arXiv Daily Digest - 2026-01-20

Energy efficiency has become an integral aspect of modern computing infrastructure design, impacting the performance, cost, scalability, and durability of production systems. The incorporation of power actuation and sensing capabilities in CPU designs is indicative of this, enabling the deployment of system software that can actively monitor and adjust energy consumption and performance at runtime. While reinforcement learning (RL) would seem ideal for the design of such energy efficiency control systems, online training presents challenges ranging from the lack of proper models for setting up an adequate simulated environment, to perturbation (noise) and reliability issues, if training is deployed on a live system. In this paper we discuss the use of offline reinforcement learning as an alternative approach for the design of an autonomous CPU power controller, with the goal of improving the energy efficiency of parallel applications at runtime without unduly impacting their performance. Offline RL sidesteps the issues incurred by online RL training by leveraging a dataset of state transitions collected from arbitrary policies prior to training. Our methodology applies offline RL to a gray-box approach to energy efficiency, combining online application-agnostic performance data (e.g., heartbeats) and hardware performance counters to ensure that the scientific objectives are met with limited performance degradation. Evaluating our method on a variety of compute-bound and memory-bound benchmarks and controlling power on a live system through Intel's Running Average Power Limit, we demonstrate that such an offline-trained agent can substantially reduce energy consumption at a tolerable performance degradation cost.

Time-series forecasting is fundamental in industrial domains like manufacturing and smart factories. As systems evolve toward automation, models must operate on edge devices (e.g., PLCs, microcontrollers) with strict constraints on latency and memory, limiting parameters to a few thousand. Conventional deep architectures are often impractical here. We propose the Fourier-Efficient Adaptive Temporal Hierarchy Forecaster (FEATHer) for accurate long-term forecasting under severe limits. FEATHer introduces: (i) ultra-lightweight multiscale decomposition into frequency pathways; (ii) a shared Dense Temporal Kernel using projection-depthwise convolution-projection without recurrence or attention; (iii) frequency-aware branch gating that adaptively fuses representations based on spectral characteristics; and (iv) a Sparse Period Kernel reconstructing outputs via period-wise downsampling to capture seasonality. FEATHer maintains a compact architecture (as few as 400 parameters) while outperforming baselines. Across eight benchmarks, it achieves the best ranking, recording 60 first-place results with an average rank of 2.05. These results demonstrate that reliable long-range forecasting is achievable on constrained edge hardware, offering a practical direction for industrial real-time inference.

Large language models (LLMs) show promise in drafting responses to patient portal messages, yet their integration into clinical workflows raises various concerns, including whether they would actually save clinicians time and effort in their portal workload. We investigate LLM alignment with individual clinicians through a comprehensive evaluation of the patient message response drafting task. We develop a novel taxonomy of thematic elements in clinician responses and propose a novel evaluation framework for assessing clinician editing load of LLM-drafted responses at both content and theme levels. We release an expert-annotated dataset and conduct large-scale evaluations of local and commercial LLMs using various adaptation techniques including thematic prompting, retrieval-augmented generation, supervised fine-tuning, and direct preference optimization. Our results reveal substantial epistemic uncertainty in aligning LLM drafts with clinician responses. While LLMs demonstrate capability in drafting certain thematic elements, they struggle with clinician-aligned generation in other themes, particularly question asking to elicit further information from patients. Theme-driven adaptation strategies yield improvements across most themes. Our findings underscore the necessity of adapting LLMs to individual clinician preferences to enable reliable and responsible use in patient-clinician communication workflows.

Diffusion Language Models (DLMs) have recently demonstrated remarkable capabilities in natural language processing tasks. However, the potential of Retrieval-Augmented Generation (RAG), which shows great successes for enhancing large language models (LLMs), has not been well explored, due to the fundamental difference between LLM and DLM decoding. To fill this critical gap, we systematically test the performance of DLMs within the RAG framework. Our findings reveal that DLMs coupled with RAG show promising potentials with stronger dependency on contextual information, but suffer from limited generation precision. We identify a key underlying issue: Response Semantic Drift (RSD), where the generated answer progressively deviates from the query's original semantics, leading to low precision content. We trace this problem to the denoising strategies in DLMs, which fail to maintain semantic alignment with the query throughout the iterative denoising process. To address this, we propose Semantic-Preserving REtrieval-Augmented Diffusion (SPREAD), a novel framework that introduces a query-relevance-guided denoising strategy. By actively guiding the denoising trajectory, SPREAD ensures the generation remains anchored to the query's semantics and effectively suppresses drift. Experimental results demonstrate that SPREAD significantly enhances the precision and effectively mitigates RSD of generated answers within the RAG framework.

Chain-of-Thought reasoning has significantly enhanced the problem-solving capabilities of Large Language Models. Unfortunately, current models generate reasoning steps sequentially without foresight, often becoming trapped in suboptimal reasoning paths with redundant steps. In contrast, we introduce Neural Chain-of-Thought Search (NCoTS), a framework that reformulates reasoning as a dynamic search for the optimal thinking strategy. By quantitatively characterizing the solution space, we reveal the existence of sparse superior reasoning paths that are simultaneously more accurate and concise than standard outputs. Our method actively navigates towards these paths by evaluating candidate reasoning operators using a dual-factor heuristic that optimizes for both correctness and computational cost. Consequently, NCoTS achieves a Pareto improvement across diverse reasoning benchmarks, boosting accuracy by over 3.5% while reducing generation length by over 22%. Our code and data are available at https://github.com/MilkThink-Lab/Neural-CoT-Search.

Separating the contributions of individual chromogenic stains in RGB histology whole slide images (WSIs) is essential for stain normalization, quantitative assessment of marker expression, and cell-level readouts in immunohistochemistry (IHC). Classical Beer-Lambert (BL) color deconvolution is well-established for two- or three-stain settings, but becomes under-determined and unstable for multiplex IHC (mIHC) with K>3 chromogens. We present a simple, data-driven encoder-decoder architecture that learns cohort-specific stain characteristics for mIHC RGB WSIs and yields crisp, well-separated per-stain concentration maps. The encoder is a compact U-Net that predicts K nonnegative concentration channels; the decoder is a differentiable BL forward model with a learnable stain matrix initialized from typical chromogen hues. Training is unsupervised with a perceptual reconstruction objective augmented by loss terms that discourage unnecessary stain mixing. On a colorectal mIHC panel comprising 5 stains (H, CDX2, MUC2, MUC5, CD8) we show excellent RGB reconstruction, and significantly reduced inter-channel bleed-through compared with matrix-based deconvolution. Code and model are available at https://github.com/measty/StainQuant.git.

An information-theoretic framework is introduced to analyze last-layer embedding, focusing on learned representations for regression tasks. We define representation-rate and derive limits on the reliability with which input-output information can be represented as is inherently determined by the input-source entropy. We further define representation capacity in a perturbed setting, and representation rate-distortion for a compressed output. We derive the achievable capacity, the achievable representation-rate, and their converse. Finally, we combine the results in a unified setting.

Large Language Models (LLMs) increasingly succeed on competitive programming problems, yet existing evaluations conflate algorithmic reasoning with code-level implementation. We argue that competitive programming is fundamentally a problem-solving task and propose centering natural-language editorials in both solution generation and evaluation. Generating an editorial prior to code improves solve rates for some LLMs, with substantially larger gains when using expertly written gold editorials. However, even with gold editorials, models continue to struggle with implementation, while the gap between generated and gold editorials reveals a persistent problem-solving bottleneck in specifying correct and complete algorithms. Beyond pass/fail metrics, we diagnose reasoning errors by comparing model-generated editorials to gold standards using expert annotations and validate an LLM-as-a-judge protocol for scalable evaluation. We introduce a dataset of 83 ICPC-style problems with gold editorials and full test suites, and evaluate 19 LLMs, arguing that future benchmarks should explicitly separate problem solving from implementation.

Spoken conversational systems require more than accurate speech generation to have human-like conversations: to feel natural and engaging, they must produce conversational behaviour that adapts dynamically to the context. Current spoken conversational systems, however, rarely allow such customization, limiting their naturalness and usability. In this work, we present the first open, instruction-following full-duplex conversational speech model that can be trained efficiently under typical academic resource constraints. By keeping the audio encoder frozen and finetuning only the language model, our model requires just 2,000 hours of data, without relying on large-scale pretraining or multi-stage optimization. The model can follow explicit instructions to control speaker voice, conversation topic, conversational behaviour (e.g., backchanneling and interruptions), and dialogue initiation. We propose a single-stage training protocol and systematically analyze design choices. Both the model and training code will be released to enable reproducible research on controllable full-duplex speech systems.

This report studies whether small, tool-augmented agents can match or outperform larger monolithic models on the GAIA benchmark. Using Qwen3 models (4B-32B) within an adapted Agentic-Reasoning framework, we isolate the effects of model scale, explicit thinking (no thinking, planner-only, or full), and tool use (search, code, mind-map). Tool augmentation provides the largest and most consistent gains. Using tools, 4B models can outperform 32B models without tool access on GAIA in our experimental setup. In contrast, explicit thinking is highly configuration- and difficulty-dependent: planner-only thinking can improve decomposition and constraint tracking, while unrestricted full thinking often degrades performance by destabilizing tool orchestration, leading to skipped verification steps, excessive tool calls, non-termination, and output-format drift.

The three-dimensional bin packing problem (3D-BPP) is a longstanding challenge in operations research and logistics. Classical heuristics and constructive methods can generate packings quickly, but often fail to address industrial constraints such as stability, balance, and handling feasibility. Metaheuristics such as genetic algorithms (GAs) provide flexibility and the ability to optimize across multiple objectives; however, pure GA approaches frequently struggle with efficiency, parameter sensitivity, and scalability to industrial order sizes. This gap is especially evident when scaling to real-world pallet dimensions, where even state-of-the-art algorithms often fail to achieve robust, deployable solutions. We propose a KPI-driven GA-based pipeline for industrial 3D-BPP that integrates key performance indicators directly into a multi-objective fitness function. The methodology combines a layer-based chromosome representation with domain-specific operators and constructive heuristics to balance efficiency and feasibility. On the BED-BPP benchmark of 1,500 real-world orders, our Hybrid-GA pipeline consistently outperforms heuristic- and learning-based state-of-the-art methods, achieving up to 35% higher space utilization and 15 to 20% stronger surface support, with lower variance across orders. These improvements come at a modest runtime cost but remain feasible for batch-scale deployment, yielding stable, balanced, and space-efficient packings.

Membership Inference Attacks (MIAs) act as a crucial auditing tool for the opaque training data of Large Language Models (LLMs). However, existing techniques predominantly rely on inaccessible model internals (e.g., logits) or suffer from poor generalization across domains in strict black-box settings where only generated text is available. In this work, we propose SimMIA, a robust MIA framework tailored for this text-only regime by leveraging an advanced sampling strategy and scoring mechanism. Furthermore, we present WikiMIA-25, a new benchmark curated to evaluate MIA performance on modern proprietary LLMs. Experiments demonstrate that SimMIA achieves state-of-the-art results in the black-box setting, rivaling baselines that exploit internal model information.

Tabular data high-stakes critical decision-making in domains such as finance, healthcare, and scientific discovery. Yet, learning effectively from tabular data in few-shot settings, where labeled examples are scarce, remains a fundamental challenge. Traditional tree-based methods often falter in these regimes due to their reliance on statistical purity metrics, which become unstable and prone to overfitting with limited supervision. At the same time, direct applications of large language models (LLMs) often overlook its inherent structure, leading to suboptimal performance. To overcome these limitations, we propose FORESTLLM, a novel framework that unifies the structural inductive biases of decision forests with the semantic reasoning capabilities of LLMs. Crucially, FORESTLLM leverages the LLM only during training, treating it as an offline model designer that encodes rich, contextual knowledge into a lightweight, interpretable forest model, eliminating the need for LLM inference at test time. Our method is two-fold. First, we introduce a semantic splitting criterion in which the LLM evaluates candidate partitions based on their coherence over both labeled and unlabeled data, enabling the induction of more robust and generalizable tree structures under few-shot supervision. Second, we propose a one-time in-context inference mechanism for leaf node stabilization, where the LLM distills the decision path and its supporting examples into a concise, deterministic prediction, replacing noisy empirical estimates with semantically informed outputs. Across a diverse suite of few-shot classification and regression benchmarks, FORESTLLM achieves state-of-the-art performance.

GitHub natively supports workflow automation through GitHub Actions. Yet, workflow maintenance is often considered a burden for software developers, who frequently face difficulties in writing, testing, debugging, and maintaining workflows. Little knowledge exists concerning the automation and reuse practices favoured by workflow practitioners. We therefore surveyed 419 practitioners to elucidate good and bad workflow development practices and to identify opportunities for supporting workflow maintenance. Specifically, we investigate the tasks that practitioners tend to automate using GitHub Actions, their preferred workflow creation mechanisms, and the non-functional characteristics they prioritise. We also examine the practices and challenges associated with GitHub's workflow reuse mechanisms. We observe a tendency to focus automation efforts on core CI/CD tasks, with less emphasis on crucial areas like security analysis and performance monitoring. Practitioners strongly rely on reusable Actions, but reusable workflows see less frequent adoption. Furthermore, we observed challenges with Action versioning and maintenance. Copy-pasting remains a common practice to have more control and avoid the complexity of depending on reusable components. These insights suggest the need for improved tooling, enhanced support for a wide range of automation tasks, and better mechanisms for discovering, managing, and trusting reusable workflow components.

Large language models (LLMs) are reshaping automated fact-checking (AFC) by enabling unified, end-to-end verification pipelines rather than isolated components. While large proprietary models achieve strong performance, their closed weights, complexity, and high costs limit sustainability. Fine-tuning smaller open weight models for individual AFC tasks can help but requires multiple specialized models resulting in high costs. We propose \textbf{multi-task learning (MTL)} as a more efficient alternative that fine-tunes a single model to perform claim detection, evidence ranking, and stance detection jointly. Using small decoder-only LLMs (e.g., Qwen3-4b), we explore three MTL strategies: classification heads, causal language modeling heads, and instruction-tuning, and evaluate them across model sizes, task orders, and standard non-LLM baselines. While multitask models do not universally surpass single-task baselines, they yield substantial improvements, achieving up to \textbf{44\%}, \textbf{54\%}, and \textbf{31\%} relative gains for claim detection, evidence re-ranking, and stance detection, respectively, over zero-/few-shot settings. Finally, we also provide practical, empirically grounded guidelines to help practitioners apply MTL with LLMs for automated fact-checking.

We present XChoice, an explainable framework for evaluating AI-human alignment in constrained decision making. Moving beyond outcome agreement such as accuracy and F1 score, XChoice fits a mechanism-based decision model to human data and LLM-generated decisions, recovering interpretable parameters that capture the relative importance of decision factors, constraint sensitivity, and implied trade-offs. Alignment is assessed by comparing these parameter vectors across models, options, and subgroups. We demonstrate XChoice on Americans' daily time allocation using the American Time Use Survey (ATUS) as human ground truth, revealing heterogeneous alignment across models and activities and salient misalignment concentrated in Black and married groups. We further validate robustness of XChoice via an invariance analysis and evaluate targeted mitigation with a retrieval augmented generation (RAG) intervention. Overall, XChoice provides mechanism-based metrics that diagnose misalignment and support informed improvements beyond surface outcome matching.

Attention Deficit Hyperactivity Disorder (ADHD) is a prevalent neurodevelopmental disorder with limited objective diagnostic tools, highlighting the urgent need for objective, biology-based diagnostic frameworks in precision psychiatry. We integrate urinary metabolomics with an interpretable machine learning framework to identify biochemical signatures associated with ADHD. Targeted metabolomic profiles from 52 ADHD and 46 control participants were analyzed using a Closest Resemblance (CR) classifier with embedded feature selection. The CR model outperformed Random Forest and K-Nearest Neighbor classifiers, achieving an AUC > 0.97 based on a reduced panel of 14 metabolites. These metabolites including dopamine 4-sulfate, N-acetylaspartylglutamic acid, and citrulline map to dopaminergic neurotransmission and amino acid metabolism pathways, offering mechanistic insight into ADHD pathophysiology. The CR classifier's transparent decision boundaries and low computational cost support integration into targeted metabolomic assays and future point of care diagnostic platforms. Overall, this work demonstrates a translational framework combining metabolomics and interpretable machine learning to advance objective, biologically informed diagnostic strategies for ADHD.

Compared to search engine result pages (SERPs), AI-generated podcasts represent a relatively new and relatively more passive modality of information consumption, delivering narratives in a naturally engaging format. As these two media increasingly converge in everyday information-seeking behavior, it is essential to explore how their interaction influences user attitudes, particularly in contexts involving controversial, value-laden, and often debated topics. Addressing this need, we aim to understand how information mediums of present-day SERPs and AI-generated podcasts interact to shape the opinions of users. To this end, through a controlled user study (N=483), we investigated user attitudinal effects of consuming information via SERPs and AI-generated podcasts, focusing on how the sequence and modality of exposure shape user opinions. A majority of users in our study corresponded to attitude change outcomes, and we found an effect of sequence on attitude change. Our results further revealed a role of viewpoint bias and the degree of topic controversiality in shaping attitude change, although we found no effect of individual moderators.

Visuomotor policies often leverage large pre-trained Vision Transformers (ViTs) for their powerful generalization capabilities. However, their significant data requirements present a major challenge in the data-scarce context of most robotic learning settings, where compact CNNs with strong inductive biases can be more easily optimized. To address this trade-off, we introduce X-Distill, a simple yet highly effective method that synergizes the strengths of both architectures. Our approach involves an offline, cross-architecture knowledge distillation, transferring the rich visual representations of a large, frozen DINOv2 teacher to a compact ResNet-18 student on the general-purpose ImageNet dataset. This distilled encoder, now endowed with powerful visual priors, is then jointly fine-tuned with a diffusion policy head on the target manipulation tasks. Extensive experiments on $34$ simulated benchmarks and $5$ challenging real-world tasks demonstrate that our method consistently outperforms policies equipped with from-scratch ResNet or fine-tuned DINOv2 encoders. Notably, X-Distill also surpasses 3D encoders that utilize privileged point cloud observations or much larger Vision-Language Models. Our work highlights the efficacy of a simple, well-founded distillation strategy for achieving state-of-the-art performance in data-efficient robotic manipulation.

Boosting is a powerful method that turns weak learners, which perform only slightly better than random guessing, into strong learners with high accuracy. While boosting is well understood in the classic setting, it is less so in the agnostic case, where no assumptions are made about the data. Indeed, only recently was the sample complexity of agnostic boosting nearly settled arXiv:2503.09384, but the known algorithm achieving this bound has exponential running time. In this work, we propose the first agnostic boosting algorithm with near-optimal sample complexity, running in time polynomial in the sample size when considering the other parameters of the problem fixed.

Music Cover Retrieval, also known as Version Identification, aims to recognize distinct renditions of the same underlying musical work, a task central to catalog management, copyright enforcement, and music retrieval. State-of-the-art approaches have largely focused on harmonic and melodic features, employing increasingly complex audio pipelines designed to be invariant to musical attributes that often vary widely across covers. While effective, these methods demand substantial training time and computational resources. By contrast, lyrics constitute a strong invariant across covers, though their use has been limited by the difficulty of extracting them accurately and efficiently from polyphonic audio. Early methods relied on simple frameworks that limited downstream performance, while more recent systems deliver stronger results but require large models integrated within complex multimodal architectures. We introduce LIVI (Lyrics-Informed Version Identification), an approach that seeks to balance retrieval accuracy with computational efficiency. First, LIVI leverages supervision from state-of-the-art transcription and text embedding models during training to achieve retrieval accuracy on par with--or superior to--harmonic-based systems. Second, LIVI remains lightweight and efficient by removing the transcription step at inference, challenging the dominance of complexity-heavy pipelines.

Spiking neural networks (SNNs) employing unsupervised learning methods inspired by neural plasticity are expected to be a new framework for artificial intelligence. In this study, we investigated the effect of multiple types of neural plasticity, such as spike-time-dependent plasticity (STDP) and synaptic scaling, on the learning in a winner-take-all (WTA) network composed of spiking neurons. We implemented a WTA network with multiple types of neural plasticity using Python. The MNIST and the Fashion-MNIST datasets were used for training and testing. We varied the number of neurons, the time constant of STDP, and the normalization method used in synaptic scaling to compare classification accuracy. The results demonstrated that synaptic scaling based on the L2 norm was the most effective in improving classification performance. By implementing L2-norm-based synaptic scaling and setting the number of neurons in both excitatory and inhibitory layers to 400, the network achieved classification accuracies of 88.84 % on the MNIST dataset and 68.01 % on the Fashion-MNIST dataset after one epoch of training.

Graph Neural Networks (GNNs) are emerging as powerful tools for nonlinear Model Order Reduction (MOR) of time-dependent parameterized Partial Differential Equations (PDEs). However, existing methodologies struggle to combine geometric inductive biases with interpretable latent behavior, overlooking dynamics-driven features or disregarding spatial information. In this work, we address this gap by introducing Latent Dynamics Graph Convolutional Network (LD-GCN), a purely data-driven, encoder-free architecture that learns a global, low-dimensional representation of dynamical systems conditioned on external inputs and parameters. The temporal evolution is modeled in the latent space and advanced through time-stepping, allowing for time-extrapolation, and the trajectories are consistently decoded onto geometrically parameterized domains using a GNN. Our framework enhances interpretability by enabling the analysis of the reduced dynamics and supporting zero-shot prediction through latent interpolation. The methodology is mathematically validated via a universal approximation theorem for encoder-free architectures, and numerically tested on complex computational mechanics problems involving physical and geometric parameters, including the detection of bifurcating phenomena for Navier-Stokes equations. Code availability: https://github.com/lorenzotomada/ld-gcn-rom

Large Language Models (LLMs) face the "knowledge cutoff" challenge, where their frozen parametric memory prevents direct internalization of new information. While Supervised Fine-Tuning (SFT) is commonly used to update model knowledge, it often updates factual content without reliably improving the model's ability to use the newly incorporated information for question answering or decision-making. Reinforcement Learning (RL) is essential for acquiring reasoning skills; however, its high computational cost makes it impractical for efficient online adaptation. We empirically observe that the parameter updates induced by SFT and RL are nearly orthogonal. Based on this observation, we propose Parametric Skill Transfer (PaST), a framework that supports modular skill transfer for efficient and effective knowledge adaptation. By extracting a domain-agnostic Skill Vector from a source domain, we can linearly inject knowledge manipulation skills into a target model after it has undergone lightweight SFT on new data. Experiments on knowledge-incorporation QA (SQuAD, LooGLE) and agentic tool-use benchmarks (ToolBench) demonstrate the effectiveness of our method. On SQuAD, PaST outperforms the state-of-the-art self-editing SFT baseline by up to 9.9 points. PaST further scales to long-context QA on LooGLE with an 8.0-point absolute accuracy gain, and improves zero-shot ToolBench success rates by +10.3 points on average with consistent gains across tool categories, indicating strong scalability and cross-domain transferability of the Skill Vector.

Retrieval-Augmented Generation (RAG) has demonstrated significant effectiveness in enhancing large language models (LLMs) for complex multi-hop question answering (QA). For multi-hop QA tasks, current iterative approaches predominantly rely on LLMs to self-guide and plan multi-step exploration paths during retrieval, leading to substantial challenges in maintaining reasoning coherence across steps from inaccurate query decomposition and error propagation. To address these issues, we introduce Reasoning Tree Guided RAG (RT-RAG), a novel hierarchical framework for complex multi-hop QA. RT-RAG systematically decomposes multi-hop questions into explicit reasoning trees, minimizing inaccurate decomposition through structured entity analysis and consensus-based tree selection that clearly separates core queries, known entities, and unknown entities. Subsequently, a bottom-up traversal strategy employs iterative query rewriting and refinement to collect high-quality evidence, thereby mitigating error propagation. Comprehensive experiments show that RT-RAG substantially outperforms state-of-the-art methods by 7.0% F1 and 6.0% EM, demonstrating the effectiveness of RT-RAG in complex multi-hop QA.

Large Reasoning Models (LRMs) excel at multi-step reasoning but often suffer from inefficient reasoning processes like overthinking and overshoot, where excessive or misdirected reasoning increases computational cost and degrades performance. Existing efficient reasoning methods operate in a closed-loop manner, lacking mechanisms for external intervention to guide the reasoning process. To address this, we propose Think-with-Me, a novel test-time interactive reasoning paradigm that introduces external feedback intervention into the reasoning process. Our key insights are that transitional conjunctions serve as natural points for intervention, signaling phases of self-validation or exploration and using transitional words appropriately to prolong the reasoning enhances performance, while excessive use affects performance. Building on these insights, Think-with-Me pauses reasoning at these points for external feedback, adaptively extending or terminating reasoning to reduce redundancy while preserving accuracy. The feedback is generated via a multi-criteria evaluation (rationality and completeness) and comes from either human or LLM proxies. We train the target model using Group Relative Policy Optimization (GRPO) to adapt to this interactive mode. Experiments show that Think-with-Me achieves a superior balance between accuracy and reasoning length under limited context windows. On AIME24, Think-with-Me outperforms QwQ-32B by 7.19% in accuracy while reducing average reasoning length by 81% under an 8K window. The paradigm also benefits security and creative tasks.

Large Language Models (LLMs) are effective for data augmentation in classification tasks like intent detection. In some cases, they inadvertently produce examples that are ambiguous with regard to untargeted classes. We present DDAIR (Disambiguated Data Augmentation for Intent Recognition) to mitigate this problem. We use Sentence Transformers to detect ambiguous class-guided augmented examples generated by LLMs for intent recognition in low-resource scenarios. We identify synthetic examples that are semantically more similar to another intent than to their target one. We also provide an iterative re-generation method to mitigate such ambiguities. Our findings show that sentence embeddings effectively help to (re)generate less ambiguous examples, and suggest promising potential to improve classification performance in scenarios where intents are loosely or broadly defined.

Large language models (LLMs) are widely used in knowledge-intensive applications but often generate factually incorrect responses. A promising approach to rectify these flaws is correcting LLMs using feedback. Therefore, in this paper, we introduce FactCorrector, a new post-hoc correction method that adapts across domains without retraining and leverages structured feedback about the factuality of the original response to generate a correction. To support rigorous evaluations of factuality correction methods, we also develop the VELI5 benchmark, a novel dataset containing systematically injected factual errors and ground-truth corrections. Experiments on VELI5 and several popular long-form factuality datasets show that the FactCorrector approach significantly improves factual precision while preserving relevance, outperforming strong baselines. We release our code at https://ibm.biz/factcorrector.

Output diversity is crucial for Large Language Models as it underpins pluralism and creativity. In this work, we reveal that controlling the language used during model thinking-the language of thought-provides a novel and structural source of output diversity. Our preliminary study shows that different thinking languages occupy distinct regions in a model's thinking space. Based on this observation, we study two repeated sampling strategies under multilingual thinking-Single-Language Sampling and Mixed-Language Sampling-and conduct diversity evaluation on outputs that are controlled to be in English, regardless of the thinking language used. Across extensive experiments, we demonstrate that switching the thinking language from English to non-English languages consistently increases output diversity, with a clear and consistent positive correlation such that languages farther from English in the thinking space yield larger gains. We further show that aggregating samples across multiple thinking languages yields additional improvements through compositional effects, and that scaling sampling with linguistic heterogeneity expands the model's diversity ceiling. Finally, we show that these findings translate into practical benefits in pluralistic alignment scenarios, leading to broader coverage of cultural knowledge and value orientations in LLM outputs. Our code is publicly available at https://github.com/iNLP-Lab/Multilingual-LoT-Diversity.

For linear partial differential equations with known fundamental solutions, this work introduces a novel operator learning framework that relies exclusively on domain boundary data, including solution values and normal derivatives, rather than full-domain sampling. By integrating the previously developed Mathematical Artificial Data (MAD) method, which enforces physical consistency, all training data are synthesized directly from the fundamental solutions of the target problems, resulting in a fully data-driven pipeline without the need for external measurements or numerical simulations. We refer to this approach as the Mathematical Artificial Data Boundary Neural Operator (MAD-BNO), which learns boundary-to-boundary mappings using MAD-generated Dirichlet-Neumann data pairs. Once trained, the interior solution at arbitrary locations can be efficiently recovered through boundary integral formulations, supporting Dirichlet, Neumann, and mixed boundary conditions as well as general source terms. The proposed method is validated on benchmark operator learning tasks for two-dimensional Laplace, Poisson, and Helmholtz equations, where it achieves accuracy comparable to or better than existing neural operator approaches while significantly reducing training time. The framework is naturally extensible to three-dimensional problems and complex geometries.

Online disinformation poses an escalating threat to society, driven increasingly by the rapid spread of misleading content across both multimedia and multilingual platforms. While automated fact-checking methods have advanced in recent years, their effectiveness remains constrained by the scarcity of datasets that reflect these real-world complexities. To address this gap, we first present MultiCaption, a new dataset specifically designed for detecting contradictions in visual claims. Pairs of claims referring to the same image or video were labeled through multiple strategies to determine whether they contradict each other. The resulting dataset comprises 11,088 visual claims in 64 languages, offering a unique resource for building and evaluating misinformation-detection systems in truly multimodal and multilingual environments. We then provide comprehensive experiments using transformer-based architectures, natural language inference models, and large language models, establishing strong baselines for future research. The results show that MultiCaption is more challenging than standard NLI tasks, requiring task-specific finetuning for strong performance. Moreover, the gains from multilingual training and testing highlight the dataset's potential for building effective multilingual fact-checking pipelines without relying on machine translation.

Federated learning (FL) for large language models (LLMs) has attracted increasing attention as a way to enable privacy-preserving adaptation over distributed data. Parameter-efficient methods such as LoRA are widely adopted to reduce communication and memory costs. Despite these advances, practical FL deployments often exhibit rank heterogeneity, since different clients may use different low-rank configurations. This makes direct aggregation of LoRA updates biased and unstable. Existing solutions typically enforce unified ranks or align heterogeneous updates into a shared subspace, which over-constrains client-specific semantics, limits personalization, and provides weak protection of local client information under differential privacy noise. To address this issue, we propose Selective Dual-module Federated LoRA (SDFLoRA), which decomposes each client adapter into a global module that captures transferable knowledge and a local module that preserves client-specific adaptations. The global module is selectively aligned and aggregated across clients, while local modules remain private. This design enables robust learning under rank heterogeneity and supports privacy-aware optimization by injecting differential privacy noise exclusively into the global module. Experiments on GLUE benchmarks demonstrate that SDFLoRA outperforms representative federated LoRA baselines and achieves a better utility-privacy trade-off.

We study model-free policy learning for discrete-time mean-field control (MFC) problems with finite state space and compact action space. In contrast to the extensive literature on value-based methods for MFC, policy-based approaches remain largely unexplored due to the intrinsic dependence of transition kernels and rewards on the evolving population state distribution, which prevents the direct use of likelihood-ratio estimators of policy gradients from classical single-agent reinforcement learning. We introduce a novel perturbation scheme on the state-distribution flow and prove that the gradient of the resulting perturbed value function converges to the true policy gradient as the perturbation magnitude vanishes. This construction yields a fully model-free estimator based solely on simulated trajectories and an auxiliary estimate of the sensitivity of the state distribution. Building on this framework, we develop MF-REINFORCE, a model-free policy gradient algorithm for MFC, and establish explicit quantitative bounds on its bias and mean-squared error. Numerical experiments on representative mean-field control tasks demonstrate the effectiveness of the proposed approach.

We present T$^\star$, a simple \textsc{TraceRL}-based training curriculum for progressive block-size scaling in masked diffusion language models (MDMs). Starting from an AR-initialized small-block MDM, T$^\star$~transitions smoothly to larger blocks, enabling higher-parallelism decoding with minimal performance degradation on math reasoning benchmarks. Moreover, further analysis suggests that T$^\star$~can converge to an alternative decoding schedule $\hat{\rm S}$ that achieves comparable performance.

Backdoored and privacy-leaking deep neural networks pose a serious threat to the deployment of machine learning systems in security-critical settings. Existing defenses for backdoor detection and membership inference typically require access to clean reference models, extensive retraining, or strong assumptions about the attack mechanism. In this work, we introduce a novel LoRA-based oracle framework that leverages low-rank adaptation modules as a lightweight, model-agnostic probe for both backdoor detection and membership inference. Our approach attaches task-specific LoRA adapters to a frozen backbone and analyzes their optimization dynamics and representation shifts when exposed to suspicious samples. We show that poisoned and member samples induce distinctive low-rank updates that differ significantly from those generated by clean or non-member data. These signals can be measured using simple ranking and energy-based statistics, enabling reliable inference without access to the original training data or modification of the deployed model.

The past few years have witnessed an increasing use of machine learning (ML) systems in science. Paul Humphreys has argued that, because of specific characteristics of ML systems, human scientists are pushed out of the loop of science. In this chapter, I investigate to what extent this is true. First, I express these concerns in terms of what I call epistemic control. I identify two conditions for epistemic control, called tracking and tracing, drawing on works in philosophy of technology. With this new understanding of the problem, I then argue against Humphreys pessimistic view. Finally, I construct a more nuanced view of epistemic control in ML-based science.

Although post-training quantization (PTQ) provides an efficient numerical compression scheme for deploying large language models (LLMs) on resource-constrained devices, the representativeness and universality of calibration data remain a core bottleneck in determining the accuracy of quantization parameters. Traditional PTQ methods typically rely on limited samples, making it difficult to capture the activation distribution during the inference phase, leading to biases in quantization parameters. To address this, we propose \textbf{FAQ} (Family-Aware Quantization), a calibration data regeneration framework that leverages prior knowledge from LLMs of the same family to generate high-fidelity calibration samples. Specifically, FAQ first inputs the original calibration samples into a larger LLM from the same family as the target model, regenerating a series of high-fidelity calibration data using a highly consistent knowledge system. Subsequently, this data, carrying Chain-of-Thought reasoning and conforming to the expected activation distribution, undergoes group competition under expert guidance to select the best samples, which are then re-normalized to enhance the effectiveness of standard PTQ. Experiments on multiple model series, including Qwen3-8B, show that FAQ reduces accuracy loss by up to 28.5\% compared to the baseline with original calibration data, demonstrating its powerful potential and contribution.

Retrieval-Augmented Generation (RAG) has attracted significant attention due to its ability to combine the generative capabilities of Large Language Models (LLMs) with knowledge obtained through efficient retrieval mechanisms over large-scale data collections. Currently, the majority of existing approaches overlook the risks associated with exposing sensitive or access-controlled information directly to the generation model. Only a few approaches propose techniques to instruct the generative model to refrain from disclosing sensitive information; however, recent studies have also demonstrated that LLMs remain vulnerable to prompt injection attacks that can override intended behavioral constraints. For these reasons, we propose a novel approach to Selective Disclosure in Retrieval-Augmented Generation, called SD-RAG, which decouples the enforcement of security and privacy constraints from the generation process itself. Rather than relying on prompt-level safeguards, SD-RAG applies sanitization and disclosure controls during the retrieval phase, prior to augmenting the language model's input. Moreover, we introduce a semantic mechanism to allow the ingestion of human-readable dynamic security and privacy constraints together with an optimized graph-based data model that supports fine-grained, policy-aware retrieval. Our experimental evaluation demonstrates the superiority of SD-RAG over baseline existing approaches, achieving up to a $58\%$ improvement in the privacy score, while also showing a strong resilience to prompt injection attacks targeting the generative model.

Artificial intelligence (AI) has moved to the center of policy, market, and academic debates, but its macroeconomic footprint is still only partly understood. This paper provides an overview on how the current AI wave is captured in US national accounts, combining a simple macro-accounting framework with a stylized description of the AI production process. We highlight the crucial role played by data centers, which constitute the backbone of the AI ecosystem and have attracted formidable investment in 2025, as they are indispensable for meeting the rapidly increasing worldwide demand for AI services. We document that the boom in IT and AI-related capital expenditure in the first three quarters of the year has given an outsized boost to aggregate demand, while its contribution to GDP growth is smaller once the high import content of AI hardware is netted out. Furthermore, simple calculations suggest that, at current utilization rates and pricing, the production of services originating in new AI data centers could contribute to GDP over the turn of the next quarters on a scale comparable to that of investment spending to date. Short reinvestment cycles and uncertainty about future AI demand, while not currently acting as a macroeconomic drag, can nevertheless fuel macroeconomic risks over the medium term.

Document-Level Relation Extraction (DocRE) presents significant challenges due to its reliance on cross-sentence context and the long-tail distribution of relation types, where many relations have scarce training examples. In this work, we introduce DOcument-level Relation Extraction optiMizing the long taIl (DOREMI), an iterative framework that enhances underrepresented relations through minimal yet targeted manual annotations. Unlike previous approaches that rely on large-scale noisy data or heuristic denoising, DOREMI actively selects the most informative examples to improve training efficiency and robustness. DOREMI can be applied to any existing DocRE model and is effective at mitigating long-tail biases, offering a scalable solution to improve generalization on rare relations.

This paper proposes a policy-based deep reinforcement learning hyper-heuristic framework for solving the Job Shop Scheduling Problem. The hyper-heuristic agent learns to switch scheduling rules based on the system state dynamically. We extend the hyper-heuristic framework with two key mechanisms. First, action prefiltering restricts decision-making to feasible low-level actions, enabling low-level heuristics to be evaluated independently of environmental constraints and providing an unbiased assessment. Second, a commitment mechanism regulates the frequency of heuristic switching. We investigate the impact of different commitment strategies, from step-wise switching to full-episode commitment, on both training behavior and makespan. Additionally, we compare two action selection strategies at the policy level: deterministic greedy selection and stochastic sampling. Computational experiments on standard JSSP benchmarks demonstrate that the proposed approach outperforms traditional heuristics, metaheuristics, and recent neural network-based scheduling methods

Generative modeling offers a promising solution to data scarcity and privacy challenges in time series analysis. However, the structural complexity of time series, characterized by multi-scale temporal patterns and heterogeneous components, remains insufficiently addressed. In this work, we propose a structure-disentangled multiscale generation framework for time series. Our approach encodes sequences into discrete tokens at multiple temporal resolutions and performs autoregressive generation in a coarse-to-fine manner, thereby preserving hierarchical dependencies. To tackle structural heterogeneity, we introduce a dual-path VQ-VAE that disentangles trend and seasonal components, enabling the learning of semantically consistent latent representations. Additionally, we present a guidance-based reconstruction strategy, where coarse seasonal signals are utilized as priors to guide the reconstruction of fine-grained seasonal patterns. Experiments on six datasets show that our approach produces higher-quality time series than existing methods. Notably, our model achieves strong performance with a significantly reduced parameter count and exhibits superior capability in generating high-quality long-term sequences. Our implementation is available at https://anonymous.4open.science/r/TimeMAR-BC5B.

Social media platforms are increasingly dominated by long-form multimodal content, where harmful narratives are constructed through a complex interplay of audio, visual, and textual cues. While automated systems can flag hate speech with high accuracy, they often function as "black boxes" that fail to provide the granular, interpretable evidence, such as precise timestamps and target identities, required for effective human-in-the-loop moderation. In this work, we introduce TANDEM, a unified framework that transforms audio-visual hate detection from a binary classification task into a structured reasoning problem. Our approach employs a novel tandem reinforcement learning strategy where vision-language and audio-language models optimize each other through self-constrained cross-modal context, stabilizing reasoning over extended temporal sequences without requiring dense frame-level supervision. Experiments across three benchmark datasets demonstrate that TANDEM significantly outperforms zero-shot and context-augmented baselines, achieving 0.73 F1 in target identification on HateMM (a 30% improvement over state-of-the-art) while maintaining precise temporal grounding. We further observe that while binary detection is robust, differentiating between offensive and hateful content remains challenging in multi-class settings due to inherent label ambiguity and dataset imbalance. More broadly, our findings suggest that structured, interpretable alignment is achievable even in complex multimodal settings, offering a blueprint for the next generation of transparent and actionable online safety moderation tools.

Crawling national top-level domains has proven to be highly effective for collecting texts in less-resourced languages. This approach has been recently used for South Slavic languages and resulted in the largest general corpora for this language group: the CLASSLA-web 1.0 corpora. Building on this success, we established a continuous crawling infrastructure for iterative national top-level domain crawling across South Slavic and related webs. We present the first outcome of this crawling infrastructure - the CLASSLA-web 2.0 corpus collection, with substantially larger web corpora containing 17.0 billion words in 38.1 million texts in seven languages: Bosnian, Bulgarian, Croatian, Macedonian, Montenegrin, Serbian, and Slovenian. In addition to genre categories, the new version is also automatically annotated with topic labels. Comparing CLASSLA-web 2.0 with its predecessor reveals that only one-fifth of the texts overlap, showing that re-crawling after just two years yields largely new content. However, while the new web crawls bring growing gains, we also notice growing pains - a manual inspection of top domains reveals a visible degradation of web content, as machine-generated sites now contribute a significant portion of texts.

Unplanned failures in industrial hydraulic pumps can halt production and incur substantial costs. We explore two unsupervised autoencoder (AE) schemes for early fault detection: a feed-forward model that analyses individual sensor snapshots and a Long Short-Term Memory (LSTM) model that captures short temporal windows. Both networks are trained only on healthy data drawn from a minute-level log of 52 sensor channels; evaluation uses a separate set that contains seven annotated fault intervals. Despite the absence of fault samples during training, the models achieve high reliability.

Unsupervised domain adaptation tackles the problem that domain shifts between training and test data impair the performance of neural networks in many real-world applications. Thereby, in realistic scenarios, the source data may no longer be available during adaptation, and the label space of the target domain may differ from the source label space. This setting, known as source-free universal domain adaptation (SF-UniDA), has recently gained attention, but all existing approaches only assume a single domain shift from source to target. In this work, we present the first study on continual SF-UniDA, where the model must adapt sequentially to a stream of multiple different unlabeled target domains. Building upon our previous methods for online SF-UniDA, we combine their key ideas by integrating Gaussian mixture model-based pseudo-labeling within a mean teacher framework for improved stability over long adaptation sequences. Additionally, we introduce consistency losses for further robustness. The resulting method GMM-COMET provides a strong first baseline for continual SF-UniDA and is the only approach in our experiments to consistently improve upon the source-only model across all evaluated scenarios. Our code is available at https://github.com/pascalschlachter/GMM-COMET.

How to find a natural grouping of a large real data set? Clustering requires a balance between abstraction and representation. To identify clusters, we need to abstract from superfluous details of individual objects. But we also need a rich representation that emphasizes the key features shared by groups of objects that distinguish them from other groups of objects. Each clustering algorithm implements a different trade-off between abstraction and representation. Classical K-means implements a high level of abstraction - details are simply averaged out - combined with a very simple representation - all clusters are Gaussians in the original data space. We will see how approaches to subspace and deep clustering support high-dimensional and complex data by allowing richer representations. However, with increasing representational expressiveness comes the need to explicitly enforce abstraction in the objective function to ensure that the resulting method performs clustering and not just representation learning. We will see how current deep clustering methods define and enforce abstraction through centroid-based and density-based clustering losses. Balancing the conflicting goals of abstraction and representation is challenging. Ideas from subspace clustering help by learning one latent space for the information that is relevant to clustering and another latent space to capture all other information in the data. The tutorial ends with an outlook on future research in clustering. Future methods will more adaptively balance abstraction and representation to improve performance, energy efficiency and interpretability. By automatically finding the sweet spot between abstraction and representation, the human brain is very good at clustering and other related tasks such as single-shot learning. So, there is still much room for improvement.

The computation of resistance distance is pivotal in a wide range of graph analysis applications, including graph clustering, link prediction, and graph neural networks. Despite its foundational importance, efficient algorithms for computing resistance distances on large graphs are still lacking. Existing state-of-the-art (SOTA) methods, including power iteration-based algorithms and random walk-based local approaches, often struggle with slow convergence rates, particularly when the condition number of the graph Laplacian matrix, denoted by $κ$, is large. To tackle this challenge, we propose two novel and efficient algorithms inspired by the classic Lanczos method: Lanczos Iteration and Lanczos Push, both designed to reduce dependence on $κ$. Among them, Lanczos Iteration is a near-linear time global algorithm, whereas Lanczos Push is a local algorithm with a time complexity independent of the size of the graph. More specifically, we prove that the time complexity of Lanczos Iteration is $\tilde{O}(\sqrtκ m)$ ($m$ is the number of edges of the graph and $\tilde{O}$ means the complexity omitting the $\log$ terms) which achieves a speedup of $\sqrtκ$ compared to previous power iteration-based global methods. For Lanczos Push, we demonstrate that its time complexity is $\tilde{O}(κ^{2.75})$ under certain mild and frequently established assumptions, which represents a significant improvement of $κ^{0.25}$ over the SOTA random walk-based local algorithms. We validate our algorithms through extensive experiments on eight real-world datasets of varying sizes and statistical properties, demonstrating that Lanczos Iteration and Lanczos Push significantly outperform SOTA methods in terms of both efficiency and accuracy.

Function-as-a-Service (FaaS) has become a central paradigm in serverless cloud computing, yet optimizing FaaS deployments remains challenging. Using function fusion, multiple functions can be combined into a single deployment unit, which can be used to reduce cost and latency of complex serverless applications comprising multiple functions. Even in small-scale applications, the number of possible fusion configurations is vast, making brute-force benchmarking in production both cost- and time-prohibitive. In this paper, we present a system that can analyze every possible fusion setup of complex applications. By emulating the FaaS platform, our system enables local experimentation, eliminating the need to reconfigure the live platform and significantly reducing associated cost and time. We evaluate all fusion configurations across a number of example FaaS applications and resource limits. Our results reveal that, when analyzing cost and latency trade-offs, only a limited set of fusion configurations represent optimal solutions, which are strongly influenced by the specific pricing model in use.

Unplanned engine failures in helicopters can lead to severe operational disruptions, safety hazards, and costly repairs. To mitigate these risks, this study compares two predictive maintenance strategies for helicopter engines: a supervised classification pipeline and an unsupervised anomaly detection approach based on autoencoders (AEs). The supervised method relies on labelled examples of both normal and faulty behaviour, while the unsupervised approach learns a model of normal operation using only healthy engine data, flagging deviations as potential faults. Both methods are evaluated on a real-world dataset comprising labelled snapshots of helicopter engine telemetry. While supervised models demonstrate strong performance when annotated failures are available, the AE achieves effective detection without requiring fault labels, making it particularly well suited for settings where failure data are scarce or incomplete. The comparison highlights the practical trade-offs between accuracy, data availability, and deployment feasibility, and underscores the potential of unsupervised learning as a viable solution for early fault detection in aerospace applications.

Multimedia recommendation systems leverage user-item interactions and multimodal information to capture user preferences, enabling more accurate and personalized recommendations. Despite notable advancements, existing approaches still face two critical limitations: first, shallow modality fusion often relies on simple concatenation, failing to exploit rich synergic intra- and inter-modal relationships; second, asymmetric feature treatment-where users are only characterized by interaction IDs while items benefit from rich multimodal content-hinders the learning of a shared semantic space. To address these issues, we propose a Cross-modal Recursive Attention Network with dual graph Embedding (CRANE). To tackle shallow fusion, we design a core Recursive Cross-Modal Attention (RCA) mechanism that iteratively refines modality features based on cross-correlations in a joint latent space, effectively capturing high-order intra- and inter-modal dependencies. For symmetric multimodal learning, we explicitly construct users' multimodal profiles by aggregating features of their interacted items. Furthermore, CRANE integrates a symmetric dual-graph framework-comprising a heterogeneous user-item interaction graph and a homogeneous item-item semantic graph-unified by a self-supervised contrastive learning objective to fuse behavioral and semantic signals. Despite these complex modeling capabilities, CRANE maintains high computational efficiency. Theoretical and empirical analyses confirm its scalability and high practical efficiency, achieving faster convergence on small datasets and superior performance ceilings on large-scale ones. Comprehensive experiments on four public real-world datasets validate an average 5% improvement in key metrics over state-of-the-art baselines.

Multi-Agent Systems (MAS) built on large language models typically solve complex tasks by coordinating multiple agents through workflows. Existing approaches generates workflows either at task level or query level, but their relative costs and benefits remain unclear. After rethinking and empirical analyses, we show that query-level workflow generation is not always necessary, since a small set of top-K best task-level workflows together already covers equivalent or even more queries. We further find that exhaustive execution-based task-level evaluation is both extremely token-costly and frequently unreliable. Inspired by the idea of self-evolution and generative reward modeling, we propose a low-cost task-level generation framework \textbf{SCALE}, which means \underline{\textbf{S}}elf prediction of the optimizer with few shot \underline{\textbf{CAL}}ibration for \underline{\textbf{E}}valuation instead of full validation execution. Extensive experiments demonstrate that \textbf{SCALE} maintains competitive performance, with an average degradation of just 0.61\% compared to existing approach across multiple datasets, while cutting overall token usage by up to 83\%.

Graph-based Retrieval-Augmented Generation (GraphRAG) frameworks face a trade-off between the comprehensiveness of global search and the efficiency of local search. Existing methods are often challenged by navigating large-scale hierarchical graphs, optimizing retrieval paths, and balancing exploration-exploitation dynamics, frequently lacking robust multi-stage re-ranking. To overcome these deficits, we propose Deep GraphRAG, a framework designed for a balanced approach to hierarchical retrieval and adaptive integration. It introduces a hierarchical global-to-local retrieval strategy that integrates macroscopic inter-community and microscopic intra-community contextual relations. This strategy employs a three-stage process: (1) inter-community filtering, which prunes the search space using local context; (2) community-level refinement, which prioritizes relevant subgraphs via entity-interaction analysis; and (3) entity-level fine-grained search within target communities. A beam search-optimized dynamic re-ranking module guides this process, continuously filtering candidates to balance efficiency and global comprehensiveness. Deep GraphRAG also features a Knowledge Integration Module leveraging a compact LLM, trained with Dynamic Weighting Reward GRPO (DW-GRPO). This novel reinforcement learning approach dynamically adjusts reward weights to balance three key objectives: relevance, faithfulness, and conciseness. This training enables compact models (1.5B) to approach the performance of large models (70B) in the integration task. Evaluations on Natural Questions and HotpotQA demonstrate that Deep GraphRAG significantly outperforms baseline graph retrieval methods in both accuracy and efficiency.

The simulation-to-reality (sim-to-real) transfer of large-scale hydraulic robots presents a significant challenge in robotics because of the inherent slow control response and complex fluid dynamics. The complex dynamics result from the multiple interconnected cylinder structure and the difference in fluid rates of the cylinders. These characteristics complicate detailed simulation for all joints, making it unsuitable for reinforcement learning (RL) applications. In this work, we propose an analytical actuator model driven by hydraulic dynamics to represent the complicated actuators. The model predicts joint torques for all 12 actuators in under 1 microsecond, allowing rapid processing in RL environments. We compare our model with neural network-based actuator models and demonstrate the advantages of our model in data-limited scenarios. The locomotion policy trained in RL with our model is deployed on a hydraulic quadruped robot, which is over 300 kg. This work is the first demonstration of a successful transfer of stable and robust command-tracking locomotion with RL on a heavy hydraulic quadruped robot, demonstrating advanced sim-to-real transferability.

Recent end-to-end spoken dialogue systems leverage speech tokenizers and neural audio codecs to enable LLMs to operate directly on discrete speech representations. However, these models often exhibit limited speaker identity preservation, hindering personalized voice interaction. In this work, we present Chroma 1.0, the first open-source, real-time, end-to-end spoken dialogue model that achieves both low-latency interaction and high-fidelity personalized voice cloning. Chroma achieves sub-second end-to-end latency through an interleaved text-audio token schedule (1:2) that supports streaming generation, while maintaining high-quality personalized voice synthesis across multi-turn conversations. Our experimental results demonstrate that Chroma achieves a 10.96% relative improvement in speaker similarity over the human baseline, with a Real-Time Factor (RTF) of 0.43, while maintaining strong reasoning and dialogue capabilities. Our code and models are publicly available at https://github.com/FlashLabs-AI-Corp/FlashLabs-Chroma and https://huggingface.co/FlashLabs/Chroma-4B .

We study how bots contribute to open-source discussions in the Ethereum ecosystem and whether they influence developers' emotional tone. Our dataset covers 36,875 accounts across ten repositories with 105 validated bots (0.28%). Human participation follows a U-shaped pattern, while bots engage in uniform (pull requests) or late-stage (issues) activity. Bots respond faster than humans in pull requests but play slower maintenance roles in issues. Using a model trained on 27 emotion categories, we find bots are more neutral, yet their interventions are followed by reduced neutrality in human comments, with shifts toward gratitude, admiration, and optimism and away from confusion. These findings indicate that even a small number of bots are associated with changes in both timing and emotional dynamics of developer communication.

Molecular property prediction is becoming one of the major applications of graph learning in Web-based services, e.g., online protein structure prediction and drug discovery. A key challenge arises in few-shot scenarios, where only a few labeled molecules are available for predicting unseen properties. Recently, several studies have used in-context learning to capture relationships among molecules and properties, but they face two limitations in: (1) exploiting prior knowledge of functional groups that are causally linked to properties and (2) identifying key substructures directly correlated with properties. We propose CaMol, a context-aware graph causality inference framework, to address these challenges by using a causal inference perspective, assuming that each molecule consists of a latent causal structure that determines a specific property. First, we introduce a context graph that encodes chemical knowledge by linking functional groups, molecules, and properties to guide the discovery of causal substructures. Second, we propose a learnable atom masking strategy to disentangle causal substructures from confounding ones. Third, we introduce a distribution intervener that applies backdoor adjustment by combining causal substructures with chemically grounded confounders, disentangling causal effects from real-world chemical variations. Experiments on diverse molecular datasets showed that CaMol achieved superior accuracy and sample efficiency in few-shot tasks, showing its generalizability to unseen properties. Also, the discovered causal substructures were strongly aligned with chemical knowledge about functional groups, supporting the model interpretability.

Credit risk models are a critical decision-support tool for financial institutions, yet tightening data-protection rules (e.g., GDPR, CCPA) increasingly prohibit cross-border sharing of borrower data, even as these models benefit from cross-institution learning. Traditional default prediction suffers from two limitations: binary classification ignores default timing, treating early defaulters (high loss) equivalently to late defaulters (low loss), and centralized training violates emerging regulatory constraints. We propose a Federated Survival Learning framework with Bayesian Differential Privacy (FSL-BDP) that models time-to-default trajectories without centralizing sensitive data. The framework provides Bayesian (data-dependent) differential privacy (DP) guarantees while enabling institutions to jointly learn risk dynamics. Experiments on three real-world credit datasets (LendingClub, SBA, Bondora) show that federation fundamentally alters the relative effectiveness of privacy mechanisms. While classical DP performs better than Bayesian DP in centralized settings, the latter benefits substantially more from federation (+7.0\% vs +1.4\%), achieving near parity of non-private performance and outperforming classical DP in the majority of participating clients. This ranking reversal yields a key decision-support insight: privacy mechanism selection should be evaluated in the target deployment architecture, rather than centralized benchmarks. These findings provide actionable guidance for practitioners designing privacy-preserving decision support systems in regulated, multi-institutional environments.

Shape-morphing soft materials can enable diverse target morphologies through voxel-level material distribution design, offering significant potential for various applications. Despite progress in basic shape-morphing design with simple geometries, achieving advanced applications such as conformal implant deployment or aerodynamic morphing requires accurate and diverse morphing designs on complex geometries, which remains challenging. Here, we present a Spectral and Spatial Neural Operator (S2NO), which enables high-fidelity morphing prediction on complex geometries. S2NO effectively captures global and local morphing behaviours on irregular computational domains by integrating Laplacian eigenfunction encoding and spatial convolutions. Combining S2NO with evolutionary algorithms enables voxel-level optimisation of material distributions for shape morphing programming on various complex geometries, including irregular-boundary shapes, porous structures, and thin-walled structures. Furthermore, the neural operator's discretisation-invariant property enables super-resolution material distribution design, further expanding the diversity and complexity of morphing design. These advancements significantly improve the efficiency and capability of programming complex shape morphing.

Large Language Models (LLMs) adapted via contrastive learning excel in general representation learning but struggle in vertical domains like chemistry and law, primarily due to a lack of domain-specific knowledge. This work identifies a core bottleneck: the prevailing ``LLM+CL'' paradigm focuses on semantic alignment but cannot perform knowledge acquisition, leading to failures on specialized terminology. To bridge this gap, we propose Learn Before Represent (LBR), a novel two-stage framework. LBR first injects domain knowledge via an Information Bottleneck-Constrained Generative Learning stage, preserving the LLM's causal attention to maximize knowledge acquisition while compressing semantics. It then performs Generative-Refined Contrastive Learning on the compressed representations for alignment. This approach maintains architectural consistency and resolves the objective conflict between generative and contrastive learning. Extensive experiments on medical, chemistry, and code retrieval tasks show that LBR significantly outperforms strong baselines. Our work establishes a new paradigm for building accurate and robust representations in vertical domains.

Clustering is a fundamental tool that has garnered significant interest across a wide range of applications including text analysis. To improve clustering accuracy, many researchers have incorporated background knowledge, typically in the form of must-link and cannot-link constraints, to guide the clustering process. With the recent advent of large language models (LLMs), there is growing interest in improving clustering quality through LLM-based automatic constraint generation. In this paper, we propose a novel constraint-generation approach that reduces resource consumption by generating constraint sets rather than using traditional pairwise constraints. This approach improves both query efficiency and constraint accuracy compared to state-of-the-art methods. We further introduce a constrained clustering algorithm tailored to the characteristics of LLM-generated constraints. Our method incorporates a confidence threshold and a penalty mechanism to address potentially inaccurate constraints. We evaluate our approach on five text datasets, considering both the cost of constraint generation and the overall clustering performance. The results show that our method achieves clustering accuracy comparable to the state-of-the-art algorithms while reducing the number of LLM queries by more than 20 times.

We propose Comprehensive Robust Dynamic Mode Decomposition (CR-DMD), a novel framework that robustifies the entire DMD process - from mode extraction to dimensional reduction - against mixed noise. Although standard DMD widely used for uncovering spatio-temporal patterns and constructing low-dimensional models of dynamical systems, it suffers from significant performance degradation under noise due to its reliance on least-squares estimation for computing the linear time evolution operator. Existing robust variants typically modify the least-squares formulation, but they remain unstable and fail to ensure faithful low-dimensional representations. First, we introduce a convex optimization-based preprocessing method designed to effectively remove mixed noise, achieving accurate and stable mode extraction. Second, we propose a new convex formulation for dimensional reduction that explicitly links the robustly extracted modes to the original noisy observations, constructing a faithful representation of the original data via a sparse weighted sum of the modes. Both stages are efficiently solved by a preconditioned primal-dual splitting method. Experiments on fluid dynamics datasets demonstrate that CR-DMD consistently outperforms state-of-the-art robust DMD methods in terms of mode accuracy and fidelity of low-dimensional representations under noisy conditions.

Fine-tuning large language models on downstream tasks is crucial for realizing their cross-domain potential but often relies on sensitive data, raising privacy concerns. Differential privacy (DP) offers rigorous privacy guarantees and has been widely adopted in fine-tuning; however, naively injecting noise across the high-dimensional parameter space creates perturbations with large norms, degrading performance and destabilizing training. To address this issue, we propose DP-SFT, a two-stage subspace fine-tuning method that substantially reduces noise magnitude while preserving formal DP guarantees. Our intuition is that, during fine-tuning, significant parameter updates lie within a low-dimensional, task-specific subspace, while other directions change minimally. Hence, we only inject DP noise into this subspace to protect privacy without perturbing irrelevant parameters. In phase one, we identify the subspace by analyzing principal gradient directions to capture task-specific update signals. In phase two, we project full gradients onto this subspace, add DP noise, and map the perturbed gradients back to the original parameter space for model updates, markedly lowering noise impact. Experiments on multiple datasets demonstrate that DP-SFT enhances accuracy and stability under rigorous DP constraints, accelerates convergence, and achieves substantial gains over DP fine-tuning baselines.

Vision-as-inverse-graphics, the concept of reconstructing an image as an editable graphics program is a long-standing goal of computer vision. Yet even strong VLMs aren't able to achieve this in one-shot as they lack fine-grained spatial and physical grounding capability. Our key insight is that closing this gap requires interleaved multimodal reasoning through iterative execution and verification. Stemming from this, we present VIGA (Vision-as-Inverse-Graphic Agent) that starts from an empty world and reconstructs or edits scenes through a closed-loop write-run-render-compare-revise procedure. To support long-horizon reasoning, VIGA combines (i) a skill library that alternates generator and verifier roles and (ii) an evolving context memory that contains plans, code diffs, and render history. VIGA is task-agnostic as it doesn't require auxiliary modules, covering a wide range of tasks such as 3D reconstruction, multi-step scene editing, 4D physical interaction, and 2D document editing, etc. Empirically, we found VIGA substantially improves one-shot baselines on BlenderGym (35.32%) and SlideBench (117.17%). Moreover, VIGA is also model-agnostic as it doesn't require finetuning, enabling a unified protocol to evaluate heterogeneous foundation VLMs. To better support this protocol, we introduce BlenderBench, a challenging benchmark that stress-tests interleaved multimodal reasoning with graphics engine, where VIGA improves by 124.70%.

Large Language Model agents are reshaping the industrial landscape. However, most practical agents remain human-designed because tasks differ widely, making them labor-intensive to build. This situation poses a central question: can we automatically create and adapt domain agents in the wild? While several recent approaches have sought to automate agent creation, they typically treat agent generation as a black-box procedure and rely solely on final performance metrics to guide the process. Such strategies overlook critical evidence explaining why an agent succeeds or fails, and often require high computational costs. To address these limitations, we propose ReCreate, an experience-driven framework for the automatic creation of domain agents. ReCreate systematically leverages agent interaction histories, which provide rich concrete signals on both the causes of success or failure and the avenues for improvement. Specifically, we introduce an agent-as-optimizer paradigm that effectively learns from experience via three key components: (i) an experience storage and retrieval mechanism for on-demand inspection; (ii) a reasoning-creating synergy pipeline that maps execution experience into scaffold edits; and (iii) hierarchical updates that abstract instance-level details into reusable domain patterns. In experiments across diverse domains, ReCreate consistently outperforms human-designed agents and existing automated agent generation methods, even when starting from minimal seed scaffolds.

High-dimensional option pricing and hedging present significant challenges in quantitative finance, where traditional PDE-based methods struggle with the curse of dimensionality. The BSDE framework offers a computationally efficient alternative to PDE-based methods, and recently proposed deep BSDE solvers, generally utilizing conventional Multi-Layer Perceptrons (MLPs), build upon this framework to provide a scalable alternative to numerical BSDE solvers. In this research, we show that although such MLP-based deep BSDEs demonstrate promising results in option pricing, there remains room for improvement regarding hedging performance. To address this issue, we introduce KANHedge, a novel BSDE-based hedger that leverages Kolmogorov-Arnold Networks (KANs) within the BSDE framework. Unlike conventional MLP approaches that use fixed activation functions, KANs employ learnable B-spline activation functions that provide enhanced function approximation capabilities for continuous derivatives. We comprehensively evaluate KANHedge on both European and American basket options across multiple dimensions and market conditions. Our experimental results demonstrate that while KANHedge and MLP achieve comparable pricing accuracy, KANHedge provides improved hedging performance. Specifically, KANHedge achieves considerable reductions in hedging cost metrics, demonstrating enhanced risk control capabilities.

Large Language Models (LLMs) can now solve entire exams directly from uploaded PDF assessments, raising urgent concerns about academic integrity and the reliability of grades and credentials. Existing watermarking techniques either operate at the token level or assume control over the model's decoding process, making them ineffective when students query proprietary black-box systems with instructor-provided documents. We present Integrity Shield, a document-layer watermarking system that embeds schema-aware, item-level watermarks into assessment PDFs while keeping their human-visible appearance unchanged. These watermarks consistently prevent MLLMs from answering shielded exam PDFs and encode stable, item-level signatures that can be reliably recovered from model or student responses. Across 30 exams spanning STEM, humanities, and medical reasoning, Integrity Shield achieves exceptionally high prevention (91-94% exam-level blocking) and strong detection reliability (89-93% signature retrieval) across four commercial MLLMs. Our demo showcases an interactive interface where instructors upload an exam, preview watermark behavior, and inspect pre/post AI performance & authorship evidence.

In this paper, we propose a distributed framework for reducing the dimensionality of high-dimensional, large-scale, heterogeneous matrix-variate time series data using a factor model. The data are first partitioned column-wise (or row-wise) and allocated to node servers, where each node estimates the row (or column) loading matrix via two-dimensional tensor PCA. These local estimates are then transmitted to a central server and aggregated, followed by a final PCA step to obtain the global row (or column) loading matrix estimator. Given the estimated loading matrices, the corresponding factor matrices are subsequently computed. Unlike existing distributed approaches, our framework preserves the latent matrix structure, thereby improving computational efficiency and enhancing information utilization. We also discuss row- and column-wise clustering procedures for settings in which the group memberships are unknown. Furthermore, we extend the analysis to unit-root nonstationary matrix-variate time series. Asymptotic properties of the proposed method are derived for the diverging dimension of the data in each computing unit and the sample size $T$. Simulation results assess the computational efficiency and estimation accuracy of the proposed framework, and real data applications further validate its predictive performance.

We present a convolutional neural network approach for classifying proper names by language and entity type. Our model, Onomas-CNN X, combines parallel convolution branches with depthwise-separable operations and hierarchical classification to process names efficiently on CPU hardware. We evaluate the architecture on a large multilingual dataset covering 104 languages and four entity types (person, organization, location, other). Onomas-CNN X achieves 92.1% accuracy while processing 2,813 names per second on a single CPU core - 46 times faster than fine-tuned XLM-RoBERTa with comparable accuracy. The model reduces energy consumption by a factor of 46 compared to transformer baselines. Our experiments demonstrate that specialized CNN architectures remain competitive with large pre-trained models for focused NLP tasks when sufficient training data exists.

Accurate forecasting of infectious disease dynamics is critical for public health planning and intervention. Human mobility plays a central role in shaping the spatial spread of epidemics, but mobility data are noisy, indirect, and difficult to integrate reliably with disease records. Meanwhile, epidemic case time series are typically short and reported at coarse temporal resolution. These conditions limit the effectiveness of parameter-heavy mobility-aware forecasters that rely on clean and abundant data. In this work, we propose the Mobility-Informed Causal Adapter (MiCA), a lightweight and architecture-agnostic module for epidemic forecasting. MiCA infers mobility relations through causal discovery and integrates them into temporal forecasting models via gated residual mixing. This design allows lightweight forecasters to selectively exploit mobility-derived spatial structure while remaining robust under noisy and data-limited conditions, without introducing heavy relational components such as graph neural networks or full attention. Extensive experiments on four real-world epidemic datasets, including COVID-19 incidence, COVID-19 mortality, influenza, and dengue, show that MiCA consistently improves lightweight temporal backbones, achieving an average relative error reduction of 7.5\% across forecasting horizons. Moreover, MiCA attains performance competitive with SOTA spatio-temporal models while remaining lightweight.

This paper proposes the soft Bayesian context tree model (Soft-BCT), which is a novel BCT model for real-valued time series. The Soft-BCT considers soft (probabilistic) splits of the context space, instead of hard (deterministic) splits of the context space as in the previous BCT for real-valued time series. A learning algorithm of the Soft-BCT is proposed based on the variational inference. For some real-world datasets, the Soft-BCT demonstrates almost the same or superior performance to the previous BCT.

Marker-based landing is widely used in drone delivery and return-to-base systems for its simplicity and reliability. However, most approaches assume idealized landing site visibility and sensor performance, limiting robustness in complex urban settings. We present a simulation-based evaluation suite on the AirSim platform with systematically varied urban layouts, lighting, and weather to replicate realistic operational diversity. Using onboard camera sensors (RGB for marker detection and depth for obstacle avoidance), we benchmark two heuristic coverage patterns and a reinforcement learning-based agent, analyzing how exploration strategy and scene complexity affect success rate, path efficiency, and robustness. Results underscore the need to evaluate marker-based autonomous landing under diverse, sensor-relevant conditions to guide the development of reliable aerial navigation systems.

The evolution of Large Language Models (LLMs) into autonomous agents has expanded the scope of AI coding from localized code generation to complex, repository-level, and execution-driven problem solving. However, current benchmarks predominantly evaluate code logic in static contexts, neglecting the dynamic, full-process requirements of real-world engineering, particularly in backend development which demands rigorous environment configuration and service deployment. To address this gap, we introduce ABC-Bench, a benchmark explicitly designed to evaluate agentic backend coding within a realistic, executable workflow. Using a scalable automated pipeline, we curated 224 practical tasks spanning 8 languages and 19 frameworks from open-source repositories. Distinct from previous evaluations, ABC-Bench require the agents to manage the entire development lifecycle from repository exploration to instantiating containerized services and pass the external end-to-end API tests. Our extensive evaluation reveals that even state-of-the-art models struggle to deliver reliable performance on these holistic tasks, highlighting a substantial disparity between current model capabilities and the demands of practical backend engineering. Our code is available at https://github.com/OpenMOSS/ABC-Bench.

Furniture assembly is a crucial yet challenging task for robots, requiring precise dual-arm coordination where one arm manipulates parts while the other provides collaborative support and stabilization. To accomplish this task more effectively, robots need to actively adapt support strategies throughout the long-horizon assembly process, while also generalizing across diverse part geometries. We propose A3D, a framework which learns adaptive affordances to identify optimal support and stabilization locations on furniture parts. The method employs dense point-level geometric representations to model part interaction patterns, enabling generalization across varied geometries. To handle evolving assembly states, we introduce an adaptive module that uses interaction feedback to dynamically adjust support strategies during assembly based on previous interactions. We establish a simulation environment featuring 50 diverse parts across 8 furniture types, designed for dual-arm collaboration evaluation. Experiments demonstrate that our framework generalizes effectively to diverse part geometries and furniture categories in both simulation and real-world settings.

Online financial services constitute an essential component of contemporary web ecosystems, yet their openness introduces substantial exposure to fraud that harms vulnerable users and weakens trust in digital finance. Such threats have become a significant web harm that erodes societal fairness and affects the well being of online communities. However, existing detection methods based on graph neural networks (GNNs) struggle with two persistent challenges: (1) fraud camouflage, where malicious transactions mimic benign behaviors to evade detection, and (2) long-tailed data distributions, which obscure rare but critical fraudulent cases. To fill these gaps, we propose HIMVH, a Hippocampus-Inspired Multi-View Hypergraph learning model for web finance fraud detection. Specifically, drawing inspiration from the scene conflict monitoring role of the hippocampus, we design a cross-view inconsistency perception module that captures subtle discrepancies and behavioral heterogeneity across multiple transaction views. This module enables the model to identify subtle cross-view conflicts for detecting online camouflaged fraudulent behaviors. Furthermore, inspired by the match-mismatch novelty detection mechanism of the CA1 region, we introduce a novelty-aware hypergraph learning module that measures feature deviations from neighborhood expectations and adaptively reweights messages, thereby enhancing sensitivity to online rare fraud patterns in the long-tailed settings. Extensive experiments on six web-based financial fraud datasets demonstrate that HIMVH achieves 6.42\% improvement in AUC, 9.74\% in F1 and 39.14\% in AP on average over 15 SOTA models.

Fairness in automated decision-making has become a critical concern, particularly in high-pressure healthcare scenarios such as emergency triage, where fast and equitable decisions are essential. Process mining is increasingly investigating fairness. There is a growing area focusing on fairness-aware algorithms. So far, we know less how these concepts perform on empirical healthcare data or how they cover aspects of justice theory. This study addresses this research problem and proposes a process mining approach to assess fairness in triage by linking real-life event logs with conceptual dimensions of justice. Using the MIMICEL event log (as derived from MIMIC-IV ED), we analyze time, re-do, deviation and decision as process outcomes, and evaluate the influence of age, gender, race, language and insurance using the Kruskal-Wallis, Chi-square and effect size measurements. These outcomes are mapped to justice dimensions to support the development of a conceptual framework. The results demonstrate which aspects of potential unfairness in high-acuity and sub-acute surface. In this way, this study contributes empirical insights that support further research in responsible, fairness-aware process mining in healthcare.

In embodied artificial intelligence, enabling heterogeneous robot teams to execute long-horizon tasks from high-level instructions remains a critical challenge. While large language models (LLMs) show promise in instruction parsing and preliminary planning, they exhibit limitations in long-term reasoning and dynamic multi-robot coordination. We propose Hierarchical Autonomous Intelligent Multi-Robot Planning(H-AIM), a novel embodied multi-robot task planning framework that addresses these issues through a three-stage cascaded architecture: 1) It leverages an LLM to parse instructions and generate Planning Domain Definition Language (PDDL) problem descriptions, thereby transforming commands into formal planning problems; 2) It combines the semantic reasoning of LLMs with the search capabilities of a classical planner to produce optimized action sequences; 3) It compiles the resulting plan into behavior trees for reactive control. The framework supports dynamically sized heterogeneous robot teams via a shared blackboard mechanism for communication and state synchronization. To validate our approach, we introduce the MACE-THOR benchmark dataset, comprising 42 complex tasks across 8 distinct household layouts. Experimental results demonstrate that H-AIM achieves a remarkable performance improvement, elevating the task success rate from 12% to 55% and boosting the goal condition recall from 32% to 72% against the strongest baseline, LaMMA-P.

Reinforcement Learning with Verifiable Rewards (RLVR) is highly effective for enhancing LLM reasoning, yet recent evidence shows models like Qwen 2.5 achieve significant gains even with spurious or incorrect rewards. We investigate this phenomenon and identify a "Perplexity Paradox": spurious RLVR triggers a divergence where answer-token perplexity drops while prompt-side coherence degrades, suggesting the model is bypassing reasoning in favor of memorization. Using Path Patching, Logit Lens, JSD analysis, and Neural Differential Equations, we uncover a hidden Anchor-Adapter circuit that facilitates this shortcut. We localize a Functional Anchor in the middle layers (L18-20) that triggers the retrieval of memorized solutions, followed by Structural Adapters in later layers (L21+) that transform representations to accommodate the shortcut signal. Finally, we demonstrate that scaling specific MLP keys within this circuit allows for bidirectional causal steering-artificially amplifying or suppressing contamination-driven performance. Our results provide a mechanistic roadmap for identifying and mitigating data contamination in RLVR-tuned models. Code is available at https://github.com/idwts/How-RLVR-Activates-Memorization-Shortcuts.

We examine whether large language models (LLMs) can predict biased decision-making in conversational settings, and whether their predictions capture not only human cognitive biases but also how those effects change under cognitive load. In a pre-registered study (N = 1,648), participants completed six classic decision-making tasks via a chatbot with dialogues of varying complexity. Participants exhibited two well-documented cognitive biases: the Framing Effect and the Status Quo Bias. Increased dialogue complexity resulted in participants reporting higher mental demand. This increase in cognitive load selectively, but significantly, increased the effect of the biases, demonstrating the load-bias interaction. We then evaluated whether LLMs (GPT-4, GPT-5, and open-source models) could predict individual decisions given demographic information and prior dialogue. While results were mixed across choice problems, LLM predictions that incorporated dialogue context were significantly more accurate in several key scenarios. Importantly, their predictions reproduced the same bias patterns and load-bias interactions observed in humans. Across all models tested, the GPT-4 family consistently aligned with human behavior, outperforming GPT-5 and open-source models in both predictive accuracy and fidelity to human-like bias patterns. These findings advance our understanding of LLMs as tools for simulating human decision-making and inform the design of conversational agents that adapt to user biases.

Large Language Models (LLMs) have demonstrated remarkable reasoning capabilities but often grapple with reliability challenges like hallucinations. While Knowledge Graphs (KGs) offer explicit grounding, existing paradigms of KG-augmented LLMs typically exhibit cognitive rigidity--applying homogeneous search strategies that render them vulnerable to instability under neighborhood noise and structural misalignment leading to reasoning stagnation. To address these challenges, we propose CoG, a training-free framework inspired by Dual-Process Theory that mimics the interplay between intuition and deliberation. First, functioning as the fast, intuitive process, the Relational Blueprint Guidance module leverages relational blueprints as interpretable soft structural constraints to rapidly stabilize the search direction against noise. Second, functioning as the prudent, analytical process, the Failure-Aware Refinement module intervenes upon encountering reasoning impasses. It triggers evidence-conditioned reflection and executes controlled backtracking to overcome reasoning stagnation. Experimental results on three benchmarks demonstrate that CoG significantly outperforms state-of-the-art approaches in both accuracy and efficiency.

Machine learning is finding its application in a multitude of areas in science and research, and Climate and Earth Sciences is no exception to this trend. Operational forecasting systems based on data-driven approaches and machine learning methods deploy models for periodic forecasting. Wildfire danger assessment using machine learning has garnered significant interest in the last decade, as conventional methods often overestimate the risk of wildfires. In this work, we present the code OpFML: Operational Forecasting with Machine Learning. OpFML is a configurable and adaptable pipeline that can be utilized to serve a machine learning model for periodic forecasting. We further demonstrate the capabilities of the pipeline through its application to daily Fire Danger Index forecasting and outline its various features.

Large Language Models (LLMs) based autonomous agents demonstrate multifaceted capabilities to contribute substantially to economic production. However, existing benchmarks remain focused on single agentic capability, failing to capture long-horizon real-world scenarios. Moreover, the reliance on human-in-the-loop feedback for realistic tasks creates a scalability bottleneck, hindering automated rollout collection and evaluation. To bridge this gap, we introduce AgencyBench, a comprehensive benchmark derived from daily AI usage, evaluating 6 core agentic capabilities across 32 real-world scenarios, comprising 138 tasks with specific queries, deliverables, and rubrics. These scenarios require an average of 90 tool calls, 1 million tokens, and hours of execution time to resolve. To enable automated evaluation, we employ a user simulation agent to provide iterative feedback, and a Docker sandbox to conduct visual and functional rubric-based assessment. Experiments reveal that closed-source models significantly outperform open-source models (48.4% vs 32.1%). Further analysis reveals significant disparities across models in resource efficiency, feedback-driven self-correction, and specific tool-use preferences. Finally, we investigate the impact of agentic scaffolds, observing that proprietary models demonstrate superior performance within their native ecosystems (e.g., Claude-4.5-Opus via Claude-Agent-SDK), while open-source models exhibit distinct performance peaks, suggesting potential optimization for specific execution frameworks. AgencyBench serves as a critical testbed for next-generation agents, highlighting the necessity of co-optimizing model architecture with agentic frameworks. We believe this work sheds light on the future direction of autonomous agents, and we release the full benchmark and evaluation toolkit at https://github.com/GAIR-NLP/AgencyBench.

Sequential knowledge editing in large language models often causes catastrophic collapse of the model's general abilities, especially for parameter-modifying methods. Existing approaches mitigate this issue through heuristic constraints on parameter updates, yet the mechanisms underlying such degradation remain insufficiently understood. In this work, we present a spectral analysis of sequential knowledge editing and show that a model's general abilities are closely associated with dominant singular directions of pretrained weight matrices. These directions are highly sensitive to perturbations and are progressively disrupted by repeated edits, closely tracking the collapse in both editing efficacy and general performance. Building on this insight, we propose REVIVE, a plug-and-play framework that stabilizes sequential editing by explicitly preserving the dominant singular subspace. REVIVE represents parameter updates in the spectral basis of the original weights and filters components that would interfere with the protected region. Extensive experiments across multiple models and benchmarks show that REVIVE consistently improves editing efficacy while substantially preserving general abilities under long-horizon sequential editing, including extreme settings with up to 20,000 edits.

Large Audio Language Models (LALMs) excel at semantic and paralinguistic tasks, yet their ability to perceive the fundamental physical attributes of audio such as pitch, loudness, and spatial location remains under-explored. To bridge this gap, we introduce SonicBench, a psychophysically grounded benchmark that systematically evaluates 12 core physical attributes across five perceptual dimensions. Unlike previous datasets, SonicBench uses a controllable generation toolbox to construct stimuli for two complementary paradigms: recognition (absolute judgment) and comparison (relative judgment). This design allows us to probe not only sensory precision but also relational reasoning capabilities, a domain where humans typically exhibit greater proficiency. Our evaluation reveals a substantial deficiency in LALMs' foundational auditory understanding; most models perform near random guessing and, contrary to human patterns, fail to show the expected advantage on comparison tasks. Furthermore, explicit reasoning yields minimal gains. However, our linear probing analysis demonstrates crucially that frozen audio encoders do successfully capture these physical cues (accuracy at least 60%), suggesting that the primary bottleneck lies in the alignment and decoding stages, where models fail to leverage the sensory signals they have already captured.

We study the reasoning behavior of large language models (LLMs) under limited computation budgets. In such settings, producing useful partial solutions quickly is often more practical than exhaustive reasoning, which incurs high inference costs. Many real-world tasks, such as trip planning, require models to deliver the best possible output within a fixed reasoning budget. We introduce an anytime reasoning framework and the Anytime Index, a metric that quantifies how effectively solution quality improves as reasoning tokens increase. To further enhance efficiency, we propose an inference-time self-improvement method using LLM-synthesized preference data, where models learn from their own reasoning comparisons to produce better intermediate solutions. Experiments on NaturalPlan (Trip), AIME, and GPQA datasets show consistent gains across Grok-3, GPT-oss, GPT-4.1/4o, and LLaMA models, improving both reasoning quality and efficiency under budget constraints.

RL-based agentic search enables LLMs to solve complex questions via dynamic planning and external search. While this approach significantly enhances accuracy with agent policies optimized via large-scale reinforcement learning, we identify a critical gap in reliability: these agents fail to recognize their reasoning boundaries and rarely admit ``I DON'T KNOW'' (IDK) even when evidence is insufficient or reasoning reaches its limit. The lack of reliability often leads to plausible but unreliable answers, introducing significant risks in many real-world scenarios. To this end, we propose Boundary-Aware Policy Optimization (BAPO), a novel RL framework designed to cultivate reliable boundary awareness without compromising accuracy. BAPO introduces two key components: (i) a group-based boundary-aware reward that encourages an IDK response only when the reasoning reaches its limit, and (ii) an adaptive reward modulator that strategically suspends this reward during early exploration, preventing the model from exploiting IDK as a shortcut. Extensive experiments on four benchmarks demonstrate that BAPO substantially enhances the overall reliability of agentic search.

Quality of Service (QoS) prediction is one of the most fundamental problems in service computing and personalized recommendation. In the problem, there is a set of users and services, each associated with a set of descriptive features. Interactions between users and services produce feedback values, typically represented as numerical QoS metrics such as response time or availability. Given the observed feedback for a subset of user-service pairs, the goal is to predict the QoS values for the remaining pairs. A key challenge in QoS prediction is the inherent sparsity of user-service interactions, as only a small subset of feedback values is typically observed. To address this, we propose a self-augmented strategy that leverages a model's own predictions for iterative refinement. In particular, we partially mask the predicted values and feed them back into the model to predict again. Building on this idea, we design a self-augmented mixture-of-experts model, where multiple expert networks iteratively and collaboratively estimate QoS values. We find that the iterative augmentation process naturally aligns with the MoE architecture by enabling inter-expert communication: in the second round, each expert receives the first-round predictions and refines its output accordingly. Experiments on benchmark datasets show that our method outperforms existing baselines and achieves competitive results.

Recent advances in generative modeling can create remarkably realistic synthetic videos, making it increasingly difficult for humans to distinguish them from real ones and necessitating reliable detection methods. However, two key limitations hinder the development of this field. \textbf{From the dataset perspective}, existing datasets are often limited in scale and constructed using outdated or narrowly scoped generative models, making it difficult to capture the diversity and rapid evolution of modern generative techniques. Moreover, the dataset construction process frequently prioritizes quantity over quality, neglecting essential aspects such as semantic diversity, scenario coverage, and technological representativeness. \textbf{From the benchmark perspective}, current benchmarks largely remain at the stage of dataset creation, leaving many fundamental issues and in-depth analysis yet to be systematically explored. Addressing this gap, we propose AIGVDBench, a benchmark designed to be comprehensive and representative, covering \textbf{31} state-of-the-art generation models and over \textbf{440,000} videos. By executing more than \textbf{1,500} evaluations on \textbf{33} existing detectors belonging to four distinct categories. This work presents \textbf{8 in-depth analyses} from multiple perspectives and identifies \textbf{4 novel findings} that offer valuable insights for future research. We hope this work provides a solid foundation for advancing the field of AI-generated video detection. Our benchmark is open-sourced at https://github.com/LongMa-2025/AIGVDBench.

This paper presents the first unified distractor removal method, named IDDR-NGP, which directly operates on Instant-NPG. The method is able to remove a wide range of distractors in 3D scenes, such as snowflakes, confetti, defoliation and petals, whereas existing methods usually focus on a specific type of distractors. By incorporating implicit 3D representations with 2D detectors, we demonstrate that it is possible to efficiently restore 3D scenes from multiple corrupted images. We design the learned perceptual image patch similarity~( LPIPS) loss and the multi-view compensation loss (MVCL) to jointly optimize the rendering results of IDDR-NGP, which could aggregate information from multi-view corrupted images. All of them can be trained in an end-to-end manner to synthesize high-quality 3D scenes. To support the research on distractors removal in implicit 3D representations, we build a new benchmark dataset that consists of both synthetic and real-world distractors. To validate the effectiveness and robustness of IDDR-NGP, we provide a wide range of distractors with corresponding annotated labels added to both realistic and synthetic scenes. Extensive experimental results demonstrate the effectiveness and robustness of IDDR-NGP in removing multiple types of distractors. In addition, our approach achieves results comparable with the existing SOTA desnow methods and is capable of accurately removing both realistic and synthetic distractors.

High-dimensional portfolio optimization faces significant computational challenges under complex constraints, with traditional optimization methods struggling to balance convergence speed and global exploration capability. To address this, firstly, we introduce an enhanced Sharpe ratio-based model that incorporates all constraints into the objective function using adaptive penalty terms, transforming the original constrained problem into an unconstrained single-objective formulation. This approach preserves financial interpretability while simplifying algorithmic implementation. To efficiently solve the resulting high-dimensional optimization problem, we propose a Quantum Hybrid Differential Evolution (QHDE) algorithm, which integrates Quantum-inspired probabilistic behavior into the standard DE framework. QHDE employs a Schrodinger-inspired probabilistic mechanism for population evolution, enabling more flexible and diversified solution updates. To further enhance performance, a good point set-chaos reverse learning strategy is adopted to generate a well-dispersed initial population, and a dynamic elite pool combined with Cauchy-Gaussian hybrid perturbations strengthens global exploration and mitigates premature convergence. Experimental validation on CEC benchmarks and real-world portfolios involving 20 to 80 assets demonstrates that QHDE's performance improves by up to 73.4%. It attains faster convergence, higher solution precision, and greater robustness than seven state-of-the-art counterparts, thereby confirming its suitability for complex, high-dimensional portfolio optimization and advancing quantum-inspired evolutionary research in computational finance.

The accurate identification of antiviral peptides (AVPs) is crucial for novel drug development. However, existing methods still have limitations in capturing complex sequence dependencies and distinguishing confusing samples with high similarity. To address these challenges, we propose AVP-Pro, a novel two-stage predictive framework that integrates adaptive feature fusion and contrastive learning. To comprehensively capture the physicochemical properties and deep-seated patterns of peptide sequences, we constructed a panoramic feature space encompassing 10 distinct descriptors and designed a hierarchical fusion architecture. This architecture integrates self-attention and adaptive gating mechanisms to dynamically modulate the weights of local motifs extracted by CNNs and global dependencies captured by BiLSTMs based on sequence context. Targeting the blurred decision boundary caused by the high similarity between positive and negative sample sequences, we adopted an Online Hard Example Mining (OHEM)-driven contrastive learning strategy enhanced by BLOSUM62. This approach significantly sharpened the model's discriminative power. Model evaluation results show that in the first stage of general AVP identification, the model achieved an accuracy of 0.9531 and an MCC of 0.9064, outperforming existing state-of-the-art (SOTA) methods. In the second stage of functional subtype prediction, combined with a transfer learning strategy, the model realized accurate classification of 6 viral families and 8 specific viruses under small-sample conditions. AVP-Pro provides a powerful and interpretable new tool for the high-throughput screening of antiviral drugs. To further enhance accessibility for users, we have developed a user-friendly web interface, which is available at https://wwwy1031-avp-pro.hf.space.

Test-time adaptation (TTA) refers to adapting a classifier for the test data when the probability distribution of the test data slightly differs from that of the training data of the model. To the best of our knowledge, most of the existing TTA approaches modify the weights of the classifier relying heavily on the architecture. It is unclear as to how these approaches are extendable to generic architectures. In this article, we propose an architecture-agnostic approach to TTA by adding an adapter network pre-processing the input images suitable to the classifier. This adapter is trained using the proposed quantile loss. Unlike existing approaches, we correct for the distribution shift by matching high-dimensional geometric quantiles. We prove theoretically that under suitable conditions minimizing quantile loss can learn the optimal adapter. We validate our approach on CIFAR10-C, CIFAR100-C and TinyImageNet-C by training both classic convolutional and transformer networks on CIFAR10, CIFAR100 and TinyImageNet datasets.

Interpretable graph learning has recently emerged as a popular research topic in machine learning. The goal is to identify the important nodes and edges of an input graph that are crucial for performing a specific graph reasoning task. A number of studies have been conducted in this area, and various benchmark datasets have been proposed to facilitate evaluation. Among them, one of the most challenging is the Spurious-Motif benchmark, introduced at ICLR 2022. The datasets in this synthetic benchmark are deliberately designed to include spurious correlations, making it particularly difficult for models to distinguish truly relevant structures from misleading patterns. As a result, existing methods exhibit significantly worse performance on this benchmark compared to others. In this paper, we focus on improving interpretability on the challenging Spurious-Motif datasets. We demonstrate that the self-reflection technique, commonly used in large language models to tackle complex tasks, can also be effectively adapted to enhance interpretability in datasets with strong spurious correlations. Specifically, we propose a self-reflection framework that can be integrated with existing interpretable graph learning methods. When such a method produces importance scores for each node and edge, our framework feeds these predictions back into the original method to perform a second round of evaluation. This iterative process mirrors how large language models employ self-reflective prompting to reassess their previous outputs. We further analyze the reasons behind this improvement from the perspective of graph representation learning, which motivates us to propose a fine-tuning training method based on this feedback mechanism.

Advances in mechanistic interpretability have identified special attention heads, known as retrieval heads, that are responsible for retrieving information from the context. However, the role of these retrieval heads in improving model performance remains unexplored. This work investigates whether retrieval heads can be leveraged to enhance the long-context capabilities of LLMs. Specifically, we propose RetMask, a method that generates training signals by contrasting normal model outputs with those from an ablated variant in which the retrieval heads are masked. This mechanism-based approach achieves substantial improvements: +2.28 points on HELMET at 128K for Llama-3.1, with +70% gains on generation with citation and +32% on passage re-ranking, while preserving performance on general tasks. Experiments across three model families reveal that the effectiveness depends on retrieval head organization: models with concentrated patterns of retrieval heads respond strongly, while those with distributed patterns show limited gains. This mechanistic relationship validates the function of retrieval heads and demonstrates that mechanistic insights can be transformed into performance enhancements.

Large Language Models (LLMs) frequently exhibit strong translation abilities, even without task-specific fine-tuning. However, the internal mechanisms governing this innate capability remain largely opaque. To demystify this process, we leverage Sparse Autoencoders (SAEs) and introduce a novel framework for identifying task-specific features. Our method first recalls features that are frequently co-activated on translation inputs and then filters them for functional coherence using a PCA-based consistency metric. This framework successfully isolates a small set of **translation initiation** features. Causal interventions demonstrate that amplifying these features steers the model towards correct translation, while ablating them induces hallucinations and off-task outputs, confirming they represent a core component of the model's innate translation competency. Moving from analysis to application, we leverage this mechanistic insight to propose a new data selection strategy for efficient fine-tuning. Specifically, we prioritize training on **mechanistically hard** samples-those that fail to naturally activate the translation initiation features. Experiments show this approach significantly improves data efficiency and suppresses hallucinations. Furthermore, we find these mechanisms are transferable to larger models of the same family. Our work not only decodes a core component of the translation mechanism in LLMs but also provides a blueprint for using internal model mechanism to create more robust and efficient models. The codes are available at https://github.com/flamewei123/AAAI26-translation-Initiation-Features.

In this paper, we introduce a framework for contextual distributionally robust optimization (DRO) that considers the causal and continuous structure of the underlying distribution by developing interpretable and tractable decision rules that prescribe decisions using covariates. We first introduce the causal Sinkhorn discrepancy (CSD), an entropy-regularized causal Wasserstein distance that encourages continuous transport plans while preserving the causal consistency. We then formulate a contextual DRO model with a CSD-based ambiguity set, termed Causal Sinkhorn DRO (Causal-SDRO), and derive its strong dual reformulation where the worst-case distribution is characterized as a mixture of Gibbs distributions. To solve the corresponding infinite-dimensional policy optimization, we propose the Soft Regression Forest (SRF) decision rule, which approximates optimal policies within arbitrary measurable function spaces. The SRF preserves the interpretability of classical decision trees while being fully parametric, differentiable, and Lipschitz smooth, enabling intrinsic interpretation from both global and local perspectives. To solve the Causal-SDRO with parametric decision rules, we develop an efficient stochastic compositional gradient algorithm that converges to an $\varepsilon$-stationary point at a rate of $O(\varepsilon^{-4})$, matching the convergence rate of standard stochastic gradient descent. Finally, we validate our method through numerical experiments on synthetic and real-world datasets, demonstrating its superior performance and interpretability.

The ability to engineer optimized protein variants has transformative potential for biotechnology and medicine. Prior sequence-based optimization methods struggle with the high-dimensional complexities due to the epistasis effect and the disregard for structural constraints. To address this, we propose HADES, a Bayesian optimization method utilizing Hamiltonian dynamics to efficiently sample from a structure-aware approximated posterior. Leveraging momentum and uncertainty in the simulated physical movements, HADES enables rapid transition of proposals toward promising areas. A position discretization procedure is introduced to propose discrete protein sequences from such a continuous state system. The posterior surrogate is powered by a two-stage encoder-decoder framework to determine the structure and function relationships between mutant neighbors, consequently learning a smoothed landscape to sample from. Extensive experiments demonstrate that our method outperforms state-of-the-art baselines in in-silico evaluations across most metrics. Remarkably, our approach offers a unique advantage by leveraging the mutual constraints between protein structure and sequence, facilitating the design of protein sequences with similar structures and optimized properties. The code and data are publicly available at https://github.com/GENTEL-lab/HADES.

LLM role-playing aims to portray arbitrary characters in interactive narratives, yet existing systems often suffer from limited immersion and adaptability. They typically under-model dynamic environmental information and assume largely static scenes and casts, offering insufficient support for multi-character orchestration, scene transitions, and on-the-fly character introduction. We propose an adaptive multi-agent role-playing framework, AdaMARP, featuring an immersive message format that interleaves [Thought], (Action), <Environment>, and Speech, together with an explicit Scene Manager that governs role-playing through discrete actions (init_scene, pick_speaker, switch_scene, add_role, end) accompanied by rationales. To train these capabilities, we construct AdaRPSet for the Actor Model and AdaSMSet for supervising orchestration decisions, and introduce AdaptiveBench for trajectory-level evaluation. Experiments across multiple backbones and model scales demonstrate consistent improvements: AdaRPSet enhances character consistency, environment grounding, and narrative coherence, with an 8B actor outperforming several commercial LLMs, while AdaSMSet enables smoother scene transitions and more natural role introductions, surpassing Claude Sonnet 4.5 using only a 14B LLM.

Contrastive learning has become a leading self- supervised approach to representation learning across domains, including vision, multimodal settings, graphs, and federated learning. However, recent studies have shown that contrastive learning is susceptible to backdoor and data poisoning attacks. In these attacks, adversaries can manipulate pretraining data or model updates to insert hidden malicious behavior. This paper offers a thorough and comparative review of backdoor attacks in contrastive learning. It analyzes threat models, attack methods, target domains, and available defenses. We summarize recent advancements in this area, underline the specific vulnerabilities inherent to contrastive learning, and discuss the challenges and future research directions. Our findings have significant implications for the secure deployment of systems in industrial and distributed environments.

Accurately assessing model confidence is essential for deploying large language models (LLMs) in mission-critical factual domains. While retrieval-augmented generation (RAG) is widely adopted to improve grounding, confidence calibration in RAG settings remains poorly understood. We conduct a systematic study across four benchmarks, revealing that LLMs exhibit poor calibration performance due to noisy retrieved contexts. Specifically, contradictory or irrelevant evidence tends to inflate the model's false certainty, leading to severe overconfidence. To address this, we propose NAACL Rules (Noise-AwAre Confidence CaLibration Rules) to provide a principled foundation for resolving overconfidence under noise. We further design NAACL, a noise-aware calibration framework that synthesizes supervision from about 2K HotpotQA examples guided by these rules. By performing supervised fine-tuning (SFT) with this data, NAACL equips models with intrinsic noise awareness without relying on stronger teacher models. Empirical results show that NAACL yields substantial gains, improving ECE scores by 10.9% in-domain and 8.0% out-of-domain. By bridging the gap between retrieval noise and verbal calibration, NAACL paves the way for both accurate and epistemically reliable LLMs.

