arXiv Daily Digest - 2026-05-19

Optical neural networks promise unmatched efficiency, bandwidth, and latency, critical benefits as demand for neural network hardware surges. However, their practical value for general-purpose acceleration or specialized applications must be proven under application-realistic conditions. We discuss recent insights and outline key research priorities.

The availability of network datasets advances research in network science, machine learning and related fields by enabling empirical analyses and their reproducibility, algorithm development, model validation and benchmarking. Existing repositories, such as SNAP and Netzschleuder, have made traditional network datasets widely accessible with metadata, metrics, and basic visualizations. However, they primarily focus on pairwise interactions, limiting data access to systems with many-body interactions. To address this gap, we created hypergraphx-data, a repository of real-world hypergraph datasets for higher-order network analysis, spanning different domains from social networks to biology and finance, and supporting configurations such as weighted, directed, temporal, and multiplex hypergraphs. Each dataset includes relational information and metadata, provided in an open JSON format and a binarized format for Hypergraphx. We provide a user-friendly interface to facilitate browsing, filtering, and accessing the datasets, while also ensuring integrity and reproducibility through hash-based verification and data versioning. The repository is available at https://hgx-team.github.io/hypergraphx-data

Cell migration is fundamental to development, tissue organization, immune response, and disease progression. Amoeboid motility is distinguished by rapid motion and strongly fluctuating cell shapes, reflecting the intrinsically nonlinear nature of active living matter far from equilibrium. Here we introduce a minimal active-shell model of an amoeboid cell that couples actin polymerization, cortical flows, and membrane deformation through nonlocal mechanical interactions. The model gives rise to a rich spectrum of emergent behaviors. A symmetric non-motile state can spontaneously break symmetry and transition toward persistent directed migration driven solely by polymerization-induced retrograde flow, even in the absence of shape deformation. Increasing activity further triggers a cascade of dynamical states, including circular trajectories, oscillatory zigzag motion, and irregular chaotic-like migration with fluctuating protrusions and multi-lobed morphologies. Although these migratory modes are observed experimentally in distinct cellular contexts, our results show that they can emerge from the same underlying physical mechanism, providing a unified framework for amoeboid dynamics. Notably, contractile stresses induced by molecular motors are not required to generate spontaneous motility, polarity, or complex migration patterns. Our findings highlight how collective active processes at the cellular scale can self-organize into complex dynamical states, revealing generic principles of nonlinear behavior in living systems.

Autonomous labs enable the integration of automated experiment execution, data analysis and decision making. The main challenge remains the integration of diverse data streams from multiple instruments, where the data is often heterogeneous and unsynchronized. The standard learning process of undetermined synthesis-process-structure-property relationships (SPSPR) usually relies on post-experiment analysis after data is fully collected, not during live experiments, and decision making is carried out independently across characterization equipment. Here, we demonstrate the Multi-instrument Autonomous Discovery (MAD) framework -- combining structural property mapping and functional property optimization simultaneously in a closed-loop manner. As an example, we applied MAD to phase change memory (PCM) materials, and, in particular on the Mn-Sb-Te ternary, a previously unexplored materials system for PCM. A multi-output model is employed to merge data from x-ray diffraction (XRD) and electrical resistance measurements simultaneously through a co-regionalization kernel that models the relationship between them. The output probabilistic posterior and uncertainty quantification facilitate decision making with shared knowledge, while the goals are different across tasks. We aimed to maximize the knowledge of crystal structure distribution using non-negative matrix factorization (NMF), while in parallel, we find the composition with the maximum resistance value, an important figure of merit for PCM. Leveraging MAD, we found promising electrical PCMs and identified the SPSPR within 25 closed-loop iterations, corresponding to a seven-fold speed-up. The framework opens a new path of study in large-scale autonomous facilities, where future experiments can be run in parallel together, not independently.