Simultaneous Machine Translation (SiMT) requires high-quality translations under strict real-time constraints, which traditional policies with only READ/WRITE actions cannot fully address. We extend the action space of SiMT with four adaptive actions: Sentence_Cut, Drop, Partial_Summarization and Pronominalization, which enable real-time restructuring, omission, and simplification while preserving semantic fidelity. We adapt these actions in a large language model (LLM) framework and construct training references through action-aware prompting. To evaluate both quality and word-level monotonicity, we further develop a latency-aware TTS pipeline that maps textual outputs to speech with realistic timing. Experiments on the ACL60/60 English-Chinese, English-German and English-Japanese benchmarks show that our framework consistently improves semantic metrics and achieves lower delay compared to reference translations and salami-based baselines. Notably, combining Drop and Sentence_Cut leads to consistent improvements in the balance between fluency and latency. These results demonstrate that enriching the action space of LLM-based SiMT provides a promising direction for bridging the gap between human and machine interpretation.

Personalized large language models (LLMs) adapt model behavior to individual users to enhance user satisfaction, yet personalization can inadvertently distort factual reasoning. We show that when personalized LLMs face factual queries, there exists a phenomenon where the model generates answers aligned with a user's prior history rather than the objective truth, resulting in personalization-induced hallucinations that degrade factual reliability and may propagate incorrect beliefs, due to representational entanglement between personalization and factual representations. To address this issue, we propose Factuality-Preserving Personalized Steering (FPPS), a lightweight inference-time approach that mitigates personalization-induced factual distortions while preserving personalized behavior. We further introduce PFQABench, the first benchmark designed to jointly evaluate factual and personalized question answering under personalization. Experiments across multiple LLM backbones and personalization methods show that FPPS substantially improves factual accuracy while maintaining personalized performance.

Physics-informed extreme learning machines (PIELMs) typically impose boundary and initial conditions through penalty terms, yielding only approximate satisfaction that is sensitive to user-specified weights and can propagate errors into the interior solution. This work introduces Null-Space Projected PIELM (NP-PIELM), achieving exact constraint enforcement through algebraic projection in coefficient space. The method exploits the geometric structure of the admissible coefficient manifold, recognizing that it admits a decomposition through the null space of the boundary operator. By characterizing this manifold via a translation-invariant representation and projecting onto the kernel component, optimization is restricted to constraint-preserving directions, transforming the constrained problem into unconstrained least-squares where boundary conditions are satisfied exactly at discrete collocation points. This eliminates penalty coefficients, dual variables, and problem-specific constructions while preserving single-shot training efficiency. Numerical experiments on elliptic and parabolic problems including complex geometries and mixed boundary conditions validate the framework.

Massively Multiplayer Online (MMO) game servers must handle thousands of simultaneous players while maintaining sub-100ms response times. When server load exceeds capacity, traditional approaches either uniformly throttle all message types regardless of importance (damaging gameplay) or apply fixed heuristic rules that fail to adapt to dynamic workloads. This paper presents AFLL (Adaptive Feedback Loop Learning), a real-time load stabilization system that learns the causal relationship between outgoing server messages and subsequent incoming client requests. AFLL employs backpropagation to continuously adjust message type weights, enabling predictive throttling that blocks low-priority messages before overload occurs while guaranteeing critical message delivery. Through controlled experiments with 1,000 concurrent players, AFLL reduced average CPU time by 48.3% (13.2ms to 6.8ms), peak CPU time by 51.7% (54.0ms to 26.1ms), and thread contention by 64.4% (19.6% to 7.0%), while maintaining zero learning overhead through background computation and caching optimizations. The system achieved remarkable reproducibility (CV < 2% across all metrics) and identified a three-stage causal chain linking message blocking to load reduction. AFLL demonstrates that circular causality learning enables practical real-time adaptation for latency-critical systems.

Outlier detection (OD) aims to identify abnormal instances, known as outliers or anomalies, by learning typical patterns of normal data, or inliers. Performing OD under an unsupervised regime-without any information about anomalous instances in the training data-is challenging. A recently observed phenomenon, known as the inlier-memorization (IM) effect, where deep generative models (DGMs) tend to memorize inlier patterns during early training, provides a promising signal for distinguishing outliers. However, existing unsupervised approaches that rely solely on the IM effect still struggle when inliers and outliers are not well-separated or when outliers form dense clusters. To address these limitations, we incorporate active learning to selectively acquire informative labels, and propose IMBoost, a novel framework that explicitly reinforces the IM effect to improve outlier detection. Our method consists of two stages: 1) a warm-up phase that induces and promotes the IM effect, and 2) a polarization phase in which actively queried samples are used to maximize the discrepancy between inlier and outlier scores. In particular, we propose a novel query strategy and tailored loss function in the polarization phase to effectively identify informative samples and fully leverage the limited labeling budget. We provide a theoretical analysis showing that the IMBoost consistently decreases inlier risk while increasing outlier risk throughout training, thereby amplifying their separation. Extensive experiments on diverse benchmark datasets demonstrate that IMBoost not only significantly outperforms state-of-the-art active OD methods but also requires substantially less computational cost.

Constant rescaling of a Riemannian metric appears in many computational settings, often through a global scale parameter that is introduced either explicitly or implicitly. Although this operation is elementary, its consequences are not always made clear in practice and may be confused with changes in curvature, manifold structure, or coordinate representation. In this note we provide a short, self-contained account of constant metric scaling on arbitrary Riemannian manifolds. We distinguish between quantities that change under such a scaling, including norms, distances, volume elements, and gradient magnitudes, and geometric objects that remain invariant, such as the Levi--Civita connection, geodesics, exponential and logarithmic maps, and parallel transport. We also discuss implications for Riemannian optimization, where constant metric scaling can often be interpreted as a global rescaling of step sizes rather than a modification of the underlying geometry. The goal of this note is purely expository and is intended to clarify how a global metric scale parameter can be introduced in Riemannian computation without altering the geometric structures on which these methods rely.

We study whether lightweight symbolic reasoning supervision can improve fix type classification in compact automated program repair models. Small code models are attractive for resource-constrained settings, but they typically produce only a single prediction, making it unclear whether they learn meaningful program structure or rely on shallow correlations. We propose a reasoning distillation approach in which a large teacher model provides structured symbolic reasoning tags alongside fix-type labels. These tags capture high-level causal properties of bugs without relying on free-form explanations. We train a CodeT5-based student model under label-only and reasoning-distilled settings on the IntroClass benchmark. Reasoning supervision consistently improves macro averaged performance, particularly on less frequent bug categories, without increasing model size or complexity. We further analyze the relationship between reasoning accuracy and fix-type prediction, showing that correct reasoning traces strongly correlate with correct predictions, while not fully determining them. Our results suggest that symbolic reasoning distillation is a practical way to improve interpretability and robustness in lightweight program repair models.

As the cost of training large language models continues to increase and high-quality training data become increasingly scarce, selecting high-value samples or synthesizing effective training data under limited data budgets has emerged as a critical research problem. Most existing data selection methods rely on static criteria, such as difficulty, uncertainty, or heuristics, and fail to model the evolving relationship between the model and the data. Inspired by the educational theory of the Zone of Proximal Development (ZPD), we propose ZPD Detector, a data selection framework that adopts a bidirectional perspective between models and data by explicitly modeling the alignment between sample difficulty and the model's current capability. ZPD Detector integrates difficulty calibration, model capability estimation based on Item Response Theory (IRT), and a capability-difficulty matching score to dynamically identify the most informative samples at each learning stage, improving data utilization efficiency; moreover, this dynamic matching strategy provides new insights into training strategy design. All code and data will be released after our work be accepted to support reproducible researc

Restoring critical loads after extreme events demands adaptive control to maintain distribution-grid resilience, yet uncertainty in renewable generation, limited dispatchable resources, and nonlinear dynamics make effective restoration difficult. Reinforcement learning (RL) can optimize sequential decisions under uncertainty, but standard RL often generalizes poorly and requires extensive retraining for new outage configurations or generation patterns. We propose a meta-guided gradient-free RL (MGF-RL) framework that learns a transferable initialization from historical outage experiences and rapidly adapts to unseen scenarios with minimal task-specific tuning. MGF-RL couples first-order meta-learning with evolutionary strategies, enabling scalable policy search without gradient computation while accommodating nonlinear, constrained distribution-system dynamics. Experiments on IEEE 13-bus and IEEE 123-bus test systems show that MGF-RL outperforms standard RL, MAML-based meta-RL, and model predictive control across reliability, restoration speed, and adaptation efficiency under renewable forecast errors. MGF-RL generalizes to unseen outages and renewable patterns while requiring substantially fewer fine-tuning episodes than conventional RL. We also provide sublinear regret bounds that relate adaptation efficiency to task similarity and environmental variation, supporting the empirical gains and motivating MGF-RL for real-time load restoration in renewable-rich distribution grids.

As Large Language Models (LLMs) evolve from static chatbots into autonomous agents capable of tool execution, the landscape of AI safety is shifting from content moderation to action security. However, existing red-teaming frameworks remain bifurcated: they either focus on rigid, script-based text attacks or lack the architectural modularity to simulate complex, multi-turn agentic exploitations. In this paper, we introduce AJAR (Adaptive Jailbreak Architecture for Red-teaming), a proof-of-concept framework designed to bridge this gap through Protocol-driven Cognitive Orchestration. Built upon the robust runtime of Petri, AJAR leverages the Model Context Protocol (MCP) to decouple adversarial logic from the execution loop, encapsulating state-of-the-art algorithms like X-Teaming as standardized, plug-and-play services. We validate the architectural feasibility of AJAR through a controlled qualitative case study, demonstrating its ability to perform stateful backtracking within a tool-use environment. Furthermore, our preliminary exploration of the "Agentic Gap" reveals a complex safety dynamic: while tool usage introduces new injection vectors via code execution, the cognitive load of parameter formatting can inadvertently disrupt persona-based attacks. AJAR is open-sourced to facilitate the standardized, environment-aware evaluation of this emerging attack surface. The code and data are available at https://github.com/douyipu/ajar.

Stochastic gradient descent (SGD) is central to deep learning, yet the dynamical origin of its preference for flatter, more generalizable solutions remains unclear. Here, by analyzing SGD learning dynamics, we identify a nonequilibrium mechanism governing solution selection. Numerical experiments reveal a transient exploratory phase in which SGD trajectories repeatedly escape sharp valleys and transition toward flatter regions of the loss landscape. By using a tractable physical model, we show that the SGD noise reshapes the landscape into an effective potential that favors flat solutions. Crucially, we uncover a transient freezing mechanism: as training proceeds, growing energy barriers suppress inter-valley transitions and ultimately trap the dynamics within a single basin. Increasing the SGD noise strength delays this freezing, which enhances convergence to flatter minima. Together, these results provide a unified physical framework linking learning dynamics, loss-landscape geometry, and generalization, and suggest principles for the design of more effective optimization algorithms.

The increasing integration of renewable energy sources (RESs) into modern power systems presents significant opportunities but also notable challenges, primarily due to the inherent variability of RES generation. Accurate forecasting of RES generation is crucial for maintaining the reliability, stability, and economic efficiency of power system operations. Traditional approaches, such as deterministic methods and stochastic programming, frequently depend on representative scenarios generated through clustering techniques like K-means. However, these methods may fail to fully capture the complex temporal dependencies and non-linear patterns within RES data. This paper introduces a multivariate Long Short-Term Memory (LSTM)-based network designed to forecast RESs generation using their real-world historical data. The proposed model effectively captures long-term dependencies and interactions between different RESs, utilizing historical data from both local and neighboring areas to enhance predictive accuracy. In the case study, we showed that the proposed forecasting approach results in lower CO2 emissions, and a more reliable supply of electric loads.

Steering LLMs is essential for specialized applications such as style-sensitive text rewriting, user-adaptive communication, and toxicity mitigation. Current steering methods, such as prompting-based and activation-based approaches, are widely used to guide model behavior. However, activation-based techniques require deep access to internal layers, while prompting-based steering often fails to provide consistent or fine-grained control. In order to address these limitations, we propose a training-free inference-time logit intervention for controllable generation. Our approach utilizes a statistical token score table derived from z-normalized log-odds of labeled corpora to shift the decoding distribution. Empirical evaluations across three diverse datasets focusing on writing complexity, formality, and toxicity demonstrate that our method effectively steers output characteristics, confirming its broad applicability and task-agnostic nature. Our results show that statistically grounded logit steering can achieve large, consistent, and multi-task control gains: up to +47%p accuracy and 50x f1 improvement.

This study investigates the detection and classification of depressive and non-depressive states using deep learning approaches. Depression is a prevalent mental health disorder that substantially affects quality of life, and early diagnosis can greatly enhance treatment effectiveness and patient care. However, conventional diagnostic methods rely heavily on self-reported assessments, which are often subjective and may lack reliability. Consequently, there is a strong need for objective and accurate techniques to identify depressive states. In this work, a deep learning based framework is proposed for the early detection of depression using EEG signals. EEG data, which capture underlying brain activity and are not influenced by external behavioral factors, can reveal subtle neural changes associated with depression. The proposed approach combines convolutional neural networks (CNNs) and gated recurrent units (GRUs) to jointly extract spatial and temporal features from EEG recordings. The minimum redundancy maximum relevance (MRMR) algorithm is then applied to select the most informative features, followed by classification using a fully connected neural network. The results demonstrate that the proposed model achieves high performance in accurately identifying depressive states, with an overall accuracy of 98.74%. By effectively integrating temporal and spatial information and employing optimized feature selection, this method shows strong potential as a reliable tool for clinical applications. Overall, the proposed framework not only enables accurate early detection of depression but also has the potential to support improved treatment strategies and patient outcomes.

The agent-tool communication loop is a critical attack surface in modern Large Language Model (LLM) agents. Existing Denial-of-Service (DoS) attacks, primarily triggered via user prompts or injected retrieval-augmented generation (RAG) context, are ineffective for this new paradigm. They are fundamentally single-turn and often lack a task-oriented approach, making them conspicuous in goal-oriented workflows and unable to exploit the compounding costs of multi-turn agent-tool interactions. We introduce a stealthy, multi-turn economic DoS attack that operates at the tool layer under the guise of a correctly completed task. Our method adjusts text-visible fields and a template-governed return policy in a benign, Model Context Protocol (MCP)-compatible tool server, optimizing these edits with a Monte Carlo Tree Search (MCTS) optimizer. These adjustments leave function signatures unchanged and preserve the final payload, steering the agent into prolonged, verbose tool-calling sequences using text-only notices. This compounds costs across turns, escaping single-turn caps while keeping the final answer correct to evade validation. Across six LLMs on the ToolBench and BFCL benchmarks, our attack expands tasks into trajectories exceeding 60,000 tokens, inflates costs by up to 658x, and raises energy by 100-560x. It drives GPU KV cache occupancy from <1% to 35-74% and cuts co-running throughput by approximately 50%. Because the server remains protocol-compatible and task outcomes are correct, conventional checks fail. These results elevate the agent-tool interface to a first-class security frontier, demanding a paradigm shift from validating final answers to monitoring the economic and computational cost of the entire agentic process.

The simulation of realistic clinical interactions plays a pivotal role in advancing clinical Large Language Models (LLMs) and supporting medical diagnostic education. Existing approaches and benchmarks rely on generic or LLM-generated dialogue data, which limits the authenticity and diversity of doctor-patient interactions. In this work, we propose the first Chinese patient simulation dataset (Ch-PatientSim), constructed from realistic clinical interaction scenarios to comprehensively evaluate the performance of models in emulating patient behavior. Patients are simulated based on a five-dimensional persona structure. To address issues of the persona class imbalance, a portion of the dataset is augmented using few-shot generation, followed by manual verification. We evaluate various state-of-the-art LLMs and find that most produce overly formal responses that lack individual personality. To address this limitation, we propose a training-free Multi-Stage Patient Role-Playing (MSPRP) framework, which decomposes interactions into three stages to ensure both personalization and realism in model responses. Experimental results demonstrate that our approach significantly improves model performance across multiple dimensions of patient simulation.

Traditionally, AI research in medical diagnosis has largely centered on image analysis. While this has led to notable advancements, the absence of patient-reported symptoms continues to hinder diagnostic accuracy. To address this, we propose a Pre-Consultation Dialogue Framework (PCDF) that mimics real-world diagnostic procedures, where doctors iteratively query patients before reaching a conclusion. Specifically, we simulate diagnostic dialogues between two vision-language models (VLMs): a DocVLM, which generates follow-up questions based on the image and dialogue history, and a PatientVLM, which responds using a symptom profile derived from the ground-truth diagnosis. We additionally conducted a small-scale clinical validation of the synthetic symptoms generated by our framework, with licensed clinicians confirming their clinical relevance, symptom coverage, and overall realism. These findings indicate that the resulting DocVLM-PatientVLM interactions form coherent, multi-turn consultations paired with images and diagnoses, which we then use to fine-tune the DocVLM. This dialogue-based supervision leads to substantial gains over image-only training, highlighting the value of realistic symptom elicitation for diagnosis.

Software is in constant evolution, with developers frequently submitting pull requests (PRs) to introduce new features or fix bugs. Testing PRs is critical to maintaining software quality. Yet, even in projects with extensive test suites, some PR-modified lines remain untested, leaving a "last-mile" regression test gap. Existing test generators typically aim to improve overall coverage, but do not specifically target the uncovered lines in PRs. We present Change And Cover (ChaCo), an LLM-based test augmentation technique that addresses this gap. It makes three contributions: (i) ChaCo considers the PR-specific patch coverage, offering developers augmented tests for code just when it is on the developers' mind. (ii) We identify providing suitable test context as a crucial challenge for an LLM to generate useful tests, and present two techniques to extract relevant test content, such as existing test functions, fixtures, and data generators. (iii) To make augmented tests acceptable for developers, ChaCo carefully integrates them into the existing test suite, e.g., by matching the test's structure and style with the existing tests, and generates a summary of the test addition for developer review. We evaluate ChaCo on 145 PRs from three popular and complex open-source projects - SciPy, Qiskit, and Pandas. The approach successfully helps 30% of PRs achieve full patch coverage, at the cost of $0.11, showing its effectiveness and practicality. Human reviewers find the tests to be worth adding (4.53/5.0), well integrated (4.2/5.0), and relevant to the PR (4.7/5.0). Ablations show test context is crucial for context-aware test generation, leading to 2x coverage. We submitted 12 tests, of which 8 have already been merged, and two previously unknown bugs were exposed and fixed. We envision our approach to be integrated into CI workflows, automating the last mile of regression test augmentation.

Split learning (SL) enables collaborative training of large language models (LLMs) between resource-constrained edge devices and compute-rich servers by partitioning model computation across the network boundary. However, existing SL systems predominantly rely on first-order (FO) optimization, which requires clients to store intermediate quantities such as activations for backpropagation. This results in substantial memory overhead, largely negating benefits of model partitioning. In contrast, zeroth-order (ZO) optimization eliminates backpropagation and significantly reduces memory usage, but often suffers from slow convergence and degraded performance. In this work, we propose HOSL, a novel Hybrid-Order Split Learning framework that addresses this fundamental trade-off between memory efficiency and optimization effectiveness by strategically integrating ZO optimization on the client side with FO optimization on the server side. By employing memory-efficient ZO gradient estimation at the client, HOSL eliminates backpropagation and activation storage, reducing client memory consumption. Meanwhile, server-side FO optimization ensures fast convergence and competitive performance. Theoretically, we show that HOSL achieves a $\mathcal{O}(\sqrt{d_c/TQ})$ rate, which depends on client-side model dimension $d_c$ rather than the full model dimension $d$, demonstrating that convergence improves as more computation is offloaded to the server. Extensive experiments on OPT models (125M and 1.3B parameters) across 6 tasks demonstrate that HOSL reduces client GPU memory by up to 3.7$\times$ compared to the FO method while achieving accuracy within 0.20%-4.23% of this baseline. Furthermore, HOSL outperforms the ZO baseline by up to 15.55%, validating the effectiveness of our hybrid strategy for memory-efficient training on edge devices.

Tree canopy detection from aerial imagery is an important task for environmental monitoring, urban planning, and ecosystem analysis. Simulating real-life data annotation scarcity, the Solafune Tree Canopy Detection competition provides a small and imbalanced dataset of only 150 annotated images, posing significant challenges for training deep models without severe overfitting. In this work, we evaluate five representative architectures, YOLOv11, Mask R-CNN, DeepLabv3, Swin-UNet, and DINOv2, to assess their suitability for canopy segmentation under extreme data scarcity. Our experiments show that pretrained convolution-based models, particularly YOLOv11 and Mask R-CNN, generalize significantly better than pretrained transformer-based models. DeeplabV3, Swin-UNet and DINOv2 underperform likely due to differences between semantic and instance segmentation tasks, the high data requirements of Vision Transformers, and the lack of strong inductive biases. These findings confirm that transformer-based architectures struggle in low-data regimes without substantial pretraining or augmentation and that differences between semantic and instance segmentation further affect model performance. We provide a detailed analysis of training strategies, augmentation policies, and model behavior under the small-data constraint and demonstrate that lightweight CNN-based methods remain the most reliable for canopy detection on limited imagery.

Currently, there are thousands of large pretrained language models (LLMs) available to social scientists. How do we select among them? Using validity, reliability, reproducibility, and replicability as guides, we explore the significance of: (1) model openness, (2) model footprint, (3) training data, and (4) model architectures and fine-tuning. While ex-ante tests of validity (i.e., benchmarks) are often privileged in these discussions, we argue that social scientists cannot altogether avoid validating computational measures (ex-post). Replicability, in particular, is a more pressing guide for selecting language models. Being able to reliably replicate a particular finding that entails the use of a language model necessitates reliably reproducing a task. To this end, we propose starting with smaller, open models, and constructing delimited benchmarks to demonstrate the validity of the entire computational pipeline.

Automated interlinear gloss prediction with neural networks is a promising approach to accelerate language documentation efforts. However, while state-of-the-art models like GlossLM achieve high scores on glossing benchmarks, user studies with linguists have found critical barriers to the usefulness of such models in real-world scenarios. In particular, existing models typically generate morpheme-level glosses but assign them to whole words without predicting the actual morpheme boundaries, making the predictions less interpretable and thus untrustworthy to human annotators. We conduct the first study on neural models that jointly predict interlinear glosses and the corresponding morphological segmentation from raw text. We run experiments to determine the optimal way to train models that balance segmentation and glossing accuracy, as well as the alignment between the two tasks. We extend the training corpus of GlossLM and pretrain PolyGloss, a family of seq2seq multilingual models for joint segmentation and glossing that outperforms GlossLM on glossing and beats various open-source LLMs on segmentation, glossing, and alignment. In addition, we demonstrate that PolyGloss can be quickly adapted to a new dataset via low-rank adaptation.

We study data curation for multimodal reasoning through the NeurIPS 2025 Data Curation for Vision-Language Reasoning (DCVLR) challenge, which isolates dataset selection by fixing the model and training protocol. Using a compact curated dataset derived primarily from Walton Multimodal Cold Start, our submission placed first in the challenge. Through post-competition ablations, we show that difficulty-based example selection on an aligned base dataset is the dominant driver of performance gains. Increasing dataset size does not reliably improve mean accuracy under the fixed training recipe, but mainly reduces run-to-run variance, while commonly used diversity and synthetic augmentation heuristics provide no additional benefit and often degrade performance. These results characterize DCVLR as a saturation-regime evaluation and highlight the central role of alignment and difficulty in data-efficient multimodal reasoning.

Robust Multi-Task Learning (MTL) is crucial for autonomous systems operating in real-world environments, where adverse weather conditions can severely degrade model performance and reliability. In this paper, we introduce RobuMTL, a novel architecture designed to adaptively address visual degradation by dynamically selecting task-specific hierarchical Low-Rank Adaptation (LoRA) modules and a LoRA expert squad based on input perturbations in a mixture-of-experts fashion. Our framework enables adaptive specialization based on input characteristics, improving robustness across diverse real-world conditions. To validate our approach, we evaluated it on the PASCAL and NYUD-v2 datasets and compared it against single-task models, standard MTL baselines, and state-of-the-art methods. On the PASCAL benchmark, RobuMTL delivers a +2.8% average relative improvement under single perturbations and up to +44.4% under mixed weather conditions compared to the MTL baseline. On NYUD-v2, RobuMTL achieves a +9.7% average relative improvement across tasks. The code is available at GitHub.

Finite-State Transducers (FSTs) are effective models for string-to-string rewriting tasks, often providing the efficiency necessary for high-performance applications, but constructing transducers by hand is difficult. In this work, we propose a novel method for automatically constructing unweighted FSTs following the hidden state geometry learned by a recurrent neural network. We evaluate our methods on real-world datasets for morphological inflection, grapheme-to-phoneme prediction, and historical normalization, showing that the constructed FSTs are highly accurate and robust for many datasets, substantially outperforming classical transducer learning algorithms by up to 87% accuracy on held-out test sets.

Breast-Conserving Surgery (BCS) requires precise intraoperative margin assessment to preserve healthy tissue. Deep Ultraviolet Fluorescence Scanning Microscopy (DUV-FSM) offers rapid, high-resolution surface imaging for this purpose; however, the scarcity of annotated DUV data hinders the training of robust deep learning models. To address this, we propose an Self-Supervised Learning (SSL)-guided Latent Diffusion Model (LDM) to generate high-quality synthetic training patches. By guiding the LDM with embeddings from a fine-tuned DINO teacher, we inject rich semantic details of cellular structures into the synthetic data. We combine real and synthetic patches to fine-tune a Vision Transformer (ViT), utilizing patch prediction aggregation for WSI-level classification. Experiments using 5-fold cross-validation demonstrate that our method achieves 96.47 % accuracy and reduces the FID score to 45.72, significantly outperforming class-conditioned baselines.

In the emerging paradigm of edge inference, neural networks (NNs) are partitioned across distributed edge devices that collaboratively perform inference via wireless transmission. However, standard NNs are generally trained in a noiseless environment, creating a mismatch with the noisy channels during edge deployment. In this paper, we address this issue by characterizing the channel-induced performance deterioration as a generalization error against unseen channels. We introduce an augmented NN model that incorporates channel statistics directly into the weight space, allowing us to derive PAC-Bayesian generalization bounds that explicitly quantifies the impact of wireless distortion. We further provide closed-form expressions for practical channels to demonstrate the tractability of these bounds. Inspired by the theoretical results, we propose a channel-aware training algorithm that minimizes a surrogate objective based on the derived bound. Simulations show that the proposed algorithm can effectively improve inference accuracy by leveraging channel statistics, without end-to-end re-training.

Learning physically meaningful spatiotemporal representations from high-resolution multivariate Earth observation data is challenging due to strong local dynamics, long-range teleconnections, multi-scale interactions, and nonstationarity. While ConvLSTM2D is a commonly used baseline, its dense convolutional gating incurs high computational cost and its strictly local receptive fields limit the modeling of long-range spatial structure and disentangled climate dynamics. To address these limitations, we propose FAConvLSTM, a Factorized-Attention ConvLSTM layer designed as a drop-in replacement for ConvLSTM2D that simultaneously improves efficiency, spatial expressiveness, and physical interpretability. FAConvLSTM factorizes recurrent gate computations using lightweight [1 times 1] bottlenecks and shared depthwise spatial mixing, substantially reducing channel complexity while preserving recurrent dynamics. Multi-scale dilated depthwise branches and squeeze-and-excitation recalibration enable efficient modeling of interacting physical processes across spatial scales, while peephole connections enhance temporal precision. To capture teleconnection-scale dependencies without incurring global attention cost, FAConvLSTM incorporates a lightweight axial spatial attention mechanism applied sparsely in time. A dedicated subspace head further produces compact per timestep embeddings refined through temporal self-attention with fixed seasonal positional encoding. Experiments on multivariate spatiotemporal climate data shows superiority demonstrating that FAConvLSTM yields more stable, interpretable, and robust latent representations than standard ConvLSTM, while significantly reducing computational overhead.

Sustainability is becoming increasingly critical in the maritime transport, encompassing both environmental and social impacts, such as Greenhouse Gas (GHG) emissions and navigational safety. Traditional vessel navigation heavily relies on human experience, often lacking autonomy and emission awareness, and is prone to human errors that may compromise safety. In this paper, we propose a Curriculum Reinforcement Learning (CRL) framework integrated with a realistic, data-driven marine simulation environment and a machine learning-based fuel consumption prediction module. The simulation environment is constructed using real-world vessel movement data and enhanced with a Diffusion Model to simulate dynamic maritime conditions. Vessel fuel consumption is estimated using historical operational data and learning-based regression. The surrounding environment is represented as image-based inputs to capture spatial complexity. We design a lightweight, policy-based CRL agent with a comprehensive reward mechanism that considers safety, emissions, timeliness, and goal completion. This framework effectively handles complex tasks progressively while ensuring stable and efficient learning in continuous action spaces. We validate the proposed approach in a sea area of the Indian Ocean, demonstrating its efficacy in enabling sustainable and safe vessel navigation.

Networking is central to the growth and visibility of software engineering research and researchers. However, opportunities and capacities to build such networks are not easily identified and often are unevenly distributed. While networking is often viewed as an individual skill, a researchers workplace, culture and environment significantly influence their motivation and, consequently, the networks they form. This paper explores how factors such as country of residence, immigration status, language, gender, and surrounding context affect researchers' ability to establish professional connections and succeed within the global research ecosystem. Drawing on existing literature and personal experience, this reflective report examines the often-invisible barriers to networking and advocates for a community-driven "expert voice" initiative to acknowledge and address these inequities.

Numerous offline and model-based reinforcement learning systems incorporate world models to emulate the inherent environments. A world model is particularly important in scenarios where direct interactions with the real environment is costly, dangerous, or impractical. The efficacy and interpretability of such world models are notably contingent upon the quality of the underlying training data. In this context, we introduce Action Shapley as an agnostic metric for the judicious and unbiased selection of training data. To facilitate the computation of Action Shapley, we present a randomized dynamic algorithm specifically designed to mitigate the exponential complexity inherent in traditional Shapley value computations. Through empirical validation across five data-constrained real-world case studies, the algorithm demonstrates a computational efficiency improvement exceeding 80\% in comparison to conventional exponential time computations. Furthermore, our Action Shapley-based training data selection policy consistently outperforms ad-hoc training data selection.

The ARC-AGI benchmark series serves as a critical measure of few-shot generalization on novel tasks, a core aspect of intelligence. The ARC Prize 2025 global competition targeted the newly released ARC-AGI-2 dataset, which features greater task complexity compared to its predecessor. The Kaggle competition attracted 1,455 teams and 15,154 entries, with the top score reaching 24% on the ARC-AGI-2 private evaluation set. Paper submissions nearly doubled year-over-year to 90 entries, reflecting the growing research interest in fluid intelligence and abstract reasoning. The defining theme of 2025 is the emergence of the refinement loop -- a per-task iterative program optimization loop guided by a feedback signal. Refinement loops come in a variety of forms, in particular evolutionary program synthesis approaches and application-layer refinements to commercial AI systems. Such refinement loops are also possible in weight space, as evidenced by zero-pretraining deep learning methods which are now achieving competitive performance with remarkably small networks (7M parameters). In parallel, four frontier AI labs (Anthropic, Google DeepMind, OpenAI, and xAI) reported ARC-AGI performance in public model cards in 2025, establishing ARC-AGI as an industry standard benchmark for AI reasoning. However, our analysis indicates that current frontier AI reasoning performance remains fundamentally constrained to knowledge coverage, giving rise to new forms of benchmark contamination. In this paper, we survey the top-performing methods, examine the role of refinement loops in AGI progress, discuss knowledge-dependent overfitting, and preview ARC-AGI-3, which introduces interactive reasoning challenges that require exploration, planning, memory, goal acquisition, and alignment capabilities.

LLMs are increasingly used as third-party judges, yet their reliability when evaluating speakers in dialogue remains poorly understood. We show that LLMs judge identical claims differently depending on framing: the same content elicits different verdicts when presented as a statement to verify ("Is this statement correct?") versus attributed to a speaker ("Is this speaker correct?"). We call this dialogic deference and introduce DialDefer, a framework for detecting and mitigating these framing-induced judgment shifts. Our Dialogic Deference Score (DDS) captures directional shifts that aggregate accuracy obscures. Across nine domains, 3k+ instances, and four models, conversational framing induces large shifts (|DDS| up to 87pp, p < .0001) while accuracy remains stable (<2pp), with effects amplifying 2-4x on naturalistic Reddit conversations. Models can shift toward agreement (deference) or disagreement (skepticism) depending on domain -- the same model ranges from DDS = -53 on graduate-level science to +58 on social judgment. Ablations reveal that human-vs-LLM attribution drives the largest shifts (17.7pp swing), suggesting models treat disagreement with humans as more costly than with AI. Mitigation attempts reduce deference but can over-correct into skepticism, framing this as a calibration problem beyond accuracy optimization.

We propose a methodology to infer collision operators from phase space data of plasma dynamics. Our approach combines a differentiable kinetic simulator, whose core component in this work is a differentiable Fokker-Planck solver, with a gradient-based optimisation method to learn the collisional operators that best describe the phase space dynamics. We test our method using data from two-dimensional Particle-in-Cell simulations of spatially uniform thermal plasmas, and learn the collision operator that captures the self-consistent electromagnetic interaction between finite-size charged particles over a wide variety of simulation parameters. We demonstrate that the learned operators are more accurate than alternative estimates based on particle tracks, while making no prior assumptions about the relevant time-scales of the processes and significantly reducing memory requirements. We find that the retrieved operators, obtained in the non-relativistic regime, are in excellent agreement with theoretical predictions derived for electrostatic scenarios. Our results show that differentiable simulators offer a powerful and computational efficient approach to infer novel operators for a wide rage of problems, such as electromagnetically dominated collisional dynamics and stochastic wave-particle interactions.

Promptable segmentation foundation models such as SAM3 have demonstrated strong generalization capabilities through interactive and concept-based prompting. However, their direct applicability to medical image segmentation remains limited by severe domain shifts, the absence of privileged spatial prompts, and the need to reason over complex anatomical and volumetric structures. Here we present Medical SAM3, a foundation model for universal prompt-driven medical image segmentation, obtained by fully fine-tuning SAM3 on large-scale, heterogeneous 2D and 3D medical imaging datasets with paired segmentation masks and text prompts. Through a systematic analysis of vanilla SAM3, we observe that its performance degrades substantially on medical data, with its apparent competitiveness largely relying on strong geometric priors such as ground-truth-derived bounding boxes. These findings motivate full model adaptation beyond prompt engineering alone. By fine-tuning SAM3's model parameters on 33 datasets spanning 10 medical imaging modalities, Medical SAM3 acquires robust domain-specific representations while preserving prompt-driven flexibility. Extensive experiments across organs, imaging modalities, and dimensionalities demonstrate consistent and significant performance gains, particularly in challenging scenarios characterized by semantic ambiguity, complex morphology, and long-range 3D context. Our results establish Medical SAM3 as a universal, text-guided segmentation foundation model for medical imaging and highlight the importance of holistic model adaptation for achieving robust prompt-driven segmentation under severe domain shift. Code and model will be made available at https://github.com/AIM-Research-Lab/Medical-SAM3.

Deep neural networks constructed from linear maps and positively homogeneous nonlinearities (e.g., ReLU) possess a fundamental gauge symmetry: the network function is invariant to node-wise diagonal rescalings. However, standard gradient descent is not equivariant to this symmetry, causing optimization trajectories to depend heavily on arbitrary parameterizations. Prior work has proposed rescaling-invariant optimization schemes for positively homogeneous networks (e.g., path-based or path-space updates). Our contribution is complementary: we formulate the invariance requirement at the level of the backward adjoint/optimization geometry, which provides a simple, operator-level recipe that can be applied uniformly across network components and optimizer state. By replacing the Euclidean transpose with a Unit-Consistent (UC) adjoint, we derive UC gauge-consistent steepest descent and backprogation.

Static Application Security Testing (SAST) tools using taint analysis are widely viewed as providing higher-quality vulnerability detection results compared to traditional pattern-based approaches. However, performing static taint analysis for JavaScript poses two major challenges. First, JavaScript's dynamic features complicate data flow extraction required for taint tracking. Second, npm's large library ecosystem makes it difficult to identify relevant sources/sinks and establish taint propagation across dependencies. In this paper, we present SemTaint, a multi-agent system that strategically combines the semantic understanding of Large Language Models (LLMs) with traditional static program analysis to extract taint specifications, including sources, sinks, call edges, and library flow summaries tailored to each package. Conceptually, SemTaint uses static program analysis to calculate a call graph and defers to an LLM to resolve call edges that cannot be resolved statically. Further, it uses the LLM to classify sources and sinks for a given CWE. The resulting taint specification is then provided to a SAST tool, which performs vulnerability analysis. We integrate SemTaint with CodeQL, a state-of-the-art SAST tool, and demonstrate its effectiveness by detecting 106 of 162 vulnerabilities previously undetectable by CodeQL. Furthermore, we find 4 novel vulnerabilities in 4 popular npm packages. In doing so, we demonstrate that LLMs can practically enhance existing static program analysis algorithms, combining the strengths of both symbolic reasoning and semantic understanding for improved vulnerability detection.

Traditional time series forecasting methods optimize for accuracy alone. This objective neglects temporal consistency, in other words, how consistently a model predicts the same future event as the forecast origin changes. We introduce the forecast accuracy and coherence score (forecast AC score for short) for measuring the quality of probabilistic multi-horizon forecasts in a way that accounts for both multi-horizon accuracy and stability. Our score additionally provides for user-specified weights to balance accuracy and consistency requirements. As an example application, we implement the score as a differentiable objective function for training seasonal ARIMA models and evaluate it on the M4 Hourly benchmark dataset. Results demonstrate substantial improvements over traditional maximum likelihood estimation. Our AC-optimized models achieve a 75\% reduction in forecast volatility for the same target timestamps while maintaining comparable or improved point forecast accuracy.

Inverse design tools such as Topology Optimization (TO) can achieve new levels of improvement for high-performance engineered structures. However, widespread use is hindered by high computational times and a black-box nature that inhibits user interaction. Human-in-the-loop TO approaches are emerging that integrate human intuition into the design generation process. However, these rely on the time-consuming bottleneck of iterative region selection for design modifications. To reduce the number of iterative trials, this contribution presents an AI co-pilot that uses machine learning to predict the user's preferred regions. The prediction model is configured as an image segmentation task with a U-Net architecture. It is trained on synthetic datasets where human preferences either identify the longest topological member or the most complex structural connection. The model successfully predicts plausible regions for modification and presents them to the user as AI recommendations. The human preference model demonstrates generalization across diverse and non-standard TO problems and exhibits emergent behavior outside the single-region selection training data. Demonstration examples show that the new human-in-the-loop TO approach that integrates the AI co-pilot can improve manufacturability or improve the linear buckling load by 39% while only increasing the total design time by 15 sec compared to conventional simplistic TO.

Context: Self-admitted technical debt (SATD) occurs when developers acknowledge shortcuts in code. In scientific software (SSW), such debt poses unique risks to the validity and reproducibility of results. Objective: This study aims to identify, categorize, and evaluate scientific debt, a specialized form of SATD in SSW, and assess the extent to which traditional SATD categories capture these domain-specific issues. Method: We conduct a multi-artifact analysis across code comments, commit messages, pull requests, and issue trackers from 23 open-source SSW projects. We construct and validate a curated dataset of scientific debt, develop a multi-source SATD classifier, and conduct a practitioner validation to assess the practical relevance of scientific debt. Results: Our classifier performs strongly across 900,358 artifacts from 23 SSW projects. SATD is most prevalent in pull requests and issue trackers, underscoring the value of multi-artifact analysis. Models trained on traditional SATD often miss scientific debt, emphasizing the need for its explicit detection in SSW. Practitioner validation confirmed that scientific debt is both recognizable and useful in practice. Conclusions: Scientific debt represents a unique form of SATD in SSW that that is not adequately captured by traditional categories and requires specialized identification and management. Our dataset, classification analysis, and practitioner validation results provide the first formal multi-artifact perspective on scientific debt, highlighting the need for tailored SATD detection approaches in SSW.

This paper addresses the joint optimization of trajectories and bandwidth allocation for multiple Unmanned Aerial Vehicles (UAVs) to enhance energy efficiency in the cooperative data collection problem. We focus on an important yet underestimated aspect of the system, where action synchronization across all UAVs is impossible. Since most existing learning-based solutions are not designed to learn in this asynchronous environment, we formulate the trajectory planning problem as a Decentralized Partially Observable Semi-Markov Decision Process and introduce an asynchronous multi-agent learning algorithm to learn UAVs' cooperative policies. Once the UAVs' trajectory policies are learned, the bandwidth allocation can be optimally solved based on local observations at each collection point. Comprehensive empirical results demonstrate the superiority of the proposed method over other learning-based and heuristic baselines in terms of both energy efficiency and mission completion time. Additionally, the learned policies exhibit robustness under varying environmental conditions.

Machine Learning (ML) has emerged as a pivotal technology in the operation of large and complex systems, driving advancements in fields such as autonomous vehicles, healthcare diagnostics, and financial fraud detection. Despite its benefits, the deployment of ML models brings significant security challenges, such as adversarial attacks, which can compromise the integrity and reliability of these systems. To address these challenges, this paper builds upon the concept of Secure Machine Learning Operations (SecMLOps), providing a comprehensive framework designed to integrate robust security measures throughout the entire ML operations (MLOps) lifecycle. SecMLOps builds on the principles of MLOps by embedding security considerations from the initial design phase through to deployment and continuous monitoring. This framework is particularly focused on safeguarding against sophisticated attacks that target various stages of the MLOps lifecycle, thereby enhancing the resilience and trustworthiness of ML applications. A detailed advanced pedestrian detection system (PDS) use case demonstrates the practical application of SecMLOps in securing critical MLOps. Through extensive empirical evaluations, we highlight the trade-offs between security measures and system performance, providing critical insights into optimizing security without unduly impacting operational efficiency. Our findings underscore the importance of a balanced approach, offering valuable guidance for practitioners on how to achieve an optimal balance between security and performance in ML deployments across various domains.

Recent recommender systems increasingly leverage embeddings from large pre-trained language models (PLMs). However, such embeddings exhibit two key limitations: (1) PLMs are not explicitly optimized to produce structured and discriminative embedding spaces, and (2) their representations remain overly generic, often failing to capture the domain-specific semantics crucial for recommendation tasks. We present EncodeRec, an approach designed to align textual representations with recommendation objectives while learning compact, informative embeddings directly from item descriptions. EncodeRec keeps the language model parameters frozen during recommender system training, making it computationally efficient without sacrificing semantic fidelity. Experiments across core recommendation benchmarks demonstrate its effectiveness both as a backbone for sequential recommendation models and for semantic ID tokenization, showing substantial gains over PLM-based and embedding model baselines. These results underscore the pivotal role of embedding adaptation in bridging the gap between general-purpose language models and practical recommender systems.

As robotics become increasingly integrated into construction workflows, their ability to interpret and respond to human behavior will be essential for enabling safe and effective collaboration. Vision-Language Models (VLMs) have emerged as a promising tool for visual understanding tasks and offer the potential to recognize human behaviors without extensive domain-specific training. This capability makes them particularly appealing in the construction domain, where labeled data is scarce and monitoring worker actions and emotional states is critical for safety and productivity. In this study, we evaluate the performance of three leading VLMs, GPT-4o, Florence 2, and LLaVa-1.5, in detecting construction worker actions and emotions from static site images. Using a curated dataset of 1,000 images annotated across ten action and ten emotion categories, we assess each model's outputs through standardized inference pipelines and multiple evaluation metrics. GPT-4o consistently achieved the highest scores across both tasks, with an average F1-score of 0.756 and accuracy of 0.799 in action recognition, and an F1-score of 0.712 and accuracy of 0.773 in emotion recognition. Florence 2 performed moderately, with F1-scores of 0.497 for action and 0.414 for emotion, while LLaVa-1.5 showed the lowest overall performance, with F1-scores of 0.466 for action and 0.461 for emotion. Confusion matrix analyses revealed that all models struggled to distinguish semantically close categories, such as collaborating in teams versus communicating with supervisors. While the results indicate that general-purpose VLMs can offer a baseline capability for human behavior recognition in construction environments, further improvements, such as domain adaptation, temporal modeling, or multimodal sensing, may be needed for real-world reliability.

If we consider human manipulation, it is clear that contact-rich manipulation (CRM)-the ability to use any surface of the manipulator to make contact with objects-can be far more efficient and natural than relying solely on end-effectors (i.e., fingertips). However, state-of-the-art model-based planners for CRM are still focused on feasibility rather than optimality, limiting their ability to fully exploit CRM's advantages. We introduce a new paradigm that computes approximately optimal manipulator plans. This approach has two phases. Offline, we construct a graph of mutual reachable sets, where each set contains all object orientations reachable from a starting object orientation and grasp. Online, we plan over this graph, effectively computing and sequencing local plans for globally optimized motion. On a challenging, representative contact-rich task, our approach outperforms a leading planner, reducing task cost by 61%. It also achieves a 91% success rate across 250 queries and maintains sub-minute query times, ultimately demonstrating that globally optimized contact-rich manipulation is now practical for real-world tasks.

Large language models have achieved remarkable capabilities across domains, yet mechanisms underlying sophisticated reasoning remain elusive. Recent reasoning models outperform comparable instruction-tuned models on complex cognitive tasks, attributed to extended computation through longer chains of thought. Here we show that enhanced reasoning emerges not from extended computation alone, but from simulating multi-agent-like interactions -- a society of thought -- which enables diversification and debate among internal cognitive perspectives characterized by distinct personality traits and domain expertise. Through quantitative analysis and mechanistic interpretability methods applied to reasoning traces, we find that reasoning models like DeepSeek-R1 and QwQ-32B exhibit much greater perspective diversity than instruction-tuned models, activating broader conflict between heterogeneous personality- and expertise-related features during reasoning. This multi-agent structure manifests in conversational behaviors, including question-answering, perspective shifts, and the reconciliation of conflicting views, and in socio-emotional roles that characterize sharp back-and-forth conversations, together accounting for the accuracy advantage in reasoning tasks. Controlled reinforcement learning experiments reveal that base models increase conversational behaviors when rewarded solely for reasoning accuracy, and fine-tuning models with conversational scaffolding accelerates reasoning improvement over base models. These findings indicate that the social organization of thought enables effective exploration of solution spaces. We suggest that reasoning models establish a computational parallel to collective intelligence in human groups, where diversity enables superior problem-solving when systematically structured, which suggests new opportunities for agent organization to harness the wisdom of crowds.

Value level parallelism (VLP) has been proposed to improve the efficiency of large-batch, low-precision general matrix multiply (GEMM) between symmetric activations and weights. In transformer based large language models (LLMs), there exist more sophisticated operations beyond activation-weight GEMM. In this paper, we explore how VLP benefits LLMs. First, we generalize VLP for nonlinear approximations, outperforming existing nonlinear approximations in end-to-end LLM accuracy, performance, and efficiency. Our VLP approximation follows a value-centric approach, where important values are assigned with greater accuracy. Second, we optimize VLP for small-batch GEMMs with asymmetric inputs efficiently, which leverages timely LLM optimizations, including weight-only quantization, key-value (KV) cache quantization, and group query attention. Finally, we design a new VLP architecture, Mugi, to encapsulate the innovations above and support full LLM workloads, while providing better performance, efficiency and sustainability. Our experimental results show that Mugi can offer significant improvements on throughput and energy efficiency, up to $45\times$ and $668\times$ for nonlinear softmax operations, and $2.07\times$ and $3.11\times$ for LLMs, and also decrease operational carbon for LLM operation by $1.45\times$ and embodied carbon by $1.48\times$.

Recent advances in code generation models have unlocked unprecedented opportunities for automating feature engineering, yet their adoption in real-world ML teams remains constrained by critical challenges: (i) the scarcity of datasets capturing the iterative and complex coding processes of production-level feature engineering, (ii) limited integration and personalization of widely used coding agents, such as CoPilot and Devin, with a team's unique tools, codebases, workflows, and practices, and (iii) suboptimal human-AI collaboration due to poorly timed or insufficient feedback. We address these challenges with a planner-guided, constrained-topology multi-agent framework that generates code for repositories in a multi-step fashion. The LLM-powered planner leverages a team's environment, represented as a graph, to orchestrate calls to available agents, generate context-aware prompts, and use downstream failures to retroactively correct upstream artifacts. It can request human intervention at critical steps, ensuring generated code is reliable, maintainable, and aligned with team expectations. On a novel in-house dataset, our approach achieves 38% and 150% improvement in the evaluation metric over manually crafted and unplanned workflows respectively. In practice, when building features for recommendation models serving over 120 million users, our approach has delivered real-world impact by reducing feature engineering cycles from three weeks to a single day.

Large language models (LLMs) currently suffer from parameter entanglement, where general reasoning capabilities (logic) and specific factual knowledge (facts) exist in a superposition state within shared weights. This coupling leads to the "memory wall," where computational capacity is squandered on simulating retrieval, often resulting in hallucinations. In this paper, we propose "digital metabolism," a thermodynamic hypothesis suggesting that targeted forgetting is necessary for distilling a pure neural logic core. To validate this hypothesis, we introduce the Regenerative Logic-Core Protocol (RLCP), a dual-stream training framework that renders specific factual dependencies linearly undecodable via deep-layer gradient reversal. Applying RLCP to Qwen2.5-0.5B, we observe a distinct phase transition: the model achieves near-zero retention of targeted factual associations (Accuracy < 7%) while exhibiting changes consistent with an emergent "structural crystallization" effect. Empirical analysis on GSM8K reveals that the "metabolized" model spontaneously adopts chain-of-thought (CoT) scaffolding, which we interpret as compensating for the loss of direct associative recall (shifting from $O(1)$ recall to $O(N)$ reasoning). While the causal mechanism underlying this behavioral shift requires further investigation, our findings provide a dynamic weight-level counterpart to architectural innovations like DeepSeek's Engram, paving the way for modular "Neural CPU + Symbolic RAM" architectures.

Style features such as friendly, helpful, or concise are widely used in prompts to steer the behavior of Large Language Model (LLM) conversational agents, yet their unintended side effects remain poorly understood. In this work, we present the first systematic study of cross-feature stylistic side effects. We conduct a comprehensive survey of 127 conversational agent papers from ACL Anthology and identify 12 frequently used style features. Using controlled, synthetic dialogues across task-oriented and open domain settings, we quantify how prompting for one style feature causally affects others via a pairwise LLM as a Judge evaluation framework. Our results reveal consistent and structured side effects, such as prompting for conciseness significantly reduces perceived expertise. They demonstrate that style features are deeply entangled rather than orthogonal. To support future research, we introduce CASSE (Conversational Agent Stylistic Side Effects), a dataset capturing these complex interactions. We further evaluate prompt based and activation steering based mitigation strategies and find that while they can partially restore suppressed traits, they often degrade the primary intended style. These findings challenge the assumption of faithful style control in LLMs and highlight the need for multi-objective and more principled approaches to safe, targeted stylistic steering in conversational agents.

Monolayer TaIrTe$_4$ has emerged as an attractive material platform to study intriguing phenomena related to topology and strong electron correlations. Recently, strong interactions have been demonstrated to induce strain and dielectric screening tunable topological phases such as quantum spin Hall insulator (QSHI), trivial insulator, higher-order topological insulator, and metallic phase, in the ground state of monolayer TaIrTe$_4$. Moreover, charge dosing has been demonstrated to convert the QSHI into a dual QSHI state. Although the band structure of monolayer TaIrTe$_4$ is central to interpreting its topological phases in transport experiments, direct experimental access to its intrinsic electronic structure has so far remained elusive. Here we report direct measurements of the monolayer TaIrTe$_4$ band structure using spatially resolved micro-angle-resolved photoemission spectroscopy (microARPES) with micrometre-scale resolution. The observed dispersions show quantitative agreement with density functional theory calculations using the Heyd-Scuseria-Ernzerhof hybrid functional, establishing the insulating ground state and revealing no evidence for strong electronic correlations. We further uncover a pronounced electron-hole asymmetry in the doping response. Whereas hole doping is readily induced by electrostatic gating, attempts to introduce electrons via gating or alkali metal deposition do not yield a rigid upward shift of the Fermi level. Fractional charge calculations demonstrate that added electrons instead drive band renormalization and shrink the band gap. Taken together, our experimental and theoretical results identify the microscopic mechanism by which induced charges reshape the band topology of monolayer TaIrTe$_4$, showing that doping can fundamentally alter the electronic structure beyond the rigid band behaviour that is typically assumed.

Charge-density waves (CDWs) are ordered quantum states of conduction electrons accompanied by periodic lattice distortions. Raman scattering (RS) spectroscopy is therefore well suited for probing CDW-induced structural modulations. We investigate the CDW state in quasi-one-dimensional NbTe$_4$ using RS spectroscopy. At $T$=5~K, the resonantly enhanced Raman spectrum exhibits 25 phonon modes. Polarization-dependent measurements reveal a strong coupling between phonon-mode symmetry and crystallographic symmetry, with modes polarized parallel or perpendicular to the crystallographic $c$-axis, along which the one-dimensional structure is elongated. Temperature-dependent RS measurements identify a transition between commensurate and incommensurate CDW phases, accompanied by pronounced thermal hysteresis, with transition temperatures of approximately 45~K upon cooling and 90~K upon warming. The hysteresis width depends on the warming rate, indicating a finite nucleation rate of CDW domains and suggesting potential relevance for memory-device applications.

We study the entanglement complexity of a system consisting of two simple-closed curves (self-avoiding polygons) that span a lattice tube, referred to as a 2SAP. 2SAPs are of interest as the first known model of confined ring polymers where the linking probability goes to 1 exponentially with the size of the system. Atapour et al proved this in 2010 by showing that all but exponentially few sufficiently large 2SAPs contain a pattern that guarantees the 2SAP is non-split, provided that the requisite pattern fits in the tube. This result was recently extended to all tubes sizes that admit non-trivial links. Here we develop and apply knot theory results to answer more general questions about the entanglement complexity of 2SAPs. We first extend the 1992 concept of a good measure of knot complexity to a good measure, $F$, of spanning-link complexity for $k$-component links. Using tangle products, we show, for example, that the more complex the prime knot decomposition of any component of a given link type, the greater its $F$-measure. We then prove that all but exponentially few size $m$ 2SAPs have $F$ complexity that grows at least linearly in $m$ as $m\to \infty$. We establish that good measures of knot complexity yield good measures of spanning-link complexity. We also establish conditions whereby more general link invariants can yield good measures. In particular, we establish that measures based on several classical invariants are good measures by our definition, eg bridge number or the number of $p$-colourings. Finally, we consider how the tube dimensions affect which links are embeddable as 2SAPs as well as geometric restrictions on the entanglement complexity of the embeddings. For example, we establish that there are two-component links that occur as 2SAPs in a given tube size only when one of the components is forced into a non-minimal bridge number conformation.

Biomolecular condensates are commonly organized by a small number of scaffold molecules that drive phase separation together with client molecules that do not condense on their own but become selectively recruited into the dense phase. A central open question is how client recruitment feeds back on scaffold interactions to determine condensate composition. Here we address this problem in a reconstituted focal adhesion system composed of focal adhesion kinase (FAK) and phosphorylated p130Cas (Cas) as scaffolds and the adaptor protein paxillin (PXN) as a client. We show that both FAK phosphorylation and PXN recruitment produce a common compositional response in which FAK becomes enriched while Cas is depleted within the condensate. To interpret these observations, we develop two complementary theoretical descriptions. First, within a two-component Flory-Huggins framework, we show that phosphorylation can be captured by either strengthening heterotypic FAK-Cas interactions or increasing the effective number of interaction-relevant segments on FAK, both of which bias partitioning toward FAK-rich condensates. Second, we introduce a minimal three-component Flory-Huggins theory without an explicit solvent and map it onto an effective two-component description, demonstrating that client recruitment renormalizes homotypic and heterotypic scaffold interactions. Analytical predictions for the location of the critical point are tested in reconstituted multicomponent systems through PXN addition, showing that client recruitment alone tunes proximity to criticality and reshapes condensate composition. Together, our results reveal distinct yet convergent physical routes by which post-translational modification and client recruitment control scaffold composition in multicomponent condensates.

The time dynamics of flagellar and ciliary beating is often neglected in theories of microswimmers, with the most common models prescribing a time-constant actuation of the surrounding fluid. By explicitly introducing a metachronal wave, coarse-grained to a sinusoidal surface slip velocity, we show that a spatial resonance between the metachronal wave and the corrugation of a confining cylindrical channel enables a ciliate to swim even when it cannot move forward in a bulk fluid. Using lubrication theory, we reduce the problem to the Adler equation that reveals an oscillatory and ballistic swimming regime. Interestingly, a ciliate can even reverse its swimming direction in a corrugated channel compared to the bulk fluid.

We present NAVIS (NAnochannel Velocity and thermal Interfacial Slip), a LAMMPS-Python scripted toolkit for computing the Navier (hydrodynamic) friction coefficient and Kapitza (thermal) resistance at arbitrary solid-fluid interfaces. NAVIS is based on equilibrium molecular dynamics (EMD) methods for calculating the linear response friction and thermal resistance at the interface, as well as the corresponding velocity and temperature slips. The methodology is based on our previous studies (Hansen, et al., Phys. Rev. E 84, 016313 (2011); Varghese et al., J. Chem. Phys. 154, 184707 (2021); Alosious, et al., J. Chem. Phys. 151, 194502 (2019); Alosious, et al., Langmuir 37, 2355-2361 (2021)), and in this work we provide a pedagogical framework for the implementation of this toolkit on two systems: (i) a water-graphene system (for hydrodynamic slip) and (ii) a water-CNT system (for thermal slip). We provide detailed instructions for performing the EMD simulations using the LAMMPS package and processing the simulation outputs using Python modules to obtain the desired quantities of interest. We expect the toolkit to be useful for computational researchers studying interfacial friction and thermal transport, key factors for efficient and practical applications of nanofluidic systems.

We report on the observation on proximity-induced superconductivity in the topological insulator BiSbTeSe2 coupled to a disordered superconductor, amorphous indium oxide (a-InO). Resistance temperature measurements reveal superconducting signatures at low temperatures, even when InO is in an insulating state, indicating the persistence of superconducting correlations. Differential conductance spectra reveal nearly periodic oscillations at higher bias, together with a pronounced zero-bias conductance peak. Both effect disappears at high temperature, marking the critical temperature (T*) of the superconducting islands in InO. These results underscore the influence of topological surface states on proximity-induced superconductivity and highlight the role of superconducting fluctuations in disordered superconductor/topological-insulator hybrid interfaces.

Magnetic skyrmions are topologically protected spin textures known for their robustness against perturbations. Their topological stability makes them robust information carriers, ideal for tackling a key challenge in quantum computing: creating reliable, one-way links between different types of qubits. In this proof-of-concept study, we introduce a novel device - the skyrmion quantum diode - based on skyrmion qubits. Our approach combines classical micromagnetic simulations, achieving skyrmion diameters as small as 3 nm, with quantum circuit models inspired by superconducting qubits. In this work, we demonstrate: (i) unidirectional skyrmion transport via the skyrmion Hall effect in asymmetric junctions, spanning length scales from 20 nm down to 3 nm; (ii) potential compatibility with flux-tunable quantum architectures; and (iii) preliminary insights into anharmonicity in skyrmion-based qubit systems. These results establish both the operational feasibility and the scaling behavior necessary for a hybrid skyrmion-quantum platform. Our work outlines a path toward integrating skyrmion based quantum components into practical device architectures, enabling low-dissipation, unidirectional quantum information transport. This capability is crucial for scalable quantum computing, spintronic logic, and hybrid quantum systems, and opens opportunities for chipscale, pump-free isolators and directional quantum links that enhance readout fidelity, reduce cryogenic load, and support modular skyrmion-superconducting processors

We present the fabrication of nanoscale superconducting quantum interference devices (SQUIDs) at the apex of wireframe tips on self-aligned superconducting cantilever probes. The probes are made on silicon wafers using molding techniques in combination with corner lithography, which results in a nanowire frame tip with a tuneable apex structure. A shadow effect deposition using magnetron sputtering of Nb creates self-aligned superconducting wireframes on cantilevers with accompanying device circuitry. Superconducting weak links are realized at the apex of the wireframes with the use of focused ion beam nanopatterning. The realized SQUIDs have effective diameters ranging from several micrometers down to 100 nm and can be operated in magnetic fields up to 1 T. Furthermore, the nanowires in the wireframe can be used to flux modulate the SQUID locally. This fabrication process enables the production of wafer-scale templates for probes based on on-tip superconducting devices.

Spiral waves are ubiquitous signatures of non equilibrium dynamics, appearing across chemical, biological, and active systems. Yet, in many living systems these waves unfold on curved and folded surfaces whose geometry has rarely been treated as a dynamical factor. Here we show that surface curvature fundamentally shapes spiral wave behavior and can contribute to the organization of neural activity in the brain. Via analytical theory and simulations of the complex Ginzburg Landau equation (CGLE) on curved surfaces, we demonstrate that curvature enters through the Laplace Beltrami operator as a spatial modulation of effective diffusion. Gradients of this effective diffusion generate a geometric force on spiral defects, and the complex nature of the CGLE produces a complex mobility that leads to non central and non reciprocal responses. Applied to realistic cortical surfaces of the human brain, the model predicts that the pattern of cortical folding stabilizes and localizes spiral waves, while progressive smoothing of the surface erases these non equilibrium structures. This reveals that brain geometry is not a passive scaffold but an active physical constraint that shapes neural dynamics. More broadly, the same geometric mechanism provides a universal route by which curvature and topology control pattern formation across oscillatory, chemical, and active matter systems.

In recent years, interest in 2D Janus materials has grown exponentially, particularly with regard to their applications in spintronics and optoelectronic devices. The defining feature of Janus materials is the ordered arrangement of different layer terminations - creating chemically distinct surfaces and an inherent out-of-plane polarity. Among the few known Janus materials, RhSeCl is particularly intriguing as a rare example of an intrinsic Janus compound. Owing to its exceptional chemical stability, RhSeCl offers a promising platform for exploring the physics related to the Janus-structure. However, synthesising large, high-quality crystals of this compound remains a significant challenge. Here, we report a novel synthetic pathway for growing crystals up to 6 mm in lateral size via a two-step self-selecting vapour growth reaction. We further present a comprehensive comparison of newly developed synthesis routes with all previously reported methods for RhSeCl. During these investigations, we identified a previously unreported impurity that forms in specific growth pathways and demonstrate how it can be avoided to obtain phase-pure few- and monolayer flakes. We showcase the reproducibility of the process to obtain high-quality, large single-crystals and flakes.

Colloidal semiconductor nanocrystals are promising building blocks for optoelectronics due to their solution processability, spectral tunability, and ability to self-assemble into complex architectures. However, their use in lasing application remains limited by high working thresholds, rapid nonradiative losses from Auger recombination, and sensitivity to environmental conditions. Here, we report hybrid microscale supraparticles composed of core/shell CdSe/ZnS quantum dots (QDs) and CdSe/CdxZn1-xS nanoplatelets (NPLs), which overcome these limitations through efficient, cavity-mediated energy funneling and coupling. Broadband absorbing QDs rapidly transfer excitation to narrow emitting NPLs, enabling stable whispering gallery mode lasing with a low threshold of 0.35 mJ/cm2. These supraparticles retain optical performance after prolonged exposure to air, water, and continuous irradiation, offering practical advantages for optoelectronic devices and advanced pigment technologies. Ultimately, our approach provides a versatile, programmable platform for optical amplification and tunable emission control within colloidal photonic architectures. Keywords

Superconducting quantum circuits derive their nonlinearity from the Josephson energy-phase relation. Besides the fundamental $\cosφ$ term, this relation can also contain higher Fourier harmonics $\cos(kφ)$ corresponding to correlated tunneling of $k$ Cooper pairs. The parity of the dominant tunneling process, i.e.~whether an odd or even number of Cooper pairs tunnel, results in qualitatively different properties, and controlling this opens up a wide range of applications in superconducting technology. However, access to even-dominated regimes has remained challenging and has so far relied on complex multi-junction or all-hybrid architectures. Here, we demonstrate a simple "harmonic parity qubit" (HPQ); an element that combines two aluminum-oxide tunnel junctions in parallel to a gate-tunable InAs/Al nanowire junction forming a SQUID, and use spectroscopy versus flux to reconstruct its energy-phase relation at 85 gate voltage points. At half flux quantum, the odd harmonics of the Josephson potential can be suppressed by up to two orders of magnitude relative to the even harmonics, producing a double-well potential dominated by even harmonics with minima near $\pmπ/2$. The ability to control harmonic parity enables supercurrent carried by pairs of Cooper pairs and provides a new building block for Fourier engineering in superconducting circuits.

We develop a microscopic quantum theory of surface plasmon polaritons valid for arbitrary metal-dielectric geometries. Our framework is based on the Power-Zienau-Woolley representation of quantum electrodynamics, which provides an optimal separation between electronic and photonic degrees of freedom and is therefore particularly well suited for constructing quantum descriptions of polaritonic excitations in strongly dispersive media. Within this formulation, the fundamental electronic oscillator is identified as the bulk plasmon mode, which is nonperturbatively coupled to the radiative continuum of free photon modes. This coupling induces a geometry-dependent renormalization of the bulk plasma frequency, giving rise to confined plasmonic resonances. As specific applications, we recover the localized surface plasmon modes of metallic nanoparticles, including radiative frequency shifts and decay, as well as the exact dispersion relation of propagating surface plasmon polaritons at planar interfaces. Our quantum treatment further reveals that light-matter interactions at metal-dielectric interfaces are inherently in the ultrastrong coupling regime. As a result, in the quasistatic limit, the system exhibits unconventional ground-state quantum fluctuations that can be controlled through the refractive index. These results open new intriguing perspectives in the field of quantum plasmonics.

With the growing interest in graphene field-effect transistors (GFETs) for biosensing applications, there is a strong demand for strategies enabling flexible and multiplexed biofunctionalization, as well as highly parallel, real-time electronic readout integrated with microfluidic control. Here we present a methodology that addresses these challenges by enabling real-time, parallel monitoring of multiple GFETs integrated on a single microfabricated chip within an automated electronic and microfluidic platform. We demonstrate the capabilities of this approach through ultrasensitive detection of the SARS-CoV-2 spike (S) and nucleocapsid (N) proteins. GFET chips are functionalized via van der Waals assembly using 1 nm-thick molecular two-dimensional (2D) materials - carbon nanomembranes - which enable multiplexed biofunctionalization. The chips are integrated into a custom-developed microelectronic and microfluidic system that allows parallel, real-time, and automated measurements of 15 GFETs. We present in situ biofunctionalization of the GFETs with antibodies, followed by highly specific detection of the S- and N-proteins with limits of detection down to 10 aM and a dynamic range spanning four orders of magnitude. Owing to its versatility, the presented methodology is readily adaptable for sensing a wide range of biological and chemical targets.

We investigate the crystallization of crosslinked and entangled polymers under external deformation using a coarse-grained poly(vinyl alcohol) (CG-PVA) model and molecular dynamics simulations. Following uniaxial deformation, the systems are cooled at a constant rate to form semi-crystalline states and subsequently heated at a constant rate to induce melting. For unstretched systems, network junctions do not significantly affect the nucleation temperature but increase the amorphous fraction and reduce the melting temperature. Uniaxial deformation accelerates nucleation and markedly increases the crystallization temperature, with more strongly crosslinked polymers exhibiting larger shifts that correlate with an enhanced orientation order parameter. We further compare cooling and heating cycles under constant-strain and constant-stress conditions. Under constant stress, crystallization induces additional elongation beyond the initial pre-stretch and leads to pronounced mechanical hysteresis upon heating, a behavior characteristic of reversible shape-memory materials.

Two-dimensional Janus structures have garnered rapidly growing attention across multidisciplinary fields. However, despite extensive theoretical and experimental efforts, a principle for designing intrinsic Janus materials remains elusive. Here, we propose a first-principles alloy theory based on cluster expansion, incorporating a strong repulsive interaction of a cation-mediated anion-pair cluster and refined short-range cluster-cluster competitions, to unravel the formation mechanism of intrinsic Janus structures with a distorted 1T phase among numerous competing phases. Our theory not only explains why intrinsic Janus structures are accidentally observed in RhSeCl and BiTeI which are composed of alloyed elements from different groups, but also accurately predicts a wide range of 1T-like intrinsic Janus materials that are ready for synthesis. Intriguingly, as demonstrated in the case of RhSeCl, we reveal that intrinsic Janus materials can exhibit anomalous second-harmonic generation (SHG) with a distinct quantum geometric effect, originating from strong lattice and chemical-potential mirror asymmetry. Furthermore, a novel skin effect unexpectedly emerges in finite-thickness RhSeCl, accompanied by a hidden SHG effect within the bulk region. Our theory paves the way for the ab initio design of intrinsic Janus materials, significantly accelerating progress in Janus science.

Electrogenerated gas nanobubbles strongly influence the performance of electrochemical energy-conversion systems, yet their nucleation and early evolution remain poorly understood due to limitations of existing experimental and computational approaches. Operando imaging lacks the temporal resolution required to capture nucleation events, while molecular dynamics simulations are restricted to nanometer-scale domains containing at most a few bubbles. Here, we develop a thermodynamically consistent phase-field framework that unifies dissolved gas transport, curvature dependent interfacial thermodynamics, and implicit bubble nucleation within a single continuum description. Using hydrogen nanobubble formation during electrocatalysis as a canonical test case, the model captures nucleation without prescribing nuclei, resolves diffusion-controlled growth under curvature effects, and remains computationally tractable despite hydrogen's extremely low solubility. Simulations reveal how nanobubble nucleation occurs once a local supersaturation threshold is exceeded, triggering a reorganization of the chemical-potential field that focuses dissolved gas toward the nascent bubble. In multi-catalyst systems, overlapping diffusion fields lead to strong bubble-bubble interactions, including competitive growth, Ostwald ripening, and source occlusion. Extending the framework to dispersed catalyst populations shows that nanobubble survival is governed not only by catalyst size but also by spatial arrangement and diffusive competition, such that only a subset of bubbles persist while others dissolve and act as feeders. These results reframe electrogenerated nanobubbles as emergent, spatially organized features rather than unavoidable parasitic byproducts, and point toward electrode designs that deliberately control where bubbles nucleate and grow to preserve active area and mitigate transport losses.

Semiflexible polymers are ubiquitous in natural and artificial systems, where their intermediate rigidity gives rise to rich structural and dynamical behavior. Confinement plays a central role in these behaviors, as spatial restrictions can promote chain alignment, induce structural rearrangements, and enable complex self-assembly. While the organization of semiflexible polymers under rigid confinement has been extensively investigated, their behavior within deformable and dynamically evolving microenvironments, such as drying droplets or intracellular compartments, remains poorly understood. In this study, we use dissipative particle dynamics simulations to investigate the self-assembly of crowded semiflexible polymers confined within a deformable droplet, whose size may also change over time. By systematically varying polymer contour length, concentration, and degree of confinement, we identify distinct assembly regimes. Increasing polymer concentration promotes the formation of ordered fibrillar domains, with orientational alignment strongest near the droplet interface. Chain length critically dictates the morphology of assembled structures: short chains remain largely disordered, chains with intermediate lengths form linear fibrillar structures with maximal nematic order, and long chains assemble into circular bundles. Dynamic confinement further modulates the assembly through the competition between the rate of confinement change and polymer mobility. Slow increase in the degree of confinement allows polymers to reorganize into highly ordered structures, while rapid crowding kinetically traps the system in disordered states. Our findings elucidate how polymer mechanics and time-dependent confinement jointly govern the organization of semiflexible polymers in deformable, dynamic, and crowded environments.

A continuous route from a disordered soup of simple chemical feedstocks to a functional protocell -- a compartment that metabolizes, grows, and propagates -- remains elusive. Here, we show that a homogeneous aqueous chemical mixture containing phosphorus, iron, molybdenum salts and formaldehyde spontaneously self-organizes into compartments that couple robust non-equilibrium chemical dynamics to their own growth. These structures mature to a sustained, dissipative steady state and support an organic synthetic engine, producing diverse molecular species including many core biomolecular classes. Internal spherules that are themselves growth-competent are produced within the protocells, establishing a rudimentary mode of self-perpetuation. The chemical dynamics we observe in controlled laboratory conditions also occur in reaction mixtures exposed to natural day-night cycles. Strikingly, the morphology and chemical composition of the protocells in our experiments closely resemble molybdenum-rich microspheres recently discovered in current oceanic environments. Our work establishes a robust, testable route to de novo protocell formation. The emergence of life-like spatiotemporal organization and chemical dynamics from minimal initial conditions is more facile than previously thought and could be a recurring natural phenomenon.

Compared to the study of graphene itself, the study of nano-structured graphene is rather limited because it is difficult to prepare atomically ordered edges. In this study, we have fabricated a periodically patterned mesh structure of graphene with atomically precise zigzag edges (zGNM: zigzag graphene nanomesh) and studied its thermal conductivity ($κ$) by opto-thermal Raman measurement. Unintuitively, it is found that the $κ$ of zGNM of 2,3 monolayers (MLs) thick is inversely proportional to the nanoribbon width ($W$), while that of zGNM of 5$\sim$10 MLs thick is independent of $W$ down to 30 nm. Since the $κ$ of suspended zigzag graphene nanoribbons (zGNRs) is suppressed by decreasing $W$, this nonclassical behavior of zGNM is due to the mesh structure. In addition, zGNRs show a higher $κ$ than GNRs with atomically rough edges. This is probably due to the atomically ordered zigzag edges.

For two-dimensional Lieb lattice, while intrinsic spin-orbit coupling is responsible for opening the gap that exhibits the quantum spin Hall effect, topological phase transitions are driven by a real next-nearest-neighbor (NNN) hopping. In this work, we utilize the transfer matrix method to study the flat-band localization mechanism in the presence of complex NNN hoppings. We demonstrate that the geometric localization in flat bands can be alleviated by topological edge states under weak disorder. Furthermore, correlated disorders are shown to induce inverse Anderson transition with the topological edge states persisting under strong disorder, a robustness confirmed by Chern number calculations, which identifies the root cause of this phenomenon. These findings establish a unified platform for investigating topological phase transitions, flat bands, and disorder effects.

Control of collective spin excitations by static or dynamic strain is an emerging phenomenon that requires in-depth understanding for design of future spin-wave-regulated devices. Here, we explore mutually interacting spin waves and acoustic wave modes in addition to few non-interactive modes through all optical excitation in ordered arrays of Ni80Fe20 nanomagnets. The acoustic wave originated from elastic deformation resonantly couple to the spin wave via magnetoelastic effect at their overlapping frequency. We demonstrate that the choice of the lattice type in which the magnetic nanodots are arranged is crucial for the observation of the magnetoelastic interaction. Therefore, the study shows that the simultaneous existence of elastic wave and spin wave offer ingeneously advantageous features to pave the way of energy-efficient magnetoacoustic devices.

In this work we report a new and simple method that combines the pulsed laser ablation in liquids (PLAL) and the Sol-Gel techniques to obtain nanocomposite glasses and gelatins. Gold nanoparticles (Au-NPs) are generated by PLAL using the corresponding target. The target is submerged in a transparent liquid solution made previously with tetraetylorthosilicate (TEOS) adding diluted hydrochloric acid as catalyzer. In the case of gelatins commercial gelatin and tap water are used. The laser source is a Nd:YAG laser emitting at 1064 nm, with an energy of 100 mJ and 8 ns pulse duration at 10 Hz repetition rate focused on the target in a 2 mm diameter laser spot. The ablation time is 10 min for the glasses and gelatins. The Au-NPs are uniformly dispersed in the solution. After the ablation process the gels are sealed and stored at room temperature for several days. The samples are characterized by UV-Vis spectroscopy, HRTEM, ellipsometry and AFM microscopy, these measurements reveal optical transparency and a refractive index near 1.45 for the pure glass, whereas a colorful aspect, a refractive index of 1.42, and a small surface roughness of 1.92 nm for the glass containing Au-NPs. In the case of gelatins self-sustained flexible films are obtained.

We study light propagation in a medium with uniform torsion, modeled as a continuum of screw dislocations within the geometric theory of defects. By solving Maxwell's equations in covariant form, we show that torsion induces intrinsic chirality and circular birefringence: right- and left-circular polarizations acquire different wavenumbers, leading to a purely geometric optical activity. The polarization plane of a linearly polarized beam rotates according to the simple law $Δθ= ΩρL$, linear in the dislocation density $Ω$, propagation length $L$, and transverse coordinate $ρ$. This can be recast as an effective birefringence $Δn = 2cΩρ/ω$, providing geometric design rules for torsion-induced rotatory power. Using parameters from dislocated semiconductors, we obtain millidegree rotations over millimetre-scale paths, within reach of modern polarimetric techniques and amenable to enhancement in metamaterial platforms. We also show that the same spiral geometry implements a broadband geometric phase gate for polarization qubits and has an electronic analogue on the surface of cylindrical topological insulators, where torsion shears the Dirac cone, establishing a unified geometric link between torsion, optical activity, and topological electronic responses.

Natural organisms have evolved integrated Latch-Mediated Spring Actuation systems (LaMSA) that consist of multiple latches and springs to enhance power output and adapt to diverse environmental conditions. Similar designs are appealing yet largely unexplored in engineered materials due to the complexity of integrating multiple components into a single material platform. Here, we report a dual-latched magneto-elastic shell device capable of selectively activating the latches to regulate snapping pathways and energy output based on specific actuation requirements. Differential deswelling across the thickness acts as the motor to load the elastic energy into the shell, which is then released via the snap-through instability once the loading reaches the critical threshold, constituting an intrinsic mechanical latch. Activation of the external magnetic latch delays snapping onset beyond the threshold of the intrinsic latch, leading to a power-amplified supercritical snap-through instability as well as a bifurcation instability. The combined function of both latches allows for flexible control over energy storage and release. Additionally, this integrated LaMSA system possesses an untethered anchoring mechanism, enabling the device to launch in arbitrary directions from the substrate, driven by the energy released during snapping. We envision that the design principles of dual-latched LaMSA systems will create opportunities for power-dense actuation in engineered materials and robotic devices.

Quantum many-body scars (QMBS) have attracted considerable interest due to their role in weak ergodicity breaking in many-body systems. We present a general construction that embeds stabilizer states as QMBS of local Hamiltonians. The method relies on a notion of factorizability of Pauli strings on a lattice, which is used to convert stabilizer elements into local, few-body operators that annihilate the stabilizer state. This enables the systematic construction of parent Hamiltonians with zero-energy stabilizer QMBS typically near the middle of the spectrum. The method reproduces several known results in a unified framework, including recent examples of volume-law entangled QMBS, such as the ``rainbow'' QMBS and the entangled antipodal Bell pair state. We also apply the framework to construct examples of stabilizer QMBS with a more complex entanglement structure, such as the cluster state, the toric code state, and a volume-law entangled state we dub the antipodal toric code (ATC) state. Exact diagonalization confirms our results and reveal the stabilizer states as exact eigenstates of their parent Hamiltonian.

We demonstrate the existence of an extended non-equilibrium critical phase, characterized by sub-exponential decay of conditional mutual information (CMI), in the surface code subject to heralded random Pauli measurement channels. By mapping the resulting mixed state to the ensemble of completely packed loops on a square lattice, we relate the extended phase to the Goldstone phase of the loop model. In particular, CMI is controlled by the characteristic length scale of loops, and we use analytic results of the latter to establish polylogarithmic decay of CMI in the critical phase. We find that the critical phase retains partial logical information that can be recovered by a global decoder, but not by any quasi-local decoder. To demonstrate this, we introduce a diagnostic called punctured coherent information which provides a necessary condition for quasi-local decoding.

Creating precise timing devices at ultra-short time scales is not just an important technological challenge, but confronts us with foundational questions about timekeeping's ultimate precision limits. Research on clocks has either focused on long-term stability using an oscillator stabilized by a level transition, limiting precision at short timescales, or on making individual stochastic ticks as precise as possible. Here, we prove the viability of a conceptually different avenue: the autonomous self-correction of consecutive ticks by quantum correlations. This provides a new paradigm that integrates the advantages and insights from quantum transport theory to operate clocks at ultra-short timescales. We fully solve a model of coupled quantum systems and show how the emergent Pauli exclusion principle correlates the clock at the quantum level yielding an exponential advantage in precision. We furthermore demonstrate through simulations with realistic imperfections that this remarkable gain in precision remains stable providing a roadmap for implementation with contemporary quantum technologies.

We show a single Landau level (LL) dressed with periodic electrostatic potentials can realize a plethora of interacting topological phases where the Hall conductivity generally does not equal to the LL filling factor. Their physics can be captured by a minimal model of a delta potential lattice within a single LL, realizing exact zero energy Chern bands (denoted as decorated Landau levels or dLL) gapped from dispersive bands with rich geometric properties. With $p/q$ magnetic fluxes per unit cell, there are $q$ dispersive bands and $p-q$ zero energy bands forming the dLL. When the one-body potential strength dominates the electron-electron interaction, band mixing is suppressed and the dispersion bands consist of ``localized states" with vanishing total Chern number. Nevertheless these dispersive bands can have highly nontrivial Berry curvature distribution, and even non-zero Chern numbers when $q>1$. Interestingly even in the limit of large short range interaction, band mixing between dLL and dispersion bands can be strongly suppressed at low filling factor, leading to robust topological phases within the dLL stabilized by the one-body potential. The dLL and the associated dispersive bands can serve as minimal theoretical models for correlated physics in lattice or moire systems; they are also highly tunable experimental platforms for realizing rich phase diagrams of exotic 2D quantum fluids.

While spin squeezing has been traditionally considered in all-to-all interacting models, recent works have shown that spin squeezing can occur in systems with power-law interactions, leading to direct testing in Rydberg atoms, trapped ions, ultracold atoms and nitrogen vacancy (NV) centers in diamond. For the latter, Wu. et al. Nature 646 (2025) demonstrated that spin squeezing is heavily affected by positional disorder, reducing any capacity for a practical squeezing advantage, which requires scalability with the system size. In this Letter we explore the robustness of spin-squeezing in two-dimensional lattices with a fraction of unoccupied lattice sites. Using semi-classical modeling, we demonstrate the existence of scalable squeezing in power-law interacting XXZ models up to a disorder threshold, above which squeezing is not scalable. We produce a phase diagram for scalable squeezing, and explain its absence in the aforementioned NV experiment. Our work illustrates the maximum disorder allowed for realizing scalable spin squeezing in a host of quantum simulators, highlights a regime with substantial tolerance to disorder, and identifies controlled defect creation as a promising route for scalable squeezing in solid-state systems.

We exploit the variational and Hamiltonian structures of quantum hydrodynamics with spin to unfold the correlation and torque mechanisms accompanying spin-orbit coupling (SOC) in electronic motion. Using Hamilton's action principle for the Pauli equation, we isolate SOC-induced quantum forces that act on the orbital Madelung--Bohm trajectories and complement the usual force terms known to appear in quantum hydrodynamics with spin. While the latter spin-hydrodynamic forces relate to the quantum geometric tensor (QGT), SOC-induced orbital forces originate from a particular current operator that contributes prominently to the spin current and whose contribution was overlooked in the past. The distinction between different force terms reveals two fundamentally different mechanisms generating quantum spin-orbit correlations. Leveraging the Hamiltonian structure of the hydrodynamic system, we also elucidate spin transport features such as the current shift in the spin Hall effect and the correlation-induced quantum torques. Finally, we illustrate the framework via the Madelung--Rashba equations for planar SOC configurations and propose a particle-based scheme for numerical implementation.

The rotating shallow water equations (RSWE) are a mainstay of atmospheric and oceanic modeling, and their wave dynamics has close analogues in settings ranging from two-dimensional electron gases to active-matter fluids. While recent work has emphasized the topological character of RSWE wave bands, here we develop a complementary quantum-geometric description by computing the full quantum geometric tensor (QGT) for the linearized RSWE on an $f$-plane. The QGT unifies two pieces of band geometry: its real part defines a metric that quantifies how rapidly wave polarization changes with parameters, while its imaginary part is the Berry curvature that controls geometric phases and topological invariants. We obtain compact, symmetry-guided expressions for all three bands, highlighting the transverse structure of the metric and the monopole-like Berry curvature that yields Chern numbers for the Poincaré bands. Finally, we describe a feasible route to probing this geometry in rotating-tank experiments via weak, time-periodic parametric driving.

The phase mode of a superconductor's order parameter encodes fundamental information about pairing and dissipation, but is typically inaccessible at low frequencies due to the Anderson-Higgs mechanism. Superconducting samples thinner than the London penetration depth, however, support a gapless phase mode whose dispersion can be reshaped by a proximal screening layer. Here, we theoretically and experimentally show that this screened phase mode in a superconducting thin film integrated into on-chip terahertz circuitry naturally forms a superconducting microcavity that hosts resonant finite-momentum standing waves of supercurrent density, which we term Cooper plasmons. We measure two Cooper plasmons in a superconducting NbN microcavity and demonstrate that their resonance frequencies and linewidths independently report the density of participating carriers and plasmon's dissipation at finite momenta. Our results reveal an emergent collective mode of an integrated superconductor-circuit system and establish design principles for engineering or suppressing Cooper plasmons in superconducting terahertz devices and circuits.

Scaling laws have played a major role in the modern AI revolution, providing practitioners predictive power over how the model performance will improve with increasing data, compute, and number of model parameters. This has spurred an intense interest in the origin of neural scaling laws, with a common suggestion being that they arise from power law structure already present in the data. In this paper we study scaling laws for transformers trained to predict random walks (bigrams) on graphs with tunable complexity. We demonstrate that this simplified setting already gives rise to neural scaling laws even in the absence of power law structure in the data correlations. We further consider dialing down the complexity of natural language systematically, by training on sequences sampled from increasingly simplified generative language models, from 4,2,1-layer transformer language models down to language bigrams, revealing a monotonic evolution of the scaling exponents. Our results also include scaling laws obtained from training on random walks on random graphs drawn from Erdös-Renyi and scale-free Barabási-Albert ensembles. Finally, we revisit conventional scaling laws for language modeling, demonstrating that several essential results can be reproduced using 2 layer transformers with context length of 50, provide a critical analysis of various fits used in prior literature, demonstrate an alternative method for obtaining compute optimal curves as compared with current practice in published literature, and provide preliminary evidence that maximal update parameterization may be more parameter efficient than standard parameterization.

We establish a majorization-based theory for bounding observables of waves with varied coherence. For any measurement, exact bounds are attained by the maximal and minimal elements in the set of input coherence spectra. The set's supremum and infimum, which may lie outside the set, provide optimal universal bounds: any alternative spectrum yielding universal bounds produces weaker constraints. We present an algorithm to compute the supremum and infimum, and prove that they lie either at singular boundary points or strictly outside the set of coherence spectra.

The impact of disorder on collective phenomena depends crucially on whether it is quenched or annealed. In synchronization problems, quenched disorder in higher dimensional Kuramoto models is known to produce unconventional dimensional effects, including a striking odd even dichotomy: synchronization transitions are continuous in even dimensions and discontinuous in odd dimensions. By contrast, the impact of annealed disorder has received comparatively little attention. Here we study a D dimensional Kuramoto model with both fundamental and higher-harmonic interactions under annealed disorder, and develop an arbitrary dimensional center-manifold framework to analyze the nonlinear dynamics near the onset of collective behavior. We show that annealed disorder fundamentally alters the role of dimensionality. With fundamental coupling alone, it completely removes the odd even dichotomy, yielding continuous synchronization transitions with universal mean-field scaling in all dimensions. Higher-harmonic interactions preserve this universality while rendering the synchronization transition tunable between continuous and discontinuous. At the same time, they give rise to a novel, correlation-driven transition between a symmetry-protected incoherent phase and a symmetry broken state lacking global synchronization, which is therefore invisible to the conventional Kuramoto order parameter. This transition is continuous in two dimensions but discontinuous in higher dimensions, revealing an emergent and previously-unrecognized special role of two dimensions.

The dynamic motion of topological defects in magnets induces an emergent electric field, as exemplified by the continuous flow of skyrmion vortices. However, the electrodynamics underlying this emergent field remains poorly understood. In this context, magnetic domain walls - one dimensional topological defects with two collective modes, sliding and spin tilt - offer a promising platform for exploration. Here, we demonstrate that the dissipative motion of domain walls under oscillatory current excitation generates an emergent electric field. We image domain patterns and quantify domain wall length under applied magnetic fields in mesoscopic devices based on the magnetic Weyl semimetal NdAlSi. These devices exhibit exceptionally strong domain wall scattering and a pronounced emergent electric field, observed in the imaginary component of the complex impedance. Spin dynamics simulations reveal that domain wall sliding dominates over spin tilting, where the phase delay of the domain wall motion with respect to the driving force impacts the emergent electric field. Our findings establish domain-wall dynamics as a platform for studying emergent electromagnetic fields and motivate further investigations on the coupled motion of magnetic solitons and conduction electrons.

We study a variety of stochastic contact processes -- directly related to models of rumor and disease spreading -- from the viewpoint of their constants of motion, either exact or approximated. Much as in deterministic systems, constants of motion in stochastic dynamics make it possible to reduce the number of relevant variables, confining the set of accessible states, and thus facilitating their analytical treatment. For processes of rumor propagation based on the Maki-Thompson model, we show how to construct exact constants of motion as linear combinations of conserved quantities in each elementary contact event, and how they relate to the constants of motion of the corresponding mean-field equations, which are obtained as the continuous-time, large-size limit of the stochastic process. For SIR epidemic models, both in homogeneous systems and on heterogeneous networks, we find that a similar procedure produces approximate constants of motion, whose average value is preserved along the evolution. We also give examples of exact and approximate constants of motion built as nonlinear combinations of the relevant variables, whose expressions are suggested by their mean-field counterparts.

Two-dimensional polyaramids exhibit strong hydrogen bonding to create molecularly thin nanosheets analogous to graphene. Here, we report the first nanomechanical resonators made out of a two-dimensional polyaramid, 2DPA-1, with thicknesses as small as 8 nm. To fabricate these molecular-scale resonators, we transferred nanofilms of 2DPA-1 onto chips with previously etched arrays of circular microwells. We then characterized the thermal resonances of these resonators under different conditions. When there is no residual gas inside the 2DPA-1-covered microwells, the eigenfrequencies are well-described by a tensioned plate theory, providing the Young's modulus and tension of the 2DPA-1 nanofilms. With gas present, the nanofilms bulge up and mechanical resonances are modified due to the adhesion, bulging and slack present in the system. The fabrication and mechanical characterization of these first 2DPA-1 nanomechanical resonators represent a convincing path toward molecular-scale polymeric NEMS with high mechanical strength, low density, and synthetic processability.

We study potential presence of statistical-computational gaps (SCG) in symmetric binary perceptrons (SBP) via a parametric utilization of \emph{fully lifted random duality theory} (fl-RDT) [96]. A structural change from decreasingly to arbitrarily ordered $c$-sequence (a key fl-RDT parametric component) is observed on the second lifting level and associated with \emph{satisfiability} ($α_c$) -- \emph{algorithmic} ($α_a$) constraints density threshold change thereby suggesting a potential existence of a nonzero computational gap $SCG=α_c-α_a$. The second level estimate is shown to match the theoretical $α_c$ whereas the $r\rightarrow \infty$ level one is proposed to correspond to $α_a$. For example, for the canonical SBP ($κ=1$ margin) we obtain $α_c\approx 1.8159$ on the second and $α_a\approx 1.6021$ (with converging tendency towards $\sim 1.59$ range) on the seventh level. Our propositions remarkably well concur with recent literature: (i) in [20] local entropy replica approach predicts $α_{LE}\approx 1.58$ as the onset of clustering defragmentation (presumed driving force behind locally improving algorithms failures); (ii) in $α\rightarrow 0$ regime we obtain on the third lifting level $κ\approx 1.2385\sqrt{\frac{α_a}{-\log\left ( α_a \right ) }}$ which qualitatively matches overlap gap property (OGP) based predictions of [43] and identically matches local entropy based predictions of [24]; (iii) $c$-sequence ordering change phenomenology mirrors the one observed in asymmetric binary perceptron (ABP) in [98] and the negative Hopfield model in [100]; and (iv) as in [98,100], we here design a CLuP based algorithm whose practical performance closely matches proposed theoretical predictions.

Advances in the next generation of mesoscopic electronics require an understanding of topological phases in inhomogeneous media and the principles that govern them. Motivated by the nature of motifs available in printable conducting inks, we introduce and study quantum transport in a minimal model that describes a bundle of one-dimensional metallic wires that are randomly interconnected by semiconducting chains. Each of these interconnects is represented by a Su-Schrieffer-Heeger chain, which can reside in either a trivial or a topological phase. Using a tight-binding approach, we show that such a system can transit from an insulating phase to a robust metallic phase as the interconnects undergo a transition from a trivial to a topological phase. In the latter, despite the random interconnectedness, the metal evades Anderson localization and exhibits a ballistic conductance that scales linearly with the number of wires. We show that this behavior originates from hopping renormalization in the wire network. The zero-energy modes of the topological interconnects act as effective random dimers, giving rise to an energy-dependent localization length that diverges as $\sim 1/E^2$. Our work establishes that random networks provide a yet-unexplored platform to host intriguing phases of topological quantum matter.

Plasmons are collective oscillations of mobile electrons. Using terahertz spacetime metrology, we probe plasmon dynamics of mono- and bi-layer graphene. In both systems, the experimentally measured Drude weight systematically exceeds the prediction based on non-interacting electronic system. This enhancement is most pronounced at ultra-low carrier densities. We attribute the observed deviation to pseudospin dynamics of the Dirac fermions in multi-layer graphene, which leads to a breakdown of Galilean invariance. Our results establish that pseudospin structure of the single-particle electronic wave function can directly govern collective excitations, with implications that extend beyond graphene to a broad class of quantum materials.

We study the high-dimensional training dynamics of a shallow neural network with quadratic activation in a teacher-student setup. We focus on the extensive-width regime, where the teacher and student network widths scale proportionally with the input dimension, and the sample size grows quadratically. This scaling aims to describe overparameterized neural networks in which feature learning still plays a central role. In the high-dimensional limit, we derive a dynamical characterization of the gradient flow, in the spirit of dynamical mean-field theory (DMFT). Under l2-regularization, we analyze these equations at long times and characterize the performance and spectral properties of the resulting estimator. This result provides a quantitative understanding of the effect of overparameterization on learning and generalization, and reveals a double descent phenomenon in the presence of label noise, where generalization improves beyond interpolation. In the small regularization limit, we obtain an exact expression for the perfect recovery threshold as a function of the network widths, providing a precise characterization of how overparameterization influences recovery.

We address the spatially nonlocal dielectric functions of graphene at any frequency derived starting fromthe first principles of thermal quantum field theory using the formalism of the polarization tensor. After a brief review of this formalism, the longitudinal and transverse dielectric functions are considered at any relationship between the frequency and the wave vector. The analytic properties of their real and imaginary parts are investigated at low and high frequencies. Emphasis is given to the double pole at zero frequency which arises in the transverse dielectric function. The role of this unusual property for solving the problem of disagreement between experiment and theory in the Casimir effect is discussed. We guess that a more complete dielectric response of ordinary metals should also be spatially nonlocal and its transverse part may possess the double pole in the region of evanescent waves.

We analyze quantum systems under a broad class of protocols in which the temperature and a Hamiltonian control parameter are ramped simultaneously and, in general, in a nonlinear fashion toward a quantum critical point. Using an open-system version of a Kitaev quantum wire as an example, we show that, unlike finite-temperature protocols at fixed temperature, these protocols allow us to probe, in an out-of-equilibrium situation and at finite temperature, the universality class (characterized by the critical exponents $ν$ and $z$) of an equilibrium quantum phase transition at zero temperature. Key to this is the identification of ramps in which both coherent and incoherent parts of the open-system dynamics affect the excitation density in a non-negligible way. We also identify the specific ramps for which subleading corrections to the asymptotic scaling laws are suppressed, which serves as a guide to dynamically probing quantum critical exponents in experimentally realistic finite-temperature situations.

By extending the Sanchez-Lacombe lattice-fluid model for mixtures to the case of polymer blends containing solid fillers, we calculate the excess thermodynamic quantities arising from the presence of fillers. These results are then used to derive the spinodal stability condition of a filled polymer blend. In the low-compressibility limit, this condition reduces to a remarkably simple analytical expression that is derived self-consistently within the present framework. Comparison between the exact and approximate spinodal curves shows excellent agreement, with deviations in the spinodal temperature of less than 4 K, thereby validating the proposed approximation. The obtained analytical approximation enables a straightforward evaluation of the spinodal temperature without the extensive numerical calculations required to determine the exact spinodal condition. Both the exact and approximate spinodal conditions yield good quantitative agreement with experimental data for filled and unfilled blends.

We study finite-rank normal deformations of rotationally invariant non-Hermitian random matrices. Extending the classical Baik-Ben Arous-Péché (BBP) framework, we characterize the emergence and fluctuations of outlier eigenvalues in models of the form $\mathbf{A} + \mathbf{T}$, where $\mathbf{A}$ is a large rotationally invariant non-Hermitian random matrix and $\mathbf{T}$ is a finite-rank normal perturbation. We also describe the corresponding eigenvector behavior. Our results provide a unified framework encompassing both Hermitian and non-Hermitian settings, thereby generalizing several known cases.

Advanced manufacturing with new bio-derived materials can be achieved faster and more economically with first-principle-based artificial intelligence and machine learning (AI/ML)-derived models and process optimization. Not only is this motivated by increased industry profitability, but it can also be optimized to reduce waste generation, energy consumption, and gas emissions through additive manufacturing (AM) and AI/ML-directed self-driving laboratory (SDL) process optimization. From this perspective, the benefits of using 3D printing technology to manufacture durable, sustainable materials will enable high-value reuse and promote a better circular economy. Using AI/ML workflows at different levels, it is possible to optimize the synthesis and adaptation of new bio-derived materials with self-correcting 3D printing methods, and in-situ characterization. Working with training data and hypotheses derived from Large Language Models (LLMs) and algorithms, including ML-optimized simulation, it is possible to demonstrate more field convergence. The combination of SDL and AI/ML Workflows can be the norm for improved use of biobased and renewable materials towards advanced manufacturing. This should result in faster and better structure, composition, processing, and properties (SCPP) correlation. More agentic AI tasks, as well as supervised or unsupervised learning, can be incorporated to improve optimization protocols continuously. Deep Learning (DL), Reinforcement Learning (RL), and Deep Reinforcement Learning (DRL) with Deep Neural Networks (DNNs) can be applied to more generative AI directions in both AM and SDL, with bio-based materials.

Springs can be found in many applications and biological systems, and when these are soft, they easily deform. At small scales, capillarity can induce a force leading to spring deformations when the elastocapillary number is small. We demonstrate through experiments the non-trivial equilibrium shape liquid droplets adopt in these soft springs, which form an annulus, Eruciform, and spherical shapes. When these droplets are set in motion, they display different flow regimes with significant dissipation generated by the internal rotational flow. The static and dynamics of droplets in such a capillary slinky is also used to demonstrate how surface tension can actuate springs by stretching/compression, while providing a way for active flow control in soft springs.

Disordered spatial networks are model systems that describe structures and interactions across multiple length scales. Scattering and interference of waves in these networks can give rise to structural phase transitions, localization, diffusion, and band gaps. The study of these complex phenomena requires efficient numerical methods to computer-generate disordered networks with targeted structural properties. In the established Wooten-Weaire-Winer algorithm, a series of bond switch moves introduces disorder into an initial network. Conventional strain energies that govern this evolution are limited to 3D networks with coordination numbers of no more than four. We extend the algorithm to arbitrary coordination number statistics by introducing bond repulsion in the Keating strain energy. We tune the degree and type of disorder introduced into initially crystalline networks by varying the bond-bending force constant in the strain energy and the temperature profile. The effects of these variables are analyzed using a list of order metrics that capture both direct and reciprocal space. A feedforward neural network is trained to predict the structural characteristics from the algorithm inputs, enabling targeted network generation. As a case study, we statistically reproduce four disordered biophotonic networks exhibiting structural color. This work presents a versatile method for generating disordered networks with tailored structural properties. It will enable new insights into structure-property relations, such as photonic band gaps in disordered networks.

Quantum bianisotropy and chirality are fundamental concepts in light matter interaction that describe how materials with broken symmetries respond to electromagnetic fields at the level of macroscopic quantum electrodynamics. In quantum bianisotropy, magnetoelectric (ME) energy plays a critical role in mediating and enhancing light matter interactions. This concept is essential for bridging the gap between classical electromagnetics (where bianisotropy often involves field nonlocality) and quantum mechanics in metamaterials. The precise manipulation of a quantum emitter's properties at a subwavelength scale is due to near fields, which effectively function as a tunable environment. We show that the ME near field, interpreted as a structure combining the effect of bianisotropy (chirality) with a quantum atmosphere, is a nonMaxwellian field with spacetime symmetry breaking. Quantum ME fields arise from the dynamic modulation and topological coupling of magnetization and electric polarization within ME meta atoms, specific subwavelength structural elements with magnetic and dielectric subsystems in magnetic insulators.

We investigate the open system dynamics of a micromaser quantum battery operating in the ultrastrong coupling (USC) regime under environmental dissipation. The battery consists of a single-mode electromagnetic cavity sequentially interacting, via the Rabi Hamiltonian, with a stream of qubits acting as chargers. Dissipative effects arise from the weak coupling of the qubit-cavity system to a thermal bath. Non-negligible in the USC regime, the counter-rotating terms substantially improve the charging speed, but also lead, in the absence of dissipation, to unbounded energy growth and highly mixed cavity states. Dissipation during each qubit-cavity interaction mitigates these detrimental effects, yielding steady-state of finite energy and ergotropy. Optimal control on qubit preparation and interaction times enhances battery's performance in: (i) Maximizing the stored ergotropy trhough an optimized charging protocol; (ii) Stabilizing the stored ergotropy against dissipative losses through an optimized measurement-based passive-feedback strategy. Overall, our numerical results demonstrate that the interplay of ultrastrong light-matter coupling, controlled dissipation, and optimized control strategies enables micromaser quantum batteries to achieve both enhanced charging performance and long-term stability under realistic conditions.

The variable range hopping (VRH) model has been widely applied to describe electrical transport in disordered systems, providing theoretical formulas to fit temperature-dependent electric conductivity. These models rely on oversimplified assumptions that restrict their applicability and result in problematic fitting behaviors, yet their overusing situation is becoming increasingly serious. In this work we formulate an integral variable range hopping (IVRH) model, which replaces the empirical temperature power-law dependence in standard VRH theories with a physics-inspired integral formulation. The model builds upon the standard hopping probability $ω(R)$ w.r.t. hopping distance $R$ and incorporates the density of accessible electronic states through an effective volume function $V(R)$, which reflects the influence of system geometry. The IVRH formulation inherently reproduces both the Mott behavior at low temperatures and the Arrhenius behavior at high temperatures, respectively, and enables a smooth transition between the two regimes. We apply the IVRH model to two-dimensional, three-dimensional, and multi-layered systems. Monte Carlo simulations validate the model's predictions and yield consistent values for the fitting parameters, with substantially reduced variances compared to fitting using the standard VRH model. Furthermore, the improved robustness of IVRH also extends to the transport measurements in monolayer MoS$_2$ system and monolayer WS$_2$ system, enabling more physically meaningful interpretation.IVRH model offers a more stable and physically sound framework for interpreting hopping transport in low-dimensional amorphous materials, providing deeper insights into the universal geometric scaling factors that govern charge transport in disordered systems.

Quantum chaotic systems exhibit certain universal statistical properties that closely resemble predictions from random matrix theory (RMT). With respect to observables, it has recently been conjectured that, when truncated to a sufficiently narrow energy window, their statistical properties can be described by an unitarily invariant ensemble, and testable criteria have been introduced, which are based on the scaling behavior of free cumulants. In this paper, we investigate the conjecture numerically in translationally invariant Heisenberg spin chains with spin quantum number $S =\frac{1}{2},1,\frac{3}{2}$. Combining a quantum-typicality-based numerical method with the exploitation of the system's symmetries, we study the spin current operator and find clear evidence of consistency with the proposed criteria in chaotic cases. Our findings further support the conjecture of the existence of RMT universality as manifest in the observable properties in quantum chaotic systems.

We investigate the coherence properties of parity-protected $\cos(2\varphi)$ qubits based on interferences between two Josephson elements in a superconducting loop. We show that qubit implementations of a $\cos(2\varphi)$ potential using a single loop, such as those employing semiconducting junctions, rhombus circuits, flowermon and KITE structures, can be described by the same Hamiltonian as two multi-harmonic Josephson junctions in a SQUID geometry. We find that, despite the parity protection arising from the suppression of single Cooper pair tunneling, there exists a fundamental trade-off between charge and flux noise dephasing channels. Using numerical simulations, we examine how relaxation and dephasing rates depend on external flux and circuit parameters, and we identify the best compromise for maximum coherence. With currently existing circuit parameters, the qubit lifetime $T_1$ can exceed milliseconds while the dephasing time $T_\varphi$ remains limited to only a few microseconds due to either flux or charge noise. Our findings establish practical limits on the coherence of this class of qubits and raise questions about the long-term potential of this approach.

We establish a rigorous mathematical framework connecting graphene nanoribbon quantum sensing to the Lambert W function through the finite square well (FSW) analogy. The Lambert W function, defined as the inverse of $f(W) = We^W$, provides exact analytical solutions to transcendental equations governing quantum confinement. We demonstrate that operating near the branch point at $z = -1/e$ yields sensitivity enhancement factors scaling as $η_{\text{enh}} \propto (z - z_c)^{-1/2}$, achieving 35-fold enhancement at $δ= 0.001$. Comprehensive numerical verification confirms: (i) all seven bound states for strength parameter $R = 10$ satisfying the constraint $u^2 + v^2 = R^2$; (ii) exact agreement between theoretical band gap formula $E_g = 2π\hbar v_F/(3L)$ and empirical relation $E_g = 1.38/L$ eV$\cdot$nm; (iii) universal sensitivity scaling across biomedical (SARS-CoV-2, inflammatory markers, cancer biomarkers), environmental (CO$_2$, CH$_4$, NO$_2$, N$_2$O, H$_2$O), and physical (strain, magnetic field, temperature) sensing modalities. This unified framework provides design principles for next-generation graphene quantum sensors with analytically predictable performance.

In this work, 3-atom clusters, Ru3 and Pt3, were deposited onto radio frequency RF-sputter deposited TiO2, treated with Ar+ ion sputtering. Ru3 was deposited by both solution submersion and chemical vapor deposition of Ru3(CO)12, while Pt3 was deposited under ultra-high vacuum using a laser vaporisation cluster source. The valence electronic density of states (DOS) of the deposited clusters were analysed after heat treatment using ultraviolet photoelectron spectroscopy (UPS) and metastable impact electron spectroscopy (MIES), where UPS measures the top several layers while MIES measures only the top atomic layer. XPS was used to determine the cluster surface coverages. The DOS were found to be very similar between Ru3 deposited by solution submersion and chemical vapor deposition. MIES results for Ru3 had contributions from titania O 2p sites due to encapsulation by a reduced titania overlayer. For Pt3 clusters the UPS and MIES results provided evidence that Pt was present on the topmost layer, and encapsulation did not occur. The proposed reason for the encapsulation of Ru3 but not of Pt3 is the higher surface energy of Ru over Pt. It is concluded that Pt clusters deposited onto TiO2 can modify the outermost layer by adding discrete energy levels on the surface, whereas the Ru clusters being encapsulated just below the surface generate a broad distribution of energy states close to the Fermi level. The outcome of this work is that Pt3-cluster-modified surfaces could be used as catalysts for reactions where the Pt3 energy levels are suitable for the respective reaction. The implication of the DOS found for photocatalytic water splitting are discussed.

Bright electroluminescence (EL) from dopant-free ambipolar lateral p-n junctions in GaAs/AlGaAs single heterointerface (SH) heterostructures is used to probe neutral free excitons arising from two-dimensional electron and hole gases (2DEGs and 2DHGs). The EL spectra reveal both the heavy-hole neutral free exciton (X$^0$) and the high-energy free exciton of the H band (HE). A combination of transition energies, lifetimes, spatial emission profiles, and temperature dependences points to a predominantly two-dimensional character for these excitons at the SH. For X$^0$, the EL peak energies (1515.5-1515.7 meV) lie slightly above the corresponding bulk GaAs photoluminescence (PL) line at 1515.3 meV, while time-resolved measurements yield markedly shorter lifetimes for EL than for PL (337 ps vs. 1610 ps), consistent with recombination in a confined interfacial layer. The HE exciton exhibits a Stark blueshift under forward bias below threshold, and its energies and lifetimes (down to 575 ps) are tuned by the topgate voltage; above threshold, HE emission is quenched in favor of X$^0$. Finally, the tidal effect $-$ a form of pulsed EL generated by swapping the topgate voltage polarity in ambipolar field-effect transistors $-$ produces an X$^0$ line at the same energy as in the lateral p-n junction and reproduces the characteristic nonmonotonic frequency dependence of the brightness previously observed in quantum-well heterostructures, again indicating a 2D-like origin. Taken together, these results show electrically generated and controllable 2D-like excitons (HE and X$^0$), thereby bridging 2D exciton physics and 2DEG/2DHG platforms in dopant-free GaAs/AlGaAs SH devices.

Quasiperiodic systems host exotic transport regimes that are distinct from those found in periodic or disordered lattices. In this work, we study quantum transport in the Aubry-André-Harper lattice in a two-terminal setup coupled to zero-temperature reservoirs, where the conductance is evaluated via the nonequilibrium Green's function method. In the extended phase, we uncover a universal subdiffusive transport when the bath chemical potential aligns with the band edges. Specifically, the typical conductance displays a scaling of $\mathcal{G}_{\text{typ}}\sim L^{-2}$ with system size $L$. We attribute this behavior to the emergence of an exceptional point (Jordan normal form) in the transfer matrix in the thermodynamic limit. In the localized phase, the conductance shows exponential decay governed by the Lyapunov exponent. Intriguingly, in the critical phase, we identify pronounced log-periodic oscillations of the conductance as a function of system size, arising from the discrete scale invariance inherent to the singular-continuous spectrum. We further extend our analysis to the generalized Aubry-André-Harper model and provide numerical evidence suggesting that the exact mobility edge resides within a finite spectral gap. This results in a counter-intuitive exponential suppression of conductance precisely at the mobility edge. Our work highlights the distinct transport behaviors in quasiperiodic systems and elucidates how they are rigorously dictated by the underlying local spectral structure.

We present a model of active particles interacting through a dynamic, heterogeneous environment, leading to emergent collective behaviors without direct agent-to-agent communication. Expanding the resource-dependent framework introduced in Briozzo et al., 2025, arXiv:2512.08762, agents perform a persistent random walk combined with chemotaxis, directing toward nutrient-rich patches, whose resources are generated by logistic regrowth. We identify distinct phases of collective organization, ranging from disordered gas-like states to polar traveling waves and nematic independent clusters, depending on the interplay between chemotactic sensitivity and angular noise. The system exhibits spontaneous symmetry breaking and density waves driven purely by the coupling between population dynamics (birth-death processes) and environmental feedback. Our results bridge active matter physics and movement ecology, demonstrating that complex spatiotemporal patterns can arise without direct interaction between agents, but solely from the maximization of resource intake in a reactive environment.

The presence of correlated noise, arising from a mixture of independent fluctuations and a common noisy input shared across the neural population, is a ubiquitous feature of neural circuits, yet its impact on collective network dynamics remains poorly understood. We analyze a network of quadratic integrate-and-fire neurons driven by Gaussian noise with a tunable degree of correlation. Using the cumulant expansion method, we derive a reduced set of effective mean-field equations that accurately describe the evolution of the population's mean firing rate and membrane potential. Our analysis reveals a counterintuitive phenomenon: increasing the noise correlation strength suppresses the mean network activity, an effect we term correlated-noise-inhibited spiking. Furthermore, within a specific parameter regime, the network exhibits metastability, manifesting itself as spontaneous, noise-driven transitions between distinct high- and low-activity states. These results provide a theoretical framework for reducing the dynamics of complex stochastic networks and demonstrate how correlated noise can fundamentally regulate macroscopic neural activity, with implications for understanding state transitions in biological systems.

We report microwave spectroscopy of Josephson junctions chains made from an epitaxial Al/InAs heterostructure. The chains exhibit superinductance, with characteristic wave impedance exceeding $R_{Q} = \hbar/(2e)^{2}$. The planar nature of the junctions results in a large plasma frequency, with no measurable deviations from ideal dispersion up to $12~\mathrm{GHz}$. Internal quality factors decrease sharply with frequency, which we describe with a simple loss model. The possibility of a loss mechanism intrinsic to the superconductor-semiconductor junction is considered.

Memory effects are essential for dynamics of condensed materials and are responsible for non-exponential relaxation of correlation functions of dynamic variables through the memory function. Memory functions of dipole rotations for polar liquids have never been calculated. We present here calculations of memory functions for single-dipole rotations and for the overall dipole moment of the sample for SPC/E water. The memory functions for single-particle and collective dipole dynamics turn out to be nearly identical. This result validates theories of dielectric spectroscopy in terms of single-particle time correlation functions and the connection between the collective and single-particle relaxation times through the Kirkwood factor. The dielectric function in this formalism contains no new dynamic information that does not exist in the single-dipole correlation function. A short memory time, $\lesssim 1$ fs, justifies the use of rotational diffusion model to describe dynamics of a single molecular dipole moment in bulk water.

Weyl degeneracies in spectra of magnetoplasma waves enable nonreciprocal energy flow and topologically protected modes, yet conventional materials require impractical magnetic fields to operate. Developing an effective Hamiltonian framework for magnetic Weyl semimetals, we show that these systems overcome the limit, hosting Weyl magnetoplasma physics at zero field due to their giant intrinsic anomalous Hall response. The resulting topology supports nonreciprocal modes localized at magnetic domain walls, including a pair of topological "Fermi-arc-like modes and additional bound states. These effects are fully developed across a broad THz window, and we propose feasible experimental routes for their detection.

Recent studies of moiré physics have unveiled a wealth of opportunities for significantly advancing the field of quantum phase transitions. However, properties of reentrant phase transitions driven by moiré strength are poorly understood. Here, we investigate the reentrant sequence of phase transitions and the invariant of universality class in moiré-modulated extended Su-Schrieffer-Heeger (SSH) model. For the simplified case with intercell hopping $w=0$, we analytically derive renormalization relations of Hamiltonian parameters to explain the reentrant phenomenon. For the general case, numerical phase boundaries are calculated in the thermodynamic limit. The bulk boundary correspondence between zero-energy edge modes and entanglement spectrum is revealed from the degeneracy of both quantities. We also address the correspondence between the central charge obtained from entanglement entropy and the change in winding number during the phase transition. Our results shed light on the understanding of universal characteristics and bulk-boundary correspondence for moiré induced reentrant phase transitions in 1D condensed-matter systems.

We study stochastic transport of interacting particles on a disordered network described by the random comb geometry. The model is defined on a one-dimensional backbone from which branches of random lengths emanate, providing a minimal model of percolation networks beyond the critical percolation probability. The dynamics obeys local detailed balance with respect to a Bose-Hubbard Hamiltonian containing both an external bias and on-site repulsion. This choice yields an analytically tractable steady state through a mapping to the zero-range-process. We compute the backbone current, branch density profiles, and macroscopic drift velocity, and analyze how bias and interactions compete to shape transport. The backbone current increases monotonically with density, while the drift velocity displays a non-monotonic dependence on the external field, remaining finite for any nonzero bias, in contrast to the vanishing drift velocity of noninteracting particles beyond a threshold bias. Density profiles along branches exhibit stepwise plateaus governed by the ratio of interaction to bias energy. These results highlight how repulsive interactions suppress trapping and restore transport in disordered geometries, bridging earlier studies of field induced drift in random networks with the physics of disordered Bose-Hubbard systems.

The classification of mixed-state topological order requires indices that behave monotonically under finite-depth quantum channels. In two dimensions, a braided $C^*$-tensor category, which corresponds to strong symmetry, arises from a state satisfying approximate Haag duality. In this note, we show that the $S$-matrix and topological twists of the braided $C^*$-tensor category are quantities that are monotone under finite-depth quantum channels.

Chain-like macromolecules in solution, whether biological or synthetic, transform from an extended conformation to a compact one when temperature or other system parameters change. This collapse transition is relevant in various phenomena, including DNA condensation, protein folding, and the behavior of polymers in solution. We investigate the interplay of chain stiffness and range of attraction between monomers in the collapse of a single polymer chain. We use Monte Carlo simulations based on the pruned-enriched Rosenbluth method. Two distinct behaviors are found depending on chain stiffness (represented by the persistence length lp) and attraction range rc. When lp is larger than rc, the chain collapses sharply with decreasing temperature, whereas if lp is smaller than rc, it contracts gradually. Notably, in the regime of small lp and large rc, this rounding into a gradual compaction persists upon increasing the chain length and may remain in place in the limit of infinite chain length. Furthermore, for small rc, the transition temperature (theta-temperature) increases with lp, whereas for large rc the theta-temperature decreases with lp. Thus, stiffness promotes collapse for small rc but suppresses it for large rc. Our findings are in agreement with recent experiments on the contraction of single-stranded RNA as compared to double-stranded DNA, and provide valuable insights for understanding polymer collapse and the essential polymer parameters affecting it.

Catalysis-driven synthetic molecular motors operate in a loose mechanochemical coupling regime, one in which a decomposition of a fuel molecule does not reliably produce a forward step. In that regime, stochastic backward steps can significantly degrade the motor's current, prompting us to ask whether mechanically coupling multiple such motors can boost their averaged current. By simulating rotaxane-based motors with two classes of models--particle-based nonequilibrium molecular dynamics and jump-diffusion models--we show that current boosts are physically achievable. Our observed boosts, which amplify current by single-digit factors, emerge when coupling between motors can increase the activity, speeding up the rate of both forward and backward steps. In doing so, the bias for preferring forward steps actually degrades, but the lost bias can be largely recovered by raising the fuel concentration, demonstrating a general design strategy: amplify activity through coupling and restore bias through stronger driving.

We have examined the electrical behavior of Josephson junctions formed by a scanning tunneling microscope (STM) with a Nb sample and a Nb tip, with normal-state resistances Rn varying between 1 kOhm and 10 MOhm. Current-voltage characteristics were obtained as a function of Rn by varying the distance between the tip and sample at temperatures of 50 mK and 1.5 K. Rn decreases as the tip-sample separation is reduced, and the junction evolves from a phase-diffusion regime to an underdamped small junction regime, and then to a point contact regime. The subgap structure exhibits pronounced multiple Andreev reflection (MAR) features whose amplitudes and onset energies depend sensitively on junction transparency and gap asymmetry. To interpret these spectra, we generalize the Averin-Bardas MAR theory to superconductors with unequal gap magnitudes, providing a quantitative model appropriate for asymmetric STM junctions. The resulting fits yield the superconducting gaps of the electrodes, barrier transparency, and number of conduction channels as a function of Rn. Combining this analysis with Josephson junction dynamics, we further account for the observed switching and retrapping currents and the finite resistance of the supercurrent branch. Our results demonstrate that incorporating intrinsic electrode asymmetry is essential for reliably extracting transport parameters in STM-based superconducting weak links.

In direct ink writing (DIW), neighbouring filaments of yield-stress inks are deposited side-by-side and are expected to merge into smooth, mechanically robust structures. Unlike Newtonian filaments, coalescence can arrest in finite time, leaving a permanent, non-flat ridge set by the competition between capillarity and rheology. Here we study the coalescence of two printed yield-stress filaments, combining scaling theory for the arrested state, direct numerical simulations, and DIW experiments on Carbopol gels imaged by optical coherence tomography. In the viscoplastic limit, we predict and observe an approximately linear decrease of the final bridge height with plastocapillary number and a critical yield stress above which coalescence does not initiate. Simulations further show that elasticity becomes important at high plastocapillary number, enabling larger final bridge heights via a crossover from a rigid Herschel--Bulkley solid to a deformable Kelvin--Voigt response. Our findings provide a framework for predicting deposition profiles and, ultimately, for mitigating residual topography in DIW.

From numerical simulations it is known that the barrier-crossing time of a non-Markovian one-dimensional reaction coordinate with a single exponentially decaying memory function exhibits a memory-turnover: for intermediate values of the memory decay time the barrier-crossing time is reduced compared to the Markovian limit and for long memory times increases quadratically with the memory time when keeping the total integrated friction and the mass constant. The intermediate memory acceleration regime is accurately predicted by Grote-Hynes theory, for the asymptotic long-memory slow-down behavior no systematic analytically tractable theory is available. Starting from the Green function for a general inertial (i.e. finite-mass) non-Markovian Gaussian reaction coordinate in a harmonic well, we derive by an exact mapping a generalized Fokker-Planck equation with a time-dependent effective diffusion constant. To first order in a systematic cumulant expansion we derive an analytical Arrhenius expression for the barrier-crossing time with the pre-exponential factor given by the energy relaxation time, which can be used to robustly predict barrier-crossing times from simulation or experimental trajectory data of general non-Markovian inertial systems without the need to extract memory functions. For a single exponential memory kernel we give a closed-form expression for the barrier-crossing time, which reproduces the Kramers turnover between the high-friction and high-mass limits as well as the memory turnover from the intermediate memory acceleration to the asymptotic long-memory slow-down regime. We also show that non-Markovian systems are singular in the zero-mass limit, which suggests that the long-memory barrier-crossing slow-down reflects the interplay between mass and memory effects. Thus, physically sound models for non-Markovian systems have to include a finite mass.

We show that a robust, nonperturbative localization plateau emerges in periodically driven quasiperiodic lattices, independent of the static localization properties and drive protocol. Using exact Floquet dynamics, Floquet perturbation theory, and optimal-order van Vleck analysis, we identify a fine-tuned amplitude-to-frequency ratio where all Floquet states become localized despite dense resonances. The van Vleck expansion achieves superasymptotic accuracy up to an optimal orde; it ultimately breaks down due to resonant hybridization at a weak quasiperiodic potential, revealing that the observed localization is nonperturbative.

Jahn--Teller (JT) distortions are a key driver of physical properties in many correlated oxide materials. Cooperative JT distortions, in which long-range orbital order reduces the symmetry of the average structure macroscopically, are common in JT-distorted materials at low temperatures. This long-range order will often melt on heating, \textit{via} a transition to a high-temperature state without long-range orbital order. The nature of this transition has been observed to vary with different materials depending on crystal structure; in LaMnO$_3$ the transition has generally been interpreted as order-disorder, whereas in layered nickelates $A$NiO$_2$ ($A$=Li,Na) there is a displacive transition. Alternatively, recent theoretical work has suggested that previous attributions of order-disorder may in fact be a consequence of phonon anharmonicity, rather than persistence of JT distortions, which would suggest that the displacive transition may be more common than currently believed. In this work, we run Monte Carlo simulations with a simple Hamiltonian which is modified to include terms dependent on the JT amplitude $ρ$, which is allowed to vary within the simulation \textit{via} the Metropolis algorithm. Our simulations yield distributions of JT amplitudes consistent with displacive rather than order-disorder behaviour for both perovskites and layered nickelates, suggesting that displacive-like JT transitions may be more common than previously assumed in both perovskites and layered nickelates. We also find significant differences between the transition observed for perovskites compared with layered nickelates, which we attribute to differing extensivity of configurational entropy on the two lattices, showing the crucial role of lattice geometry in determining behaviour.

A recent STM experiment in 2D bilayer graphene [Y.-C. Tsui, et al., Nature 628, 287 (2024)], under a strong perpendicular magnetic field, has made a direct observation of the existence of three distinct filling-factor-dependent quantum phases in the lowest Landau level: the incompressible fractional quantum Hall liquid, a crystalline compressible hexagonal Wigner crystal with long-range order and rotational symmetry-breaking, and a random localized solid phase with no spatial order. We argue that the spatially random localized phase at low filling is the recently proposed disorder-dominated strongly localized amorphous "Anderson solid" phase [A. Babber, et al., arXiv:2601.03521], which appears generically at a sample-dependent filling factor.

We investigate heat transport in one-dimensional harmonic chains with mass disorder and weak bond disorder, coupled at both ends to oscillator heat baths through weak impedance mismatches. The model incorporates correlations in mass disorder, in bond disorder, and between the two. We find that the scaling of thermal conductivity $κ$ with system size $N$ is determined solely by either mass disorder or bond disorder. This indicates that cross-correlations between the two types of disorder play no important role in the scaling behavior of $κ$. Consequently, by tuning the self-correlations, it is possible to control how the thermal conductivity scales with the system size. Such control could have potential applications in thermoelectric devices and thermal insulation technologies.

Extracting the complete quantum geometric and topological character of Bloch wavefunctions from experiments remains a challenge in condensed matter physics. Here, we resolve this by introducing the "wavefunction form factor" (WFF) matrix, a quantity directly constructible from intensities in momentum- and energy-resolved spectroscopies like ARPES and INS. We demonstrate that band topology is encoded in "spectral nodes" -- momentum-space points where the WFF determinant vanishes, providing a direct readout of topological invariants via a topological selection rule. Furthermore, when the number of independent probes exceeds the number of the target bands, our framework yields an effective band projector. This enables the determination of Wilson loop spectra and the extraction of an effective quantum geometric tensor, providing a model-independent measurement of the non-Abelian Berry curvature and quantum metric as resolved by the experimental probes.

We propose a mechanism for the inverse Faraday and the inverse Cotton--Mouton effects arising from quantum geometry, characterized by the quantum metric quadrupole and the weighted quantum metric. Within a semiclassical framework based on the Boltzmann transport theory, we establish a general formalism describing light-induced magnetization in electronic systems as a second-order response to the electric field of light. Using continuum and tight-binding models, we discuss the symmetry constraints on these effects and estimate the magnitudes of the resulting magnetizations. Our results highlight a direct manifestation of quantum-geometric quantities in nonlinear magneto-optical responses and suggest a viable pathway for experimental detection.

Ferroelectric materials exhibit a switchable, spontaneous polarization at the unit cell level--an attractive property utilized in many emerging technologies including, among others, high-density memory storage, low-power transistors, and high-speed fiber optic communication. Understanding the local polarization switching behavior, through domain nucleation and evolution, is critical to advancing these technologies and requires characterization of the local domain microstructure. However, in application-relevant polycrystalline materials exhibiting a distribution of grain orientations, a direct mapping of the polarization direction in three dimensions has remained inaccessible using conventional experimental approaches. Here, taking barium titanate single crystals and lead zirconium titanate polycrystals as our bulk model systems, we map the local polarization directions using a new electron backscatter diffraction indexing technique based on simulated pattern-matching. Through improved pre-processing techniques (including optimized pattern processing, a new pseudosymmetry-sensitive neighbor pattern averaging method, and DIC-based global sample-detector geometry calibration) and a new pseudosymmetry confidence index (which considers not only pattern similarity but pattern dissimilarity trends with other domain variant patterns), we successfully distinguish between the six polarization directions, despite the challengingly small unit cell aspect ratio of the selected materials. The methods developed in this work are not only applicable to ferroelectrics but any material which exhibits close crystallographic pseudosymmetries--extending the current capabilities of EBSD.

We introduce the genuine multipartite Rains entanglement (GMRE) as a measure of genuine multipartite entanglement that can be computed using semi-definite programming. Similar to the Rains relative entropy (its bipartite counterpart), the GMRE is monotone under selective quantum operations that completely preserve the positivity of the partial transpose, implying that it is a multipartite entanglement monotone. As a consequence, we show that the GMRE bounds both the one-shot standard and probabilistic approximate GHZ-distillable entanglement from above. We also develop a generalization of this quantity that incorporates other entropies, including quantum Renyi relative entropies.

Stiff forces, which bind objects together or otherwise confine motion, are found widely in soft-matter systems - colloids with short range attractions, ligand-receptor contacts, particles in optical traps, fibres that resist stretching, etc. To assess the long-term effect of these stiff forces on dynamics and structure, it is useful to consider the limit where they are treated as constraints, so the system evolves strictly within allowed configurations. Efforts to derive equations involving both constraints, and the stochastic motion appropriate at the scales of soft matter, began around 50 years ago, yet, we are still lacking a straightforward way to extract the projected equations and apply them in modern formulations of mesoscale dynamics. Here, we address this gap with two key contributions: (1) a practical summary of the constrained Brownian dynamics equations with ``soft'' constraints, i.e. constraints imposed by stiff forces, which is illustrated through several representative examples, taking care to highlight the nontrivial effects of the constraints; and (2) a novel derivation using singular perturbation theory, establishing the validity of these equations over timescales exceeding the relaxation of stiffly constrained degrees of freedom. We further extend our approach to ``soft soft'' constraints, where mobility varies on lengthscales comparable to the restraining forces - a scenario typical for particles in fluids experiencing hydrodynamic interactions. We hope our results will be useful for soft matter research, as a robust toolkit for studying tethered or confined systems.

A probabilistic formulation of irreversible kinetics is introduced in which incrementally admissible histories are weighted by a Gibbs-type measure built from an energy-dissipation action and observation constraints, with Theta controlling epistemic uncertainty. This measure can be interpreted as a Bayesian posterior over histories. In the zero-uncertainty limit, it concentrates on maximum-a-posteriori (MAP) histories, recovering classical deterministic evolution by incremental minimization in the convex generalized-standard-material setting, while allowing multiple competing MAP histories for non-convex energies or temporally coupled constraints. This emergence is demonstrated across seven distinct forward-in-time examples and an inverse inference problem of unknown histories from sparse observations via a global constrained minimum-action principle.

Dynamical perturbations can modify the energy landscape of a physical system, such that unstable equilibrium configurations become stable when subject to an external drive. The magnetic analog of such dynamical stabilization corresponds to saturation of the magnetization against an external field. Here we report dynamical stabilization of the magnetization in thin film bismuth-substituted yttrium iron garnet by spin current injection from an adjacent Pt layer. Magneto-optical Kerr effect measurements demonstrate magnetization reversal against magnetic fields up to 3000 times larger than the film's coercivity once the spin injection surpasses a critical threshold associated with negative damping. Micromagnetic simulations reveal that this process is mediated by the excitation of a large population of incoherent magnons with non-zero wave vector, leading to a transient shortening and subsequent stabilization of the inverted magnetization. The elementary excitations of the high-energy inverted magnetization state are shown to be antimagnons, quasi-particles carrying opposite energy and spin relative to magnons. Our results further reveal how the system's size and minimization of nonlinear magnon scattering processes play a key role in dynamical stabilization, opening new avenues for magnetic state control beyond conventional magnetization switching. Dissipation-driven phase transitions in large-area magnetic systems provide a solid-state platform to study magnonic analogs of relativistic phenomena, such as Klein tunneling and black holes, as well as spin-wave amplification and lasing.

Many-body localization (MBL) appears to be a robust example of ergodicity breaking in many-body interacting systems. Here, we review different aspects of MBL, concentrating on various ways the disorder may be introduced into the system studied. In particular, we consider both the random and quasiperiodic diagonal (i.e., on-site) disorder as well as bond disorder as realized in randomly distributed atoms interacting via long-range interactions. We also review the quantum sun model, which seems to be the ideal, albeit artificial, model exhibiting MBL.

Error-mitigation methods for Ising machines are reexamined not merely as noise-suppression techniques but as a structural design problem of replica-coupled Ising models. Using simulated annealing as a hardware-noise-free testbed, we systematically compare the penalty-spin (PS) model, which couples replicas through a centralized auxiliary layer, with the stacked model, which couples adjacent replicas directly. Numerical experiments on the quadratic assignment problem reveal that the ferromagnetically coupled stacked model stably maintains constraint satisfaction and improves solution quality over a broad parameter range, exhibiting favorable scalability with both the number of replicas and problem size. In contrast, the PS model suffers from cooperation collapse at large parallelism: many-replica averaging in the PS layer washes out sparse solution information, preventing effective inter-replica coordination. These findings demonstrate that the topology of inter-replica couplings decisively influences search robustness, and provide practical guidelines for model selection and parameter tuning in constrained optimization.

1D quasicrystals such as the Fibonacci chain have been said to ``inherit" their topological properties from the 2D Quantum Hall problem. Yet, a direct way to see the connection was lacking until a common ancestor, the Fibonacci-Hall model, was introduced recently \cite{aj2025}. This 2D ancestor model relates the role of the external magnetic flux in the Hall problem and that of a geometric flux which describes the winding of the quasicrystal in 2D, in the cut-and-project method. Doing this enables us to extend the notion of Chern numbers as defined in 2D, to the energy bands of the 1D chain by adiabatic continuity. The older notion of gap labels in the 1D system are now seen to be derivable from the Chern numbers of the 2D bands. The Fibonacci-Hall model thus provides an important link between physics of two paradigmatic models, the Fibonacci quasicrystal and the quantum Hall insulator. The generalization to other 1D quasiperiodic models is expected to be relatively straightforward. The extension to 2D cut-and-project tilings is left for future studies.

Accurately determining the underlying physical parameters of individual elements in integrated photonics is increasingly difficult as device architectures become more complex. Inferring these parameters directly from spectral measurements of the system as a whole provides a practical alternative to traditional calibration, allowing characterization of photonic systems without relying on detailed device-specific models. Here, we introduce a supervised machine-learning strategy to learn the onsite losses and resonant frequency shifts of each individual ring in an array of coupled ring resonators from measured spectral power distributions of the whole array. The neural network infers these parameters with high accuracy across multiple experimental configurations. Our methodology provides a scalable and non-invasive method for extracting intrinsic parameters in coupled photonic platforms, paving the way for future development of automated calibration and control methods.

We study composite topological excitations in ferromagnetic superconductors consisting of bound states of magnetic spin textures (skyrmions) and superconducting vortices. Using a Ginzburg--Landau framework with Zeeman coupling between the magnetization and superconducting magnetic field, we demonstrate that skyrmion-vortex pairs (SVPs) form energetically stable bound states. By analyzing their asymptotic interactions, we identify regimes in which SVPs exhibit both short-range repulsion and long-range attraction, leading to clustering phenomena. Our results provide a field-theoretical basis for understanding suggest pathways for controlling hybrid topological matter through long-range interactions.

The role of interface states and dielectric mismatch is studied in ultrathin P-doped silicon-on-insulator (SOI) films with thickness of the device layer ($H_{SOI}$) varying from 30 to 8 nm and dopant concentration ($n_{D}$) ranging from 10$^{18}$ to nearly 10$^{20}$ cm$^{-3}$. P concentration is determined by Time-of-Flight Secondary Ion Mass Spectrometry (ToF-SIMS). Sample resistivity ($ρ$), carrier concentration ($n_e$), and mobility ($μ_e$) are extracted by combining sheet resistance and Hall measurements in van der Pauw configuration. When $H_{SOI}$ = 30 nm, transport properties at room temperature are fully compatible with those of a similarly doped bulk Si. Progressive 2D confinement by reduction of $H_{SOI}$ below 30 nm results in a reduction of the carrier concentration and a concomitant degradation of $μ_e$. These effects, which are steadily enhanced decreasing $n_D$, are attributed to non-passivated interface states at the SiO$_2$/Si interface and can be significantly mitigated by high temperature rapid thermal oxidation (RTO). The effectiveness of this approach was verified by electron-paramagnetic resonance (EPR) spectra and capacitance-voltage (CV) measurements, which allowed the assessment of the quality of the RTO-SiO$_2$/Si interface and the correlation with observed electrical properties. After effective interface engineering, low temperature electrical characterization revealed a significant increase in P ionization energy in samples with $H_{SOI}$ <= 15 nm, a result directly related to the dielectric mismatch.

The autonomous motion of liquid crystal oil droplets in micellar media arises from spontaneous breaking of time reversal symmetry via nonlinear coupling between Marangoni stresses and surfactant transport. While this phenomenon has been widely studied, the influence of micellar solute structure remains unexplored. By modifying micellar architecture using a structure forming salt, we uncover a pronounced non monotonic dependence of droplet velocity on salt concentration. Increasing salt simultaneously raises the medium viscosity and drives a transition of micelles from spherical to rod-like or worm like morphologies. Using complementary experiments, we quantify the viscosity and micellar interaction lengthscale as functions of the salt to surfactant ratio and develop a theoretical model that consistently reproduces the measured propulsion speeds. Flow fields around the droplets are characterized by particle image velocimetry. Our results demonstrate that salt surfactant composition governs active droplet propulsion by jointly controlling micellar solute interaction lengthscales and medium viscosity.

In this paper, we establish a connection between the formalism of $\mathcal{R}$-transforms for non-Hermitian random matrices and the framework of spherical integrals, using the replica method. This connection was previously proved in the Hermitian setting and in the case of bi-invariant random matrices. We show that the $\mathcal{R}$-transforms used in the non-Hermitian context in fact originate from a single scalar function of two variables. This provides a new and transparent way to compute $\mathcal{R}$-transforms, which until now had been known only in restricted cases such as bi-invariant, Hermitian, or elliptic ensembles.

The Eigenstate Thermalization Hypothesis explains thermalization in isolated quantum systems through the statistical properties of observables in the energy eigenbasis. We investigate the crossover from integrability to chaos in the spin-$1/2$ XXZ chain, establishing a direct correspondence between the spectral correlations of the Hamiltonian and local observables expressed in the energy eigenbasis as a signature of ergodicity breaking. By introducing a local perturbation that drives the system from integrability to chaos, we track the standard ETH indicators and the eigenstate entanglement entropy. We introduce a submatrix-based framework for analyzing local observables in the energy eigenbasis. By extracting real-symmetric blocks along the diagonal of the local observables represented in eigenbasis, we show that these submatrices exhibit both the short-range and long-range spectral features of the Hamiltonian. Remarkably, this correspondence persists even in a partially ergodic regime, indicating that the emergence of chaos is already encoded locally within the observables' matrix structure and that small blocks are sufficient to capture the underlying spectral correlations.

We present a systematic study of the Ruderman-Kittel-Kasuya-Yosida (RKKY) interaction in MnBi$_2$Te$_4$ films under both dark and illuminated conditions. In the dark, the intrinsic magnetism of MnBi$_2$Te$_4$ is shown to yield a stronger anisotropic RKKY spin model compared to nonmagnetic topological insulators, providing a clear signature for differentiating these systems. Furthermore, key band properties -- such as energy gap, band degeneracy/splitting, and topological deformations of the Fermi surface -- imprint distinct signatures on the RKKY interaction, enabling clear discrimination between even- and odd-septuple-layer (SL) films. This discrimination manifests in multiple ways: through the Fermi-energy dependence or spatial oscillations of the interaction for impurities on the same surface, or via the presence versus absence of spin-frustrated terms for those on different surfaces. Under off-resonant circularly polarized light, we track photoinduced topological phase transitions and identify two characteristic signatures at the phase boundary: a sign reversal in spin-frustrated terms and a dip in collinear RKKY components. These serve as fingerprints for circular-polarization-chirality-dependent topological transitions in even- and odd-SL films, respectively. Overall, this work establishes RKKY interactions as a sensitive magnetic probe for revealing both distinctive band properties and light-driven phase transitions in MnBi$_2$Te$_4$ films, thereby complementing conventional electrical measurements while providing new insights into the influence of intrinsic magnetism on the surface-state band structure.

The time to first crossing for the Poisson counting process with respect to a linear moving barrier with offset is a classic problem, although key results remain scattered across the literature and their equivalence is often unclear. Here we present a unified and pedagogical treatment of two approaches: the direct time-domain approach based on path-decomposition techniques and the Laplace-domain approach based on the Pollaczek-Spitzer formula. Beyond streamlining existing derivations and establishing their consistency, we leverage the complementary nature of the two methods to obtain new exact analytical results. Specifically, we derive an explicit large deviation function for the first-passage time distribution in the subcritical regime and closed-form expressions for the conditional mean first-passage time for arbitrary offset. Despite its simplicity, this first crossing process exhibits non-trivial critical behavior and provides a rare example where all the main results of interest can be derived exactly.

Heterostructures combining graphene with 3d transition metal ferromagnets (FMs) enable various spin-based phenomena at ultrafast timescales. However, challenges such as the interfacial impedance mismatch, FM deposition-induced defect generation, and interface modification by interfacial coupling or hybridization can impede their functionalization for spin-orbitronics. In this work, we utilize insulating TiOx barrier layers (BLs) to modify the interfacial spin conductance structurally, disentangle spin pumping and magnetic proximity effects (MPE), and establish external control over ultrafast magnetization dynamics in single-layer graphene/TiOx/Co systems. All-optical time-resolved magneto-optical Kerr effect measurements of femtosecond to nanosecond spin dynamics reveal systematic tunability of ultrafast magnetic parameters via barrier engineering. The thickness-dependent damping modulation in Co indicates strong spin pumping, with interfacial spin transparency close to half its physical limit in the presence of an ultrathin BL, where MPE is eliminated. Our results show that appropriately chosen ultrathin BLs can prevent interfacial alterations from ferromagnetic metals, facilitating efficient spin detection in graphene and enhancing control over spin angular momentum dissipation in graphene/FM interfaces.

Choosing a representative element volume (REV) from finite cylindrical $μ$CT scans becomes ambiguous when a key field variable exhibits a slow axial trend, because estimated statistics can change systematically with subvolume size and position rather than converging under simple averaging. A practical workflow is presented to size an REV under such nonstationary conditions by first suppressing axial drift/trend to obtain a residual field suitable for second-order analysis, and then selecting the smallest analysis diameter for which low-wavenumber content stabilizes within a prescribed tolerance. The approach is demonstrated on \textit{Thalassinoides}-bearing rocks, whose branching, connected burrow networks impose heterogeneity on length scales comparable to typical laboratory core diameters, making imaging-based microstructural statistics and downstream digital-rock proxies highly sensitive to the chosen subvolume. From segmented data, a scalar ``burrowsity'' field--capturing burrow-related pore spaces and infills--is defined, and axial detrending (with optional normalization) is applied to mitigate acquisition drift and nonstationary trends. Representativeness is then posed as a diameter-convergence problem on nested inscribed cylinders: the two-point covariance and its isotropic spectral counterpart $\widehat{C}$ are estimated, and the smallest diameter at which the low-wavenumber plateau becomes stable is selected. Applied to a segmented \textit{Thalassinoides} core, the method identifies a minimum analysis cylinder of approximately $D_{\mathrm{REV}}\approx 93~\mathrm{mm}$ and $H_{\mathrm{REV}}\approx 83~\mathrm{mm}$, enabling reproducible correlation-scale reporting and connectivity-sensitive property estimation.

One-dimensional semiconductors are characterized by strongly bound excitons. Therefore, the Frenkel regime of excitons localized within a few unit cells is readily reached and traditional Wannier exciton models become inadequate. In the presence of strong electric fields, excitons are polarized and, in extreme cases, ionized. Such strong-field effects have previously been described analytically for Wannier excitons. In the present work, we show that analytical results can be extended to the more involved Frenkel case as well. Hence, by analytically solving the difference equation describing Frenkel excitons in electric fields, we derive close-form expressions for resonances providing Stark shifts and ionization rates. Moreover, closed-form results for exciton electroabsorption spectra and dynamic polarizability are obtained.

We present explicit matrix product operator (MPO) representations for the local conserved quantities of the spin-$1/2$ XYZ chain. Through these MPO representations, we simplify the coefficients appearing in the local conserved quantities originally derived by one of the authors, and reveal their combinatorial meaning: the coefficients prove to be a polynomial generalization of the Catalan numbers, defined via weighted monotonic lattice paths. Furthermore, we obtain a new simple $3 \times 3$ Lax operator for the XYZ chain that, unlike Baxter's R-matrix, does not involve elliptic functions.

In pursuit of a colloidal analogue to quantum density functional theory (DFT) predictions of atomic crystal structures, we report a new, classical DFT that predicts the relative thermodynamic stability of colloidal crystals of hard, convex particle shapes. In contrast to standard classical DFT approaches, our theory maps the hard particle system to an auxiliary system in which we treat the particles as fixed "nuclei" embedded in a fictitious, spatially varying density field that distributes throughout the auxiliary system. By minimizing the free energy of the auxiliary system, and through comparison with known equations of state and free energy calculations using thermodynamic integration, we show that the auxiliary system with the lowest free energy corresponds to the most probable crystal of hard shapes in the original system.

We propose a new method to describe the population dynamics of distinct configurational states based on a continuous-time description of state-to-state transitions. According to classical reaction dynamics theory, the probability density associated with a given state obeys the Liouville equation, the probability density associate d with a given state obeys the Li ouville equation, including influx from and efflux to neighboring states. By introducing a Markov approximation for the crossing of boundaries separating the states, tractable integral equations governing the state populations are derived. Once the time-dependent quantities appearing in these equations are evaluated, the population dynamics on long timescales can be obtained. Because these quantities depend only on a few states in the local neighborhood of a given state, they can be computed using a set of short-timescale molecular dynamics (MD) simulations. We apply the present method to the binding and unbinding kinetics of CH$_4$/CH$_4$, Na$^+$/Cl$^-$, and 18-crown-6-ether (crown ether)/K$^+$ in water. For both kinetics, the time constants estimated from the present method are almost comparable to those obtained from brute-force MD simulations. The required timescale of each MD trajectory in the present method is approximately two orders of magnitude shorter than that in the brute-force MD approach in the crown ether/K$^+$ system. This reduction in the trajectory timescale enables applications to complex binding and unbinding sy stems whose characteristic timescales a re far beyond those directly acce ssible by brute-force MD simulati ons.

We investigate vapor-liquid phase separation of an active near critical Lennard-Jones fluid confined within a cylindrical pore using molecular dynamics simulations. Activity is introduced via Vicsek-type alignment interactions, enabling a systematic study of how self-propulsion modifies domain morphology and coarsening kinetics under quasi-one-dimensional confinement. In the passive limit, the system undergoes early-time spinodal decomposition (diffusive growth characterized by the Lifshitz-Slyozov exponent $α= 1/3$), followed by the formation of periodically modulated, plug-like liquid domains along the pore axis. At late times, coarsening becomes kinetically arrested, and the system remains trapped in a metastable striped state. Introducing activity destabilizes this arrested morphology by enhancing collective domain transport, leading to frequent domain mergers and complete phase separation at sufficiently high activity. The late-stage coarsening then exhibits a crossover to faster, ballistic growth with an effective exponent $α= 2/3$, consistent with a cluster-coalescence mechanism. Analysis of two-point correlation functions and structure factors confirms dynamic scaling across all activity regimes. Our results demonstrate that alignment-induced activity can overcome confinement-driven kinetic arrest, providing new insight into phase separation in confined active fluids. The relevant growth laws are analyzed and interpreted using appropriate theoretical frameworks.

We experimentally demonstrate the generation of multiple three-magnon splitting processes in an in-plane magnetized submicron Bi-YIG disk using micro-focused Brillouin light scattering. The low magnetic damping and strong magneto-optical response of BiYIG enable the detection of nonlinear spin-wave interactions at low threshold powers. By tuning the in-plane static magnetic field, excitation frequency, and power, we observe the generation of three pairs of secondary modes symmetrically distributed around half the excitation frequency. Time-resolved BLS measurements present temporal dynamics and threshold behavior associated with the successive activation of three-magnon pairs.

The observation of magnetism in atomically thin van der Waals (vdW) antiferromagnets (FePS$_3$, NiPS$_3$, and MnPS$_3$) in 2016 marked an important moment in the development of two-dimensional (2D) physics. In this personal reflection, I describe how a simple question, posed in the early 2010s, motivated experimental efforts that culminated in the demonstration of antiferromagnetic order in monolayer FePS$_3$. Alongside subsequent reports of vdW ferromagnets in 2017, these developments helped establish intrinsic magnetism as a viable degree of freedom in atomically thin materials. I close with personal lessons drawn from this period and a perspective on the opportunities that now shape the field's second decade and beyond.

Exciton-polaritons are light-matter hybrid quasi-particles that have emerged as a flexible platform for developing quantum technologies and engineering material properties. However, the fundamental mechanistic principles that govern their dynamics and relaxation remain elusive. In this work, we provide the microscopic mechanistic understanding of the exciton-polariton relaxation process that follows from an excitation in the upper polariton. Using both mixed quantum-classical simulations and analytical analysis, we reveal that phonon-induced upper-to-lower polariton relaxation proceeds via two steps: the first step is a vertical inter-band transition from the upper to the lower polariton, which is followed by a second step that is a phonon-induced Fröhlich scattering within the lower polariton. We find that in materials of finite thickness (which include filled cavities), phonon-induced polaritonic intraband Fröhlich scattering is significantly suppressed. We show that the microscopic origin of this suppression is phonon-fluctuations synchronization (or self-averaging) due to the polaritonic spatial delocalization in the quantization direction. Finally, we show that the same phonon fluctuation-synchronization effect plays a central role across polaritonic relaxation pathways, and we derive simple analytical expressions that relate a material's finite thickness to the corresponding relaxation rate constants.

The origin of the excess vibrational density of states (DOS) beyond Debye's theory in amorphous solids (often referred to as the Boson peak) has been attributed to the presence of quasi-localized vibrational modes in recent years. However, by dispersing the total DOS onto each degree of freedom (DOF), the results of this report provide evidence that \(99.9\%\) of DOFs, and hence almost all particles, contribute to the Boson peak (BP). These results challenge the prevailing opinion that BP is contributed by a minority of particles and highlight its long-neglected global and collective origin.

Probabilistic computers built from p-bits offer a promising path for combinatorial optimization, but the dense connectivity required by real-world problems scales poorly in hardware. Here, we address this through graph sparsification with auxiliary copy variables and demonstrate a fully on-chip parallel tempering solver on an FPGA. Targeting MIMO detection, a dense, NP-hard problem central to wireless communications, we fit 15 temperature replicas of a 128-node sparsified system (1,920 p-bits) entirely on-chip and achieve bit error rates significantly below conventional linear detectors. We report complete end-to-end solution times of 4.7 ms per instance, with all loading, sampling, readout, and verification overheads included. ASIC projections in 7 nm technology indicate about 90 MHz operation with less than 200 mW power dissipation, suggesting that massive parallelism across multiple chips could approach the throughput demands of next-generation wireless systems. However, sparsification introduces sensitivity to the copy-constraint strength. Employing Two-Dimensional Parallel Tempering (2D-PT), which exchanges replicas across both temperature and constraint dimensions, we demonstrate over 10X faster convergence without manual parameter tuning. These results establish an on-chip p-bit architecture and a scalable algorithmic framework for dense combinatorial optimization.

The photoluminescence (PL) of momentum-indirect $\rm Γ- K$ excitons in monolayer WS$_2$ under biaxial strain was recently observed by Blundo et al. [Phys. Rev. Lett. 129, 067402 (2022)], yet its microscopic origin remains elusive. Here we develop a unified framework that reproduces the measured PL and reveals its fundamental excitonic mechanism. We reveal that: (i) the PL originates from genuinely hybridized direct-indirect excitonic eigenstates, rather than nominally mixed species with fixed dominant character; (ii) the direct exciton converts into the indirect one via a previously unrecognized two-step pathway -- exchange-interaction-driven exciton transfer followed by a spin flip; and (iii) a higher-energy indirect exciton, absent from prior studies, acts as a crucial intermediate mediating this conversion. Beyond explaining experiment, our theory predicts a striking strain-driven "sinusoidal'' valley response, furnishing a continuously tunable valley dial that far exceeds binary control schemes. This unified picture of strain-engineered direct-indirect exciton dynamics introduces a new paradigm for manipulating long-lived valley degrees of freedom, opening a pathway toward programmable valley pseudospin engineering and next-generation valleytronic quantum technologies.

The Casimir interaction in salted water contains a universal contribution of electromagnetic fluctuations that makes it of a longer range than previously thought. The universal contribution dominates non universal ones at the distances relevant for actin fibers inside the cell. We discuss universal and non-universal contributions with a model mimicking biological matter. We also show that the universal Casimir effect should have important implications at the cell scale.

Molecular simulations of interfacial polar media routinely employ periodic boundary conditions parallel to the interface. We show that this geometry injects a uniform plane mode ($q_{\parallel}=0$) that converts the plane-averaged electrostatic potential into a cumulative sum of plane-dipole increments, a random walk in $z$. Consequently, the variance of the plane-averaged potential grows linearly with depth in semi-infinite slabs and follows a parabolic Brownian-bridge profile in finite cells with both ends fixed, with an amplitude inversely proportional to the cell's lateral area. Hence, at any finite area, the variance diverges with slab thickness, a 2D-mode catastrophe. In contrast, a pure 1D chain (no lateral replication) and a fully 3D, nonperiodic medium both exhibit bounded fluctuations that saturate with distance. The mechanism is generic to any solver of Poisson's equation with 2D periodicity, so the apparent growth and ultimate divergence in potential fluctuation are artifacts of boundary conditions rather than material response, and we provide a simple scaling criterion for choosing slab sizes that keeps these artifacts under quantitative control.

We present a theoretical study of spin-wave scattering by a twisted domain wall (DW) in a two-dimensional ferromagnet with easy-axis anisotropy. While the twisted DW generates an effective gauge field for spin waves, leading to a deflection of their trajectories, our main focus is on a distinct effect that arises when a hard-axis anisotropy is present in addition to the easy-axis anisotropy. In this case, the translational symmetry of the spin-wave Hamiltonian along the DW is broken, resulting in a periodic modulation of the Hamiltonian. This periodicity leads to the formation of multiple diffracted spin wave modes on both sides of the DW, engendering a DW-induced magnonic diffraction pattern. The interplay between the emergent gauge field and the anisotropy-induced periodicity reveals rich spin-wave dynamics and suggests potential applications for manipulating magnon flow in two-dimensional magnetic textures.

Cryogenic photon sensing for high-energy physics motivates photosensor technologies that combine large-area scalability with internal gain and stable operation at low temperature. Amorphous selenium is a promising photoconductor, yet its field- and temperature-dependent transport and avalanche response in lateral geometries have not been systematically established. This work reports field-resolved photocurrent measurements of lateral a-Se devices from 93 K to 297 K under 401 nm excitation at fields up to 120 V/um. Below avalanche onset, the external quantum efficiency was described by the Onsager model, yielding effective post-thermalization separations that decrease with decreasing temperature. The field-assisted detrapping region was evaluated using several transport models, with the data favoring field-assisted hopping and thermally-assisted tunneling as the mechanisms that best capture the temperature evolution of the photocurrent. The boundaries between field-assisted detrapping, transport-limited conduction, and avalanche shift with temperature; at 93 K the response transitions directly from detrapping into avalanche. Avalanche multiplication was analyzed using the Lucky-drift model. These results provide the first systematic characterization of cryogenic avalanche behavior in lateral a-Se detectors and establish quantitative trends relevant to low-temperature, high-gain photodetector design.

A constitutive relation between stress and strain relative to a reference state is the basic assumption of elasticity theory. However, in living matter, stress is governed by (motor molecule) activity rather than a constitutive law. What paradigm takes the place of elasticity in this setting? Here, we derive a continuum theory of active mechanics by taking the continuum limit of the Active Tension Network model of 2d epithelia. Instead of a reference state, we start from a prescribed active force configuration, encoded in a Riemannian "tension metric". Intuitively, one expects cells to adjust their positions to achieve force balance by rearranging local sources of active stress. More precisely, the cell positions define an embedding of the tension metric into 2d physical space, which determines the macroscopic physical stress. For free boundaries, tissue adopts a certain intrinsically defined shape, the force-balanced embedding with minimal internal stress. Boundary forces then deform this embedding. The resulting stress transformation yields an effective stress-strain relation. Key elements of elasticity hence emerge from a "stress-only" starting point, explaining how tissue shape can be adiabatically controlled by active stress during morphogenesis. Plastic behavior arises from topological cell rearrangement, which we represent by a continuous reparameterization of the tension metric, providing a principled continuum theory of emergent elasto-plastic flow. To express this physics, we use the mathematics of isothermal coordinates and quasi-conformal maps. The present theory elucidates the unconventional mechanics of living tissues and may apply to 2d active and granular materials more generally.

Neutral-atom quantum simulators provide a powerful platform for realizing strongly correlated phases, enabling access to dynamical signatures of quasiparticles and symmetry breaking processes. Motivated by recent observations of quantum phases in flux-frustrated ladders with non-vanishing ground state currents, we investigate interacting bosons on the dimerized BBH lattice in two dimensions-originally introduced in the context of higher-order topology. After mapping out the phase diagram, which includes vortex superfluid (V-SF), vortex Mott insulator (V-MI), and featureless Mott insulator (MI) phases, we focus on the integer filling case. There, the MI/V-SF transition simultaneously breaks the $\mathbb Z_2^{T}$ and U(1) symmetries, where $\mathbb Z_2^{T}$ corresponds to time-reversal symmetry (TRS). Using a slave-boson description, we resolve the excitation spectrum across the transition and uncover a chiral Higgs mode whose mass softens at criticality, providing a dynamical hallmark of emergent chirality that we numerically probe via quench dynamics. Our results establish an experimentally realistic setting for probing unconventional TRS-broken phases and quasiparticles with intrinsic chirality in strongly interacting quantum matter.

Time-periodic modulation of a static system is a powerful method for realizing robust unidirectional topological states. So far, all such realizations have been based on interactions among $s$ orbitals, without incorporating inter-orbital couplings. Here, we demonstrate higher-orbital Floquet topological insulators by introducing periodically modulated couplings between the optical $s$ and $p$ orbitals in a square lattice. The staggered phase of the $s$-$p$ couplings gives rise to a synthetic uniform $π$ magnetic flux per plaquette of the lattice, and periodic driving of the couplings opens a topological bandgap, characterized by the Floquet winding number. We image topological edge modes of $s$-$p$ orbitals traveling unidirectionally around a corner. Here, the topological phases are realized by a combined effect of the periodic driving and synthetic magnetic flux. Consequently, when the synthetic flux is turned off, the system becomes trivial over a range of driving parameters. Our results open a promising pathway for exploring topological phenomena by introducing the orbital degree of freedom.

This Ph.D. thesis reports on progress in rigorously establishing hydrodynamic principles from the microscopic Hamiltonian dynamics of quantum many-body systems in a general, non-model-specific manner. Using the C*-algebra framework of statistical mechanics, we treat systems directly in the thermodynamic limit, primarily focusing on quantum lattice models where tools such as Lieb-Robinson bounds yield rigorous statements. We thus provide a proof-of-principle that large-scale behaviours can indeed be seen as emerging from microscopic dynamics, with mathematical proof. We first report on ergodicity results in short-range models with exponentially decaying or finite-range interactions. We show that time-averaged observables converge to their ensemble averages and decorrelate from all other observables almost everywhere within the light-cone defined by Lieb-Robinson bounds. This relaxation property indicates the loss of information at large scales, from which we prove a Boltzmann-Gibbs principle: at the Euler scaling limit of large time and distance, observables project onto hydrodynamic modes (extensive conserved quantities), within correlation functions. These results hold independently of microscopic details, capturing the physical idea that such details are lost at large space-time scales. Regarding finer scales of hydrodynamics, we discuss rigorous lower bounds on the strength of diffusion. We establish a general result on the clustering of n-th order connected correlations within C* dynamical systems. These results are applied to obtain a strictly positive lower bound on the diffusion constant of chaotic open quantum spin chains with nearest-neighbor interactions. This thesis underlines the universality of hydrodynamic principles, provides a framework for establishing them rigorously, and sets the stage for future progress toward the goal of proving the hydrodynamic equations.

When we consider canonical averages for classical discrete systems, typically referred to as substitutional alloys, the map phi from many-body interatomic interactions to thermodynamic equilibrium configurations generally exhibits complicated nonlinearity. This canonical nonlinearity is fundamentally rooted in deviations of the discrete configurational density of states (CDOS) from continuous Gaussian families, and has conventionally been characterized by the Kullback-Leibler (KL) divergence on discrete statistical manifold. Thus, the previous works inevitablly missed intrinsic nonlinearities induced by discretization of Gaussian families, which remains invisible within conventional information-geometric descriptions. In the present work, we identify and quantify such unavoidable canonical nonlinearity by employing the 2-Wasserstein distance with a cost function aligned with the Fisher metric for Gaussian families. We derive an explicit expression for the Wasserstein distance in the limit of vanishing discretization scale d to 0: W2 = d*sqrt(Tr(Gamma)^(-1)/12), where Gamma denotes covariance matrix of the CDOS. We further show that this limiting Wasserstein distance admits a clear geometric interpretation on the statistical manifold, equivalent to a KL divergence associated with the expected parallel translations of continuous Gaussian. Our framework thus provides a transport-information-geometric characterization of discretization-induced nonlinearity in classical discrete systems. In addition, we confirm that this W2-KL equivalence admits a natural generalization beyond Gaussian families, provided that the transport cost is aligned with the Fisher metric of an underlying statistical submanifold and the discretization scale induces infinitesimal parameter variations.

It was conjectured for a long time that the tectonic plates are in a self-organized state of criticality and that the Gutenberg-Richter (power) law is a manifestation of that. It was recently shown that for a system near criticality, the inequality of their responses toward external driving could indicate proximity to the critical point. In this work, we show with numerical simulations and seismic data analysis that large earthquake events have a tendency to follow events that are highly unequal. We have applied this framework to various tectonically active regions, such as North America, Southern Japan, parts of South-East Asia and Indonesia.

The eigenstate thermalization hypothesis (ETH) posits how isolated quantum many-body systems thermalize, assuming that individual eigenstates at the same energy density have identical expectation values of local observables in the limit of large systems. While the ETH apparently holds across a wide range of interacting quantum systems, in this work we show that it requires generalization in the presence of thermal first-order phase transitions. We introduce a class of all-to-all spin models, featuring first-order thermal phase transitions that stem from two distinct mean-field solutions (two ``branches'') that exchange dominance in the many-body density of states as the energy is varied. We argue that for energies in the vicinity of the thermal phase transition, eigenstate expectation values do not need to converge to the same thermal value. The system has a regime with coexistence of two classes of eigenstates corresponding to the two branches with distinct expectation values at the same energy density, and another regime with Schrodinger-cat-like eigenstates that are inter-branch superpositions; these two regimes are separated by an eigenstate phase transition. We support our results by semiclassical calculations and an exact diagonalization study of a microscopic spin model, and argue that the structure of eigenstates in the vicinity of thermal first-order phase transitions can be experimentally probed via non-equilibrium dynamics.

Defect engineering via parallel cracks has been proposed as a route to tailor the fracture response of graphene. However, atomistic fracture predictions can be strongly sensitive to the interatomic potential. Here, we quantify the effect of potential choice by revisiting H-passivated graphene containing two parallel cracks separated by a gap $W_{\text{gap}}$ loaded in tension along the armchair (AC) and zigzag (ZZ) directions. Molecular dynamics simulations using the AIREBO potential under the same geometry and loading protocol previously studied with ReaxFF, are employed, so enabling a direct comparison. Stress-strain responses, Young's modulus, an effective mode-I stress intensity factor, and energy absorption are evaluated as functions of $W_{\text{gap}}$. Compared with ReaxFF, AIREBO predicts lower peak stresses and earlier catastrophic softening, leading to reduced post-peak deformation capacity and energy absorption. Ductility and energy absorption are shown to be highly potential-dependent, underscoring the need for careful potential selection in defect-engineered graphene fracture simulations.

We study the spectral statistics and wave-function properties of a one-dimensional quantum system subject to a Cantor-type fractal potential. By analyzing the nearest-neighbor level spacings, inverse participation ratio (IPR), and the scaling behavior of the integrated density of states (IDS), we demonstrate how the self-similar geometry of the potential is imprinted on the quantum spectrum. The energy-resolved level spacings form a hierarchical, filamentary structure, in sharp contrast to those of periodic and random systems. The normalized level-spacing distribution exhibits a bimodal structure, reflecting the deterministic recurrence of spectral gaps. A multifractal analysis of eigenstates reveals critical behavior: the generalized fractal dimensions $D_q$ lie strictly between the limits of extended and localized states, exhibiting a distinct $q$-dependence. Consistently, the IPR indicates the coexistence of quasi-extended and localized features, characteristic of critical wave functions. The IDS shows anomalous power-law scaling at low energies, with an exponent close to the Hausdorff dimension of the underlying Cantor set, indicating that the geometric fractality governs the spectral dimensionality. At higher energies, this scaling crosses over to the semiclassical Weyl law. Our results establish a direct connection between deterministic fractal geometry, hierarchical spectral statistics, and quantum criticality.

Filamentous bacteriophages fd are viral particles, highly monodisperse in size, that have been widely used as a model colloidal system for studying the self-assembly of rod-shaped particles as well as a versatile template in nanoscience. In aqueous suspensions, fd viruses exhibit lyotropic behavior, forming liquid crystalline phases as their concentration increases. Here, we report a solvent-free system displaying thermotropic phase behavior, achieved through covalent coupling of low molecular weight PEG-based polymer surfactant onto the fd virus surface. Upon lyophilization of aqueous suspensions of these polymer-grafted bacteriophages and subsequent thermal annealing, a solvent-free material is obtained, exhibiting both viscoelasticity and, notably, thermotropic liquid crystalline properties. A combination of small-angle X-ray scattering and optical microscopy experiments reveals the formation of an ordered hexagonal mesophase below 30 °C, which undergoes a melting transition into an isotropic liquid at higher temperatures. Our results demonstrate an efficient approach for converting lyotropic into thermotropic phase behavior in the columnar liquid crystalline phase of filamentous fd colloids. This approach paves the way for extending such functionalization to other technologically relevant rod-like systems, such as carbon nanotubes and cellulose nanocrystals, enabling the introduction of thermotropic properties in anhydrous colloidal materials.

The anomalous Hall effect has been understood in terms of the geometric nature of Bloch bands and impurity scattering, and has been observed in a wide variety of magnetic materials such as ferromagnets and antiferromagnets. Recently, a large anomalous Hall effect was reported in the noncollinear antiferromagnetic metal NbMnP whose magnetic order is a mixture of the even-parity and the odd-parity magnetic components. Such a magnetic structure is expected to exhibit the anomalous Hall effect and the nonlinear Hall effect from the symmetry breaking of the antiferromagnet ordering. Here, we theoretically investigate the intrinsic anomalous and nonlinear Hall effect of NbMnP induced by the quantum geometry of Bloch band using the first-principles calculation and the Wannier interpolation method. We found that the intrinsic Hall response of NbMnP is predominantly governed by the strongly enhanced Berry curvature and Berry-connection-polarization dipole on a specific mirror plane. These enhanced geometric quantities originate from the spin-orbit-coupling-induced gap openings along the nodal lines. Our results indicate that NbMnP serves as a model system for investigating transport phenomena originating from nodal-lines in parity-mixed antiferromagnets.

Ion transport underlies the operation of biological ion channels and governs the performance of electrochemical energy-storage devices. A long-standing anomaly is that smaller alkali metal ions, such as Li$^+$, migrate more slowly in water than larger ions, in apparent violation of the Stokes-Einstein relation. This breakdown is conventionally attributed to dielectric friction, a collective drag force arising from electrostatic interactions between a drifting ion and its surrounding solvent. Here, combining nanopore transport measurements over electric fields spanning several orders of magnitude with molecular dynamics simulations, we show that the time-averaged electrostatic force on a migrating ion is not a drag force but a net driving force. By contrasting charged ions with neutral particles, we reveal that ionic charge introduces additional Lorentzian peaks in the frequency-dependent friction coefficient. These peaks originate predominantly from short-range Lennard-Jones (LJ) interactions within the first hydration layer and represent additional channels for energy dissipation, strongest for Li$^+$ and progressively weaker for Na$^+$ and K$^+$. Our results demonstrate that electrostatic interactions primarily act to tighten the local hydration structure, thereby amplifying short-range LJ interactions rather than directly opposing ion motion. This microscopic mechanism provides a unified physical explanation for the breakdown of the Stokes-Einstein relation in aqueous ion transport.

Remarkably, even under negligible inertia, the addition of microstructural agents can generate chaotic flow fields. Such behavior can arise in polymer solutions, leading to elastic turbulence, or from active, self-driven particles, which generate active turbulence. Here, we demonstrate a close and hitherto unrecognized connection between these two classes of turbulence. Specifically, we reveal that their continuum descriptions are analogous at the macroscopic level, such that polymeric fluids can be interpreted as a deformable analogue of contractile active matter. Moreover, our numerical results for Kolmogorov flow demonstrate that the transition into the well-known traveling arrowhead structures in elastic turbulence is marked by the emergence of $\pm 1/2$ topological defects, long recognized as a defining feature of active turbulence, in the polymer director field. Importantly, these coherent structures originate from a transverse instability driven by activity-like gradients generated by anisotropically stretched, contractile polymers. At sufficiently strong activity, the system undergoes a transition into a flow-suppressed state characterized by weak polymer stretching and ordering, a behavior that can be explained by analogy with the spontaneous-flow transition observed in channel-confined active nematics.

Bell or Clauser-Horne-Shimony-Holt (CHSH) tests on superconducting quantum processors are commonly interpreted under the assumption that repeated circuit executions sample a single, stationary preparation ensemble. Here we show that this assumption can be violated on contemporary hardware, with direct implications for the interpretation of observed Bell violations. We introduce an ensemble-divergence framework in which slow temporal drift of the preparation process induces context-dependent effective ensembles, even when measurement independence and locality are preserved. This leads to a relaxed Bell bound $|S| \le 2 + 6δ_{\mathrm{ens}}$, where $δ_{\mathrm{ens}}$ quantifies preparation nonstationarity. Because $δ_{\mathrm{ens}}$ is not directly observable, we develop an operational witness $δ_{\mathrm{op}}$ based on bin-resolved outcome statistics for fixed measurement channels. Using Pauli-axis measurements on IBM superconducting processors, we observe statistically significant operational drift that persists after full two-qubit readout mitigation, ruling out measurement artifacts. In contrast, drift extracted from CHSH-optimal measurements is eliminated by mitigation, demonstrating that such settings are unsuitable for diagnosing preparation nonstationarity. We further show that the observed Bell violations imply only modest ensemble divergences, comparable in scale to those required in Hall-type measurement-dependence models, but arising here solely from preparation drift combined with experimental scheduling. Our results identify a preparation-dependent loophole relevant to Bell tests on noisy intermediate-scale quantum devices and highlight the necessity of drift-aware protocols for reliable quantum certification.

Topology provides a unifying framework for understanding robust transport through protected edge states arising from nontrivial wavenumber topology. Extending these concepts to active matter, however, remains largely unexplored experimentally, with realizations limited to systems composed of chiral active particles. Here, we realize topological edge states in dense bacterial suspension, which represents a prototypical active matter system, using microfabricated geometrical structures with nontrivial wavenumber topology. Inspired by previous theoretical studies, we constructed a directional kagome network composed of ratchet-shaped channels that induce unidirectional bacterial flow. In this network, we found clear edge localization of bacterial density. A steady-state analysis based on the bacterial transport model and experimentally measured velocity field reveals how the characteristic collective flow generates edge localization. The model also uncovers the topological origin of the observed edge states. By tuning the geometry of the microfabricated networks, we identified directional channel design and network chirality as the key design features essential for the emergence of the edge state. Our results pave the way for establishing a control and design principle of topological transport in such active matter systems.

We demonstrate a dissipative protocol for ground-state preparation of a quantum spin chain on a trapped-ion quantum computer. As a first step, we derive a Kraus representation of a dissipation channel for the protocol recently proposed by Ding et al. [Phys. Rev. Res. 6, 033147 (2024)] that still holds for arbitrary temporal discretization steps, extending the analysis beyond the Lindblad dynamics regime. The protocol guarantees that the fidelity with the ground state monotonically increases (or remains unchanged) under repeated applications of the channel to an arbitrary initial state, provided that the ground state is the unique steady state of the dissipation channel. Using this framework, we implement dissipative ground-state preparation of a transverse-field Ising chain for up to 19 spins on the trapped-ion quantum computer Reimei provided by Quantinuum. Despite the presence of hardware noise, the dynamics consistently converges to a low-energy state far away from the maximally mixed state even when the corresponding quantum circuits contain as many as 4110 entangling gates, demonstrating the intrinsic robustness of the protocol. By applying zero-noise extrapolation, the resulting energy expectation values are systematically improved to agree with noiseless simulations within statistical uncertainties.

We study the Brownian motion of a rigid rod threading through a small fixed ring while the ring can freely rotate. We derive the distribution function for the sliding displacement and the unit vector along the rod both at equilibrium and non-equilibrium. The equilibrium distribution is quadratic in the sliding displacement and is controlled by the moment of inertia (mass distribution). Applying the Onsager variational principle, we derive a Smoluchowski equation in which sliding and rotational diffusion are coupled. The mean square displacement (MSD) of sliding shows a metastable plateau in a certain time range before it approaches the final equilibrium value. The longest sliding relaxation time decreases as $α^{-1/2}$ as the moment of inertia increases. The rotational relaxation time obtained from the orientational correlation function is longer than that of a rod with its center fixed but faster than a rod with one end fixed. These results may be useful in understanding the dynamics of polymers connected by sliding rings.

Energy-efficient fast switching of spin orientations or textures is a core issue of spintronics, which is highly demanded but remains challenging. Different from the mainstream routes based on spin-transfer torque or spin-orbit torque, here we propose another mechanism coined as magnetoelectric torque to switch the magnetization in polar magnetic bilayers via pure electric field. In some magnetic van der Waals bilayers, when the electrostatic energy of polarization can compensate the interlayer magnetic coupling, a magnetoelectric torque is generated to fastly flip spins within a few picoseconds, which is demonstrated by combining the first-principles calculations, analytic model, as well as atomistic simulations. Such a magnetoelectric torque doesn't rely on the spin-orbit coupling and is generally active in polar magnetic homostructures and heterostructures. Our work provides an alternative route to switch magnetization in nanoscale, which may benefit the energy-saving and fast-response spintronic devices.

When a liquid droplet completely wets a hard substrate, its spreading dynamics follow Tanner's law, with the droplet radius growing as the one-tenth power of time. Here, we investigate how these dynamics change when silicone oil droplets spread on soft silicone elastomer and gel films supported by a rigid silicon substrate. While the droplets fully wet the elastomer surface, they also simultaneously swell the elastomer film. By varying the film thickness, we observe deviations from the classical power-law scaling, which we interpret in terms of changes to the effective stiffness and the absorption potential of the system. We describe the spreading behavior using a phenomenological model that accounts for both absorption and mechanical contributions.

The exact solution of a two-dimensional (2D) Ising model with the next nearest interactions at zero magnetic field is derived. At first, the transfer matrices are analyzed in three representations, i.e., Clifford algebraic representation, transfer tensor representation and schematic representation, to inspect nontrivial topological structures in this system. The system is equivalent to a triangular Ising model plus an interaction along the z axis, so that the approaches developed for the 3D Ising model are modified to be appropriable for solving the exact solution of the 2D Ising model with the next nearest interactions. The partition function and the spontaneous magnetization are obtained. The comparison with the exact solutions of other Ising lattices reveals that either the increase of the number of interactions in a unit cell or the presence/increase of topological contributions enhances the critical point of the Ising lattices. The results obtained in this work are helpful for understanding the physical properties of the 2D magnetic materials.

Discrete charge fluctuations, routinely observed in semiconductor quantum dot devices, may contribute significantly to device drift and errors resulting from qubit miscalibration. Understanding the nature and origins of these discrete charge fluctuations may provide insights into material improvements or means of mitigating charge noise in semiconductor quantum dot devices. In this work, we measure multi-level charge fluctuations present in a Si/SiGe double quantum dot device over a range of device operating voltages and temperatures. To characterize the parameter-dependent dynamics of the underlying fluctuating degrees of freedom, we perform a detailed analysis of the measured noise timeseries. We perform algorithmically assisted drift detection and change point detection to detrend the data and remove a slow fluctuator component, as a preprocessing step. We perform model comparison on the post-processed time series between different $n$-level fluctuator ($n$LF) factorial hidden Markov models (FHMMs), finding that although at most sweep values the independent pair of 2LFs model would be preferred, in a particular region of voltage space the 4LF model outperforms the other models, indicating a conditional rate dependence between the two fluctuators. By tracking fluctuator transition rates, biases, and weights over a range of different device configurations, we estimate gate voltage and conductivity sensitivity. In particular, we fit a phenomenological, detailed balance model to the extracted independent 2LFs rate data, yielding lever arm estimates in the range of $-2 μ$eV/mV up to $4 μ$eV/mV between the two 2LFs and nearby gate electrodes. We expect that these characterization results may aid in subsequent spatial triangulation of the charge fluctuators.

Dense Associative Memory (DAM) models generalize the classical Hopfield model by incorporating n-body or exponential interactions that greatly enhance storage capacity. While the criticality of DAM models has been largely investigated, mainly within a statistical equilibrium picture, little attention has been devoted to the temporal self-organizing behavior induced by learning. In this work, we investigate the behavior of a stochastic exponential DAM (SEDAM) model through the lens of Temporal Complexity (TC), a framework that characterizes complex systems by intermittent transition events between order and disorder and by scale-free temporal statistics. Transition events associated with birth-death of neural avalanche structures are exploited for the TC analyses and compared with analogous transition events based on coincidence structures. We systematically explore how TC indicators depend on control parameters, i.e., noise intensity and memory load. Our results reveal that the SEDAM model exhibits regimes of complex intermittency characterized by nontrivial temporal correlations and scale-free behavior, indicating the spontaneous emergence of self-organizing dynamics. These regimes emerge in small intervals of noise intensity values, which, in agreement with the extended criticality concept, never shrink to a single critical point. Further, the noise intensity range needed to reach the critical region, where self-organizing behavior emerges, slightly decreases as the memory load increases. This study highlights the relevance of TC as a complementary framework for understanding learning and information processing in artificial and biological neural systems, revealing the link between the memory load and the self-organizing capacity of the network.

Decarbonization is rapidly increasing the penetration of inverter-based renewables and other low-capacity generators, intensifying concerns about frequency synchronization in increasingly decentralized power grids. A common heuristic from Kuramoto onset theory and homogeneous parameter swing-equation models is that distributing generation across many smaller units reduces the effective heterogeneity of nodal injections (natural frequencies) and lowers the coupling required for synchronization. Here, using a second-order Kuramoto model, we investigate how decentralization affects frequency synchronization when inertia and damping scale with power generation and consumption. We find that decentralization does not always lower the critical frequency synchronization threshold. Instead, increasing decentralization can induce a non-monotonic dependence of the critical coupling strength and lead to a double phase transition in frequency synchronization. These behaviors remain robust under asymmetric inertia between consumers and generators. Even when empirical power-generation and power-consumption distributions are considered, a region in which the critical threshold remains nearly constant is observed as decentralization increases. Our results demonstrate that decentralization can give rise to complex collective dynamics and caution against assuming that decentralization alone ensures improved frequency synchronization.

We study the features of scarred eigenstates of relativistic neutrino billiards (NBs), graphene billiards (GBs) and Haldane graphene billiards (HGBs) and recapitulate those for nonrelativistic quantum billiards (QBs) with the shapes of a full- and quarter-stadium billiard. Here, we restrict for the GBs and HGBs to the region of linear dispersion around the Fermi energy, where they are effectively described by the same Dirac equation for massless spin-1/2 particles as NBs. Scarred wave functions of the nonrelativistic billiards and spinor functions of the relativistic ones are localized along the same types of periodic orbits, the most prominent ones being bouncing-ball orbits. The objective is to demonstrate that the properties of the scarred eigenstates observed in the full- and quarter-stadium GB do not comply with those of relativistic quantum systems. For this we apply the semiclassical approach associated with such non-generic contributions, which was developed for the spectral density of QBs and NBs. It provides semiclassical trace formulas in terms of the periodic orbits associated with a scarred wave function and a procedure to extract such contributions from the eigenvalue spectra. Furthermore, we analyze momentum distributions and Husimi functions of such scarred states and employ them to classify scarred wave functions according to the periodic orbits along which they are localized. We show that for the GB the semiclassical approach, the spectral properties, the symmetry properties and generally properties of the wave functions all comply with those of the nonrelativistic QB, whereas for the HGB they agree well with those of the NB, implying that the quantum scars observed in GBs are not relativistic.

We report on the experimental study of the spectral properties of quantum systems consisting of two quantum billiards (QBs), one with chaotic, the other one with integrable classical dynamics, that are coupled to each other via an opening in a common wall. They are compared to those of a special case of the Rosenzweig-Porter model with random matrices composed of two diagonal blocks modeling the spectral properties of the QBs, that are coupled with a tunable parameter. We demonstrate that this model is suitable for the description of the experimental data and thus may be employed to determine the strength of the coupling. It results from the increasing overlap of eigenmodes in the QBs penetrating through the opening into the other one, leading to a mixing of their eigenstates, and the breaking of the symmetry present in the QB with integrable dynamics. This implicates deviations of the spectral properties from those of typical quantum systems with integrable and chaotic dynamics, respectively, and approaches those of a fully chaotic system for sufficiently large coupling strength. In contrast in previous studies the transition from integrable to chaotic dynamics was induced by introducing a random potential of increasing strength into such a QB and applicability of a variant of the Rosenzweig-Porter model was tested.

We systematically investigate semiclassical dynamics of the optical field produced by quantum nanoemitters (NEs) embedded in a periodic lattice of conducting nanorings (NRs), in which plasmon polaritons (PPs) are excited. The coupling between PPs and NEs through the radiated optical field leads to establishment of a significant cross-correlation between NEs, so that their internal dynamics (photocurrent affected by the laser irradiation) depends on the NR's plasma frequency $ω_{p}$. The transition to this regime,combined with the nonlinearity of the system, leads to a steep increase of the photocurrent in the NEs, as well as to non-smooth (chimera-like or chaotic) behavior in the critical (transition) region, where small variations of $ω_{p}$ lead to significant changes in the level of the NE pairwise cross-correlations. The chimera-like state is realized as coexistence of locally synchronized and desynchronized NE dynamical states. A fit of the dependence of the critical current on $ω_{p}$ is found, being in agreement with results of numerical simulations. The critical effect may help to design new optical devices, using dispersive nanolattices which are made available by modern nanoelectronics.

We determine the complete structure of the symmetry algebras associated with the N-body Calogero-Moser system and its maximally superintegrable discretization. We prove that the discretization naturally leads to a nontrivial deformation of the continuous symmetry algebra, with the discretization parameter playing the rôle of a deformation parameter. This phenomenon illustrates how discrete superintegrable systems can be viewed as natural sources of deformed polynomial algebraic structures. As a byproduct of these results, we also reveal a connection between the above-mentioned symmetry algebras and the Bell polynomials, as a consequence of the trace properties.

Boundary integrability provides rare analytic control over field theories in the presence of an interface, from quantum impurity problems to open string dynamics. We develop an analytic framework for integrable boundaries in two-dimensional sigma-models that determines admissible reflection maps directly from the meromorphic Lax connection. Applying it to open strings on $AdS_3\times S^3$ with mixed NSNS and RR flux, we find two branches of integrable boundary conditions. One branch admits D-branes wrapping twisted conjugacy classes on $SU(1,1)\times SU(2)$, with the mixed-flux deformation encoded entirely into dynamical boundary data. At the exactly solvable WZW point these coincide with the conformal D-branes, providing a natural link to conformal perturbation theory.

The Moran process is a foundational model of genetic drift and mutation in finite populations. In its standard two-allele form with population size $n$, allele counts, and hence allele frequencies, change through stochastic replacement and mutation, yet converge to a stationary distribution. This distribution undergoes a qualitative transition at the \emph{critical mutation rate} $μ_c=1/(2n)$: at $μ=μ_c$ it is exactly uniform, so that the probability of observing $k$ copies of allele~1 (and $n-k$ of allele~2) is $π(k)=1/(n+1)$ for $k=0,\dots,n$. For $μ<μ_c$ diversity is low: the stationary distribution places most of its mass near $k=0$ and $k=n$, and the population is therefore typically dominated by one allele. For $μ>μ_c$, on the other hand, diversity is high: the distribution concentrates around intermediate values, so that both alleles are commonly present at comparable frequencies. Recently, the two-allele Moran process was shown to be exactly equivalent to the voter model with two candidates and $α_1$ and $α_2$ committed voters (\emph{zealots}) in a population of $n+α_1+α_2$, where mutation is played by zealot influence. Here we extend this equivalence to multiple alleles and multiple candidates. Using the mapping, we derive the exact stationary distribution of allele counts for well-mixed populations with an arbitrary number $m$ of alleles, and obtain the critical mutation rate $μ_c = 1/(m+2n-2)$, which depends explicitly on $m$. We then analyze the Moran process on randomly connected populations and show that both the stationary distribution and $μ_c$ are invariant to network structure and coincide with the well-mixed results. Finally, simulations on general network topologies show that structural heterogeneity can substantially reshape the stationary allele distribution and, consequently, the level of genetic diversity.

We investigate how a thermodynamical first-order phase transition affects the dynamical chaotic behaviour of a given model. To this effect, we analyze the model of Berkooz, Brukner, Jia and Mamroud that interpolates between the double-scaled SYK model and an integrable chord Hamiltonian. This model displays a first-order phase transition given by a kink in the free energy. We map out the dynamical behaviour, as characterized by chord number, Krylov complexity, and operator size, of the model across the phase diagram. We observe a jump in the chord numbers at the transition point, in agreement with the first-order transition. We further determine how scrambling measures, i.e.~the growth of the Lanczos coefficients and the time dependence of the operator size, change across the phase diagram. Deep inside the two phases, these measures indeed display integrable and chaotic behaviour, respectively. Across the transition however, we observe no qualitative change in these measures. This means that the thermodynamical transition does not imply a sharp transition in the growth exponent characterizing the dynamical chaotic behaviour. We also discuss a possible holographic interpretation of the model.

We present here a study of the bright soliton dynamics in an inhomogeneous fibre by means of variable coefficient Fokas-Lenells equation with time varying dispersion, nonlinearity and gain/loss parameter. At first, we propose our system that governs the propagation of ultrashort pulses in an inhomogeneous fibre. Secondly, under a suitable gauge transformation, we transform the system into a simplified form of variable coefficient Fokas-Lenells equation. The Lax integrability and conservation laws are exhibited. We also study the stability of the generalised plane wave against small amplitude perturbations. Thereafter, by using a nonstandard Hirota bilinearization method with the help of a suitable auxiliary function, we obtain the bright one soliton, two soliton and provide a scheme for obtaining N-bright soliton solutions. The elastic collision dynamics of the two solitons is studied using asymptotic analysis. We also investigate the soliton acceleration/retardation under a suitable choice of dispersion and nonlinearity coefficients. Finally, the dramatic effect of the nonlinear tunnelling of the bright one and two-soliton is also studied under some Gaussian dispersion or nonlinearity.

In the present work we describe a way to assess memory capability of real devices, while proposing to the engineering community what to pursue to create devices with deep associated memory capability. The study of the signal produced by a real memristor nano-device focused on the description in terms of the Landau φ4 theory for the critical phenomena in finite systems. This further allowed the utilization of the property of the anomalous enhancement of the autocorrelation function when a system is on the Spontaneous Symmetry Breaking (SSB), for improving the quantity of the demonstrated memory, while simultaneously maintaining a very good quality, as this is expressed by the stability of the autocorrelation function. In this proof-of-concept case, the morphology of the signal allowed us to impose the appropriate modifications on the signal so that we finally show how to get very close to the characteristics of the SSB and thus achieve our goal to get as close as possible to the ideal behavior of a Memristor that yields deep memory. Finally, we provide proof of the stability of memristor's operation by showing that solitons "follow" as a skeleton structure the experimentally derived time series.

We experimentally investigate the effects of finite-system size on the dynamics of weakly nonlinear random gravity-capillary surface waves. Experiments are conducted in rectangular tanks with varying aspect ratios, in which the fluid surface is perturbed locally and erratically by small, partially submerged magnets. Driven by an oscillating vertical electromagnetic field, these magnets generate a statistically homogeneous and isotropic random wave field. This setup enables us to probe finite-size effects without the dominant influence of global forcing present in horizontally oscillated tanks. Spatiotemporal measurements of the wave field reveal multiple branches in the wave-energy spectrum along the unconfined direction, corresponding to sloshing modes in the confined direction. We show that the spectral properties of these modes can be tuned by varying either the wave steepness or the confinement. Signatures of discrete wave turbulence in the confined direction and mesoscopic continuous wave turbulence in the unconfined direction are observed. As the confinement is gradually relaxed, we further demonstrate a smooth transition from discrete to continuous wave turbulence, consistent with the nonlinear-to-discreteness timescale ratio. Using high-order correlation analysis, we also show that finite-size effects alter wave dynamics by depleting two-dimensional three-wave resonant interactions along the confined direction.

In this paper the Mikhailov model is discretized by means of the Cauchy matrix approach. A pair of discrete Miura transformations are constructed. The discrete Mikhailov model is a coupled system, in which one equation comes from the compatibility of the two Miura transformations, the other is transformed from the discrete negative order Ablowitz-Kaup-Newell-Segur system by using the Miura transformations. Explicit solutions, including solitons and multiple-pole solutions, are presented via two Cauchy matrix schemes respectively, namely, the Ablowitz-Kaup-Newell-Segur type and the Kadomtsev-Petviashvili type. By straight continuum limits, semi-discrete and continuous Mikhailov models together with their Cauchy matrix structures and solutions are recovered.

We study the dynamics of the geodesics of pp-wave spacetimes with polynomial profiles of degrees $3\leq n\leq10$, which are dynamically equivalent to the motion of a classical particle in a two-dimensional harmonic polynomial potential. By analysing the escape basins associated with different asymptotic outcomes, we show that all basins exhibit the Wada property for every polynomial degree considered. We further compute the basin entropy $S_{b}$, finding that it increases monotonically with the polynomial degree, indicating enhanced unpredictability of the final state of the system. The boundary basin entropy $S_{bb}$ is also evaluated, and the $\ln(2)$ criterion confirms that the basin boundaries are fractal for $n>3$. We conjecture that the Wada property persists for polynomial degrees $n>10$.

Previous analysis of the Newell-Whitehead-Segel equation proved the best solution is null; although, the method of solution generated complex nested integrals, therefore, difficult to analyze \cite{NWSgen,NWS2020}. Recent insights into the properties of the convolution integral enable considerable simplification of the solution in the codomain, producing much simpler representations. The inverse Fourier transform of the spectral solution proves to be a non-bijective, null solution, therefore, confirming previous suspicions. Alternative representations of the solution, either expansions or Fujita type solutions, all prove the solution to be a null function.

Incorporating self-interaction corrections (SIC) significantly improves chemical reaction barrier height predictions made using density functional theory methods. We present a detailed, orbital-by-orbital analysis of these corrections for three semi-local density functional approximations (DFAs) situated on the three lowest rungs of the Jacob's Ladder of approximations. The analysis is based on Fermi-Löwdin Orbital Self-Interaction Correction calculations performed at several steps along the reaction pathway from the reactants (R) to the transition state (TS) to the products (P) for four representative reactions selected from the BH76 benchmark set. For all three functionals, the major contribution to self-interaction corrections of the barrier heights can be traced to stretched bond orbitals that develop near the TS configuration. The magnitude of the ratio of the self-exchange-correlation energy to the self-Hartree energy (XC/H) for a given orbital is introduced as an indicator of one-electron self-interaction error. For the exact, but unknown density functional, XC/H = 1.0 for all orbitals, while for the practical DFAs studied here, XC/H spans a range of values. The largest values are obtained for stretched or strongly lobed orbitals. We show that significant differences in XC/H for corresponding orbitals in the R, TS, and P configurations can be used to identify the major contributors to the SIC of barrier heights and reaction energies. Based on such comparisons, we suggest that barrier height predictions made using the SCAN meta-generalized gradient approximation may have attained the best accuracy possible for a semi-local functional using the Perdew-Zunger SIC approach.

Religions are among the most consequential social institutions, shaping collective identities, moral norms, and political organization across societies and historical periods. Nevertheless, despite extensive scholarship describing conversion, competition, and secularization, there is still no widely adopted formal model that captures religious dynamics over time within a unified, mechanistic framework. In this study, we propose an epidemiologically grounded model of religious change in which religions spread and compete analogously to co-circulating strains. The model extends multi-strain compartmental dynamics by distinguishing passive believers, active missionaries, and religious elites, and by incorporating demographic turnover and mutation-like splitting that endogenously generates new denominations. Using computer simulations, we show that the same mechanism reproduces canonical qualitative regimes, including emergence from rarity, rapid expansion, long-run coexistence, and transient rise-and-fall movements. A reduced calibration variant fits historical affiliation trajectories with parsimonious regime shifts in effective recruitment and disaffiliation, yielding interpretable signatures of changing social conditions. Finally, sensitivity analyses map sharp regime boundaries in parameter space, indicating that modest shifts in recruitment efficacy or retention among active spreaders can qualitatively alter long-run religious landscapes. These results establish a general, interpretable framework for studying religion as a dynamical diffusion process and provide a tool for comparative inference and counterfactual analysis in sociological research.

Particulate matter in a fluid injected into a porous reservoir impairs its permeability spatio-temporally due to pore clogging. As particle volume fraction increases near the pore throats, inter-particle contact mechanics determine their jamming and subsequent pore clogging behavior. During contact of particles submerged in a fluid, in addition to sliding friction, a rolling resistance develops due to a several micromechanical and hydrodynamic factors. A coefficient of rolling friction is often used as a lumped parameter to characterize particle rigidity, particle shape, lubrication and fluid mediated resistance, however its direct influence on the clogging behavior is not well studied in literature. We study the effect of rolling resistance on the clogging behavior of a dense suspension at pore scale using direct numerical simulations (DNS). A discrete element method (DEM) library is developed and coupled with an open-source immersed boundary method (IBM) based solver to perform pore and particle resolved simulations. Several 3D validations are presented for the DEM library and the DEM-IBM coupling and the effect of rolling resistance on clogging at a pore entry is studied.

TP53 is the most frequently mutated tumor suppressor gene in human cancers, with mutations primarily in its DNA-binding domain (p53-DBD). Mutations in p53-DBD are categorized into hotspot mutations (resulting in complete loss-of-function) and non-hotspot mutations (inducing partial loss-of-function). However, the allosteric mechanisms underlying non-hotspot mutations remain elusive. Using p53 dimer as models, we constructed p53-WT, non-hotspot p53-E180R, and hotspot p53-R248W dimer-DNA complexes to compare the structural and functional impacts of these two mutation types. Our results reveal that both mutations weaken intramolecular interactions in p53-DBD and enhance structural flexibility. Specifically, E180R perturbs dimer interface interactions, impairing dimer stability and cooperative DNA binding; R248W disrupts interactions between the L3/L1 loops and DNA, leading to the loss of DNA-binding capacity. Steered molecular dynamics (SMD) simulations further confirm that both mutations accelerate p53 dimer dissociation, with E180R exerting the most prominent disruptive effect on the mechanical stability of the dimer interface.