Spectroscopy requires high-precision wavelength discrimination but typically requires bulky, alignment-sensitive instrumentation. To address this, we present a compact computational spectrometer built from a single germanium PN photodiode. The system exploits optoelectronic chromatic dispersion (OED), a phenomenon whereby wavelength-dependent absorption depth produces carrier diffusion delays that encode spectral information as measurable RF amplitude and phase signatures in the photodiode output. We extract DC voltage, RF amplitude, and RF phase across 15 modulation frequencies (0.1-1.5 MHz), forming a 31-dimensional feature vector per optical input. Spectral reconstruction was formulated as a high-dimensional inverse problem and solved using five machine learning models, utilizing group-wavelength splitting and k-fold cross-validation to prevent spectral leakage and ensure unbiased evaluation. Across the C- and L-bands, single-wavelength reconstruction using Gaussian Process Regression (GPR) achieves an accuracy of 0.178 nm on a wavelength-grouped, held-out test set spanning seven optical power levels. Five-fold cross-validation yields a robust Root Mean Square Error (RMSE) of (0.342 +/- 0.117) nm, confirming excellent generalization under wavelength and power variations. For dual-wavelength inputs, GPR yields accuracies of 0.362 nm for the swept wavelength and 0.434 nm for the fixed wavelength. This is the first spectral reconstruction method exploiting a multi-frequency OED feature space from a single photodiode. By merging the physics of OED with data-driven learning, this work enables alignment-free, on-chip-compatible spectrometers suitable for portable optical sensing, smartphone-integrated diagnostics, and field-deployable environmental monitoring.

Ray-tracing (RT) has become central to site-specific electromagnetic propagation modeling in dynamic complex environments. Yet its computational burden grows sharply as high-fidelity digital twins of these environments scale to millions of facets whose material parameters must be continuously updated as the environment changes. The challenge is amplified at mmWave and sub-THz frequencies, where surface roughness becomes comparable to the wavelength and so diffuse scattering can account for up to 40% of the received power, making accurate yet tractable models essential. The popular Effective Roughness (ER) approach offers physical consistency but become increasingly costly when highly directive lobes are required or when parameters must be iteratively tuned. This communication introduces a directive, reciprocal diffuse scattering model that preserves the structure of the ER while enabling an order-of-magnitude reduction in computational cost. Validation across eight materials shows no loss in accuracy - and a slight improvement - demonstrating a scalable and physically meaningful solution for RT in scenarios where diffuse scattering is non-negligible.

Weak force amplification describes the process of amplifying a faint low-frequency signal by means of an additional high-frequency modulation, which plays a vital role in quantum sensing and high-precision measurement. However, the potential enhancement of weak-force amplification in non-Markovian environments has received little attention. In this paper, we firstly study the amplification of weak forces within cavity-optomechanical systems incorporating a degenerate optical parametric amplifier (DOPA) under the Markovian assumption, which can be amplified via using two high-frequency signals via vibrational resonance through adjusting the strength and phase of the DOPA with different pumping frequencies. Moreover, we extend the study of the amplification of the weak force to the non-Markovian environment composed of an ensemble of infinite oscillators. We reveal that the amplification exhibits a conversion from the non-Markovian regime to Markovian regime by controlling environmental spectral width. Such a transition facilitates a remarkable improvement in amplification, and this enhancement originates from the excitation backflow generated via the interplay between the cavity and the non-Markovian environment. By controlling DOPA to amplify weak forces, the study achieves amplification in the non-Markovian regime, offering new directions for quantum optics research.

Optical neural networks promise ultrafast, low-energy information processing by performing computation directly with photons. Current implementations, however, are largely restricted to steady-state operation and rely on high-latency electro-optical conversion for nonlinear activation. To address these limitations, we propose an all-optical fully connected neural network architecture in which the basic neuronal functions are realized by coherent transient quantum dynamics. Within this framework, phase-tunable nonlocal interference in a giant cavity implements programmable synaptic weights; an integrator operating in the bad cavity regime performs temporal summation by coherently combining sequential wavepackets; and transient Rabi dynamics of a driven two-level system provide nonlinear activation. Full-physics simulations demonstrate high classification accuracy on MNIST and colored-object recognition tasks. These results eliminate the optoelectronic activation bottleneck, reduce latency, and establish transient light-matter dynamics as a native physical resource for high-dimensional nonlinear information processing, paving the way toward fully optical neuromorphic computing.

The force experienced by a spin in a magnetic field gradient underlies many proposals for hybrid quantum systems. These include schemes for mechanically mediated quantum gates, spin squeezing, searches for exotic forces, and motional superpositions for probing the interface between quantum and gravity. Yet, experimentally observing this spin-force for anything larger than atomic scales has proved challenging. In our work, we demonstrate controllable Center-of-Mass motion of a $128 \rm\: mg$ diamagnetically levitated oscillator due to force from an ensemble of Nitrogen-Vacancy (NV) defects in diamond. We induce coherent motion in the oscillator by periodic optical initialisation of the NV spin states, achieving motional amplitudes exceeding $100 \rm\:nm$. Our results mark a key milestone towards spin-based engineering of motional states deep in the high-mass regime.

Heterogeneous integration of gain and strongly nonlinear materials with ultra-low-loss silicon nitride (SiN) photonics offers a route to scalable quantum circuits, but concurrent wafer-scale manufacturability, low interlayer loss, and high performance have been challenging to realize. Here we demonstrate a wafer-scale III-V-on-SiN quantum photonic platform that directly integrates III-V layers to foundry-fabricated SiN circuits. The SiN layer provides 200-300 nm thick waveguides with $<1$ dB/m loss and a mature passive photonics ecosystem, while III-V materials provide large $χ^{\left(2\right)}$ and $χ^{\left(3\right)}$ nonlinearities for parametric gain, frequency conversion and quantum light generation. Adiabatic interlayer couplers yield $<25$ mdB loss to InGaP waveguides and resonators with intrinsic quality factors exceeding $10^6$, enabling $15\times$ brighter entanglement sources and efficient nonlinear conversion on SiN. Integrated components--including low-loss beam splitters, waveguide crossers, and tunable interferometers--are complemented by III-V lasers and InP photodetectors with amplifiers achieving up to $99^{+1}_{-12}\%$ quantum efficiency and $3$ GHz bandwidth. This architecture unites ultra-efficient sources, nonlinear elements and detectors on a wafer-scale, low-loss platform, establishing a path toward large-scale, low-noise quantum photonic systems.

Ultracompact cladding-free waveguide arrays with zero inter-channel spacing and negligible crosstalk open a new avenue for high-density integrated photonic circuits. However, existing cladding-free waveguide arrays typically rely on conventional trivial bulk modes, making them highly susceptible to scattering losses at sharp bends or in the presence of obstacles and defects. To overcome this limitation, we theoretically propose and experimentally demonstrate a robust, crosstalk-free, and cladding-free photonic waveguide array based on chiral anomaly bulk states (CABSs) in photonic crystals. By interfacing distinct Dirac photonic crystals that host Dirac cones at different high-symmetry points (Γ and K) in the Brillouin zone and carefully engineering the boundary conditions, the boundary-induced CABSs in adjacent channels become effectively decoupled due to a large momentum separation, thereby eliminating inter-channel crosstalk. More importantly, we experimentally demonstrate that these crosstalk-free CABSs are robust to perturbations, including metallic obstacles, air defects, and sharp bends. We further extend the CABS-based waveguide array to two dimensions and demonstrate a cladding-free triangular resonator and a crosstalk-free waveguide crossing, both of which are previously unattainable. Our work establishes a new design paradigm for cladding-free, crosstalk-free, and ultracompact topological photonic devices, paving the way for robust, highly integrated photonic circuits.

Twisting two dissimilar monolayer semiconductors induces structural chirality that remains largely elusive in linear optics but becomes remarkably pronounced in the nonlinear regime. Here we demonstrate that MoS2/WSe2 heterobilayers exhibit giant, twist-tunable nonlinear chirality in second-harmonic generation (SHG). The sign of SHG circular dichroism is governed by structural handedness, and its magnitude reaches 1.96 near a 30° twist angle under 1260-nm excitation, approaching the theoretical limit of 2. Furthermore, reversed chirality is observed when light is incident from opposite directions. Using a layer-resolved model, we attribute this phenomenon to helicity-dependent interference between the two monolayer SHG fields, mediated by a nonlinear Pancharatnam-Berry phase. These findings establish that the relative orientation of atomically thin layers can deterministically control nonlinear chiral responses, identifying twisted 2D heterostructures as a versatile platform for nonlinear chiral photonics, frequency conversion, and ultracompact light-matter interfaces.