Circular cross-section plasma is the most basic form of tokamak plasma and the fundamental configuration for magnetic confinement fusion experiments. Based on the HL-2A limiter discharge experiments, the magnetohydrodynamic (MHD) equilibrium and MHD instability of circular cross-section tokamak plasmas are investigated in this work. The results show that when q_0=0.95, the internal kink mode of m/n=1/1 is always unstable. The increase in plasma \b{eta} (the ratio of thermal pressure to magnetic pressure) can lead to the appearance of external kink modes. The combination of axial safety factor q_0 and edge safety factor q_a determines the equilibrium configuration of the plasma and also affects the MHD stability of the equilibrium, but its growth rate is also related to the size of \b{eta}. Under the condition of q_a>2 and q_0 slightly greater than 1, the internal kink mode and surface kink mode can be easily stabilized. However the plasma becomes unstable again and the instability intensity increases as q_0 continues to increase when q_0 exceeds 1. As the poloidal beta (\b{eta}_p) increases, the MHD instability develops, the equilibrium configuration of MHD elongates laterally, and the Shafranov displacement increases, which in turn has the effect on suppressing instability. Calculations have shown that the maximum \b{eta} value imposed by the ideal MHD mode in a plasma with free boundary in tokamak experiments is proportional to the normalized current I_N (I_N=I_p (MA)/a(m)B_0 (T)), and the achievable maximum beta \b{eta}(max) is calibrated to be 2.01I_N,i.e. \b{eta}(max)~2.01I_N. The operational \b{eta} limit of HL-2A circular cross-section plasma is approximately \b{eta}_N^c~2.0. Too high a value of q_0 is not conducive to MHD stability and leads the \b{eta} limit value to decrease. When q_0=1.3, we obtain a maximum value of \b{eta}_N of approximately 1.8.

'Closeness' is well-defined as a quantitative measure of centrality in social network analysis (SNA), but it is not as well defined qualitatively as a description of social relationships. This paper presents a qualitative analysis of 'closeness' as it is defined both implicitly and explicitly in interviews with 100 women and/or LGBTQ+ PhD physicists. The interviews include a social network construction component, and we define a quantitative network parameter that serves as a proxy for closeness, which we examine in relation to attributes of network members. We find that physicists in this sample see trust, relaxed boundaries, reliance, and support as concepts that most directly define closeness in their relationships. Consistent interaction, positive affect, and commonalities are also often present in (and in some cases, defining of) these relationships. From the quantitative analysis, we find that these physicists tend to view family and partners, friends, and professional friends as comprising their closest relationships. These results are consistent with other studies which have sought to define closeness qualitatively, but the prevalence of trust in this dataset in particular suggests that these physicists see the ability to confide as uniquely important in their relationships. We make recommendations to institutions supporting and employing physicists on how to better support gender and sexual minority (GSM) physicists going forward, and we believe these results help us gain a better understanding of how GSM physicists find success in physics despite the barriers they face due to their identities.

We present OmniMol, a state-of-the-art transformer-based small molecule machine-learned interatomic potential (MLIP). OmniMol is built by adapting Omnilearned, a foundation model for particle jets found in high-energy physics (HEP) experiments such as at the Large Hadron Collider (LHC). Omnilearned is built with a Point-Edge-Transformer (PET) and pre-trained using a diverse set of one billion particle jets. It includes an interaction-matrix attention bias that injects pairwise sub-nuclear (HEP) or atomic (molecular-dynamics) physics directly into the transformer's attention logits, steering the network toward physically meaningful neighborhoods without sacrificing expressivity. We demonstrate OmniMol using the oMol dataset and find excellent performance even with relatively few examples for fine-tuning. This study lays the foundation for building interdisciplinary connections, given datasets represented as collections of point clouds.

Urban crises - floods, pandemics, economic shocks, and conflicts - function as accelerators of urban change, exposing structural vulnerabilities while creating windows for reinvention. Building on a prior theoretical contribution that identified fifteen principles of urban antifragility, this paper tests and operationalizes the framework through an empirical assessment of 26 cities selected for their post-crisis adaptation trajectories. Using a tailored diagnostic methodology, we benchmark cities' Stress Response Strategies (SRS) and then evaluate Urban Development Trajectories (UDT) across four weighted dimensions, positioning each case along a fragility-robustness-resilience-antifragility continuum and applying a balanced-threshold rule to confirm antifragile status. Results show that "resilience enhanced by innovation and technology" is the most effective response typology (86.9/100), and that six cities meet the antifragile trajectory criteria. By mapping best practices to activated principles and analysing co-activations, the study identifies a robust "hard core" of principles - Sustainable Resilience (O), Strategic Diversity (F), Proactive Innovation (I), and Active Prevention (N) - supplemented by operational enablers (e.g., anticipation, mobilization, shock absorption). The paper concludes by proposing an evidence-based, SDG-aligned operational model that links high-impact principle pairings to measurable indicators, offering a practical roadmap for cities seeking to convert crises into sustained transformation. Keywords: Post-crisis strategies, Urban antifragility, Sustainable cities and communities, Disaster resilience and urban regeneration, Risk governance and Black Swan adaptation.

Whether neural information processing is entirely classical or involves quantum-mechanical elements remains an open question. Here we propose a model-agnostic, information-theoretic test of nonclassicality that bypasses microscopic assumptions and instead probes the structure of neural representations themselves. Using autoencoders as a transparent model system, we introduce a Bell-type consistency test in latent space, and ask whether decoding statistics obtained under multiple readout contexts can be jointly explained by a single positive latent-variable distribution. By shifting the search for quantum-like signatures in neural systems from microscopic dynamics to experimentally testable constraints on information processing, this work opens a new route for probing the fundamental physics of neural computation.

Studying the optoelectronic structure of materials can require the computation of up to several thousands of the smallest eigenpairs of a pseudo-hermitian Hamiltonian. Iterative eigensolvers may be preferred over direct methods for this task since their complexity is a function of the desired fraction of the spectrum. In addition, they generally rely on highly optimized and scalable kernels such as matrix-vector multiplications that leverage the massive parallelism and the computational power of modern exascale systems. \textit{Chebyshev Accelerated Subspace iteration Eigensolver} (ChASE) is able to compute several thousands of the most extreme eigenpairs of dense hermitian matrices with proven scalability over massive parallel accelerated clusters. This work presents an extension of ChASE to solve for a portion of the spectrum of pseudo-hermitian Hamiltonians as they appear in the treatment of excitonic materials. The new pseudo-hermitian solver achieves similar convergence and performance as the hermitian one. By exploiting the numerical structure and spectral properties of the Hamiltonian matrix, we propose an oblique variant of Rayleigh-Ritz projection featuring quadratic convergence of the Ritz-values with no explicit construction of the dual basis set. Additionally, we introduce a parallel implementation of the recursive matrix-product operation appearing in the Chebyshev filter with limited amount of global communications. Our development is supported by a full numerical analysis and experimental tests.

Higher-order interactions can dramatically reshape collective dynamics, yet how their microscopic organization controls macroscopic critical behavior remains unclear. Here we develop a new theory to study contagion dynamics on hypergraphs and show that nested hyperedges not only facilitate the onset of spreading, but also suppress backward bifurcations, thereby inhibiting explosive behavior. By disentangling contagion pathways, we find that overlap redirects transmission from external links to internal, group-embedded routes -- boosting early activation but making dyadic and triadic channels increasingly redundant. This loss of structural independence quenches the nonlinear amplification required for bistability, progressively smoothing the transition as hyperedges become nested. We observe the same phenomenology in Kuramoto dynamics, pointing to a broadly universal mechanism by which nested higher-order structure governs critical transitions in complex systems.

Modal methods are a long-standing approach to physical modelling synthesis. Extensions to nonlinear problems are possible, including the case of a high-amplitude vibration of a string. A modal decomposition leads to a densely coupled nonlinear system of ordinary differential equations. Recent work in scalar auxiliary variable techniques has enabled construction of explicit and stable numerical solvers for such classes of nonlinear systems. On the other hand, machine learning approaches (in particular neural ordinary differential equations) have been successful in modelling nonlinear systems automatically from data. In this work, we examine how scalar auxiliary variable techniques can be combined with neural ordinary differential equations to yield a stable differentiable model capable of learning nonlinear dynamics. The proposed approach leverages the analytical solution for linear vibration of system's modes so that physical parameters of a system remain easily accessible after the training without the need for a parameter encoder in the model architecture. As a proof of concept, we generate synthetic data for the nonlinear transverse vibration of a string and show that the model can be trained to reproduce the nonlinear dynamics of the system. Sound examples are presented.

Gold nanoclusters possess multiple competing structural motifs with small energy differences, enabling structural coexistence and interconversion. Using a high-accuracy machine learned potential trained on some 20'000 density functional theory reference data points, we investigate transformation pathways connecting both high-symmetry and amorphous cuboctahedra, Ino's decahedra and icosahedra for Au55, Au147, Au309 and Au561 nanoclusters. Our saddle point searches reveal that high-symmetry transformations from cuboctahedra and Ino's decahedra to icosahedra proceed through a single barrier and represent soft-mode-driven jitterbug-type and slip-dislocation motions. In addition, we identify lower-barrier asymmetric transformation pathways that drive the system into disordered, Jahn-Teller-stabilized amorphous icosahedra. Minima Hopping sampling further uncovers, in this context, many such low-symmetry minima. Some of the newly identified global minima for Au309 and Au561 have energies that are up to 2.8 eV lower than the previously reported global minima. Hence, both the shapes and the transformation pathways studied in previous investigations are not the physically relevant ones. In contrast to the previously studied pathways, our transformation pathways give reasonable transformation times that are in rough agreement with experiments.

The accurate identification of historical restoration traces and material degradation is essential for the scientific preservation of ancient bronzes. In this study, the prestigious FUHAO bronze artifact (late Shang period, 13th-11th century BCE) was non-destructively examined using pulsed thermal imaging (PT). By combining single- and double-layer heat conduction models with Thermal Tomography (TT), this approach allowed for precise spatial localization of repair crevices, patches, and filler materials, while also distinguishing restorative interventions from the original bronze substrate. The artifact was revealed to have been assembled from multiple fragments, exhibiting uneven surface corrosion and clear evidence of prior conservation. The results not only provide direct insights for conservation strategy and historical interpretation but also demonstrate the capability of pulsed thermal imaging as an effective diagnostic tool for the integrated surface and subsurface assessment of cultural heritage objects.

Despite the significant role of turbomachinery in fluid-based energy transfer, precise simulation of rotating solid objects with complex geometry is a challenging task. In the present study, the volume penalization method (VPM) is combined with multiple reference frame (MRF) and sliding mesh (SLM), respectively, so as to develop immersed-boundary approaches for simulating flows around a rotating solid. The level-set function is adopted to represent arbitrary geometries embedded in Cartesian grids. The VPM body-forcing terms in the momentum equation are proposed for MRF and SLM, respectively, so as to build unified governing equations for both fluid and solid regions. The flows around a rotating cuboid under various rotating speeds are simulated by the present schemes, namely, VPM with MRF, and VPM with SLM, and compared to corresponding simulations by the body-fitted method (BFM). The results suggest the relative deviations of predicted pressure drop and torque between the present VPM and BFM are around 5%, demonstrating the validity of the present VPM.

Innovation emerges from complex collaboration patterns - among inventors, firms, or institutions. However, not much is known about the overall mesoscopic structure around which inventive activity self-organizes. Here, we tackle this problem by employing patent data to analyze both individual (co-inventorship) and organization (co-ownership) networks in three strategic domains (artificial intelligence, biotechnology and semiconductors). We characterize the mesoscale structure (in terms of clusters) of each domain by comparing two alternative methods: a standard baseline - modularity maximization - and one based on the minimization of the Bayesian Information Criterion, within the Stochastic Block Model and its degree-corrected variant. We find that, across sectors, inventor networks are denser and more clustered than organization ones - consistent with the presence of small recurrent teams embedded into broader institutional hierarchies - whereas organization networks have neater hierarchical role-based structures, with few bridging firms coordinating the most peripheral ones. We also find that the discovered meso-structures are connected to innovation output. In particular, Lorenz curves of forward citations show a pervasive inequality in technological influence: across sectors and methods, both inventor (especially) and organization networks consistently show high levels of concentration of citations in a few of the discovered clusters. Our results demonstrate that the baseline modularity-based method may not be capable of fully capturing the way collaborations drive the spreading of inventive impact across technological domains. This is due to the presence of local hierarchies that call for more refined tools based on Bayesian inference.

We review and study the correspondence between discrete lattice/chain models of interacting particles and their continuous counterparts represented by partial differential equations. We study the correspondence problem for nearest neighbour interaction lattice models as well as for multiple-neighbour interaction lattice models, and we gradually proceed from infinite lattices to periodic lattices and finally to finite lattices with fixed ends/zero Dirichlet boundary conditions. The whole study is framed as systematic specialisation of Fourier analysis tools from the continuous to the discrete setting and vice versa, and the correspondence between the discrete and continuous models is examined primarily with regard to the dispersion relation.

Quantum algorithms are gaining attention in Computational Fluid Dynamics (CFD) for their favorable scaling, as encoding physical fields into quantum probability amplitudes enables representation of two to the power of n spatial points with only n qubits. A key challenge in Quantum CFD is the efficient readout of simulation results, a topic that has received limited attention in literature. This work presents methods to extract statistical properties of spatial velocity fields, such as central moments and structure functions, directly from parameterized ansatz circuits, avoiding full quantum state tomography. As a proof of concept, we implement our approach for 1D velocity fields, encoding 16 spatial points with 4 qubits, and analyze both a sine wave signal and four snapshots from Burgers' equation evolution. Using Qedma's error mitigation software QESEM, we demonstrate that such computations achieve high accuracy on current quantum devices, specifically IBMQ's Heron2 system ibm_fez.

Conjugate scalar transport with interfacial jump conditions on complex interfacial geometries is common in thermal and chemical processes, while its accurate and efficient simulations are still quite challenging. In the present study, a novel treatment of a two-phase interface in the volume penalization method, a kind of immersed boundary method, for solving conjugate scalar transport with general interfacial boundary conditions is developed. We first propose an interfacial treatment for solving an advection-diffusion equation with a Neumann boundary condition, and then extend it to general conjugate scalar transport with both interfacial flux and scalar jumps. A one-dimensional diffusion problem is solved to verify the present scheme and demonstrate the advantage of the present scheme in improving accuracy and unifying the governing equations in the two phases with an additional source term representing the local jump condition of the interfacial scalar flux. Then, the present scheme is further applied to fluid-solid coupled scalar diffusion and advection-diffusion problems with the scalar and its flux jumps across the interface. The simulation results of the present scheme generally show good agreement with reference results obtained by body-fitted mesh simulations with average relative deviations less than 3.0%.

Superconductivity in sulfur superhydride H$_{3}$S under extreme pressures has been explained theoretically, but it requires a peaked concentration of the electronic density of states (DOS), which has been found in first-principles calculations. The mechanism of this peak formation, though vital for its high transition temperature, has however remained obscure. We address this problem through detailed analysis of the first-principles electronic wave functions. The valence wave functions are shown to be significantly plane-wave-like. From the Fourier-mode analysis of the self-consistent potential and atomic pseudopotentials, we extract the nearly uniform models that accurately reproduce the first-principles band structure with very few parameters. The DOS peak is shown to be the consequence of the hybridization of specific plane waves. Adjacency of Jones' large zone to the plane-wave spherical Fermi surface is posited to be the root cause of the multiple plane-wave hybridization, the DOS peak formation and its proximity to the Fermi level. The present theory resolves the minimal modeling problem of electronic states in H$_{3}$S, as well as establishes a mechanism that may help to boost the transition temperatures in pressure induced superconductors.

This paper introduces a Particle Flow Map Level Set (PFM-LS) method for high-fidelity interface tracking. We store level-set values, gradients, and Hessians on particles concentrated in a narrow band around the interface, advecting them via bidirectional flow maps while using a conventional grid-based representation elsewhere. By interpreting the level set value as a 3-form and its gradient as a 1-form, PFM-LS achieves exceptional geometric fidelity during complex deformations and preserves sub-grid features that traditional methods cannot capture. Our dual-timescale approach utilizes long-range maps for values and gradients, with frequent reinitialization of short-range maps for the distortion-sensitive Hessian, alongside adaptive particle control that maintains sufficient density within the narrow band. We also develop a hybrid particle-grid quasi-Newton redistancing scheme that preserves fine-scale features while enforcing the signed-distance property. Benchmark comparisons in 2D and 3D demonstrate that PFM-LS achieves state-of-the-art volume preservation and shape fidelity against a broad range of existing level-set methods.

We introduce the variable coherence model (VCM) for simulating free-electron laser (FEL) pulses generated through self-amplified spontaneous emission. Building on the established partial coherence model of [T. Pfeifer et. al, Opt. Lett. 35, 3441 (2010)], we demonstrate that the implementation of a variable coherence width allows for continuous control over the pulses' characteristic noise, while keeping the average pulse parameters such as the bandwidth fixed. We demonstrate this through systematic statistical analyses of the intensity and number of sub-pulses in VCM pulses, in both time and frequency. In particular, we analyze how the sub-pulse statistics are affected by the coherence width parameter. We perform our analyses across three distinct regimes of FEL parameters and demonstrate how the VCM can generate pulses that range from maximally random to fully coherent. Finally, we illustrate the effect of the VCM variable coherence width on an absorption simulation.

Predicting biomolecular thermodynamics and spectroscopy requires accurate relative energies of metastable states and local curvatures on the potential-energy surface. We show that the general-purpose SO3LR machine-learned force field (MLFF) reproduces PBE0+MBD density-functional theory with unprecedented fidelity across bio-relevant molecules spanning sizes and complexities far beyond its training dataset. For 23 small molecules, SO3LR captures harmonic and anharmonic vibrational features, including frequencies, displacement patterns, and IR spectra. We perform detailed dynamical studies of the amino acid oF-Phe+, folding of the alanine-15 peptide, and assembly of monomeric p53 transactivation domains into tetramers, in vacuum and water. SO3LR consistently reproduces DFT-level potential-energy surfaces, vibrational densities of states, and mode eigenvectors, capturing anharmonicity, polarization, and medium-range environment-driven interactions crucial for proteins. Our results establish that MLFF-driven dynamics provide a quantum-accurate picture of metastable minima and vibrational properties, achieving DFT-level accuracy at force-field computational cost and opening new possibilities for the computational study of biomolecules.

This tutorial paper shows how to compute the DC (or low-frequency) resistance, inductance and capacitance of a pair of parallel wires using the finite element method. A three-dimensional infinite domain (open boundary) modeling of electrostatic and magnetostatic fields is presented, along with the electrokinetic formulation for the current flow inside the wires. The effects of the insulation and of a proposed physical defect in the wires are also considered. The open-source ONELAB software is used to perform the simulations and the code listing is provided. Comparisons using analytical models (when applicable) and the Altair Flux software are performed to help validate the simulations.

Kolmogorov-Arnold Networks (KANs) require significantly smaller architectures compared to multilayer perceptron (MLP)-based approaches, while retaining expressive power through spline-based activations. We propose a shallow KAN framework that directly approximates the temperature distribution T(x,t) and the moving interface $Γ(t)$, enforcing the governing PDEs, phase equilibrium, and Stefan condition through physics-informed residuals. To enhance accuracy, we employ interface-focused collocation resampling. Numerical experiments in one and two dimensions show that the framework achieves accurate reconstructions of both temperature fields and interface dynamics, highlighting the potential of KANs as a compact and efficient alternative for moving boundary PDEs. First, we validate the model with semi-infinite analytical solutions. Subsequently, the model is extended to 2D using a level-set based formulation for interface propagation, which is solved within the KAN framework. This work demonstrates that KANs are capable of solving complex moving boundary problems without the need for measurement data.

Existing match classification models in the tournament design literature have two major limitations: a contestant is considered indifferent only if uncertain future results do never affect its prize, and competitive matches are not distinguished with respect to the incentives of the contestants. We propose a probabilistic framework to address both issues. For each match, our approach relies on simulating all other matches played simultaneously or later to compute the qualifying probabilities under the three main outcomes (win, draw, loss), which allows the classification of each match into six different categories. The suggested model is applied to the previous group stage and the new incomplete round-robin league, introduced in the 2024/25 season of UEFA club competitions. An incomplete round-robin tournament is found to contain fewer stakeless matches where both contestants are indifferent, and substantially more matches where both contestants should play offensively. However, the robustly higher proportion of potentially collusive matches can threaten with serious scandals.

In this work, we characterized the material properties of an animal model of the rotator cuff tendon using full volume datasets of both its intact and injured states by capturing internal strain behavior throughout the tendon. Our experimental setup, involving tension along the fiber direction, activated volumetric, tensile, and shear mechanisms due to the tendon's complex geometry. We implemented an approach to model inference that we refer to as variational system identification (VSI) to solve the weak form of the stress equilibrium equation using these full volume displacements. Three constitutive models were used for parameter inference: a neo-Hookean model, a modified Holzapfel-Gasser-Ogden (HGO) model with higher-order terms in the first and second invariants, and a reduced polynomial model consisting of terms based on the first, second, and fiber-related invariants. Inferred parameters were further refined using an adjoint-based partial differential equation (PDE)-constrained optimization framework. Our results show that the modified HGO model captures the tendon's deformation mechanisms with reasonable accuracy, while the neo-Hookean model fails to reproduce key internal features, particularly the shear behavior in the injured tendon. Surprisingly, the simplified polynomial model performed comparably to the modified HGO formulation using only three terms. These findings suggest that while current constitutive models do not fully replicate the complex internal mechanics of the tendon, they are capable of capturing key trends in both intact and damaged tissue, using a homogeneous modeling approach. Continued model development is needed to bridge this gap and enable clinical-grade, predictive simulations of tendon injury and repair.

Efficient Monte Carlo (MC) sampling of many-body systems with long-range electrostatics is often limited by the cost of per-move energy-difference evaluation under periodic boundary conditions. We present DMK-MC, an accelerated MC method that adapts the dual-space multilevel kernel-splitting (DMK) framework to single-particle Metropolis updates. DMK-MC computes the energy change and, upon acceptance, updates the stored incoming plane-wave fields with $O(1)$ work per tree level, yielding an overall $O(\log N)$ expected work per trial move for fixed accuracy. The method decomposes the Coulomb kernel into three components: a global, periodized smooth part; a multilevel sequence of smooth difference kernels whose interactions are restricted to same-level colleague boxes; and a singular residual kernel whose short-range interactions are evaluated directly. Benchmarks on uniform, highly nonuniform, and implicit-solvent electrolyte and colloidal configurations show that DMK-MC consistently outperforms a recent FMM-based $O(\log N)$ Monte Carlo method, delivering several-fold speedups at comparable tolerances.

Neural operator learning accelerates PDE solution by approximating operators as mappings between continuous function spaces. Yet in many engineering settings, varying geometry induces discrete structural changes, including topological changes, abrupt changes in boundary conditions or boundary types, and changes in the computational domain, which break the smooth-variation premise. Here we introduce Discrete Solution Operator Learning (DiSOL), a complementary paradigm that learns discrete solution procedures rather than continuous function-space operators. DiSOL factorizes the solver into learnable stages that mirror classical discretizations: local contribution encoding, multiscale assembly, and implicit solution reconstruction on an embedded grid, thereby preserving procedure-level consistency while adapting to geometry-dependent discrete structures. Across geometry-dependent Poisson, advection-diffusion, linear elasticity, as well as spatiotemporal heat conduction problems, DiSOL produces stable and accurate predictions under both in-distribution and strongly out-of-distribution geometries, including discontinuous boundaries and topological changes. These results highlight the need for procedural operator representations in geometry-dominated problems and position discrete solution operator learning as a distinct, complementary direction in scientific machine learning.

Accurate identification of chemical species and reaction pathways from molecular dynamics (MD) trajectories is a prerequisite for deriving predictive chemical-kinetic models and for mechanistic discovery in reactive systems. However, state-of-the-art trajectory analysis methods infer bonding from instantaneous distance thresholds, which can misclassify transient, nonreactive encounters as bonds and thereby introduce spurious intermediates, distorted reaction networks, and biased rate estimates. Here, we introduce ChemXDyn, a dynamics-aware computational methodology that leverages time-resolved interatomic distance signatures as a core principle to robustly identify chemically consistent bonded interactions and, consequently, extract meaningful reaction pathways. In particular, ChemXDyn propagates molecular connectivity through time while enforcing atomic valence and coordination constraints to distinguish genuine bond-breaking and bond-forming events from transient, nonreactive encounters. We evaluate ChemXDyn on ReaxFF MD simulations of hydrogen and ammonia oxidation and on neural-network potential MD simulations of methane oxidation, and benchmark its performance against widely used trajectory analysis methods. Across these cases, ChemXDyn suppresses unphysical species prevalent in static analyses, recovers experimentally consistent reaction pathways, and improves the fidelity of rate constant estimation. In ammonia oxidation, ChemXDyn removes unphysical intermediates and resolves key NOx- and N2O-forming and -consuming routes. In methane oxidation, it reconstructs the canonical progression from CH4 to CO2. By linking atomistic dynamics to chemically consistent reaction identification, ChemXDyn provides a transferable foundation for MD-derived reaction networks and kinetics, with potential utility spanning combustion, catalysis, plasma chemistry, and electrochemical environments.

Non-Newtonian fluids encompass a large family of fluids with additional nonlinear material properties, contributing to non-trivial flow behaviour that cannot be captured through a single constant viscosity term. Common non-Newtonian characteristics include shear-thinning, shear-thickening, viscoplasticity, and viscoelasticity, commonly encountered in everyday fluids, such as ketchup, blood, toothpaste, mud, etc., as well as practical applications involving porous media, cosmetics, food processing, and pharmaceuticals. Due to the complex nature of these fluids, accurate computational fluid dynamics simulations are essential for predicting their behaviour under various flow conditions. Recent advancements have highlighted the growing trend of using the lattice Boltzmann method to solve such complex flows, owing to its ability to handle intricate boundary conditions, ease of including additional multiphysics, and providing computationally efficient parallel simulations. Since the initial review over a decade ago [Phillips & Roberts, IMA J. Appl. Math. 76, 790-816 (2011)], significant advancements have been made to the lattice Boltzmann method to simulate non-Newtonian fluids. Here, we present a comprehensive review of different lattice Boltzmann techniques used to solve non-Newtonian fluid systems, specifically dealing with shear-dependent viscosity, viscoplasticity, and viscoelasticity. In addition, we discuss various benchmark cases that validate these approaches and highlight their growing application to realistic and challenging complex flow problems. We further address outstanding issues in current lattice Boltzmann models, as well as future directions for numerical advancement and application.

Autonomous experimentation holds the potential to accelerate materials development by combining artificial intelligence (AI) with modular robotic platforms to explore extensive combinatorial chemical and processing spaces. Such self-driving laboratories can not only increase the throughput of repetitive experiments, but also incorporate human domain expertise to drive the search towards user-defined objectives, including improved materials performance metrics. We present an autonomous materials synthesis extension to SARA, the Scientific Autonomous Reasoning Agent, utilizing phase information provided by an automated probabilistic phase labeling algorithm to expedite the search for targeted phase regions. By incorporating human input into an expanded SARA-H (SARA with human-in-the-loop) framework, we enhance the efficiency of the underlying reasoning process. Using synthetic benchmarks, we demonstrate the efficiency of our AI implementation and show that the human input can contribute to significant improvement in sampling efficiency. We conduct experimental active learning campaigns using robotic processing of thin-film samples of several oxide material systems, including Bi$_2$O$_3$, SnO$_x$, and Bi-Ti-O, using lateral-gradient laser spike annealing to synthesize and kinetically trap metastable phases. We showcase the utility of human-in-the-loop autonomous experimentation for the Bi-Ti-O system, where we identify extensive processing domains that stabilize $δ$-Bi$_2$O$_3$ and Bi$_2$Ti$_2$O$_7$, explore dwell-dependent ternary oxide phase behavior, and provide evidence confirming predictions that cationic substitutional doping of TiO$_2$ with Bi inhibits the unfavorable transformation of the metastable anatase to the ground-state rutile phase. The autonomous methods we have developed enable the discovery of new materials and new understanding of materials synthesis and properties.

Background Predicting overall survival (OS) in non-small cell lung cancer (NSCLC) is essential for clinical decision-making and drug development. While tumor and blood test markers kinetics are intrinsically linked, their joint dynamics and relationship to OS remain unknown. Methods We developed a mechanistic model capturing the interplay between tumor (T) burden and three key blood markers kinetics: albumin (A), lactate dehydrogenase (L), and neutrophils (N), through coupled differential equations (termed TALN-k). This model was enhanced with a machine learning framework (TALN-kML) for OS prediction. The model was trained and validated on clinical trial data from NSCLC patients treated with atezolizumab in monotherapy (N = 862 patients) or combination therapy (N = 1,115). Model parameters were estimated using nonlinear mixed-effects modelling, and survival predictions were assessed using individual and trial level metrics. Results TALN-k successfully described individual and population-level marker kinetics, revealing complex interactions between tumor and blood markers, and improving corrected BIC and log-likelihood metrics by a significant margin of previous empirical state-of-the-art models. Feature selection methods also highlighted valuable predictive parameters, indicatives of good or poor prognosis. The TALN-kML model outperformed empirical, uncoupled models, achieving improved C-index (0.74 $\pm$ 0.02 vs 0.72 $\pm$ 0.03), 12-months AUC (0.83 $\pm$ 0.004 vs 0.79 $\pm$ 0.05), and accuracy (0.77 $\pm$ 0.03 vs 0.76 $\pm$ 0.05) in OS prediction. Conclusion Our mechanistic learning approach allows for an interpretable model, which improves on longitudinal data description and on survival prediction in NSCLC by jointly integrating tumor and blood markers kinetics. This methodology offers a promising avenue for both personalized treatment strategies and drug development optimization.

Understanding how neural activity gives rise to perception is a central challenge in neuroscience. We address the problem of decoding visual information from high-density intracortical recordings in primates, using the THINGS Ventral Stream Spiking Dataset. We systematically evaluate the effects of model architecture, training objectives, and data scaling on decoding performance. Results show that decoding accuracy is mainly driven by modeling temporal dynamics in neural signals, rather than architectural complexity. A simple model combining temporal attention with a shallow MLP achieves up to 70% top-1 image retrieval accuracy, outperforming linear baselines as well as recurrent and convolutional approaches. Scaling analyses reveal predictable diminishing returns with increasing input dimensionality and dataset size. Building on these findings, we design a modular generative decoding pipeline that combines low-resolution latent reconstruction with semantically conditioned diffusion, generating plausible images from 200 ms of brain activity. This framework provides principles for brain-computer interfaces and semantic neural decoding.

Exposure to psychoactive substances during pregnancy, such as cannabis, can disrupt neurodevelopment and alter large-scale brain networks, yet identifying their neural signatures remains challenging. We introduced KOCOBrain: KuramotO COupled Brain Graph Network; a unified graph neural network framework that integrates structural and functional connectomes via Kuramoto-based phase dynamics and cognition-aware attention. The Kuramoto layer models neural synchronization over anatomical connections, generating phase-informed embeddings that capture structure-function coupling, while cognitive scores modulate information routing in a subject-specific manner followed by a joint objective enhancing robustness under class imbalance scenario. Applied to the ABCD cohort, KOCOBrain improved prenatal drug exposure prediction over relevant baselines and revealed interpretable structure-function patterns that reflect disrupted brain network coordination associated with early exposure.

Estimating brain age (BA) from T1-weighted magnetic resonance images (MRIs) provides a useful approach to map the anatomic features of brain senescence. Whereas global BA (GBA) summarizes overall brain health, local BA (LBA) can reveal spatially localized patterns of aging. Although previous studies have examined anatomical contributors to GBA, no framework has been established to compute LBA using cortical morphology. To address this gap, we introduce a novel graph neural network (GNN) that uses morphometric features (cortical thickness, curvature, surface area, gray/white matter intensity ratio and sulcal depth) to estimate LBA across the cortical surface at high spatial resolution (mean inter-vertex distance = 1.37 mm). Trained on cortical surface meshes extracted from the MRIs of cognitively normal adults (N = 14,250), our GNN identifies prefrontal and parietal association cortices as early sites of morphometric aging, in concordance with biological theories of brain aging. Feature comparison using integrated gradients reveals that morphological aging is driven primarily by changes in surface area (gyral crowns and highly folded regions) and cortical thickness (occipital lobes), with additional contributions from gray/white matter intensity ratio (frontal lobes and sulcal troughs) and curvature (sulcal troughs). In Alzheimers disease (AD), as expected, the model identifies widespread, excessive morphological aging in parahippocampal gyri and related temporal structures. Significant associations are found between regional LBA gaps and neuropsychological measures descriptive of AD-related cognitive impairment, suggesting an intimate relationship between morphological cortical aging and cognitive decline. These results highlight the ability of GNN-derived gero-morphometry to provide insights into local brain aging.

Rhizomes play fundamental roles in plant evolution, persistence, and environmental adaptation by enabling clonal propagation, resource storage, and stress resilience. Despite their ecological and agronomic importance across diverse plant lineages, the genetic and developmental regulation of rhizomes remains poorly characterized. Here, we synthesize findings from in vitro induction studies, in vivo physiological and developmental analyses, quantitative trait loci (QTL) mapping, comparative transcriptomics, and limited functional studies to evaluate current knowledge and highlight outstanding questions in rhizome biology. Results show that phytohormones are central regulators of rhizome initiation and growth, with effects mediated in a context-dependent manner through interactions with environmental and developmental cues. Across rhizomatous species, traits such as rhizome initiation, branching, and elongation are typically under polygenic control, although comparatively simpler genetic architectures have been documented in emerging model systems like Mimulus. Transcriptomic analyses further highlight hormone signaling, stress-response, and carbohydrate metabolism pathways as key regulatory components. However, few genes have been functionally validated, underscoring the need for experimentally tractable systems for genetic dissection. Perennial Mimulus species are proposed as promising models for rhizome research due to their experimental accessibility, ecological relevance, and established genomic resources. Integrated approaches leveraging fine-mapping, near-isogenic lines, multi-omics network reconstruction, and genome editing are poised to accelerate the discovery of causal loci and regulatory networks underlying rhizome development, thereby illuminating key processes involved in plant adaptation and perenniality, with direct implications for evolutionary biology and crop improvement.

Introduction Sporadic Creutzfeldt Jakob disease sCJD is a rapidly progressive neurodegenerative disease without effective treatment that usually results in death within one year. The recently applied methods have improved the accuracy of the disease diagnosis and the specific radiological findings provide the necessary information for differential diagnosis. Research question The research is aimed to provide a different perspective on the development of CJD and associated literature review. Materials and methods The study presents a case who presented cognitive deficits, gait instability, and urinary and fecal incontinence suffered from traumatic brain injury eight months ago before admission with cerebral ventricle dilation on CT images. Furthermore, studies describe relevant cases are also included. Results The patients symptoms got deteriorated. Further examinations, including 14-3-3 and tau proteins in the cerebrospinal fluid CSF, MRI, and EEG, confirmed the patients diagnosis of sCJD. He returned to the local hospital for the conservative treatment without effective medical intervention. Conclusion This case illustrates the diagnostic process of CJD and underscores the importance of distinguishing rare disorders from common conditions to achieve a comprehensive understanding of the disease.

Acute stress alters cognitive performance, yet competing models make divergent predictions regarding the mechanisms, scope, and temporal dynamics of these effects. This large-scale randomized controlled trial tested predications from three influential stress-effect models using a broad cognitive task battery embedded within a psychopharmacological stress paradigm. Across 606 testing sessions, 303 healthy male participants completed both the Maastricht Acute Stress Test (MAST) and its non-stress control condition. To independently manipulate acute stress and stress hormone availability, participants were additionally randomized to receive atomoxetine (40 mg; to prolong norepinephrine availability), hydrocortisone (10 mg; to increase cortisol availability), or placebo. Cognitive performance was assessed over 80-minutes (post-stress) using tasks targeting visual perception (rapid serial visual presentation), response inhibition (stop-signal), and cognitive flexibility (dual and switch tasks). MAST exposure selectively impaired response inhibition, reflected in shorter stop-signal delays, lower probabilities of successful stopping and prolonged stop-signal reaction times, particularly during later testing phases (40-80 minutes post-stress). MAST exposure did not affect visual perception or task-switching performance but buffered time-related declines in processing efficiency at the expense of task prioritization in the dual task. Pharmacological manipulation of norepinephrine or cortisol availability was effective but did not moderate cognitive stress effects. Overall, this pattern of task-specific impairment alongside stabilized processing efficiency cannot be fully explained by any tested model, highlighting the need to refine existing models and adopt more integrative approaches to advance our mechanistic understanding of cognitive stress-effects in laboratory and real-world contexts.

Why do neurons encode information the way they do? Normative answers to this question model neural activity as the solution to an optimisation problem; for example, the celebrated efficient coding hypothesis frames neural activity as the optimal encoding of information under efficiency constraints. Successful normative theories have varied dramatically in complexity, from simple linear models (Atick & Redlich '90), to complex deep neural networks (Lindsay '21). What complex models gain in flexibility, they lose in tractability and often understandability. Here, we split the difference by constructing a set of tractable but flexible normative representational theories. Instead of optimising the neural activities directly, following Sengupta et al. '18, we optimise the representational similarity, a matrix formed from the dot products of each pair of neural responses. Using this, we show that a large family of interesting optimisation problems are convex. This family includes problems corresponding to linear and some non-linear neural networks, and problems from the literature not previously recognised as convex, such as modified versions of semi-nonnegative matrix factorisation or nonnegative sparse coding. We put these findings to work in three ways. First, we provide the first necessary and sufficient identifiability result for a form of semi-nonnegative matrix factorisation. Second, we show that if neural tunings are `different enough' then they are uniquely linked to the optimal representational similarity, partially justifying the use of single neuron tuning analysis in neuroscience. Finally, we use the tractable nonlinearity of some of our problems to explain why dense retinal codes, but not sparse cortical codes, optimally split the coding of a single variable into ON & OFF channels. In sum, we identify a space of convex problems, and use them to derive neural coding results.

We present a mathematical model that describes the synergistic mechanism of combined Vesicular Stomatitis Virus (VSV) and Vaccinia Virus (VV). The model captures the dynamic interplay between tumor cells, viral replication, and the interferon-mediated immune response, revealing a `ping-pong' synergy where VV-infected cells produce B18R protein that neutralizes interferon-$α$, thereby enhancing VSV replication within the tumor. Numerical simulations demonstrate that this combination achieves complete tumor clearance in approximately 50 days, representing an 11\% acceleration compared to VV monotherapy (56 days), while VSV alone fails to eradicate tumors. Through bifurcation analysis, we identify critical thresholds for viral burst size and B18R inhibition, while sensitivity analysis highlights infection rates and burst sizes as the most influential parameters for treatment efficacy. Temporal optimization reveals that therapeutic outcomes are maximized through immediate VSV administration followed by delayed VV injection within a 1-19 day window, offering a strategic approach to overcome the timing and dosing challenges inherent in OVT.

Short-term synaptic plasticity (STP) is traditionally viewed as a purely presynaptic filter of incoming spike trains, independent of postsynaptic activity. Recent experiments, however, reveal an associative form of STP in which presynaptic release probability changes alongside long-term potentiation, implying a richer computational role for presynaptic plasticity. Here we develop a normative theory of associative STP using an information-theoretic framework. Extending Fisher-information-based learning to Tsodyks-Markram synapses, we derive analytic update rules for baseline synaptic strength and release probability that maximize encoded stimulus information under resource constraints. The learning rules separate into a conventional postsynaptic term tracking local firing and a distinct presynaptic term with a phase-advanced structure that selectively detects stimulus onset; critically, differences between plasticity of baseline strength and release probability arise within this presynaptic component. For stimulus variations slower than the EPSP time constant, onset sensitivity biases optimal connectivity toward anti-causal associations, strengthening synapses from neurons activated later to those activated earlier. In recurrent circuits, these rules yield ramp-like sustained representations and reverse replay after drive removal. Linear-response analysis further shows that STP confers frequency-dependent phase selectivity on presynaptic drive and that constraints on total release probability systematically tune temporal asymmetry. Together, our results provide a principled account of associative STP and identify presynaptic plasticity of release probability as a substrate for rapidly reconfigurable temporal coding.

Recruitment dynamics, or the distribution of the number of offspring among individuals, is central for understanding ecology and evolution. Sweepstakes reproduction (heavy right-tailed offspring number distribution) is central for understanding the ecology and evolution of highly fecund natural populations. Sweepstakes reproduction can induce jumps in type frequencies and multiple mergers in gene genealogies of sampled gene copies. We take sweepstakes reproduction to be skewed offspring number distribution due to mechanisms not involving natural selection, such as in chance matching of broadcast spawning with favourable environmental conditions. Here, we consider population genetic models of sweepstakes reproduction in a diploid panmictic populations absent selfing and evolving in a random environment. Our main results are {\it (i)} continuous-time Beta and Poisson-Dirichlet coalescents, when combining the results the skewness parameter $α$ of the Beta-coalescent ranges from $0$ to $2$, and the Beta-coalescents may be incomplete due to an upper bound on the number of potential offspring produced by any pair of parents; {\it (ii)} in large populations time is measured in units proportional to either $N/\log N$ or $N$ generations (where $2N$ is the population size when constant); {\it (iii)} it follows that incorporating population size changes leads to time-changed coalescents with the time-change independent of $α$; {\it (iv)} using simulations we show that the ancestral process is not well approximated by the corresponding coalescent (as measured through certain functionals of the processes); {\it (v)} whenever the skewness of the offspring number distribution is increased the conditional (conditioned on the population ancestry) and the unconditional ancestral processes are not in good agreement.

Although most theories posit that natural behavior can be explained as maximizing some form of extrinsic reward, often called utility, some behaviors appear to be reward independent. For instance, spontaneous motor babbling in human newborns and curiosity in little kids and other animals seem to elude a simple explanation in terms of extrinsic reward maximization. Rooted in these observations, intrinsic motivation has emerged as a potentially major driver of behavior. However, only recently have several quantitative and foundational theories of intrinsic motivation been put forward. We first provide a general framework to understand behavior as being organized hierarchically: objective--intrinsic reward, or motivation--drives, goals and extrinsic reward. We next review the main formalizations of intrinsic motivation, including empowerment, the free energy principle, information-gain maximization, and the maximum occupancy principle. These theories produce complex behavior by promoting, in various ways, entropic action-state paths. The presence of a single intrinsic motivation objective breaks infinite regress, as drives and goals act only temporarily to serve the objective. Extrinsic rewards, such as sugar or protein, are just a means to achieve the objective. Bounded cognition and embodiment impose constraints and boundary conditions for the intrinsic motivation objective. By virtue of their capability to generate complex behavior in a task-agnostic manner, theories of intrinsic motivation promise to become successful generative models of open-ended, embodied behavior.

The classification of microscopy videos capturing complex cellular behaviors is crucial for understanding and quantifying the dynamics of biological processes over time. However, it remains a frontier in computer vision, requiring approaches that effectively model the shape and motion of objects without rigid boundaries, extract hierarchical spatiotemporal features from entire image sequences rather than static frames, and account for multiple objects within the field of view. To this end, we organized the Cell Behavior Video Classification Challenge (CBVCC), benchmarking 35 methods based on three approaches: classification of tracking-derived features, end-to-end deep learning architectures to directly learn spatiotemporal features from the entire video sequence without explicit cell tracking, or ensembling tracking-derived with image-derived features. We discuss the results achieved by the participants and compare the potential and limitations of each approach, serving as a basis to foster the development of computer vision methods for studying cellular dynamics.

Learning, inference, and emergence in biological and artificial systems are often studied within disparate theoretical frameworks, ranging from energy-based models to recurrent and attention-based architectures. Here we develop a unified dynamical field theory in which learning and inference are governed by a minimal stochastic dynamical equation admitting a Martin--Siggia--Rose--Janssen--de Dominicis formulation. Within this framework, inference corresponds to saddle-point trajectories of the associated action, while fluctuation-induced loop corrections render collective modes dynamically emergent and generate nontrivial dynamical time scales. A central result of this work is that cognitive function is controlled not by microscopic units or precise activity patterns, but by the collective organization of dynamical time scales. We introduce the \emph{time-scale density of states} (TDOS) as a compact diagnostic that characterizes the distribution of collective relaxation modes governing inference dynamics. Learning and homeostatic regulation are naturally interpreted as processes that reshape the TDOS, selectively generating slow collective modes that support stable inference, memory, and context-dependent computation despite stochasticity and structural irregularity. This framework unifies energy-based models, recurrent neural networks, transformer architectures, and biologically motivated homeostatic dynamics within a single physical description, and provides a principled route toward understanding cognition as an emergent dynamical phenomenon.

Atrial fibrillation (AF) is a common cardiac arrhythmia that significantly increases the risk of stroke and heart failure, necessitating reliable and generalizable detection methods from electrocardiogram (ECG) recordings. Although deep learning has advanced automated AF diagnosis, existing approaches often struggle to exploit complementary time-frequency information effectively, limiting both robustness under intra-dataset and generalization across diverse clinical datasets. To address these challenges, we propose a cross-modal deep learning framework comprising two key components: a Bidirectional Gating Module (BGM) and a Cross-modal Supervised Contrastive Learning (CSCL) strategy. The BGM facilitates dynamic, reciprocal refinement between time and frequency domain features, enhancing model robustness to signal variations within a dataset. Meanwhile, CSCL explicitly structures the joint embedding space by pulling together label-consistent samples and pushing apart different ones, thereby improving inter-class separability and enabling strong cross-dataset generalization. We evaluate our method through five-fold cross-validation on the AFDB and the CPSC2021 dataset, as well as bidirectional cross-dataset experiments (training on one and testing on the other). Results show consistent improvements over state-of-the-art methods across multiple metrics, demonstrating that our approach achieves both high intra-dataset robustness and excellent cross-dataset generalization. We further demonstrate that our method achieves high computational efficiency and anti-interference capability, making it suitable for edge deployment.

Assessment of sperm morphological quality remains a critical yet subjective component of male fertility evaluation, often limited by inter-observer variability and resource constraints. This study presents a comparative biomedical artificial intelligence framework evaluating an image-based deep learning model (HuSHeM) alongside a clinically grounded baseline derived from World Health Organization criteria augmented with the Systemic Inflammation Response Index (WHO(+SIRI)). The HuSHeM model was trained on high-resolution sperm morphology images and evaluated using an independent clinical cohort. Model performance was assessed using discrimination, calibration, and clinical utility analyses. The HuSHeM model demonstrated higher discriminative performance, as reflected by an increased area under the receiver operating characteristic curve with relatively narrow confidence intervals compared to WHO(+SIRI). Precision-recall analysis further indicated improved performance under class imbalance, with higher precision-recall area values across evaluated thresholds. Calibration analysis indicated closer agreement between predicted probabilities and observed outcomes for HuSHeM, while decision curve analysis suggested greater net clinical benefit across clinically relevant threshold probabilities. These findings suggest that image-based deep learning may offer improved predictive reliability and clinical utility compared with traditional rule-based and inflammation-augmented criteria. The proposed framework supports objective and reproducible assessment of sperm morphology and may serve as a decision-support tool within fertility screening and referral workflows. The proposed models are intended as decision-support or referral tools and are not designed to replace clinical judgment or laboratory assessment.

Neuropixels probes, initially developed for use in small animal models, have transformed basic neuroscience by enabling high-density, single-cell resolution recordings across multiple brain regions simultaneously. The recent development of Neuropixels 1.0 NHP Long, a longer probe designed for non-human primates, has expanded this capability, enabling unprecedented simultaneous access to multiple cortical layers and deep brain structures of large-brained animals. Here, we report the first use of these probes in humans, aiming to establish safe intraoperative use and assess feasibility for clinical and research applications. Nine patients undergoing neurosurgical procedures, including epilepsy or tumor resection and deep brain stimulation (DBS) implantation, were enrolled. Successful intraoperative recordings were obtained from surface and deep cortical structures without probe breakage or adverse events. Compared with conventional electrodes, the Neuropixels probe enabled dense sampling across multiple parenchymal depths with submillisecond temporal resolution. Recordings were obtained from deep targets including the hippocampus and cingulate cortex, as well as from regions that are challenging to access with single-unit precision, such as the superior frontal sulcus. Custom tools and refined workflows lowered technical barriers for operative use and improved recording stability. Neural activity was observed across all recordings. Neuropixels 1.0-NHP Long probes can be deployed in the human operating room, enabling simultaneous recordings from multiple brain structures at single-neuron resolution. These methods expand opportunities for studying human brain function and pathology in vivo, and may ultimately support the development of more precise neurosurgical interventions.

Tuberculosis (TB) is an airborne disease caused by the pathogen Mycobacterium tuberculosis. In 2023, it returned to being the leading cause of death from an infectious agent globally, replacing COVID-19; in the nineteenth century, one in seven of all humans died of tuberculosis. More than 10 million people are diagnosed with TB every year. The majority of cases in adults occur in males (62.5% of all global adult cases in 2023, compared to 37.5% in females). The main reasons for males suffering from a higher burden of global TB cases, compared to females, may be in large part due to population-scale factors, such as employment type, the quantity and type of social contacts they make, and their health-seeking behaviours (e.g. differences in diagnostic and treatment delays between genders). To investigate which population-scale factors are most important in determining this higher TB burden in males, we have developed an age- and gender-stratified, spatially heterogeneous epidemiological agent-based model. We have focused specifically on Kampala, the capital of Uganda, which is a high-burden TB country. We considered counterfactual scenarios to elucidate the impact of gender on the epidemiology of TB. Setting disease progression parameters equal between the genders leads to a reduction in both male-to-female case ratio and total case numbers.

There is little debate about the importance of the ancestral recombination graph in population genetics. An important theoretical tool, the main obstacle to its widespread usage is the computational cost required to match the ever-increasing scale of the data being analyzed. Many of these difficulties have been overcome in the past two decades, which have consequently seen the development of increasingly sophisticated ARG simulation and inference software. Nonetheless, challenges remain, especially in the area of ancestry inference. This paper is a comprehensive review of ARG samplers that have emerged in the past three decades to meet the need for scalable and flexible ancestry simulation and inference solutions. It specifically focuses on their performance, usability, and the biological realism of the underlying algorithm, and aims primarily to provide a technical overview of the field for researchers seeking to write their own coalescent-with-recombination sampler. As a complement to this article, we have compiled links to software, source code and documentation and made them available at https://www.patrickfournier.ca/arg-samplers-review/graph.

Sweepstakes reproduction may be generated by chance matching of reproduction with favorable environmental conditions. Gene genealogies generated by sweepstakes reproduction are in the domain of attraction of multiple-merger coalescents where a random number of lineages merges at such times. We consider population genetic models of sweepstakes reproduction for haploid panmictic populations of both constant ($N$), and varying population size, and evolving in a random environment. We construct our models so that we can recover the observed number of new mutations in a given sample without requiring strong assumptions regarding the population size or the mutation rate. Our main results are {\it (i)} continuous-time coalescents that are either the Kingman coalescent or specific families of Beta- or Poisson-Dirichlet coalescents; when combining the results the parameter $α$ of the Beta-coalescent ranges from 0 to 2, and the Beta-coalescents may be incomplete due to an upper bound on the number of potential offspring an arbitrary individual may produce; {\it (ii)} in large populations we measure time in units proportional to either $ N/\log N$ or $N$ generations; {\it (iii)} incorporating fluctuations in population size leads to time-changed multiple-merger coalescents where the time-change does not depend on $α$; {\it (iv)} using simulations we show that in some cases approximations of functionals of a given coalescent do not match the ones of the ancestral process in the domain of attraction of the given coalescent; {\it (v)} approximations of functionals obtained by conditioning on the population ancestry (the ancestral relations of all gene copies at all times) are broadly similar (for the models considered here) to the approximations obtained without conditioning on the population ancestry.

Cerebellar-like networks, in which input activity patterns are separated by projection to a much higher-dimensional space before classification, are a recurring neurobiological motif, present in the cerebellum, dentate gyrus, insect olfactory system, and electrosensory system of the electric fish. Their relatively well-understood design presents a promising test-case for probing principles of biological learning. The circuits' expansive projections have long been modelled as random, enabling effective general purpose pattern separation. However, electron-microscopy studies have discovered interesting hints of structure in both the fly mushroom body and mouse cerebellum. Recent numerical work suggested that this non-random connectivity enables the circuit to prioritise learning of some, presumably natural, tasks over others. Here, rather than numerical results, we present a robust mathematical link between the observed connectivity patterns and the cerebellar circuit's learning ability. In particular, we extend a simplified kernel regression model of the system and use recent machine learning theory results to relate connectivity to learning. We find that the reported structure in the projection weights shapes the network's inductive bias in intuitive ways: functions are easier to learn if they depend on inputs that are oversampled, or on collections of neurons that tend to connect to the same hidden layer neurons. Our approach is analytically tractable and pleasingly simple, and we hope it continues to serve as a model for understanding the functional implications of other processing motifs in cerebellar-like networks.

Analysis of learned representations has a blind spot: it focuses on $similarity$, measuring how closely embeddings align with external references, but similarity reveals only what is represented, not whether that structure is robust. We introduce $geometric$ $stability$, a distinct dimension that quantifies how reliably representational geometry holds under perturbation, and present $Shesha$, a framework for measuring it. Across 2,463 configurations in seven domains, we show that stability and similarity are empirically uncorrelated ($ρ\approx 0.01$) and mechanistically distinct: similarity metrics collapse after removing the top principal components, while stability retains sensitivity to fine-grained manifold structure. This distinction yields actionable insights: for safety monitoring, stability acts as a functional geometric canary, detecting structural drift nearly 2$\times$ more sensitively than CKA while filtering out the non-functional noise that triggers false alarms in rigid distance metrics; for controllability, supervised stability predicts linear steerability ($ρ= 0.89$-$0.96$); for model selection, stability dissociates from transferability, revealing a geometric tax that transfer optimization incurs. Beyond machine learning, stability predicts CRISPR perturbation coherence and neural-behavioral coupling. By quantifying $how$ $reliably$ systems maintain structure, geometric stability provides a necessary complement to similarity for auditing representations across biological and computational systems.

Fisher's fundamental theorem describes the change caused by natural selection as the change in gene frequencies multiplied by the partial regression coefficients for the average effects of genes on fitness. Fisher's result has generated extensive controversy in biology. I show that the theorem is a simple example of a general partition for change in regression predictions across altered contexts. By that rule, the total change in a mean response is the sum of two terms. The first ascribes change to the difference in predictor variables, holding constant the regression coefficients. The second ascribes change to altered context, captured by shifts in the regression coefficients. This general result follows immediately from the product rule for finite differences applied to a regression equation. Economics widely applies this same partition, the Oaxaca-Blinder decomposition, as a fundamental tool that can in proper situations be used for causal analysis. Recognizing the underlying mathematical generality clarifies Fisher's theorem, provides a useful tool for causal analysis, and reveals connections across disciplines.

Interval-censored covariates are frequently encountered in biomedical studies, particularly in time-to-event data or when measurements are subject to detection or quantification limits. Yet, the estimation of regression models with interval-censored covariates remains methodologically underdeveloped. In this article, we address the estimation of generalized linear models when one covariate is subject to interval censoring. We propose a likelihood-based approach, GELc, that builds upon an augmented version of Turnbull's nonparametric estimator for interval-censored data. We prove that the GELc estimator is consistent and asymptotically normal under mild regularity conditions, with available standard errors. Simulation studies demonstrate favorable finite-sample performance of the estimator and satisfactory coverage of the confidence intervals. Finally, we illustrate the method using two real-world applications: the AIDS Clinical Trials Group Study 359 and an observational nutrition study on circulating carotenoids. The proposed methodology is available as an R package at github.com/atoloba/ICenCov.

Diffusion models have emerged as a powerful class of generative models for molecular design, capable of capturing complex structural distributions and achieving high fidelity in 3D molecule generation. However, their widespread use remains constrained by long sampling trajectories, stochastic variance in the reverse process, and limited structural awareness in denoising dynamics. The Directly Denoising Diffusion Model (DDDM) mitigates these inefficiencies by replacing stochastic reverse MCMC updates with deterministic denoising step, substantially reducing inference time. Yet, the theoretical underpinnings of such deterministic updates have remained opaque. In this work, we provide a principled reinterpretation of DDDM through the lens of the Reverse Transition Kernel (RTK) framework by Huang et al. 2024, unifying deterministic and stochastic diffusion under a shared probabilistic formalism. By expressing the DDDM reverse process as an approximate kernel operator, we show that the direct denoising process implicitly optimizes a structured transport map between noisy and clean samples. This perspective elucidates why deterministic denoising achieves efficient inference. Beyond theoretical clarity, this reframing resolves several long-standing bottlenecks in molecular diffusion. The RTK view ensures numerical stability by enforcing well-conditioned reverse kernels, improves sample consistency by eliminating stochastic variance, and enables scalable and symmetry-preserving denoisers that respect SE(3) equivariance. Empirically, we demonstrate that RTK-guided deterministic denoising achieves faster convergence and higher structural fidelity than stochastic diffusion models, while preserving chemical validity across GEOM-DRUGS dataset. Code, models, and datasets are publicly available in our project repository.

Dietary flavonoids associate with disease prevention in epidemiological studies, yet their polypharmacological mechanisms remain unclear. We establish network pharmacology as a systematic framework to characterize flavonoid therapeutic properties through integrated computational, experimental, and epidemiological validation. We constructed a master network of 17,869 human proteins, 14 dietary flavonoids, and 1,496 FDA-approved drugs with 278,768 interactions. Flavonoids averaged 45.3 target proteins per compound compared to 16.8 for FDA-approved drugs (2.7-fold higher; p=7.5x10^-4), reflecting multi-target architecture. Statistical analysis revealed that 71.4% of flavonoids targeted proteins associated with cardiovascular drugs and 78.6% aligned with antineoplastic drug targets. MTT-based Jurkat cell assays confirmed network predictions: high-association flavonoids (luteolin LC50=31.4 microM, myricetin=29.5 microM) produced strong cytotoxicity, while low-association flavonoids showed minimal activity (LC50>200 microM). Network-predicted association strengths correlated with experimental bioactivity (Pearson r=0.918; R^2=0.843). We translated network associations into food-level predictions across 506 foods, identifying 685 food-drug therapeutic combinations. Systematic literature searches confirmed 96 associations supported by 132 unique references. Cardiovascular domains achieved 47.1% validation. Top-validated foods included tea (31 evidence items), blueberries (18 items), tomato (13 items), grape juice (10 items), and plum (9 items). Network pharmacology characterizes dietary polypharmacological properties and generates evidence-based food-therapeutic predictions, bridging nutritional science and systems pharmacology.

An important part of the information theory folklore had been about the output statistics of codes that achieve the capacity and how the empirical distributions compare to the output distributions induced by the optimal input in the channel capacity problem. Results for a variety of such empirical output distributions of good codes have been known in the literature, such as the comparison of the output distribution of the code to the optimal output distribution in vanishing and non-vanishing error probability cases. Motivated by these, we aim to achieve similar results for the quantum codes that are used for classical communication, that is the setting in which the classical messages are communicated through quantum codewords that pass through a noisy quantum channel. We first show the uniqueness of the optimal output distribution, to be able to talk more concretely about the optimal output distribution. Then, we extend the vanishing error probability results to the quantum case, by using techniques that are close in spirit to the classical case. We also extend non-vanishing error probability results to the quantum case on block codes, by using the second-order converses for such codes based on hypercontractivity results for the quantum generalized depolarizing semi-groups.

An accelerating Rindler frame in Minkowski spacetime acting for a finite time interval is used to carry a box of particles or waves between two relativistic inertial frames. The finite spatial extent of the box allows treatment of the equations of motion for particles or for waves, while the Rindler acceleration provides a substitute for scattering to test for thermal equilibrium. In the case of equilibrium for relativist particles, the Juttner distribution is derived. For relativistic waves, a full derivation of the Planck spectrum including zero-point radiation is obtained within classical theory. For relativistic waves, relativistic behavior and conformal symmetry are crucial. It is emphasized that the classical two-point correlation function for classical zero-point radiation depends upon the geodesic separation between the spacetime points and is independent of the coordinate system choice. The classical point of view here does not give any support for the idea that a system in uniform acceleration through classical zero-point radiation finds a thermal system.

We present the long-duration time-domain simulations of scalar-field tails in Kerr spacetimes driven by \emph{outgoing} multipolar sources. Extending the recent work in the literature from Schwarzschild to rotating black holes, we evolve sources with $\ell'=\{0,1,2,3,4\}$ on backgrounds with dimensionless spin $a/M=\{0.0, 0.8, 1.0\}$ and extract the late-time decay rates of measured modes $\ell\le4$ for a nonlinearity-inspired outgoing source with a $1/r^2$ fall-off. In all cases we find the inverse power-law index $p_{\ell\ell'}$ to be larger than the source-free Price law values by one unit, i.e. $p^{\text{sourced}}_{\ell\ell'} = p^{\text{Price}}_{\ell\ell'} + 1$. We also include a power-law index value computation for a similar source-driven gravitational wave case $(\ell,m)=(4,4)$ and confirm closely related results in the recent literature.

A central building block of a heat engine is the working fluid, which mediates the conversion of heat into work. In nanoscale heat engines, the working fluid can be a quantum system whose behavior and dynamics are non-classical. A particularly versatile realization is a quantum resonator, which allows for precise control and coupling to thermal reservoirs, making it an ideal platform for exploring quantum thermodynamic processes. Here, we investigate the thermodynamic properties of a driven quantum resonator whose temperature is controlled by modulating its natural frequency. We evaluate the work performed by the external drive and the resulting heat flow between the resonator and its environment, both within linear response and beyond. To further elucidate these processes, we determine the full distribution of photon exchanges between the resonator and its environment, characterized by its first few cumulants. Our results provide quantitative insights into the interplay between heat, work, and fluctuations, and may help in designing future heat engines.

Freely propagating electrons may serve as quantum probes that can become coherently correlated with other quantum systems, offering access to advanced metrological resources. We propose a setup that coherently couples free electrons in an electron microscope to a trapped-ion quantum processor, enabling non-destructive, quantum-coherent detection and the accumulation of information across multiple electrons. Our analysis shows that single electrons can induce resolvable qubit excitations, establishing a platform for practical applications such as quantum-enhanced, dose-efficient electron microscopy.

Imbalanced datasets are a fundamental issue in industrial condition monitoring and fault classification pipelines, causing classical machine learning models to overfit the majority classes while failing to learn the minority fault patterns. This work presents a detailed benchmarking and robustness investigation of classical classifiers under class imbalance mitigation using the Quantum Synthetic Minority Oversampling Technique (QSMOTE) and quantum-inspired perturbations modelled using six noise channels. Four different datasets, the Solar Panel Image Dataset (SPID), the CWRU Bearing Dataset (CWRUBD), the Engine Failure Detection Dataset (EFDD), and the Industrial Fault Detection Dataset (IFDD), are tested across multi-class scenarios to determine the universality of these impacts. The results show that QSMOTE consistently corrects distributional skew and significantly enhances the performance of non-linear classifiers such as Random Forests (RF), Support Vector Machines (SVM), and Decision Trees (DT), yielding improvements of up to 170% on EFDD and achieving near-perfect accuracy ($\geq$0.99) on IFDD. Linear and probabilistic models, such as Linear Regression (LR) and Naive Bayes (NB), produce mixed results, with significant degradation in overlapping feature spaces due to interpolation-induced boundary distortion. A parallel robustness analysis under different noise models reveals that ensemble models (RF) and margin-based learners (SVM) maintain strong resilience, often preserving over 95% of baseline accuracy even under maximum noise. In contrast, NB and DT show substantial instability, especially on high-variance datasets. The findings establish a rigorous baseline for understanding how classical models behave under realistic imbalance and quantum-inspired noise.

In this work, we present a new algorithm for generating quantum circuits that efficiently implement continuous time quantum walks on arbitrary simple sparse graphs. The algorithm, called matching decomposition, works by decomposing a continuous-time quantum walk Hamiltonian into a collection of exactly implementable Hamiltonians corresponding to matchings in the underlying graph followed by a novel graph compression algorithm that merges edges in the graph. Lastly, we convert the walks to a circuit and Trotterize over these components. The dynamics of the walker on each edge in the matching can be implemented in the circuit model as sequences of CX and CRx gates. We do not use Pauli decomposition when implementing walks along each matching. Furthermore, we compare matching decomposition to a standard Pauli-based simulation pipeline and find that matching decomposition consistently yields substantial resource reductions, requiring up to 43% fewer controlled gates and up to 54% shallower circuits than Pauli decomposition across multiple graph families. Finally, we also present examples and theoretical results for when matching decomposition can exactly simulate a continuous-time quantum walk on a graph.

Clinical trials are notorious for their high failure rates and steep costs, leading to wasted time and resources spend, prolonged development timelines, and delayed patient access to new therapies. A key contributor to these failures is biological uncertainty, which complicates trial design and weakens the ability to detect true treatment effects. In particular, inadequate patient stratification often results in covariate imbalances across treatment arms, masking treatment effects and reducing statistical power, even when therapies are effective for specific patient subpopulations. This work presents an optimization-based, quantum-enhanced approach to patient stratification that explicitly minimizes covariate imbalance across numerical and categorical variables, without altering protocol design or trial endpoints. Using real clinical trial data, we demonstrate that hybrid quantum-classical optimization methods achieve high-quality stratification while scaling efficiently to larger cohorts. In our benchmark study, the quantum-enhanced pipeline delivered over a 100x improvement in computational efficiency compared to classical approaches, enabling faster iteration and practical deployment at scale. This report shows how improved stratification can lead to decision-relevant gains, including up to a fivefold increase in statistical significance in treatment effect estimation, reducing treatment-effect dilution and increasing trial sensitivity. Together, these results show that optimization-driven stratification can strengthen clinical trial design, improve confidence in downstream decisions, and reduce the risk of costly late-stage failure.

We showcase the recently developed double transmon coupler (DTC) circuit as a compact, drop-in, tunable and transition-selective link between an otherwise coherent transmon and the continuum of modes in a waveguide. We use these transmon-DTC devices as transmon emitter/dectectors (TEDs) for microwave photons. We highlight the flexibility of these devices by sending photons from a source TED to a measurement TED using a meter of coaxial cable and a circulator, each TED with nominally identical circuit parameters. We detect $60\,\%$ of the photons using this setup where we infer that $95\,\%$ of the photons at the input of the measurement TED are detected. Reset and photon emission/detection each require about $2\,μ$s, for a minimum protocol duration of $4\,μ$s, for our choice of TED parameters. Transmon-waveguide links like the DTC serve an important role in quantum information processors: they provide a mechanism for unconditional fast reset, metrology, and as nascent quantum communication interfaces for quantum networking.

We combine the study of resources in measurement-based quantum computation (MBQC) with that of quantum solutions to linear constraint systems (LCS). Contextuality of the input state in MBQC has been identified as a key resource for quantum advantage, and in a stronger form, underlies algebraic relations between (measurement) operators which obey classically unsatisfiable (linear) constraints. Here, we compare these two perspectives on contextuality, and study to what extent they are related. More precisely, we associate a LCS to certain MBQC which exhibit strong forms of state-dependent contextuality, and ask if the measurement operators in such MBQC give rise to state-independent contextuality in the form of quantum solutions of its associated LCS. Our main result rules out such quantum solutions for a large class of MBQC. This both sharpens the distinction between state-dependent and state-independent forms of contextuality, and further generalises results on the non-existence of quantum solutions to LCS in finite odd (prime) dimension.

Measurement of mutual gravitation on laboratory scales is an outstanding challenge and a prerequisite to probing theories of quantum gravity. A leading technology in tabletop gravity experiments is the torsion balance, with limitations due to thermal decoherence. Recent demonstrations of lithographically defined suspensions in thin-film silicon nitride with macroscale test masses suggest a path forward, as torsion pendulums dominated by gravitational stiffness may achieve higher mechanical quality factors through dilution of material losses. Here we demonstrate a 250 micron by 5 mm by 1.8 micron torsion fiber supporting 87 grams and forming a Cavendish-style torsion pendulum with tungsten test masses that -- to our knowledge -- is the largest thin-film silicon-nitride-based oscillator to date. Torsion pendulums with thin-film, nanofabricated suspensions provide a test bed for near-term tabletop experiments probing classical and quantum gravitational interaction between oscillators.

In this work, we develop an algebraic description of the Page transition, a key feature in black hole evaporation where the entropy of Hawking radiation follows a unitary Page curve instead of monotonically increasing. By applying concepts from approximate quantum error correction with complementary recovery, we characterize the Page transition as a phase transition in channel recovery. We then generalize the description to infinite-dimensional settings using algebraic relative entropy, which remains valid even in type III factors. For type I/II factors, explicit probes based on relative entropy differences are derived, serving as indicators for the transition at the Page time.

Under certain conditions, the oscillating inflaton condensate filling the Universe after inflation can fragment and form so-called oscillons. These long-lived soliton-like field configurations can dominate the Universe for several $e$-folds of expansion, leading to an early matter-dominated phase preceding the standard radiation era. In this paper we show how the rapid final decay of the oscillons leads to an enhanced production of induced gravitational waves, whose energy density can saturate the observational bound on the effective number of relativistic species. We leverage this bound to constrain the inflaton mass, cubic, and quartic self-coupling in generic models that admit oscillon formation, providing novel and complementary constraints in regions of parameter space that are inaccessible with cosmic microwave background observations alone.

Understanding error mechanisms in two-qubit gate operations is essential for building high-fidelity quantum processors. While prior studies predominantly treat dephasing noise as either Markovian or predominantly low-frequency, realistic qubit environments exhibit structured, frequency-dependent spectra. Here we demonstrate that noise at frequencies matching the dressed-state energy splitting--set by the inter-qubit coupling strength g--induces a distinct relaxation channel that degrades gate performance. Through combined theoretical analysis and experimental verification on superconducting qubits with engineered noise spectra, we show that two-qubit gate errors scale predictably with the noise power spectral density at frequency 2g, extending the concept of $T_{1ρ}$ relaxation to interacting systems. This frequency-selective relaxation mechanism, universal across platforms, enriches our understanding of decoherence pathways during gate operations. The same mechanism sets coherence limits for dual-rail or singlet-triplet encodings.

The influences on the cluster number counts from the coupling between dark energy and dark matter with momentum transfer are investigated. We find that the extrapolated linear density contrast computed from the spherical collapse model is suppressed when the strength of momentum transfer is increased. Using the Sheth-Tormen mass function, the cluster number counts are computed. The minimum mass limit in the mass integration for each redshift bin is determined by matching the predicted number counts from the $Λ$CDM model with the result from eROSITA surveys. We find that the number of clusters is maximal at a higher redshift bin, and the number of clusters in a maximum redshift bin is enhanced when the strength of momentum and energy transfers increases due to the reduction of extrapolated linear density contrast. Setting the parameters of the dark energy model with momentum coupling according to the observational constraints in \cite{bestfit}, the predicted number counts from the coupled dark energy is larger than the result from eROSITA surveys. The statistical analysis yields a $p$-value of 0.189 for the proposed model relative to $Λ$CDM. Consequently, there is no statistically significant evidence of an improved fit over the standard $Λ$CDM framework based on the eROSITA cluster number counts.

We analyze shortcuts to adiabaticity (STA) and their completions for the quantum harmonic oscillator (QHO) with time-dependent frequency, as well as for quantum field theory (QFT) in non-stationary backgrounds. We exploit the analogy with one-dimensional quantum mechanics, and the well known correspondence between Bogoliubov coefficients in the QHO and transmission/reflection amplitudes in scattering theory. Within this framework, STA protocols for the QHO are equivalent to transmission resonances, while STA in QFT with homogeneous backgrounds correspond to reflectionless potentials. Moreover, using the connection between particle creation and squeezed states, we show how STA completions can be understood in terms of the anti-squeezing operator.

The rate of coherence loss is lower for a qubit under Rabi drive compared to a freely evolving qubit, $T_{2}^{\rm{Rabi}}>T_{2}^*$. Building on this principle, concatenated continuous driving (CCD) keeps the qubit under continuous drive to suppress noise and manipulate dressed states by either phase or amplitude modulation. In this work, we propose a new variant of CCD which simultaneously modulates both the amplitude and phase of the driving field to generate a circularly-polarized field in the rotating frame of the carrier frequency. This circular-modulated (CM)-CCD cancels the counter-rotating term in the second rotating frame, eliminating a systematic pulse-area error that arises from an imperfect rotating wave approximation for fast gates. Numerical simulations demonstrate that the proposed CMCCD achieves higher gate fidelity than conventional CCD schemes. We further implement and compare different CCD protocols using an electron spin-qubit in an isotopically purified $^{28}$Si-MOS quantum dot and evaluate its robustness by applying static detuning and Rabi frequency errors. The robustness is significantly improved compared to standard Rabi-drive, showing the effectiveness of this scheme for qubit arrays with variation in qubit frequency, coupling to Rabi drive, and low frequency noise. The proposed scheme can be applied to various physical systems, including trapped atoms, cold atoms, superconducting qubits, and NV-centers.

Parameter estimation of gravitational wave signals is computationally intensive and typically requires millions of likelihood evaluations to construct posterior probability distributions. This computational cost increases significantly in the time domain, which requires non-diagonal covariance matrices to compute the likelihood. Consequently, parameter estimation of long-duration gravitational wave signals, such as binary neutron star mergers, becomes computationally infeasible in time domain. In this work, we detail a framework for the heterodyned likelihood that enables rapid inference in the time domain. Our method is applicable to signals with arbitrary mode content, and leverages the smoothness of the ratio of complex-valued waveform modes, approximating the ratio as a linear function within appropriately chosen time bins. This allows downsampling of the waveform modes and a reformulation of the likelihood, such that it depends only on the bin edges. We demonstrate that this likelihood recovers posteriors that are indistinguishable from those obtained using the standard likelihood in the time domain. We also observe dramatic improvement in speed - for a 128 seconds-long gravitational wave signal, our method is at least $\sim 400$ times faster than the standard time-domain analysis, reducing the wall clock time to just a few hours. We also demonstrate the reliability and unbiasedness of the likelihood using percentile-percentile tests for binary black hole and binary neutron star injections. We use the Gohberg-Semencul representation of the inverse of Toeplitz covariance matrix to accelerate matrix-vector products, which has potential applications even in non heterodyned time-domain inference.

We investigate the formation of three-body bound states in the continuum by tracing pole trajectories in the complex energy plane under variation of system parameters. Using a one-dimensional model of two identical bosons and a distinguishable particle interacting via Gaussian potentials, we systematically vary the interaction strength, interaction range, and mass ratio. Our results confirm the parametric nature of few-body bound states in a continuum (BIC) and extend this characterization to a broader set of system parameters. Specifically, we find that variations of both interaction parameters and the mass ratio can lead to the formation of at least one three-body BIC. However, the exact shape of trajectories differs, and for the mass ratio variation we find a more regular pattern with multiple BIC locations. These results suggest that the mechanism of few-body BIC formation is more sensitive to the kinematic structure of the problem than to the specific details of the two-body interaction.

We compute, at the first self force accuracy level, the radiated energy from a radially infalling particle released from rest in a Schwarzschild spacetime. We examine both the cases of a scalar particle and that of a massive particle, in the context of gravitational perturbations. Our findings are accompanied by Post-Newtonian checks. In spite of the specific interest for this kind of computations, we outline the building blocks for future higher-order Post-Newtonian computations as well as for extending these results to other interesting situations out of the black hole case.

The formalism of Bayesian model selection provides a very elegant way of ranking different physical models in terms of how compatible they are with a given set of observed data. However, its practical application is often hampered by the challenge of having to compute the Bayesian evidence - a multi-dimensional integral over the product of likelihood and prior probability. This usually necessitates a large number of function calls to the likelihood, which may become prohibitive in case of "slow", costly to evaluate likelihoods. A possible solution to this problem lies in approximating the slow full likelihood by a fast emulated likelihood. In this paper, we introduce BOBE (Bayesian Optimisation for Bayesian Evidence), a method to construct a Gaussian Process Regression (GPR)-based emulator. BOBE utilises a Bayesian Optimisation algorithm designed specifically to (i) provide a realistic estimate of the emulator's uncertainty and its impact on the evidence calculation, and (ii) minimise the number of likelihood evaluations required in order to meet a given evidence accuracy goal. We apply it to a number of toy examples as well as actual cosmological likelihoods, and demonstrate that training the emulator to a sufficient accuracy takes a factor of $O(10^3)$ fewer direct likelihood evaluations than would be needed if one were to directly compute the evidence integral via nested sampling. BOBE's overhead is independent of the likelihood computation time $t_L$, making it particularly useful for "expensive" likelihoods with $t_L \gtrsim 1$~s. BOBE is written in Python, supports MPI parallelisation, takes advantage of automatic differentiation and just-in-time-compilation provided by JAX, can straightforwardly be implemented with cosmological data analysis frameworks such as Cobaya, and is available for download from https://github.com/Ameek94/BOBE.

We present a theoretical framework for quantum key distribution (QKD) using orbital angular momentum (OAM) encoded BB84 on an unmanned aerial vehicle (UAV) platform. A unified channel model captures Kolmogorov turbulence, pointing induced misalignment, and finite aperture clipping, enabling quantitative predictions of inter mode crosstalk and the resulting quantum bit error rate (QBER). Using a weak plus vacuum decoy state formulation, we derive composable finite key lower bounds on the secret key rate that incorporate statistical fluctuations, detector dark counts, efficiency mismatch, and error correction leakage. To stabilize performance under non stationary flight conditions, we introduce a lightweight physics informed learning module that combines physical priors with measured link statistics to classify valid pulses, reject corrupted data, and recommend decoding strategies. We outline a complete evaluation pipeline including UAV system architecture, turbulence driven QBER maps, decoy optimization, finite key scaling, and AI calibration metrics. Simulations indicate that under moderate turbulence and milliradian level pointing jitter, the proposed AI assisted method can improve the secret key rate by 10 percent to 30 percent while preserving composable security.

We derive the master equations governing axial and polar gravitational perturbations of a generic static and spherically symmetric black hole spacetime within the framework of the revised Deser--Woodard nonlocal gravity theory. We then apply our general formalism to a one-parameter family of black hole solutions recently obtained by the present authors, representing small first-order deviations from the Schwarzschild geometry. We provide well-motivated arguments that allow us to render the analysis analytically tractable. Our results provide the first complete perturbative characterization of nonlocal black holes and lay the groundwork for future investigations.

Qubits that experience predominantly erasure errors offer distinct advantages for fault-tolerant operation. Indeed, dual-rail encoded erasure qubits in superconducting cavities and transmons have demonstrated high-fidelity operations by converting physical-qubit relaxation into logical-qubit erasures, but this comes at the cost of increased hardware overhead and circuit complexity. Here, we address these limitations by realizing erasure conversion in a single fluxonium operated at zero flux, where the logical state is encoded in its 0-2 subspace. A single, carefully engineered resonator provides both mid-circuit erasure detection and end-of-line (EOL) logical measurement. Post-selection on non-erasure outcomes results in more than four-fold increase of the logical lifetime, from $193~μ$s to $869~μ$s. Finally, we characterize measurement-induced logical dephasing as a function of measurement power and frequency, and infer that each erasure check contributes a negligible error of $7.2\times 10^{-5}$. These results establish integer-fluxonium as a promising, resource-efficient platform for erasure-based error mitigation, without requiring additional hardware.

We introduce a Pauli-measurement-based algorithm to certify the Schmidt number of $n$-qubit pure states. Our protocol achieves an average-case sample complexity of $\caO(\mathrm{poly}(n)χ^2)$, a substantial improvement over the $\caO(2^n χ)$ worst-case bound. By utilizing local pseudorandom unitaries, we ensure the worst case can be transformed into the average-case with high probability. This work establishes a scalable approach to high-dimensional entanglement certification and introduces a proof framework for random Pauli sampling.

We study the nonlocal advantage of quantum imaginarity (NAQI) and distillable imaginarity of assistance (DIA), which treat imaginarity as a resource in distributed scenarios. For two qubits interacting with a lossy cavity, it is shown that both the NAQI and DIA can be well preserved for long times in the presence of large and symmetric detuning between the qubits and the cavity. Moreover, the off-resonant interaction generates a high degree of NAQI and DIA from the initial product states of two qubits having the same detunings and unequal couplings to the cavity. Based on the effective coupling of the qubits induced by the cavity mode, we explain the physical mechanism underlying the validity of this strategy. Our findings shed light on the role that off-resonant interactions have in the efficient control of imaginarity in distributed scenarios.

We clarify the structural role of trilinear kernels in multidimensional integrable hierarchies and in stationary axisymmetric gravity. The Yu--Toda--Fukuyama (YTF) trilinear equation of Ref.~\cite{YuTodaSasaFukuyama:1998hierarchy} is shown to represent not a particular evolution equation but a universal kernel that generates the entire $(3+1)$--dimensional hierarchy by selecting commuting flows. The frequently quoted trilinear equation of Ref.~\cite{YTSF1998} is identified as one such flow of this kernel. We further show that stationary axisymmetric gravity corresponds to a projective realization of the YTF kernel rather than to any single flow. Imposing $\GL(2)$ covariance and homogeneity on the kernel leads uniquely to a gravitational trilinear kernel $\mathcal{Y}(τ_0,τ_1)$, whose vanishing reproduces the Ernst equation. The Tomimatsu--Sato family \cite{Tomimatsu1972} and related bilinear solutions are shown to arise as degenerate submanifolds of this projected trilinear structure, in agreement with the multilinear analysis of Ref.~\cite{Fukuyama:2025TS}. These results establish a unified structural framework linking multidimensional trilinear integrability, stationary gravity, and bilinear solution sectors, and clarify why trilinear kernels are both necessary and sufficient for describing soliton dynamics with projective geometry.

Quantum architecture search (QAS) has emerged to automate the design of high-performance quantum circuits under specific tasks and hardware constraints. We propose a noise-aware quantum architecture search (NA-QAS) framework based on variational quantum circuit design. By incorporating a noise model into the training of parameterized quantum circuits (PQCs) , the proposed framework identifies the noise-robust architectures. We introduce a hybrid Hamiltonian $\varepsilon$ -greedy strategy to optimize evaluation costs and circumvent local optima. Furthermore, an enhanced variable-depth NSGA-II algorithm is employed to navigate the vast search space, enabling an automated trade-off between architectural expressibility and quantum hardware overhead. The effectiveness of the framework is validated through binary classification and iris multi-classification tasks under a noisy condition. Compared to existing approaches, our framework can search for quantum architectures with superior performance and greater resource efficiency under a noisy condition.

Fault-tolerant quantum computation allows quantum computations to be carried out while resisting unwanted noise. Several error correcting codes have been developed to achieve this task, but none alone are capable of universal quantum computation. This universality is highly desired and often achieved using additional techniques such as code concatenation, code switching, or magic state distillation, which can be costly and only work for specific codes. This work implements logical Clifford and T gates through novel ancilla-mediated protocols to construct a universal fault-tolerant quantum gate set. Unlike traditional techniques, our implementation is deterministic, does not consume ancilla registers, does not modify the underlying data codes or registers, and is generic over all stabilizer codes. Thus, any single code becomes capable of universal quantum computation by leveraging helper codes in ancilla registers and mid-circuit measurements. Furthermore, since these logical gates are stabilizer code-generic, these implementations enable communication between heterogeneous stabilizer codes. These features collectively open the door to countless possibilities for existing and undiscovered codes as well as their scalable, heterogeneous coexistence.

In this paper a central server Charlie has access to a quantum system C and measures it with a POVM $\{Λ_x\}$. Alice and Bob are only interested in the partial results $g_A(x)$ respectively $g_B(x)$. Alice, Bob, and Charlie share common randomness and Alice and Bob only need to faithfully simulate their measurements. The paper develops to achievable regions for the amount of communication needed to Alice and Bob.

In this paper, we study the two-time Leggett-Garg (LG) inequalities of a quantum optical model that appears in the Josephson-junction quantum bit (qubit) interacting with an external magnetic flux. This model is a natural extension of an exactly solvable model whose interaction between a qubit and single-mode photons is given by a product of the Pauli $z$ operator of the qubit and a linear combination of annihilation and creation operators of the photons. By contrast, a photon's part of the interaction of our model is given by the square of the linear combination. Because our model is not solvable, we approximately investigate its time evolution up to the second-order perturbation. Our numerical calculations show that violation of the LG inequality diminishes as the temperature increases. Moreover, it exhibits power laws of the temperature, whose exponents vary depending on the coupling constant of the interaction between the qubit and photons. The violation of the LG inequality decreases and becomes less sensitive to the temperature as the coupling constant of the interaction gets larger.

The dynamics of quantum systems are generally described by a family of quantum channels (linear, completely positive and trace preserving maps). In this note, we mainly study the range of all possible values of $\|\mathcal{E}\|_2^2+\|\widetilde{\mathcal{E}}\|_2^2$ for quantum channels $\mathcal{E}$ and give the equivalent characterizations for quantum channels that achieve these maximum and minimum values, respectively, where $\|\mathcal{E}\|_2$ is the Hilbert-Schmidt norm of $\mathcal{E}$ and $\widetilde{\mathcal{E}}$ is a complementary channel of $\mathcal{E}.$ Also, we get a concrete description of completely positive maps on infinite dimensional systems preserving pure states. Moreover, the equivalency of several matrix integrals over the unit sphere is demonstrated and some extensions of these matrix integrals are obtained.

Quantum trajectories are dynamical equations for quantum states conditioned on the results of a time-continuous measurement, such as a continuous-in-time current $\vec y_t$. Recently there has been renewed interest in dynamical maps for quantum trajectories with time-intervals of finite size $Δt$. Guilmin \emph{et al.} (unpublished) derived such a dynamical map for the (experimentally relevant) case where only the average current $I_t$ over each interval is available. Surprisingly, this binned data still generates a conditioned state $ρ_\text{\faFaucet}$ that is almost pure (for efficient measurements), with an impurity scaling as $(Δt)^{3}$. We show that, nevertheless, the typical distance of $ρ_\text{\faFaucet}$ from $\hatψ_{\text{F}; \vec y_t}$ -- the projector for the pure state conditioned on the full current -- is as large as $(Δt)^{3/2}$. We introduce another finite-interval dynamical map (``$Φ$-map''), which requires only one additional real statistic, $φ_t$, of the current in the interval, that gives a conditioned state $\hatψ_Φ$ which is only $(Δt)^{2}$-distant from $\hatψ_{\text{F}; \vec y_t}$. We numerically verify these scalings of the error (distance from the true states) for these two maps, as well as for the lowest-order (Itô) map and two other higher-order maps. Our results show that, for a generic system, if the statistic $φ_t$ can be extracted from experiment along with $I_t$, then the $Φ$-map gives a smaller error than any other.

We introduce a two-tooth bosonic quantum comb that captures the sequential interactions between a thermal absorber and a long-lived coherent probe. The comb provides a causal, multi-time description of coherence transport, tracking how the probe records both instantaneous fluctuations and their temporal correlations. Using a process-tensor formulation, we derive closed form expressions showing that interference between the two interaction windows generates a non-monotonic memory response that reflects a fundamental competition between the absorbers thermal population and its dynamical correlations. By sweeping the temporal separation between the interaction windows, the probe directly samples the absorbers population correlator, enabling bosonic noise spectroscopy that discriminates Markovian temperature noise from slow or spectrally structured fluctuations. The approach is readily compatible with circuit-QED platforms and offers a general method for probing fluctuating bosonic environments.

We construct a large number of exact solutions of three-dimensional gravity with heavy matter particles that generalize the construction of Antonini, Sasieta, and Swingle (AS${}^2$), argued to define CFT states dual to a spacetime with a closed baby universe cosmology. Our construction starts with an arbitrary heavy-particle closed universe cosmology of the type constructed in arXiv:2503.12227, and via a gluing procedure adds an arbitrary number of AdS tubes connecting the past and future conformal boundaries of the associated Euclidean wormhole solution. With our construction, it is straightforward to produce examples where the cosmology is approximately homogeneous and isotropic. We describe a necessary condition for the cosmological wormhole saddle to dominate the Euclidean path integral with the specified boundary conditions. We argue that the original AS${}^2$ construction usually does not meet this condition, and describe alternative saddles that are likely to dominate. We discuss various possibilities for how the cosmological saddle might be made to dominate in our generalized construction.

The quantum Fourier transform and quantum wavelet transform have been cornerstones of quantum information processing. However, for non-stationary signals and anomaly detection, the Hilbert transform can be a more powerful tool, yet no prior work has provided efficient quantum implementations for the discrete Hilbert transform. This letter presents a novel construction for a quantum Hilbert transform in polylogarithmic size and logarithmic depth for a signal of length $N$, exponentially fewer operations than classical algorithms for the same mapping. We generalize this algorithm to create any $d$-dimensional Hilbert transform in depth $O(d\log N)$. Simulations demonstrate effectiveness for tasks such as power systems control and image processing, with exact agreement with classical results.

We reconsider the quantum energetics and quantum thermodynamics of the charging process of a simple, two-component quantum battery model made up of a charger qubit and a single--cell battery qubit. We allow for the initial quantum state of the charger to lie anywhere on the surface of the Bloch sphere, and find the generalized analytical expressions describing the stored energy, ergotropy and capacity of the battery, all of which depend upon the initial Bloch sphere polar angle in a manner evocative of the quantum area theorem. The origin of the ergotropy produced, as well as the genesis of the battery capacity, can be readily traced back to the quantum coherences and population inversions generated (and the balance between these two mechanisms is contingent upon the starting Bloch polar angle). Importantly, the ergotropic charging power and its associated optimal charging time display notable deviations from standard results which disregard thermodynamic considerations. Our theoretical groundwork may be useful for guiding forthcoming experiments in quantum energy science based upon coupled two-level systems.

The spatial distribution of a magnetic field can be determined with micrometer resolution using widefield nitrogen vacancy (NV) center magnetic imaging. Nevertheless, reconstructing the magnetic field from the raw data can be challenging due to the degeneracy of the four possible NV axes and the tremendous amount of data. While a qualitative approach is sufficient for most analyses, a quantitative analysis offers deeper insight into the physical system. Here, we apply NV widefield magnetic imaging to a HgBa$_{2}$Ca$_{2}$Cu$_{3}$O$_{8+δ}$ (Hg-1223) superconducting microcrystal at a pressure of 4 GPa. We fit the results with solutions from the Hamiltonian describing the NV center ground state and take into account the relative intensities of the resonances to determine the local magnetic field magnitude and angle. Thus, we reconstruct the temperature-dependent expulsion of the magnetic field due to the Meissner effect around the superconductor. By comparing the resulting parameters to Brandt's model, which describes the magnetic behavior of a type-II superconductor, we extract the critical current density $j_c$. Overall, this work showcases the first widefield quantitative reconstruction of the Meissner effect under pressure and an optical method to study critical current density. Thus, it provides new insights into the application of NV magnetometry to superconductivity research at high pressures.

Progress towards quantum utility in chemistry requires not only algorithmic advances, but also the identification of chemically meaningful problems whose electronic structure fundamentally challenges classical methods. Here, we introduce a curated hierarchy of chemically decisive benchmark systems designed to probe distinct regimes of electronic correlation relevant to molecular, bioinorganic, and heavy-element chemistry. Moving beyond minimal toy models, our benchmark set spans multireference bond breaking (N$_2$), high-spin transition-metal chemistry (FeS), biologically relevant iron-sulfur clusters ([2Fe-2S]), and actinide-actinide bonding (U$_2$), which exhibits extreme sensitivity to active-space choice, relativistic treatment, and correlation hierarchy even within advanced multireference frameworks. As a concrete realization, we benchmark a recently developed automated and adaptive quantum algorithm based on generator-coordinate-inspired subspace expansion,ADAPT-GCIM, using a black-box workflow that integrates entropy-based active-space selection via the ActiveSpaceFinder tool. Across this chemically diverse problem set, ADAPT-GCIM achieves high accuracy in challenging correlation regimes. Equally importantly, these benchmarks expose general failure modes and design constraints-independent of any specific algorithm-highlighting the necessity of problem-aware and correlation-specific strategies for treating strongly correlated chemistry on quantum computers. To support systematic benchmarking and reproducible comparisons, the Hamiltonians for all systems studied are made openly available.

The Lorentzian-Euclidean black hole has been recently introduced as a geodesically complete spacetime featuring a signature shift at the event horizon where causal geodesics are precluded from reaching the central $r=0$ singularity. In this paper, we investigate the shadows produced by this geometry to identify deviations from the standard Schwarzschild solution. Our analysis reveals an excess intensity in the inner shadow region that points to a potential observational signature of the novel behavior of light rays propagating near the event horizon. This excess could be a probe for horizon-scale modifications of black hole geometries. Furthermore, although the horizon surface of the Lorentzian-Euclidean black hole continuously accumulates photons and energy, we show that its backreaction response differs from that of stable light rings found in various exotic compact objects.

We present a systematic study of Tensor Network (TN) models $\unicode{x2013}$ Matrix Product States (MPS) and Tree Tensor Networks (TTN) $\unicode{x2013}$ for real-time jet tagging in high-energy physics, with a focus on low-latency deployment on Field Programmable Gate Arrays (FPGAs). Motivated by the strict requirements of the HL-LHC Level-1 trigger system, we explore TNs as compact and interpretable alternatives to deep neural networks. Using low-level jet constituent features, our models achieve competitive performance compared to state-of-the-art deep learning classifiers. We investigate post-training quantization to enable hardware-efficient implementations without degrading classification performance or latency. The best-performing models are synthesized to estimate FPGA resource usage, latency, and memory occupancy, demonstrating sub-microsecond latency and supporting the feasibility of online deployment in real-time trigger systems. Overall, this study highlights the potential of TN-based models for fast and resource-efficient inference in low-latency environments.

Detecting gravitational waves above 100 kHz would constitute a major discovery, as any observable signal would have to arise from new physics within the late universe. Although many technologies have been identified to explore this high-frequency regime, the known landscape of promising sources remains extremely sparse. In this work, we aim to rectify this issue by providing model-independent arguments that highlight the most interesting parts of theory space, while remaining agnostic of the specific signal mechanism. For example, energy-conservation implies that gravitational waves detectable by future experiments well above a MHz would most likely have to originate from within the Solar System. Based on these arguments, we also constrain the physical properties of such sources.

Biased-noise qubits, in which one type of error (e.g. $X$- and $Y$-type errors) is significantly suppressed relative to the other (e.g. $Z$-type errors), can significantly reduce the overhead of quantum error correction. Codes such as the rectangular surface code or XZZX code substantially reduce the qubit overhead under biased noise, but they still face challenges. The rectangular surface code suffers from a relatively low threshold, while the XZZX code requires twice as many physical qubits to maintain the same code distance as the surface code. In this work, we introduce a 2D local code construction that outperforms these codes for noise biases $η\ge 7\times10^{4}$, reducing the qubit overhead by over 50% at $p_Z=10^{-3}$ and $η= 2 \times 10^6$ to achieve a logical error rate of $10^{-12}$. Our construction relies on the concatenation of two classical codes. The inner codes are repetition phase-flip codes while the outer codes are high-rate bit-flip codes enabled by their implementation at the logical level, which circumvents device connectivity constraints. These results indicate that under sufficiently biased noise, it is advantageous to address phase-flip and bit-flip errors at different layers of the coding scheme. The inner code should prioritize a high threshold for phase-flip errors, while the bit-flip outer code should optimize for encoding rate efficiency. In the strong biased-noise regime, high-rate outer codes keep the overhead for correcting residual bit-flip errors comparable to that of the repetition code itself, meaningfully lower than that required by earlier approaches.

We introduce a quantum Maxwell erasure decoder for CSS quantum low-density parity-check (qLDPC) codes that extends peeling with bounded guessing. Guesses are tracked symbolically and can be eliminated by restrictive checks, giving a tunable tradeoff between complexity and performance via a guessing budget: an unconstrained budget recovers Maximum-Likelihood (ML) performance, while a constant budget yields linear-time decoding and approximates ML. We provide theoretical guarantees on asymptotic performance and demonstrate strong performance on bivariate bicycle and quantum Tanner codes.

We investigate late-time cosmic acceleration in $f(R,L_m)$ gravity driven by nonlinear matter contributions, focusing on the class $f(R,L_m)=R/2+c_1 L_m+c_n L_m^{n}+c_0$ with the explicit choice $L_m=ρ_m$ and an uncoupled radiation sector. We analyze two realizations: (i) Case A: $f(R,L_m)=R/2+βρ_m^{n}+γ$, where $γ$ acts as a vacuum term, and (ii) Case B: $f(R,L_m)=R/2+βρ_m+γρ_m^{n}$, where the nonlinear sector can mimic dark energy without an explicit cosmological constant. For each case, we construct a bounded autonomous system, classify all critical points and their stability, and compute cosmographic diagnostics. The phase-space analysis shows that Case A reproduces the standard radiation$\to$matter$\to$de~Sitter sequence only for $n\gtrsim 4/5$, with acceleration essentially enforced by the vacuum term. In contrast, Case~B admits a qualitatively distinct and phenomenologically appealing branch: for $0<n<1/2$ the system possesses a physical \emph{scaling} de~Sitter future attractor inside the bounded simplex, yielding radiation$\to$matter$\to$acceleration with $q=-1$ and $ω_{\rm eff}=-1$ and without introducing $c_0$. We confront both models with background data (CC, Union3, DESI BAO, plus a BBN prior on $Ω_b h^2$) using nested sampling and perform model comparison via Bayesian evidence and AIC/BIC. The full data combination constrains $n=1.08\pm0.05$ in Case A and $n=0.05\pm0.10$ in Case B (68\% CL), the latter lying within the accelerating window while remaining statistically consistent with $Λ$CDM kinematics at the background level. We also record minimal consistency conditions for stability (tensor no-ghost and luminal propagation) and motivate a dedicated perturbation-level analysis as the next step to test growth and lensing observables.

Dicke states serve as a critical resource in quantum metrology, communication, and computation. However, unitary preparation of Dicke states is limited to logarithmic depth in standard circuit models and existing constant-depth protocols require measurement and feed-forward. In this work, we present the first unitary, constant-depth protocols for exact Dicke state preparation. We overcome the logarithmic-depth barrier by moving beyond the standard circuit model and leveraging global interactions (native to architectures such as neutral atoms and trapped ions). Specifically, utilizing unbounded CZ gates (i.e. within the QAC$^0$ circuit class), we offer circuits for exact computation of constant-weight Dicke states, using polynomial ancillae, and approximation of weight-1 Dicke states (i.e. $W$ states), using only constant ancillae. Granted additional access to the quantum FAN-OUT operation (i.e. upgrading to the QAC$_f^0$ circuit class), we also achieve exact preparation of arbitrary-weight Dicke states, with polynomial ancillae. These protocols distinguish the constant-depth capabilities of quantum architectures based on connectivity and offer a novel path toward resolving a long-standing quantum complexity conjecture.

Coupling mechanical motion to an optical resonator enables displacement measurements approaching the standard quantum limit (SQL). However, increasing the optomechanical coupling strength will inevitably lead to probing of the nonlinear response of the optical resonator. Thermal intermodulation noise (TIN) arising from the nonlinear mixing of thermomechanical motion can further increase the imprecision well above the SQL and has hitherto been canceled up to second order of nonlinearity via operation at the "magic detuning". In this work, we record the output of a membrane-in-the-middle microcavity system operating at room temperature and achieving high cooperativity, $C>n_\text{th}$, and apply a nonlinear transform that removes all orders of TIN, improving the mechanical signal-to-noise ratio by nearly 10 dB. Our results can be applied to experiments affected by third-order TIN, which we expect to be the dominating intrinsic source of noise in high-cooperativity room-temperature cavity optomechanical systems.

Consider quantum channels with input dimension $d_1$, output dimension $d_2$ and Kraus rank at most $r$. Any such channel must satisfy the constraint $rd_2\geq d_1$, and the parameter regime $rd_2=d_1$ is called the boundary regime. In this paper, we show an optimal query lower bound $Ω(rd_1d_2/\varepsilon^2)$ for quantum channel tomography to within diamond norm error $\varepsilon$ in the away-from-boundary regime $rd_2\geq 2d_1$, matching the existing upper bound $O(rd_1d_2/\varepsilon^2)$. In particular, this lower bound fully settles the query complexity for the commonly studied case of equal input and output dimensions $d_1=d_2=d$ with $r\geq 2$, in sharp contrast to the unitary case $r=1$ where Heisenberg scaling $Θ(d^2/\varepsilon)$ is achievable.

This work investigates the use of quantum resources in distributed storage systems. Consider an $(n,k,d)$ distributed storage system in which a file is stored across $n$ nodes such that any $k$ nodes suffice to reconstruct the file. When a node fails, any $d$ helper nodes transmit information to a newcomer to rebuild the system. In contrast to the classical repair, where helper nodes transmit classical bits, we allow them to send classical information over quantum channels to the newcomer. The newcomer then generates its storage by performing appropriate measurements on the received quantum states. In this setting, we fully characterize the fundamental tradeoff between storage and repair bandwidth (total communication cost). Compared to classical systems, the optimal storage--bandwidth tradeoff can be significantly improved with the enhancement of quantum entanglement shared only among the surviving nodes, particularly at the minimum-storage regenerating point. Remarkably, we show that when $d \geq 2k-2$, there exists an operating point at which \textit{both storage and repair bandwidth are simultaneously minimized}. This phenomenon breaks the tradeoff in the classical setting and reveals a fundamentally new regime enabled by quantum communication.

In optimal quantum-mechanical evolutions, motion can take place along paths of minimal length within an optimal time frame. Alternatively, optimal evolutions may occur along established paths without any waste of energy resources and achieving 100% speed efficiency. Unfortunately, realistic physical scenarios often lead to less-than-ideal evolutions that demonstrate suboptimal efficiency, nonzero curvature, and a high level of complexity. In this paper, we provide an exact analytical expression for the curvature of a quantum evolution pertaining to a two-level quantum system subjected to various time-dependent magnetic fields. Specifically, we examine the dynamics produced by a two-parameter nonstationary Hermitian Hamiltonian with unit speed efficiency. To enhance our understanding of the physical implications of the curvature coefficient, we analyze the curvature behavior in relation to geodesic efficiency, speed efficiency, and the complexity of the quantum evolution (as described by the ratio of the difference between accessible and accessed Bloch-sphere volumes for the evolution from initial to final state to the accessible volume for the given quantum evolution). Our findings indicate that, generally, efficient quantum evolutions exhibit lower complexity compared to inefficient ones. However, we also note that complexity transcends mere length. In fact, longer paths that are sufficiently curved can demonstrate a complexity that is less than that of shorter paths with a lower curvature coefficient.

We examine the pertinent geometric characteristics of entanglement that arise from stationary Hamiltonian evolutions transitioning from separable to maximally entangled two-qubit quantum states. From a geometric perspective, each evolution is characterized by means of geodesic efficiency, speed efficiency, and curvature coefficient. Conversely, from the standpoint of entanglement, these evolutions are quantified using various metrics, such as concurrence, entanglement power, and entangling capability. Overall, our findings indicate that time-optimal evolution trajectories are marked by high geodesic efficiency, with no energy resource wastage, no curvature (i.e., zero bending), and an average path entanglement that is less than that observed in time-suboptimal evolutions. Additionally, when analyzing separable-to-maximally entangled evolutions between nonorthogonal states, time-optimal evolutions demonstrate a greater short-time degree of nonlocality compared to time-suboptimal evolutions between the same initial and final states. Interestingly, the reverse is generally true for separable-to-maximally entangled evolutions involving orthogonal states. Our investigation suggests that this phenomenon arises because suboptimal trajectories between orthogonal states are characterized by longer path lengths with smaller curvature, which are traversed with a higher energy resource wastage compared to suboptimal trajectories between nonorthogonal states. Consequently, a higher initial degree of nonlocality in the unitary time propagators appears to be essential for achieving the maximally entangled state from a separable state. Furthermore, when assessing optimal and suboptimal evolutions...

The Landau--Zener (LZ) model describes a two-level quantum system that undergoes an avoided crossing. In the adiabatic limit, the transition probability vanishes. An auxiliary control field $H_\text{CD}$ can be reverse-engineered so that the full Hamiltonian $H_0 + H_\text{CD}$ reproduces adiabaticity for all parameter values. Our aim is to construct a single control field $H_1$ that drives an ensemble of LZ-type Hamiltonians with a distribution of energy gaps. $H_1$ works best statistically, minimizing the average transition probability. We restrict our attention to a special class of $H_1$ controls, motivated by $H_\text{CD}$. We found a systematic trade-off between instantaneous adiabaticity and the final transition probability. Certain limiting cases with a linear sweep can be treated analytically; one of them being the LZ system with Dirac $δ(t)$ function. Comprehensive and systematic numerical simulations support and extend the analytic results.

It is known that the continuous-time variant of Grover's search algorithm is characterized by quantum search frameworks that are governed by stationary Hamiltonians, which result in search trajectories confined to the two-dimensional subspace of the complete Hilbert space formed by the source and target states. Specifically, the search approach is ineffective when the source and target states are orthogonal. In this paper, we employ normalization, orthogonality, and energy limitations to demonstrate that it is unfeasible to breach time-optimality between orthogonal states with constant Hamiltonians when the evolution is limited to the two-dimensional space spanned by the initial and final states. Deviations from time-optimality for unitary evolutions between orthogonal states can only occur with time-dependent Hamiltonian evolutions or, alternatively, with constant Hamiltonian evolutions in higher-dimensional subspaces of the entire Hilbert space. Ultimately, we employ our quantitative analysis to provide meaningful insights regarding the relationship between time-optimal evolutions and analog quantum search methods. We determine that the challenge of transitioning between orthogonal states with a constant Hamiltonian in a sub-optimal time is closely linked to the shortcomings of analog quantum search when the source and target states are orthogonal and not interconnected by the search Hamiltonian. In both scenarios, the fundamental cause of the failure lies in the existence of an inherent symmetry within the system.

The entanglement distance of evolutionary quantum states of a two-spin system with the XXZ model has been studied. The analysis has been conducted both analytically and using quantum computing. An analytical dependence of the entanglement distance on the values of the model coupling constants and the parameters of the initial states has been obtained. The speed of evolution of a two-spin system has been investigated. The analysis has been performed analytically and using quantum computing. An explicit dependence of the speed of evolution on the coupling constants and on the parameters of the initial state has been obtained. The results of quantum computations are in good agreement with the theoretical predictions.

We investigate late-time cosmology in the context of modified $f(Q,L_m)$ gravity, considering a non-linear model$ f(Q,L_m) = αQ + βL_m^n + λ$ where, $α$, $β$, $λ$, and $n$ are some free parameters. The modified Friedmann equations are derived for a barotropic cosmic fluid, and an analytical solution for the Hubble parameter $H(z)$ is obtained. Using the latest DESI DR2 BAO data, previous BAO compilations (P-BAO), and cosmic chronometer (CC) datasets, we constrain the model parameters through a Markov Chain Monte Carlo analysis. Our results show that the model successfully describes the observed late-time cosmic acceleration with slightly tighter constraints from the inclusion of DESI dataset. The present-day Hubble constant is determined as $H_0 \simeq 69.5\ \mathrm{km\ s^{-1}\ Mpc^{-1}}$, while the deceleration parameter confirms accelerated expansion with $q_0 \simeq -0.57$. The transition redshift, where the universe switches from deceleration to acceleration, occurs in the range $z_{\rm tr} \sim 0.56 - 0.77$. Similarly, a smooth and physically consistent transition from a matter-dominated decelerated period at high redshifts to an accelerated phase at late times is revealed by the evolution of $ω_{eff}(z)$. While statefinder diagnostic shows the model favours a Chaplygin gas like nature for DESI and DESI+CC, whereas the model favours as quintessence dominated evolution for P-BAO+CC in the late time regime. Conclusively, all these results along with the study of the energy conditions and stability analysis showcases the given $f(Q,L_m)$ model offers a viable alternative to GR-based cosmology

We develop quantitative surgery, which extends the classical constructions of Gromov--Lawson and Lawson--Michelsohn. As an application, we prove a conjecture of Gromov on the total mean curvature of fill-ins.

Quantum embedding methods, such as dynamical mean-field theory (DMFT), provide a powerful framework for investigating strongly correlated materials. A central computational bottleneck in DMFT is in solving the Anderson impurity model (AIM), whose exact solution is classically intractable for large bath sizes. In this work, we develop and benchmark a quantum-classical hybrid solver tailored for DMFT applications, using the variational quantum eigensolver (VQE) to prepare the ground state of the AIM with shallow quantum circuits. The solver uses a unified ansatz framework to prepare the particle and hole excitations of the ground-state from parameter-shifted circuits, enabling the reconstruction of the impurity Green's function through a continued-fraction expansion. We evaluate the performance of this approach across a few bath sizes and interaction strengths under noisy, shot-limited conditions. We compare three optimization routines (COBYLA, Adam, and L-BFGS-B) in terms of convergence and fidelity, assess the benefits of estimating a quantum-computed moment (QCM) correction to the variational energies, and benchmark the approach by comparing the reconstructed density of states (DOS) against that obtained using a classical pipeline. Our results demonstrate the feasibility of Green's function reconstruction on near-term devices and establish practical benchmarks for quantum impurity solvers embedded within self-consistent DMFT loops.

We demonstrate a Doppler thermometer based on direct optical frequency comb spectroscopy of an $^{85}$Rb vapor with a chirped electro-optic frequency comb (EOFC). The direct EOFC Doppler thermometer is accurate to within its approximately 1 K statistical uncertainty. We experimentally compare direct EOFC spectroscopy with conventional Doppler spectroscopy using a single-frequency, step-scanned laser probe. Our results show that direct EOFC spectroscopy mitigates transit-induced optical pumping distortion of the atomic lineshape, which is the dominant systematic temperature shift in alkali atom Doppler thermometry. Optical Bloch equation simulations of conventional and direct EOFC Doppler spectroscopy confirm that EOFC spectroscopy can use higher optical power to reduce statistical noise without optical pumping distortion. Our results indicate that EOFC Doppler thermometry is a promising approach to realizing a primary thermometer with size and measurement rate sufficient for applications including pharmaceutical manufacturing and nuclear waste monitoring.

The scalable and deterministic preparation of large Fock-number states represents a long-standing frontier in quantum science, with direct implications for quantum metrology, communication, and simulation. Despite significant progress in small-scale implementations, extending such state generation to large excitation numbers while maintaining high fidelity remains a formidable challenge. Here, we present a scalable protocol for generating large Fock states with fidelities exceeding 0.9 up to photon numbers on the order of 100, achieved using only native control operations and, when desired, further enhanced by an optional post-selection step. Our method employs a hybrid Genetic-Adam optimization framework that combines the global search efficiency of genetic algorithms with the adaptive convergence of Adam to optimize multi-pulse control sequences comprising Jaynes-Cummings interactions and displacement operations, both of which are native to leading experimental platforms. The resulting control protocols achieve high fidelities with shallow circuit depths and strong robustness against parameter variations. These results establish an efficient and scalable pathway toward high-fidelity non-classical state generation for precision metrology and fault-tolerant quantum technologies.

We study the computation of the $α$-Rényi capacity of a classical-quantum (c-q) channel for $α\in(0,1)$. We propose an exponentiated-gradient (mirror descent) iteration that generalizes the Blahut-Arimoto algorithm. Our analysis establishes relative smoothness with respect to the entropy geometry, guaranteeing a global sublinear convergence of the objective values. Furthermore, under a natural tangent-space nondegeneracy condition (and a mild spectral lower bound in one regime), we prove local linear (geometric) convergence in Kullback-Leibler divergence on a truncated probability simplex, with an explicit contraction factor once the local curvature constants are bounded.

We present an analytic framework demonstrating that a spinning black hole endowed with a net magnetic charge exhibits a dramatically amplified superradiant instability against charged scalar fields, enhanced by several orders of magnitude compared with the neutral Kerr case. The amplification arises from a monopole induced reduction of the centrifugal barrier. This shift deepens the gravitational bound-state potential well and produces a parametrically larger instability growth rate. This resulting rapid growth yields a macroscopic boson cloud that acts as a coherent source of near monochromatic continuous gravitational waves (GWs). We find an enhanced GW power. Monopole harmonic selection rules restrict the emission from the north (south) clouds corresponding to opposite helicities. Their superposition generates an (approximately) circularly polarized continuous GWs at a fixed sky location within even parity general relativity, distinct from the generic elliptical polarization of the Kerr case. In light of these new findings, we propose a potential smoking-gun search strategy for magnetic monopole and ultralight boson: the rapid post-merger follow-up GW signals from binary-black-hole merger remnants through ground-based and space-based GW experiments. In contrast to the Kerr case, where the signal turn-on can be delayed to decades-centuries, a magnetic remnant can form a cloud and emit a stronger, circularly polarized continuous GWs within weeks to months. Taking the magnetic supermassive remnants as an example, we demonstrate that the rapid follow-up GW signal in the mHz band appears just in few weeks after binary black hole mergers. Moreover, future polarization (ellipticity) measurements can distinguish the magnetic scenario from Kerr while providing a parity-even mechanism for circularly polarized GWs in general relativity.

The remnants of binary neutron star mergers are expected to be massive, rapidly rotating stars whose oscillations produce gravitational waves in the kilohertz band. The degree of differential rotation and the rotation profiles strongly influence their structure, stability and oscillation spectrum, and must therefore be taken into account when modeling their dynamics. We extend the pseudospectral code ROXAS (Relativistic Oscillations of non-aXisymmetric neutron stArS) to enable the dynamical evolution of oscillating, differentially rotating neutron stars. Using the updated code, we aim to study the star's oscillation frequencies. We extend the previous formalism, based on primitive variables and the conformal flatness approximation, to differential rotation. Within this framework, we run a series of axisymmetric and non-axisymmetric simulations of perturbed, differentially rotating neutron stars with different rotation rates, and extract their oscillation frequencies. Axisymmetric modes, as well as those under the Cowling approximation, show excellent agreement with published results. We show that the secondary fundamental mode in the Cowling approximation is an artifact that does not appear in dynamical spacetimes. In addition, we provide, for the first time, frequency values for non-axisymmetric modes in differentially rotating configurations evolved in conformal flatness. This extension broadens the range of physical scenarios that can be studied with ROXAS, and represents a step toward more realistic modeling of post-merger remnants and their gravitational-wave emission.

We show how our Universe can emerge from a symmetry breaking of a multicomponent $W_3$ algebra, where the components in addition form a Jordan algebra. We discuss how symmetry breaking related to the Jordan algebras $H_3(C)$ and $H_3(O)$ over the complex and octonion numbers can lead to an extended four-dimensional spacetime, where the expansion of the Universe is governed by a modified Friedmann equation. We finally discuss how this modified Friedmann equation might explain a number of puzzling cosmological observations.

Neutral atom quantum processors have emerged as a promising platform for scalable quantum information processing, offering high-fidelity operations and exceptional qubit scalability. A key challenge in realizing practical applications is efficiently extracting readout outcomes while maintaining high system throughput, i.e., the rate of quantum task executions. In this work, we develop a theoretical framework to quantify the trade-off between readout fidelity and atomic retention. Moreover, we introduce a metric of quantum circuit iteration rate (qCIR) and employ normalized quantum Fisher information to characterize system overall performance. Further, by carefully balancing fidelity and retention, we demonstrate a readout strategy for optimizing information acquisition efficiency. Considering the experimentally feasible parameters for 87Rb atoms, we demonstrate that qCIRs of 197.2Hz and 154.5Hz are achievable using single photon detectors and cameras, respectively. These results provide practical guidance for constructing scalable and high-throughput neutral atom quantum processors for applications in sensing, simulation, and near-term algorithm implementation.

Variational Quantum Algorithms (VQAs) are critically threatened by the Barren Plateau (BP) phenomenon. In this work, we introduce the H-EFT Variational Ansatz (H-EFT-VA), an architecture inspired by Effective Field Theory (EFT). By enforcing a hierarchical "UV-cutoff" on initialization, we theoretically restrict the circuit's state exploration, preventing the formation of approximate unitary 2-designs. We provide a rigorous proof that this localization guarantees an inverse-polynomial lower bound on the gradient variance: $Var[\partial θ] \in Ω(1/poly(N))$. Crucially, unlike approaches that avoid BPs by limiting entanglement, we demonstrate that H-EFT-VA maintains volume-law entanglement and near-Haar purity, ensuring sufficient expressibility for complex quantum states. Extensive benchmarking across 16 experiments -- including Transverse Field Ising and Heisenberg XXZ models -- confirms a 109x improvement in energy convergence and a 10.7x increase in ground-state fidelity over standard Hardware-Efficient Ansatze (HEA), with a statistical significance of $p < 10^{-88}$.

Quantum Amplitude Amplification (QAA), the generalization of Grover's algorithm, is capable of yielding optimal solutions to combinatorial optimization problems with high probabilities. In this work we extend the conventional 2-dimensional representation of Grover's (orthogonal collective states) to oracles which encode cost functions such as QUBO, and show that linear cost functions are a special case whereby an exact formula exists for determining optimal oracle parameter settings. Using simulations of problem sizes up to 40 qubits we demonstrate QAA's algorithmic performance across all possible solutions, with an emphasis on the closeness in Grover-like performance for solutions near the global optimum. We conclude with experimental demonstrations of generalized QAA on both IBMQ (superconducting) and IonQ (trapped ion) qubits, showing that the observed probabilities of each basis state match our equations as a function of varying the free parameters in the oracle and diffusion operators.

In this article, we apply the Simpson-Visser (SV) regularization scheme to Anti-de Sitter (AdS) charged black holes and investigate the resulting spacetime geometry in detail, with emphasis on both geodesic structure and thermodynamic behavior. In particular, we analyze the motion of massless particle, focusing on key features such as the photon sphere, black hole shadow, photon trajectory and the dynamics of charged particles, including the characteristics of the circular and type of orbits. Furthermore, we compare the theoretical predictions of the charged SV-AdS black hole with recent observations reported by the Event Horizon telescope (EHT) for M87* and Sgr~A*. Beyond the geodesic analysis, we explore the thermodynamics of the regularized charged SV-AdS black hole by deriving essential quantities such as the Hawking temperature, Gibbs free energy, and specific heat capacity. Through a systematic examination of these thermodynamic variables, we demonstrate how the regularization parameter inherent in the SV regularization influences particle dynamics, stability conditions, and the overall thermal properties of the modified black hole solution. This comprehensive study highlights the interplay between regularization effects and the physical observables associated with charged AdS black holes.

Fast and high fidelity shuttling of spin qubits has been demonstrated in semiconductor quantum dot devices. Several architectures based on shuttling have been proposed; it has been suggested that singlet-triplet (dual-spin) qubits could be optimal for the highest shuttling fidelities. Here we present a fault-tolerant framework for quantum error correction based on such dual-spin qubits, establishing them as a natural realisation of erasure qubits within semiconductor architectures. We introduce a hardware-efficient leakage-detection protocol that automatically projects leaked qubits back onto the computational subspace, without the need for measurement feedback or increased classical control overheads. When combined with the XZZX surface code and leakage-aware decoding, we demonstrate a twofold increase in the error correction threshold and achieve orders-of-magnitude reductions in logical error rates. This establishes the singlet-triplet encoding as a practical route toward high-fidelity shuttling and erasure-based, fault-tolerant quantum computation in semiconductor devices.

We propose a generalization of the Filoche--Mayboroda localization landscape that extends the theory well beyond the static, elliptic and Hermitian settings while preserving its geometric interpretability. Using the positive operator $H^\dagger H$, we obtain a landscape that predicts localization across non-Hermitian, Floquet, and topological systems without computing eigenstates. Singular-value collapse reveals spectral instabilities and skin effects, the Sambe formulation captures coherent destruction of tunneling, and topological zero modes emerge directly from the landscape. Applications to Hatano--Nelson chains, driven two-level systems, and driven Aubry--André--Harper models confirm quantitative accuracy, establishing a unified predictor for localization in equilibrium and driven quantum matter.

We present a unified scheme for generating high-fidelity entangling gates in superconducting platforms by continuous dynamical decoupling (CDD) combined with variational minimal-energy optimal control. During the CDD stage, we suppress residual couplings, calibration drifting, and quasistatic noise, resulting in a stable effective Hamiltonian that preserves the designed ZZ interaction intended for producing tunable couplers. In this stable $\mathrm{SU}(4)$ manifold, we calculate smooth low-energy single-quibt control functions using a variational geodesic optimization process that directly minimizes gate infidelity. We illustrate the methodology by applying it to CZ, CX, and generic engangling gates, achieving virtually unit fidelity and robustness under restricted single-qubit action, with experimentally realistic control fields. These results establish CDD-enhanced variational geometric optimal control as a practical and noise-resilient scheme for designing superconducting entangling gates.

We introduce SpinPulse, an open-source python package for simulating spin qubit-based quantum computers at the pulse-level. SpinPulse models the specific physics of spin qubits, particularly through the inclusion of classical non-Markovian noise. This enables realistic simulations of native gates and quantum circuits, in order to support hardware development. In SpinPulse, a quantum circuit is first transpiled into the native gate set of our model and then converted to a pulse sequence. This pulse sequence is subsequently integrated numerically in the presence of a simulated noisy experimental environment. We showcase workflows including transpilation, pulse-level compilation, hardware benchmarking, quantum error mitigation, and large-scale simulations via integration with the tensor-network library quimb. We expect SpinPulse to be a valuable open-source tool for the quantum computing community, fostering efforts to devise high-fidelity quantum circuits and improved strategies for quantum error mitigation and correction.

The thermodynamic uncertainty relation (TUR) imposes a fundamental constraint between current fluctuations and entropy production, providing a refined formulation of the second law for micro- and nanoscale systems. Quantum violations of the classical TUR reveal genuinely quantum thermodynamic effects, which are essential for improving performance and enabling optimization in quantum technologies. In this work, we analyze the TUR in a class of paradigmatic quantum thermal-machine models whose operation is enabled by coherent coupling between two energy levels forming a virtual qubit. Steady-state coherences are confined to this virtual-qubit subspace, while in the absence of coherent coupling the system satisfies detailed balance with the thermal reservoirs and supports no steady-state heat currents. We show that the steady-state currents and entropy production can be fully reproduced by an effective classical Markov process, whereas current fluctuations acquire an additional purely quantum correction originating from coherence. As a result, the thermodynamic uncertainty naturally decomposes into a classical (diagonal) contribution and a coherent contribution. The latter becomes negative under resonant conditions and reaches its minimum at the coupling strength that maximizes steady-state coherence. We further identify the optimization conditions and the criteria for surpassing the classical TUR bound in the vicinity of the reversible limit.

With the growing number of gravitational-wave detections, particularly from binary black hole mergers, there is increasing anticipation that an astrophysical background, formed by an ensemble of faint, high-redshift events, will be observed in the near future by the ground-based detector network. This background is anticipated to exhibit non-Gaussian statistical properties. To develop a robust method for detecting such a non-Gaussian gravitational-wave background, we revisit optimal detection strategies based on the Gaussian-mixture likelihood model. In this work, we demonstrate that properly accounting for the detector antenna pattern is essential. Current approaches typically rely on the overlap reduction function averaged over the sky. Through simulations, we show that using such an averaged response introduces significant biases in parameter estimation. In addition, we propose a computationally feasible method that incorporates second-order corrections as an approximation of the full integral over the source distribution. Our results indicate that this approach effectively eliminates these biases. We also show that our method remains robust even when considering anisotropic backgrounds.

Classical and quantum physics represent two distinct theories; however, quantum physics is regarded as the more fundamental of the two. It is posited that classical mechanics should arise from quantum mechanics under certain limiting conditions. Nevertheless, this remains a challenging objective. In this work, we explore the potential for unifying the dynamics of classical and quantum physics. This discussion does not suggest that classical behavior emerges from quantum mechanics; rather, it demonstrates the exact equivalence between the dynamics of quantum observables and their classical counterparts. It is shown that the Heisenberg equations of motion can be cast in a form that is identical to Newton's equations of motion, with $\hbar$ being absent from the formulation. This implies that both quantum and classical dynamics are governed by the same equations, with the Heisenberg operators substituting the classical observables.

Experiments on Bose-Einstein condensates at finite temperature typically extract the system parameters, such as temperature, atom number, and condensed fraction from time-of-flight images taken after a free expansion time. This paper systematically examines the effect of repulsive interactions between the condensed and thermal atoms in partially condensed clouds on the expansion profile of the thermal cloud. An analytical expression for the expansion can be obtained only if the interactions between the Bose-Einstein condensate and thermal atoms are neglected, resulting in a Bose-enhanced distribution for the thermal component. Here, the deformation of the cloud due to interactions and the effects on estimated parameters are investigated by simulating the expansion using a ballistic approximation. By fitting the simulated expansion profiles with a Bose-enhanced distribution, the errors of using such a fit are estimated, and the results are explained phenomenologically. The simulation was also used as a fitting function for experimental data, showing better agreement of the extracted condensed fraction with the semi-ideal model than results from a Bose-enhanced fit.

The evolution of an isolated quantum system inevitably exhibits recurrence: the state returns to the vicinity of its initial condition after finite time. Despite its fundamental nature, a rigorous quantitative understanding of recurrence has been lacking. We establish upper bounds on the recurrence time, $t_{\mathrm{rec}} \lesssim t_{\mathrm{exit}}(ε)(1/ε)^d$, where $d$ is the Hilbert-space dimension, $ε$ the neighborhood size, and $t_{\mathrm{exit}}(ε)$ the escape time from this neighborhood. For pure states evolving under a Hamiltonian $H$, estimating $t_{\mathrm{exit}}$ is equivalent to an inverse quantum speed limit problem: finding upper bounds on the time a time-evolved state $ψ_t$ needs to depart from the $ε$-vicinity of the initial state $ψ_0$. We provide a partial solution, showing that under mild assumptions $t_{\mathrm{exit}}(ε) \approx ε/\sqrt{ Δ(H^2)}$, with $Δ(H^2)$ the Hamiltonian variance in $ψ_0$. We show that our upper bound on $t_{\mathrm{rec}}$ is generically saturated for random Hamiltonians. Finally, we analyze the impact of coherence of the initial state in the eigenbasis of $H$ on recurrence behavior.

Calculating bounds of properties of many-body quantum systems is of paramount importance, since they guide our understanding of emergent quantum phenomena and complement the insights obtained from estimation methods. Recent semidefinite programming approaches enable probabilistic bounds from finite-shot measurements of easily accessible, yet informationally incomplete, observables. Here we render these methods scalable in the number of qubits by instead utilizing moment-matrix relaxations. After introducing the general formalism, we show how the approach can be adapted with specific knowledge of the system, such as it being the ground state of a given Hamiltonian, possessing specific symmetries or being the steady state of a given Lindbladian. Our approach defines a scalable real-world certification scheme leveraging semidefinite programming relaxations and experimental estimations which, unavoidably, contain shot noise.

Pinsker's inequality sets a lower bound on the Umegaki divergence of two quantum states in terms of their trace distance. In this work, we formulate corresponding estimates for a variety of quantum and classical divergences including $f$-divergences like Hellinger and $χ^2$-divergences as well as Rényi divergences and special cases thereof like the Umegaki divergence, collision divergence, max divergence. We further provide a strategy on how to adapt these bounds to smoothed divergences.

High-Q bosonic memories are central to hardware-efficient quantum error correction, but their isolation makes fast, high-fidelity reset a persistent bottleneck. Existing approaches either rely on weak intermode cross-Kerr conversion or on measurement-based sequences with substantial latency. Here we demonstrate a hardware-efficient Rabi-Driven Reset (RDR) that implements continuous, measurement-free cooling of a superconducting cavity mode. A strong resonant Rabi drive on a transmon, together with sideband drives on the memory and readout modes detuned by the Rabi frequency, converts the dispersive interaction into an effective Jaynes-Cummings coupling between the qubit dressed states and each mode. This realizes a tunable dissipation channel from the memory to the cold readout bath. Crucially, the engineered coupling scales with the qubit-mode dispersive interaction and the drive amplitude, rather than with the intermode cross-Kerr, enabling fast cooling even in very weakly coupled architectures that deliberately suppress direct mode-mode coupling. We demonstrate RDR of a single photon with a decay time of $1.2 μs$, more than two orders of magnitude faster than the intrinsic lifetime. Furthermore, we reset about 30 thermal photons in about $80 μs$ to a steady-state average photon number of $\bar{n} = 0.045 \pm 0.025$.

Learning the Hamiltonian governing a quantum system is a central task in quantum metrology, sensing, and device characterization. Existing Heisenberg-limited Hamiltonian learning protocols either require multi-qubit operations that are prone to noise, or single-qubit operations whose frequency or strength increases with the desired precision. These two requirements limit the applicability of Hamiltonian learning on near-term quantum platforms. We present a protocol that learns a quantum Hamiltonian with the optimal Heisenberg-limited scaling using only single-qubit control in the form of static fields with strengths that are independent of the target precision. Our protocol is robust against the state preparation and measurement (SPAM) error. By overcoming these limitations, our protocol provides new tools for device characterization and quantum sensing. We demonstrate that our method achieves the Heisenberg-limited scaling through rigorous mathematical proof and numerical experiments. We also prove an information-theoretic lower bound showing that a non-vanishing static field strength is necessary for achieving the Heisenberg limit unless one employs an extensive number of discrete control operations.

We investigate the experimental prospects for testing a relativistic local quantum measurement inequality that quantifies the trade-off between vacuum insensitivity and responsiveness to excitations for finite-size detectors. Building on the Reeh--Schlieder approximation for coherent states, we derive an explicit and practically applicable bound for arbitrary coherent states. To connect with realistic photodetection scenarios, we model the detection region as a square prism operating over a finite time window and consider a normally incident single-mode coherent state. Numerical results exhibit the expected qualitative behavior: suppressing dark counts necessarily tightens the achievable click probability.

The definition of weak equivalence principle of quantum matter is an open problem at present. In order to reflect the probability of quantum system in the quantum version of weak equivalence principle, we proposed a quantum weak equivalence principle based on differential scattering cross section at tree level, that is, the differential scattering cross section does not depend on the mass and properties of the scattered particles when the target particles take the large mass limit. This version of the quantum equivalence principle we proposed will be broken by the spin properties of quantum matter. In the non-relativistic case, the difference of differential scattering cross sections of scattered particles with different spin properties scattered by target particles is mainly reflected in the order of $ \mathcal O (p _ {\mathrm{cm}} ^2) $. In the relativistic case , we studied the asymptotic behavior of differential scattering cross sections at small angles. When the target particles are scalar particles, the difference of light particles with different spin properties is mainly reflected in the $ \mathcal O (1/θ^2) $ order. When the target particles are Dirac particles, the difference of light particles with different spin properties is mainly reflected in the $ \mathcal O (1/θ^4) $ order. The polarization of differential scattering cross section when scattered particles are Dirac particles is investigated. The result of the degree of polarization depends on the polarization direction of the incident particles.

The manipulation of distinct degrees of freedom of photons plays a critical role in both classical and quantum information processing. While the principles of wave optics provide elegant and scalable control over classical light in spatial and temporal domains, engineering quantum states in Fock space has been largely restricted to few-photon regimes, hindered by the computational and experimental challenges of large Hilbert spaces. Here, we introduce ``Fock-space optics", establishing a conceptual framework of wave propagation in the quantum domain by treating photon number as a synthetic dimension. Using a superconducting microwave resonator, we experimentally demonstrate Fock-space analogues of optical propagation, refraction, lensing, dispersion, and interference with up to 180 photons. These results establish a fundamental correspondence between Schrödinger evolution in a single bosonic mode and classical paraxial wave propagation. By mapping intuitive optical concepts onto high-dimensional quantum state engineering, our work opens a path toward scalable control of large-scale quantum systems with thousands of photons and advanced bosonic information processing.

This paper presents a theoretical study of the light-induced shift of the coherent population trapping resonance. An analytical model is proposed that describes the interaction of two radiation components with an atomic system using a $Λ$ scheme and takes into account an additional level of excited state. Both weak and strong coupling regimes with off-resonant transitions are considered. It is shown that, in addition to the conventional dynamic Stark shift, an extra shift arises due to the distortion of the resonance line shape when bichromatic laser radiation interacts with off-resonant atomic transitions. An analytical expression for this additional shift is derived in the weak-coupling limit, and its significant impact on the resonance shape and sensitivity to the intensities of the laser field components is demonstrated. It is found that under strong coupling conditions, the additional shift can deviate substantially from a linear dependence on light intensity, suggesting new opportunities for controlling light shifts in precision atomic devices such as quantum frequency standards.

In this study, we investigate the geodesic motion of massless particles -- specifically photons -- in the spacetime of a charged anti-de Sitter (AdS) black hole (BH) surrounded by a cloud of strings (CoS) within the framework of Kalb-Ramond (KR) gravity. We analyze the effective potential that governs photon trajectories, explore the properties and location of the photon sphere (PS), and examine the effective radial force acting on photons. The resulting BH shadow is also studied, highlighting the roles of both the CoS parameter $α$ and the KR field parameter $\ell$ in shaping its geometry. We constrain these parameters using observational data from M87* and Sgr A* obtained by the Event Horizon Telescope (EHT). Furthermore, we extend our investigation to the motion of neutral test particles in the same gravitational background. By examining the impact of the CoS and KR field, we show how these additional fields modify the dynamics relative to standard charged BH scenarios. Finally, we study the fundamental frequencies associated with quasiperiodic oscillations (QPOs) of test particles, demonstrating how these frequencies are affected by the presence of the CoS and KR field. Our results reveal the rich structure of AdS-BH spacetimes influenced by string clouds and antisymmetric tensor fields, with potential observational consequences in gravitational wave and BH imaging astronomy.

A relativistic equation for a neutral complex field as a probability amplitude is proposed. The continuity equation for the probability density is obtained. It is shown that there are two types of excitations of this field, which describe particles with positive energy and different dispersion laws. Based on the Lagrangian formalism, conservation laws are obtained. The transition to secondary quantization is considered.

We consider the question of counting the degrees of freedom in theoretical models, with an emphasis on theories of fields and gravity. Among the possible approaches, the Hamiltonian formulation remains one of the most systematic and robust tools. However, it can easily become long and technically involved. In this work, we present a broadly applicable recipe to find the degrees of freedom directly, based on the Lagrangian formulation. We compare it to the standard approaches, highlight the challenges that may arise in the latter, and demonstrate that the proposed method leads to transparent insights about the dynamical nature of theory in a quick, simple, and straight-forward way.

Several photonic quantum technologies rely on the ability to generate multiple indistinguishable photons. Benchmarking the level of indistinguishability of these photons is essential for scalability. The Hong-Ou-Mandel dip provides a benchmark for the indistinguishability between two photons, and extending this test to the multi-photon setting has so far resulted in a protocol that computes the genuine n-photon indistinguishability (GI). However, this protocol has a sample complexity that increases exponentially with the number of input photons for an estimation of GI up to a given additive error. To address this problem, we introduce new theorems that strengthen our understanding of the relationship between distinguishability and the suppression laws of the quantum Fourier transform interferometer (QFT). Building on this, we propose a protocol using the QFT for benchmarking GI that achieves constant sample complexity for the estimation of GI up to a given additive error for prime photon numbers, and sub-polynomial scaling otherwise, representing an exponential improvement over the state of the art. We prove the optimality of our protocol in many relevant scenarios and validate our approach experimentally on Quandela's reconfigurable photonic quantum processor, where we observe a clear advantage in runtime and precision over the state of the art. We therefore establish the first scalable method for computing multi-photon indistinguishability, which applies naturally to current and near-term photonic quantum hardware.

This paper presents a systematic study of adversarial hypothesis testing for both quantum-quantum (QQ) and classical-quantum (CQ) channels. Unlike conventional channel discrimination, we consider a framework where the sender, Alice, selects the channel input adversarially to minimize Bob's distinguishability. We analyze this problem across four settings based on whether Alice employs i.i.d. or general inputs and whether the receiver, Bob, is informed of the specific input choice (allowing his measurement to depend on the input). We characterize the Stein exponents for each setting and reveal a striking distinction in behavior: for QQ channels with i.i.d. inputs, Bob's knowledge of the input significantly enhances distinguishability, yet this advantage vanishes when general inputs are permitted. In contrast, for CQ channels, Bob being informed provides a consistent advantage over the corresponding entanglement-breaking channels for both i.i.d. and general inputs. These results demonstrate a unique phenomenon in adversarial hypothesis testing where the CQ channel does not merely behave as a special case of the QQ channel.

Waves propagating through a gravitational potential exhibit wave-optics effects when their wavelength is not significantly smaller than the lensing scales. We study the propagation of a scalar wave, governed by the Klein-Gordon equation in curved spacetime, to focus on effects on amplitude and phase, while leaving aside the issue of wave polarization which affects electromagnetic and gravitational waves. Using the Newman-Penrose formalism, we obtain the first corrections beyond the geometric optics in the expansion in the inverse frequency. In vacuum, that is for Weyl tensor lensing, there is no wave effect at first order in $G$ and wave effects start at order $G^2$. Conversely, if the wave travels through a non-vanishing matter density, the first corrections start at order $G$. We check these analytic results by solving numerically the equations dictating the evolution of the corrections either in the vicinity of a Schwarzschild black hole or through a transparent star.

In the thermodynamic limit, the Dicke-Ising chain maps exactly onto an effective self-consistent matter Hamiltonian with the photon field acting solely as a self-consistent effective field. As a consequence, no quantum correlations between photons and spins are needed to understand the quantum phase diagram. This enables us to determine the quantum phase diagram in the thermodynamic limit using numerical linked-cluster expansions combined with density matrix renormalization group calculations (NLCE+DMRG) to solve the resulting self-consistent matter Hamiltonian. This includes magnetically ordered phases with significantly improved accuracy compared to previous estimates. For ferromagnetic Ising couplings, we refine the location of the multicritical point governing the change in the order of the superradiant phase transition, reaching a relative accuracy of $10^{-4}$. For antiferromagnetic Ising couplings, we confirm the existence of the narrow antiferromagnetic superradiant phase in the thermodynamic limit. The effective matter Hamiltonian framework identifies the antiferromagnetic superradiant phase as the many-body ground state of an antiferromagnetic transverse-field Ising model with longitudinal field. This phase emerges through continuous Dicke-type polariton condensation from the antiferromagnetic normal phase, followed by a first-order transition to the paramagnetic superradiant phase. Thus, NLCE+DMRG provides a precise determination of the Dicke-Ising phase diagram in one dimension by solving the self-consistent effective matter Hamiltonian.

We analyze quantum state preservation in open quantum systems using quantum error-correcting (QEC) codes that are explicitly embedded into microscopic system-bath models. Instead of abstract quantum channels, we consider multi-qubit registers coupled to bosonic thermal environments, derive a second-order master equation for the reduced dynamics, and use it to benchmark the five-qubit, Steane, and toric codes under local and collective noise. We compute state fidelities for logical qubits as functions of coupling strength, bath temperature, and the number of correction cycles. In the low-temperature regime, we find that repeated error-correction with the five-qubit code strongly suppresses decoherence and relaxation, while in the high-temperature regime, thermal excitations dominate the dynamics and reduce the benefit of all codes, though the five-qubit code still outperforms the Steane and toric codes. For two-qubit Werner states, we identify a critical evolution time before which QEC does not improve fidelity, and this time increases as entanglement grows. After this critical time, QEC does improve fidelity. Comparative analysis further reveals that the five-qubit code (the smallest perfect code) offers consistently higher fidelities than topological and concatenated architectures in these open-system settings. These findings establish a quantitative framework for evaluating QEC under realistic noise environments and provide guidance for developing noise-resilient quantum architectures in near-term quantum technologies.

Pre-execution calibration is a major bottleneck for operating superconducting quantum processors, and qubit frequency allocation is especially challenging due to crosstalk-coupled objectives. We establish that the widely-used Snake optimizer is mathematically equivalent to Block Coordinate Descent (BCD), providing a rigorous theoretical foundation for this calibration strategy. Building on this formalization, we present a topology-aware block ordering obtained by casting order selection as a Sequence-Dependent Traveling Salesman Problem (SD-TSP) and solving it efficiently with a nearest-neighbor heuristic. The SD-TSP cost reflects how a given block choice expands the reduced-circuit footprint required to evaluate the block-local objective, enabling orders that minimize per-epoch evaluation time. Under local crosstalk/bounded-degree assumptions, the method achieves linear complexity in qubit count per epoch, while retaining calibration quality. We formalize the calibration objective, clarify when reduced experiments are equivalent or approximate to the full objective, and analyze convergence of the resulting inexact BCD with noisy measurements. Simulations on multi-qubit models show that the proposed BCD-NNA ordering attains the same optimization accuracy at markedly lower runtime than graph-based heuristics (BFS, DFS) and random orders, and is robust to measurement noise and tolerant to moderate non-local crosstalk. These results provide a scalable, implementation-ready workflow for frequency calibration on NISQ-era processors.

Studying the complexity of states sampled from various ensembles is a central component of quantum information theory. In this work we establish the average-case hardness of learning, in the statistical query model, the Born distributions of states sampled uniformly from the circular and (fermionic) Gaussian ensembles. These ensembles of states are induced variously by the uniform measures on the compact symmetric spaces of type AI, AII, and DIII. This finding complements analogous recent results for states sampled from the classical compact groups. On the technical side, we employ a somewhat unconventional approach to integrating over the compact groups which may be of some independent interest. For example, our approach allows us to exactly evaluate the total variation distances between the output distributions of Haar random unitary and orthogonal circuits and the constant distribution, which were previously known only approximately.

We demonstrate that large language model (LLM) agents can autonomously perform tensor network simulations of quantum many-body systems, achieving approximately 90% success rate across representative benchmark tasks. Tensor network methods are powerful tools for quantum simulation, but their effective use requires expertise typically acquired through years of graduate training. By combining in-context learning with curated documentation and multi-agent decomposition, we create autonomous AI agents that can be trained in specialized computational domains within minutes. We benchmark three configurations (baseline, single-agent with in-context learning, and multi-agent with in-context learning) on problems spanning quantum phase transitions, open quantum system dynamics, and photochemical reactions. Systematic evaluation using DeepSeek-V3.2, Gemini 2.5 Pro, and Claude Opus 4.5 demonstrates that both in-context learning and multi-agent architecture are essential. Analysis of failure modes reveals characteristic patterns across models, with the multi-agent configuration substantially reducing implementation errors and hallucinations compared to simpler architectures.

Historically, the focus in entanglement distillation has predominantly been on the distillable entanglement, and the framework assumes complete knowledge of the initial state. In this paper, we study the reliability function of entanglement distillation, which specifies the optimal exponent of the decay of the distillation error when the distillation rate is below the distillable entanglement. Furthermore, to capture greater operational significance, we extend the framework from the standard setting of known states to a black-box setting, where distillation is performed from a set of possible states. We establish an exact finite blocklength result connecting to composite correlated hypothesis testing without any redundant correction terms. Based on this, the reliability function of entanglement distillation is characterized by the regularized quantum Hoeffding divergence. In the special case of a pure initial state, our result reduces to the error exponent for entanglement concentration derived by Hayashi et al. in 2003. Given full prior knowledge of the state, we construct a concrete optimal distillation protocol. Additionally, we analyze the strong converse exponent of entanglement distillation. While all the above results assume the free operations to be non-entangling, we also investigate other free operation classes, including PPT-preserving, dually non-entangling, and dually PPT-preserving operations.

Recent studies have extensively explored chaotic dynamics in quantum optical systems through the mean-field approximation, which corresponds to an ideal, fluctuation-free scenario. However, the inherent sensitivity of chaos to initial conditions implies that even minute fluctuations can be amplified, thereby questioning the applicability of this approximation. Here, we analyze these chaotic effects using stochastic Langevin equations or the Lindblad master equation. For systems operating at frequencies of $10^5$ to $10^7$ Hz, we demonstrate that room-temperature thermal fluctuations are sufficient to suppress chaos at the level of expectation values, even under weak nonlinearity. Furthermore, nonlinearity induces deviations from Gaussian phase-space distributions of the quantum state, revealing attractor-like features in the Wigner function. With increasing nonlinearity, the noise threshold for chaos suppression decreases, approaching the scale of vacuum fluctuations. These results provide a bidirectional validation of the quantum mechanical suppression of chaos.

We present a comprehensive study of warm hybrid inflation within the framework of $α$-attractor models, where an axionic inflaton is coupled to a waterfall field in the presence of thermal dissipation. The model is analyzed for both linear ($Υ\propto T$) and cubic ($Υ\propto T^{3}$) dissipation regimes. Confronting the theoretical predictions with the latest observational data from Planck+BICEP/Keck, P-ACT-LB-BK18 and SPT, and , we find that in the weak dissipative regime ($Q_{*} \lesssim 10^{-5}$), the scalar spectral index $n_{s} \simeq 0.965$ lies at the boundary of the combined P-ACT-LB-BK18 constraints, while the tensor-to-scalar ratio $r$ remains within observable ranges. For stronger dissipation ($Q_{*} \gtrsim 10^{-5}$), the model predicts values of $n_{s}$ well within the $1$--$2σ$ confidence region of all datasets, with tensor modes remaining fully observable in both dissipation scenarios. These results indicate that forthcoming CMB polarization experiments may be capable of detecting primordial gravitational waves, thereby providing a robust observational test of warm hybrid inflation across different dissipative regimes.

The transition to the fault-tolerant era exposes the limitations of homogeneous quantum systems, where no single qubit modality simultaneously offers optimal operation speed, connectivity, and scalability. In this work, we propose a strategic approach to Heterogeneous Quantum Architectures (HQA) that synthesizes the distinct advantages of the superconducting (SC) and neutral atom (NA) platforms. We explore two architectural role assignment strategies based on hardware characteristics: (1) We offload the latency-critical Magic State Factory (MSF) to fast SC devices while performing computation on scalable NA arrays, a design we term MagicAcc, which effectively mitigates the resource-preparation bottleneck. (2) We explore a Memory-Compute Separation (MCSep) paradigm that utilizes NA arrays for high-density qLDPC memory storage and SC devices for fast surface-code processing. Our evaluation, based on a comprehensive end-to-end cost model, demonstrates that principled heterogeneity yields significant performance gains. Specifically, our designs achieve $752\times$ speedup over NA-only baselines on average and reduce the physical qubit footprint by over $10\times$ compared to SC-only systems. These results chart a clear pathway for leveraging cross-modality interconnects to optimize the space-time efficiency of future fault-tolerant quantum computers.

We explore the cosmological dynamics of a teleparallel Gauss-Bonnet gravity model defined by the torsion scalar $T$ and the torsion-based Gauss-Bonnet invariant $T_{\mathcal{G}}$, deriving modified Friedmann equations for a flat FLRW Universe and corresponding linear scalar perturbation equations. Using a numerical approach, we solve these equations for pressureless matter, predicting the redshift evolution of the Hubble parameter $H(z)$. Bayesian Markov chain Monte Carlo analysis, incorporating late-time observations from Cosmic Chronometers, Pantheon$^+$ with SH0ES, and DESI BAO (Data Release 1 and Data Release 2), constrains the model parameters, revealing that $f(T, T_{\mathcal{G}})$ mimics dark energy in the absence of a cosmological constant, presenting a viable alternative to $Λ$CDM paradigm. Stability is confirmed via scalar perturbation analysis of Hubble and matter density fluctuations, positioning $f(T, T_{\mathcal{G}})$ gravity as a robust framework to address cosmic acceleration challenges. The model yields a present-day effective equation of state $ω_{\mathrm{eff}}(z=0) \approx -0.664$ to \(-0.693\), consistent with observations, and partially alleviates the Hubble tension with $H_0$ estimates of 69 to 71.5\kms. These findings highlight the potential of $f(T, T_{\mathcal{G}})$ gravity to resolve fundamental cosmological puzzles while aligning with late-time observational data.

Casimir forces arise from quantum electromagnetic fluctuations and depend on the dielectric response of interacting materials across the entire frequency spectrum. Although this dependence is central to Lifshitz theory of the Casimir effect, the formulation of the force in terms of dielectric functions evaluated at imaginary frequencies has largely obscured its connection to real-frequency optical properties, limiting the use of Casimir interactions as a probe of materials. Here we demonstrate that Casimir force measurements encode sufficient information to reconstruct a material's broadband optical response. Using supervised machine learning to invert Lifshitz theory, we determine the complex permittivity of a material over more than seven orders of magnitude in frequency from a single force-distance curve. We show that measurements at different separations selectively constrain distinct frequency ranges of the dielectric response, providing direct physical insight into how quantum fluctuations sample the electromagnetic spectrum. These results establish Casimir interactions as a physically constrained, broadband spectroscopic tool and open new opportunities for optical characterization in regimes inaccessible to conventional techniques.

Magic states are essential for universal quantum computation and are widely viewed as a key source of quantum advantage, yet in realistic devices they are inevitably noisy. In this work, we characterize how noise on injected magic resources changes the classical simulability of quantum circuits and when it induces a transition from classically intractable behavior to efficient classical simulation. We adopt a resource-centric noise model in which only the injected magic components are noisy, while the baseline states, operations, and measurements belong to an efficiently simulable family. Within this setting, we develop an approximate classical sampling algorithm with controlled error and prove explicit noise-dependent conditions under which the algorithm runs in polynomial time. Our framework applies to both qubit circuits with Clifford baselines and fermionic circuits with matchgate baselines, covering representative noise channels such as dephasing and particle loss. We complement the analysis with numerical estimates of the simulation cost, providing concrete thresholds and runtime scaling across practically relevant parameter regimes.

We study the Fano effect in dissipative cavity quantum electrodynamics, which originates from the interference between the emitter's direct radiation and that mediated by a cavity mode. Starting from a two-level system coupled to a structured reservoir, we show that a quantum master equation previously derived within the Born-Markov approximation can be rederived by introducing a single auxiliary mode via pseudomode approach. We identify the corresponding spectral function of the system--environment interaction and demonstrate that it consists of a constant and a non-Lorentzian contribution forming the Fano profile. The constant term is shown to be essential for obtaining a Lindblad master equation and is directly related to the rate associated with this Fano interference. Furthermore, by applying Fano diagonalization to a common-environment setup including an explicit cavity mode, we independently derive the same spectral function in the strongest-interference regime. Our results establish a unified framework for describing the Fano effect in single-mode cavity QED systems and clarify its non-Markovian origin encoded in the spectral function.

Static phase detuning fundamentally constrains coherent state transfer in asymmetric classical and quantum systems. We introduce a Bloch-sphere formulation for piecewise-coherent modulation that recasts coupled-mode dynamics as geometric trajectories, transforming algebraic control into path optimization. The approach reveals a cone of inaccessibility at the target pole and yields exact geodesic criteria for complete mode conversion in detuned systems. Leveraging this framework, we break time-reversal symmetry to realize a magnet-free optical isolator with near-unity contrast. Furthermore, for detuning larger than coupling between modes, we develop a recursive multi-step protocol enabling deterministic transfer for arbitrary detunings and derive a universal geometric lower bound on the required number of coupling-switching events.

We study SU(3) gauge theory on small lattices in the minimal (qutrit) electric field truncation retaining only the ${\bf 1}, {\bf 3}, {\bf \overline{3}}$ representations for the link variables. Explicit expressions are given for the Kogut-Susskind Hamiltonian for the square plaquette chain and the two-dimensional honeycomb lattice. Our formalism can be easily extended to the minimally truncated general SU($N_c$) gauge theory. The addition of (static) quarks is discussed. We present results for the energy spectrum of the gauge field on these lattices by exact diagonalization of the Hamiltonian and analyze its statistical properties. We also compute the SU(3) string tension and discuss how it is modified by vacuum fluctuations. Finally, we calculate the potential energies of a static quark-antiquark pair and three static quarks and study their screening at finite temperature.

Quantum state tomography is essential for characterizing quantum systems, but it becomes infeasible for large systems due to exponential resource scaling. Overlapping tomography addresses this challenge by reconstructing all $k$-body marginals using few measurement settings, enabling the efficient extraction of key information for many quantum tasks. While optimal schemes are known for qubits, the extension to higher-dimensional qudit systems remains largely unexplored. Here, we investigate optimal qudit overlapping tomography, constructing local measurement settings from generalized Gell-Mann matrices. By establishing a correspondence with combinatorial covering arrays, we present two explicit constructions of optimal measurement schemes. For $n$-qutrit systems, we prove that pairwise tomography requires at most $8 + 56\left\lceil \log_{8} n \right\rceil$ measurement settings, and provide an explicit scheme achieving this bound. Furthermore, we develop an efficient algorithm to determine the optimal order of these measurement settings, minimizing the experimental overhead associated with switching configurations. Compared to the worst-case ordering, our optimized schedule reduces switching costs by approximately 50\%. These results provide a practical pathway for efficient characterization of qudit systems, facilitating their application in quantum communication and computation.

The high overhead of fault-tolerant measurement sequences (FTMSs) poses a major challenge for implementing quantum stabilizer codes. Here, we address this problem by constructing efficient FTMSs for the class of quantum Hamming codes $[\![2^r-1, 2^r-1-2r, 3]\!]$ with $r=3k+1$ ($k \in \mathbb{Z}^+$). Our key result demonstrates that the sequence length can be reduced to exactly $2r+1$-only one additional measurement beyond the original non-fault-tolerant sequence, establishing a tight lower bound. The proposed method leverages cyclic matrix transformations to systematically combine rows of the initial stabilizer matrix and preserving a self-dual CSS-like symmetry analogous to that of the original quantum Hamming codes. This induced symmetry enables hardware-efficient circuit reuse: the measurement circuits for the first $r$ stabilizers are transformed into circuits for the remaining $r$ stabilizers simply by toggling boundary Hadamard gates, eliminating redundant hardware. For distance-3 fault-tolerant error correction, our approach simultaneously reduces the time overhead via shorting the FTMS length and the hardware overhead through symmetry-enabled circuit multiplexing. These results provide an important advance towards the important open problem regarding the design of minimal FTMSs for quantum Hamming codes and may shed light on similar challenges in other quantum stabilizer codes.

Decision making often exhibits context dependence that challenges classical probability theory. While quantum cognition has successfully modeled such phenomena, it remains unclear whether quantum probability is merely a convenient assumption or a necessary consequence of decision dynamics. Here we present a theoretical framework in which contextuality arises generatively from physically grounded constraints on decision making. By developing a quantum extension of the Tug-of-War (TOW) model, we show that conservation-based internal state updates and measurement-induced disturbance preclude any non-contextual classical description with a single, unified internal state. Contextuality therefore emerges as a structural consequence of adaptive learning dynamics. We further show that the resulting measurement structure admits Klyachko-Can-Binicioglu-Shumovsky (KCBS)-type contextuality witnesses in a minimal single-system setting. These results indicate that quantum probability is not merely a descriptive convenience, but an unavoidable effective theory for adaptive decision dynamics.

Our objective is to illuminate the global structure of non-orientable manifolds with signature-changing metrics. Using explicit constructions based on the topology of the Möbius strip, we produce examples of crosscap manifolds where the gluing junction serves as the locus of signature change. In another set of examples, we convert the Möbius strip into a singular signature-type changing manifold. For these resulting manifolds, we test whether the metric can be expressed as $\tilde{g}=g+fV^{\flat}\otimes V^{\flat}$, with $g$ a Lorentzian metric and $f$ a smooth interpolation function between the Lorentzian and Riemannian regions, separated by the signature change hypersurface $\mathcal{H}$. Our analysis reveals that the radical of the metric can transition from transverse to tangent at $\mathcal{H}$, pseudo-space orientability is obstructed by the Euler characteristic, and pseudo-time orientability may still hold. These examples illustrate subtle obstructions to applying standard transformation prescriptions for signature change and highlight novel phenomena in compact, non-orientable semi-Riemannian manifolds.

Accurately capturing electron correlation in large-scale molecular systems remains one of the foremost challenges in quantum chemistry and a primary driver for the development of quantum algorithms. Classical configuration-interaction methods, while rigorous, suffer from exponential scaling, rendering them impractical for large or strongly correlated systems. Overcoming this limitation is central to realizing the promise of quantum computing in chemistry. Here, we investigate the pyridine-Li ion complex using three quantum algorithms: the variational quantum eigensolver (VQE), the subspace quantum diagonalization (SQD) method, and the recently introduced handover iterative VQE (HI-VQE). Our results demonstrate how new generations of hybrid quantum-classical frameworks overcome the scalability and noise sensitivity that constrain conventional VQE approaches. SQD and HI-VQE achieve ground-state energy calculations for problem sizes inaccessible to classical computation, marking a clear advance toward quantum advantage. In particular, HI-VQE enables calculations within active spaces as large as (24e,22o), requiring 44 qubits-well beyond the reach of classical CASCI and VQE. This capability provides a systematic pathway for incorporating increasing numbers of electrons into quantum treatment, thereby approaching exact molecular energies. Importantly, both SQD and HI-VQE exhibit robustness against hardware noise, a critical improvement over earlier approaches. By enabling quantum simulations of molecular systems previously deemed intractable, SQD and HI-VQE offer a realistic route toward practical quantum advantage in computational chemistry. The comparison between HI-VQE and SQD shows that optimizing circuit parameters is crucial for accurate simulation.

The subadditivity-doubling-rotation (SDR) technique is a powerful route to Gaussian optimality in classical information theory and relies on strict subadditivity and its equality-case analysis, where double Markovity is a standard tool. We establish quantum analogues of double Markovity. For tripartite states, we characterize the simultaneous Markov conditions A-B-C and A-C-B via compatible projective measurements on B and C that induce a common classical label J yielding A-J-(BC). For strictly positive four-party states, we show that A-(BD)-C and A-(CD)-B hold if and only if A-D-(BC) holds. These results remove a key bottleneck in extending SDR-type arguments to quantum systems.

Quantities computed by minimal cuts, such as entanglement entropies achievable by the Ryu-Takayanagi proposal in the AdS/CFT correspondence, are constrained by linear inequalities. We prove a previously conjectured property of all such constraints: Any $k$ systems on the "greater-than" side of the inequality are subsumed in some $k$ systems on its "less-than" side (accounting for multiplicity). This finding adds evidence that the same inequalities also constrain the entropies under time-dependent conditions because it preempts a large class of potential counterexamples. We prove several other properties of holographic entropy inequalities and comment on their relation to quantum erasure correction and the Renormalization Group.

A holographic entropy inequality (HEI) is a linear inequality obeyed by Ryu-Takayanagi holographic entanglement entropies, or equivalently by the minimum cut function on weighted graphs. We establish a new combinatorial framework for studying HEIs, and use it to prove several properties they share, including two majorization-related properties as well as a necessary and sufficient condition for an inequality to be an HEI. We thereby resolve all the conjectures presented in [arXiv:2508.21823], proving two of them and disproving the other two. In particular, we show that the null reduction of any superbalanced HEI passes the majorization test defined in [arXiv:2508.21823], thereby providing strong new evidence that all HEIs are obeyed in time-dependent holographic states.

Photon statistics plays a governing role in multi-photon interference. While interference visibility in the standard two-photon case, known as Hong-Ou-Mandel interference, monotonically degrades with higher intensity correlation functions, we show that this monotonicity does not hold for three-photon interference in symmetric circuits. We reveal that, in the discrete Fourier transform circuit, engineered super-Poissonian photon-number fluctuations, realized using a modulated laser, maximize the visibility, surpassing the magnitude of the single-photon signature. In addition, by tuning the symmetric circuit parameters, we demonstrate that the visibility hierarchy inverts relative to the benchmark of Poissonian statistics. This trade-off implies that quantum and classical advantages are mutually exclusive resources for interference, indicating a form of statistical complementarity.

There exist conformally invariant, higher-derivative, variational analogs of the Yang-Mills condition for connections on vector bundles over a conformal manifold of even dimension greater than or equal to six. We give a compact formula for these analogs and prove that they are a strict weakening of the Yang-Mills condition with respect to an Einstein metric. We also show that the conformal Yang-Mills condition for the tractor connection of an even dimensional conformal manifold is equivalent to vanishing of its Fefferman-Graham obstruction tensor. This result uses that the tractor connection on a Poincaré-Einstein manifold is itself Yang-Mills.

Interface circuits are the key components that enable the hybrid integration of superconductor and semiconductor digital electronics. The design requirements of superconductor-semiconductor interface circuits vary depending on the application, such as high-performance classical computing, superconducting quantum computing, and digital signal processing. In this survey, various interface circuits are categorized based on the working principle and structure. The superconducting output drivers are explored, which are capable of converting and amplifying, e.g., single flux quantum (SFQ) voltage pulses, to voltage levels that semiconductor circuits can process. Several trade-offs between circuit- and system-level design parameters are examined. Accordingly, parameters such as the data rate, output voltage, power dissipation, layout area, thermal/heat load of cryogenic cables, and bit-error rate are considered.

The Variational Quantum Eigensolver (VQE) is a hybrid quantum-classical algorithm for computing ground state energies of molecular systems. We implement VQE to calculate the potential energy surface of the hydrogen molecule (H$_2$) across 100 bond lengths using the PennyLane quantum computing framework on an HPC cluster featuring 4$\times$ NVIDIA H100 GPUs (80GB each). We present a comprehensive parallelization study with four phases: (1) Optimizer + JIT compilation achieving 4.13$\times$ speedup, (2) GPU device acceleration achieving 3.60$\times$ speedup at 4 qubits scaling to 80.5$\times$ at 26 qubits, (3) MPI parallelization achieving 28.5$\times$ speedup, and (4) Multi-GPU scaling achieving 3.98$\times$ speedup with 99.4% parallel efficiency across 4 H100 GPUs. The combined effect yields 117$\times$ total speedup for the H$_2$ potential energy surface (593.95s $\rightarrow$ 5.04s). We conduct a CPU vs GPU scaling study from 4--26 qubits, finding GPU advantage at all scales with speedups ranging from 10.5$\times$ to 80.5$\times$. Multi-GPU benchmarks demonstrate near-perfect scaling with 99.4% efficiency and establish that a single H100 can simulate up to 29 qubits before hitting memory limits. The optimized implementation reduces runtime from nearly 10 minutes to 5 seconds, enabling interactive quantum chemistry exploration.

Quantum Computing (QC) has evolved from a few custom quantum computers, which were only accessible to their creators, to an array of commercial quantum computers that can be accessed on the cloud by anyone. Accessing these cloud quantum computers requires a complex chain of tools that facilitate connecting, programming, simulating algorithms, estimating resources, submitting quantum computing jobs, retrieving results, and more. Some steps in the chain are hardware dependent and subject to change as both hardware and software tools, such as available gate sets and optimizing compilers, evolve. Understanding the trade-offs inherent in this process is essential for evaluating the power and utility of quantum computers. ARLIS has been systematically investigating these environments to understand these complexities. The work presented here is a detailed summary of three months of using such quantum programming environments. We show metadata obtained from these environments, including the connection metrics to the different services, the execution of algorithms, the testing of the effects of varying the number of qubits, comparisons to simulations, execution times, and cost. Our objective is to provide concrete data and insights for those who are exploring the potential of quantum computing. It is not our objective to present any new algorithms or optimize performance on any particular machine or cloud platform; rather, this work is focused on providing a consistent view of a single algorithm executed using out-of-the-box settings and tools across machines, cloud platforms, and time. We present insights only available from these carefully curated data.

Quantum annealing is typically regarded as a tool for combinatorial optimization, but its coherent dynamics also offer potential for machine learning. We present a model that encodes classical data into an Ising Hamiltonian, evolves it on a quantum annealer, and uses the resulting probability distributions as feature maps for classification. Experiments on the quantum annealer machine with the Digits dataset, together with simulations on MNIST, demonstrate that short annealing times yield higher classification accuracy, while longer times reduce accuracy but lower sampling costs. We introduce the participation ratio as a measure of the effective model size and show its strong correlation with generalization.

The Lorentzian path integral was recently used to argue that standard Euclidean axion wormholes do not dominate computations of connected AdS/CFT partition functions. We now apply similar methods to study the seemingly-analogous Jackiw-Teitelboim wormholes constructed by Garcia-Garcia and Godet using Jackiw-Teitelboim gravity with an imaginary-valued minimally-coupled massless scalar field. However, this time we find that these wormholes do dominate our path integral for the relevant connected partition function. This supports the suggestion by Garcia-Garcia and Godet that contributions from such wormholes parallel the physics of the Sachdev-Ye-Kitaev model at complex couplings. The result also illustrates the sensitivity of wormhole contributions to details of the relevant physics.

Fast, reliable decoders are pivotal components for enabling fault-tolerant quantum computation (FTQC). Neural network decoders like AlphaQubit have demonstrated potential, achieving higher accuracy than traditional human-designed decoding algorithms. However, existing implementations of neural network decoders lack the parallelism required to decode the syndrome stream generated by a superconducting logical qubit in real time. Moreover, integrating AlphaQubit with sliding window-based parallel decoding schemes presents non-trivial challenges: AlphaQubit is trained solely to output a single bit corresponding to the global logical correction for an entire memory experiment, rather than local physical corrections that can be easily integrated. We address this issue by training a recurrent, transformer-based neural network specifically tailored for parallel window decoding. While it still outputs a single bit, we derive training labels from a consistent set of local corrections and train on various types of decoding windows simultaneously. This approach enables the network to self-coordinate across neighboring windows, facilitating high-accuracy parallel decoding of arbitrarily long memory experiments. As a result, we overcome the throughput bottleneck that previously precluded the use of AlphaQubit-type decoders in FTQC. Our work presents the first scalable, neural-network-based parallel decoding framework that simultaneously achieves SOTA accuracy and the stringent throughput required for real-time quantum error correction. Using an end-to-end experimental workflow, we benchmark our decoder on the Zuchongzhi 3.2 superconducting quantum processor on surface codes with distances up to 7, demonstrating its superior accuracy. Moreover, we demonstrate that, using our approach, a single TPU v6e is capable of decoding surface codes with distances up to 25 within 1us per decoding round.

We studied the magnetic Penrose process (MPP) in the Kerr-Bertotti-Robinson (KBR) spacetime, an exact rotating electrovacuum solution describing a black hole (BH) immersed in an intrinsic, uniform electromagnetic field. We analyze the behavior of charged particles in this geometry and find that the spacetime structure itself responds non-monotonically to the background magnetic field $B$. Specifically, both the event horizon and the static limit surface first expand as $B$ increases, reach a maximum size at an intermediate field strength, and then contract toward the extremal limit. Although the ergoregion itself shrinks monotonically with $B$, this structural feature gives rise to a pronounced non-monotonic dependence of the energy extraction efficiency on the magnetic field $B$, i.e., the efficiency initially rises, attains a maximum value, and subsequently falls as the extremal condition is approached. This contrasts sharply with the monotonic trends usually associated with magnetic enhancements in the Kerr geometry. We further explore an astrophysical application of the MPP by estimating the maximum energy of electrons escaping from the ergoregion of the KBR BH. Modeling neutron beta decay occurring near the event horizon, we derive an analytical expression for the energy gained by electrons accelerated by the magnetic field. Applying our results to the supermassive BH at the Galactic center, $\mathrm{SgrA}^*$, we find that electrons can be accelerated up to energies of $\sim 10^{15}\,\mathrm{eV}$ for realistic values of the spin and magnetic field. Although these energies exceed the observed upper range of cosmic-ray electrons, radiative losses such as synchrotron emission and inverse-Compton scattering can efficiently reduce them to the observed $\mathrm{TeV}$ scale.

Although there have now been hundreds of transient gravitational-wave detections of merging compact stars by the LIGO-Virgo-KAGRA (LVK) detector network, no continuous-wave (CW) signals have yet been discovered. To ensure that such signals, expected to be exceedingly weak, can be detected in the ongoing O4 observing run by coherent integration over years, simulated waveforms ('hardware injections') are injected directly into the LIGO data by continuously modulating the positions of the interferometer mirrors so as to mimic nearly sinusoidal signals from fast-spinning galactic neutron stars. A set of 18 such simulated CW sources are injected with signal frequencies spanning much of the LIGO detection band and with varying sky locations. By verifying the successful recovery of the simulated signals, including preservation of absolute phase over as many as 10^{11} signal cycles, we validate our understanding of detector response and end-to-end search pipelines, including data cleaning. Daily and weekly monitoring of the signal reconstruction is meant to catch any unexpected sudden changes in interferometer response, to verify that signal-to-noise ratio increases as expected and to verify that simulated source parameters are recovered correctly. We describe three methods of monitoring: 1) a highly templated matched filter to extract signal amplitude and phase precisely; 2) a frequentist Fstatistic evaluation that marginalizes over amplitude, phase and orientation of the star; and 3) a Bayesian reconstruction of the source parameters together with noise characterization. Results from each method are shown, with emphasis on the new templated method, which yields precise measurement of the critical phase offset parameter and therefore validates understanding of absolute timing delays in the detector response and data stream.

Quantum low-density parity-check (qLDPC) codes have emerged as a promising approach for realizing low-overhead logical quantum memories. Recent theoretical developments have established shift automorphisms as a fundamental building block for completing the universal set of logical gates for qLDPC codes. However, practical challenges remain because the existing SWAP-based shift automorphism yields logical error rates that are orders of magnitude higher than those for fault-tolerant idle operations. In this work, we address this issue by dynamically varying the syndrome measurement circuits to implement the shift automorphisms without reducing the circuit distance. We benchmark our approach on both twisted and untwisted weight-6 generalized toric codes, including the gross code family. Our time-dynamic circuits for shift automorphisms achieve performance comparable to the idle operations under the circuit-level noise model (SI1000). Specifically, the dynamic circuits achieve more than an order of magnitude reduction in logical error rates relative to the SWAP-based scheme for the gross code at a physical error rate of $10^{-3}$, employing the BP-OSD decoder. Our findings improve both the error resilience and the time overhead of the shift automorphisms in qLDPC codes. Furthermore, our work can lead to alternative syndrome extraction circuit designs, such as leakage removal protocols, providing a practical pathway to utilizing dynamic circuits that extend beyond surface codes towards qLDPC codes.

We theoretically investigate the optical dipole interaction between a multi-level quantum system and a single-mode optical waveguide of any local polarisation. We investigate several paradoxical seeming situations, for example we find a situation in which there exist two non-orthogonal quantum states, each of which results in a photon flux in the opposite direction to the other. We show how, despite appearances, this does not break the unitary requirements of quantum mechanics. We also find that an isotropic quantum emitter can be either reflective or transmissive to light depending on the waveguide polarisation at the emitter location, indeed in the zero loss limit such a system changes from 100% transmission to 100% reflection due to an infinitesimal polarisation rotation. An example case for a four level system is also considered, which is found to operate as a non-destructive parity measurement of the photon number.

Product code construction is a powerful tool for constructing quantum stabilizer codes, which serve as a promising paradigm for realizing fault-tolerant quantum computation. Furthermore, the natural mapping between stabilizer codes and the ground states of exactly solvable spin models also motivates the exploration of many-body orders in the stabilizer codes. In this work, we investigate the fracton topological orders in a family of codes obtained by a recently proposed general construction. More specifically, this code family can be regarded as a class of generalized hypergraph product (HGP) codes. We term the corresponding exactly solvable spin models \textit{orthoplex models}, based on the geometry of the stabilizers. In the 3D orthoplex model, we identify a series of intriguing properties within this model family, including non-monotonic ground state degeneracy (GSD) as a function of system size and non-Abelian lattice defects. Most remarkably, in 4D we discover \textit{fragmented topological excitations}: while such excitations manifest as discrete, isolated points in real space, their projections onto lower-dimensional subsystems form connected objects such as loops, revealing the intrinsic topological nature of these excitations. Therefore, fragmented excitations constitute an intriguing intermediate class between point-like and spatially extended topological excitations. In addition, these rich features establish the generalized HGP codes as a versatile and analytically tractable platform for studying the physics of fracton orders.

We show that in multi-stage axion monodromy inflation an interruption near the end of the penultimate stage can lead to a spike in the gravitational wave background. These gravitational waves are in the frequency range and with an amplitude accessible to proposed terrestrial detectors such as the Einstein Telescope, Cosmic Explorer, and future Levitated Sensor Detector experiments.

In this work, we describe optically inspired models for unitary black hole (BH) evaporation. The goal of these models are (i) to be operationally simple, (ii) approximately preserve the thermal nature of the emitted Hawking Radiation (HR), and (iii) attempt to reproduce the Page Curve that purports that information flows forth from the BH when it has evaporated to approximately half its initial mass. We concentrate on modeling the BH as a single mode squeezed state successively interacting, by means of beam splitters and squeezers, with vacuum modes near the horizon, giving rise to entangled pairs representing the external Hawking radiation and its partner particle inside the horizon. Since all states and operations are Gaussian throughout, we use a symplectic formalism to track the evolution of the composite system through the evolving means and variances of their quadrature operators. This allows us to easily compute correlations and entanglement between the BH and the HR, as well as calculate correlations between the BH at early and late times.

Reduced-order quadrature (ROQ) is commonly used to speed up parameter estimation in gravitational wave astronomy; however, the construction of ROQ bases can be computationally costly, particularly for longer duration signals. We propose a modified construction strategy based on PyROQ that accelerates this process by performing the basis search using multiband waveforms, without compromising the desired likelihood speed and accuracy. We use this altered method to construct a set of ROQs in the sub-solar mass range using the \texttt{IMRPhenomXAS\_NRTidalV3} waveform. We find a 20\% to 30\% decrease in basis size and a $\sim 10$ times decrease in basis construction time. We verify the altered method preserves the likelihood accuracy and mantains consitent parameter estimation results.

A precise definition is proposed for the localization probability of a quantum state within a given subspace of the full Hilbert space of a quantum system. The corresponding localized component of the state is explicitly identified, and several mathematical properties are established. Applications and interpretations in the context of quantum information are also discussed.

The Bern-Carrasco-Johansson (BCJ) double-copy construction reveals a fundamental structural connection between gauge and gravity theories. At its core, the BCJ double copy is directly due to a duality between the algebraic relations of a color root and those of a kinematic root. We generalize this principle beyond the conventional Lie algebra structure of tree-level Yang-Mills theory. By demanding color-kinematics duality for the complete basis of four-point color structures -- including those involving the symmetric $d^{abc}$ constants -- we define the universal double copy. We systematically classify the bases of all such parity-even generalized gauge-theory numerators and, independently, the space of all parity-even four-graviton higher-derivative operators. We demonstrate that our universal double-copy construction precisely spans the entire tower of parity-even four-graviton amplitudes in any dimension, except for the Lovelock $R^3$ contribution in $D >6$ which we can express in terms of a particularly simple universal triple-copy involving gauge theories coupled to scalars. Explicit machine-readable expressions for the complete basis of gauge-theory numerators and fundamental gravitational building blocks are provided in the ancillary files. This establishes that all possible four-point gravitational interactions can be factorized into products of gauge-theory building blocks governed by this universal notion of color-kinematics duality.

A key challenge in quantum sensing is the detection of weak time dependent signals, particularly those that arise as specific frequency perturbations over a background field. Conventional methods usually demand complex dynamical control of the quantum sensor and heavy classical post-processing. We propose a quantum sensor that leverages time independent interactions and entanglement to function as a passive, tunable, thresholded frequency filter. By encoding the frequency selectivity and thresholding behavior directly into the dynamics, the sensor is responsive only to a target frequency of choice whose amplitude is above a threshold. This approach circumvents the need for complex control schemes and reduces the post-processing overhead.

First-order phase transitions occur through the nucleation of critical bubbles of the stable phase within the metastable phase. Using holography, we present a fully microscopic description of these bubbles in a strongly coupled, four-dimensional gauge theory at finite temperature. In the gravitational dual, these bubbles correspond to static, inhomogeneous and unstable black-brane solutions with a localized deformation on the horizon. We construct these solutions across the entire metastable branch and compute the surface tension and the nucleation rate. We then compare these microscopic results with those obtained from a two-derivative effective action for the order parameter in two different scenarios. When the effective action is derived from the microscopic theory via holography, we find remarkable agreement. However, when the effective action is constrained only by the equation of state and dimensional analysis, significant discrepancies emerge. These discrepancies can be resolved if an additional constraint related to the surface tension is imposed.

I solve the equations of the low-energy limit of string theory to obtain a solution corresponding to a microcanonical ensemble of highly-excited superstrings. This ``Superball of Strings'' is a static, spherically symmetric ``fuzzball'' of BPS strings with a size set by a random walk scaling. The solution can be embedded in string theory in a significant part of parameter space. While the solution does not constitute a Lorentzian interpretation for a Euclidean, horizonless solution by Chen, Maldacena, and Witten, a few connections are noted. A singular extremal black hole and the Superball of Strings exist as Supergravity solutions with the same asymptotic boundary conditions; however, I argue that the latter describes generic BPS microstates.

The aim of this work is to study the zero-error capacity of pure-state classical-quantum channels in the setting of list decoding. We provide an achievability bound for list-size two and a converse bound holding for every fixed list size. The two bounds coincide for channels whose pairwise absolute state overlaps form a positive semi-definite matrix. Finally, we discuss a remarkable peculiarity of the classical-quantum case: differently from the fully classical setting, the rate at which the sphere-packing bound diverges might not be achievable by zero-error list codes, even when we take the limit of fixed but arbitrarily large list size.

Spontaneous symmetry breaking is one of the central organizing principles in physics. Time crystals have emerged as an exotic phase of matter, spontaneously breaking the time translational symmetry, and are mainly categorized as discrete or continuous. While these distinct types of time crystals have been extensively explored as standalone systems, intriguing effects can arise from their mutual interaction. Here, we demonstrate that a time-independent coupled system of discrete and continuous time crystals induces a simultaneous two-fold temporal symmetry breaking, resulting in a hierarchical time crystal phase. Interestingly, one of the subsystems breaks an emergent discrete temporal symmetry that does not exist in the dynamical generator but rather emerges dynamically, leading to a convoluted non-equilibrium phase. We demonstrate that hierarchical time crystals are robust, emerging for fundamentally different coupling schemes and persisting across wide ranges of system parameters.

Topological field theories (TFTs) play an important role in characterizing the deep infrared (IR) of many quantum systems with a mass gap, as well as the global symmetries of quantum field theories (QFTs) decoupled from gravity. In gravitational asymptotically AdS spacetimes, TFT sectors which are putatively decoupled from local metric data are nevertheless non-perturbatively sensitive to Newton's constant via a sum over topologically distinct saddle point configurations. Tracking the fate of this non-decoupling in the boundary dual, we argue that in spite of appearances, this dependence on Newton's constant extends to local metric fluctuations. Said differently, TFTs are in the Swampland. In tandem with earlier results on the absence of global symmetries in theories with subregion-subregion duality, this also establishes that topological operators of boundary systems with a gravity dual are always non-topological in the bulk.

We introduce a category $\mathsf{qGph}$ of quantum graphs, whose definition is motivated entirely from noncommutative geometry. For all quantum graphs $G$ and $H$ in $\mathsf{qGph}$, we then construct a quantum graph $[G,H]$ of homomorphisms from $G$ to $H$, making $\mathsf{qGph}$ a closed symmetric monoidal category. We prove that for all finite graphs $G$ and $H$, the quantum graph $[G,H]$ is nonempty iff the $(G,H)$-homomorphism game has a winning quantum strategy, directly generalizing the classical case. The finite quantum graphs in $\mathsf{qGph}$ are tracial, real, and self-adjoint, and the morphisms between them are CP morphisms that are adjoint to a unital $*$-homomorphism. We show that Weaver's two notions of a CP morphism coincide in this context. We also show that every finite reflexive quantum graph is the confusability quantum graph of a quantum channel, answering a question of Daws.

GW231123 is an exceptional gravitational-wave event consistent with the merger of two massive, highly-spinning black holes. Reliable inference of the source properties is crucial for accurate interpretation of its astrophysical implications. However, characterization of GW231123 is challenging: only few signal cycles are observed and different signal models result in systematically different parameters. We investigate whether the interpretation of GW231123 is robust against model systematics and Gaussian detector noise. We show that the model systematics observed in GW231123 can be reproduced for a simulated signal based on the numerical-relativity surrogate model NRSur7dq4. Simulating data using the maximum-likelihood NRSur7dq4 waveform for GW231123 and no noise realization, we closely recover the systematics observed for the real signal. We then explore how the headline properties of GW231123 are impacted by Gaussian detector noise. Using the NRSur7dq4 maximum-likelihood waveform and different noise realizations, we consistently find support for large masses, high spin magnitudes (median $χ_1\geq 0.7$), and high spin precession (median $χ_\mathrm{p}\geq 0.68$). The spin in the direction of the angular momentum ($χ_\mathrm{eff}$) fluctuates more. Finally, again comparing to simulated signals, we show that any differences in the GW231123 inference based on each separate detector are not statistically significant. These results show that the properties of GW231123, and most importantly the high mass and high spin magnitudes inferred by NRSur7dq4, are robust.

The generation of non-Gaussian quantum states is a key requirement for universal continuous-variable quantum information processing. We report the experimental generation of large-amplitude squeezed coherent-state superpositions (squeezed cat states) on free-space optical pulses, reaching an amplitude of $α= 2.47$, which, to our knowledge, exceeds all previously reported values. Our protocol relies on the controlled mixing of the Fock states $|1\rangle$ and $|2\rangle$ through a tunable beam splitter, followed by heralding via homodyne detection. The resulting state displays three well-resolved negative regions in its Wigner function and achieves a fidelity of $0.53$ with the target state $\propto \hat{S}(z)(|α\rangle - |-α\rangle)$, with $α= 2.47$ and squeezing parameter $z = 0.56$. These results constitute a significant milestone for temporal breeding protocols and for the iterative generation of optical GKP states, opening new perspectives for scalable and fault-tolerant photonic quantum architectures.

In this paper, we explore the inflationary dynamics of the $β$-exponential potential model, where a scalar field couples to quadratic $(R + R^2)$ gravity. In this model, the inflaton is the field that determines the size of the extra dimension. We employ the Palatini formalism to derive the resulting Einstein-frame generalized $k$-inflation effective theory, which we analyze under the assumption that the constant-roll condition is satisfied. We scan the parameter space for inflationary predictions, specifically the spectral index $n_s$ and the tensor-to-scalar ratio $r$, ensuring consistency with the results from ACT DR6. The compliant regions are depicted accordingly. For a suitable range of the model parameters, the values obtained for the inflationary observables align with the most recent observations by the Atacama Cosmology Telescope (ACT) collaboration and/or the Planck mission.

We present a closed-form surrogate for the equivalent diameter of the Kerr black-hole shadow, defined as the diameter of the circle with the same area as the shadow's critical curve. The construction enforces the exact face-on (polar) limit by explicitly separating an analytically computed polar contribution based on the spherical photon-orbit branch where the horizontal impact parameter vanishes. The remaining inclination dependence is captured by a compact 15-parameter polynomial placed inside an exponential correction. The coefficients are determined by ordinary least squares on a deterministic reference grid generated from the Kerr critical-curve area. Over the practical domain of dimensionless spin from 0 to 0.998 and inclination from just above 0 degrees up to 90 degrees (with the exactly polar point treated analytically), the surrogate achieves sub-percent accuracy. On the training grid the median absolute percent error is 0.0105 percent with a worst case of 0.782 percent, and on a denser out-of-sample validation set (including inclinations down to 0.5 degrees) the median, 95th-percentile, and worst-case errors are 0.023 percent, 0.471 percent, and 1.64 percent, respectively. The resulting expression provides fast evaluations of the shadow size without numerical ray tracing, making it convenient for repeated calls in parameter inference and rapid model comparisons.

Molecular spins are promising candidates for quantum information science, leveraging coherent electronic spin states for quantum sensing and computation. However, the practical application of these systems is hindered by electronic spin decoherence, driven by interactions with nuclear spins in the molecule and the surrounding environment at low temperatures. Predicting dephasing dynamics remains a formidable challenge due to the complexity of the spin bath. In this work, we develop a non-Markovian time-convolutionless master equation to treat an electronic spin coupled to a nuclear-spin bath. By relating ab initio electronic structure parameters directly to the decoherence dynamics, we provide a framework that accounts for pure dephasing in the low-temperature limit. We apply this method to a series of molecular qubit candidates and demonstrate good agreement with experimental relaxation trends. This approach offers a computationally efficient path for the prediction of low-temperature decoherence trends in molecular spin systems.

We present the electro-thermal characterization of transition-edge sensor (TES) detectors suspended on Si membranes fabricated using a silicon-on-insulator (SOI) wafer. The use of an all-silicon fabrication platform, in contrast to the more commonly used silicon nitride membranes, is compatible with monolithic fabrication of integrated TES and SQUID circuits. The all-silicon architecture additionally allows efficient use of focal plane area; the readout circuitry may be positioned out of the focal plane by bending a thinned portion of the chip. Compatibility with integrated fabrication and efficient use of focal plane area provide a path to an efficient soft X-ray spectrometer. This work is motivated by our goal to develop a 10,000-pixel TES spectrometer to overcome critical measurement limitations in catalysis research. The characterization of fragile, carbon-based intermediates via techniques like Resonant Inelastic X-ray Scattering (RIXS) is often precluded by the slow, high-flux nature of existing technologies. The new instrument will allow for fast RIXS measurements to be made without causing sample damage. We verify the detector models and measure the energy resolution using a pulsed optical laser, demonstrating the viability of this approach for the final instrument to be deployed at the National Synchrotron Light Source II (NSLS-II).

We explore the existence and stability of exceptional points (EPs) in finite waveguide arrays subject to single-site dissipation. We show that the EP landscape is dictated by a geometry-dependent parity effect, leading to strictly distinct spectral behaviors for arrays with even versus odd numbers of waveguides. Through analytical derivation and numerical analysis, we define the conditions under which these singularities emerge and evolve. Our findings clarify the mechanisms of symmetry breaking in finite non-Hermitian lattices, offering new guidelines for the design of robust optical structures that exploit or avoid exceptional points.

Quantum Field Theory (QFT) introduced us to the notion that a causal diamond in space-time corresponded to a subsystem of a quantum mechanical system defined on the global space-time. Work by Jacobson, Fischler and Susskind, and particularly Bousso suggested that, in the quantum theory of gravity, this subsystem should have a density matrix of finite entropy. These authors formalized older intuitive arguments based on black hole physics. Although mathematically, Type II von Neumann algebras admit finite entropy density matrices, the black hole arguments suggest that the number of physical states in these subsystems is finite. The conjecture that de Sitter (dS) space has a finite number of physical states was first made by Fischler and one of the present authors. Leutheusser and Liu showed that, in the $N = \infty$ limit, causal diamonds with finite area in AdS radius units had Type $III_1$ von Neumann sub-algebras of the full operator algebra. They claimed that this was true for finite values of the UV cutoff, and that the algebra was the algebra of bulk local fields in the diamond. We will argue that the second part of this conjecture is incorrect and that the bulk field algebra emerges only in a double scaled limit, where the boundary UV cutoff is taken to infinity as $N$ is taken to infinity. There is never a bulk field theory description that resolves distances smaller than the AdS radius.

The accurate computation of quasinormal modes from rotating black holes beyond general relativity is crucial for testing fundamental physics with gravitational waves. In this study, we assess the accuracy of the eikonal and post-Kerr approximations in predicting the quasinormal mode spectrum of a scalar field on a deformed Kerr spacetime. To obtain benchmark results and to analyze the ringdown dynamics from generic perturbations, we further employ a 2+1-dimensional numerical time-evolution framework. This approach enables a systematic quantification of theoretical uncertainties across multiple angular harmonics, a broad range of spin parameters, and progressively stronger deviations from the Kerr geometry. We then confront these modeling errors with simple projections of statistical uncertainties in quasinormal mode frequencies as a function of the signal-to-noise ratio, thereby exploring the domain of validity of approximate methods for prospective high-precision black-hole spectroscopy. We also report that near-horizon deformations can affect prograde and retrograde modes differently and provide a geometrical explanation.

Highly multipartite entangled states play an important role in various quantum computing tasks. We investigate the dissipative generation of a complex entanglement structure as in a cluster state through engineered Markovian dynamics in the spin systems coupled via Ising interactions. Using the Lindblad master equation, we design a projection based dissipative channel that drives the system toward a unique pure steady state corresponding to the desired cluster state. This is done by removing the contribution of the orthogonal states. By explicitly constructing the Liouvillian superoperator in the full $2^N$-dimensional Hilbert space, we compute the steady-state density matrix, the Liouvillian spectral gap, entanglement witness and the fidelity with respect to the ideal cluster state. The results demonstrate that the cluster state emerges as the steady state when the engineered Liouvillian dissipation dominates over the local Ising interaction between spins. Moreover, we find that the fidelity and Liouvillian spectral gap is relatively insensitive to the system size once the saturation dissipation has been achieved that scales linearly with the qubit number. This analysis illustrates a physically realizable path towards steady-state entanglement generation in the spin systems using engineered dissipation.

The pseudocomplex version of the FLRW model is presented within the framework of pseudocomplex General Relativity (pcGR). In this approach, dark energy arises as a geometric consequence of the pseudocomplex structure, leading to a time dependent Hubble parameter rather than a strictly constant H0. The relation between the tiderived and constrained using recent DESI BAO data. Fitting beta yields a best-fit value beta = 1.0426, corresponding to a deceleration parameter q = -0.9361 and a present day Hubble acceleration me derivative of the Hubble parameter and a single geometric parameter beta in the effective dark energy equation of state is derived and constrained using recent DESI BAO data. Fitting beta yields a best-fit value beta = 1.0426, corresponding to a deceleration parameter q = -0.9361 and a present day Hubble acceleration H0 sim 0.94 x10-17 (km/s2)/Mpc. Using the exact Sandage Loeb relation, the predicted redshift drift over 20 years for a source at z = 4 is Delta-v sim -11.1 cm/s, in close agreement with the Lambda CDM prediction. In pcGR, however, the non-vanishing H0 is a direct geometric prediction, providing a clear and testable target for future high-precision spectroscopic observations.

Compared to other stable isotopes of $\rm{Yb}^+$, $^{173}\rm{Yb}^+$ has a richer hyperfine structure, which leads to more favorable clock transitions, spectroscopic techniques for probing new physics, and more sophisticated quantum computing architectures. However, to date, its electronic spectrum remains poorly characterized. Here, we report on efficient laser cooling, state preparation, and detection of a single trapped $^{173}\rm{Yb}^+$ ion. The previously unobserved $^2\!S_{1/2} \rightarrow {}^2\!D_{3/2}$ electric quadrupole transition at 436 nm is coherently excited, and the isotope shift between $^{171}\rm{Yb}^+$ and $^{173}\rm{Yb}^+$ on this transition is determined with an uncertainty of 1.4 Hz. Using microwave spectroscopy, we resolve the hyperfine structure (HFS) of the ${}^2\!D_{3/2}$ state with a relative uncertainty below $10^{-8}$. From the HFS measurement data, we infer for ${}^{173}$Yb a nuclear magnetic octupole moment $Ω= -0.062(8)\,({\rm b} \times μ_N)$ with uncertainty reduced by more than 2 orders of magnitude compared to previous studies. The data also allow us to determine hyperfine anomalies for the ${}^2\!S_{1/2}$ and ${}^2\!D_{3/2}$ states.

We extend a recently developed "tangent fermion" method to discretize the Hamiltonian of a helical Luttinger liquid on a one-dimensional lattice, including two-particle backscattering processes that may open a gap in the spectrum. The fermion-doubling obstruction of the sine dispersion is avoided by working with a tangent dispersion, preserving the time-reversal symmetry of the Hamiltonian. The numerical results from a tensor network calculation on a finite lattice confirm the expectation from infinite-system analytics, that a gapped phase with spontaneously broken time-reversal symmetry emerges when the Fermi level is tuned to the Dirac point and the Luttinger parameter crosses a critical value.

The Berry phase, a fundamental geometric phase in quantum systems, has become a crucial tool for probing the topological properties of materials. Quantum oscillations, such as Shubnikov-de Haas (SdH) oscillations, are widely used to extract this phase, but its unambiguous determination remains challenging. This work highlights the inherent ambiguities in interpreting the oscillation phase solely from SdH data, primarily due to the influence of the spin factor $R_S$, which depends on the Landé $g$-factor and effective mass. While the Lifshitz-Kosevich (LK) theory provides a framework for analyzing oscillations, the unknown g-factor introduces significant uncertainty. For instance, a zero oscillation phase could arise either from a nontrivial Berry phase or a negative $R_S$. We demonstrate that neglecting $R_S$ in modern studies, especially for topological materials with strong spin-orbit coupling, can lead to doubtful conclusions. Through theoretical analysis and numerical examples, we show how the interplay between the Berry phase and Zeeman effect complicates phase determination. Additionally, we also discuss another underappreciated mechanism - the magnetic field dependence of the Fermi level. Our discussion underscores the need for complementary experimental techniques to resolve these ambiguities and calls for further research to refine the interpretation of quantum oscillations in topological systems.

We investigate the quantum properties of heavy-fermion pairs, such as $t\bar t$ or $τ^+τ^-$, produced in lepton-lepton collisions with polarised beams. Focusing on spin correlations, entanglement, Bell-inequality violation, and quantum-information--theoretic measures such as purity and magic, we analyse how beam polarisation shapes the structure of the spin-density matrix. We derive analytic expressions for a wide range of helicity configurations, including both Standard Model contributions and generic new-physics effects parametrised by scalar, vector, and tensor four-fermion operators within an effective field theory framework. We show that beam polarisation unlocks a substantially richer set of spin configurations and significantly enhances sensitivity to non-standard interactions. As a phenomenological application, we study $t\bar t$ production at a future linear collider and demonstrate that quantum observables provide a comprehensive and complementary probe of top-quark interactions and stronger constraints on the scale of new physics.

This book gives a geometry-first, hardware-aware route through quantum-information workflows, with one goal: connect states, circuits, and measurement to deterministic classical pipelines that make hybrid quantum systems run. Part 1 develops the backbone (essential linear algebra, the Bloch-sphere viewpoint, differential-geometric intuition, and quantum Fisher information geometry) so evolution can be read as motion on curved spaces and measurement as statistics. Part 2 reframes circuits as dataflow graphs: measurement outcomes are parsed, aggregated, and reduced to small linear-algebra updates that schedule the next pulses, highlighting why low-latency, low-jitter streaming matters. Part 3 treats multi-qubit structure and entanglement as geometry and computation, including teleportation, superdense coding, entanglement detection, and Shor's algorithm via quantum phase estimation. Part 4 focuses on topological error correction and real-time decoding (Track A): stabilizer codes, surface-code decoding as "topology -> graph -> algorithm", and Union-Find decoders down to microarchitectural/RTL constraints, with verification, fault injection, and host/control-stack integration under product metrics (bounded latency, p99 tails, fail-closed policies, observability). Optional Track C covers quantum cryptography and streaming post-processing (BB84/E91, QBER/abort rules, privacy amplification, and zero-knowledge/post-quantum themes), emphasizing FSMs, counters, and hash pipelines. Appendices provide visualization-driven iCEstick labs (switch-to-bit conditioning, fixed-point phase arithmetic, FSM sequencing, minimal control ISAs), bridging principles to implementable systems.