The particle representation model (PRM) and interacting particle representation model (IPRM) describe homogeneous turbulence through orientation-conditioned structural states. In their original form, the conditional state is organized by the unit spectral direction, while the radial spectral coordinate is integrated out. We introduce a scale-conditioned Ray-Column extension in which the spectral vector is decomposed into orientation and radial wavenumber, and the conditional structure state is projected onto finite radial bands. The formulation starts from the continuum spectral tensor and is then reduced to the ray-packet ensemble sums used in the implementation. The bands are projections of an orientation-wavenumber tensor density and retain scale-conditioned structural populations for closure evaluation. The rapid dynamics remain ray-packet resolved, while the nonlinear slow and terminal closure coefficients are evaluated from band-aggregate structure tensors formed by integrating over orientation and wavenumber within each band. The present reference closure omits conservative cascade modeling among bands. A reference closure is built from PRM rapid kinematics, band-local effective-gradient response, slow rotational randomization, and an active large-scale enstrophy (LSE) terminal-drain map. In the active-LSE closure, the misalignment-sensing factor Psi_fd regularizes the LSE structure-to-dissipation map; the Ray-Column formulation evaluates this map on band-aggregate structural populations. The model is assessed in irrotational strain, homogeneous shear, elliptic-streamline, and rotating-shear configurations. The rotating-shear comparison with filtered LES data illustrates the payoff of retaining band information: filtered or low-pass observables can be formed before scale information is lost in the one-point reconstruction.

Electron Beam-Induced Deposition (EBID) enables site-specific nanofabrication but suffers from significant carbon contamination, limiting its applicability in plasmonics, nanoelectronics, and sensing. In this study, we investigate the relationship between EBID process parameters such as beam current, acceleration voltage, and dwell time, and the platinum-to-carbon composition of deposited nanostructures. Using Energy Dispersive X-Ray Spectroscopy (EDX), we establish a hindered exponential growth model that correlates deposit composition with fabrication conditions. To enhance metal purity, we apply plasma oxygen treatment, exposing EBID deposits to a 30 W plasma for 30 minutes in a tabletop plasma generator. Post-treatment EDX analysis confirms a systematic increase in platinum content, while SEM inspection reveals nanostructure shrinkage due to carbon removal. This work aims to provide a framework for optimizing EBID fabrication and post-processing strategies to enhance material performance.

A dual-polarization dielectric metasurface sensor based on TiO$_2$ nanobar pairs with a 20\,nm BeS gap insert is numerically investigated in the near-infrared. The BeS layer introduces dielectric symmetry breaking without requiring geometric asymmetry, enabling polarization-selective excitation of two distinct resonances. Under TE illumination, the structure supports a quasi-BIC resonance at 879.2\,nm with $Q=128$, whereas TM excitation produces a broader magnetic dipole resonance at 910.8\,nm with $Q=36$. The spectral separation between the two modes enables simultaneous tracking of both polarization channels within a single measurement window. For background refractive indices from 1.00 to 1.05, the TE and TM resonances exhibit sensitivities of 243.1 and 178.8\,nm/RIU, respectively. The corresponding figures of merit reach 35 and 7\,RIU$^{-1}$, with detection limits on the order of $10^{-5}$\,RIU. Field distributions show strong confinement of the TE mode inside the gap region, leading to enhanced overlap with the surrounding analyte. Because the two resonances respond differently to refractive-index variations, the metasurface produces a polarization-dependent spectral fingerprint that may provide additional selectivity beyond conventional single-channel dielectric sensors. The proposed platform further shows good tolerance against dimensional variation and consistent resonance behavior across independent FDTD solvers.

This paper proposes a systematic and explicit quantum circuit framework for solving advection-diffusion equations with boundary conditions, based on the Linear Combination of Hamiltonian Simulations (LCHS) method. By employing the Finite Volume Method (FVM) combined with various flux construction schemes, we elaborate the design of quantum circuits tailored explicitly for Robin boundary conditions (including Dirichlet and Neumann boundary conditions as special cases) and periodic boundary conditions. In contrast to prior works on quantum simulation of advection-diffusion equations, we present a detailed error analysis for the linear combination of unitaries (LCU) induced by the constructed quantum circuits. A comprehensive gate complexity analysis demonstrates the quantum advantages over classical computing in high-dimensional scenarios. We simulate the proposed circuits on a fault-tolerant emulator, and numerical results validate the effectiveness of the proposed framework across homogeneous, inhomogeneous, and high-dimensional cases. The proposed framework is compatible with numerous spatial discretization methods and numerical schemes, extends naturally to other linear PDEs, and establishes a practical foundation for solving large-scale PDE problems on future fault-tolerant quantum computers.