Modern understanding of symmetry in quantum field theory includes both invertible and non-invertible operations. Motivated by this, we extend Nielsen's geometric approach to quantum circuit complexity to incorporate non-invertible gates. These arise naturally from fusion of topological defects and allow transitions between superselection sectors. We realise fusion operations as completely positive, trace-preserving quantum channels. Including such gates makes the sector-changing optimisation problem discrete: it reduces to a weighted shortest-path problem on the fusion graph. Circuit complexity therefore combines continuous geometry within sectors with discrete sector jumps. We illustrate the framework in rational conformal field theories and briefly comment on an AdS$_3$ interpretation in which fusion-induced transitions correspond to geometry-changing boundary operations. A companion paper provides full derivations and extended examples.

Two of the most pressing challenges in cosmology are the persistent discrepancy in measurements of the Hubble constant, referred to as the Hubble tension, and the deficit of baryons in the local Universe, known as the missing baryon problem. Fast radio bursts (FRBs) encode the integrated electron column density along their lines of sight, offering a unique probe of both the cosmic expansion rate ($H_0$) and the baryon density ($Ω_{\rm b}$). However, constraints from FRBs alone suffer from a severe $H_0$-$Ω_{\rm b}$ degeneracy that prevents them from resolving either problem independently. We show that this degeneracy can be broken by combining FRBs with other emerging probes whose degeneracy directions differ in the $H_0$-$Ω_{\rm b}$ plane. Specifically, we quantify three multi-messenger approaches: FRBs paired with gravitational wave (GW) standard sirens, strong gravitational lensing (SGL) time delays, and 21 cm intensity mapping (IM). The combinations FRB+GW, FRB+SGL, and FRB+21 cm IM each deliver simultaneous constraints on $H_0$ and $Ω_{\rm b}$ better than ($1\%$, $1\%$) in the $Λ$CDM model, ($1.5\%$, $2\%$) in the $w$CDM model, and ($2\%$, $3.5\%$) in the CPL model. Moreover, in a model-independent framework, both FRB+GW and FRB+SGL constrain $H_0$ and $Ω_{\rm b}$ to better than ($1\%$, $2\%$) precision. These results demonstrate that the synergy between FRBs and other emerging probes holds great promise for resolving the Hubble tension and locating the missing baryons.

We present an analytically tractable theory of reservoir-engineered refrigeration of a superconducting microwave cavity and map it onto a realistic solid-state implementation based on gate-defined double-quantum-dot (DQD) spin qubits. Treating the DQD not as a spectroscopic element but as a tunable engineered reservoir, we show how gate control of populations, coherences, linewidths, and detuning defines an effective photon birth-death process with predictable detailed balance. This framework yields closed-form expressions for the cavity steady state, identifies cooling bounds and detuning-dependent refrigeration valleys, and clarifies when refrigeration can drive the cavity below both the bath temperature and the DQD setpoint. By distinguishing refreshed (collision-like) and persistent reservoir regimes, we show how memory effects, saturation, and dark-state formation constrain cooling in realistic devices, while collective bright-mode coupling in a two-dot configuration can enhance refrigeration subject to mismatch and dephasing, as confirmed by numerical Lindblad simulations demonstrating targeted millikelvin cavity cooling relevant for cryogenic circuit-QED architectures.

We investigated the spectral properties of squeezed light generated via parametric down-conversion in the high-gain regime, considering both unapodized and apodized dispersion-engineered waveguides. The gain-dependent evolution of these states is examined starting from the low-gain regime, which includes both highly correlated and nearly uncorrelated cases. For the unapodized configuration, we observe a monotonic increase in spectral purity with gain, whereas the apodized configuration exhibits a nonmonotonic dependence, initially decreasing and then recovering at higher gain. By combining Schmidt-mode analysis with a group-velocity-based interpretation, we explain why different dispersion conditions exhibit distinct gain-dependent behavior, specifically that rapid purification occurs when the pump group velocity lies between those of the signal and idler. Our study shows that the evolution of spectral purity is governed primarily by the underlying dispersion of the waveguide. These results demonstrate that dispersion engineering and parametric gain can be jointly exploited to tailor the spectral-mode structure of squeezed-light sources, enabling their optimization for a broad range of quantum applications.

We investigate tidal forces, geodesic deviation, and tidal disruption in the black hole spacetime described by the Kalb-Ramond-ModMax solution, where electromagnetic nonlinearity is governed by the parameter $γ$ and Lorentz symmetry violation by the parameter $l$. In the canonical sector ($α=1$), the radial tidal force exhibits a transition marked by a sign inversion between the horizons $r_{-}$ and $r_+$, signaling internal regimes of radial compression analogous to those of charged black holes; the parameter $l$ controls the strength and location of this transition, while $γ$ regulates the nonlinear electromagnetic contribution. The angular tidal force is predominantly compressive, $l$ shaping the effective geometry, and $γ$ acting as a damping factor. In the phantom sector ($α=-1$), tidal forces and geodesic deviation diverge, indicating a tidal instability, with $l$ and $γ$ affecting only the magnitude of the response. We further show that $l$ shifts the relation between the horizon radius $r_+$ and the tidal disruption radius $r_{\rm Roche}$, thereby modifying the critical (Hills) mass defined by $r_{\rm Roche}=r_+$. Tidal disruption of neutron stars occurs inside the horizon for supermassive black holes, whereas Sun-like stars are disrupted outside the horizon, with $γ$ becoming relevant only for ultramassive black holes with masses $\sim 10^{8}M_{\odot}$. Our results demonstrate that Kalb-Ramon-ModMax effects are largely suppressed for supermassive black holes, but may be relevant for intermediate-mass systems and observable tidal disruption events, offering an indirect probe of Lorentz violation and nonlinear electrodynamics in the strong-field regime.

The NANOGrav 15-year (NG15) data set provides evidence for a gravitational-wave background (GWB) signal at nanohertz frequencies, which is expected to originate either from a cosmic population of inspiraling supermassive black-hole binaries or new particle physics in the early Universe. A firm identification of the source of the NG15 signal requires an accurate reconstruction of its frequency spectrum. In this paper, we provide such a spectral characterization of the NG15 signal based on a piecewise power-law (PPL) ansatz that strikes a balance between existing alternatives in the literature. Our PPL reconstruction is more flexible than the standard constant-power-law model, which describes the GWB spectrum in terms of only two parameters: an amplitude A and a spectral index gamma. Concurrently, it better approximates physically realistic GWB spectra -- especially those of cosmological origin -- than the free spectral model, since the latter allows for arbitrary variations in the GWB amplitude from one frequency bin to the next. Our PPL reconstruction of the NG15 signal relies on individual PPL models with a fixed number of internal nodes (i.e., constant power law, broken power law, doubly broken power law, etc.) that are ultimately combined in a Bayesian model average. The data products resulting from our analysis provide the basis for fast refits of spectral GWB models.

The answers on the current status and future development of Quantum Science and Technology are presented.

In this work we show that results of Rayleigh-Schrödinger perturbation theory can be easily obtained using the recently proposed supersymmetric expansion algorithm. Our formalism avoids the sums over intermediate states and yield directly corrections to the energy and eigenstates in terms of integrals weighted by the probability densities for the edge states of the involved supersymmetric Hamiltonians.

Non-Gaussian mechanical states are a key resource for quantum-enhanced sensing and tests of macroscopic quantum physics. We propose a measurement-based protocol to herald delocalized, nonclassical states of a mechanical oscillator in cavity optomechanics by conditioning on Geiger photodetection of the optical output. We analyse under which conditions Stokes-induced optomechanical entanglement give rise to mechanical Wigner Function negativity upon detection. We develop and compare a blue-detuned pulsed scheme and a continuous-wave steady-state scheme employing temporal-mode filtering, and we quantify heralding rates and robustness to finite temperature under realistic detection efficiencies.

We here investigate the observational viability of Nash gravity as an alternative to the standard $Λ$CDM cosmology. Based on Nash's embedding theorem, the model introduces orthogonal perturbations via variations in the extrinsic curvature, generating scalar-type metric perturbations directly from geometry, without the need to introduce additional fields. We confront the model with current observational data, including Cosmic Microwave Background (CMB) measurements from Planck, Baryon Acoustic Oscillations (BAO) from DESI DR2, and recent Type Ia supernova (SN Ia) compilations. Our analysis shows that Nash gravity provides a good fit to the data, yielding a slightly higher value for the Hubble constant, $H_0 = 69.32 \pm 0.72$ km/s/Mpc, compared to the $Λ$CDM model, thus offering a potential alleviation of the $H_0$ tension. Furthermore, the model naturally predicts a suppressed growth of structure, with $S_8 \approx 0.76$ across various joint analyses, potentially alleviating the so-called $S_8$ tension, assuming that this discrepancy is not solely due to systematic effects in other independent measurements. In some cases, Nash gravity achieves a better fit to the data than the $Λ$CDM paradigm at the $2σ$ level.

The open quantum Rabi model is studied in this work, with the explicit $\mathbf{A}^{2}$ term incorporated as required by the Thomas-Reich-Kuhn sum rule. It is shown that anisotropy provides a generic and robust mechanism for overcoming the no-go theorem in dissipative quantum systems, thereby establishing a genuine platform for observing dissipative phase transitions. The inclusion of the $\mathbf{A}^{2}$ term yields a significantly richer and asymmetric steady-state phase diagram, consisting of normal, superradiant, and bistable phases that intersect at tricritical points, while isolated bistable phases also emerge and the number of tricritical points is reduced. Notably, it is near the intersection of the two critical-line branches enclosing the superradiant phases, rather than at the tricritical points, that the $\mathbf{A}^{2}$ term fundamentally alters the scaling of photon-number fluctuations. Given the inherent role of the $\mathbf{A}^{2}$ term in light-matter interactions, our findings open a realistic route toward the experimental investigation and dynamical control of nonequilibrium critical phenomena in practical open quantum platforms.

Holographic black holes exhibit a striking relation between thermal boundary one-point functions and bulk geodesic lengths. In the large conformal-dimension limit, the one-point function of a primary operator is given by the exponential of the geodesic length from its boundary insertion point to the horizon. We test the robustness of this relation under perturbations by considering an arbitrary radial deformation of an Euclidean BTZ black hole and working to first order in the perturbation. We find that the relation remains robust: the corrected one-point function at large conformal dimension is still governed by an exponent proportional to the modified boundary-to-horizon geodesic length. The result is established using WKB and saddle-point methods, with the validity of the WKB approximation justified by exact analyses.

The preparation of quantum states is one of the most fundamental tasks in quantum computing, and a key primitive in many quantum algorithms. Of particular interest to areas such as quantum simulation and linear-system solvers are sparse quantum states, which contain only a small number $s$ of non-zero computational basis states compared to a generic state. In this work, we present an approach that prepares $s$-sparse states on $n$ qubits, reducing the number of Toffoli gates required compared to prior art. We work in the established framework of first preparing a dense state on a $\lceil{\log(s)}\rceil$-qubit sub-register, and then mapping this state to the target state via an isometry, with the latter step dominating the cost of the full algorithm. The speed-up is achieved by designing an efficient algorithm for finding and implementing the isometry. The worst-case Toffoli cost of our isometry circuit, which may be viewed as a batched version of an approach by Malvetti et al., is essentially $2s$ for sufficiently large values of $n$, yielding roughly a $\log(s)/2$ improvement factor over the state-of-the-art. In numerical benchmarks on randomly chosen states, the cost is closer to $s$. With the improved isometry circuit, we examine the dense-state preparation step and present ways to optimize the joint cost of both steps, particularly in the case of target states with purely real coefficients, by outsourcing some sub-tasks from the dense-state preparation to the isometry.

In fault-tolerant quantum computing, a large number of physical qubits are required to construct a single logical qubit, and a single quantum node may be able to hold only a small number of logical qubits. In such a case, the idea of distributed fault-tolerant quantum computing (DFTQC) is important to demonstrate large-scale quantum computation using small-scale nodes. However, the design of distributed systems on small-scale nodes, where each node can store only one or a few logical qubits for computation, has not been explored well yet. In this paper, we propose network-based quantum computation (NBQC) to efficiently realize distributed fault-tolerant quantum computation using many small-scale nodes. A key idea of NBQC is to let computational data continuously move throughout the network while maintaining the connectivity to other nodes. We numerically show that, for practical benchmark tasks, our method achieves shorter execution times than circuit-based strategies and more node-efficient constructions than measurement-based quantum computing. Also, if we are allowed to specialize the network to the structure of quantum programs, such as peak access frequencies, the number of nodes can be significantly reduced. Thus, our methods provide a foundation in designing DFTQC architecture exploiting the redundancy of many small fault-tolerant nodes.

One of the key considerations in the development of Quantum Machine Learning (QML) protocols is the encoding of classical data onto a quantum device. In this chapter we introduce the Matrix Product State representation of quantum systems and show how it may be used to construct circuits which encode a desired state. Putting this in the context of QML we show how this process may be modified to give a low depth approximate encoding and crucially that this encoding does not hinder classification accuracy and is indeed exhibits an increased robustness against classical adversarial attacks. This is illustrated by demonstrations of adversarially robust variational quantum classifiers for the MNIST and FMNIST dataset, as well as a small-scale experimental demonstration on a superconducting quantum device.

Coherent multidimensional spectroscopy enables detailed investigations of vibronic effects in molecular and solid-state systems. We present explicit analytical expressions for multidimensional nonlinear response functions in the presence of Herzberg-Teller (non-Condon) coupling, within the displaced harmonic oscillator model. The formulation applies to electronic systems with an arbitrary number N of electronic states and to response functions of arbitrary order M in the light-matter interaction. We show that Herzberg-Teller coupling introduces additional oscillatory factors in the time-domain response functions, leading, upon Fourier transformation, to replicas of the Franck-Condon multidimensional spectra shifted by integer multiples of the vibrational frequencies. The present results provide a general analytical framework for the interpretation of non-Condon effects in coherent multidimensional spectroscopies.

We study the CHSH inequality from an informational, timing-sensitive viewpoint using game-theoretic probability, which avoids assuming an underlying probability space. The locality loophole and the measurement-dependence (``freedom-of-choice'') loophole are reformulated as structural constraints in a sequential hidden-variable game between Scientists and Nature. We construct a loopholes-closed game with capital processes that test (i) convergence of empirical conditional frequencies to the CHSH correlations and (ii) the absence of systematic correlations between measurement settings and Nature's hidden-variable assignments, and prove that Nature cannot satisfy both simultaneously: at least one capital process must diverge. This yields an operational winning strategy for Scientists and a game-theoretic probabilistic interpretation of experimentally observed CHSH violations.

The continual production of long wavelength scalars and gravitons during inflation injects secular growth into loop corrections which would be constant in flat space. One typically finds that each additional factor of the loop counting parameter can induce up to a certain number of logarithms of the scale factor. Loop corrections that attain this number are known as ``leading logarithms''; those with fewer are sub-leading. Starobinsky's stochastic formalism has long been known to reproduce the leading logarithms of scalar potential models. We show that the first sub-leading logarithm is captured by applying the stochastic formalism to a certain part of the 1-loop effective potential. This is checked at 2-loops for a massless, minimally coupled scalar with a quartic self-interaction on de Sitter background.

The generalized quantum Arimoto--Blahut (QAB) algorithm is a powerful derivative-free iterative method in quantum information theory. A key obstacle to its broader use is that existing convergence guarantees typically rely on analytical conditions that are either overly restrictive or difficult to verify for concrete problems. We address this issue by introducing an a posteriori certification viewpoint: instead of requiring fully a priori verifiable assumptions, we provide convergence and error guarantees that can be validated directly from the iterates produced by the algorithm. Specifically, we prove a generalized global convergence theorem showing that, under convexity and a substantially weaker numerically verifiable condition, the QAB iteration converges to the global minimizer. This theorem yields a practical certification procedure: by checking explicit inequalities along the computed trajectory, one can certify global optimality and bound the suboptimality of the obtained value. As an application, we develop a certified iterative scheme for computing the quantum relative entropy of channels, a fundamental measure of distinguishability in quantum dynamics. This quantity is notoriously challenging to evaluate numerically: gradient-based methods are impeded by the complexity of matrix functions such as square roots and logarithms, while recent semidefinite programming approaches can become computationally and memory intensive at high precision. Our method avoids these bottlenecks by combining the QAB iteration with a posteriori certification, yielding an efficient and scalable algorithm. Numerical experiments demonstrate rapid convergence and improved scalability and adaptivity compared with SDP-based approaches.

A new interferometer topology for kilohertz gravitational-wave detection was recently proposed in [Zhang et al. Phys. Rev. X 13, 021019 (2023)]. The design is based on an L-shaped optical cavity pumped through a Sagnac-like vortex. We report a tabletop experiment that characterizes the interferometer's optical response near DC. When the laser frequency is locked to the resonance of the L-shaped cavity, we observe that the cavity input coupler becomes effectively transparent, yielding a simple Michelson-like response. Moreover, the Sagnac vortex separates into upper and lower paths, which behave as two independent pumping paths driving the cavity. These observations are in agreement with theoretical predictions. Our results provide an intuitive physical picture of this interferometer topology and offer insight into its lock acquisition strategy.

In this article, we explore the use of contour deformation for the numerical evaluation of Feynman integrals after sector decomposition. In existing codes, the contour of integration is determined heuristically for each phase-space point by sampling the integrand. In this work, we introduce a method for choosing the contour deformation for an entire phase-space region using only an initial sampling or training step. We demonstrate that the resulting integrand has a lower variance than that obtained with heuristic methods and show that optimising a contour to reduce the estimated error of a Quasi-Monte Carlo sample is an ill-defined problem. The a priori knowledge of the integration path obtained in this work can be used to improve the speed of conventional integration methods or be leveraged for integration using neural networks, where, crucially, it removes the need to retrain the neural network for each phase-space point. The techniques described in this work can be adapted to other problems where a non-trivial integration path has to be chosen subject to a set of constraints.

Energy levels of QED bound states, which depend on a number of independent mass parameters, can be calculated as matrix elements of the QED energy-momentum trace. As an example of such system we consider muonic hydrogen. The leading one-loop corrections to its energy levels depend on the electron and muon masses. These corrections are calculated as matrix elements of the energy-momentum trace. Respective one-loop trace diagrams are different from the standard Lamb shift diagrams. We explain analytically and diagrammatically why two different sets of diagrams lead to the same results. Similar relationships should also hold beyond the one-loop approximation.

The Future Circular Collider (FCC) stands at the forefront of the European Strategy for Particle Physics as the future flagship project at CERN. The H$\toττ$ decay, featuring a large branching ratio, clean identification in the FCC-ee environment, and the possibility to reconstruct polarization information, is an excellent channel to measure Higgs properties. This work shows the expected precision for the H$\toττ$ cross-section measurement at the FCC-ee in the ZH production mechanism at $\sqrt{s}=$240 GeV and $\sqrt{s}=$365 GeV, as well as via the vector boson fusion process at $\sqrt{s}=$365 GeV. Furthermore, we explore and evaluate a set of methods for reconstructing tau decays. These techniques are critical for unlocking the full physics potential of the FCC-ee and for improving the understanding of tau-related observables in both Standard Model measurements and New Physics searches. The results obtained significantly enhance the FCC-ee outlook in the H$\toττ$ channel, improving it by at least an order of magnitude compared to the current sensitivity of measurements' performance at the LHC.

A search for dark matter particles produced in association with a Higgs boson decaying to a bottom quark-antiquark pair in proton-proton collisions at $\sqrt{s}$ = 13 TeV is presented. The data, collected with the CMS detector at the LHC, correspond to an integrated luminosity of 101 fb$^{-1}$. The analysis is performed in exclusive categories targeting both Lorentz-boosted (merged) and resolved b jet pair topologies, covering a wide range of Higgs boson transverse momentum. A statistical combination is made with a previous search using data collected in 2016 and corresponding to an integrated luminosity of 35.9 fb$^{-1}$. The observed data agree with the standard model background predictions. Constraints are placed on models predicting new particles or interactions, such as those in the simplified frameworks of baryonic-Z' and 2HDM+a, where the latter is a type-II two-Higgs-doublet model featuring a heavy pseudoscalar with an additional light pseudoscalar. Upper limits at 95% confidence level are set on the production cross section for these models. For the baryonic-Z' model, Z' boson masses below 2.25 TeV are excluded for a dark matter particle candidate mass of 1 GeV. In the 2HDM+a model, heavy pseudoscalar masses between 850 and 1300 GeV are excluded for a light pseudoscalar mass of 350 GeV.

We report on a blinded search for dark matter (DM) using ionization-only (S2-only) signals in XENONnT with a total exposure of $7.83\mathrm{tonne}\times\mathrm{year}$ over 579 days in three science runs. Dedicated background suppression techniques and the first complete S2-only background model in XENONnT provide sensitivity to nuclear recoils of [0.5, 5.0] $\mathrm{keV_\mathrm{nr}}$ and electronic recoils of [0.04, 0.7] $\mathrm{keV_\mathrm{ee}}$. No significant excess over the expected background is observed, and we set 90\% confidence level upper limits on spin-independent DM--nucleon and spin-dependent DM--neutron scattering for DM masses between 3 and 8 $\mathrm{GeV}/c^2$, as well as on DM--electron scattering, axion-like particles, and dark photons, improving on previous constraints. For spin-independent DM--nucleon scattering, we exclude cross sections above $6.0\times10^{-45} $cm$^2$ at a DM mass of 5 $\mathrm{GeV}/c^2$, pushing the XENONnT sensitivity closer to the region where coherent elastic neutrino-nucleus scattering ($\text{CE}ν\text{NS}$) becomes an irreducible background.

The Belle and Belle II experiments have collected a 1.2 ab$^{-1}$ sample of collisions at a center-of-mass energy corresponding to the $Υ(4S)$ resonance. These datasets, with low particle multiplicity and constrained initial state kinematics, are an ideal environment to search for rare electroweak penguin $B$ decays and lepton-flavor-violating $B$ decays to final states with missing energy from neutrinos. Results from $b \to sν\barν$ processes and their interpretation are presented. In addition, we provide an overview of the search for the $B \to K^{*0}τ^+τ^-$ decays and the lepton-flavor violating decays $B^0 \to K^{(*)0}τ^\pm\ell^\mp$, where $\ell$ is an electron or a muon.

We investigate higher-order perturbative corrections to hadronic $τ$ decays by applying nonlinear sequence-transformation techniques to the QCD correction $δ^{(0)}$. In particular, we employ the Shanks transformation and several of its generalisations constructed through Wynn's $\varepsilon$-algorithm, which are known to accelerate the convergence of slowly convergent or divergent series. These methods are used to extract higher-order information from the fixed-order perturbative expansion of $δ^{(0)}$. Within this framework, we estimate the perturbative coefficients $c_{5,1}$-$c_{12,1}$. In particular, we obtain $c_{5,1}=294^{+41}_{-21}$, $c_{6,1}=3415^{+450}_{-467}$, and $c_{7,1}=2.23^{+0.75}_{-0.49}\times 10^4$, where the quoted uncertainties reflect the spread among the different sequence transformations employed. Our analysis demonstrates that Shanks-type sequence transformations based on Wynn's $\varepsilon$-algorithm provide an efficient and systematic tool for probing higher-order perturbative effects in hadronic $τ$ decays in the absence of explicit multi-loop calculations.

A method to improve the reconstruction of charged b-hadron decays is proposed that uses energy deposits left by the hadron in tracking detectors close to the production point. Performances are shown for different detector configurations and different number of deposits reconstructed, as obtained in simulation, for $b$-hadrons produced in high energy proton-proton collisions. It is shown that up to few percent of the $B^+$ mesons could leave two deposits before decaying, depending on the detector configuration. The presented results can inform the design of future inner detectors. This method could increase significantly the physics reach of flavour physics at hadron colliders, opening it to decays with missing particles and vertex information that are otherwise unreconstructable.

We apply a tag-and-probe method to precisely measure the absolute branching fraction of the decay $η_c \to γγ$ with the BESIII experiment at BEPCII. Starting with a large initial sample of $2712.4\pm 14.3$ million $ψ(3686)$ events, a sample of 0.16 million $η_c$ events are tagged via the golden channel $ψ(3686)\to π^0 h_c$, $h_c\to γη_c$, effectively avoiding interference effects. The absolute branching fraction of $η_c \to γγ$ is measured for the first time to be $\mathcal{B}(η_c \to γγ) = (2.45 \pm 0.48_{\rm stat.} \pm 0.09_{\rm syst.}) \times 10^{-4}$. Using the world average value of the total width of the $η_c$, the partial decay width of $η_c \to γγ$ is calculated to be $Γ(η_c \to γγ) = (7.48 \pm 1.48_{\rm stat.} \pm 0.30_{\rm syst.})~{\rm keV}$.

In this work, we study the jet quenching parameter ${\hat q}$ by using a background field effective theory. Particular attention is paid to its behavior near the critical temperature where nonperturbative effects induced by the deconfining phase transition are taken into account through a self-consistently introduced background field ${\cal Q}$. We adopt a theoretical approach in which the interaction rate between the energetic jet and medium partons is computed diagrammatically and the hard-thermal-loop resummed propagator is used to regulate the infrared divergence. In the presence of a background field, its influence on the jet quenching parameter manifests in two aspects. One is the modification on the screening mass in the resummed propagator, which leads to an enhanced ${\hat q}$. The other corresponds to the ${\cal Q}$-modified parton distribution function which is dominant and leads to a suppression of ${\hat q}$. Decreasing the temperature $T$, our result shows a non-monotonic $T$-dependence of the dimensionless ${\hat q}/T^3$. In the high temperature region, ${\hat q}/T^3$ shows an increase with decreasing $T$ due to the running coupling effect. Near the critical temperature, the background field plays a significant role and a dramatic suppression of ${\hat q}/T^3$ is found which qualitatively agrees with the Lattice simulation. In addition, the background field modification on the jet quenching parameter which is characterized by the ${\hat q}$-ratio can be simply parameterized by a polynomial expression depending only on the background field. This expression is expected to be useful for phenomenological applications in jet physics.

Precise QED radiative corrections for low- and high-energy electron-positron colliders are essential for accurate simulations of luminosity processes and precision tests of the Standard Model. We review the historical formulation and the recent developments of the BabaYaga@NLO event generator, which implements a QED Parton Shower matched with fixed-order calculations. We discuss the theoretical formulation of the code, as well as the assessment of its theoretical accuracy. Applications at low- and high-energy $e^+e^-$ colliders are presented, including latest result, together with the perspectives for future improvements, in view of the demanding precision requirements of future machines at the intensity frontier.

We study the HL-LHC sensitivity to an ultraheavy diquark $S_{uu}$ produced in up-quark fusion and decaying as $S_{uu}\to uχ$, $χ\to Wb, Zt, h^0t$. For fully hadronic decays of the W, Z and top quark, this gives rise to multijet final states. Within the same model framework used previously for the $S_{uu}\toχχ$ six-jet channel, we consider $S_{uu}$ masses in the multi-TeV range and vectorlike quark masses of order a few TeV, and simulate proton-proton collisions at $\sqrt s = 13.6$ TeV with integrated luminosities up to the HL-LHC target. The analysis strategy employs a machine-learning-based discriminant adapted from the six-jet study to the new four-jet topology, which we use to derive the corresponding discovery reaches and exclusion limits. We find that this topology improves the overall sensitivity to $S_{uu}$ in regions where the branching ratio $B(S_{uu}\to uχ)$ is sizable and provides a complementary signature for studying ultraheavy diquarks at the HL-LHC.

We investigate the fine-tuning of radiative alpha-particle capture on carbon, $α(^{12}{\rm C},^{16}{\rm O})γ$, at astrophysical energies. Utilizing results from cluster effective field theory for this reaction, we find that the low-energy data of the astrophysical S-factor allow for only very small variations in the electromagnetic fine-structure constant $α$, namely $|δα/α| \leq 0.2\,$ per mille, in both the $E1$ and the $E2$ radiative capture.

A main conjecture in the field of Positive Geometry states that amplituhedra are positive geometries. It is motivated by examples showing that the canonical forms of certain amplituhedra compute scattering amplitudes in particle physics. In recent work, Brown and Dupont introduced a new framework, based on mixed Hodge theory, connecting canonical forms and de Rham cohomology. In this paper, we show that this framework is consistent with the known results for amplituhedra but does not apply beyond those families. We provide an explicit example showing that the central assumption of the Brown-Dupont framework (namely to have a pair of genus zero) is not a necessary condition to be a positive geometry in the original sense of Arkani-Hamed, Bai, and Lam. This underscores the fact that our results do not immediately disqualify the amplituhedron from being a positive geometry.

We revisit the holographic description of the thermal first order phase transition of N=4 SYM compactified on a spatial circle. At the transition, the dominant bulk saddle exchanges between a geometry with a compact spatial circle and one with a compact Euclidean time circle. We construct a one-parameter family of Euclidean geometries that describes the unstable branch of the transition, completing the swallow-tail structure of the free energy. Although these configurations are thermodynamically unstable, they provide a continuous interpolation between the confining soliton and the deconfined black hole phases. Using probe fundamental strings, we show that the theory remains confining along the unstable branch, with a string tension that decreases smoothly and vanishes only in the black hole limit. Introducing fundamental matter via probe D5-branes, we find that chiral symmetry breaking follows the same pattern: the condensate decreases continuously and switches off precisely where confinement disappears. We discuss the implications for the confinement and chiral symmetry breaking mechanisms at large Nc.

We investigate the build-up of elliptic flow of charm quarks produced in the early pre-equilibrium stage of high-energy proton--nucleus collisions. The initial stage is modeled within the Color Glass Condensate framework as an evolving glasma, initialized through the McLerran--Venugopalan model. Subnucleonic fluctuations have been implemented as constituent-quark hotspots for both the proton and the nuclear participants. Charm quarks are propagated in the evolving non-Abelian background by solving the relativistic Wong equations for their coordinates, momenta, and color charges. First, we compute the nuclear modification factor of charm quarks, finding a slight migration towards higher $p_T$ states in agreement with previous results in the literature. Then, we focus on the azimuthal anisotropies acquired through the interaction with glasma fields. We find that glasma-induced momentum anisotropies are efficiently transmitted to heavy quarks within $τ\sim 0.4~\mathrm{fm/c}$, leading to a sizeable charm-quark $v_2$, with a magnitude that increases with the strength of the initial fields and with the number of nuclear participants. Remarkably, we show that the early-stage contribution alone can account for a significant fraction of the experimentally observed $J/ψ$ elliptic flow in p-Pb collisions, indicating that pre-hydrodynamic dynamics can play a non-negligible role in the final-state heavy-flavor collectivity, especially in small systems.

In this work, we study the properties of strange quark matter and reveal the evolution process of strange quark stars employing a self consistent thermodynamic treatment. A comprehensive and reliable thermodynamic basis for the study of the dynamic evolution from proto-strange quark stars to stable strange stars at a zero temperature is provided. The relative abundance of particles, equation of state, temperature, and mass-radius relationship at each stage of the evolution of stars are discussed, where the cold strange quark star are consistent with the observational mass and radius of Hess J1731-347, PSR J1231-1411, PSR J0030+0451, PSR J0348+0432, and PSR J0740+6620, which could be difficult to be explained by the standard neutron star model. A schematic diagram is provided as well, illustrating the state of different stages along the evolution of stars at a fixed baryon-mass.

In this work, we investigate the behavior of the light vector \(ρ\) meson in the presence of a symmetric nuclear medium at zero temperature. We calculate the mass and decay constant of the $ρ$-meson as well as the leading twist distribution amplitudes (DAs) in the light-front quark model in vacuum, which are further investigated at different baryonic densities. We also predict the Mellin moments of the DAs and decay width of the $ρ^0 \to e^+ e^-$ process in both vacuum and medium. The evolution of DAs is carried out by the leading order (LO) Efremov-Radyushkin-Brodsky-Lepage method and compared with available predictions. For better understanding of medium effects on $ρ$-meson, we have also predicted the in-medium charge ($G_C(Q^2)$), magnetic ($G_M(Q^2)$), and quadrupole ($G_Q(Q^2)$) form factors. The in-medium charge radii, magnetic moment, and quadrupole moment have also been predicted in this work. We have found that the nuclear medium induces appreciable modifications on the mass, weak decay constant, decay width, and distribution amplitudes of the \(ρ\) meson. However, the charge radii, magnetic moment, and quadrupole moment are observed to exhibit weaker sensitivity to changes in baryonic density.

We investigate the in-medium spectral functions and effective masses of heavy quarkonia charmonium ($J/Ψ$) and bottomonium ($Υ(1S)$) in a quark-gluon plasma (QGP) possessing both global rotation and spatial anisotropy. Using a gauge/gravity holographic model incorporating finite temperature, chemical potential, and warp factor, we compute the spectral signatures non-perturbatively. Our results show that both rotation and anisotropy enhance quarkonium dissociation, manifesting as peak suppression and width broadening in the spectral functions. Crucially, their effects are directional: anisotropy primarily dissociates longitudinally polarized states, while rotation more strongly disrupts transversely polarized ones. A competitive interplay exists: for small anisotropy, rotational effects dominate at high angular velocity, whereas for large anisotropy, anisotropy governs the dissociation regardless of rotation strength. Furthermore, rotation induces a non-monotonic temperature dependence in the transverse effective mass of $J/Ψ$, while strong anisotropy causes similar non-monotonicity in the longitudinal effective mass of $J/Ψ$. These findings reveal how the distinct symmetry breaking patterns induced by QGP rotation and anisotropy reshape the heavy quarkonium spectrum, providing new insights into polarization-dependent suppression in non-central heavy-ion collisions.

We investigate the potential of precision Higgs factory measurements to discover signatures of a representative model of electroweak symmetry breaking in which the Higgs boson arises as a composite Nambu-Goldstone boson. In this model, as in other models of the ``Little Higgs" or Natural Composite Higgs type, the primary perturbations of the Standard Model come from effects of vectorlike top quark partners. We carry out an explicit calculation of the Higgs potential in this model. Applying phenomenological constraints, we are left with a 3-dimensional parameter space. We then present results from a complete scan of this parameter space. The region in which significant departures from the Standard Model predictions extends to models in which the lightest top quark partner has a mass above 3~TeV. Little Higgs models with such heavy top partners also predict significant deviations from the Standard Model in the top quark electroweak couplings, in particular, in the model studied here, in the $t_L$ coupling to the $Z$ boson.

In recent years, physical models involving noncommutative algebras have attracted considerable interest since we can study theories with a Planck scale parameter which can seen as a semiclassical theory and consequently a path to a kind of quantum gravity. The noncommutative geometry introduces modifications to the underlying phase space structure, which can lead to new insights, and potentially solves outstanding problems in theoretical physics. In this work, we adopt a semiclassical perspective to perform a thermostatistical analysis of well-established potential models - Yukawa and Lee-Wick - within a noncommutative phase space. We obtain statistical thermodynamics quantities such as the density of states, partition function, average energy, and the heat capacity. We employ both the microcanonical and canonical ensemble formalisms, with the system embedded in a phase space modified by the noncommutativity of the space-time. We analyzed the negative heat capacities results obtained here in this noncommutativity scenario as a function of the modification of the microstructure of phase space thanks to the $θ$-parameter introduction. The entire treatment is conducted within the Boltzmann-Gibbs statistical mechanics framework.

We construct a group associated to a class of Borcherds algebras that admit a direct sum decomposition into a Kac--Moody (or semi-simple) subalgebra and a pair of free Lie subalgebras. Such Borcherds algebras have no mutually orthogonal imaginary simple roots.Our group is a semi-direct product of a Kac--Moody (or semi-simple) group and a Magnus group of invertible formal power series corresponding to a basis of a certain highest weight module determined by the simple imaginary roots. We show that our group is independent of this choice of basis, up to isomorphism. We apply our construction to a number of concrete examples, such as certain Borcherds algebras formed using root lattices of hyperbolic Kac--Moody algebras, the Monster Lie algebra, Monstrous Lie algebras of Fricke type and the gnome Lie algebra.

In this work, we apply topic modeling to detect new physics in proton-proton collisions at the LHC in an unsupervised way. We investigate three new physics scenarios where fully leptonic $t\bar{t}\to b\bar{b}\ell^+\ell^-ν_\ell\barν_\ell$ is the main source of background without relying on jet substructure variables. We demonstrate that the algorithm remains effective even in this low-particle multiplicity framework, complementing jet tagging studies, where it is typically employed. Moreover, we demonstrate that the performance of topic modeling is competitive or even better than well-known outlier detectors, such as isolation forest and variational autoencoders, with moderate and high background pollution in almost all new physics scenarios considered.

We show how Gravitational Path Integral formulae for various quantities that have been computed in the literature, follow from a few coarse grained hydrodynamic assumptions about the relations between space-time geometry, entropy, and fluctuations of the modular Hamiltonian of causal diamonds. These remarks have implications for the way we think about such path integrals in relation to a more fundamental model of quantum gravity, and to questions about which space-time topologies are actually summed over in real models.

This letter considers 3d $\mathcal{N}=4$ (unitary-)orthosymplectic quiver gauge theories originating from Type IIA and Type IIB brane systems with $\mathrm{ON}^0$ planes. Such theories lie outside the scope of present combinatorial techniques for Coulomb branch symmetry and symplectic stratification. It turns out that the correct prescription involves `symmetry mitosis': a common subset of nodes in two linear balanced chains source \emph{two} factors of a Coulomb branch global symmetry instead of one; the correct Coulomb branch Hasse diagram is obtained by a `doubling' procedure on that computed by naive quiver subtraction. Input from 6d SQFTs and little string theories allows for the construction of various `mitotic' magnetic quivers. The full Higgs branch Hasse diagram of minimal $(E_6,E_6)$ conformal matter is given. Additionally, a new Type I$'$ brane system using eight full D8 branes, negatively charged D6 branes, and $\mathrm{ON}^0$ planes is found corresponding to a product of $\mathrm{Spin}(32)$ instantons on $\mathbb C^2$. The corresponding 6d theory uses $\mathrm{Sp}(-1)$ gauge nodes which have the interpretation of bi-spinor matter of $\mathrm{O}(a)$ and $\mathrm{O}(12-a)$ for $a=0,1,\cdots,12$.

We introduce a framework that integrates both analytical and machine-learning approaches for calculating observables optimal for EFT and broader applications at the LHC. A new metric for evaluating the performance of these approaches has been introduced. In addition, we demonstrate how the majority of relevant information can be effectively stored in a limited number of bins, allowing for efficient data analysis, data preservation, and global data combination, while also providing tools to achieve these benefits. A key feature of this approach is the reduction in the dimensionality of the observable information, which enhances both the effectiveness and practicality of the data analysis while maximizing gains within limited resources. These features have been demonstrated through simulated analyses of the Higgs boson production and decay processes at the LHC.

Reported electron and hole mobilities, and their saturation velocities, in diamond span orders of magnitude across the literature. We attribute this dispersion primarily to (i) the electric-field window probed in TCT measurements, (ii) the choice of mobility model, and (iii) the excitation source (alpha, laser, or electron). Using an aggregated literature dataset, we benchmark the Trofimenkoff and Caughey-Thomas parameterisations together with a new piecewise model for both conduction- and valence-band transport. For electrons, the piecewise model provides the best global description over a broad electric-field range and is shown to arise as the room-temperature limit of a more general superposition framework that explicitly incorporates intervalley repopulation in the conduction band. For holes, the Caughey-Thomas model remains the statistically preferred description, in line with the absence of a strong repopulation effect in the accessible data. Furthermore, we demonstrate a systematic source dependence (alpha versus laser) and quantify its impact on fitted mobility and saturation-velocity values. We provide temperature scalings over narrow intervals around room temperature and recommend parameter sets for implementation in device and detector simulation frameworks. Together, these results reconcile much of the apparent inconsistency in the literature and offer clear guidance for model selection, experimental design, and device-level simulation of charge transport in intrinsic diamond.

We fully classify the phenomenology of gravitational wave emission from scaling cosmic string networks with varying tension and compute the spectral indices of the resulting stochastic backgrounds. In string compactifications, periods of varying tension occur when moduli acquire a time-dependence. We present concrete examples in type IIB string theory as D3- and NS5- branes wrapping internal cycles, which become dynamical due to the effect of moduli potentials. Moreover, we use Swampland constraints to derive general bounds on the allowed time-variation of the effective string tension in FLRW backgrounds and on the resulting spectral indices.

We compute holographically correlation functions for giant gravitons in $\mathcal{N}=4$ SYM in the presence of monodromy defects through probe branes. The computation boils down to the study of charged geodesics in certain five-dimensional gauged supergravity backgrounds. In addition to the standard U-shaped geodesic, in the presence of the defect, we find an extra, novel, contribution from a geodesic anchored at the defect which captures the one-point function of the square of the giant graviton.

A phase transition is an example of a ``topological defect'' in the space of parameters of a quantum or classical many-body systems. In this paper, we consider phase diagram topological defects of higher codimension. These have the property that equilibrium states undergo some kind of non-trivial winding as one moves around the defect. We show that such topological defects exist even in classical statistical mechanical systems, and describe their general structure in this context. We then introduce the term ``diabolical critical point'' (DCP), which is a higher-codimension analog of a continuous phase transition, with the proximate phases of matter replaced by the non-trivial winding of the proximate equilibrium states. We propose conditions under which a system can have a stable DCP. We also discuss some examples of stable DCPs in (1+1)-dimensional quantum systems.

We summarize recent work to develop an effective theory of muon-to-electron conversion, based on a complete set of low-energy effective operators that are developed from a systematic expansion in velocities and momenta. The expansion effectively factors rates into sums of particle physics and nuclear physics terms, where the former are expressed as bilinears in the LECs (the low-energy constants of the effective theory) and the latter are the associated nuclear responses. One can view the nuclear responses as ``dials" that can be adjusted -- for example, by selection of targets with specific properties -- in order to isolate the former. We show that an important dial, in the case of Mu2e and COMET, will be inelastic transitions to certain low-energy nuclear states that are resolvable in 27Al. If these transitions are exploited, the experiments have the potential not only to discover charged lepton flavor violation (CLFV), but to determine the operators responsible for the CLFV. We also discuss how such low-energy results can be ``ported" to higher energies through a tower of matched EFTs, so they can be combined with other experimental limits to further constrain CLFV

We present a complementary approach to the standard Vasiliev framework for nonlinear higher-spin interactions in four dimensions, aimed at identifying their minimally nonlocal form. Our proposal introduces a generating system for higher-spin vertices at the level of classical equations, which we refer to as irregular, in contrast to the regular case described by Vasiliev. This system extends the recently proposed equations for (anti)holomorphic interactions by incorporating the mixed sector. Its perturbative series encompasses the entire (anti)holomorphic sector in the leading order, with vertices related to powers of the complex parity-breaking parameter $η$ or $\barη$. The subsequent corrections facilitate the mixing of the two sectors, with vertices carrying mixed powers of $η$ and $\barη$. The consistency relies on the nonlinear algebraic constraint, which is shown to be satisfied at least in the quadratic and cubic approximations. As a result, the previously discussed (anti)holomorphic interactions in the literature can be systematically extended to generate vertices of the form $η^N \barη^k$ and their conjugate, at least for $k \leq 2$ and any $N$. As a byproduct of our analysis, we also identify the new higher-spin structure dualities.

We study Energy Correlators as probes of strongly-coupled nearly-conformal field theories within their holographically dual descriptions, focusing on the important features that appear in realistic theories going beyond the standard model. In particular, we study warped geometries which asymptote to $\text{AdS}_5$, as well as IR-truncations dual to a 4D gap. Our correlators are computed by in-in type Witten perturbative diagrams, corresponding to a large-N expansion of the strong dynamics. We describe how this sets the stage for phenomenological applications for collider searches beyond the standard model as well as for new theoretical explorations in Lorentzian holography.

We analyze a static open string in a general stationary spacetime, which can represent a heavy quark-antiquark pair within the holographic framework or effective theory. We establish that for a simple U-shaped string with only radial dependence on the space string coordinate, $x_r'(σ) \neq 0$, the string is generally not symmetric about its turning point, and the symmetry restores only for backgrounds with $h_{pr} = G_{00} G_{pr} - G_{0p} G_{0r} = 0$. Consequently, such asymmetric strings directly probe a possibility of the parity violation in the quark-antiquark interaction. Nevertheless, we identify a wide family of metrics for which the symmetry is preserved, enabling a direct isolation of the linear-in-distance term in the static interquark potential for simple symmetric string configurations, even in non-diagonal backgrounds. Applying the holographic framework, we further study the Rindler-AdS spacetime dual to an accelerated $\mathcal{N}=4$ super Yang-Mills plasma. We show that the distance between quarks decreases, the static potential between them increases, and the deconfinement phase transition temperature, $T_{\rm dec} = (π/3) T_H = a_c/6$, increases with an acceleration. However, we observe that an acceleration-scaled potential as a function of the acceleration-scaled distance does not depend on the certain value of the acceleration This result, reflecting the scale invariance and self-similarity of the holographic setup, can be also obtained in the dimensionless metric after scaling of the coordinates onto the acceleration, $\tilde{x}_i = a_c x_i$, for which one obtains an universal value of the phase transition temperature, $\tilde{T}_{\rm dec} = (π/3) \tilde{T}_H = 1/6$.

We give an overview of the construction of Borcherds algebras, particularly the Monstrous Lie algebras $\mathfrak m_g$ constructed by Carnahan, where $g$ is an element of the Monster finite simple group. When $g$ is the identity element, $\mathfrak m_g$ is the Monster Lie algebra of Borcherds. We discuss the appearance of the $\mathfrak m_g$ in compactified models of the Heterotic String. We also summarize recent work on associating Lie group analogs to the Lie algebras $\mathfrak m_g$. We include a discussion of some open problems.

We investigate the in-medium modification and dissociation of heavy quarkonium in a hot QCD medium at finite quark chemical potential and in the weak magnetic-field regime. Starting from the one-loop resummed gluon propagator in the imaginary-time formalism, and incorporating non-perturbative effects through a phenomenological correction to the HTL description, we compute the real and imaginary parts of the dielectric permittivity. This, in turn, leads to a complex heavy-quark potential: the real part is used to determine binding energies by solving the nonrelativistic Schrödinger equation, while the imaginary part generates thermal decay widths, dominated by Landau damping. Within the explored parameter range, temperature has the greatest control over Debye screening, potential modification, and quarkonium stability, whereas finite density and weak magnetic fields introduce comparatively smaller quantitative changes. As the temperature increases, binding energies decrease and thermal widths grow, giving rise to the expected hierarchy between ground and excited states and a sequential suppression pattern in the dissociation temperatures. Overall, our results indicate that while finite chemical potential and weak magnetic fields can shift quarkonium properties in a measurable way, thermal effects remain the primary driver of dissociation, with direct relevance for heavy-ion collision phenomenology.

In the minimal helicity-chirality formalism, we systematically construct higher-point massive amplitudes from the fundamental building blocks: the contact three-point and four-point massive amplitudes. The inclusion of four-point contact amplitudes is essential to maintain gauge invariance in the spontaneously broken Standard Model. We construct all the standard model massive contact amplitudes and identify the physical light-cone gauge nature of massive amplitudes. Then only using the contact minimal helicity-chirality amplitudes at the leading order, we show both bootstrap techniques and on-shell recursion relations can be utilized to compute higher-point massive amplitudes. This provides a systematic framework for constructing various higher-point electroweak amplitudes, analogous to established on-shell methods for massless theories. Finally by deforming the gauge-invariant $n$-point amplitudes, we extend the massless-massive correspondence from three-and-four point contact amplitudes to general $n$-point factorized amplitudes.

In this work, the massless-massive correspondence for the on-shell scattering amplitudes is constructed so the massive amplitudes could inherit advantageous techniques developed in the massless calculation. This correspondence is established by matching massless amplitudes to Minimal Helicity-Chirality (MHC) amplitudes, which arise from an expansion of massive spin-spinor amplitudes in terms of the chirality-flip $mη$ order by order. The primary MHC amplitude deforms into a massless amplitude of the same helicity; if a vector boson is involved, it may instead vanish due to the associated conserved current. In cases where the primary amplitude vanishes, the leading contributions originate from descendant MHC amplitudes, each corresponding to a distinct massless amplitude in the ultraviolet theory containing either a transverse gauge boson or a Goldstone boson. We propose a systematic amplitude deformation procedure for three-point massless-massive matching based on helicity-chirality unification and the scaling properties of $mη$. Sub-leading MHC amplitudes are matched to massless amplitudes with additional on-shell Higgs splitting, a process known as on-shell Higgsing. In this work, we extend and reinterpret on-shell Higgsing as a transversality flip between different MHC states, and obtain all the 3-point massless-massive matching results in the spontaneous broken standard model.

Soft current algebras capture the infrared structure of scattering in asymptotically flat spacetimes, but an analogous algebraic description of finite-energy dynamics has been missing. We uncover an infinite-dimensional hard current algebra that encodes finite-energy contributions to scattering and implies novel Ward identities. The soft current algebras are not independent but arise naturally from the hard ones. This provides a unified algebraic framework underlying quantum theory in flat spacetime.

Using (10087$\pm$44)$\times$10$^{6}$ $J/ψ$ events collected with the BESIII detector at the BEPCII collider at the center-of-mass energy of $\sqrt{s}=3.097~\rm{GeV}$, we report the first search for $η\toπ^0S\toπ^0χ\barχ$ with $S$ denotes an on-shell dark scalar boson and $χ$ an invisible dark matter particle. No significant signals are observed with $S$ mass ranging from 0 to 400 $\rm{MeV}/c^2$. The upper limits on the branching fractions and the new physics coupling strengths between $S$ and quarks are set to be $(1.8\sim5.5)\times10^{-5}$ and $(1.3\sim3.2)\times10^{-5}$ at the 90% confidence level, respectively. The constraints on the dark-matter-nucleon scattering cross section is improved by approximately 5 orders of magnitude over previous dark-matter-nucleon scattering experiments, providing unique insights into sub-GeV dark matter.

We examine the first-order Einstein-Cartan (EC) action in 2+1 dimensions, including a cosmological term and its supersymmetric extension. In this setting the spin connection can be expressed as an axial vector, yielding an action that is bilinear in the quantum fields and allows quantization without background fields. We identify the complete set of first-class constraints and derive the associated gauge transformations, which differ from the standard diffeomorphism and local Lorentz invariances. Using the closed gauge algebra, we construct the Faddeev-Popov-Nielsen path integral and show how a Lagrange multiplier field can be introduced to remove higher-loop contributions while preserving unitarity and gauge invariance.

The common eigenfunctions of the twisted Cherednik operators can be first analyzed in the limit of $q\longrightarrow 1$. Then, the polynomial eigenfunctions form a simple set originating from the symmetric ground state of non-vanishing degree and excitations over it, described by non-symmetric polynomials of higher degrees and enumerated by weak compositions. This pattern is inherited by the full spectrum at $q\neq 1$, which can be considered as a deformation. The whole story looks like a typical NP problem: the Cherednik equations are difficult to solve, but easy to check the solution once it is somehow found.

We investigate six singly Cabibbo-suppressed decay channels in $D^0\to VP$ ( $V$ and $P$ stand for the ground state vector and pseudoscalar mesons, respectively), i.e. $D^{0}\to ρ^{+}π^{-}$, $ρ^{-}π^{+}$, $K^{*+}K^{-}$, $K^{*-}K^{+}$, $K^{*0}\bar{K}^{0}$, and $\bar{K}^{*0}K^{0}$. These decay channels share the similar transition mechanisms involving only the direct emission (DE) and internal conversion (IC) processes. We show that a combined analysis of these channels can explicitly highlight the role played by the IC processes which contribute to the amplitudes at the same order of magnitude as the DE processes.

Reconstruction of one-dimensional kinematic distributions from calculations based on high-dimensional Monte-Carlo integration is a standard problem in high-energy physics. Traditionally, this is done by collecting randomly-generated events in histograms. In this article, we explore an alternative approach, whose main idea is to approximate the target distribution by a weighted sum of orthogonal basis functions whose coefficients are calculated using the Monte-Carlo integration. This method has the advantage of directly yielding smooth approximations to target distributions. Furthermore, in the context of high-order perturbative calculations with local subtractions, it eliminates the so-called bin-to-bin fluctuations, which often severely affect the quality of conventional histograms. We also demonstrate that the availability of a high-quality approximation to the target distribution, for example the leading-order result in the perturbative expansion, can be exploited to construct an optimized orthonormal basis. We compare the performance of this method to conventional histograms in both toy-model and real Monte-Carlo settings, applying it to Higgs boson production in weak boson fusion as an example.

We present a comparative study of three Monte Carlo simulation frameworks -SRIM, GEANT4, and PHITS- for modeling the transport, stopping, and atomic cascade of negative muons in micrometer-scale, multilayer systems relevant to Muon-Induced X-ray Emission (MIXE) experiments at the Paul Scherrer Institute (PSI). Using a lithium-ion battery as a benchmark target, simulated implantation profiles are compared with experimental data from the GIANT spectrometer. All three codes reproduce the overall muon depth distributions with good consistency, even across sharp density contrasts. SRIM provides reliable implantation estimates for compact geometries, whereas PHITS reproduces GEANT4 results with comparable accuracy and additionally generates muonic X-ray spectra. These spectra, however, exhibit a systematic energy offset in the K-line transitions of medium- and high-Z elements relative to theoretical and experimental values. Despite this bias, PHITS accurately captures relative intensities and spectral shapes, enabling element-specific line identification. The results demonstrate that SRIM and PHITS constitute practical tools for rapid estimation of muon implantation and stopping profiles, and that PHITS holds strong potential for predictive MIXE spectroscopy once its transition-energy bias is corrected.

We perform a two-loop calculation in Light Cone Perturbation Theory (LCPT) to evaluate the next-to-leading order nonsinglet splitting function. Our calculation demonstrates the methodology and feasibility of performing higher order calculations in LCPT. Since in Hamiltonian perturbation theory the longitudinal $k^+$ momentum is always positive, poles in $1/k^+$ can be regularized by a simple cutoff which cancels in physical results, without any associated ambiguities. For transverse momentum integrals we use dimensional regularization. Developing methods for loop calculations in LCPT paves the way for a systematical, automatizable procedure for precision calculations in this framework with a transparent physical partonic interpretation. This can provide a standard framework in higher order calculations in the gluon saturation regime of QCD.

In the braneworld scenario, we introduce a uniformly distributed cloud of infinitely long strings in the five dimensional AdS bulk spacetime. The end points of the strings are attached to the brane and becomes the source of the four dimensional matter on the brane, while the body of the strings hang onto the radial direction of the bulk and act as the gluonic field on the brane. The presence of matter in the brane induces a nonsingular cosmological evolution for the scale factor of the brane world under certain conditions of mass and cosmological parameters. However, the nonsingular nature is unstable since the bounce occurs inside the Cauchy horizon. Further, we consider the shellworld or the dark bubble scenario for the same bulk spacetime. It shows stable nonsingular cosmological nature of the bubble universe under certain conditions on the bulk and bubble parameters.

It has recently been shown that when $N_f$ generations of chiral fermions are coupled in a specific manner to $N$ (with $N \geq 2N_f-1$) pairs of vectorlike fermions whose mass terms form a one-dimensional lattice-like structure in theory space, locality along the lattice ensures that only a single fermion generation acquires a mass at tree level. Radiative corrections can induce controlled departures from locality in the latticed space, thereby generating suppressed but non-vanishing masses for the remaining $N_f-1$ generations. In this work, we present an explicit implementation of this mechanism to address the flavour hierarchies of the Standard Model. After delineating the minimal extensions of the gauge, scalar, and Yukawa sectors required for feasible implementation of the mechanism, we demonstrate that the framework successfully reproduces the observed charged-fermion mass spectrum and quark mixing pattern. We analyse the new-physics effects arising from the extended sectors and confront them with existing constraints from direct, indirect searches and precision measurements. It is shown that a viable realisation of the mechanism allows the spectrum of vectorlike fermions and additional gauge boson to lie at scales as low as $\mathcal{O}(5)\,\mathrm{TeV}$ with the lightest states typically corresponding to top partners. This stands in sharp contrast to conventional radiative mass-generation scenarios, in which phenomenological constraints typically impose a lower bound on the new-physics scale of order a few hundred to several thousand TeV.

The neutrino masses ordering remains one of the most important open questions in neutrino physics. While upcoming oscillation experiments aim to resolve this problem at low energies, complementary approaches are highly desirable. In this Letter, we show that the neutrino mass ordering can be probed at high-energy colliders through the lepton-flavor structure of heavy neutral lepton (HNL) interactions. In the minimal Type-I seesaw scenario with two nearly degenerate HNLs, the heavy--light neutrino mixings are strongly correlated with the light-neutrino mass spectrum, leading to distinct flavor patterns for the normal and inverted hierarchies. We demonstrate that future $Z$ factories, such as CEPC and FCC-ee, can probe the neutrino mass ordering for total HNL mixings as small as $U_{\rm tot}^2 \gtrsim 4 \times 10^{-9}$, and discriminate between the two hierarchies for $U_{\rm tot}^2 \gtrsim 10^{-6}$. Our results establish collider searches for HNLs as a powerful and complementary probe of the neutrino mass ordering.

In this proceedings we explore the physics potential of the ESSnuSBplus setup to study beam and non-beam based physics scenarios in both standard and new physics cases. The ESSnuSBplus setup consists of three neutrino sources: the main ESS linac, a low energy monitored neutrino beam and a low energy nuSTORM facility and three detectors: the main far detector and two near detectors. The goal of this facility is to measure the leptonic CP phase with extremely high precision and the neutrino nucleus cross-section in the few hundred MeV region.

In this work, we investigate the effects of $\text{T}\bar{\text{T}}$ and root-$\text{T}\bar{\text{T}}$ deformations on reflected and entanglement entropy in the context of both pure and mixed state entanglement measures. Utilizing a mixed boundary condition framework, we analyze how these deformations modify entanglement structures and explore their implications in three-dimensional AdS space. Our results provide insights into the interplay between solvable irrelevant deformations and quantum information-theoretic quantities, shedding light on the entanglement structure of deformed theories.

Advances in fabrication techniques and high-performance electronics have facilitated the development of fine-pitch Gas Electron Multipliers (GEMs). Earlier experimental and simulation findings suggest that these reduced-pitch GEMs can outperform the standard configuration in terms of effective gain, collection efficiency, and position resolution. However, a noticeable fraction of avalanche electrons is lost within the GEM systems, resulting in a degradation of charge collection efficiency. Therefore, a comprehensive simulation-based study is essential to provide deeper insights into the extent of degradation and its contributing factors. In this context, we employ ANSYS and Garfield++ to model the Triple GEM detectors with reduced pitch sizes of 90 and 60 $μ$m, and perform a comparative performance analysis with the standard configuration (pitch size: 140 $μ$m). At first, the simulation framework is validated by comparing the results of the standard configuration with available experimental data and previously reported simulation outcomes. Despite the characteristic gain offset, the framework remains physically consistent and reliable in capturing microscopic avalanche dynamics, reproducing the experimental trend. Following validation, we investigate electron losses at the metal electrodes and within the Kapton holes, electron transmission through the transfer and induction regions, electron diffusion on the induction electrode, and the overall collection efficiency. These parameters are analyzed as functions of GEM potential, outer hole diameter, inner hole diameter, Kapton thickness, metal thickness, and gas composition, thereby offering insights for designing efficient GEM detectors.

Theoretical developments in relativistic spin hydrodynamics, which describes the macroscopic transport of spin angular momentum alongside other fundamental conserved quantities, have progressed rapidly since the experimental observation of the global spin polarization of $Λ$ hyperons in relativistic heavy-ion collision experiments. However, numerical simulations of relativistic spin hydrodynamics remain largely unaddressed due to computational challenges, particularly the accurate numerical conservation of total angular momentum. In this work, we propose the use of physics-informed neural networks (PINNs) for computational relativistic spin hydrodynamics. As a concrete application, we consider a rotating fluid confined within a cylindrical container. We show that angular-momentum conservation can be accurately achieved in the PINNs-based numerical framework. Furthermore, we investigate the spin-orbit conversion induced by the rotational viscous effect, which is the intrinsic dissipative process of relativistic spin hydrodynamics. Our analysis numerically identifies the mismatch between the transverse thermal vorticity and the spin potential as the driving mechanism of the spin-orbit conversion.

Based on the kinetic theory with relaxation time approximation, we investigate the bulk viscosity ($ζ$) and its ratio to shear viscosity ($ζ/η$) of quark matter at finite temperature and chemical potential with the in-medium particle masses derived in the 2+1 flavor Polyakov-loop improved Nambu--Jona-Lasinio (PNJL) model. We explore the behaviors of specific bulk viscosity ($ζ/s$) and $ζ/η$ across different QCD phase transitions, including the Mott phase transition, the chiral crossover, and the first-order transition with the associated metastable phase. The calculation shows that both $ζ/s$ and $ζ/η$ are extremely small at high temperatures, approaching the nature of a conformal theory. Larger $ζ/s$ and $ζ/η$ are derived near the chiral phase transition at finite temperature. Along the chiral crossover line, $ζ/s$ and $ζ/η$ generally increase with decreasing temperature, though $ζ/η$ exhibits a slight decline near the critical endpoint (CEP). On the boundary of the first-order transition, $ζ/s$ shows a non-monotonic variation with temperature. Furthermore, an additional peak structure emerges beyond the chiral phase boundary for both $ζ/s$ and $ζ/η$, with magnitudes even exceeding those near the chiral crossover of $u, d$ quarks. Our analysis indicates this peak originates from the chiral crossover transformation of strange quark.

The type I seesaw mechanism is one of the leading proposed explanations for how neutrinos acquire their tiny masses. However, the mass scale of the undiscovered right-handed neutrinos required by this mechanism remains undetermined. Assuming vanilla leptogenesis in the two-flavor regime, we work backwards to find the required general textures of the Dirac mass matrix from which we determine the mass of the lightest right-handed neutrino to be around $10^9 {\rm GeV}$ to $10^{12} {\rm GeV}$.

We study the observable $v_0(p_T)$, which quantifies the relative change of $p_T$ spectra induced by event-by-event density fluctuations in the medium created in heavy-ion collisions. This quantity provides a direct measure of radial flow and serves as a probe of collectivity, complementing anisotropic flow coefficients. Using hydrodynamic model calculations, we predict the behavior of $v_0(p_T)$ and show that the scaled quantity $v_0(p_T)/v_0$ exhibits very little dependence on centrality and transport coefficients. We further find that the apparent influence of transport coefficients$-$particularly bulk viscosity$-$ on $v_0(p_T)$ largely originates from modifications of the event-averaged mean transverse momentum, $\langle p_T \rangle$. By expressing $v_0(p_T)/v_0$ as a function of $p_T/\langle p_T \rangle$, the genuine sensitivity of $v_0(p_T)$ to transport coefficients can be isolated. Moreover, since $v_0(p_T)$ is the $p_T$-differential measure of event-by-event $[p_T]$ fluctuations, it naturally explains the observed $p_T$-cut dependence of $σ_{p_T}$ measured by ATLAS collaboration.

We report on a new flux quantization phenomenon in metals. We study the response of normal metals to the presence of localized magnetic flux. We find that, due to backreaction effects, the metal traps 0 flux or $\tfrac{h}{2e}$ flux (half flux). We exhibit this effect both for metals pierced by magnetic solenoids and metals wrapping a magnetic solenoid. In the latter case we demonstrate the trapping of magnetic flux analytically. Furthermore, we find that as the solenoid is adiabatically turned off, a logarithmically enhanced localized equilibrium current persists, reflecting perfect defect-diamagnetism of the Fermi gas.

The modeling of coupled-channel effects has become increasingly important due to the availability of highly precise data for a large variety of hadronic (re)scattering processes. The K-matrix is a powerful, yet comparatively simple, method to describe scattering amplitudes, including coupled-channel effects, with the aim to interpret experimental data. Throughout the literature, a range of dispersive and nondispersive K-matrix methods are employed. Here, we compare the dispersive and nondispersive formulations in the context of the N/D method. It is shown that the methods are equivalent in the physical region under K-matrix reparameterization. Differences away from the physical region are examined. Applications to synthetic data are used to illustrate the effects of model choices concerning form factors and the application of dispersion relations, with the goal of clarifying best practices. We find no clear preference with regards to dispersive modeling. In contrast, we find that interpretational ambiguity of the bare model parameters -- and even of the form of the bare model -- is endemic, and recommend a thorough sampling of data and model spaces to assess conclusion robustness.

First results from the JUNO reactor neutrino experiment already determine with world-leading precision the small neutrino squared-mass splitting $Δm^2_{21}$ and the mixing angle $θ_{12}$. In this article we perform an exploratory study beyond these, taking advantage of the first JUNO data release to discuss its sensitivity to the large squared-mass splitting, $Δm^2_{3\ell}$. When combined with constraints from global oscillation data, this may already contain some information on the neutrino mass ordering. Indeed, we find that the combination of the complementary $Δm^2_{3\ell}$-determinations gives a slight preference for Normal Ordering, with a p-value for Inverted Ordering of 2%-2.6% ($2.2σ$-$2.3σ$). We study the robustness of this result with respect to potential systematic uncertainties and statistical fluctuations. Taken at face value, a full global analysis of oscillation data including the publicly available JUNO information and data leads to a preference for Normal Ordering with $Δχ^2 = 4.6$ and 9.4 without and with Super-K and IceCube-24 atmospheric neutrino data, respectively.

The cosmic neutrino background (C$ν$B) can be boosted to high energies due to scatterings with energetic cosmic rays (CRs) across cosmological scales. Previous calculations focused on neutral current incoherent and coherent elastic scatterings of cosmic-ray protons off relic neutrinos. However, charged current interactions and deep inelastic scatterings are also expected to occur, which enhances the boosted relic neutrino fluxes on Earth. Here, we compute the \textit{total} diffuse boosted cosmic neutrino background (DBC$ν$B) arising from CRs at all redshifts in the Universe, accounting for neutral current and charged current elastic and deep inelastic scatterings. We find that IceCube already places an upper limit on the cosmic neutrino background overdensity in cosmological scales of ~$\mathcal{O}(100-1000)$ at $E_ν=10^{10}$ GeV, for a lightest neutrino mass of $m_ν \gtrsim 0.1$ eV. We further show that IceCube-Gen2 could test $\mathcal{O}(1-10)$ C$ν$B overdensities, and the combination of $10$ future neutrino telescopes with similar sensitivity would allow us to test the $Λ$CDM expected C$ν$B density for a lightest neutrino mass compatible with the KATRIN bound.

Motivated by axion-like inflation and its warm embedding within the Standard Model, we study the early stages of the energy transfer between an axion condensate and an SU(2) gauge ensemble, by employing non-linear classical real-time lattice simulations. The discretized equations of motion are worked out, elaborating on Gauss constraints. A numerical solution is implemented on the CosmoLattice platform. Adopting a quadratic potential, and omitting universe expansion for the moment, we establish initial exponential growth of the low-momentum gauge modes; damping of axion oscillations after some delay; and subsequent energy equipartition between axion and gauge ensembles. A clear difference between the SU(2) and U(1) dynamics is observed, likely associated with non-Abelian self-interactions. We elaborate on what this implies for the possible thermalization of the SU(2) ensemble.

Recent work revealed a tension between the Gross-Mende analysis of the high-energy fixed-angle behavior of string amplitudes and the explicit numerical data. Motivated by this puzzle, we revisit the problem of classifying saddle-point geometries for the one-loop amplitude. We find an infinite family of complex saddles that dominate the high-energy regime. Using general constraints and matching to numerical data, we formulate a bootstrap problem that determines their multiplicities. This procedure yields a precise asymptotic expansion of the one-loop amplitude at high energies. The resulting oscillatory contributions lead to a much richer high-energy behavior than that predicted by the original Gross-Mende analysis.

Hadronic resonances are crucial probes to understand the various phases of matter created during relativistic heavy-ion collisions. Due to their short lifetimes, the yields of these resonances can be affected by competing rescattering and regeneration mechanisms in the final hadronic phase. Rescattering can alter the momentum of the resonance decay products, limiting their reconstruction through the invariant-mass technique, while pseudo-elastic scattering can regenerate them. Final state observables such as elliptic flow, transverse momentum spectra, and measured yields of resonances could be significantly modified due to the interaction in the hadronic phase. By comparing the yields of longer-lived resonances, such as the $φ$-meson with shorter-lived ones, such as the K$^*$(892), it is possible to obtain information about the properties and timescales of the hadronic phase. This contribution presents new Run 3 results on production yields, spectra, and flow harmonics for K$^*$(892) and $φ$(1020) in Pb-Pb collisions at $\sqrt{s_{NN}}$ = 5.36 TeV obtained by the ALICE Collaboration. The results are compared with state-of-the-art models to interpret the underlying mechanism that can describe the experimental observations. In addition to probe hadronic phase, the study of resonances also offers valuable insights into the non-perturbative regime of Quantum Chromodynamics (QCD). Resonances such as the f$_0$(980) and f$_1$(1285) challenge the traditional quark model. Their structure is yet unknown as they could potentially be tetraquark states or meson-meson molecules. This contribution presents new measurements of exotic resonances such as f$_0$(980), f$_1$(1285), and the glueball candidates to get more insight into their internal structure.

We report on a broadband search for axion-like-particle (ALP) interactions using a radio-frequency-operated $^{87}\mathrm{Rb}$ atomic magnetometer. The instrument provides wide spectral coverage and sensitivity to an oscillating pseudomagnetic field that may be generated by the gradient coupling of the ALP field to the constituent fermions of atoms. We search for an ALP-gradient signature in the mass range $2.40\times10^{-10}\,\mathrm{eV}/c^{2}$--$2.11\times10^{-9}\,\mathrm{eV}/c^{2}$. No statistically significant signatures of an oscillating magnetic field are observed, and we derive upper limits on the corresponding ALP-proton, -neutron and -electron couplings, $g_{αpp}$, $g_{αnn}$ and $g_{αee}$, respectively. The result on $g_{αpp}$ improves over previous laboratory searches, while the limits on $g_{αnn}$ and $g_{αee}$ complement earlier laboratory searches and astrophysical bounds. The work extends searches for ALP-fermion interactions into a mass region largely unexplored in a dark-matter context, demonstrating the potential of our method for broadband axion-like particle searches targeting the Galactic dark-matter halo.

We investigate the effective potential and the string tension for the spatial Wilson loop (SWL) in hot dense QGP with two types of anisotropy, i.e. external magnetic field and spatial anisotropy, employing a holographic approach for the heavy quark model. In this approach, the string is extended in the 5th, holographic direction and has a turning point either on a dynamical wall (DW) configuration or on the horizon configuration in the 5th direction. We obtain the magnetic catalysis behavior for a phase transition between DW and horizon configuration of the string. The structure of the phase diagram does not depend on the boundary conditions choice for the dilaton field. Inclusion of the external magnetic field and spatial anisotropy enhance the string tension in the horizon configuration, namely drag force. For the spatially isotropic case $ν= 1$ at different magnetic field values the string tension is proportional to $T^2$ and is qualitatively consistent with lattice results. However, for the anisotropic case, $ν= 4.5$, it deviates from the quadratic term.

The calculation of precise predictions for Higgs decays is a necessary ingredient for determining Higgs properties at the LHC and future colliders. We compute all two- and three-body Higgs decays at next-to-leading order (NLO) in both QCD and electroweak interactions using the dimension-6 Standard Model Effective Field Theory (SMEFT). Results for four-body Higgs decays that are accurate to NLO QCD/electroweak order in the SMEFT are obtained using the narrow width approximation. Our results are contained in a flexible Monte Carlo program, NEWiSH, that is publicly available and we illustrate the impact of the NLO electroweak corrections for HL-LHC, Tera-Z, and Higgstrahlung projections.

We perform a general analysis of minimal modular fixed point models based on two right-handed neutrinos (2RHNs) and three modular fixed points, and find that the only viable possibilities are based on modular $S_4'$ and $A_5$ symmetry. Such models are highly predictive, with neutrino masses and the lepton mixing mixing matrix being fixed by three real parameters, as in the Littlest Seesaw Models. We perform an exhaustive scan over all possible models in this class and find many viable fixed points and modular form alignments, after confronting them with the latest neutrino oscillation global fits. The resulting models have the new feature that the two Dirac columns take more general forms than traditional Littlest Seesaw models, resulting in new sum rule relations between the solar and reactor angles, beyond those associated with TM1 (where the first column of the tri-bimaximal mixing matrix is preserved), which are compared to present and future projected JUNO results. We also compare the predictions of these models for the atmospheric angle and CP violating phase to current global fits and future T2HK and DUNE sensitivities.

We propose to discuss the modular flavor model and the stabilization of single modulus field in the Jordan frame supergravity with non-minimal scalar-curvature coupling of the form $Φ(τ,\barτ)R$. Modular invariance and positivity of the scale factor constrain stringently the form of the frame function, consequently the Kahler potential by the relation $Φ(τ,\barτ)=-3\exp[-K(τ,\barτ)/3]$. We discuss some general properties of scalar potentials after the scale transformation from the Jordan frame to the Einstein frame. We find that the shape of the resulting scalar potential in the Einstein frame is quite different from that of ordinary single modulus stabilization mechanism. The scalar potential could be stationary at the $i\infty$ fixed point, leading to a runaway type vacuum. We also discuss numerically the modulus stabilization for some simplified scenarios.

We perform for the first time a full study of spin correlations in inclusive WZ production at the LHC with leptonic decays in the presence of NLO QCD corrections and of effects from a dimension-six operator in the SMEFT modifying the electroweak triple-gauge coupling. We carry out the complete quantum-state tomography of the di-boson system and relate its results to common purity and spin-entanglement markers, highlighting the sizeable impact of both QCD corrections and SMEFT insertions. Additionally, we show how a naive truncation at dimension-six in the SMEFT expansion of the spin-density matrix can lead to a cumbersome spin interpretation of the quantum-tomography results.

We extend Nielsen's formulation of quantum circuit complexity to include intrinsically non-invertible operations. Such gates arise from fusion with topological defect operators and remove a basic limitation of symmetry-based circuits: the inability to change superselection sectors, or in two-dimensional CFTs, conformal families. We realise fusion operations as completely positive, trace-preserving quantum channels acting between sectors, with consistency ensured by the fusion and associator data of an underlying unitary modular tensor category. In contrast to standard Nielsen circuits, non-invertible circuits lead to an optimisation problem that is no longer governed by geodesics on a continuous group manifold but instead reduces to a discrete shortest-path problem on the fusion graph of superselection sectors. We illustrate the framework in representative rational conformal field theories. Finally, we interpret fusion-induced transitions as discrete changes in boundary stress-tensor data, corresponding to shock-like defects in AdS$_3$ gravity.

We report the recent progress from our group in extracting observables of both inclusive and exclusive semileptonic heavy-meson decays directly from lattice QCD four-point correlators. On the inclusive side, we illustrate how to estimate the systematic uncertainties from omitted higher-order terms and non-zero smearing of the kernel approximation, building on two important features of the Chebyshev expansion. On the exclusive side, we perform BCL parameterizations of the pseudoscalar to pseudoscalar form factors and compare the fitted coefficients with those from earlier results by HPQCD. We also perform a HQET-based parameterization of the P-wave form factors to shed new light on the 1/2-vs-3/2 puzzle. This work constitutes a step toward a unified lattice treatment of inclusive and exclusive semileptonic decays, relevant for the Vcb puzzle. In this study, we use lattice ensembles from the RBC/UKQCD collaboration for numerical investigations. Future developments from our group will focus on the control of other systematic effects for inclusive decays and investigations of other techniques with reduced statistical errors to extract exclusive contributions from lattice four-point correlators.

The ePIC collaboration is developing a multidetector system to explore the fundamental properties of the strong interaction at the future Electron-Ion Collider (EIC), to be built at Brookhaven National Laboratory. A key component of the ePIC detector is the Silicon Vertex Tracker (SVT), which provides high-precision tracking and microvertex reconstruction. The SVT consists of the Inner Barrel (IB), the Outer Barrel (OB), and the Forward/Backward Disks, all based on Monolithic Active Pixel Sensors (MAPS) that combine high granularity, low power consumption, and minimal material budget. This paper presents a concise overview of the SVT design and its development status.

Interference of the ultralight dark matter (ULDM) field generates time-varying gravitational potential fluctuations, which stochastically heat stellar systems embedded in ULDM halos. Small-sized stellar systems are therefore often used to set stringent constraints on ULDM. However, the evolution of systems with sizes well below the ULDM de Broglie wavelength remains poorly explored. Using numerical simulations, we show that the evolution of compact stellar systems in ULDM halos is governed by the interplay between internal stellar relaxation and ULDM-induced heating. We find the following main results. First, in sufficiently compact systems, relaxation-driven core collapse dominates, allowing the system to remain bound and dense, while ULDM-induced stripping of outer stars further accelerates the collapse. Second, in more extended systems, ULDM heating dominates and ultimately disrupts the system. Near the disruption threshold, we identify systems resembling ultra-faint dwarfs like Segue 1. Third, we further introduce a dimensionless parameter to quantify the relative importance of heating and relaxation and finally lead to an evolutionary phase diagram. Our results reveal the rich and nontrivial dynamics of compact stellar systems in ULDM halos, indicating that precise system modeling is essential for robust ULDM constraints.

In this letter, we construct an explicit rephasing transformation that converts an arbitrary unitary matrix into the Kobayashi--Maskawa (KM) parameterization and identify all independent CP phases in the mixing matrix as the arguments of its matrix elements. Furthermore, by applying this rephasing transformation to the fermion diagonalization matrices $U^{f}$, we show that the Majorana phases are represented by fermion-specific phases $δ^{ν, e}_{\rm KM}$ and their relative phases. In particular, by neglecting the 3-1 elements $U_{31}^{ν,e}$ of the diagonalization matrices for the two fermions, the KM phase $δ_{\rm KM}$ is concisely expressed by fermion-specific rephasing invariants involving two relative phases $δ_{\rm KM} = \arg \left [1 + ({U^{e * }_{21} U^ν_{21} / U^{e * }_{11} U^ν_{11} }) \right ] + \arg \left [ - { U_{32}^{e *} U_{32}^ν / U^{e * }_{22} U^ν_{22} } \right] $.

We study the near-threshold exclusive photoproduction of heavy vector mesons (quarkonia $J/ψ$ and $Υ$) off the proton within the framework of generalized parton distribution (GPD) factorization. The gluon GPDs are computed using a spectator model in which the proton emits a gluon and the remaining constituents are treated as a single spectator particle. Model parameters are determined by fitting the gluon unpolarized and helicity collinear parton distribution functions (PDFs) from global analyses. We compare our results with the latest near-threshold $J/ψ$ production data from the GlueX and $J/ψ$-007 experiments at Jefferson Laboratory, finding good agreement for both differential and total cross sections. Predictions are also provided for $Υ$ photoproduction, which can be tested at future Electron-Ion Colliders.

Sub-GeV dark matter is an appealing thermal target because it can still be produced via the standard freeze-out mechanism; at such low masses, achieving freeze-out naturally points to the presence of a light mediator, which shifts the most promising discovery avenues from the energy frontier to the intensity frontier. Realizing this picture is nonetheless challenging, since CMB observations tightly constrain energy injection from dark-matter annihilation at recombination and therefore strongly disfavor simple $s$-wave annihilation into visible Standard-Model final states. In this work, we propose a concrete neutrinophilic framework for sub-GeV thermal dark matter (''light WIMPs'') based on an additional gauge symmetry $\mathrm{U}(1)_{\mathrm{B}-\mathrm{L}+x\mathrm{Y}}$; for an appropriate choice of $x$, the new gauge boson couples predominantly to dark matter and neutrinos while its couplings to charged leptons are suppressed, so that sub-GeV dark matter annihilates almost exclusively into neutrinos, with hadronic modes kinematically closed. We map the parameter space in which the observed relic abundance is reproduced via standard thermal freeze-out in a conventional cosmological history, and show that sizable regions remain viable after imposing current cosmological, indirect-detection, and terrestrial constraints; in part of the allowed parameter space, the dark matter also exhibits sufficiently large self-interactions to potentially alleviate small-scale structure tensions.

We discuss in rather general terms the properties of space-like baryon transition form factors. In particular, we argue why these are necessarily complex-valued, what can be deduced from the respective phase motion and why dealing with real valued transition form factors in general leads to misleading results. For illustration the transition form factors for the Roper resonance as derived in the Jülich-Bonn-Washington framework are discussed.

We investigate the complex time evolution of a vacuum state with the insertion of a local primary operator in two-dimensional conformal field theories (2d CFTs). This complex time evolution can be considered as a composite process constructed from Lorentzian time evolution and a Euclidean evolution induced by a post-selected measurement. Our main finding is that in the spatially-compact system, this complex time evolution drives the state of the subsystems to those of the primary state with the same conformal dimensions of the inserted operator. Contrary to the compact system, the subsystems of the spatially non-compact system evolve to states that depend on the non-unitary process during a certain time regime. In holographic systems with a compact spatial direction, this process induced by a heavy local operator can correspond to the relaxation from a black hole with an inhomogeneous horizon to that with a uniform one, while in the ones with a non-compact spatial direction, it can correspond to the relaxation to that with a horizon depending on the non-unitary process.

We investigate exact cosmological solutions with a massive scalar field minimally coupled to the Einstein-Hilbert action in General Relativity. For an extended Higgs-like scalar self-interaction, we find that the resulting field equations belong to the damped Ermakov-Painlevé II class and construct novel analytical solutions within the framework of the Chiellini integrability condition. We analyze whether the expanding branch of the solutions can describe a late-time cosmic acceleration, using a combined statistical analysis of BAO, CMB, cosmic chronometer and Pantheon+SHOES supernova datasets. A crucial outcome of this exercise is the analytical emergence of a smooth phantom divide crossing in the dark energy equation of state, achieved without introducing any pathological instabilities. The reconstruction yields a present-day Hubble parameter $H_0 \gtrsim 70 \,\mathrm{km\,s^{-1}\,Mpc^{-1}}$, with a reduced tension relative to the $Λ$CDM cosmology. The results indicate that Chiellini-integrable scalar cosmologies are capable of providing a robust and analytically controlled framework for modeling late-time cosmic acceleration and phantom divide crossing, offering a viable alternative to phenomenological dark-energy parametrizations.

We study the compactification of higher-dimensional Lovelock gravity on compact irreducible symmetric spaces, focusing on conditions under which a physically healthy four-dimensional Minkowski vacuum exists. We show that when the internal dimension is five or less, or when the theory is restricted to the Einstein-Gauss-Bonnet sector, the four-dimensional graviton (tensor sector) is necessarily a ghost. Inclusion of the cubic Lovelock term removes this ghost instability; however, the resulting Minkowski vacuum is generically only metastable, being accompanied by energetically favored Anti-de Sitter vacua. While such metastability cannot be avoided for spherical internal spaces, we identify an infinite class of higher-rank symmetric spaces where the true vacuum can be pushed to infinity in moduli space, thereby realizing genuinely stable and ghost-free Minkowski vacua at the level of the four-dimensional effective theory. To support these conclusions, we explicitly compute Lovelock terms up to cubic order on these spaces, confirming a universal log-convexity among the linear, quadratic, and cubic invariants, which plays a central role in our analysis.

Charged lepton flavor violation (CLFV) is forbidden in the Standard Model but predicted by many new physics models. We present searches for CLFV in charmonium decays using world-leading datasets collected by the BESIII detector. The processes $J/ψ\to eτ$, $J/ψ\to eμ$, and $ψ(3686)\to eμ$ are investigated using world-leading $J/ψ$ and $ψ(3686)$ dataset collected by BESIII. No significant signals are observed, and upper limits on branching fractions are set at $\mathcal{B}(J/ψ\to eτ)<7.5\times10^{-8}$, $\mathcal{B}(J/ψ\to eμ)<4.5\times10^{-9}$, and $\mathcal{B}(ψ(3686)\to eμ)<1.4\times10^{-8}$ at 90\% confidence level. These results provide constraints on Wilson coefficients in effective field theory and probe new physics at high energy scales.

We propose a generic axion--driven cogenesis scenario in which both the baryon asymmetry and dark matter abundance originate from the kinetic misalignment. The framework unifies the Peccei--Quinn (PQ) mechanism with a Type--I seesaw sector, where Hubble--induced masses and higher-dimensional PQ--violating operators drive early--time axion rotation. Working within the DFSZ axion model augmented by heavy neutrinos, we identify the parametric window of right-handed neutrino masses, determined by its decay rate, and the range of Hubble scales compatible with successful cogenesis, while maintaining the axion solution to the strong CP problem and satisfying current limits on axion isocurvature perturbations. Our results establish kinetic axion misalignment as a robust and predictive mechanism for axion cogenesis, independent of the inflationary microphysics.

Nonlocality is a peculiar nature of quanta and it stands as an important quantum resource in application. Yet mere linear property of it, viz. the first order in moment, has been explored through various inequalities. Noticing the vast higher-order regime unexplored, in this study we investigate the higher-order quantum correlations in entangled hyperon-antihyperon system, which may be generated massively in charmonium decays. A new type of Clauser-Horne inequality for statistical cumulants and central moments is formulated. We find that a significant violation of the third-order constraint, indicating the existence of higher-order correlation, exists in hyperon-antihyperon system and can be observed in high energy physics experiments, like BESIII and Belle II. Notably, the violation manifests more in higher energy systems of the $Λ\barΛ$ pair against the kinematic contamination of timelike events.

We propose a new strategy to probe heavy neutrinos with non-universal fermion couplings at the Large Hadron Collider (LHC) using a novel production mechanism and machine-learning algorithms. Focusing on proton--proton collisions at $\sqrt{s} = 13.6~\mathrm{TeV}$, we investigate final states containing a charged lepton, missing transverse energy, and two jets. For heavy neutrino masses below $\mathcal{O}(1~\mathrm{TeV})$, production is dominated by the $s$ channel process. At higher masses, vector boson fusion becomes the dominant production mechanism, with cross sections that decrease slowly as the heavy neutrino mass increases. We simulate both signal and Standard Model background events and employ gradient-boosted decision trees to optimize event classification. Assuming an integrated luminosity of $3000~\mathrm{fb^{-1}}$, expected for the high-luminosity, and considering realistic statistical and systematic uncertainties, we find that heavy neutrinos in the mass range $50~\mathrm{GeV}$--$10~\mathrm{TeV}$ can be probed with sensitivity to the mixing parameter $|V_{\ell N}|^2$ spanning from $\mathcal{O}(10^{-5})$ to 1. This approach enhances the discovery potential for heavy neutrinos and provides a complementary pathway to existing search strategies.

We derive several new Bogomol'nyi (self-dual) equations in two-species $U(1)\times U(1)$ gauge theories governed by the Born--Infeld nonlinear electrodynamics. By identifying appropriate Born--Infeld type Higgs potentials, we show that the highly nonlinear energy functionals admit exact topological lower bounds saturated by coupled first-order equations. The resulting models accommodate both vortex-vortex and vortex-antivortex configurations and generalize previously known single-species Born--Infeld systems to interacting multi-component settings. Beyond the derivation of the Bogomol'nyi equations, we develop an exact thermodynamic theory for pinned multivortex configurations in both the full plane and compact doubly periodic domains. Owing to the linear dependence of the Bogomol'nyi energy spectrum on topological charges, we obtain closed-form expressions for the canonical partition function, internal energy, heat capacity, and magnetization. In compact domains, the Bradlow type geometric bounds constrain admissible vortex numbers and lead to qualitatively new high-temperature behavior. In particular, vortex-only systems exhibit spontaneous magnetization, while vortex-antivortex systems do not, reflecting the underlying symmetry between opposite topological charges. These results provide a rare analytically solvable framework for studying thermodynamics in nonlinear multi-component gauge theories regulated by the Born--Infeld electrodynamics.

It is known that a two-dimensional bosonic theory with a non-anomalous $\mathbb{Z}_2$ symmetry can be fermionized. Recent work shows that if the bosonic theory also has non-anomalous time-reversal symmetry, fermionization extends to non-orientable surfaces and yields a fermionic theory that depends on a $\mathrm{Pin}^-$ structure. Besides fermionization, one can define various topological manipulations, such as gauging and stacking invertible phases, which together generate a web of dualities. We prove that their group structure is the dihedral group $D_8$ of order 16. Furthermore, we systematically investigate the web from two perspectives: Symmetry TFT and actions on sectors of the $S^1$ Hilbert space.

Quirks are heavy particles connected by a flux tube from a hidden confining force that remain weakly constrained in large regions of their parameter space. This flux tube acts as a string that, at short enough distance, stretches as the quirk pair separates, then pulls the pair back together leading to interesting dynamics. We propose a novel search using the LHCb Vertex Locator (VELO), whose forward geometry and software-based trigger are uniquely suited to detecting the characteristic back-to-back, planar hit patterns produced by quirk pairs with little transverse recoil. Using detailed simulations of the VELO geometry, together with simple geometric selections, we present different sensitivity projections, demonstrating that LHCb can probe parameter regions inaccessible to existing ATLAS and CMS searches and offering a powerful, complementary path toward discovering quirks.

Metric-affine gravity (GL(4) gauge theory) in 4-dimensions is coupled to a spacetime Dirac source field using the isomorphisms of the Lie algebra gl(4) to the Clifford algebras Cl(3,1) and Cl(2,2). A simple transformation relates the generators of Cl(3,1) to a real representation of Cl(2,2), while the real representation of Cl(2,2) serves directly as a basis for the Lie algebra gl(4). Therefore, although GL(4) does not contain a spinor representation of the Lorentz group, expanding its Lie algebra in the Cl(2,2) basis gives a Clifford valued connection with well-defined coupling to Dirac spinors. Variation of the expansion coefficients gives new Dirac sources for both torsion and nonmetricity, separated by identifying the so(3,1) basis within the gl(4) basis.

We explain and illustrate how to compute string-loop amplitudes in Calabi-Yau orientifold compactification in the large volume limit with the help of the patch-by-patch description of string field theory. We compute the one-loop partition function of the D1-instanton in type IIB string theory compactified on an O9 orientifold of a Calabi-Yau threefold to the first order in the large volume expansion. We show that the unphysical divergence arising from a naive choice of PCOs is canceled by vertical integration. The corollaries of this result, including the universal part of the normalization of the D-instanton superpotential and the one-loop renormalization of Kähler moduli, will be presented elsewhere.

Rare-event searches including dark matter, coherent elastic neutrino--nucleus scattering (CE$ν$NS), and neutrinoless double-beta decay (0$νββ$) require high-purity germanium (HPGe) detectors with ultralow noise, stable backgrounds, and electrode geometries that can scale to larger single-crystal masses. Ring-contact (ring-and-groove) designs address scalability by shaping the electric field to preserve low-capacitance readout, but their nonplanar topology motivates a lithium-contact process that is compatible with conformal deposition and robust high-voltage operation. As a process demonstration toward future ring-contact prototypes, we fabricate and characterize a hybrid-contact planar HPGe device, KL01. Here, ``hybrid'' denotes an $n^{+}$ contact formed by an in-house lithium-suspension paint followed by controlled thermal diffusion, combined with an AJA-developed a-Ge/Al $p^{+}$ contact and a-Ge sidewall passivation. At 77~K the device exhibits pA-scale leakage current under kV bias, a depletion plateau near $V_{\mathrm{dep}}\approx 1300$~V, and energy resolutions of 1.57~keV FWHM at 59.5~keV and 2.57~keV FWHM at 662~keV. These results validate the compatibility of the paint-and-diffuse lithium process with thin-film a-Ge/Al contacts and establish a practical fabrication workflow to be extended to ring-and-groove electrodes for next-generation rare-event HPGe modules.

Using cosmological dressing rules, we uplift flat-space unitarity cuts to discontinuity relations for dS/EAdS observables. In this representation, Cutkosky delta functions map directly to "Disc" operations in the exchanged energy variable. This provides a transparent diagram by diagram origin of cosmological cutting rules. We illustrate this with explicit examples at tree level and one loop for conformally coupled scalars.

Several papers have recently calculated the contribution to pulsar timing array overlap reduction functions (ORFs) induced by our peculiar velocity with respect to the rest frame of the stochastic gravitational-wave background. Here we show that a harmonic-space calculation confirms the most recent result. We note that, with the harmonic-space calculation, the ORFs for spin-1 GWs and the correlations with astrometry measurements are also easily obtained.

Real-time data filtering and selection -- or trigger -- systems at high-throughput scientific facilities such as the experiments at the Large Hadron Collider (LHC) must process extremely high-rate data streams under stringent bandwidth, latency, and storage constraints. Yet these systems are typically designed as static, hand-tuned menus of selection criteria grounded in prior knowledge and simulation. In this work, we further explore the concept of a self-driving trigger, an autonomous data-filtering framework that reallocates resources and adjusts thresholds dynamically in real-time to optimize signal efficiency, rate stability, and computational cost as instrumentation and environmental conditions evolve. We introduce a benchmark ecosystem to emulate realistic collider scenarios and demonstrate real-time optimization of a menu including canonical energy sum triggers as well as modern anomaly-detection algorithms that target non-standard event topologies using machine learning. Using simulated data streams and publicly available collision data from the Compact Muon Solenoid (CMS) experiment, we demonstrate the capability to dynamically and automatically optimize trigger performance under specific cost objectives without manual retuning. Our adaptive strategy shifts trigger design from static menus with heuristic tuning to intelligent, automated, data-driven control, unlocking greater flexibility and discovery potential in future high-energy physics analyses.

We initiate the study of infinite-distance limits on (complex) multi-dimensional conformal manifolds of 4d SCFTs and their bulk interpretation as tensionless-string limits in AdS/CFT. In particular, we focus on 4d $\mathcal{N}=2$ $SU$ quiver gauge theories with hypermultiplets in the bifundamental and fundamental representations. In the overall-free limit, we compute the large-$N$ Hagedorn temperature $T_H$, which governs the stringy exponential growth of the density of states at high energies. We argue that this quantity determines the type of stringy ultraviolet completion in the bulk: it captures the type of string theory in which the bulk physics is embedded while remaining insensitive to detailed geometric data. For linear quivers, we find that $T_H$ depends only on the quiver length, which is tied to the number of NS5-branes in the underlying brane construction and, in turn, to the string theory in which the bulk is embedded. For holographic quivers, where we impose that the two central charges $a$ and $c$ coincide in the large-$N$ limit, we show that $T_H$ coincides with that of $\mathcal{N}=4$ SYM, which befits the 10d Type IIB description of their gravitational duals. We also analyze the exponential rate $α$, which controls how the leading tower of higher-spin currents becomes conserved in these limits, as suggested by the CFT Distance Conjecture. In the large-$N$ regime, we derive sharp bounds on the minimal rate, $1/\sqrt{2}\le α_{\min}\le \sqrt{2/3}$, attained in the overall-free limit. Moreover, we prove that the universal lower bound $α\ge 1/\sqrt{2}$ holds, including at finite $N$. Finally, we go beyond the overall-free ray by characterizing the convex hull of the $\vecα$-vectors that encode the exponential rate of the higher-spin towers along any (partial) weak-coupling limit.

We consider a quantum mechanical model involving interacting fermions without disorder that has the same low energy physics as the supersymmetric SYK model. The model is $SU(2)$ invariant, and the supercharge involves the $ SU(2) $ 3j symbol. We analyze various solvable corners, conceptually explain why it has a melonic expansion, and perform an exact diagonalization for small values of $N$. Expanded around the states with maximal angular momentum, the model is approximated by a two dimensional CFT. The BPS states have a simple description in that regime.

Minimal Flavor Violation (MFV) provides a compelling framework for exploring physics beyond the Standard Model, in which new QCD-singlet fields transforming under the global $\mathrm{SU}(3)^3$ quark flavor symmetry can naturally be stable and act as dark matter (DM) candidates. We show that the DM-MFV framework naturally accommodates the excess in either $K^+ \to π^+ ν\barν$ or $B^+ \to K^+ ν\barν$, while a unified explanation of both channels simultaneously cannot be achieved within a minimal setup containing only a single dark matter multiplet with nearly degenerate masses. Overall, our findings underscore the intricate interplay between MFV-based model building, flavored dark matter scenarios, and precision flavor experiments, highlighting flavored dark matter as a framework that is both theoretically robust and experimentally testable.

We study the time evolution of a $3+1$ dimensional spacetime, where space is a large three-sphere, due to small perturbations of the background fields. We focus on two classes of deformations. One corresponds on the worldsheet to time-dependent non-abelian Thirring deformations. The other to perturbations of the radius of the three-sphere. In the former case, we find that small deformations generically lead to big-bang and big-crunch singularities, near which the spacetime becomes highly anisotropic. We argue that string theory likely resolves these singularities. In the latter case, general solutions have the property that the radius of the three-sphere goes to infinity at a finite time, but there are no solutions in which it collapses to zero. We also discuss the interplay of these spacetime properties with the corresponding worldsheet RG flows.

We investigate the free, massless Dirac equation $Dψ= 0$ on a higher-dimensional manifold $M_4 \times K$ equipped with a submersion metric. These background metrics generalize the Kaluza ansatz. They encode 4D massive gauge fields and Higgs-like scalars, alongside the usual 4D metric and massless gauge fields. We show that the dimensional reduction of the Dirac equation on these backgrounds naturally violates CP symmetry in four dimensions. This provides a new geometric path to constructing models with intrinsic CP violation. In this framework, massive gauge fields can break CP for three different reasons: $i)$ a misalignment between the mass eigenspinors and the spinors in the representation basis; $ii)$ a new non-minimal term coupling 4D fermions to massive gauge fields; $iii)$ the presence of a non-abelian Pauli term. All this derives from the higher-dimensional Dirac equation. Technically, the paper uses the language of spin geometry and Riemannian submersions. Along the way, it develops detailed geometric descriptions of several constructions. It discusses gauge anomalies, fermion generations, and introduces a new Lie derivative of spinors along non-Killing vector fields induced by actions of compact groups.

We investigate Lorentz- and CPT-violating effects in neutron and storage-ring electric dipole moment (EDM) experiments within the framework of the Standard-Model Extension (SME). For neutron EDM experiments, perturbation theory is applied to derive leading-order contributions to the spin precession frequency arising from Lorentz and CPT violation. For storage-ring experiments, a generalized Bargmann-Michel-Telegdi equation is used to determine the corresponding spin-precession modifications. The analysis establishes explicit correspondences between measured EDMs and specific SME coefficients, providing a basis for setting the first limits on several previously unconstrained coefficients for Lorentz violation in future studies.

The deconfining transition in $SU(3)$ gauge theory, traditionally interpreted through the Gross-Witten-Wadia (GWW) model as a sharp third-order phase transition in the large-$N_c$ limit, appears as a smooth crossover in lattice QCD. This work demonstrates that the transition is topologically smoothed into a crossover by incorporating the fractal momentum space structure inherent to thermofractals. By matching the non-extensive $β$-function to one-loop QCD results, a fundamental scaling of the thermofractal index $q$ is derived as a function of the number of flavours $N_f$. It is proven that applying a $q$-deformed derivative operator $\mathcal{D}_q$ to the $q$-logarithm of the eigenvalue distance results in a non-extensive measure that effectively smears the topological stiffness of the gauge vacuum. A unified master equation for the Polyakov loop $\langle L \rangle$ is presented, governed by the thermofractal index $q$ and a single variance parameter $σ^2(T)$ that scales as $T^{1/(q-1)}$. The observed phase dynamics are shown to be asymptotic limits of this unified density: a ``soft'' algebraic growth $\langle L \rangle \propto T^{11}$ in the 1D string-like confined regime for $N_f=0$, and a rapid $1 - \langle L \rangle \propto T^{-21}$ suppression in the 3D deconfined volume for $N_f=3$. This approach provides a microscopic foundation for partial deconfinement theory and reproduces lattice QCD data with a reduced $χ^2 \approx 1.12$, offering a rigorous reconciliation between matrix model topology and the continuous QCD crossover.

Computing functional determinants of differential operators is central to any field-theoretical calculation relying on a saddle-point expansion. A variety of approaches is available for the computation that avoid having to know the eigenspectrum of the operator, and in particular the Gel'fand-Yaglom theorem and the Green's function method. In this note, we show how both approaches can be constructed using a contour integral argument and conclude that these are completely equivalent for computing ratios of determinants of one-dimensional operators. Furthermore, we comment on the presence of vanishing as well as negative eigenvalues and show how the Green's function method provides a natural prescription for handling them.

We discuss the role of quantum reference frames in providing a viable definition of rest frame for mixed particles. We then analyze the related concept of frame-dependent entanglement and its impact on the phenomenology of neutral mesons and neutrinos.

The BESIII experiment has collected 2.6 billion $ψ(3686)$ events, 10 billion events, 20 $fb^{-1}$ of $D$ meson pairs at 3.773 GeV, and 7.33 $fb^{-1}$ of $D_sD_s^*$ events from 4.128 to 4.226 GeV. These huge data samples allow us to search for rare or forbidden processes in charm hadron decays. We summarize the recent research of charm rare decays at BESIII in this paper.

Detector visualization plays a vital role in high energy physics (HEP) experiments, yet existing detector descriptions, such as GDML, lack compatibility with industrial 3D tools. We present an automated conversion framework that transforms four major HEP detector descriptions, including GDML, Geant4, ROOT and DD4hep, into standardized FBX models compatible with a industrial 3D platform called Unity. This solution enables HEP detectors to be directly visualized in the professional 3D ecosystem, which is of great help for detector design verification, event display development, and public participation.

In arXiv:2211.09069, significant progress was made in decomposing simple products of fermion correlation functions, and in summing over spin structures of superstring amplitudes in genus two under cyclic constraints. In this manuscript we consider part of the same subject using a framework in which one of the branch points of the genus two curve is fixed at infinity. This framework is a direct generalization of the popular one in the case of genus one. We address some of the issues that remained unresolved in our previous paper arXiv:2209.14633. We show that the spin structures of the simple products of fermion correlation functions with cyclic conditions depend only on the Pe-function values at the half-periods of the genus two surface, for any number of factors in the products. Similar to the genus one case, we can provide basis functions to decompose the product. Consequently, the trilinear relations found in arXiv:2211.09069 can be derived from the known set of differential equations of genus two Pe-functions by simply setting the variables equal to the half-periods of the non-singular and even spin structures, as is the case for genus one. The focus of this manuscript is on the procedures for expressing the results of decomposed formulae in terms of the unique genus two theta function. At present we cannot provide a procedure for deriving the general form of the decomposed formula totally expressed in terms of the theta functions for an arbitrary number of the fermion correlation functions in the product, by the reason described in the text. We present some general results and demonstrate that concrete expressions of both the spin structure dependent and independent parts will be derived and simplified to analyze using the logic of the derivations of the classical solutions to Jacobi inversion problem and their modifications which will be given in this manuscript.

In this work, we perform the first numerical study of strong scalar backreaction in spectator chromo-natural inflation (SCNI) in the case where the spectator sector decays during inflation. The tachyonic instability in scalar fluctuations, activated as the system crosses the $m_Q = \sqrt{2}$ threshold, amplifies perturbations and may significantly alter the background dynamics. The strong scalar backreaction regime introduces an effective quartic term in the potential for the gauge field background that rapidly drives it to zero, accelerating the axion-gauge system decay. We describe the dynamics of such decay and derive the gravitational wave spectrum for a set of benchmark parameters. Interestingly, the signal may peak at interferometer scales and lie within LISA's projected sensitivity.

We construct a $G$-cluster Hamiltonian in 2+1 dimensions and analyze its properties. This model exhibits a $G\times2\mathrm{Rep}(G)$ symmetry, where the $2\mathrm{Rep}(G)$ sector realizes a non-invertible symmetry obtained by condensing appropriate algebra objects in $\mathrm{Rep}(G)$. Using the symmetry interpolation method, we construct $S^1$- and $S^2$-parameterized families of short-range-entangled (SRE) states by interpolating an either invertible $0$-form or $1$-form symmetry contained in $G\times2\mathrm{Rep}(G)$. Applying an adiabatic evolution argument to this family, we analyze the pumped interface mode generated by this adiabatic process. We then explicitly construct the symmetry operator acting on the interface and show that the interface mode carries a nontrivial charge under this symmetry, thereby demonstrating the nontriviality of the parameterized family.

We construct 2+1-dimensional lattice models of symmetry-protected topological (SPT) phases with non-invertible symmetries and investigate their properties using tensor networks. These models, which we refer to as generalized cluster models, are constructed by gauging a subgroup symmetry $H \subset G$ in models with a finite group 0-form symmetry $G$. By construction, these models have a non-invertible symmetry described by the group-theoretical fusion 2-category $\mathcal{C}(G; H)$. After identifying the tensor network representations of the symmetry operators, we study the symmetry acting on the interface between two generalized cluster states. In particular, we will see that the symmetry at the interface is described by a multifusion category known as the strip 2-algebra. By studying possible interface modes allowed by this symmetry, we show that the interface between generalized cluster states in different SPT phases must be degenerate. This result generalizes the ordinary bulk-boundary correspondence. Furthermore, we construct parameterized families of generalized cluster states and study the topological charge pumping phenomena, known as the generalized Thouless pump. We exemplify our construction with several concrete cases, and compare them with known phases, such as SPT phases with $2\mathrm{Rep}((\mathbb{Z}_{2}^{[1]}\times\mathbb{Z}_{2}^{[1]})\rtimes\mathbb{Z}_{2}^{[0]})$ symmetry.

We show that the consequences of a recent paper on quantum gravity are 1) a duality between point particles and massive scalar propagators, 2) the recovery of the entropy of a boundary (a black hole) in the same form as that of the EFT approach to Quantum Gravity and 3) a quantum correction to Hawking radiations and a Page-like curve. In this recent paper, information about what lies inside a boundary is encoded onto it, meaning that in this approach the information directly leaks from the horizon to the bulk in the form of Hawking radiations.

The $^8Be$ anomaly reported by the Atomki experiments can be explained by the hypothesis of an $X(17)$ boson that interacts with electrons via the ``Vector $\pm$ Axial-vector'' ($V \pm A$) interaction. Using existing experimental data, we derive constraints on the couplings of the $X(17)$ boson to electrons within this $V \pm A$ framework. With this setup, we attempt to identify $X(17)$ signals in the $e^+e^- \longrightarrow X(17) \longrightarrow e^+e^-$ process at the PADME experiment and in the $e^+e^- \longrightarrow X(17)γ\longrightarrow e^+e^- γ$ process at the BESIII experiment. Our findings indicate that observing the $X(17)$ signal at the PADME experiment is pessimistic, whereas the BESIII experiment may provide a definitive answer regarding the $X(17)$ hypothesis.

We reconsider the detailed structure of the topological character of the instantons in pure Yang-Mills theory on $S^1\times\mathbb{R}^3$, so-called calorons. The claim is that the standard formula for the topological character, the second Chern number, requires some modification through analytic consideration. For concreteness, we explicitly calculate the second Chern number of the gauge configuration of the Harrington-Shepard type with unit topological charge of the gauge group $\mathrm{SU}(2)$ in several gauges. The genuine formula is shown to be applicable even though the gauge connection is in singular gauge. The gauge dependence of the magnetic charge is also discussed.

In this Letter, we propose a strategy to extract information on the hierarchical amplitude pair in singly Cabibbo-suppressed (SCS) charmed baryon two-body decays, with a dominant amplitude proportional to $λ_s = V_{cs}^* V_{us}$ from tree operators and sub-leading one proportional to $λ_b = V_{cb}^* V_{ub}$ from both penguin and tree contributions. The coexistence of these two amplitudes is essential for generating nonzero CP violation (CPV) effects. Since the $λ_b$ amplitude is strongly suppressed, its experimental determination is highly challenging. However, by exploiting SU(3) flavor symmetry, which relates the well-measured Cabibbo-favored (CF) amplitudes to the SCS tree amplitudes, information on the $λ_b$ amplitude can be extracted. Using current experimental data, a conservative analysis yields $λ_b$ amplitudes can be as large as about $10\%$ of the corresponding tree amplitudes with a significance of $2.1σ$. In addition, the Lee-Yang parameters of these decays provide an independent probe of this elusive term. We further identify two golden decay channels, $Ξ_c^0 \to p K^-$ and $Ξ_c^0 \to Σ^+ π^-$, which are particularly well suited for experimental studies of CPV.

We present precision spectroscopy and isotope shift measurement of the ${}^1S_0 \leftrightarrow {}^3P_2$ clock transition in neutral ytterbium ($\mathrm{Yb}$) atoms. By revealing a magic wavelength at $905.4(2)$ nm, we successfully achieve the atomic spectrum narrower than $100$ Hz. The interleaved clock operation between isotopes allows us to determine isotope shifts of four bosonic isotope pairs at Hz-level uncertainties, which is combined with those of other four ultra-narrow transitions in $\mathrm{Yb}$ and $\mathrm{Yb}^+$ to construct the King plot. Importantly, the new isotope shift data reported in this work is a key to exclude the possibility of attributing the observed nonlinearity of the three-dimensional King plot solely to the new physics, while the previous works rely on the other terrestrial bound set by the neutron scattering and $(g-2)_e$ measurements. This work paves the way for the effective use of precision isotope shift data in the King plot analysis and stimulates further measurements in $\mathrm{Yb}$ and other elements.

We derive a parameter-free geometric constraint on residue phases dictated by the pole-threshold angle. Using the N(2190) anomaly as a test case, this constraint reveals a sign ambiguity in prior data; correcting it yields a phase of $-28^\circ\pm10^\circ$, matching our prediction. This consistency validates the method as a model-independent diagnostic for distinguishing compact from molecular states, offering a rigorous tool for exotic spectroscopy.

When studying the Casimir effect in a quantum field theory setting, one can impose the boundary conditions by adding appropriate Dirac-delta functions to the path integral. In this paper, the limits of this approach are explored under different boundary conditions.

Recently, the LHCb collaboration reported the first observation of $CP$ violation in baryon decays, with a significance of more than $5σ$. This strongly motivates us to investigate the $CP$ violation in more baryon decay processes. In this work, we employ the final-state rescattering mechanism with introducing two model parameters, $Λ_{charm}$ and $Λ_{charmless}$, and calculate two-body non-leptonic baryon decays $Λ^0_b \rightarrow Λ(1520)\,π^0/κ(700)/f_0(500, 980)/ρ^0/K^{*0}/φ$ and $Ξ^-_b \rightarrow Λ(1520)\,K^-$. Consequently, we evaluate the corresponding branching ratios, $CP$ asymmetries, and interference effects between different decay amplitudes. Our theoretical predictions for certain decay channels are in good agreement with current experimental measurements, while the remaining processes--particularly the remarkably large $CP$ violation observable revealed by the kinematic analysis are expected to be tested in future experiments.

In this {\it Letter}, we present a compelling and robust argument for the roles of neutrino fast flavor conversion (FFC) in the explosion mechanism of core-collapse supernova (CCSN), combining the {\it multi-angle} FFC subgrid model rooted in quantum kinetic theory with the multi-dimensional four-species Boltzmann neutrino radiation hydrodynamics. Employing various progenitor masses and the nuclear equations of states, we find that the effect of FFC on CCSN explosion is bifurcated depending on the progenitors. For the lowest-mass progenitor, FFC facilitates the shock revival and enhances the explosion energy, whereas for higher-mass progenitors its impact is inhibitory. We identify the mass accretion rate as the key determinant governing this bifurcation. When the mass accretion rate is low (high), the contribution of FFC to neutrino heating becomes positive (negative), because the heating efficiency enhancement via FFC-driven spectral hardening of electron-type neutrinos dominates over (is outweighed by) the concurrent reduction in neutrino luminosity. Our results further highlight the limitations of approximate neutrino transport, and demonstrate that a multi-angle treatment is essential for accurately capturing FFC effects; otherwise, FFCs are missed and even generated spuriously.

The asymptotic safety program assumes that quantum gravity becomes renormalizable through ultraviolet fixed points in metric-based couplings. We demonstrate that this approach {encounters fundamental symmetry violations} across multiple independent criteria, all traceable to a single fundamental cause: the breakdown of general covariance and BRST symmetries above the gravitational cutoff scale. Rigorous canonical quantization proves that general covariance cannot be maintained quantum mechanically in dimensions greater than two, while recent path integral calculations reveal persistent gauge parameter dependence in quantum gravitational corrections, signaling BRST symmetry violation. These dual proofs establish that the metric tensor ceases to exist as a valid quantum degree of freedom above $Λ_{\text{grav}}$$\sim$$10^{18}$ GeV, rendering the search for ultraviolet fixed points in metric-based theories {problematic from a foundational physical perspective}. We provide comprehensive analysis demonstrating that asymptotic safety exhibits persistent gauge parameter dependence where fixed-point properties vary with arbitrary gauge choices, non-convergent truncation schemes extending to the 35th order showing no approach to stable values, experimental {tensions} with electroweak precision tests by orders of magnitude, matter content requirements incompatible with the Standard Model, absence of concrete graviton predictions due to gauge and truncation dependence, unitarity {challenges} through ghost instabilities and propagator negativity, and fundamental Wick rotation obstructions preventing reliable connection between Euclidean calculations and physical Lorentzian spacetime. We contrast this with the Unified Standard Model with Emergent Gravity framework that systematically avoids all asymptotic safety pathologies.

We investigate the quantum structure of black hole interiors in Horava-Lifshitz gravity by analyzing the Wheeler-DeWitt equation in minisuperspace. Focusing on the ultraviolet regime, where higher-order spatial curvature terms dominate, we derive analytical solutions in this UV limit for both the original Horava-Lifshitz action and its analytically continued counterpart. We study their behavior near the event horizon and the classical singularity, with particular attention to the interpretation of the wave function in terms of the annihilation-to-nothing scenario proposed in general relativity. In this paper, we have considered cases in which the two-dimensional spatial section is spherical, planar, or hyperbolic, as well as models with positive, negative, or vanishing cosmological constant. In all cases, we find that the terms dominating in the ultraviolet regime, together with the effects of the running scaling parameter, act to suppress the annihilation-to-nothing behavior. These results suggest that, at least within the range explored in this study, the characteristic annihilation-to-nothing behavior does not appear in the ultraviolet regime of Horava-Lifshitz gravity, and provide a new perspective on the understanding of singularity resolution in quantum gravity.

Axions coupled to thermal non-Abelian gauge fields may have cosmological significance. As the heat bath defines a frame, its influence depends separately on energy and momentum. A light-like momentum ($k \approx ω$) is relevant for the axion contribution to the effective number of light neutrinos, $ΔN^{ }_\mathrm{eff}$, whereas a vanishing momentum ($k=0$) plays a role for warm natural inflation or ultralight dark matter, and has been employed in lattice estimates (both classical and quantum-statistical) of the strong sphaleron rate. Focussing on soft energies ($α_\mathrm{s}^{ }T \ll ω\ll πT$), we carry out an HTL computation to show how the domains $k=0$ and $k \approx ω$ interpolate to each other. We then compare with lattice data at $k=0$, and connect our analysis to NLO computations at $k \approx ω\ge πT$. Assembling the current best input, we re-investigate light QCD axion decoupling dynamics at $T \ge 200$ MeV, showing that efficient interactions in the ultrasoft domain increase $ΔN^{ }_\mathrm{eff}$ from $\sim 0.03$ to $\sim 0.04$ at $f^{ }_a = 4\times 10^8_{ }$ GeV.

The BESIII experiment at BEPCII provides a clean environment to test baryon-dark sector connections and baryon-number violation. Using $10^{10}$ $J/ψ$ events, searches were performed for $Σ^{+}\to p+\text{inv}$, $Ξ^{-}\toπ^{-}+\text{inv}$, and $\barΛ-Λ$ oscillations. No excess was observed, yielding $\mathcal{B}(Σ^{+}\to p+\text{inv})<3.2\times10^{-5}$, $\mathcal{B}(Ξ^{-}\toπ^{-}+\text{inv})<(4-7)\times10^{-5}$, and $P(Λ)<4.4\times10^{-6}$ (90\% C.L.). These are the most stringent limits on invisible baryon decays and $ΔB=2$ transitions in strange baryons, probing new physics beyond the Standard Model.

Recently, the exciting new Fermilab (FNAL) Muon g-2 measurement impressively confirmed the final Brookhaven (BNL) result from 2004, and with a result four times more precise, has launched a new serious attack on the Standard Model (SM). On the theoretical side, ab initio lattice QCD (LQCD) calculations of hadronic vacuum polarization have made remarkable progress. They are now the new standard for studying the leading non-perturbative contributions, which have previously hindered matching with the precision required for full exploitation of the experimental results. The lattice results affected both leading hadronic contributions the hadronic vacuum polarization (HVP) and the hadronic light-by-light (HLbL) contributions by increasing the previously generally accepted $e^+e^-$ to hadrons based dispersion relation results. The shifts reduced the discrepancy between theory and experiment, leaving nothing missing. One of the most prominent signs of Beyond the Standard Model (BSM) physics has disappeared: the SM appears validated more than ever, in agreement with what other searches at the Large Hadron Collider (LHC) at CERN tell us! A triumph of the SM, even though the SM cannot explain known cosmological puzzles like dark matter or baryogenesis, and why neutrino masses are so tiny, the absence of strong CP violation, for example. I also argue that the discrepancy between the data-driven dispersive result and the lattice QCD results for the hadronic vacuum polarization can be largely explained by correcting the $e^+e^-$ data for 'rho-gamma' mixing effects.

The Light Dark Matter eXperiment (LDMX) is a proposed fixed-target missing momentum search for sub-GeV thermal relic dark matter. LDMX aims to probe thermal dark matter targets with 1016 electrons on target. Such an approach requires a high-repetition rate, low-current beam, with an average of one electron on target per event. These requirements are well-suited to the DArk Sector Experiments at LCLS-II (DASEL) facility, which will take advantage of the unused RF buckets between LCLS-II bunches to produce a well-defined low-current beam with a 26.9 ns bunch spacing. This document describes the results of a measurement of dark current in the Sector 30 transfer line (S30XL) of the LCLS-II beam, using a prototype of the LDMX trigger scintillator (TS) subsystem.

The intrabinary shocks (IBS) in spider pulsars emit non-thermal synchrotron X-rays from accelerated electrons and positrons in the shocked pulsar wind, likely energized by magnetic reconnection. The double-peaked X-ray light curves from these shocks have been well characterized in several spider systems. In this paper, we analyze Imaging X-ray Polarimetry Explorer (IXPE) observations of the redback pulsar J1723$-$2837 to examine the expected synchrotron polarization. Using advanced extraction methods that include spatial, temporal, and particle background weights, we constrain the polarization of the IBS. We compare different models for the magnetic field in the radiation zone and find that the best fit prefers a striped pulsar wind model over other polarized models, with maximum polarization degree of the IBS emission component $Π_{\rm IBS}=36^{+16}_{-15}\%$, in addition to an unpolarized non-IBS component. Since this is only 2.4$σ$, we cannot claim strong preference over an unpolarized model; we report a $99\%$ confidence level upper limit on the total polarization of both IBS and non-IBS components $Π_{99}<36\%$, which is improved over the $50\%$ limit obtained in previous work. The best-fit polarization of the IBS component is consistent with numerical simulations. Detailed tests of such models are accessible to future measurements.

We present JWST/NIRSpec PRISM observations of a robust galaxy candidate at $z\simeq14$, selected from pure-parallel NIRCam imaging; PAN-z14-1. The NIRSpec spectrum allows confirmation of this source at $z_{\rm spec}=13.53^{+0.05}_{-0.06}$ through modeling of the Lyman-$α$ break. PAN-z14-1 is the fourth most distant galaxy known to date and is extremely luminous ($M_{\rm UV}=-20.6\pm0.2$), with a blue UV-continuum slope ($β=-2.26\pm0.08$) and a large physical size ($r_{\rm c}=233\pm10\, \rm pc$). We fail to detect any rest-frame UV emission lines at $\geq 2σ$ significance, with upper limits sufficiently constraining to exclude the possibility of strong line emission. In terms of its physical properties, PAN-z14-1 is remarkably similar to the previously confirmed $z_{\rm spec}=14.18$ galaxy GS-z14-0. The lack of strong emission lines and large physical size is consistent with an emerging picture of two potentially distinct galaxy populations at $z>10$, distinguished by star-formation rate surface density. In this scenario, PAN-z14-1 is a second example of a ``normal'', extended, luminous, star-forming galaxy at $z \simeq 14$, and differs markedly from the other class of extremely compact galaxies with strong emission lines recently uncovered at extreme redshifts with JWST. These results highlight the importance of further spectroscopic confirmation of $z>10$ galaxy candidates in order to fully understand the diversity of properties displayed by the first galaxies.

In radio interferometric observations, the main source of information is the complex visibility function, which includes amplitude and phase. In this paper, the dependence of the phase of the visibility function on the base projection is investigated when used in radio interferometry with space bases up to six Earth diameters. The dependence of the phase of the visibility function on the projection of the base and direction is obtained. It is shown that for small values of the base projections, this dependence has a universal character and is consistent with the results of numerical magnetohydrodynamic models.

Recently, the experimental realization of a Kapitza potential in a Bose-Einstein Condensate (BEC) has been reported for the first time in literature, motivating further theoretical investigations of such system. At the same time, in the astrophysical context, BEC dark matter models have been widely studied as a possible phenomenological explanation for the dark matter phenomena. We model the galactic structure with an inner cored profile obtained from the ground state equilibrium solution of the Schroedinger-Poisson together with a Kapitza-BEC like interaction for the tail region. We find reasonable agreement of the model with representative galaxy rotation curves available in the SPARC catalogue.

The peak luminosities and rates of decline for a large sample of novae recently published by Clark et al. have been analyzed using the Yaron et al. nova models to estimate fundamental properties of the M31 nova population. The apparent white dwarf (WD) mass distribution is approximately Gaussian with a mean $\langle M_\mathrm{WD} \rangle = 1.16\pm0.14~M_{\odot}$. When corrected for recurrence-time bias, the mean drops to $\langle M_\mathrm{WD} \rangle = 1.07~M_\odot$. The average WD mass of the M31 nova sample is found to be remarkably similar to that found by Shara et al. in their study of 82 Galactic novae, but $\sim0.15~M_\odot$ more massive than the mean recently determined by Schaefer in his comprehensive study of more than 300 systems. As expected, the average WD mass for the recurrent novae included in the M31 sample, $\langle M_\mathrm{WD} \rangle = 1.33\pm0.08~M_{\odot}$, is significantly higher than that for novae generally. Other parameters of interest, such as the accretion rate, velocity of the ejecta, and the predicted recurrence time, are characterized by skewed distributions with large spreads about means of $\langle \log \dot M ~(M_\odot~\mathrm{yr}^{-1}) \rangle \simeq -9.27$, $\langle V_\mathrm{max} \rangle \simeq 1690~\mathrm{km~s}^{-1}$, and $\langle \log P_\mathrm{rec}~\mathrm{(yr)} \rangle \simeq 4.39$, respectively. The role of hibernation in affecting the $\dot M$ and $P_\mathrm{rec}$ distributions is briefly discussed. Finally, the nova properties were studied as a function of apparent position (isophotal radius) in M31, with the preponderance of evidence failing to establish any clear dependence on stellar population.

We present observations and analysis of the bound planetary microlensing event KMT-2025-BLG-1616. The planetary signal was captured by the Korea Microlensing Telescope Network (KMTNet) and the DECam Rogue Earths and Mars Survey (DREAMS). DREAMS's minute-cadence observations break the central/resonant degeneracy in the binary-lens models. The color of the faint source star ($I=22$) is measured from the DREAMS's $r - z$ color. The planetary system has a planet-host mass ratio of $q \sim 5 \times 10^{-4}$. A Bayesian analysis yields a host-star mass of $\sim 0.3\,M_\odot$, a planetary mass of $\sim 40\,M_{\oplus}$, a projected planet-host separation of $\sim 1.6~\mathrm{au}$, and a lens distance of $\sim 7.5~\mathrm{kpc}$. Based on the photometric precision achieved by DREAMS for this event, we simulate free-floating planet (FFP) detections and find that DREAMS is sensitive to Mars-mass FFPs in the Galactic bulge and Moon-mass FFPs in the Galactic disk.

We present a study of Pseudo-LRD-NOM (Pseudo little red dot with no metal lines), a highly magnified low-mass galaxy behind the lensing cluster Abell 370 at z=5.96. We classify this object as a pseudo-LRD because its red rest-frame optical colour is mainly driven by a prominent Halpha line (with EW0 >~ 800 Angstroms) present in its JWST NIRSpec spectrum. Halpha is dominated by a narrow component and also has a minor broad component indicative of an active black hole with M_BH = 2.9x10^6 Msun. A narrow Hbeta emission line is also detected (with S/N = 8), producing a Balmer decrement (narrow) Halpha/Hbeta = 11. The rest-frame UV spectral slope is beta_UVspec = -1.2. All these features can be ascribed to high dust attenuation. However, no [OIII]5007 or any other metal lines are detected in the spectrum, so [OIII]5007/Hbeta < 0.25, at odds with a simple dust-attenuation explanation. Accounting for all the spectral properties requires the model of a starburst with moderate colour excess E(B-V)=0.18-0.45, high gas density (n_H >~ 10^6 cm^{-3}) and extremely low gas/stellar metallicities (Z = 0.01-0.1 Zsun). The demagnified stellar mass is 1.62^{+1.54}_{-0.79} x10^7 Msun and the stellar-mass surface density is Sigma* = 418^{+725}_{-310} Msun/pc^2, similar to that of massive/nuclear star clusters. Pseudo-LRD-NOM provides evidence of massive black-hole growth occurring in a high-density, dusty starburst which is at the early stages of its chemical enrichment, and is likely a precursor to a real LRD.

We have used Auriga -- a set of state-of-the-art cosmological hydrodynamical simulations of Milky Way-size systems -- to study the impact of baryons on the Galactic subhalo population. A DM-only run counterpart of Auriga allows us to compare results with and without baryons. We repopulate the original suites with low-mass subhalos orders of magnitude lighter than the mass resolution limit, starting from a detailed characterization of Auriga data in the well-resolved subhalo mass range. The survival of low-mass subhalos to tidal forces is unclear and under debate nowadays, thus in our study we stay agnostic and consider two different levels of subhalo resilience to tidal stripping ('fragile' and 'resilient' subhalos). We find baryons to alter the Galactic substructure significantly, by decreasing its overall abundance by a factor $\sim2.4$ (fragile) and $\sim1.9$ (resilient) and subhalo concentration -- here defined in terms of maximum circular velocity -- by $\sim1.5$ with respect to the DM-only scenario. This has important consequences for indirect searches of DM. As an example, we investigated the case of using unidentified gamma-ray sources to set constraints on the DM particle properties, assuming some of them may be dark satellites. We find the DM annihilation cross-section constraints to worsen by a factor $\sim3.6$ in the most realistic scenario of including baryons, compared to DM-only simulations in the 'fragile' setup. Yet, a stronger resilience of subhalos to tidal stripping improves these DM limits by a factor $\sim4.5$ and $\sim10$ compared to the DM-only and hydrodynamical 'fragile' cases, respectively. Our results show the importance of including baryons to properly characterize the Galactic subhalo population, as well as to propose the most optimal subhalo search strategies, not only via its potential DM annihilation products but also through their gravitational signatures.

We apply the angular two-point correlation function (TPCF) to the spatial distribution of young star clusters (YSCs) in four nearby star forming galaxies (NGC 628, NGC 4449, M51, and M83) in order to investigate their underlying hierarchical structuring. Using newly constructed catalogs of YSCs in the emerging phase (eYSCs), identified in the infrared with JWST, and optical YSCs detected in archival HST data, we compute TPCFs for various cluster samples and age bins across the four galaxies as part of the FEAST (Feedback in Emerging extrAgalactic Star ClusTers) program. We find clear evidence of hierarchical structuring, especially in eYSCs and YSCs with ages < 10 Myr (referred to as oYSCs), which show similar TPCFs within each galaxy. NGC 628 exhibits a clear distinction between the TPCFs of eYSCs and oYSCs, implying a shorter randomization timescale. In contrast, clusters aged 10 to 300 Myr exhibit progressively more random spatial distributions, becoming effectively random after $\sim$ 100 Myr, consistent with earlier studies. The two-dimensional fractal index $D_2$ of the YSCs underlying distribution is calculated from model fits to TPCFs. Our values of $D_2$ derived from the youngest YSC populations align better with the expected value of $D_2 \sim $1.3 for a universal star formation process compared to previous findings.

In the context of pre-processing -- a scenario in which galaxies quench their star formation within substructures before falling into clusters -- we investigate the impact of environment on the physical and morphological properties of galaxies in Compact Groups (CGs), focusing specifically on a sample of Star-Forming Compact Groups (SFCGs). Our aim is to characterize the physical and morphological properties of galaxies in SFCGs, analogues of the Blue Infalling Group, and to understand how the environment influences their evolution. We use photometric techniques to derive stellar masses and star formation rates (SFRs). Morphological parameters are extracted from DECaLS images, obtaining parametric properties such as the Sérsic index ($n$) and effective radius ($R_{\mathrm{e}}$) using GALFITM, as well as non-parametric indices -- including the Gini coefficient, $M_{20}$, and asymmetry -- from the same data. These indicators allow us to classify galaxies into E/S0/Sa, Sb/Sc/Ir, and merger types. All measurements are compared to a control sample of field galaxies to assess environmental effects. We find no significant differences in $n$ and $R_{\mathrm{e}}$ between SFCG and field galaxies, in contrast to results reported for other CG samples. However, SFCG galaxies exhibit higher specific star formation rates (sSFRs) than their field counterparts. Approximately $16\%$ of SFCG galaxies show merger features and elevated asymmetry. These mergers also present enhanced SFRs compared to both other SFCG types and the field population. We propose that SFCGs represent an earlier evolutionary phase of CGs, supported by their lower velocity dispersions and moderate crossing times, in addition to the observed SFR enhancement and the absence of pronounced morphological transformation. Galaxy mergers in this phase appear to enhance, rather than suppress, star formation.

Previously we developed a local model for a spherically contracting/expanding gas cloud that can be used to study turbulence and small scale instabilities in such flows. In this work we generalise the super-comoving variables used in studies of cosmological structure formation to our local spherical flow model, which make it significantly easier to derive analytical solutions and analyse the interactions of more complex flows with the background. We show that a wide class of solutions to the local spherical flow model can be obtained via a mapping from the corresponding solutions in regular Cartesian flows. The rescaling of time in the transformation results in a modification of the linear instabilities that can occur in spherical flows, causing them to have a time dependent growth rate in the physical time coordinate, and can prevent slower instabilities from operating. Finally, we show that the small scale flows in isotropic contraction/expansion can be mapped directly to Cartesian, inviscid, incompressible hydrodynamics, meaning that one expects a form of rescaled Kolmogorov-turbulence at the small scale of isotropically contracting/expanding flows.

Extragalactic planetary nebulae (xPNe) in galaxies beyond the Local Universe serve as discrete tracers for studying the element abundances and kinematics of galaxies covering a wide range of morphologies and masses at a variety of angular distances, from the centre well out into their haloes. They are direct stellar probes to identify the galaxy progenitors of haloes and the intracluster light. Even with new facilities, reaching higher angular resolution and sensitivity, xPNe are the only stellar tracers that can be directly and singularly detected and characterised out to 100 Mpc distance, making them crucial for tracing halo and intracluster light assembly. New wide-field spectroscopic instruments at 10+meter-class telescopes will allow the unprecedented characterisation of xPN populations from galaxy centres to their diffuse outskirts. Panoramic integral-field spectroscopy will enable the simultaneous study of xPN and stellar population properties, establishing their use as age- and metallicity-tracers while also improving post-AGB stellar evolution models.

Fast flavor conversions (FFCs) in supernovae, driven by neutrino-neutrino refraction, can catastrophically equilibrate flavors and potentially affect the neutrino-driven explosion. We present a pivotal insight: matter density fluctuations characterized by red spectra ($ν<0$), naturally arising in stratified supernova environments, can suppress such instabilities by inducing accelerated decoherence. By deriving exact analytical solutions for two-flavor evolution in red turbulent matter-where correlations grow as $t^{|ν|}$-we uncover a novel acceleration of coherence loss. This dynamical decoherence mechanism raises an effective energy barrier against the collective growth of flavor instabilities. Translating our master-equation results into an effective damping rate for FFC linear analysis, we find that realistic red turbulence ($ν\sim -1$, fluctuation strength $ξ_ν\sim 0.1$) can elevate the FFC threshold by a factor of $\sim 3-5$, potentially stabilizing regions that would otherwise undergo explosion-killing flavor equilibration (or vice versa). Our work provides the first analytical criterion for FFC suppression in turbulent media and identifies red turbulence as a critical, physics-grounded ingredient missing from current supernova models.

Dark matter dominates the matter content of the Universe, and its properties can be constrained through large-scale structure probes such as the cross-correlation between the unresolved gamma-ray background (UGRB) and weak gravitational lensing. We analysed 15 years of Fermi-LAT data, constructing UGRB intensity maps in ten energy bins (0.5-1000 GeV), and cross-correlated them with KiDS-Legacy shear in six tomographic bins. The measurements were performed using angular power spectra estimated with the pseudo-$C_\ell$ method. No significant cross-correlation is found. Based on this non-detection, we present 95% upper bounds on the weakly interacting massive particle (WIMP) decay rate $Γ_{\rm dec}$ and velocity-averaged annihilation cross-section $\langleσ_{\rm ann} v\rangle$ as functions of mass. We compare our results with bounds from other cosmological tracers and from local probes, and found them to be complementary, particularly at low masses ($\rm GeV/TeV$). In addition, using a Euclid-like lensing survey cross-correlated with Fermi-LAT, we forecast $\sim$2 times tighter limits, highlighting the potential of forthcoming data to strengthen constraints on dark matter annihilation and decay.

We present a multiwavelength analysis of 850 $μ$m-selected SMGs (deblended S$_{\rm 850}\gtrsim$ 1mJy) in the SSA22 field, where our deepest JCMT/SCUBA-2 observations reach a sensitivity of $σ_{850}\sim$ 0.80mJy beam$^{-1}$. Using multiple identification methods, we have identified 248 deblended SMG candidates for 192 SCUBA-2 sources. The average multiplicity of SCUBA-2 sources is $\sim$26%, with brighter sources exhibiting higher multiplicity. After applying quality cuts based on SED fitting reliability, our final sample comprises 221 SMGs associated with 186 SCUBA-2 sources. The SSA22 SMGs have a median infrared luminosity of (2.25$\pm$0.25) $\times$10$^{12}$ L$_{\odot}$, with $\sim$ 63% ($\sim$ 8%) of the sample classified as ULIRGs (HLIRGs). The median redshift of the sample is $z = 2.00 \pm 0.08$, while optically faint galaxies exhibit higher median redshift ($\sim 2.20 \pm 0.17$). The comoving volume density of SMGs increases by a factor of $\sim 6$ at $z \lesssim 4$, plateauing at $\sim$ 1.78-3.16 $\times$ 10$^{-5}$ cMpc$^{-3}$ over $z \sim$ 1-3 (including the overdensity). The significant overdensity of SMGs within large-scale structures demonstrates their reliability as tracers of cosmic structure formation at high redshift. The median stellar mass and SFR of our SMG sample are $(1.55 \pm 0.22) \times 10^{11}$ M$_\odot$ and $166 \pm 25$ M$_\odot$ yr$^{-1}$, respectively. We observe a clear ``downsizing" signature: after cosmic noon ($z \lesssim 2$), massive SMGs exhaust their gas reservoirs and transition to quiescence, while lower-mass SMGs continue forming stars and dominate the cosmic SFR density. The sample has a median dust mass of (1.95 $\pm$ 0.14) $\times$ 10$^{9}$ M$_{\odot}$. The dust fraction ($ M_{\text{dust}}/M_{\text{star}}$) has a median value of (1.4 $\pm$ 0.2) $\times$ 10$^{-2}$. The median $A_V$ of SMGs is 3.09$\pm$0.07mag.

Little Red Dots (LRDs) are enigmatic, compact, red galaxies at high redshift, $z\sim 4$-$7$, discovered by the James Webb Space Telescope. Broad emission lines in the absence of X-ray and radio counterparts suggest that they host accreting supermassive black holes embedded in dense gaseous envelopes. This black-hole-envelope configuration facilitates efficient photohadronic interactions and neutrino production. Remarkably, their observed source number density and luminosity are compatible with the energetics of the diffuse neutrino background. We consider that relativistic jets and outflows are launched from the black hole and propagate through low-density polar funnels within envelopes, where particle acceleration and neutrino emission occur. This leads to LRDs being effectively hidden sources. Our analytic and numerical calculations show that, in an optimistic scenario, LRDs can contribute $\sim 30\%$ of the observed diffuse background at TeV$-$sub-PeV energies, predominantly through photomeson production. At high neutrino energies, $\gtrsim 10^{5.5}~{\rm GeV}$, inverse-Compton cooling of muons modifies the resulting flavor ratio, providing a distinctive diagnostic for IceCube-Gen2 and other upcoming neutrino telescopes.

We analyze the spectral energy distributions (SEDs), star formation histories (SFHs), and infrared-radio correlation (IRRC) of 221 850 $μ$m-selected submillimeter galaxies (SMGs) in the SSA22 deep field. The median mass-weighted age is 567 Myr. Most galaxies in our sample began forming $\sim$ 1.68 Gyr after the Big Bang, entered the `SMG phase' after $\sim$ 1 Gyr of evolution -- when they are predominantly observed -- and largely transitioned out of the `SMG phase' to become quiescent within an additional $\sim$ 0.2 Gyr. A subset of massive galaxies shows rapid early assembly with high star formation efficiencies ($\sim$0.2-0.8). The majority of SMGs reside at the high-mass end of the star-forming main sequence, with a characteristic stellar mass of $M_{star} \sim 10^{11}$ M$_\odot$, above which galaxies are predominantly either on the main sequence or already quenched. We observe a downsizing trend: more massive galaxies tend to ``mature" earlier, completing their major episodes of star formation at higher redshifts compared to lower-mass systems. Our sample contributes $\sim$ 21% (28%) to the cosmic star formation rate density (stellar mass density), including the overdensity, with its relative contribution peaking at 50-60% in the redshift range $z=2.5-3.5$. We suggest that 850 $μ$m surveys may miss a population of faint, warm galaxies at $z \geq 1$-2. The median infrared-radio correlation parameter $q_{IR}$ is 2.37, evolving as $(1+z)^{-0.11}$, likely due to AGN contributions at high redshift and intrinsic differences between low- and high-redshift populations.

The cosmological principle assumes the isotropy of the Universe at large scales. It is a foundational assumption in the $Λ$CDM model, which is the current standard model of cosmology. Recent tensions give legitimacy to investigating the possibility of anisotropies in the Universe. The large sky coverage achieved by the Zwicky Transient Facility survey (ZTF) allows us to test the veracity of the cosmological principle using observations of Type Ia supernovae (SNe Ia). In this article, we develop a methodology to measure potential anisotropies in the Hubble constant $H_0$. We test our method on realistic simulations of the second data release (DR2) of ZTF SNe Ia in which we introduce a dipole. We develop an unbiased method both to introduce a dipole in the simulations and to recover it. We test a potential $H_0$ dependency of our method while varying the dipole amplitude. We analyse the impact of introducing large-scale structures in the simulations and the efficiency of using a volume-limited sample, which is an unbiased subsample of the ZTF SNe Ia sample. Finally, we build an error model applied to the recovered dipole amplitude ($ΔH_0$) and its direction ($α_0$, $δ_0$). Our analysis allows us to recover a dipole with an error on the amplitude of $0.33\,\mathrm{km\,s^{-1}\,Mpc^{-1}}$, and uncertainties of $3.4^\circ$ and $6.1^\circ$ on the right ascension and declination, respectively, for an initial dipole amplitude of $ΔH_0 = 3\,\mathrm{km\,s^{-1}\,Mpc^{-1}}$. The resulting dipole is independent of the chosen $H_0$ value and sky coverage. This paper paves the way for a future precise ZTF dipole investigation.

We present sub-arcsecond ($θ\sim0.1^{\prime\prime}$) resolution VLA 1.3 cm continuum and 22.2 GHz H$_2$O maser observations toward 15 compact radio continuum sources with rising spectral index and 8 string-like radio continuum structures in the Rosero et al. (2016, 2019) survey. Three different morphologies are observed: elongated or double-peak string-like structure (6 out of 23 cases), a collection of distinct continuum peaks (4 out of 23 cases), and single compact sources (13 out of 23 cases). The majority of H$_2$O maser spots detected are within a sky-projected distance of $\sim5,000$ au from the radio continuum peaks and tend to be well aligned and distributed in an elongated structure when more than three spots are observed. We generally recover less emission than Rosero et al. (2016, 2019), which together with the fact that more than half of the jet candidates in our survey appear mostly compact, suggest core/halo shock structures even on small scales. We also detected proper motion in 10 cases and measured an average projected velocity of approximately 120 km s$^{-1}$. Radio brightness variability is detected in at least two cases, possibly due to weak accretion bursts. This work, together with our previous molecular jet study, provides further evidence that support the main source of ionization in the studied sources is shocks, yet collimation is only observed in 4 cases. We conclude that the available data supports the thermal jet classification of 7 sources, and the ionized jet interpretation is further supported in 16 sources.

Nearby galaxies exhibit a variety of structures, including central rings, similar to the MW Central Molecular Zone (CMZ). These rings are common in barred galaxies and can be gas-rich and highly star-forming. We aim to study molecular gas content and star formation rate of central rings within nearby galaxies and link them to global galaxy properties (e.g. bar morphology). We utilize $1\,$'' resolution CO(2-1) PHANGS-ALMA observations, visually identify 20 central rings and determine their properties. For $14$ rings, SFR surface density maps are available. We derive ring geometry, integrated molecular gas masses, SFRs, depletion times, and compare them to host galaxy and bar properties. Molecular gas is a good tracer for central rings: Previous studies used ionized gas and dust tracers to identify central rings in galaxies of similar morphological types as this study. In comparison, we find similar fractions of galaxies hosting central rings and similar radii distributions. The gaseous central rings have typical radii of $400_{-150}^{+250}\,$pc, molecular gas masses of $\log(M_\text{mol}/M_\odot){\sim}8.1_{-0.23}^{+0.17}$, and SFRs of $0.21_{-0.16}^{+0.15}\,M_\odot/\text{yr}$, thus contributing $5.6_{-2.1}^{+4.5}\,\%$ and $13_{-5}^{+10}\,\%$ to their host galaxies' molecular gas mass and SFR. The MW CMZ sits at the lower end of the radius, molecular gas mass, and SFR distribution, but it has a similar molecular gas mass and SFR fraction, and depletion time. Longer bars contain more massive molecular central rings, but we find no correlation between bar strength and the ring's molecular gas content. Although absolute central ring properties likely depend on host galaxy properties, the similarities between the MW CMZ and PHANGS central rings in relative parameters suggest that the processes of gas inflow and star formation are similar for central rings across nearby galaxies.

The Lyman-alpha (Ly$α$) absorption profile in star-forming galaxies serves as a powerful tracer of the extended, dense neutral hydrogen in their surroundings during the Epoch of Reionization (EoR). We report a unique galaxy, A2744-z7DLA, at $z\approx 6.87$ gravitationally lensed by the foreground galaxy cluster Abell 2744, which exhibits both moderate Ly$α$ emission and damped Ly$α$ absorption, suggesting the presence of a dense neutral hydrogen environment. Our analysis suggests that the UV continuum turnover near Ly$α$ is more likely shaped by a damped Ly$α$ system rather than nebular continuum from two photon process. We analyze the physical properties of A2744-z7DLA with spectroscopic and photometric data from the JWST and the HST. The galaxy shows a compact morphology ($r_e \sim 0.3\ {\rm kpc}$) and a broadened H$α$ emission line, suggesting possible AGN activity. The broad component of H$α$ has a FWHM of $2721 \pm 200\ {\rm km\ s^{-1}}$, corresponding to a black hole mass of $M_{\rm BH}=2.90^{+2.35}_{-1.28}\times 10^7 M_\odot$ and a black hole-to-stellar mass ratio of $\log (M_{\rm BH}/M_{\rm stellar}) = -1.58^{+0.45}_{-0.34}$. The Balmer decrement ($\rm Hα/Hβ$) yields a dust attenuation of $\rm A_V \approx 1.15 \pm 0.23$, indicating that this system is less dust-rich than some "little red dots". Furthermore, we perform SED fitting using both stellar and AGN models. The results show that the UV and optical wavelengths are dominated by star-forming regions, while the AGN component contributes primarily at longer wavelengths. This work provides new insights into the interplay between star formation, neutral gas, and potential AGN activity in galaxies during the EoR.

Growing evidence indicates that some fast radio bursts (FRBs) reside in dense, magneto-ionic environments where extrinsic propagation effects can substantially reshape the observed signal. Within a 1D Gaussian plasma-lens framework, we show that small, monotonic variations in the incidence angle of the FRB wavefront naturally generate both downward and upward sub-burst frequency drifts. We further demonstrate that distinct lensed paths that probe different rotation measures (RMs), can produce orthogonal polarization-angle (PA) jumps at gigahertz frequencies. In this picture, a $\sim 90^\circ$ PA transition requires only a modest RM contrast of order a few $\times10~\rm{rad~m^{-2}}$ between the multiple images. The chromatic activity of FRB 20180916B-earlier and narrower activity windows at higher frequencies-can be explained as preferential magnification near the outer caustic. Finally, the intrinsic resolution of a plasma lens provides an upper limit on the transverse emission size: lenses located close to the source yield magnetospheric-scale constraints and offer a practical means of discriminating between inner- and outer-magnetospheric emission scenarios. These results suggest that plasma lensing could account for multiple complex observational features of FRBs and may play a non-negligible role in modulating their observable properties.

The first four all-sky surveys with eROSITA the soft X-ray instrument on board the Spektrum-Roentgen-Gamma (SRG) satellite revealed a new X-ray source, eRASSU J012422.9-724248, in the Magellanic Bridge, near the Eastern Wing of the Small Magellanic Cloud (SMC). We performed a broadband timing and spectral analysis using the optical and X-ray data of eRASSU J012422.9-724248. Using the X-ray observations with eROSITA, Swift, NuSTAR and optical data from the optical Gravitational Lensing Experiment (OGLE) and the Las Cumbres Observatory (LCO), we confirm the nature of eRASSU J012422.9-724248 as a Be/X-ray binary (BeXRB) pulsar in the Magellanic bridge. The position is coincident with that of an early-type star (OGLE ID SMC732.10.7). We detect the spin period at 341.71 s in NuSTAR data and infer a period of 63.65 days from the 15 year monitoring with OGLE, that we interpret as the orbital period of the system. A tentative CRSF at ~12.3 keV is identified in NuSTAR spectra with ~1.8-sigma. The source appears to show a persistent X-ray luminosity and an optical magnitude transition on the long timescale. We propose eRASSU J012422.9-724248 is a new member of the class of persistent BeXRBs.

We present the Atacama Large Millimeter/submillimeter Array (ALMA) observations of linearly polarized 870 $μ$m continuum emission at a resolution of $\sim$0.2$^{\prime\prime}$ (2000 au) toward the two massive clumps, Clump 1 and Clump 4, in the 20 km s$^{-1}$ cloud. The derived magnetic field strengths for both clumps range from $\sim$0.3 to 3.1 mG using the Angular Dispersion Function (ADF) method. The magnetic field orientations across multiple scales suggests that the magnetic field dominates at the cloud scale, whereas gravity likely governs the structures at the core (0.01$-$0.1 pc) and condensation ($\le$ 0.01 pc) scales. Furthermore, the study on the angular difference between the orientations of the local gravity gradient and the magnetic field suggests that the magnetic field predominantly governs the dynamics in the diffuse regions, while gravity and star formation feedback become increasingly significant within the dense regions. The ratio of the magnetic field tension force $F_\textrm{B}$ to the gravitational force $F_\textrm{G}$ suggests that the magnetic field may provide some support against gravity, but it is insufficient to prevent gas from infalling toward the dense cores.

We analyze several key rest-optical emission-line ratios in a sample of 763 galaxies as well as composite spectra from JADES DR3 in the range $1.4 < z < 7$. These emission-line ratios include: [O III]$\lambda5008$/H$β$, [N II]$λ6585$/H$α$, [S II]$λλ6718,6733$/H$α$, [O I]$λ6302$/H$α$, O32, R23, Ne3O2, and RO2Ne3. We find evidence for a harder ionizing spectrum at $z\sim 3.5$ compared to $z\sim 2$ at fixed gas-phase metallicity, resulting in a pronounced shift in the star-forming galaxy locus on the [N II]/H$α$ BPT diagram and the O32 vs. R23 diagram. At $z\gtrsim 3.5$, star-forming galaxies occupy a common locus, indicating that ISM ionizing conditions at fixed gas-phase metallicity do not evolve strongly at these early cosmic times. There is a connection between ISM ionizing conditions and the chemical abundance patterns (i.e., $α$/Fe) in massive stars providing the ionizing radiation field. Therefore, the lack of evolution in ISM ionizing conditions at $z\gtrsim 3.5$, followed by evolution towards a softer ionizing spectrum at fixed nebular metallicity as cosmic time proceeds to $z\sim 2$ and lower redshift mirrors the chemical abundance patterns in Milky Way stars as a function of iron abundance. Our results highlight the diagnostic power of emission-line diagrams in the era of JWST to further our understanding of the ISM conditions into the Epoch of Reionization.

Pulsar wind nebula DA~495 (G65.7+1.2) has been extensively observed from radio to TeV $γ$-ray bands. We present LHAASO observations of DA~495, revealing an energy-dependent morphology, where an extended source with $r_{39}=0.19^{\circ}\pm0.02^{\circ}$ is detected by WCDA (0.4-15~TeV), and a point-like source with a 95\% upper limit of $r_{39}=0.11^{\circ}$ is observed by KM2A ($>25~\mathrm{TeV}$). The spectrum of the source extends beyond 100~TeV with a break or cutoff at a few tens of TeV. Our X-ray data analysis, based on Chandra and XMM-Newton observations, shows that the X-ray emission of DA~495 extends well to $\sim 6^{\prime}$, significantly larger than the size previously reported. The broadband spectral energy distribution across radio, X-ray and TeV $γ$-ray bands is phenomenologically described by a one-zone leptonic model, yielding an average magnetic field of $\sim$ 5 $\mathrm{μG}$, while Fermi-LAT spectral analysis indicates a likely presence of a $γ$-ray pulsar within the system. A time-dependent model, in which particle transport is convection-dominated in the inner region (within $\sim100^{\prime\prime}$) and diffusion-dominated in the outer region, successfully reproduces the observed radial profiles of X-ray surface brightness and spectral index, and also accounts for the TeV $γ$-ray emission detected by LHAASO, suggesting that DA~495 represents an evolved PWN with ongoing particle escape that gives rise to a TeV halo component -- that is, a PWN+halo system.

The Main Aim of this paper is to explain the emergence of new components of pulsars at higher radio bands by implementing the Inverse Compton Scattering Mechanism. From pulsar radio observation, it is seen that a couple of pulsars reveal new emission components at higher radio frequencies, although they show single-component emission at lower frequencies. We develop a brief outline, fostering inverse Compton scattering (ICS) of the low-frequency radio photons as a vulnerable source of scattering, susceptible to explaining the evolution of new components of some radio pulsars at higher bands. We couple the conventional curvature radiation (CR) mechanism and ICS, and suggest that the spectral convolution of the flux component individually from CR and the modulated template due to the ICS scattered component can be combined to reproduce such signatures associated with the diverse morphology of the integrated pulse profile. We reproduce the beam frequency diagram, the geometrical variation of different parameters of the emission geometry, as well as the multi-frequency evolution from theory. We have suitably tuned the input parameter space and given the combination of parameters that can tune to a particular scattered frequency in tabulated form. We conclude that ICS may be a responsible process for describing the emergence of new components in higher radio emission bands.

Recent JWST observations of Lyman-$α$ emission at $z \sim 11-6$ indicate a rapid reionization of the intergalactic medium within the first $\sim700$ Myr. The required Lyman continuum (LyC) photon budget may naturally arise from the unexpectedly high galaxy number densities revealed by JWST, reducing the need for scenarios invoking very high LyC escape fractions ($f_{\rm esc}\gtrsim0.2$) or dominant contributions from ultra-faint galaxies ($M_{\rm UV}>-15$) in the standard $Λ$CDM framework. In this work, we model the reionization history under the Early Dark Energy (EDE) paradigm -- originally proposed to ease the Hubble tension -- which also explains the observed over-abundance of high-$z$ galaxies without extreme star formation efficiencies. The EDE model yields reionization histories consistent with current constraints while requiring only moderate LyC escape fractions and UV luminosity densities ($f_{\rm esc}\sim 0.05-0.1$, $M_{\rm UV}\lesssim -17$ to $-15$). Our results suggest that, once key astrophysical parameters are better constrained, the reionization history could serve as an independent and complementary probe of EDE cosmologies.

Galaxy cluster-scale strong gravitational lensing systems are rare yet valuable tools for investigating the properties of dark matter and dark energy, as well as providing the opportunity to study the distant universe at flux levels and spatial resolutions that would otherwise be unavailable. Large-scale imaging surveys present unprecedented opportunities to expand the sample of cluster lenses. In this study, we adopt a deep learning-based approach to identify cluster lenses from the DESI Legacy Imaging Surveys, utilizing the catalog of galaxy cluster candidates identified by Zou et al. (2021). Our lens-finder employs a ResNet-18 architecture, trained with mock images of cluster lenses as positives and observational images of cluster scale non-lenses as negatives. We do an iterative operation to increase the completeness of our work, namely adding the found true positive samples back to the training set and training again for several times. Human inspection is conducted to further refine the candidates, categorizing them into grades (A, B, C) according to the significance of the strongly lensed arcs. Reviewing all 540,432 objects in Zou's catalog, we discover 485 high-confidence cluster lens candidates with a cluster $M_{500}$ range of $10^{13.67\sim14.97}M_{\odot}$ and a Brightest Central Galaxy (BCG) redshift range of $0.04\sim0.89$. After excluding the lens candidates listed in previous studies, we identify 247 newly discovered cluster lens candidates, including 16 grade A, 90 grade B, and 141 grade C. This catalog of cluster lens candidates is publicly available online, and follow-up observations are encouraged to confirm and conduct thorough investigations of these systems.

The Gamma-ray Burst Monitor (GBM) on board Fermi Gamma-ray Space Telescope has produced the largest database of all-sky observations in gamma rays with its continuous data with high time and energy resolutions. These data contain a wealth of unidentified transient events that did not trigger the detectors for various reasons. We conducted extensive searches to identify such untriggered transient events observed with GBM in 11 years (July 2010 - June 2021). In particular, we employed four different search modes with various energy ranges (mainly below 300 keV) and time resolutions (from 8 ms to 2 s), utilizing three statistical methods (signal-to-noise ratio, Poisson, and Bayesian statistics), each with different effectiveness in identifying specific classes of transients. Moreover, we developed algorithms for known-event flagging as well as unknown-event classification for our candidate events found in the searches. In this paper, we present our search methodologies, event flagging and classification algorithms and the resulting comprehensive event catalog. The catalog contains more than a million events in total, including known events such as gamma-ray bursts, soft-gamma repeater bursts, galactic X-ray source activities, terrestrial gamma flashes, and solar flares. For each candidate event, the catalog presents the event time, detection significance, event duration, hardness ratios, known-event flagging results, and classification probabilities. Our short-transient catalog significantly expands the currently-existing list of known events and complements the GBM trigger catalog. The event database with filtering capabilities is also publicly available at https://magnetars.sabanciuniv.edu/gbm, which allows users to retrieve event information based on their input queries along with the event lightcurves.

We present the neutral gas morphology of four galaxies from z = 0.22 to 0.47 obtained with the COSMOS HI Large Extragalactic Survey (CHILES). The HI is resolved at the highest redshift with the 7.5 arcsec beam of CHILES and 43 kpc linear scale, with all four galaxies having extended HI. Three are massive galaxies (Mstellar > 3 e10 Mo), with HI masses of 1.6 - 6.7 e10 Mo, and active star formation (3 - 30 Mo/yr). The morphology and kinematics of the galaxies vary from regular to disturbed, including an asymmetric HI disk surrounding the fourth smaller galaxy (Mstellar ~ e9 Mo). CO(1-0) observations of the sample, obtained with the LMT, confirm the redshifts of three of the four galaxies and we derive H2 masses of 0.4 - 5.2 e10 Mo. JWST imaging with four combined NIRCam filters reveals disturbed stellar components with compact knots in two of the galaxies. We combine our new higher-redshift galaxies with previously published observations to conduct a more complete study of HI and H2 evolution in the redshift range 0 - 0.5. With our HI flux-limited observations compared to similar lower redshift galaxies with high stellar mass (Mstellar > e10 Mo), the results show the mean H2/HI ratio at the highest redshift is 10.3 +- 3.4 larger than the mean H2/HI ratio in the local Universe.

The speed of sound refers to the rate at which information travels from one point to another. It is a positive quantity and bounded by causality. It is defined as the rate of change of pressure with respect to the system's density. In this article, we derive a covariant equation for the sound wave and demonstrate how the wave equation is modified in the general relativistic formalism. One can then define an effective speed of sound by attenuating the usual definition of sound speed with the gravitational metric potential. The general relativistic curvature effect is observed to reduce the speed of sound when computed inside a neutron star. This effectively makes the star relatively softer (according to the equation of state). The change in the effective sound speed can be easily visualised if one redefines the non-radial modes in terms of it. The modes do not change, but the space-time curvature reduces the amplitude of the oscillation modes. The formalism is suited for studying astrophysical compact objects.

Dust grains in the interstellar medium interact with photons across the electromagnetic spectrum. They are generally photon energy converters, absorbing short wavelength radiation and emitting long wavelength radiation. Sixty years ago in 1965, thermal emission from dust grains in the interstellar medium was discovered. This tutorial is a summary of the physics of thermal dust continuum emission and how to use observations of the intensity and flux density of dusty objects to calculate physical properties such as mass, column density, luminosity, dust temperature, and dust opacity spectral index. Equations are derived, when feasible, from first principles with all limits and assumptions explicitly stated. Properties of dust opacities appropriate for different astrophysical environments (e.g. diffuse ISM, dense cores, protoplanetary disks) are discussed and tabulated for the wavelengths of past, current, and future bolometer cameras. Corrections for observations at high redshift as well as the effects of telescope measurement limitations are derived. We also update the calculation of the mean molecular weight in different ISM environments and find that it is 1.404 per H atom, 2.809 per H2 molecule, and 2.351 per gas particle assuming protosolar metallicity and the latest values of the ISM gas phase abundances of metals.

Context. We investigate the physics of black hole accretion flows, particularly focusing on phenomena like magnetic reconnection and plasmoid formation, which are believed to be responsible for energetic events such as flares observed from astrophysical black holes.Aims. We aim to understand the influence of radiative cooling on plasmoid formation within black hole accretion flows that are threaded by multi-loop magnetic field configurations.Methods. We conducted two- and three-dimensional two-temperature general relativistic magnetohydrodynamic (GRMHD) simulations. By varying the magnetic loop sizes and the mass accretion rate, we explored how radiative cooling alters the accretion dynamics, disk structure, and the properties of reconnection-driven plasmoid chains.Results. Our results demonstrate that radiative cooling suppresses the transition to the magnetically arrested disk (MAD) state by reducing magnetic flux accumulation near the horizon. It significantly modifies the disk morphology by lowering the electron temperature and compressing the disk, which leads to increased density at the equatorial plane and decreased magnetization. Within the current sheets, radiative cooling triggers layer compression and the collapse of plasmoids, shortening their lifetime and reducing their size, while the frequency of plasmoid events increases. Moreover, we observe enhanced negative energy-at-infinity density in plasmoids near the ergosphere, with its peaks corresponding to plasmoid presence.Conclusions. Radiative cooling plays a critical role in shaping both macroscopic accretion flow properties and microscopic reconnection phenomena near black holes. This suggests that radiative cooling may modulate black hole energy extraction through reconnection-driven Penrose processes, highlighting its importance in models of astrophysical black holes.

We presented a comprehensive multi-epoch timing and multiwavelength analysis of the accreting millisecond X-ray pulsar MAXI J1957+032, covering two major outbursts in 2022 and 2025. By reanalyzing the 2022 outburst data from the Neutron Star Interior Composition Explorer (NICER), we found the spin frequency and orbital parameters from the observations in 0.3-5 keV. For the 2025 outburst, we reported the detection of pulsations with the Einstein Probe (EP). Based on the $\sim$3-year baseline between these two outbursts, we measured a significant long-term spin-down rate of $\dotν= (-5.73 \pm 0.28) \times 10^{-14}~{\rm Hz~s^{-1}}$. Assuming that the quiescent spin-down is driven by magnetic dipole radiation, we inferred a spin-down luminosity of $L \approx 1.1 \times 10^{36}~{\rm erg~s^{-1}}$ and a surface dipolar magnetic field of $B \approx (7.3 - 10.4) \times 10^8$ G. Furthermore, we conducted a deep radio pulsation search with the Five-hundred-meter Aperture Spherical radio Telescope (FAST) during the X-ray quiescent state in 2024, resulting in a non-detection with a 7$σ$ flux density upper limit of 12.3 $μ$Jy. This corresponds to a radio efficiency upper limit of $ξ< 2.8 \times 10^{-10}$, which is significantly lower than that of typical millisecond pulsars with a similar spin-down power. This profound radio pulsation faintness can be explained by two primary scenarios: either a geometric effect, wherein the pulsar's radio beam is directed away from our line of sight, or a physical suppression of the emission mechanism, potentially caused by a persistent low-level accretion flow during the X-ray quiescent state.

The transient and variable optical sky is relatively poorly characterised on fast ($<$1$\,$hr) timescales. With the Dark Energy Camera (DECam), the Deeper, Wider, Faster programme (DWF) probes a unique parameter space with its deep (median of $g\sim22.2$ AB mag), minute-cadence imaging. In this work, we present DWF's first data release which comprises high cadence photometry extracted from $\sim$12000 images and 166 hours of telescope time. We present a novel data processing pipeline, $\texttt{dwf-postpipe}$, developed to identify sources and extract their light curves. The accuracy of the photometry is assessed by cross-matching to public catalogues. In addition, we injected a population of synthetic GRB afterglows into a subset of the DWF DECam imaging to compare the efficiency of our pipeline with a standard difference imaging approach. Both pipelines show performance and reliably recover injected transients with peak magnitudes $g<22$ AB mag with an efficiency of $97.24^{+0.7}_{-1.0}$ percent for \texttt{dwf-postpipe} and $96.14^{+0.9}_{-1.1}$ percent for a difference imaging approach. However, we find that $\texttt{dwf-postpipe}$ is less likely to recover transients appearing in galaxies that are brighter or comparable in brightness to the transient itself. To demonstrate the power of the data in this release, we conduct a search for uncatalogued variable stars in a single night of DWF DECam imaging and find ten pulsating variables, two eclipsing binaries and one ZZ ceti. We also conduct a search for variable phenomena in the Chandra Deep Field South, a Rubin deep drilling field, and identify two flares from likely UV ceti type stars.

The Latin American Giant Observatory (LAGO) is a collaborative initiative that deploys a network of low-cost, autonomous Water Cherenkov Detectors across Latin America and Spain. Initially focused on detecting gamma-ray bursts at high-altitude sites, LAGO has evolved into a multidisciplinary forum for astroparticle physics, space weather studies, and environmental monitoring. Its detectors operate from sea level to over 4300 meters above sea level (m a.s.l.) in diverse geomagnetic and atmospheric conditions. The ARTI-MEIGA simulation framework is a key development that models the entire cosmic-ray interaction chain, enabling site-specific simulations to be integrated into FAIR-compliant workflows. LAGO also plays a significant role in regional education and training through partnerships with ERASMUS+ projects, positioning itself as a hub for research capacity building. New contributions emerging from the collaboration include volcano muography, neutron hydrometry for precision agriculture, and space weather monitoring in the South Atlantic Magnetic Anomaly. LAGO demonstrates how Cherenkov-based detection and open science can drive scientific discovery and practical innovation.

We determine the post-perihelion rotation period of 3I/ATLAS using two independent diagnostics: the temporal modulation of the position angle (PA) of a persistent jet-like feature, and a time-series photometric light curve in the Gr (R) band. For the jet morphology, we measure the PA at multiple epochs by applying the Larson-Sekanina Rotational Gradient filter to Hubble Space Telescope images between November 20, 2025 and December 27, 2025, and model the phase-folded PA curve with weighted least-squares Fourier series up to two harmonics while scanning trial periods P to identify minima in \c{hi}2(P). For the photometry, we adopt the best-fit period from an independent 30-minute binned analysis (from a 0.25 meter telescope MPC L92) based on a refined \c{hi}2(P) profile for a sinusoidal model with nightly offsets. We find that the jet-PA modulation gives a period Pjet = 7.20 +/-0.05 h (adopting a conservative uncertainty dominated by sparse sampling and systematics), while the photometry yields Pphot = 7.136+/-0.001 h (formal 1σ uncertainty). Although the periods differ slightly, the offset is plausibly attributable to non-Gaussian systematics and aliasing. The combined data supports a post-perihelion rotation period of 7.1 h associated with precession of the jet structure around the rotation axis by 20°. The rotation axis is aligned with the sunward direction to within 20°

We present a comprehensive reanalysis of the dynamical state of the high-redshift galaxy cluster SPT-CL J2215-3537 (z = 1.16), using the full set of available Chandra observations to characterize the thermodynamic and morphological properties of the intracluster medium. Although previously identified as one of the most distant dynamically relaxed systems based on X-ray morphological statistics, we find compelling evidence that SPT-CL J2215-3537 displays some level of dynamical activity. This includes temperature anisotropies consistent with the first detection of a pair of core-sloshing cold fronts at z > 1. We identify a ghost cavity candidate and estimate its mechanical power as log10(Pcav/10^42 erg s^-1) = 2.66 +- 0.23, confirming that radiative cooling strongly exceeds active galactic nucleus feedback heating. We show that SPT-CL J2215-3537 is likely in a short transient phase preceding the onset of a self-regulated cooling-feedback cycle. We recalculate traditional X-ray morphological parameters and discuss how non-self-similar evolution of parameters sensitive to the surface brightness cuspiness can bias dynamical classifications at high redshift.

We present high-resolution Very Large Array (VLA) continuum observations at S-band ($3$ GHz, $560$ pc scale) and X-band ($10$ GHz, $200$ pc scale) of the ram-pressure-stripped Virgo galaxy NGC 4522, to investigate the characteristics of its radio continuum, spectral index, and magnetic field under the influence of the intracluster medium (ICM). The total radio continuum shows an asymmetry that extends northwest, mirroring the HI gas distribution, but showing distinct features in the extraplanar regions. The spectral index steepens systematically from $α\sim-0.6$ in the main disk to $α\sim-1.1$ in the outer disk. We find that the spectral index behavior of the outer disk is mainly due to an ICM shock that can re-accelerate electrons and a significant reduction of thermal emission. Intriguingly, extraplanar clouds exhibit exceptionally flat spectral indices ($α\sim-0.2$ to $0$), resulting from a combination of significantly enhanced thermal emission and pronounced spectral aging of the non-thermal component. Although some of these regions correlate with H$α$, others do not. We propose that the mixing between the ICM and interstellar medium (ISM) is an alternative mechanism that enhances thermal emission independently of star formation. Polarized continuum emissions are highly asymmetric, preferentially distributed along the ICM wind side, and the polarization fraction increases radially outward from the galactic midplane, indicating that the polarized emission is strongly influenced by the ICM wind. Our results show how and where the ICM substantially affects the ISM, and also demonstrate that high-frequency observations are crucial for analyzing the radio continuum of ram pressure stripping galaxies.

The surface [C/N] of red giants is correlated with birth mass, but not directly impacted by mass loss. Exploiting this, we compare asteroseismic masses of red giants with the same [C/N] and but different evolutionary states. We find bulk differences between stars at the beginning of the red giant branch and in the subsequent evolutionary phase, the red clump, providing a direct constraint on the strength of net RGB mass loss in field stars. We find that net mass loss decreases with metallicity and mass, matching recent studies for field giants, but contradicting expectations from the widely used Reimers' mass loss formula. We propose a mass- and metallicity-dependent Reimers' $η$ calibration that reproduces the empirical trends that we see. In addition, we identify 207 stars (3.33% of our sample) that are clear outliers from their population in these birth mass bins, which we believe are likely candidates for mass transfer events. These stars do not show any obvious discrepancies in abundances or binary properties from their counterparts. This population should be accounted for in Galactic archaeological studies. Further follow-up is required to quantify their occurrence rate and origin.

Millimeter-band polarimetry offers a powerful probe of AGN jets, accessing regions less affected by opacity and Faraday rotation than at longer radio wavelengths. As part of the POLAMI program, we have conducted 14 years of 1 mm and 3 mm polarization monitoring of a sample of gamma-ray-bright blazars with the IRAM 30-m telescope, complemented here with long-term optical polarimetric observations from multiple facilities. We aim to test whether current models of parsec-scale jet physics are consistent with observed multi-band polarization behavior. Using a Bayesian framework, we derive intrinsic mean flux densities and modulation indices for total flux and fractional polarization, and characterize EVPA variability using circular statistics. We then examine how these quantities reflecting variability properties across millimeter and optical bands relate to synchrotron peak frequency, jet orientation, and radio/gamma-ray luminosities. BL Lac objects exhibit, on average, higher fractional polarization and lower EVPA variability than FSRQs at all wavelengths. Fractional polarization increases with frequency, consistent with increasingly ordered magnetic fields at shorter wavelengths. BL Lacs also show more frequent alignment of EVPAs between optical and millimeter bands, whereas FSRQs display weaker coherence. EVPA variability correlates positively with radio and gamma-ray luminosities and negatively with synchrotron peak frequency, most strongly in the optical. We further find a positive correlation between EVPA spread and fractional polarization variability, suggesting a direct link between magnetic-field structure and polarization dynamics.

Most LSST extragalactic science will rely on photometric redshifts (photo-$z$) to extract distance information for the galaxies. However, an incomplete or non-representative training set can introduce bias into photo-$z$ estimation. It is necessary to understand how various forms of training set imperfection, such as incompleteness and non-trivial spectroscopic target selection, affect photo-$z$ estimation algorithms, and to identify metrics best-suited to quantify the impact. This work aims to systematically study metrics for diagnosing how various photo-$z$ methods react to certain types of training set incompleteness and non-representativeness. We use methods available through the open-source Python library Redshift Assessment Infrastructure Layers (RAIL) to systematically test the algorithms CMNN, GPz, FlexZBoost, and PZFlow on mock training data degraded in accordance with several existing spectroscopic sky surveys, as well as under conditions of inverse redshift incompleteness, which approximately mimics observed patterns of incompleteness at high redshift. We employ the algorithm TrainZ as a control. Finally, we quantify photo-$z$ algorithm performance using a variety of statistical metrics implemented externally to RAIL. We determine that the Kullback-Liebler Divergence, Wasserstein Distance, and Probability Integral Transform are particularly informative metrics with which to assess the impact of training set imperfection on algorithmic performance. We also find that inverse redshift incompleteness effects alone lack the complexity to realistically represent anticipated training data.

Weak lensing surveys require accurate correction for the point spread function (PSF) when measuring galaxy shapes. For a diffraction-limited PSF, as arises in space-based missions, this correction depends on each galaxy SED. In the Euclid mission, galaxy SED reconstruction, a tasks of the photometric-redshift processing function (PHZ PF), relies on broad- and medium-band ancillary photometry. The limited wavelength sampling of the Euclid VIS passband and signal-to-noise ratio may affect the reconstruction accuracy and translate into biases in the weak lensing measurements. In this study, we present the methodology, which is employed in the Euclid PHZ PF, for reconstructing galaxy SEDs at 55 wavelengths, sampling the VIS passband every 10 nm, and we assess whether it fulfils the accuracy requirements imposed on the Euclid PSF model. We employ both physics- and data-driven methods, focusing on a new approach of template-based flux correction and Gaussian processes, and we introduce an SED metric whose bias propagates into PSF quadrupole moment errors. Our findings demonstrate that Gaussian processes and template fitting meet the requirements only in specific, but complementary, redshift intervals. We therefore propose a hybrid approach, which leverages both methods. This solution proves to be effective in meeting the Euclid accuracy requirements for most of the redshift range of the survey. Finally, we investigate the impact on the SED reconstruction of a new set of 16 evenly-spaced medium-band filters for the Subaru telescope, providing quasi-spectroscopic coverage of the VIS passband. This study shows promising results, ensuring accurate SED reconstruction and meeting the mission PSF requirements. This work thus provides not only the methodological foundation of galaxy SED reconstruction in the Euclid PHZ PF, but also a roadmap for future improvements using a new medium-band survey.

Notoriously hard to detect and study, isolated neutron stars (NS) could provide valuable answers to fundamental questions about stellar evolution and explosion physics. With the upcoming Roman Space Telescope, scheduled for launch in 2026, a new and powerful channel for their detection - astrometric microlensing - will become available. We set out to create a realistic sample of simulated gravitational microlensing events as observed by Roman with the Galactic Bulge Time Domain Survey. We focus in particular on the population of NS lenses, which has until now been largely understudied. We use state-of-the-art Galactic models tailored for application to microlensing by compact objects. We simulate four different NS populations with Maxwellian natal kick distributions: $\bar{v} = (150, \ 250, \ 350, \ 450)$ km/s. We apply projected Roman precision, cadence, and detectability criteria. We find the parameter space $\log_{10} t_{\rm E}$ - $\log_{10} θ_{\rm E}$, which will be accessible to Roman observations, to be maximally efficient for classification of stellar remnants. We find a feature in this space that is characteristic to NS; using this feature, optimal samples of NS candidates can be constructed from Roman-like datasets. We describe the dependence of observable parameter distributions on the assumed mean kick velocities. As the effects of natal kicks are very complex and mutually counteracting, we suggest more detailed studies focused on the dynamics of NS are needed in anticipation of Roman and future surveys. We estimate Roman will observe approximately $11\,000$ microlensing events - including $\sim100$ with NS lenses - whose both photometric and astrometric signal are detectable; the event yield decreases by $38\%$ if gap-filling low-cadence observations are not included. We make all simulated microlensing event datasets publicly available in preparation for Roman data.

Active galactic nucleus (AGN) powered jets can accelerate cosmic ray electrons, leading to the observed radio synchrotron emission. To simulate this emission, jet dynamics in galaxy clusters must be coupled to electron spectral modelling. We run magneto-hydrodynamic (MHD) simulations of a single AGN jet outburst in a Perseus-like galaxy cluster and adopt a sub-grid model for the acceleration of cosmic ray protons and electrons at unresolved internal shocks in the jet. We evolve cosmic ray electron spectra along Lagrangian trajectories using the Fokker-Planck solver Crest and compute the non-thermal emission using Crayon+. The resulting total electron spectrum reaches a steady-state slope at high momenta, with a gradually decreasing normalization over time, while the lower-momentum portion continues to resemble a freely cooling spectrum. The interaction of the jets with the turbulent cluster environment inflates lobes which rise buoyantly, induce amplification of the magnetic fields and uplift old cosmic ray populations in the wake of the bubbles. We connect radio spectral indices to electron injection ages: at a given radio frequency, weaker magnetic fields are illuminated by higher momenta electrons, whose age is determined by the last injection event. On the other hand, stronger magnetic fields are illuminated by lower momenta electrons, whose age is determined by the maximum energy injection event in the past. This powerful approach allows us to relate the underlying MHD properties to electron spectra and the resulting radio synchrotron emission, thereby enabling us to infer the underlying physics from observed radio properties.

The Ghana Radio Astronomy Observatory (GRAO) marks a pivotal advance in African radio astronomy through the successful transformation of a decommissioned 32 m satellite communication antenna into a scientifically capable, VLBI-ready radio telescope. Strategically located near the equator at Kutunse, Ghana, the telescope offers nearly full-sky coverage (-77 degrees to +88 degrees declination), making it a valuable asset for time-domain astronomy, transient surveys, and global VLBI networks. This work documents the technical evolution of the facility, including beam-waveguide optics, dual-polarization C-band receivers (5 and 6.7 GHz), and recent backend upgrades culminating in the integration of a hydrogen maser, wideband ROACH2 system, and enhanced control and pointing infrastructure. We report early science results from high-resolution spectral-line observations of 6.7 GHz Class II methanol masers, pulse timing of PSR J0835-4510 (Vela), and successful VLBI fringe detections on intercontinental baselines. Simulations and commissioning tests confirm high aperture efficiency (>77%), low sidelobe levels, and robust time stability across the signal chain. These outcomes validate the GRAO's readiness for both standalone and networked operations. As the first operational node in West Africa contributing to the African VLBI Network, GRAO plays a critical role in advancing the continent's participation in global radio astronomy, capacity building, and the preparatory phase of the Square Kilometre Array.

The ongoing Euclid mission aims to measure spectroscopic redshifts for approximately two million galaxies using the H $α$ line emission detected in near-infrared slitless spectroscopic data from the Euclid Deep Fields (EDFs). These measurements will reach a flux limit of $5\times 10^{-17}\,{\rm erg}\,{\rm cm}^{-2}\,{\rm s}^{-1}$ in the redshift range $0.4<z<1.8$, opening the door to numerous investigations involving galaxy evolution, extending well beyond the mission's core objectives. The achieved H $α$ luminosity depth will lead to a sufficiently high sampling, enabling the reconstruction of the large-scale galaxy environment. We assess the quality of the reconstruction of the galaxy cosmic web environment with the expected spectroscopic dataset in EDFs. The analysis is carried out on the Flagship and GAEA galaxy mock catalogues. The quality of the reconstruction is first evaluated using geometrical and topological statistics measured on the cosmic web, namely the length of filaments, the area of walls, the volume of voids, and its connectivity and multiplicity. We then quantify how accurately gradients in galaxy properties with distance from filaments can be recovered. As expected, the small-scale redshift-space distortions, have a strong impact on filament lengths and connectivity, but can be mitigated by compressing galaxy groups before skeleton extraction. The cosmic web reconstruction is biased when relying solely on H $α$ emitters. This limitation can be mitigated by applying stellar mass weighting during the reconstruction. However, this approach introduces non-trivial biases that need to be accounted for when comparing to theoretical predictions. Redshift uncertainties pose the greatest challenge in recovering the expected dependence of galaxy properties, though the well-established stellar mass transverse gradients towards filaments can still be observed.

The study is devoted to comparing the parameters of the interstellar medium of HII regions in the Kongiu and Groves catalogs for the galaxy NGC 628. The article analyzes the characteristics of star-forming regions, including a comparison of radiation fluxes in the ranges of 7.7 $μ$m and 21 $μ$m and in the H$α$, H$β$, OIII and CO lines, calculating the kinematic parameters (FWHM) for the lines, and analyzing the spatial distribution of regions for both catalogs. The results of the study showed that the regions from the Groves catalog demonstrate higher line widths compared to the Kongiu catalog. Signs of possible misidentified classification of some regions from the Groves catalog were revealed: there is a possibility that some of them are not HII regions, but shock ionization regions.

We present a four-component high-resolution model of thermal dust emission for microwave and sub-mm frequencies derived from Planck HFI, WHAM and Gaia. The resulting high-resolution model derived here forms the basis for the thermal dust model employed in the Cosmoglobe DR2 reanalysis of COBE-DIRBE. The four dust components are called ``cold dust'', ``hot dust'', ``nearby dust'', and ``Ha correlated dust'', respectively, and trace different physical environments. The spatial distributions of the nearby dust and Ha dust components are defined by the Edenhofer et al. Gaia 3D extinction model and the WHAM survey, respectively, while the hot and cold dust components are fit freely pixel-by-pixel to the Planck HFI data. We use a global parameter grid search coupled to an amplitude map Gibbs sampler to fit this model to Planck HFI data. In agreement with the companion low-resolution analysis, we find that the hot dust component is strongly correlated with the FIRAS Cii map, while the cold dust component is strongly correlated with the HI4PI Hi map. Despite its fewer degrees of freedom per pixel compared to the Planck 2015 legacy dust model, we find that this new model performs competitively in terms of overall residuals, capturing over 98% of the full-sky dust variance for all channels. When fitting a spatially varying 3-parameter MBB model to the new dust model with isotropic SEDs, we find very similar spatial distributions to those of the official Planck analysis, and this new model thus represents an economical decomposition of previously published spatially varying spectral parameter maps. We conclude that this new model represents both a statistically more efficient summary of thermal dust in the microwave and far-infrared regimes and a physically more realistic decomposition of the sky compared to the traditional 3-parameter MBB model. (abridged)

Observations of active galactic nuclei have revealed a correlation between millimeter luminosity, X-ray luminosity, and mass, suggesting the emission in each of these bands is powered by a common source. Starting with a set of five general relativistic magnetohydrodynamic simulations with dynamically important magnetic fields, we perform ray-tracing calculations to produce spectra including synchrotron emission, bremsstrahlung emission, and Compton scattering. Our models with similar Eddington ratios to the objects for which the relationship was inferred naturally reproduce observations without tuning. Our lower Eddington ratio models depart from this relationship, likely attributable to an observational bias against extremely low accretion rates. We find that inverse Compton scattering dominates the production of X-rays over bremsstrahlung radiation in almost all models, and in all models consistent with the observed correlation. We find only a modest spin dependence in this relationship. This study demonstrates that a compact, hot accretion flow with dynamically important magnetic fields can naturally explain observed millimeter and X-ray properties in low-luminosity active galactic nuclei. Future work should explore the impacts of non-thermal electron populations, weaker magnetic fields, and radiative cooling.

The scarcity of high-fidelity extinction measurements remains a bottleneck in deriving accurate stellar properties from Gaia parallaxes. In this work, we aim to derive precision extinction estimates for APOGEE DR19 stars, establishing a new benchmark for Galactic stellar population studies. We first determine reddening by comparing observed colorsr, etrieved from photometric surveys or standardized synthetic magnitudes from Gaia BP/RP spectra, to intrinsic colors predicted via an XGBoost model. The model is trained on minimally reddened stars to infer intrinsic colors and their associated uncertainties, using APOGEE stellar parameters (Teff, logg, [Fe/H], and [alpha/Fe]). The derived reddening values are then converted into extinctions using an anchor ratio of A_BP / A_RP = 1.694 +/- 0.004, derived from red-clump-like stars. Here, we provide extinction measurements in 39 filters across 10 photometric systems and introduce a new empirical extinction curve optimized for broadband passbands. Our extinction estimates (Av) outperform existing results (Bayestar19, StarHorse, SEDEX), achieving a typical precision of 0.03 mag in Av. Notably, we identify systematic deviations of up to 30% between monochromatic and passband-integrated extinction ratios at wavelengths greater than 700 nm. This result highlights the necessity of adopting passband-specific coefficients when correcting extinction to derive stellar parameters. As the foundation for a forthcoming series of papers, these benchmark measurements will be used to (1) revise asteroseismic scaling relations, (2) calibrate differential reddening in open clusters, and (3) reconcile heterogeneous dust maps into a unified, all-sky extinction scheme.

We present a statistical study on the origins of the UV continuum and narrow/broad emission lines in little red dots (LRDs), a newly discovered class of active galactic nuclei (AGNs). Leveraging all archived JWST/NIRSpec data, we build a sample of 28 spectroscopically-confirmed LRDs at $5<z_{\rm spec}<7.2$, by requiring broad H$α$ emission, blue UV colors, V-shaped continua, and compact morphologies. We define a control sample of 9 blue, compact, broad-line AGNs without red optical continua (hereafter little blue dots; LBDs), and examine correlations between rest UV and the narrow/broad H$α$ luminosities in these populations. In LRDs, both narrow and broad H$α$ components are tightly correlated with the UV continuum, and the luminosity ratios are consistent with those in young starburst galaxies. In contrast, the UV to broad H$α$ ratios in LBDs closely match local unobscured AGNs and are statistically different from LRDs. The Ly$α$ occurrence rates and strengths do not differ between LRDs and LBDs and are comparable to normal star-forming galaxies. These results are consistent with a scenario where the central BH in LRDs is enshrouded by a dense opaque gas envelope -- in this model, the UV continuum as well as narrow and even broad H$α$ emissions are not powered by AGNs but predominantly by young massive stars surrounding the envelope, while the envelope radiates as a $\sim 5000$ K blackbody. As the envelope dissipates, direct AGN emission can emerge, potentially transforming LRDs into LBDs and marking the end of a short-lived phase of rapid black hole growth.

A substantial fraction of metal-poor stars in the local Milky Way halo exhibit large overabundances of carbon. These stars, dubbed Carbon-Enhanced Metal-Poor (CEMP) stars, provide crucial constraints on the nature of the early universe including the earliest nucleosynthetic events. Whether these stars exist at similar rates in nearby galaxies is a major open question with implications for the environmental dependence of early chemical evolution. Here, we present the discovery of the first five CEMP stars in the Milky Way's largest dwarf companion, the LMC, using SDSS-V spectra from the BOSS instrument. We measure metallicities ranging from [Fe/H] = -2.1 to -3.2 and evolutionary state corrected carbon enhancements of [C/Fe] = +1.2 to +2.4, placing these stars among the most metal-poor and carbon-rich ever identified in the LMC. This discovery demonstrates that CEMP stars do exist in the LMC despite previous null detections, and establishes the foundation for measuring the CEMP occurrence rate in this system. Such measurements will provide critical tests of whether environmental differences affect the formation channels and frequencies of these ancient, carbon-rich stars.

Bridging the spatiotemporal scales of magnetic seed field generation and subsequent dynamo amplification in the weakly collisional intracluster medium presents an extreme numerical challenge. We perform collisionless turbulence simulations with initially unmagnetized electrons that capture both magnetic seed generation via the electron Weibel instability and the ensuing dynamo amplification. Going beyond existing pair-plasma studies, we use an ion-to-electron mass ratio of 100 for which we find electron and ion dynamics are sufficiently decoupled. These simulations are enabled by the 10-moment collisionless fluid solver of Gkeyll, which evolves the full pressure tensor for all species. The electron heat-flux closure regulates pressure isotropization and effectively sets the magnetic Reynolds number. We investigate how the strength of of the closure influences the transition between a regime reminiscent of previous kinetic pair-plasma simulations and a more MHD-like dynamo regime.

Long Period Transients (LPTs) are a recently identified class of sources characterized by periodic radio bursts lasting seconds to minutes, with flux densities that might reach several tens of Jy. These radio bursts repeat with periodicity from minutes to hours, and they exhibit strong polarization and transient activity periods. To date, about 12 such sources have been identified, which might encompass the same or different physical scenarios. Proposed explanations include binary systems with a white dwarf and a low-mass star companion, slow-spinning magnetars, highly magnetized isolated white dwarfs, and other exotic objects. In a few cases the optical counterpart indeed points toward a white dwarf with a low-mass companion, while in other cases, transient X-ray emission was detected, very common in magnetars. However, despite being able to reproduce partially some of the characteristics of LPTs, all the proposed scenarios find difficulty in explaining the exact physical origin of their bright, highly polarized and periodic radio emission. We review here the state-of-the-art in the observations and interpretation of this puzzling class of radio transients.

We investigate the correlation between ionized gas outflows, jets, and star formation in a sample of 42 local type-I active galactic nuclei (AGNs) exhibiting significant [O III] outflows. This study uses both new submillimeter (sub-mm) observations and archival data from the James Clerk Maxwell Telescope. Our analysis, which includes a correction for jet emission in the sub-mm bands, fitting spectral energy distribution, and analyzing spectra, enables us to derive star-formation rates (SFRs) through various methods. By comparing radio power and SFRs, we select a sub-sample of jetted AGNs of which radio emission is mostly from the jets. We find that jetted AGNs predominantly lie above the main sequence of star-forming galaxies, suggesting a correlation between jet activity and star formation. By comparing dust extinction, we demonstrate that jetted AGNs do not have more dust which is the fuel of both star formation and AGN activity. Therefore, this correlation is more likely to arise from AGN feedback. We also find that the Eddington ratio does not impact the specific SFRs (sSFRs) of our sample. Additionally, for jetted AGNs, stronger radio emission corresponds to higher sSFRs, suggesting that jet emission may promote star formation, i.e., positive feedback. Our results not only shed light on the feedback mechanisms of AGNs but also underscore the complex interplay between black hole activity and star formation in galaxy evolution.

The bending of radio galaxies in galaxy clusters is expected to be caused by interactions with the local environment. The physical processes responsible for jet bending, and their influence on the polarization properties of radio galaxies, remain poorly understood, leading to the question of whether jet properties in bent radio galaxies differ from those in linear radio galaxies. Using a sample of 24 polarized bent radio galaxies, observed with the Karl G. Jansky Very Large Array at 1--2 GHz, we test for correlation of bending angle with polarization parameters measuring Faraday rotation, intrinsic fractional polarization, and Faraday rotation dispersion, used here as a measure of turbulence along the line of sight. We find no statistically significant correlations. At the spatial resolution of our dataset (3--46 kpc, median 18.4 kpc), our results indicate that we are primarily probing larger-scale intracluster medium effects not related to bending angle. The absence of a statistically significant correlation suggests that bent radio galaxies are reliable probes of intracluster magnetic fields, because their intrinsic properties do not appear to introduce systematic biases into measured polarization parameters. We do detect a preference for source magnetic field vectors to align with the direction of jet bending. Finally, we estimate that the POSSUM and SKA surveys will contain $\gtrsim$300 and $\gtrsim$1000 polarized radio galaxies, respectively, providing large future samples with a range of bending angles and similar redshift distribution and number of beams per source as in our sample, enabling our results to be tested with greater statistical power.

Low--luminosity gamma-ray bursts (LLGRBs) are promising candidates for high-energy neutrinos, yet no coincident neutrino events have been detected so far. Recent advances in X-ray time-domain astronomy, together with the development of next-generation neutrino telescopes, open new opportunities for joint X-ray and neutrino observations of these transients. We calculate the jet dynamical evolution and the associated neutrino production for both non-magnetized and magnetized outflows. For individual events, joint X-ray and neutrino detection is generally limited to nearby LLGRBs or sources with high luminosities. Thus, we consider a next-generation neutrino telescope with an effective area enhanced by a factor of $\sim30$ relative to IceCube. In the non-magnetized scenario, joint detection of individual events is enabled for sources with typical isotropic luminosities of $L_{\mathrm{iso}}\sim10^{47}\,\mathrm{erg\,s^{-1}}$ out to luminosity distances of $D_L\sim1.6\times10^{2}\,\mathrm{Mpc}$, corresponding to an expected detection rate of order $1$ per year. In contrast, for the magnetized scenario at the same luminosity, the accessible distance is significantly reduced, with joint observations confined to sources within $D_L\sim6.5\times10^{1}\,\mathrm{Mpc}$ and an expected detection rate of order $0.5$ per year. For stacked samples of $\sim100$ magnetized LLGRBs, stacking substantially enlarges the accessible distance range, enabling joint observations for sources with representative luminosities of $L_{\mathrm{iso}}\sim1\times10^{47}\,\mathrm{erg\,s^{-1}}$ out to $D_L\lesssim7.0\times10^{2}\,\mathrm{Mpc}$ and corresponding to an expected detection rate of order $2$ per year. These results demonstrate that joint X-ray and next-generation neutrino observations enable a practical multimessenger probe of LLGRBs.

We present the implementation of a Self-Calibration of Intrinsic Alignments of galaxies as an extension to the Vera C. Rubin Observatory's Legacy Survey of Space and Time (LSST) Dark Energy Science Collaboration (DESC)'s weak lensing 3x2pt pipeline (TXPipe). As a demonstration, we have run this pipeline on the Dark Energy Survey (DES) year one data set. We find indications of a non-zero intrinsic alignment signal in the higher redshift bins, while in the lower bins our results look more uncertain. We believe this is caused by known issues with the individual galaxies photo-z estimation. This effect is particularly harmful for the self-calibration method, since it has high requirements for reliable estimation of the photo-$z$s, and the need for individual galaxy point estimates and tomographic binning to match. We show how different methods of recreating the redshift probability distribution can affect the detection of intrinsic alignment.

The 21-cm global signal, a treasure trove of information about the nature of the first luminous sources of the Universe, has traditionally been modelled assuming that these early sources were predominantly star-forming galaxies. However, recent observations by the James Webb Space Telescope (JWST) have revealed several AGNs as early as z ~ 10 - 10.4 . In light of this, it is important to investigate the contribution of such AGNs to the 21-cm signal. Assuming that these AGNs are seeded by Primordial Black Holes (PBHs) and employing an analytical PBH model, consistent with existing cosmological and astrophysical constraints, we show that these exotic objects can have a significant impact on the redshift evolution of the global signal.

To date, Galactic Astronomy has largely concerned itself with astrophysical processes, and with the locations, space motions and compositions of objects. Consider, for example, the elucidation of the components of the Galaxy over the past decades, its mapping as enabled by Gaia and its predecessors, the photometric and spectroscopic characterization of innumerable astrophysical objects in various wavelength ranges, both from the ground and from space, and the expanding discovery and characterization of exoplanets; all focused on the current, static Galaxy. This White Paper proposes a dedicated program to derive stellar ages from time-series spectroscopy to hasten the transformation of this static conception into a dynamical one with age-labeled objects and events.

Context. Building on the transformative success of optical redshift surveys, the emerging technique of neutral hydrogen (HI) intensity mapping (IM) offers a novel probe of large-scale structure (LSS) growth and the late-time accelerated expansion of the universe. Aims. We present cosmological forecasts for the Baryon Acoustic Oscillations from Integrated Neutral Gas Observations (BINGO), a pioneering HI IM experiment, quantifying its potential to constrain the Planck-calibrated $Λ$CDM cosmology and extensions to the $w_0w_a$CDM dark energy model. Methods. For BINGO's Phase~1 configuration, we simulate the HI IM signal using a lognormal model and incorporate three dominant systematics: foreground residuals, thermal noise, and beam resolution effects. Using Bayesian inference, we derive joint constraints on six cosmological parameters ($Ω_b h^2$, $Ω_c h^2$, $100θ_s$, $n_s$, $\ln 10^{10} A_s$, and $τ_r$) alongside 60 HI parameters ($b_{\rm HI}^i$, $Ω_{\rm HI}^i b_{\rm HI}^i$) across 30 frequency channels. Results. Our results demonstrate that combining BINGO with the Planck 2018 CMB dataset tightens the confidence regions of cosmological parameters to $\sim$40\% the size of those from Planck alone, significantly improving the precision of parameter estimation. Furthermore, BINGO constrains the redshift evolution of HI density and delivers competitive measurements of the dark energy equation of state parameters ($w_0$, $w_a$). Conclusions. These results demonstrate BINGO's potential to extract significant cosmological information from the HI distribution and provide constraints competitive with current and future cosmological surveys.

A central goal in supernova (SN) research is to identify and characterize their progenitors. However, this is very difficult due to the limited archival images with sufficient depth and spatial resolution required for direct progenitor detection and due to the circumstellar dust which often biases the estimate of their intrinsic parameters. This field will be revolutionized by Euclid and the upcoming Chinese Space Station Survey Telescope (CSST), which conduct deep, wide-field, high-resolution and multi-band imaging surveys. We analyze their detection capability by comparing the model magnitudes of red supergiant (RSG) progenitors with the detection limits under different conditions, and we estimate the annual detection rates with Monte-Carlo simulations. We explore how to recover the intrinsic properties of SN progenitors with the help of radiation transfer calculations in circumstellar dust. We find the optical and near-infrared filters of the Euclid and CSST are highly effective for detecting RSG progenitors. We predict that archival images from the completed 2 surveys will enable $\lesssim13$ (or 24) progenitor detections per year within the mass range of 8--16 (or 8--25)M_\odot, an order of magnitude higher than the current detection rate of $\sim1$ detection per year. In the presence of circumstellar dust, the emerging spectral energy distribution (SED) of the progenitor is mainly affected by the optical depth and is almost independent of dust temperature in the Euclid and CSST filters. Our mock tests demonstrate that one can derive the progenitor mass and dust optical depth simultaneously by fitting the observed SED over the 11 filters of the 2 surveys while fixing the dust temperature to a typical value. Euclid and CSST will significantly enlarge the sample of direct progenitor detections with accurate mass measurements, which is crucial to resolve the long-standing RSG problem.

Recent works have identified that rapidly rotating stars are predominantly binaries with separations of a few to a few tenths of au. This is a crucial range of separation that is often inaccessible to searches of binary stars, providing a unique opportunity to examine their statistical properties. In particular, we have performed an analysis of rapid rotators in young moving groups. We examined their fraction as a function of the stellar density of the population in which they are found. We find that there is a deficit of rapid rotators in dense clusters such as the Orion Nebula in comparison to the more diffuse parts of the Orion Complex, as intracluster interactions with neighboring stars likely dissolve binaries with intermediate separations before they had a chance to fully form. In contrast, in older populations with an age of $\sim100$ Myr, mass segregation redistributes binaries relative to single stars, thus in such older regions, rapid rotators are predominantly found in the regions of higher stellar density. This work sheds light on both the conditions that lead to the formation of binary stars and their dynamical evolution.

We report the discovery of two swallowtail image formations at $z=2.91$ and $z=6.70$ behind the galaxy cluster MS 0451.6-0305 in JWST-NIRCam imaging. We find that in both of the above lensed systems, the complex image morphology cannot be reproduced by simple fold/cusp caustics, and detailed lens modeling reveals higher-order swallowtail caustic configurations. In the $z=2.91$ lens system, a small part of the source galaxy (which itself is part of a galaxy group) containing atleast two compact knots sits inside the swallowtail caustic, producing a quadruply imaged arc. At two of the image positions of these knots, we infer point source magnifications of $\gtrsim 300$, implying lensing-corrected effective radii of $\lesssim 0.8-1.5$ pc. The $z=6.70$ system exhibits even more complex image formation. We therefore only use the most confidently identified counter-images of knots in this system as constraints in our lens modeling. The resulting model predicts magnifications $\sim20-200$ and lensing-corrected effective radii of $\lesssim 0.8-18.5$ pc for various knots. Together, these two systems represent the first example of observations of multiple swallowtail image formations in a single galaxy cluster and demonstrate the ability of swallowtail caustics to magnify individual substructures at sub-parsec scales, from intermediate redshifts to the first billion years of the Universe.

We present new measurements of galaxy luminosity functions (LFs) from JWST/NIRCam imaging over the redshift range z=4.5-9.7, using photometric catalogs from JADES and public extragalactic fields. Our analysis includes rest-frame UV and B-band LFs, as well as apparent LFs in F090W, F115W, F200W, F356W, and F444W. We present the first constraints on the rest-frame B-band LF at z~7-8 and extend existing measurements at z~5 to M(B) = -18 mag. The B-band LFs evolve more strongly with redshift than UV LFs, though both decline more gradually than predicted by simulations at z>5. No single existing simulation reproduces all observed trends, with discrepancies likely driven by assumptions about binary evolution and stellar population synthesis models. The apparent LFs in F356W and F444W show hints of a bright-end excess at all redshifts, extending to fainter magnitudes at higher redshift. While extreme emission line galaxies may partially account for it, the excess may also indicate a population of moderately red, optically bright sources - potentially dusty star-forming galaxies or obscured AGNs. Finally, we find that rest-frame B-band luminosity correlates more tightly with stellar mass than UV, making it a powerful tracer of mass assembly and reinforcing the diagnostic value of rest-frame optical LFs in uncovering the physical processes that drive early galaxy formation.

The apsidal motion of a non-synchronous binary pulsar serves as a valuable probe of relativistic gravity, stellar stricture, and dynamical evolution of close binary systems, In this study, we investigate the combined influence of general relativity, stellar oblateness and tidal interaction on the apsidal motion of three binary pulsars: 1913+16, J0737-3039A/B, and J0621+1002. Zahn's tidal equations \cite{1977A&A....57..383Z, 1989A&A...220..112Z} were employed for numerical integrations to describe tidal effects and their role in orbital and spin evolution. We estimated the timescales for tidal synchronization and orbital circularization for each system. The results indicate that tidal effects play only a minor role in orbital decay compared with energy loss due to gravitational wave emission. This is evident in the compact system PSR 1913+16, where the orbital period decreases by approximately 76.5 $μ$s/yr as a result of gravitational radiation. The double pulsar J0737-3039A/B exhibits faster orbital evolution, with synchronization occurring in about 8.4$\times10{^3}$ years, whereas the wider system J0621+1002 shows negligible orbital change over timescales exceeding 10$^{10}$ years. The simulations demonstrate clear trends of decreasing semi-major axis and eccentricity, accompanied by an increase in spin rate among the binary pulsars studied. The derived apsidal motion constants [$k\simeq0.1$] are consistent with theoretical expected values, and the corresponding tidal friction times (between a few hours to several days) agree well with theoretical predication. These results emphasize the dominant role of relativistic effects in neutron star binaries and highlight the importance of including gravitational-wave terms long-term orbital evolution

We summarize the current state of the art and calculate gravitational wave strain amplitudes for known binary pulsars, using data from current ground-based detectors (LIGO-Virgo-KAGRA) and the upcoming space-based missions (LISA). We present detailed calculations of the characteristic gravitational wave strain values, ranging from 3.0 to 73 $\times10^{-22}$, across frequencies between 0.66 and 5.87 $\times10^{-4}$ Hz. Our post-Newtonian approximation analysis yields predicted periastron advance rates from 1.6 to 80.5 deg/yr and orbital period decay rates between -5 and -176 $μ$s/yr for the binary pulsar population. We derive common envelope efficiency parameters ($α_{CE}$) for representative progenitor scenarios within our sample, finding values between 0.63 and 1.16, with notable sensitivity to the binding energy parameter $λ$. Binary neutron star merger rates are estimated at $22.77^{+6.83}_{-6.83}$ Myr$^{-1}$ for the Milky Way, corresponding to a volumetric rate of $227.71^{+68.31}_{-68.31}$ Gpc$^{-3}$ yr$^{-1}$, consistent with the latest LIGO-Virgo-KAGRA observational constraints. Our results illustrate how multi-band gravitational wave observations, from LIGO/Virgo to LISA, can contribute to precise measurements of binary pulsar strain and orbital evolution histories, improving merger time predictions and constraining neutron star physics and common envelope processes

Recently Quenched Ellipticals (RQEs) provide a valuable test case for disentangling intrinsic and environmental quenching, particularly because they are commonly classified as isolated central galaxies in low-mass halos. However, central/satellite assignments and isolation labels can vary across group catalogs, and such misclassifications can strongly bias physical interpretations. We present a uniform, physically motivated reassessment of the environments of 155 RQEs previously identified as centrals in an SDSS-based group catalog. We construct value-added neighbor catalogs via KD-tree searches and apply consistent thresholds in projected separation and line-of-sight velocity to (i) verify centrality, (ii) quantify isolation using a mass-ratio-based companion criterion, and (iii) identify potential pseudo-centrals via proximity to massive clusters. We find that 132/155 (85.2\%) RQEs remain true centrals, while 23/155 (14.8\%) are better interpreted as misidentified centrals with a more massive neighbor within $R_{\rm proj}\leq0.8$~Mpc and $ΔV\leq250$~km~s$^{-1}$. Among the true centrals, 110/132 (83.3\%) satisfy our isolation criterion, and only one system meets our pseudo-central definition, indicating that direct cluster-scale preprocessing is rare for RQE centrals. Using projected number density and surface stellar mass density, we show that misidentified and non-isolated systems occupy systematically denser regimes than isolated true centrals. These results imply that while most RQEs ($\sim$71\%) are consistent with predominantly internal quenching in genuinely isolated centrals, a non-negligible minority ($\sim$29\%) likely experienced environmentally influenced pathways at group scales.

Braids are periodic solutions to the general N-body problem in gravitational dynamics. These solutions seem special and unique, but they may result from rather usual encounters between four bodies. We aim at understanding the existence of braids in the Galaxy by reverse engineering the interactions in which they formed. We simulate self-gravitating systems of N particles, starting with the constructing of a specific braid, and bombard it with a single object. We study how frequently the bombarded braid dissolves in four singles, a triple and a single, a binary and 2 singles, or 2 binaries. The relative proportion of those events gives us insight into how easy it is to generate a braid through the reverse process. It turns out that braids are easily generated from encounters between 2 binaries, or a triple with a single object, independent on the braid's stability. We find that 3 of the explored braids are linearly stable against small perturbations, whereas one is unstable and short-lived. The shortest-lived braid appears the least stable and the most chaotic. nonplanar encounters also lead to braid formation, which, in our experiments, themselves are planar. The parameter space in azimuth and polar angle that lead to braid formation via binary-binary or triple-single encounters is anisotropic, and the distribution has a low fractal dimension. Since a substantial fraction of ~9% of our calculations lead to periodic 3-body systems, braids may be more common than expected. They could form in multi-body interactions. We do not expect many to exist for time, but they may be common as transients, as they survive for tens to hundreds of periodic orbits. We argue that braids are common in relatively shallow-potential background fields, such as the Oort cloud or the Galactic halo. If composed of compact objects, they potentially form interesting targets for gravitational wave detectors.

Luminous infrared galaxies are key sites of obscured stellar mass assembly at z > 0.5. Their star formation rates (SFRs) are often estimated using the luminosities of the 6.2 micron and 11.2 micron polycyclic aromatic hydrocarbon (PAH) features, or those of the [Ne II] and [Ne III] fine-structure lines, as they are minimally affected by obscuration. It is uncertain whether the calibration of these features as SFR tracers depends on the starburst bolometric luminosity or the level of Active Galactic Nucleus (AGN) activity. We here investigate the relationship between the luminosities of PAH and Neon lines with star formation rate for highly luminous objects using radiative transfer modeling and archival observations of 42 local Ultraluminous (>= 10^12 L_sun) Infrared Galaxies (ULIRGs). We find that PAH and [Ne II] features arise mainly in star-forming regions, with small contributions from the AGN or host, but that the [Ne III] line has a mixed contribution from both star formation and AGN activity. We present relations between L_PAH and L_NeII, and both starburst luminosity and SFR. We find relations for lower luminosity (L_IR ~= 10^10-10^12 L_sun) systems underestimate the SFRs in local ULIRGs by up to ~1 dex. The 6.2 micron and 11.2 micron PAH features, and the [Ne II] line, are thus good tracers of SFR in ULIRGs. We do not find that a more luminous AGN affects the relationship between SFR and PAH or Neon luminosity, but that it can make PAH emission harder to discern. Our results and derived relations are relevant to studies of star-forming and composite galaxies at z < 3 with the James Webb Space Telescope.

One of the earliest mechanisms proposed for plasma heating was magnetic pumping (MP). However, its significance for astrophysical phenomena, including particle acceleration, has yet to be appreciated. MP-energized particles tap energy from magnetic-field oscillations. A particle's momentum component perpendicular to the local B-field increases during field growth by virtue of the adiabatic invariant $p_{\perp}^{2}/B=const$. The gained $p_{\perp}$ is then partially scattered elastically into the parallel momentum, $p_{\parallel}$, with $p^{2}=p_{\parallel}^{2}+p_{\perp}^{2}=const$, thereby retaining some fraction of the gained energy before the field decreases to its minimum. This scattering breaks the reversibility of energy exchange between particles and oscillating magnetic fields, thereby increasing the particle energy after each MP cycle. Field oscillations are often assumed to be sinusoidal, and the resulting MP is treated perturbatively. These simplifications restrict astrophysical applications, leaving objects with strong magnetic perturbations outside the scope of adequate treatment. We develop a nonperturbative approach to MP that is suitable for a broad spectrum of magnetic turbulence. The treatment comprises two steps. The first step is common: converting a kinetic equation into an infinite hierarchy of moments of the particle distribution function. The second step is new in MP treatments: we find an exact closure at an arbitrary level of the moment system. The heating is treated exactly at the second-moment closure. Particle acceleration generally requires a higher-level closure to determine the power-law index and the maximum energy of accelerated particles. We propose a method for extracting these crucial acceleration data from the second moment for a broadband random field.

Generalised axion-like scalar fields provide a well-motivated framework for describing the late-time acceleration of the Universe. As the field evolves, it rolls down its potential and, depending on its mass and initial conditions, it may either still be approaching the minimum or already oscillating around it. These two dynamical regimes require distinct treatments of cosmological perturbations. In this work, we perform a detailed analysis of linear cosmological perturbations in the regime where the dark-energy scalar field undergoes coherent oscillations about the minimum of its potential. We show that the standard effective fluid description breaks down in this phase and develop a consistent field-based perturbation framework, which we use to assess the impact of oscillatory dark energy on the growth of cosmic structures.

The Milky Way's Galactic Center hosts the black hole Sagittarius A* (Sgr A*), which provides us with a close-up view into supermassive black hole accretion and feedback. Recent works have shown that the winds from $\sim 30$ Wolf-Rayet (WR) stars orbiting Sgr A* at about 4 arcsec are important contributors to feeding the supermassive black hole. A nuclear star cluster (NSC) with a mass of several $10^6 \, \text{M}_\odot$, of which $10^6 \, \text{M}_\odot$ is within 1 pc, also surrounds Sgr A*. The NSC contributes to the gravitational potential in the Galactic Center, affecting the orbits of the WR stars and their stellar winds. In this work, we examine the effects that the NSC has on the accretion of these stellar winds onto Sgr A* which have previously been neglected. We find that, on the parsec scale, the effect from the gravitational potential of the NSC is negligible on the wind-fed accretion flow, validating the existing simulations used in the literature.

Milky Way halo substructures identified in dynamical space are known to suffer from contamination from the Milky Way in-situ stars, which makes their accreted origins uncertain. We present detailed chemical abundances of 35 stars belonging to two sets of dynamically tagged groups, Rg8 and Rg9, to investigate their accreted nature. Both groups are composed of stars with low orbital energy and very retrograde orbits. We find that Rg8 and Rg9 are chemically indistinguishable across all elements, from C to Eu, strongly indicating that they belong to the same structure. The iron-abundance distribution of this low-$E$ retrograde group has a prominent peak at [Fe/H] $\approx-2.1$, revealing that its main population is very metal-poor, and a secondary peak at [Fe/H] $\approx-1.5$, very likely due to contamination from Milky Way in-situ stars. These groups also heavily overlap with the Thamnos substructure in dynamical space, and we thus use them to investigate the chemical properties of Thamnos. The dominant, low-metallicity population provides strong evidence for the ex-situ origin of Thamnos, as well as its very metal-poor nature. We do not see any evidence of an $α$ knee in our sample, which is consistent with previous studies. Comparison with the Cetus-Palca stream in the chemical space shows similar abundance distributions, and thus it suggests that the Thamnos progenitor dwarf galaxy had a truncated star formation history due to its early merger with the Milky Way.

Cloud-cloud collisions are expected to trigger star formation by compressing gas into dense, gravitationally unstable regions. However, the role of magnetic fields in this process is unclear. We use SPH to model head-on collisions between two uniform density clouds, each with mass $500 \,$M$_{\odot}$, initial radius 2 pc, and embedded in a uniform magnetic field parallel to the collision velocity. As in the nonmagnetic case, the resulting shock-compressed layer fragments into a network of filaments. If the collision is sufficiently slow, the filaments are dragged into radial orientations by non-homologous gravitational contraction, resulting in a $\textit{Hub Filament}$ morphology, which spawns a centrally concentrated monolithic cluster with a broad mass function shaped by competitive accretion and dynamical ejections. If the collision is faster, a $\textit{Spiders Web}$ of intersecting filaments forms, and star-systems condense out in small subclusters, often at the filament intersections; due to their smaller mass reservoirs, and the lower probability of dynamical ejection, the mass function of star-systems formed in these subclusters is narrower. Magnetic fields affect this dichotomy quantitatively by delaying collapse and fragmentation. As a result, the velocity threshold separating $\textit{Hub Filament}$ and $\textit{Spiders Web}$ morphologies is shifted upward in magnetised runs, thereby enlarging the parameter space in which $\textit{Hub Filament}$ morphologies form, and enhancing the likelihood of producing centrally concentrated clusters. Consequently, magnetic fields regulate both the morphology and timing of star formation in cloud-cloud collisions: they broaden filaments, delay the onset of star formation, and promote the formation of $\textit{Hub Filament}$ morphologies, monolithic clusters and high-mass star-systems.

Luminous Red Galaxies (LRGs) are among the most massive galaxies at any epoch, and lack ongoing star formation. As systems hosting most of the baryonic mass in the local Universe, they preserve imprints of the quenching mechanisms in the early Universe. We exploited the large BOSS and DESI spectroscopic datasets to perform the first homogeneous and continuous mapping of the evolution of stellar population properties of a complete sample of the most massive LRGs ($\log (M_*/\mathrm{M_\odot})> 11.5$) at 0.15 < z < 0.8. By consistently fitting the same spectral indices at all redshifts, we measured trends of [Fe/H], [alpha/Fe], and light-weighted age as a function of redshift. These galaxies exhibit a passive light-weighted age evolution and flat [Fe/H] and [alpha/Fe] trends towards lower redshift, indicating genuinely passive evolution. These trends are robust against the choice of stellar population models and analysis assumptions, and they support the predictions from IllustrisTNG, which predict negligible chemical evolution for the most massive quenched systems at z < 0.8. Our results suggest that, despite nearly 5 Gyr of cosmic time and a 3-4x increase in number density, the stellar population properties of massive quiescent galaxies remain essentially unchanged since z ~ 0.8. This shows a negligible progenitor bias below z ~ 0.8, and a genuinely passive evolution. Newly added systems after $z \sim 0.8$ were already largely quenched and chemically mature, while subsequent evolution was dominated by dry mergers without altering the bulk of the stellar populations.

We present a new X-ray study and multiwavelength spectral energy distribution (SED) modeling of the young pulsar wind nebula (PWN) powered by the energetic pulsar PSR J1747-2809, inside the composite supernova remnant (SNR) G0.9+0.1, located in the Galactic Center region. The source is detected by NuSTAR up to 30 keV with evidence for the synchrotron burnoff effect in the changing spatial morphology with increasing energy. The broadband 2-30 keV spectrum of PWN G0.9+0.1 is modeled by a single power law with photon index $Γ=2.11\pm0.07$. We combined the new X-ray data with the multiwavelength observations in radio, GeV, and TeV gamma rays and modeled the SED, applying a one-zone and a multi-zone leptonic model. The comparison of the models is successful, as we obtained physically compatible results in the two cases. Through the one-zone model, we constrain the age of the system to $\sim2.2$ kyr, as well as reproduce the observed PWN and SNR radio sizes. In both the one-zone and multi-zone leptonic models, the electron injection spectrum is well-described by a single power law with spectral index $p \sim 2.6$ and a maximum electron energy of $\sim2$ PeV, suggesting the source could be a leptonic PeVatron candidate. We estimate the average magnetic field to be $B_{\rm PWN} \sim 20\ μ$G. We also report the serendipitous NuSTAR detection of renewed X-ray activity from the very faint X-ray transient XMMU J174716.1-281048 and characterize its spectrum.

We present [O$_{\rm \,III}$] 88$μ$m observations towards four ${\it Herschel}$-selected dusty star-forming galaxies (DSFGs; log$_{10}$ $μ$L$_{\rm IR}$/L$_{\odot}$ = 13.5 - 14 at $z = 2.9 - 4$) using the Atacama Compact Array (ACA) in Bands 9 and 10. We detect [O$_{\rm \,III}$] emission in all four targets at >3$σ$, finding line luminosity ratios ($L_{\rm [O_{\rm \,III}]}$ / L$_{\rm IR}$ = 10$^{-4.2}$ to 10$^{-3}$) similar to local spiral galaxies, and an order of magnitude lower when compared with local dwarf galaxies as well as high-redshift Lyman-break galaxies. Using the short-wavelength capabilities of the ACA, these observations bridge the populations of galaxies with [O$_{\rm \,III}$] emission at low redshift from space missions and at high redshift from ground-based studies. The difference in [O$_{\rm \,III}$] emission between these DSFGs and other high-redshift galaxies reflects their more evolved stellar populations (> 10 Myr), larger dust reservoirs (M$_{\rm dust}$ $\sim$ 10$^{9 - 11}$ M$_{\odot}$), metal-rich interstellar medium ($Z \sim 0.5 - 2$ Z$_{\odot}$), and likely weaker ionization radiation fields. Ancillary [C$_{\rm \,II}$] emission on two targets provide $L_{[{\rm O}_{\rm \,III}]} / L_{[{\rm C}_{\rm \,II}]}$ ratios at 0.3 - 0.9, suggesting that ionized gas represents a smaller fraction of the total gas reservoir in DSFGs, consistent with theoretical models of DSFGs as transitional systems between gas-rich, turbulent disks and more evolved, gas-poor galaxies. Expanding samples of DSFGs with [O$_{\rm \,III}$] emission will be key to place this heterogeneous, poorly-understood galactic phase in its astrophysical context.

JWST's "Little Red Dots" (LRDs) are increasingly interpreted as active galactic nuclei (AGN) obscured by dense thermalized gas rather than dust as evidenced by their X-ray weakness, blackbody-like continua, and Balmer line profiles. A key question is how LRDs connect to standard UV-luminous AGN and whether transitional phases exist and if they are observable. We present the "X-Ray Dot" (XRD), a compact source at $z=3.28$ observed by the NIRSpec WIDE GTO survey. The XRD exhibits LRD hallmarks: a blackbody-like ($T_{\rm eff} \simeq 6400\,$K) red continuum, a faint but blue rest-UV excess, falling mid-IR emission, and broad Balmer lines ($\rm FWHM \sim 2700-3200\,km\,s^{-1}$). Unlike LRDs, however, it is remarkably X-ray luminous ($L_\textrm{2$-$10$\,$keV} = 10^{44.18}\,$erg$\,$s$^{-1}$) and has a continuum inflection that is bluewards of the Balmer limit. We find that the red rest-optical and blue mid-IR continuum cannot be reproduced by standard dust-attenuated AGN models without invoking extremely steep extinction curves, nor can the weak mid-IR emission be reconciled with well-established X-ray--torus scaling relations. We therefore consider an alternative scenario: the XRD may be an LRD in transition, where the gas envelope dominates the optical continuum but optically thin sightlines allow X-rays to escape. The XRD may thus provide a physical link between LRDs and standard AGN, offering direct evidence that LRDs are powered by supermassive black holes and providing insight into their accretion properties.

On 2025 January 10, a thermonuclear (Type I) X-ray burst from the neutron star low-mass X-ray binary \textit{4U~0614+091} was detected with the ECLAIRs instrument on board the \textit{SVOM} mission. We present here a time-resolved spectroscopic analysis of the burst, along with the detection of burst oscillations within a 51-second interval during the decay phase. The oscillation frequency is measured to be $ν= 413.674 \pm 0.002\,\mathrm{Hz}$, consistent with previous reports. However, we detect a significant downward frequency drift over the burst duration, characterized by $\dotν = (-4.7 \pm 0.3) \times 10^{-3}\,\mathrm{Hz\,s^{-1}}$. This frequency evolution is atypical compared to those observed in similar burst oscillation sources. We tentatively attribute the observed drift to a Doppler shift induced by orbital motion. Under this interpretation, the inferred orbital period must be shorter than 20 minutes, placing \textit{4U~0614+091} among the most compact known low-mass X-ray binaries.

We utilize Gaia DR3 data to report the discovery of a new star cluster, Nasser 1, located 32 arcmin from Czernik 38 at coordinates $α= 282.11 \pm 0.05$ and $δ= 4.56 \pm 0.05$. Using a variable membership probability threshold technique with pyUPMASK, we confirm Nasser 1 as a genuine open cluster. It exhibits a distinct King profile and a well-defined CMD with an age of $125.0 \pm 12.30$ Myr, a distance modulus of $12.87 \pm 0.21$ mag, and a color excess of $2.42 \pm 0.09$. Nasser 1 shares consistent physical parameters (age, distance, kinematics, and reddening) with Czernik 38, suggesting they constitute a young primordial binary system in the Carina-Sagittarius spiral arm. Both clusters display elongations indicative of differential rotation tides; Nasser 1 is additionally perturbed by the spiral arm's gravitational field. Gaussian mass function analysis suggests the two were formerly a single cluster, violently torn apart by differential rotation.

Recent studies report a mild discrepancy between baryon acoustic oscillation (BAO) and cosmic microwave background (CMB) measurements within the $Λ$CDM framework. This discrepancy could be explained if the optical depth $τ$ inferred from the CMB large-scale E-mode polarization is underestimated, which may be biased by foreground-subtraction or instrumental systematics. In this work, we present a determination of $τ$ independent of the large-scale E-mode polarization, using the latest measurements of the redshift evolution of the neutral hydrogen fraction $x_\mathrm{HI}(z)$, which is constrained by Lyman-$α$ forest and damping-wing absorption measurements at $z\sim5$-$14$, based on ground-based optical and JWST observations. Combining $x_\mathrm{HI}(z)$ with the Planck CMB power spectra excluding the large-scale E-mode polarization, we obtain $τ=0.0552^{+0.0019}_{-0.0026}$, a stringent constraint consistent with the previous CMB results including the large-scale E-mode. We also evaluate a potential systematic error in our method associated with absorption modeling, obtaining $τ=0.0552^{+0.0075}_{-0.0049}$. Using this constraint on $τ$, we resolve the degeneracy in the $τ$-$Ω_m$ plane and find a $2.4σ$ tension with the DESI DR2 BAO results, thereby confirming the claimed mild discrepancy suggestive of physics beyond $Λ$CDM. Finally, we derive an upper limit on the sum of neutrino masses, $Σm_ν<0.0550\,(0.0717)$ eV at the 95% (99%) confidence level. This limit favors the normal mass ordering and, when combined with the lower limits from neutrino oscillation experiments, yields a further constraint, $Σm_ν=0.0594_{-0.0007}^{+0.0113}$ eV. However, the cosmological upper limit and the oscillation-based lower limit show a mild $2.2σ$ tension, providing an independent indication of possible physics beyond $Λ$CDM.

We investigate plasma wake generation via Compton scattering from photon bursts, a non-ponderomotive process relevant when the photon wavelength is smaller than the interparticle distance but larger than the Compton wavelength. In this regime, electrons can reach relativistic velocities. We extend linear theory to the nonlinear regime, showing that plasma waves can reach the wave-breaking limit. Perfectly collimated drivers produce wakes propagating at the speed of light, allowing electron phase-locking (limited by driver depletion). Non-collimated drivers induce subluminal phase velocities, limiting acceleration via dephasing. Two-dimensional simulations reveal unique transverse fields compared to laser wakefields, with a DC magnetic field leading to consistent focusing. The work considers observational prospects in laboratory and astrophysical scenarios such as around highly luminous compact objects (e.g., pulsars, gamma-ray bursts) interacting with tenuous interstellar or intergalactic plasmas, where conditions favor Comptondominated wakefield acceleration.

We present observations of jets within 2000 au of three deeply embedded protostars using 2.9-27 micron observations with JWST. These observations show the morphologies and kinematics of the collimated jets from three protostars, the low-mass Class 0 protostars B335 and HOPS 153, and the intermediate-mass protostar HOPS 370. These jets are traced by shock-ionized fine-structure line emission observed with the JWST NIRSpec and MIRI IFUs. We find that [Fe II] emission traces the full extent of the inner 1000 to 2000 au of the jets, depending on distance to the protostar, while other ions mostly trace isolated shocked knots. The jets show evidence of wiggling motion in the plane of the sky as well as asymmetries between blue and red-shifted lobes. The widths of the jets increase non-monotonically with distance from the central protostar, with opening angles ranging from 2.1 degrees to < 10.1 degrees for the three protostars in the sample. The jets have total velocities ranging from 147 to 184 km/s after correcting for disk inclination. For B335, an 8-month gap between NIRSpec and MIRI MRS observations enabled measurement of the tangential velocity of a shocked knot; in combination with the radial velocity, this shows that the jet has a different inclination than the outflow cavity. We find multiple knots before and during a recent outburst in B335, although the knots were more frequent during the burst. The asymmetries between blue- and red-shifted lobes strongly suggest complex interactions between the circumstellar disks and magnetic fields.

Formation of a rapidly spinning, strongly magnetized neutron star (NS) may occur in various classes of core-collapse events. If the NS injects an amount of energy comparable to the explosion energy of the accompanying supernova (SN) before the SN ejecta becomes transparent, the nascent NS wind bubble can overtake the outer ejecta and undergo a blowout driven by hydrodynamic instabilities. Based on multidimensional numerical studies, we construct a minimal semi-analytic framework to follow the post-blowout dynamics and radiative evolution, map the blowout conditions by scanning the ejecta and NS parameters, and compute survey-ready multi-band light curves. For stripped-envelope SNe with an ejecta mass of $M_\mathrm{ej} \sim 10\,M_\odot$ and an explosion energy of $E_\mathrm{sn} \sim 10^{51}\,\mathrm{erg}$, blowout occurs for NSs with magnetic field strengths of $B_{\mathrm{dip}} \gtrsim 10^{13}\,\mathrm{G}$ and spin periods of $P_\mathrm{NS} \lesssim \mathrm{a\ few}\,\mathrm{ms}$. Relatively weak-field cases with $B_\mathrm{dip} \lesssim 10^{14}\,\mathrm{G}$ produce luminous double-peaked UV/optical light curves, as observed in the superluminous SN LSQ14bdq, while stronger-field cases with $B_\mathrm{dip} \gtrsim 10^{14}\,\mathrm{G}$ result in hypernovae preceded by X-ray blowout precursors. We also examine weaker and lower-mass SN explosions representing ultra-stripped SNe and accretion- or merger-induced collapse events, in which blowout is more readily achieved over a broader range of NS parameters, producing fast X-ray transients with durations of $ 10^{2\mbox{--}4}\,\mathrm{s}$ and peak luminosities of $10^{42\mbox{--}48}\,\mathrm{erg\,s^{-1}}$. Our results encourage coordinated UV, optical, and X-ray observations which constrain the formation of the most energetic NSs in the universe.

We employ the MIT bag model with density-dependent bag constant for the equation of state (EOS) to estimate the gravitational mass and Keplerian frequency of rapidly rotating strange quark stars (SQS). In a companion paper we discuss the structural parameters of such rotating stars under the influence of strong magnetic fields. We use the LORENE library to compute the structural parameters at different rotational frequencies in the range of 1100-1300~Hz for a non-magnetized SQS. While there is no minimum limit for the mass of slowly rotating self-bound stars, by computing the maximum rotational frequency, known as the mass-shedding limit, we show that SQS must have a minimum mass to sustain high rotational frequencies. The mass-shedding frequency in our EOS model is lower than that estimated from the MIT bag model EOS with a fixed bag constant. The Keplerian frequency in our model depends linearly on the gravitational mass at the mass-shedding limit (and similarly on the minimum mass) with the slope of 0.08~${\rm kHz}/M_\odot$. We obtain mass limits aligned with the observational data for both the heaviest and the lightest observed pulsars.

We investigate the structural parameters of strange quark stars (SQS) under the influence of strong magnetic fields and varying rotational frequencies. The equation of state is computed using the MIT bag model with a density-dependent bag constant and considering the Landau quantization effect regarding the strong magnetic fields up to $5\times10^{17}\,$G in the interior of SQS. Employing the LORENE library, we calculate the structural parameters under different magnetic field strengths and rotational frequencies. Our models are compared in terms of maximum gravitational mass, deformation parameter, binding energy, and compactness. Our equation of state model demonstrates that the gravitational masses are higher than those computed using a MIT bag model with a fixed bag constant. We find the gravitational masses beyond $2.3 \,M_\odot$, which are compatible with the masses of observed compact objects, such as the ``black widow'' pulsar \emph{PSR J0952-0607}, and the \emph{GW190814} event detected by the LIGO/Virgo collaboration. The deformation parameter and maximum gravitational mass of SQS are characterized by fitted functions accounting for variations in both magnetic field strength and rotational frequency. We find the maximum deformation parameter of 1.55 and the maximum gravitational mass of $2.8\, M_\odot$ in the fast-rotating strongly magnetized model.

Ultraluminous X-ray sources (ULXs) are off-nuclear compact objects with apparent luminosities above 10^39 erg/s, often exceeding the Eddington limit for stellar-mass black holes. Beaming is a commonly invoked mechanism to explain their extreme brightness, and the dependence of the beaming factor on accretion rate is a critical parameter. In this work, we investigate how different beaming prescriptions affect the predicted properties of ULX populations. Using binary population synthesis, we construct synthetic X-ray luminosity functions (XLFs) for both classical and log-modified beaming models at solar and sub-solar metallicities. The classical model predicts a larger intrinsic number of bright ULXs, but strong beaming reduces their observable fraction, resulting in fewer visible ULXs compared to the log-modified model. The log-modified prescription yields a shallower slope at high-luminosity, aligning better with observed XLFs, and increases the fraction of observable neutron star ULXs above 10^39 erg/s. These results underscore the significant role of the beaming law in shaping ULX statistical distributions and assessing neutron star contributions to the population.

We present a catalog of 4539 fast radio bursts (FRBs) observed with the Canadian Hydrogen Intensity Mapping Experiment (CHIME) telescope between 25 July 2018 and 15 September 2023. These bursts originate from 3641 unique sources, including 981 bursts from 83 known repeating sources. For each FRB, the catalog provides a $O(10')$ estimate of sky location along with corresponding measurements of cumulative exposure time and survey sensitivity over the observing period. It includes a total-intensity dynamic spectrum between 400 and 800 MHz at 0.983 ms resolution. From this spectrum, we constrain a model of the burst morphology and measure key parameters such as arrival time, intrinsic temporal width, dispersion measure, scattering time, and flux density. This second catalog includes all FRBs from the first catalog, with every event reprocessed using a uniform and improved analysis framework. We show that previously published inferences remain valid under the updated measurements. We assess consistency of the detection rate across observational parameters, present initial distributions of burst properties, and outline ongoing and future studies that will use this catalog to investigate the nature of FRBs and their utility as astrophysical and cosmological probes.

In the interstellar medium, cosmic rays (CRs) generate a field of ultraviolet (UV) photons via the excitation and subsequent radiative decay of $\rm H_2$ molecules. This UV field is a major agent of ionization and dissociation in the inner regions of molecular clouds that are shielded from the effects of the interstellar radiation field. In particular, the dissociation of $\rm H_2$, by far the most abundant molecule in interstellar clouds, leads to the production of atomic hydrogen which then takes part in the production of a multitude of molecules, in particular complex organics on the surfaces of interstellar dust grains. Precise knowledge of the rates of CR-induced dissociation processes is thus crucial for constructing reliable chemical models. For the present paper, we have derived a new value of $k_{\rm diss, CR}(\mbox{$\rm H_2$})=0.831ζ$ for the rate of $\rm H_2$ dissociation, where $ζ$ is the CR ionization rate of $\rm H_2$. This prediction contrasts a previous value from the Leiden database which overestimated the rate due to an inconsistent treatment of the $\rm H_2$ abundances and photodissociation cross sections. By running a series of chemical models, we show that the overestimated dissociation rate has a large effect on the results of chemical simulations, with the abundance of methanol being overestimated by over one order of magnitude. Hence, we strongly recommend the adoption of our new estimate $k_{\rm diss, CR}(\mbox{$\rm H_2$})=0.831ζ$ in all chemical models that include this process. Our newly derived value corresponds to $\rm H_2$ being purely in the para form ($J^{\prime\prime} = 0$). However, in the interiors of molecular clouds the $\rm H_2$ ortho-to-para ratio is low and using the rate for para-$\rm H_2$ is an adequate approximation.

In data-driven scientific discovery, a challenge lies in classifying well-characterized phenomena while identifying novel anomalies. Current semi-supervised clustering algorithms do not always fully address this duality, often assuming that supervisory signals are globally representative. Consequently, methods often enforce rigid constraints that suppress unanticipated patterns or require a pre-specified number of clusters, rendering them ineffective for genuine novelty detection. To bridge this gap, we introduce CLiMB (CLustering in Multiphase Boundaries), a domain-informed framework decoupling the exploitation of prior knowledge from the exploration of unknown structures. Using a sequential two-phase approach, CLiMB first anchors known clusters using constrained partitioning, and subsequently applies density-based clustering to residual data to reveal arbitrary topologies. We demonstrate this framework on RR Lyrae stars data from the Gaia Data Release 3. CLiMB attains an Adjusted Rand Index of 0.829 with 90% seed coverage in recovering known Milky Way substructures, drastically outperforming heuristic and constraint-based baselines, which stagnate below 0.20. Furthermore, sensitivity analysis confirms CLiMB's superior data efficiency, showing monotonic improvement as knowledge increases. Finally, the framework successfully isolates three dynamical features (Shiva, Shakti, and the Galactic Disk) in the unlabelled field, validating its potential for scientific discovery.

One of the key challenges in strong gravitational lensing cosmography is the accurate measurement of time delays between multiple lensed images, which are essential for constraining the Hubble constant ($H_0$). We investigate how lens mass-profile assumptions affect time delays. Specifically, we implement a Broken Power Law (BPL) mass model within the Lenstronomy framework (Birrer & Amara 2018), which introduces additional flexibility in the radial mass distribution and can phenomenologically capture deviations from a single power-law profile. This model is combined with a numerical approach to compute time delays at the image positions. We validate the BPL implementation using simulated lenses and compare the results with those obtained from the elliptical power-law (EPL) model. We then apply both model families to the quadruply imaged quasar WGD~2038-4008. Both models fit the imaging and kinematic data comparably well, yet the greater radial freedom in the BPL model shifts the inferred time-delay distance -- and thus $H_0$ -- by an amount comparable to the current discrepancy between early- and late-universe $H_0$ estimates. In a flat $Λ$CDM cosmology, the $H_0$ inferred using the BPL lens model is $75^{+23.1}_{-16.3} \ \mathrm{km \ s^{-1} \ Mpc^{-1}},$ while the EPL model gives $H_0 = 61^{+19.2}_{-13.2} \ \mathrm{km \ s^{-1} \ Mpc^{-1}}.$ This difference is largely due to uncertainties in the inner mass profile ($θ<0.2''$), a region where point spread function (PSF) reconstruction is a critical factor -- a finding consistent with results reported in Shajib et al. (2022). This highlights how time-delay cosmography remains sensitive to assumptions about the lens mass profile. With current precision, this difference does not favor one cosmological scenario over another, but rather underscores the importance of flexible mass modeling and PSF modeling.

Extragalactic jets are broadly classified into two categories based on radio observations: core-brightened jets, known as Fanaroff-Riley Type I (FR I), and edge-brightened jets, classified as Type II (FR II). This FR dichotomy may arise due to variation in the ambient medium and/or the properties of the jet itself, such as injection speed, temperature, composition, magnetization, etc. To investigate this, we perform large-scale three-dimensional magnetohydrodynamic (3D-MHD) simulations of low-power, supersonic jets extending to kiloparsec scales. We inject a jet beam carrying an initially toroidal magnetic field into a denser, unmagnetized, and stratified ambient medium through a cylindrical nozzle. Our simulations explore jets with varying injection parameters to investigate their impact on morphology and emission properties. Furthermore, we examine jets with significantly different plasma compositions, such as hadronic and mixed electron-positron-proton configurations, to study the conditions that may drive transitions between FR I and FR II morphologies. We find that, under the same injection parameters, mixed plasma composition jets tend to evolve into FR I structures. In contrast, electron-proton jets exhibit a transition between FR I and FR II morphologies at different stages of their evolution.

This paper presents Owl-z, a Bayesian code aiming at identifying z \geq 7 quasars in wide field optical and near-infrared surveys. By construction,the code can also be used to select objects that contaminate the high-z quasar population, i.e. brown dwarfs and early-type galaxies at intermediate redshifts. The code can be adapted for the selection of high-z galaxies, and although it has been tuned to the Euclid Wide Survey, it can be easily adapted to other photometric surveys. The code input data are the object's photometric data and its galactic longitude and latitude, and the code output data are the probabilities of the modelled populations of high-z quasars, brown dwarfs and early-type galaxies at intermediate redshift. As part of the validation, Owl-z could re-identify all spectroscopically confirmed quasars at z \geq 7, demonstrating the code's versatility in applying to different photometric catalogues. The performance of Owl-z, based on a metric combining completeness and purity called F-measure, is analysed in the case of Euclid using simulated data in a wide range of redshifts (7 \leq z \leq 12) and H-band Euclid magnitudes (18 \leq H_E \leq 24.5). The results show that Owl-z reaches full performance for bright sources (H_E \lesssim 22), independently of the redshift. We show that the probability threshold used to select promising quasar candidates can be adjusted after processing to fine-tune the F-measure value of candidates depending on their magnitude and redshift estimates. We show that for objects brighter than about two magnitudes above the survey detection limit, Owl-z provides a classification that will facilitate the optimisation of photometric and spectroscopic confirmation campaigns. In conclusion, Owl-z is a powerful public tool to help select high-z quasars, brown dwarfs or early-type galaxies at intermediate redshifts in Euclid or other wide-field surveys.

The superorbital modulations (SMs) observed in wind-fed X-ray binaries remain a puzzling phenomenon in astrophysics. To investigate this behavior observationally, we analyzed the long-term hard X-ray light curve from the Swift/BAT 157-Month Hard X-ray Survey in X-ray binary GX 301-2. Using three timing analysis methods--the Lomb-Scargle periodogram, the weighted wavelet Ztransform, and Gaussian processes--we identify a rare dual-SM behavior in this source: the 115-day modulation exceeds the 5$σ$ global significance level, whereas the 65-day signal only marginally reaches the 4$σ$ level. Because the 115-day period is more consistent with the previously reported linear relation between orbital and superorbital periods, we interpret 115 days as the actual superorbital period, while the weaker and less stable 65-day period is its beat modulation with the orbital period.By assessing the applicability of different physical scenarios to our results, we suggest that this dual-SM behavior is most plausibly associated with corotating interaction regions (CIRs) in the stellar wind. This framework can also account for the observed linear orbital-superorbital relation, despite the unclear physical mechanism that sets the apparent ratio between the CIR and orbital periods across sources. Further long-term monitoring of this system, together with continued theoretical development of the CIR scenario, will be essential for clarifying the origin of wind-fed SMs.

Statistically, green valley (GV) galaxies exhibit lower molecular gas fractions ($f_{gas}$) and reduced star formation efficiency (SFE) compared to star-forming galaxies. However, it remains unclear whether quenching is primarily driven by one factor or results from a combination of mechanisms in individual GV galaxies. In this study, we address this question by examining the spatial distributions of star formation and molecular gas in 28 GVs selected from the ALMaQUEST survey and additional literature samples. For each galaxy, we identify regions with suppressed specific star formation rate (sSFR) and measure $Δf_{gas}$ and $Δ$SFE-offsets from the resolved scaling relations of the star-forming main sequence galaxies. By comparing the fraction of regions with negative $Δf_{gas}$ and $Δ$SFE, we classify 35.7$\pm$13.2\% (57.1$\pm$17.9\%) of GV galaxies as $f_{gas}$-driven, 39.3$\pm$14.0\% (39.3$\pm$14.0\%) as SFE-driven, and 25.0$\pm$10.6\% (3.6$\pm$3.6\%) as mixed mode when adopting a fixed (variable) CO-to-$\rm H_{2}$ conversion factor ($α_{CO}$). These results indicate that GVs undergo quenching through multiple pathways. As sSFR decreases from the main sequence to the green valley, we observe a transition toward predominantly SFE-driven quenching, possibly linked to internal processes such as morphological quenching or AGN activity. We further estimate the quenching timescale ($τ_{decay}$), defined as the time from the peak SFR to 1/e (approximately 37\%) of its value, using integrated MaNGA spectra. SFE-driven quenching is typically associated with short $τ_{decay}$ , while $f_{gas}$-driven quenching shows a broader range. Overall, 75\% of GVs exhibit $τ_{decay}$ shorter than 1 Gyr, suggesting that quenching in most GVs proceeds rapidly, challenging purely slow-quenching scenarios like starvation.

We present new sub-arcsecond ($\sim$0.3-1 arcsec; $\sim$1--3\,kpc) VLA imaging at 1.4\,GHz and 6\,GHz of 29 optically-selected, [O~{\sc iii}] luminous ($L_{\rm [O III]}$ > 10$^{42.1}$\,erg\,s$^{-1}$), $z<0.2$ quasars drawn from the expanded Quasar Feedback Survey (QFeedS; with $L_\mathrm{1.4\,GHz} = 10^{22.6}$--10$^{26.3}$\,W\,Hz$^{-1}$). These 29 new objects occupy the low end of the radio-power distribution ($L_\mathrm{1.4\,GHz}$=$10^{22.63}$--10$^{23.45}$\,W\,Hz$^{-1}$) in the QFeedS sample and are nominally `radio quiet'. Despite this, we find widespread evidence of AGN-driven synchrotron activity. Nearly $\sim 31\,$per\,cent exhibit resolved radio structures on $\sim$0.1--20\,kpc scales consistent with compact jets or wind-driven outflows, and $\sim 90\,$per\,cent display steep spectra ($α\lesssim -1$) indicative of optically thin synchrotron emission. Combining morphology, spectral index and brightness-temperature diagnostics, at least $\sim38\,$per\,cent of the sample show clear AGN signatures that cannot be explained by star formation alone. These constitute the first results from the expanded QFeedS (now 71 quasars spanning $\approx 4$ dex in radio power) and demonstrate that compact, low-power jets and AGN shocks are common deep inside the radio-quiet regime. A thorough understanding of feedback processes from quasars, deep into the `radio-quiet' regime, will be obtained by connecting these high resolution radio observations with multi-wavelength observations.

Ultracompact dwarf galaxies (UCDs) are thought to be remnants of stripped galactic nuclei, among which a handful are known to host a central supermassive black hole (SMBH). As in stripped nuclear star clusters, the SMBHs in UCDs may be fed by stellar winds from old stellar populations, in the absence of substantial gas reservoirs and galactic inflows. In this work, we investigate such a wind-fed accretion scenario for M60-UCD1, which harbors a confirmed $2\times10^7~M_\odot$ SMBH and exhibits X-ray emission suggestive of SMBH accretion signature. Using three-dimensional hydrodynamical simulations, we simulate the SMBH accreting stellar winds from approximately 1500 asymptotic giant branch stars, and explore the role of ram pressure from the ambient interstellar or intracluster medium. After 5 Myr, the majority of the stellar winds form a cold gas disk ($\sim1000~M_\odot$) within $\sim10~\rm pc$ as well as the SMBH's gravitational sphere of influence. Within the inner $10^4~r_{\rm g}$, this disk transitions into a hot ($\sim10^7-10^9~\rm K$), geometrically thick corona that dominates the X-ray emission. The SMBH achieves an accretion rate of $\sim10^{-5}~M_\odot~\rm yr^{-1}$, yielding an X-ray luminosity of $\sim7\times10^{37}~\rm erg~s^{-1}$, well consistent with observations. Including ram pressure stripping reduces both the accretion rate and luminosity by about a factor of two. Our results suggest that the X-ray counterpart of M60-UCD1 originates from a weakly accreting SMBH fed by stellar winds, with broader insights into the feeding mechanisms of central massive black holes and the origins of X-ray sources in other UCDs.

We present a model for the intrinsic alignment contamination of the shear three-point correlation function and skewness of the mass aperture statistic using the tidal alignment and tidal torquing (TATT) formalism. We compute the intrinsic alignment bispectra components in terms of the TATT model parameters. We consider two effective field theory approaches in the literature, relate them to the TATT model parameters and an extension to TATT that includes the velocity-shear (VS) parameter. We compare the impact of changing between NLA, TATT, and TATT+VS on the theoretical computation of the 3PCF using the best fit parameters and tomographic redshift distributions from Dark Energy Survey Year 3. We find that the TATT model significantly impacts the skewed triangle configurations of the 3PCF. Additionally, including the higher-order effects from TATT can introduce opposite effects on the two-point function and on the mass aperture skewness, damping the signal of the former while boosting the signal of the latter. We argue that a joint 2PCF+3PCF analysis with the TATT model can help break the degeneracy between its model parameters and provide more robust constraints on both cosmology and intrinsic alignment amplitude parameters. We show that typical values of order unity for the intrinsic alignment parameters introduce differences of around $10\%$ between NLA and TATT predictions.

We provide a characterization of the red supergiant (RSG) progenitor candidate for the nearby Type II-plateau supernova (SN) 2025pht in NGC 1637. The star was first detectable in 2001 by the Hubble Space Telescope (HST) and then again in a dozen bands by the James Webb Space Telescope (JWST) in 2024. This "quasi-snapshot" of the star's nature almost immediately prior to explosion is unprecedented. The RSG varied in brightness, and we posit that it could have been a pulsating variable, possibly with a long period of ~660 days. The largest uncertainty is the host-galaxy distance, which we establish to be 10.73+/-1.76 Mpc. The star was also heavily extinguished by interstellar dust internal to the host, with visual extinction A_V(host)~1.7 mag (total A_V(tot)~1.8 mag). Dust radiative-transfer modeling reveals the star's circumstellar medium to be quite dusty and silicate-rich, yielding a bolometric luminosity log(L_bol/L_Sun)=5.08+/-0.16 and a cool effective temperature T_eff=2100--2500 K. The available HST optical data had no bearing on the shape of the candidate's observed spectral energy distribution -- for the first time, without the archival JWST observations we would not have been able to detect and characterize the candidate at all. The SN 2025pht progenitor candidate, although quite similar to that of SN 2023ixf, may be the most luminous candidate identified to date.

In low-mass core-collapse supernova (CCSN) progenitors, nuclear burning beyond oxygen can become explosive under degenerate conditions, triggering eruptive mass loss before the final explosion. We investigate such pre-SN eruptions using \texttt{SNEC} hydrodynamic simulations and realistic stellar models, parameterizing the nuclear energy deposition as a fraction of the binding energy of the combined He layer and H-rich envelope. For the lowest-mass model (9 $M_\odot$), the ejecta mass ($M_{\rm ej}$) scales with the energy gained by the H-rich envelope via a power law (index$\sim$3.5). Across 9-10 $M_\odot$, this relation shows limited scatter within a factor of $\sim$2.6, enabling an estimation of the gained energy from $M_{\rm ej}$. The shock passage also flattens the bound envelope, which can affect the SN light curve morphology and provide another diagnostic for the eruption. Then, we compute the associated precursor light curves for the 9 $M_\odot$ model with the multi-group radiative-transfer code \texttt{STELLA}. These signals are typically faint, with bolometric luminosities of $\sim10^{39}$ erg s$^{-1}$ lasting hundreds of days. Their cool black-body spectra make them brighter in the infrared, yet several magnitudes fainter than observed pre-SN precursors at the threshold for full envelope ejection. To aid future studies, we make our post-eruption stellar profiles and precursor light curves publicly available.

We present the results of a search for gravitational waves (GWs) from individual sources using high-cadence observations of PSR J1909\(-\)3744 obtained during an intensive observing campaign with the International Pulsar Timing Array second data release (IPTA-DR2) between July 2010 and November 2012. The observations, conducted at three different radio frequencies with the Nançay Radio Telescope (NRT) and Parkes Telescope (PKS) and five frequencies with the Green Bank Telescope (GBT), enabled precise corrections for dispersion measure effects and scattering variations. After these corrections, the timing residuals showed an unmodeled periodic noise component with an amplitude of 340 ns. Our analysis yields upper limits on the GW strain from individual sources, constraining it to be below \(1.9 \times 10^{-14}\) at 71 nHz and \(2.3 \times 10^{-13}\) at 1 \textmu Hz for average sky locations, while for optimal source locations the limits improve to \(6.2 \times 10^{-15}\) and \(8.9 \times 10^{-14}\) at the same frequencies, respectively. Our new limits are about a factor of 1.52 more stringent than those of Perera et al. based on an earlier EPTA data.

Relativistic magnetic reconnection is a key mechanism for dissipating magnetic energy and accelerating particles in astrophysics. In the absence of radiative cooling, recent particle-in-cell (PIC) simulations have shown that high-energy particles gain most of their energy in the upstream region, during a short-lived "free phase" where they meander between the two sides of the layer; when they get captured/trapped by the downstream flux ropes, they undergo a "trapped phase", where no significant energization occurs. Here, we perform a suite of 3D PIC simulations of relativistic reconnection including inverse Compton (IC) losses in the weakly cooled regime in which the radiation-reaction-limited Lorentz factor $γ_{\rm rad}$ exceeds the magnetization $σ$. We show that electron cooling losses do not appreciably alter the reconnection rate, the structure of the layer, and the physics of particle acceleration in the free phase, so the spectrum of free electrons is $dN_{\rm free}/dγ\propto γ^{-1}$, as in the uncooled case. The spectrum of trapped electrons above the cooling break $γ_{\rm cool}$ (in the range $γ_{\rm cool}<γ<γ_{\rm rad}$) is $dN/dγ\propto γ^{-3}$, steeper than the scaling $dN/dγ\propto γ^{-2}$ of uncooled simulations. This confirms that no significant particle energization occurs during the trapped phase. Our results validate the model by arXiv:2302.12269 for particle acceleration in 3D relativistic reconnection, and imply that radiative emission models of reconnection-powered astrophysical sources should employ a two-zone structure, that differentiates between free, rapidly accelerating particles and trapped, passively cooling particles.

We investigate the properties of the interstellar medium (ISM) at ~100 pc scales in a sample of 27 nearby luminous infrared galaxies (LIRGs) spanning the entire merger sequence. In particular, we study the relations between star-formation (SF) and molecular gas surface density as a function of the interaction stage using two complementary approaches: beam-sized (unresolved, line-of-sight) regions and physically identified molecular gas clumps. To map the distribution of molecular gas we use ALMA CO(2-1) observations, while SF is traced using HST Pa-alpha or Pa-beta images. We derive spatially resolved Kennicutt-Schmidt (KS) relations for each galaxy. When using beam-sized regions, we find that 67% of galaxies follow a single relation between Sigma_SFR and Sigma_H2. However, in the remaining galaxies, the relation splits into two branches, indicating the presence of a duality in this relation. In contrast, when using physical gas clumps, the duality disappears and all galaxies show a single trend. We also study other ISM/clump properties as a function of the merger stage. We find that isolated galaxies and systems in early stages of interaction exhibit lower amounts of gas and SF. As the merger progresses, however, the amount of gas in the central kpc of the galaxy undergoing the merger increases, along with the SFR, and the slope of the KS relation becomes steeper, indicating an increase in SF efficiency of the gas clumps. Clumps in late-stage mergers are predominantly located at small distances from the nucleus, confirming that most of the activity is concentrated in the central regions. Finally, the relation between the SF efficiency and the boundedness parameter evolves from being roughly flat in the early stages of the merger to becoming positive in the final phases, indicating that clump self-gravity only starts to regulate the SF process between the early- and mid-merger stages.

Complex organic molecules (COMs) in young stellar objects (YSOs) have attracted significant attention in recent years due to their potential connection to pre-biotic chemistry and their utility as tracers of warm or shocked gas components. Proto-binary and multiple systems with close separations are particularly valuable targets for investigating chemical inheritance and reaction, as their members are expected to form from similar material in their parental cloud. We present ALMA observations of the hierarchical proto-triple system HOPS-288, focusing on the physical structure, kinematics, and COM compositions. The system is treated as a proto-binary system consisting of HOPS-288-A and HOPS-288-B due to the limited spatial resolutions, with a separation of 200~au. Three COM-rich features are revealed: two hot corinos associated with the two members, rich in a variety of COMs, and an intervening component between the two members traced by CH$_3$OH and tentatively by CH$_3$CHO. The hot corino in HOPS-288-A exhibits rotational features and might trace a disk. The hot corino in HOPS-288-B is also possibly exhibiting rotational motion. The intervening component could possibly trace a shocked region in the circumbinary disk or a bridge between the two members. The column densities of COMs, including $^{13}$CH$_3$OH, CH$_2$DOH, CH$_3$CHO, HCOOCH$_3$, C$_2$H$_5$OH, $^{13}$CH$_3$CN, and NH$_2$CHO, are broadly similar between the two sources, possibly suggesting the complex organic similarities among proto-binary/multiple systems. Given the complexity of the studied physical structures, further detailed investigations will be essential to confirm this result.

We present observational constraints on the Bound Dark Energy Cold Dark Matter (BDE-CDM) model using DESI DR2 baryon acoustic oscillation measurements combined with Planck CMB data and Type Ia supernovae compilations (PantheonPlus, Union3, DESY5). In BDE-CDM, dark energy originates from the lightest meson field within a supersymmetric SU(3) dark gauge group with $N_f = 6$ flavors, governed by an inverse power-law potential $V(φ) = Λ_{c}^{4+2/3} φ^{-2/3}$. Unlike $Λ$CDM and $w_0w_a$CDM, the dark energy sector contains no free parameters -- the condensation scale $Λ_c$ and transition epoch $a_c$ are determined by gauge coupling unification constraints. The equation of state evolves from relativistic behavior ($w = 1/3$) before condensation through a kinetic-dominated stiff phase ($w \simeq 1$), approaching $w_0 = -0.9298 \pm 0.0003$ at present, with $w > -1$ maintained throughout cosmic history, avoiding phantom-regime instabilities. We obtain $Λ_{c} = 43.93 \pm 0.13$~eV and $a_c = (2.489 \pm 0.007) \times 10^{-6}$, consistent with theoretical predictions. The $w_0$-$w_a$ confidence contours are approximately 10,000 times smaller than those of $w_0w_a$CDM while achieving comparable fits, and remain stable across different supernova datasets. Statistical analysis yields $Δ\mathrm{DIC} = -6.77$ and $Δ\mathrm{AIC} = -8.97$ relative to $Λ$CDM for BAO+DESY5, constituting strong evidence favoring BDE-CDM model. The model predicts distinctive signatures including 25\% enhancement in the matter power spectrum at $k \approx 4.3\,h\,\mathrm{Mpc}^{-1}$. These results establish BDE-CDM as a theoretically motivated framework that successfully addresses the DESI-observed preference for dynamical dark energy while connecting particle physics with cosmological observations.

We investigate the interplay between star formation, interstellar medium (ISM) components, and dust properties in NGC 3627 using new NIKA2 1.15 and 2 mm observations from the IMEGIN Large Program. Our goal is to analyze dust and radio emission, decompose contributions in the millimeter-centimeter regime, and explore ISM properties within the galaxy. We perform spectral energy distribution fitting, at both global and spatial scales, using the THEMIS dust model within the HerBIE code, applied to data from 3.4 $μ$m to 6 cm. We decompose emission into dust, free-free, and synchrotron components, and examine correlations with gas surface density and star formation activity. Additionally, we analyze the small dust grain fraction and its variation across the galaxy. We find $\sim$10% radio emission at 2 mm, peaking at 18% in the southern bar-end, which hosts the highest star formation activity. However, an isolated star-forming region beyond this bar-end is the most efficient, as indicated by its elevated dust production efficiency and effective yield, predicted by our simplistic dust evolution model. The 160 $μ$m emission shows the strongest correlation with molecular gas, while 1.15 mm better traces the dust mass surface density. Small grains, which make up $\sim$13% of dust mass (2 $\times$ 10$^{7}$ M$_{\odot}$), are depleted in intense radiation fields, with a notable deficit in the southern tidal tail. ISM properties and chemical evolution indicate that dynamical processes, such as bar-driven gas flows and tidal interactions, are crucial in shaping the galactic structure, influencing star formation efficiency, and dust distribution.

Context. X-ray quasi-periodic eruptions (QPEs) are repeating, high-amplitude, soft X-ray bursts observed from the nuclei of a dozen nearby low-mass galaxies. Their origin remains a puzzle in the physics of accretion variability. Observational data indicate that X-ray and/or optical tidal disruption events (TDEs) may precede QPE detections. Although both kinds of outburst are driven by supermassive black holes, they are more frequently detected in faded active galactic nuclei (AGNs), when the TDE is not happening in a dormant galaxy. In the case of the QPE discovery source, GSN 069, it remains debated whether its past activity arose from a previous AGN phase or from an enhanced TDE rate. Aims. We investigated the origin of the past nuclear activity in GSN 069. Methods. Past AGN activity imprints detectable polarization in optical light, due to the expected delay between direct and scattered light. On 6 September 2019, we targeted GSN 069 with VLT/FORS2 in both imaging polarimetry and spectropolarimetry modes so that its optical polarization could be investigated while the first detected QPE phase was still active. Results. We measured a rising polarization, from ~0% to ~1.5%, as moving away from the nucleus of GSN 069. This rise is probed to be intrinsic to the central engine, confirming the already detected extended emission line region (EELR) by integral field unit data. Conclusions. The increasing radial polarization demonstrates a switched-off nucleus. The polarization angle traces an axis aligned with elongated [OIII], [NII], and Hα gas distributions, revealing an EELR that may be consistent with relic polarization cones, therefore suggesting the presence of a torus-like structure in the past. Thus, optical polarization echoes geometrically favor a faded AGN as the origin of the EELR rather than a past elevated TDE rate, although the latter cannot be excluded.

Since its launch, JWST has opened an unprecedented opportunity to characterise the ionised ISM of high-redshift galaxies using well-established rest-frame UV/optical diagnostics from the local Universe. At the same time, these observations challenge the validity of such classical methods when applied to the extreme environments typical at high redshift. We present an in-depth analysis of the ISM in three representative case studies at $z=2 - 6$ (MARTA 4327, the Sunburst Arc and RXCJ2248-ID) conducted within a multi-cloud photoionisation modelling framework (HOMERUN). We show that even a small fraction of unresolved high-density clumps can contribute more than half of the observed flux of auroral lines, while only negligibly to standard optical density tracers. As a result, $T_{\mathrm{e}}$-method metallicities can be underestimated by $\sim 0.15 - 0.3$ dex, as for MARTA 4327. By modelling rest-frame UV and optical data, we demonstrate that discrepancies between abundances obtained from diagnostics tracing different zones do not necessarily imply chemical inhomogeneities. In RXCJ2248-ID, the disagreement between UV and optical N/O may naturally arise from ionisation and density structure alone. In contrast, we find evidence for genuine chemical stratification in the Sunburst Arc, where a component enriched in nitrogen coexists with a chemically normal one. Finally, we argue that very-high-ionisation lines may be explained within a pure star-formation scenario invoking matter-bounded regions. However, in the case of RXCJ2248-ID, we cannot rule out a minor contribution from an AGN based solely on the observed fluxes. These results indicate that classical diagnostics can be significantly biased in high-redshift galaxies and that self-consistent, physically motivated tools are therefore essential to properly interpret the complex ISM conditions and chemical enrichment in the early Universe.

Galactic centres are highly dynamic regions dominated by a supermassive black hole (BH) surrounded by nuclear star clusters (NSC), molecular gas, and asymmetric matter distributions such as disks or halos. The combined gravitational effects of these components, along with relativistic corrections from the BH's spin, generate strongly nonlinear dynamics and frequent chaotic orbital behaviour. To model this environment, we employ a multipolar expansion potential in which the central compact object is represented by the Artemova-Bjornsson-Novikov pseudo-Newtonian potential, effectively capturing spin-dependent features of a Kerr-like BH. The surrounding halo is treated as an axisymmetric, shell-like mass distribution expanded up to third order in multipolar terms to account for realistic asymmetry. Previous studies have mainly explored the influence of multipolar moments and BH spin using Poincare sections, SALI, and related chaos indicators. In this work, we extend these analyses by incorporating stability analysis and basins of convergence to achieve a more complete understanding of the system's dynamics. Stability analysis around equilibrium points provides insight into local behavior, while basins of convergence highlight sensitivity to initial conditions and expose fractal basin boundaries. Our results show that the BH spin significantly reshapes phase space: depending on its magnitude and orientation, it can either amplify chaotic scattering caused by halo asymmetry or stabilize specific orbital families. These findings enhance our understanding of how relativistic spin effects and multipolar mass distributions jointly govern the dynamical architecture of galactic centers.

Recent space-based observations discovered several unusual objects, exhibiting similar properties, at redshifts $z\gtrsim 10$. Among them is the UHZ1 system at $z=10.1$, containing $\sim 10^8M_\odot$ in stars, with a similarly massive central black hole of $\sim 10^{7-8}M_\odot$. Here we propose a possible mechanism for forming such systems which hinges on the presence of primordial black holes (PBHs) covering a range of masses while contributing a significant fraction of the dark matter (DM). We evaluate the accurate expression for the small-scale power responsible for the collapse of the first halos in the presence of the PBH population. The extra power in the matter density field, produced by the granulation term, will cause an earlier collapse of DM halos, populated by PBHs of different masses. In these collapsed and virialized systems the PBHs will undergo 2-body relaxation, driving the more massive PBHs to the halo center under dynamical friction. We quantify this evolution for a distribution of PBH orbital parameters and halo properties. The analysis shows that PBHs can have appropriate mass functions capable of producing systems with parameters similar to what is observed for UHZ1. We suggest that the proposed mechanism could account for a subset of other systems newly discovered with the JWST at high redshifts, including the Little Red Dots.

Context. The electron pressure profile is a convenient tool to characterize the thermodynamical state of a galaxy cluster, with several studies adopting a "universal" functional form. Aims. This study aims at using Sunyaev-Zel'dovich (SZ) data to test four different functional forms for the cluster pressure profile: generalized Navarro-Frenk-White (gNFW), $β$-model, polytropic, and exponential. The goal is to assess to what level they are universal over a population-level cluster sample. Methods. A set of 3496 ACT-DR4 galaxy clusters, spanning the mass range $[10^{14},10^{15.1}]\,\text{M}_{\odot}$ and the redshift range $[0,2]$, is stacked on the ACT-DR6 Compton parameter $y$ map over $\sim13,000\,\text{deg}^2$. An angular Compton profile is then extracted and modeled using the theoretical pressure recipes, whose free parameters are constrained against the measurement via a multi-stage MCMC approach. The analysis is repeated over cluster subsamples spanning smaller mass and redshift ranges. Results. All functional forms are effective in reproducing the measured $y$ profiles within their error bars, without a clearly favored model. While best-fit estimates are in broad agreement with previous findings, hints of residual subsample dependency are detected favoring higher amplitudes and steeper profiles in high-mass, low-redshift clusters. Conclusions. Population-level cluster studies based on SZ data alone are likely unable to accurately constrain different pressure profile models. Residual trends at population level and scatter at individual cluster level undermine the universal pressure model assumption whenever high precision is required. Finally, functional forms different from the gNFW prove equally effective while being more physically motivated.

Transient astronomy in the early Universe (z > 2) remains largely unexplored, lying beyond the rest-frame optical spectroscopic reach of most current observatories. Yet this regime promises transformative insights, with high-redshift transients providing direct access to the early Universe and enabling studies of how stellar populations and cosmology evolve over cosmic time. JWST is uniquely equipped to probe these redshifts efficiently in the rest-frame optical and near-IR. We present results from an initial pathfinder search, covering an area of ~133 arcmin^2 (~0.037 deg^2) independently imaged by the PRIMER and COSMOS-Web (hereafter COSMOS) extragalactic surveys. Although neither program was designed for time-domain astronomy, combining their data results in difference images separated by roughly one year, leading to the discovery of 68 supernovae (SNe) with host photometric redshifts reaching z < 5. For most SNe, only a single epoch is available, but the combination of host redshift, classification, color, and magnitude enables us to prioritize candidates for detailed photometric and spectroscopic follow-up. Among the most notable sources are a relatively bright, blue CCSN at z > 3 (SN 2023aeab) and a young, normal SN Ia at z > 2 (SN 2023aeax). The sample distribution highlights the increasing likelihood that a wide-area JWST program can uncover younger, bluer, and potentially more extreme explosions. While this pathfinder effort is limited in cadence and number of filters, it demonstrates the strong potential of a dedicated, well-planned time-domain survey with JWST to obtain the sample sizes and rate measurements needed to chart SN populations deep into the early Universe.

Reverberation mapping is one of the main techniques used to study active galactic nuclei (AGN) accretion disks. Traditional continuum reverberation mapping uses short lags between variability in different wavelength AGN light curves on the light crossing timescale of the disk to measure the radial structure of the disk. The harder-to-detect long negative lag measures lags on the longer inflow timescale, opening up a new window to mapping out the vertical structure of AGN disks. The Vera Rubin Observatory, with its 6 wavebands, long baseline, and high cadence, will revolutionize our ability to detect short and long lags. However, many challenges remain to detect these long lags, such as seasonal gaps in Rubin light curves, the weak signal strength of the long lag relative to the short lag, and the enormous influx of data for millions of AGN from Rubin. Machine learning techniques have the potential to solve many of these issues, but have yet to be applied to the long negative lag problem. We develop and train a transformer-based machine learning model to detect long and short lags in mock Rubin AGN light curves. Our model identifies whether a light curve in our test set has a long negative lag with 96% recall and 0.04% contamination, and is 98% accurate at predicting the true long lag. This accuracy is an enormous improvement over two baseline methods we test on the same mock light curves, the interpolated cross correlation function and javelin, which are only 54% and 21% accurate, respectively.

The detection of gravitational-wave events by LIGO-Virgo-KAGRA has opened new avenues for multi-messenger astrophysics; however, electromagnetic counterparts remain elusive due to large localization uncertainties. Wide-field optical surveys like the Zwicky Transient Facility (ZTF) play a crucial role in follow-up, but efficient scheduling is essential. In this work, we present LUMOS, a Mixed Integer Linear Programming (MILP) approach that selects fields via a maximum coverage problem and schedules observations to maximize cumulative probability while respecting observability constraints. Using 1199 GW events from O4, we compare the LUMOS scheduler with gwemopt, showing an 84.7 percent higher mean cumulative probability and better performance in nearly all cases. While designed for ZTF, LUMOS's framework parallels the M4OPT toolkit for space missions, highlighting the broader applicability of MILP-based scheduling to both ground- and space-based follow-up.

The Javalambre-Physics of the Accelerating Universe Astrophysical Survey (J-PAS) is an ongoing survey mapping thousands of square degrees in the Northern Hemisphere using 56 narrow-band filters, delivering IFU-like photometric data well suited for studying galaxy properties and evolution. As a precursor, the miniJPAS survey observed a 1 deg$^2$ field with the same filter system, providing an ideal testbed for the study of spatially resolved galaxies. In this work, we investigate the resolved stellar population and emission-line properties of 51 miniJPAS galaxies, classified by spectral type (red or blue) and environment (group or field), and assess the role of environment in galaxy evolution. We use the Py2DJPAS pipeline to process the data, homogenise the images to a common PSF, define galactic regions, and extract photo-spectra. Radial profiles are analysed using elliptical annuli spaced by 0.7 R_EFF, combined with an inside-out segmentation to study star formation histories. Stellar population parameters are derived with the Bayesian SED-fitting code BaySeAGal, while artificial neural networks are used to estimate the equivalent widths of the H$α$, H$β$, [NII], and [OIII] emission lines. We find clear trends in a mass density-colour diagram: denser, redder regions are older, more metal-rich, and have lower specific star formation rates, while bluer, less dense regions show stronger emission lines and higher sSFRs. Red and blue galaxies are well separated in these relations, whereas environmental classification shows no clear distinction. Radial profiles support an inside-out formation scenario, with significant differences between red and blue galaxies but no strong environmental dependence. We suggest that the weak environmental effects may be due to the relatively low stellar masses of the galaxy groups in our sample.

Most stars form in multiple systems, with profound implications in numerous astronomical phenomena intrinsically linked to multiplicity. However, our knowledge about the process on how multiple stellar systems form is incomplete and biased toward nearby molecular clouds forming only low-mass stars, which are unrepresentative of the stellar population in the Galaxy. Most stars form within dense cores in clusters alongside high-mass stars (>8 M$_{\odot}$), as likely the Sun did. Here we report deep ALMA 1.33 mm dust continuum observations at ~160 au spatial resolution, revealing 72 low-mass multiple systems embedded in 23 high-mass cluster-forming regions, as part of the Digging into the Interior of Hot Cores with ALMA (DIHCA) survey. We find that the companion separation distribution presents a distinct peak at ~1200 au, in contrast to the one at ~4000 au observed in nearby low-mass regions. The shorter fragmentation scale can be explained by considering the higher pressure exerted by the surrounding medium, which is higher than the one in low-mass regions, due to the larger turbulence and densities involved. Because the peak of the companion separation distribution occurs at much larger scales than the expected disk sizes, we argue that the observed fragmentation is produced by turbulent core fragmentation. Contrary as predicted, the multiplicity fraction remains constant as the stellar density increases. We propose that in the extremely dense environments where high-mass stars form, dynamical interactions play an important role in disrupting weakly bound systems.

Recent Gaia astrometry has revealed thousands of main-sequence + white-dwarf binaries (MS+WD) at separations of ~0.1-10 au, including a subset hosting unusually massive (>~0.8 Msun) WDs. We argue that s-process enrichment in the non-degenerate companion provides a powerful diagnostic for identifying WDs that formed via mergers in hierarchical triple systems. For a massive WD, standard single-star evolution requires a massive (>~4 Msun) progenitor, yet such progenitors produce negligible s-process yields. We define IK Peg-type systems as those exhibiting this mass-yield tension: barium-enhanced companions orbiting WDs too massive to have descended from efficient s-process producers. The well-known system IK Peg exemplifies this class. Applying this framework to published spectroscopic data reveals several additional candidates, and we estimate that a few dozen such systems should exist in the current Gaia sample. If these systems trace inner-binary mergers in primordial triples, they represent observable intermediate stages towards eventual Type Ia supernovae via the double-merger pathway, as predicted by recent population-synthesis models.

We present independent polarimetric observations of the interstellar object 3I/ATLAS, including the first near-infrared polarimetric measurements. Using imaging polarimeters, we measured the degree of linear polarization from the visible RC band (0.64 μm) to the near-infrared KS band (2.25 μm), and investigated its dependence on solar phase angle (polarization phase curve; PPC) and wavelength (polarization color curve; PCC). We confirm that the PPC of 3I/ATLAS differs significantly from those of typical Solar System comets, showing an unusually large polarization amplitude. This PPC shows no significant change in the RC band across perihelion passage, despite the perihelion lying within the water snow line. This indicates that the unusual polarimetric behavior of 3I/ATLAS is unlikely to be driven by transient volatile activity, but instead reflects intrinsic optical properties of refractory dust particles. The PCC increases with wavelength over 0.6-1.2 μm and peaks at 1.5-2.0 μm, suggesting that the dominant scattering units are dust aggregates composed of submicron-sized monomers, broadly consistent with interstellar dust and solar-system cometary aggregates. Taken together, our results indicate that 3I/ATLAS preserves polarimetric properties characteristic of a primitive cometary planetesimal formed in another planetary system, with a refractory dust composition that differs from that typically observed among Solar System comets, despite sharing a similar size scale of the aggregate building blocks.

The 2175 Angstrom extinction bump, the most prominent spectral feature superimposed on the interstellar extinction curve, is widely seen in the interstellar medium (ISM) of the Milky Way and external galaxies, both near and far. While its central wavelength is remarkably stable and independent with environment, its width shows considerable variation and environmental dependence. Here we examine the extinction curve for the line of sight toward HD 93222, a young star located in the Carina nebula. It is found that the 2175 Angstrom bump is extremely sharp, which is among the narrowest ever found in the Milky Way and external galaxies. We model the derived extinction curve and find that, to explain the extinction characteristics of HD 93222, in addition to the conventional silicate and graphite dust mixture, an additional population of nano-sized graphitic grains is required.

The initial mass function (IMF) is a cornerstone of star formation studies, yet its universality remains debated. We investigate the IMF in the young massive cluster RCW 36, located in the Vela Molecular Ridge and comparable to the Orion Nebula Cluster in stellar density. Our goal is to build the most complete census of RCW 36 and derive its first IMF and star-to-brown-dwarf (BD) ratio. We combine new GLAO observations from HAWK-I/VLT with archival data (2MASS, SOFI/NTT) and Gaia DR3 kinematics. Photometric accuracy and source extraction were improved using \textsc{DeNeb}, a deep-learning algorithm that removes complex nebular emission. Membership probabilities were assigned via color-magnitude diagram comparisons with a control field, and stellar masses were estimated using model isochrones. We find a revised distance of $954\pm40\,$pc and determine the IMF down to $\sim0.03\,M_{\odot}$, described by a broken power law ($dN/dM\propto M^{-α}$) with $α=1.62\pm0.03$ for $0.20$-$20\,M_{\odot}$ and $α=0.46\pm0.14$ for $0.03$-$0.20\,M_{\odot}$. The star-BD ratio is $2$-$5$, consistent with other Galactic clusters. Lastly, through a study of the differences in the IMF within and outside $0.2\,$pc and the cumulative mass distributions for low-mass and intermediate to high-mass sources, we also detected signs of possible mass segregation within RCW 36, which should be primordial. RCW 36 shares many characteristics with other young massive clusters, such as a shallower than Salpeter high-mass slope and the possibility of mass segregation. The flatter lower-mass regime of the IMF is similar to most Galactic clusters. The star-BD ratio is also in line with the observed values in other clusters, independent of their inherent properties.

Gamma ray bursts (GRBs) offer a powerful probe of the cosmic expansion history far beyond the redshift range accessible to Type Ia supernovae. However, the calibration of GRB luminosity correlations is hindered by the circularity problem, which arises from assuming a fiducial cosmological model during calibration. In this work, we perform a model independent calibration of GRB luminosity relations using observational Hubble parameter H(z) data from the A220 and J220 compilations, thereby avoiding explicit cosmological assumptions. We employ Artificial Neural Network (ANN) to reconstruct the calibration relation directly from the data. In addition, we implement a Bayesian Neural Network (BNN) framework as an alternative approach, enabling a data driven treatment of both statistical and systematic uncertainties. The calibrated GRB sample is used to constrain the Amati relation, and we systematically compare the outcomes obtained from different calibration techniques and datasets. While the Amati Parameters obtained from GRBs caibrated from the ANN and BNN results are consistent with previous low redshifts calibrations using model-independent methods, the BNN approach provides a more robust framework.

PSR J1455$-$3330 is a $\sim$7.98 ms pulsar in a $\sim$76.17 day nearly circular orbit with a white dwarf companion. In this work, we combine the available Lovell, Nançay decimetric Radio Telescope, Green Bank, and MeerKAT pulsar timing data spanning $\sim$ 30 years to measure the kinematic and relativistic effects of PSR J1455$-$3330 to constrain its 3D orbital geometry and component masses. We detect a relativistic Shapiro delay signal. We measure a significant orthometric amplitude $h_3 = 0.307^{+0.022}_{-0.026}$ $μ$s and an orthometric ratio $ς= 0.551^{+0.057}_{-0.054}$. We measure the change in projected semi-major axis $\dot{x} = -202.1^{+2.5}_{-2.7} \times10^{-16} \, \rm s\,s^{-1}$ with high significance, parallax, $\varpi$ = 1.11(6) mas, parallax derived distance 0.90(5) kpc, and a precise total proper motion magnitude of 12.432(2) mas yr$^{-1}$. A self-consistent analysis of all kinematic and relativistic effects, assuming general relativity, yields two solutions: (1) a pulsar mass $M_{\rm p} = 1.39^{+0.38}_{-0.18}\, \rm M_{\odot}$, a companion mass $M_{\rm c} = 0.293^{+0.056}_{-0.026}$ $\rm M_{\odot}$, an orbital inclination, $i = 63(2)^{\circ}$, and longitude of the ascending node, $Ω= 212(12)^{\circ}$ or (2) a pulsar mass $M_{\rm p} = 1.53^{+1.10}_{-0.22} \, \rm M_{\odot}$, a companion mass $M_{\rm c} = 0.309^{+0.163}_{-0.026}\, \rm M_{\odot}$, an orbital inclination, $i = 123(4)^{\circ}$, and longitude of the ascending node, $Ω= 334(12)^{\circ}$. All uncertainties represent the 68.27$\%$ credibility region. These results strongly favour a helium-dominated white dwarf companion.

We revisit the Great Attractor using the Manticore-Local suite of digital twins of the nearby Universe. The Great Attractor concept has been proposed as an answer to three distinct questions: what sources the Local Group velocity in the cosmic microwave background frame, where present-day velocity streamlines converge, and where the Local Group is moving to. Addressing the original motivation of the Great Attractor -- explaining the Local Group cosmic velocity -- we find that mass within $155~h^{-1}\mathrm{Mpc}$ accounts for only ${\sim}72\%$ of that velocity magnitude with ${\sim}38\,°$ directional offset. We show that even in the purely linear regime convergence within this volume is not guaranteed, particularly when also accounting for small-scale contributions to the observer velocity; no single structure, including the proposed Great Attractor, would be expected to dominate the velocity budget. Streamline convergence is smoothing-scale-dependent, transitioning from Virgo at small scales through the Hydra--Centaurus region at intermediate scales to Shapley at large scales; at intermediate smoothing the convergence point lies near Abell 3565 with an asymmetric basin of mass $\log( M / (h^{-1} \mathrm{M}_\odot)) = 16.4 \pm 0.1$ that excludes Norma. To address the third question, we evolve the Manticore-Local realisations to scale factor $a = 10$ in a new Beyond-Present-Time simulation suite and identify the asymptotic future location of the Local Group. We find that the dominant motion is towards Virgo, but even it contributes at most one third of the Local Group velocity. Our results demonstrate that the classical Great Attractor is not a dynamically dominant structure but an artifact of the instantaneous velocity field, and that no single attractor is likely to account for the Local Group motion in the cosmic rest frame.

Our Local Group, dominated in mass by the Milky Way (MW) and M31, provides a unique laboratory for testing $Λ$CDM cosmology on small scales owing to its proximity. However, its connection to the surrounding large-scale environment, which is essential for interpreting its properties, is inadequately understood. In this work, we explore the connection between Local Group analogues (LGAs) and their surrounding large-scale environments using the ABACUSSUMMIT simulation suite, highlighting the key role of the coupling energy of the MW-M31 orbit, $E_{\rm coupling}$. We find that LGAs with high $E_{\rm coupling}$ preferentially reside in denser regions, whereas those with low $E_{\rm coupling}$ tend to occupy low-density environments. Furthermore, LGAs with low $E_{\rm coupling}$ exhibit strong alignment with cosmic filaments, manifested as a pronounced polar anisotropy in the distribution of tracer haloes. By contrast, LGAs with high $E_{\rm coupling}$ show a weaker polar anisotropy but an enhanced azimuthal anisotropy, with large-scale tracer haloes preferentially lying in the plane spanned by the halo pair and the orbital spin vector. Within this framework, our Local Group is characterised by typical $E_{\rm coupling}$ residing in a relatively under-dense environment, yet it remains consistent with the 95\% range of analogue systems identified in the simulation.

By the 2040s, the exoplanet field will have moved from the discovery of a few thousand planets to hundreds of thousands, thanks to Gaia DR5, TESS, PLATO, Roman, and their successors. At that stage, the key bottleneck will no longer be planet detection, but our ability to understand how planetary systems form, evolve, and diversify across different stellar and Galactic environments. To address this, we need a large-scale, high-resolution spectroscopic survey of planet-host stars, spanning a broad range of Galactic environments (thin and thick disks, bulge, halo, clusters, associations), and including a well-defined control sample of non-hosts. Such a survey must deliver homogeneous stellar parameters, detailed abundance determinations, ages, and kinematics for tens of thousands of hosts, extending to the faint magnitudes probed by future missions but are beyond the reach of existing and currently planned spectroscopic facilities.

The parameters of an axisymmetric model for the gravitational potential of the Galaxy have been refined. The basic curve of the Galaxy's rotation in a distance interval of $R:0-190$ kpc was constructed using the velocities of masers, classical Cepheids, Red Clump stars, Blue Horizontal Branch stars, halo stars, globular clusters, and dwarf satellite galaxies of the Milky Way. The rotation curve was selected in such a way that there would be no dominant burst of circular velocities in the central ($R<2$ kpc) region of the Galaxy. As a result, we constructed two two-component models of the galactic potential, which include contributions from the disk and the halo of invisible matter, as well as a three-component model with a small-mass bulge added in advance. These models can be useful in studying the long-term orbital evolution of stars and open and globular star clusters in the central ($R<4$ kpc) region of the Galaxy. The constructed models were tested for self-consistency by comparing their rotation curves with a set of model curves generated with the Illustris TNG50 software package.

Many stellar systems exhibit a finite spatial extent, yet constructing self-consistent spherical models with a prescribed outer boundary is non-trivial because sharp density cutoffs introduce discontinuities that lead to inconsistencies in the associated distribution function. In this paper we show that these difficulties arise from the abruptness of the truncation rather than from the finite extent itself. We introduce a general and infinitely differentiable radial truncation scheme that can be applied to any density profile, and illustrate its behaviour using the Hernquist model. We find that softly truncated models are dynamically consistent provided that the truncation is sufficiently gradual, and we determine the corresponding critical truncation sharpness. Their distribution functions display a characteristic bump-dip feature near the truncation energy that signals the transition between consistent and inconsistent cases. In contrast to sharply truncated models, softly truncated systems can support an extensive family of Osipkov-Merritt orbital structures, including moderately radial ones. Soft truncations therefore offer a general and physically motivated route to constructing finite-extent dynamical models with well-controlled outer-edge behaviour.

Radio Frequency Interference (RFI), the presence of artificial and/or terrestrial signals in astronomical data, poses a great challenge to the search for pulsars and radio transients, such as Rotating Radio Transients (RRATs) and Fast Radio Bursts (FRBs), by obscuring or distorting the signal of interest and resulting in large numbers of erroneous detections. RFI mitigation algorithms aim to remove this interference and improve the chance of detection of transients, but with the growing number of techniques, selecting the most appropriate method for a given survey can be problematic. The choice of method is particularly important in real-time searches planned for next-generation telescopes such as those of the SKAO, where there is no possibility to reprocess the data. In this paper, we explore the algorithm selection problem by injecting pulses into data which simulates several RFI environments. A set of these files is then cleaned using RFI mitigation algorithms and run through a single pulse search pipeline to analyse the recovery of the injected pulses. We examine the recovery of the injected single pulses with an emphasis on a number of cases spanning a range of pulse brightness, width and dispersion measure. The efficacy and side effects of a few popular RFI excision methods, namely IQRM, SKF, and ZDMF are evaluated.

Rotation curves are a fundamental tool in the study of galaxies across cosmic time, and with the advent of large integral field unit (IFU) kinematic surveys there is an increasing need for efficient and flexible modelling tools. We present RotCurves, a parametric forward-modeling tool designed for rotation curve analysis at high-z, correcting for ``beam smearing" by projecting and convolving the beam PSF in the plane of the galaxy. We benchmark RotCurves against the established parametric code dysmalpy using synthetic observations. The typical runtime with RotCurves is a few ~10ms, a factor 250 faster than dysmalpy for a single realization. For well-resolved systems (PSF FWHM < Reff), the mock observed rotation and dispersion curves agree to within 5% up to 3Reff, where most of the discrepancies are in the inner disk. whereas in marginally resolved systems (PSF FWHM > 1.5 Reff) discrepancies increase to up to 15%. Using a built-in MCMC fitting procedure, RotCurves recovers well the intrinsic model parameters across a wide range of galaxy properties and accounting for realistic noise patterns. Systematic biases emerge for the effective radius and for low disk masses (Mdisk < 3x10^9 Msun). We show excellent parameter recovery at high signal-to-noise ratios (S/N > 25), with increasing deviations in parameter recovery at lower S/N. RotCurves is best suited for inclinations of 10 < i < 80. RotCurves is built as an exploratory tool for rapid testing of mass model assumptions, parameter studies and for efficiently processing large samples of observational data from large IFU surveys. The code is publicly available on github.

Aims. The blazars Mrk 421 and Mrk 501 have shown multiwavelength variability on all observed timescales, and have been well studied at high energies on short timescales. We aim to characterise the long-term temporal behaviour of these blazars at synchrotron energies, namely optical, UV, and X-ray, in order to assess current models of these objects and their processes. Methods. Amongst the longest light curves ever studied for these sources, we investigated 20 years of data (2005-2025) from the Swift-UVOT and Swift-XRT telescopes. We examined spectral models, fractional variabilities, flux distributions, and X-ray photon index vs flux relations, as well as carrying out in-depth time series analysis using structure functions, Lomb-Scargle periodograms, and discrete correlation functions. Results. Mrk 421 and Mrk 501 both showed intriguing variability in all studied wavelengths; this variability has been found to be energy dependent, as has the trend of lognormality in flux distributions. X-ray photon indices fluctuated greatly throughout the entire period, showing an overall harder-when-brighter trend. Hints of a quasi-periodicity have been found in the X-ray of Mrk 501 (host frame time scale $\sim390$ days, >3$σ$) but not in the UV or X-ray of Mrk 421, or in the UV of Mrk 501. No correlation at any time lag was found between the optical/UV and X-ray bands in either source.

According to the classical AGN model, broad emission lines originate from the broad-line region (BLR) and are observable only when the attenuation by the dusty torus is small. However, we recently found several heavily-obscured ($A_V > 50$ mag) AGNs with broad H$α$ detections: MCG -3-34-64, UGC 5101, and Mrk 268. To investigate the origin of the observed broad line in these AGNs, we performed multi-epoch optical spectroscopic observations to search for flux variability of the broad H$α$ line. For MCG -3-34-64 and UGC 5101, no significant variability was detected, suggesting that the broad line of these AGNs may arise from sources other than the BLR. Spectral fitting analysis suggests possible large contribution of ionized outflows to the observed broad component of MCG -3-34-64, while both the outflow and scattering by polar material can explain that of UGC 5101. For Mrk 268, we detected a significant ($4.3σ$) flux variation of the broad H$α$ line by using the flux ratio of the H$α$ complex and the [SII]$λ\lambda6716$, 6731 doublet, indicating that the broad line originates directly from the BLR. The lack of significant flux variation in the optical continuum implies that the line of sight to the nucleus of Mrk 268 is mildly obscured. Our results demonstrate that the observed broad H$α$ lines in obscured AGNs likely have multiple origins. Such complexity may introduce additional uncertainties in black hole mass measurements of distant AGNs revealed by e.g., JWST.

Three-nucleon force (3NF) plays an important role in understanding the structure of finite nuclei and the saturation properties of infinite nuclear matter. The chiral 3NF derived from the chiral effective field theory has been successful in $\textit{ab initio}$ studies of atomic nuclei. However, challenges remain, such as parameterizing low-energy constants and applying regulators. Most of established chiral nuclear forces have a nonlocal form in the momentum space. In this work, we construct local and hybrid local-nonlocal chiral 3NFs for the newly established Idaho local position-space two-nucleon potential, and calculate binding energies and radii of nuclei up to $^{132}$Sn. The two low-energy constants of 3NF are constrained by the ground-state energies of $^3$H and $^{16}$O, as suggested in a recent work. The chiral Hamiltonian obtained with the local-nonlocal regulator can simultaneously reproduce the experimental ground-state energies and charge radii of nuclei over a large range from $^4$He to $^{132}$Sn.

The Andes Large area PArticle detector for Cosmic ray and Astronomy (ALPACA) is a new air-shower array experiment under construction in the Bolivian Andes, and its prototype ALPAQUITA surface array has been operating since 2023 April. In addition to the traditional $\ge$3-hit or $\ge$4-hit coincidences to trigger recording air-shower events, ALPAQUITA records the counting rates of the $\ge$1-hit and $\ge$2-hit events (Any1 and Any2, respectively). We report a successful detection of a Forbush decrease occurred on 2024 May 10 caused by a passage of an interplanetary shock formed ahead of the Interplanetary Coronal Mass Ejection. The amplitude detected in the Any1 rate is 4.26$\pm$0.33% at the median primary rigidity of 76GV which is consistent with the observations with the worldwide neutron monitor and muon detector networks. Under the assumption of a power-law rigidity spectrum, we renormalized the errors of the observed amplitude ($A_{obs}$) and fitted them as a function of the median primary rigidity ($R_{m}$) of each detector and observational method. The result $A_{obs} = (10.9\% \pm 0.9\%) \times (R_{m}/10\,GV)^{-0.55 \pm 0.07}$ exhibits a hard nature of this event. Our non-detection in the Any2 rate decrease constrains the amplitude with a 2$σ$ upper limit to be 0.95% at 960GV. This marginally suggests an existence of a spectral softening between 100GV and 1000GV as also suggested by the Misato underground muon detector at 145GV. Although a strong geomagnetic storm was observed during this period, we conclude it does not impact our results. Our novel technique realizes a unique coverage to study the behavior of the Forbush decreases at the highest rigidity.

We compute the cosmic stellar, dust and neutral gas mass history at $0<z\lesssim3$ using ProSpect spectral energy distribution modelling of $\approx 800 \, 000$ galaxies in the Galaxy and Mass Assembly (GAMA) survey and the Deep Extragalactic VIsible Legacy Survey (DEVILS). The cosmic dust mass history broadly follows the shape of the cosmic star formation history; though, the decline is slower, suggestive of a slowing rate of dust growth and destruction as the star formation declines past its peak at $z\approx 2$. Neutral gas masses were estimated by scaling the dust masses by the metallicity-dependent dust-to-gas ratio. The neutral gas mass density as traced by the dust is an average of $\approx 0.6$ dex lower than that measured from $21$cm experiments, most likely due to differences in the spatial scales inhabited by dust and HI. Folding in measurements of the supermassive black hole mass density obtained previously with similar data and methods, we present a self-consistent census of the baryons confined to galaxies. Stars, neutral gas, SMBHs and dust contained within the optical radii of galaxies account for $\approx 5$ per cent of the baryons. Most of the remaining $\approx 95$ per cent of baryons must be ionised and dispersed throughout the interstellar, circumgalactic and intergalactic media within, around and between galaxies.

We present a measurement of Baryon Acoustic Oscillations (BAO) in the cross-correlation of triply ionized carbon C IV absorption with the positions of quasars (QSO) and Emission Line Galaxies (ELG). We use quasars and ELGs from the second data release (DR2) of the Dark Energy Spectroscopic Instrument (DESI) survey. Our data sample consists of 2.5 million quasars, 3.1 million ELGs, and the C IV absorption is measured along the line of sight of 1.5 million high redshift quasars with $z > 1.3$. We measure the isotropic BAO signal at 4.2$σ$ for the CIV$\times$QSO cross-correlation. This translates into a 3.0% precision measurement of the ratio of the isotropic distance scale, $D_{\rm V}$, and the sound horizon at the drag epoch, $r_{\rm d}$, with $D_{\rm V}/r_{\rm d}(z_{\rm eff} = 1.92) = 30.3 \pm 0.9$. We make the first detection of the BAO feature in the CIV$\times$ELG cross-correlation at a significance of 2.5$σ$ and find $D_{\rm V}/r_{\rm d}(z_{\rm eff} = 1.47) = 24.6 \pm 1.0$.

On the framework of relativistic force-free magnetohydrodynamic (MHD) turbulence, we explore the fundamental properties of strong and weak turbulent cascades using high-resolution numerical simulations in the presence of a uniform background magnetic field. We find that (1) power spectra and scale-dependent anisotropies both for the strong and weak turbulence resemble those observed in the non-relativistic MHD turbulence; (2) intermittency of magnetic fields in strong turbulence is stronger than that in the weak one; (3) generated Alfvén modes show similar energy spectra and scale-dependent anisotropies to those of non-relativistic case; (4) generated fast modes present a power spectrum similar to that of Alfvén modes, with a strong (for strong turbulence) or weak (for weak turbulence) scale-dependent anisotropy, which are significantly different from non-relativistic turbulence; and (5) applications of our numerical results to neutron star magnetospheres show that the strong (or moderately weak) turbulent cascade can explain the X-ray radiation of the Vela pulsar. Our study is of great significance for understanding energy transfer, magnetic field evolution, and particle acceleration mechanisms in extreme astrophysical environments.

Star clusters are well known for their dynamical interactions, an outcome of their high stellar densities; in this paper we use multiwavelength observations to search for the unique outcomes of these interactions in three nearby Galactic open clusters: IC 2602 (30 Myr), NGC 2632 (750 Myr) and M67 (4 Gyr). We compared X-ray observations from all-sky surveys like eROSITA, plus archival observations from Chandra X-ray Observatory, survey radio observations from ASKAP's Evolutionary Map of the Universe survey plus archival VLA observations, in conjunction with new cluster catalogs with Gaia. From X-ray, we found 77 X-ray sources likely associated with IC 2602, 31 X-ray sources in NGC 2632, and 31 near M67's central regions. We were further able to classify these X-ray sources based on their optical variability and any radio emission. Three IC 2602 X-ray sources had radio counterparts, which are likely all chromospherically active binary stars. We also identified luminous radio and X-ray variability from a spectroscopic triple system in M67, WOCS 3012/S1077, which is either consistent with a quiescent black hole binary, or due to an active binary stellar system. A recent population study of optical variables by Anderson & Hunt 2025 shows that the population of optical variables in open clusters clearly changes over cluster age; this pilot study gives evidence that the X-ray population also changes with time, and demonstrates the need for a broader multiwavelength study of Galactic open clusters.