We present a semi-analytical model to analyze multi-tile metasurface antennas consisting of a set of metasurface tiles and a practical power-dividing network that excites the tiles. The metasurface tiles consist of arrays of rectangular waveguides with subwavelength metamaterial radiators etched into their top walls, each of which can be accurately modeled as polarizable dipoles. The feed structure for the arrays comprises a slotted waveguide attached to their bottom wall, with coupling slots inserted into the common wall that are likewise modeled as polarizable dipoles. The proposed semi-analytical model employs a coupled-dipole framework that accurately captures dipolar interactions among constituent elements within the metasurface tiles, along with a multi-port network analysis technique that accounts for electromagnetic interactions between the tiles and the power divider, thereby forming a self-consistent formulation. The proposed model enables the prediction of key performance metrics, including overall S-parameters, radiation patterns, and gain, and is validated through full-wave numerical simulations. By significantly reducing the computational complexity associated with electrically large apertures, the proposed framework enables rapid and efficient modeling of the overall structure, thereby facilitating iterative optimization. The proposed model has potential applications as an efficient forward model for the design of wireless systems requiring large-aperture metasurface antennas, including remote sensing and next-generation wireless communication networks.

This article examines the case of two papers published in Naturwissenschaften by the physicist Max Planck that were retrospectively marked as retracted on Springer digital platform. Rather than originating in scientific fraud, these withdrawals appear to result from contemporary digitization and copyright-management procedures applied anachronistically to historical publications. Through an investigation of the circulation history of Planck 1940 and 1942 philosophical essays, the article shows that republication across multiple formats was a common and legitimate practice within the scientific publishing culture of the early 20th century. Such practices only became problematic with the later transformation of the scientific article into a countable and proprietary unit within systems of bibliometric evaluation and commercial academic publishing. This article argues that contemporary notions such as duplicate publication and self-plagiarism are historically situated categories that cannot be applied retrospectively without distorting the historical record. More broadly, the Planck case reveals how digital scholarly infrastructures controlled by large commercial publishers can limit the accessibility of the scientific past. Ironically, the original papers remain accessible today through the nonprofit digital platform Internet Archive rather than through the publisher that originally issued the journal.

Spectral deconvolution is essential for extracting peak structures that encode material properties and chemical structures, but conventional automated methods often fail when spectra contain high-intensity noise or unknown background components. In practice, scientists rarely interpret spectra in isolation. Instead, they identify physically meaningful peaks by relating spectral structures to auxiliary information such as physical-property values, chemical structures, and trends across related measurements. Here, we propose a Bayesian framework that integrates spectral deconvolution with a model of expert scientific reasoning. In this work, expert scientific reasoning refers to the practice of evaluating candidate spectral structures by their consistency with independently measured physical-property values, rather than to manual expert intervention during inference. We formalize this reasoning as a physical-property regression layer, implemented using Gaussian process regression, and couple it with Bayesian spectral deconvolution. By averaging the physical-property likelihood over posterior predictive spectra inferred from Bayesian spectral deconvolution, the proposed method selects spectral models according to the consistency between inferred spectral structures and physical-property information. We validate the framework using synthetic spectra with high-intensity noise or unknown backgrounds and infrared spectra of poly(lactic acid). The method recovers physically meaningful peak structures that conventional Bayesian spectral deconvolution misses or misidentifies from spectra alone, including weak peaks in poly(lactic acid) IR spectra related to measured degradation rates. These results demonstrate that integrating expert scientific reasoning with Bayesian spectral deconvolution enables robust peak estimation under conditions where spectrum-only inference is unreliable.

Lasers that combine narrow linewidths with rapid tunability are critical for applications such as coherent optical ranging, distributed fiber-optic sensing, and precision spectroscopy. Despite significant progress in integrated laser technologies, the concurrent realization of low phase noise and frequency agility on a single integrated platform remains challenging owing to a fundamental architectural trade-off: conventional integrated laser designs typically suppress phase noise via high-$Q$ resonators, yet the extended photon lifetimes inherent to such resonators intrinsically constrain tuning speed. Here, we address this noise-agility trade-off by introducing a laser architecture that achieves ultralow phase noise and ultrafast tunability simultaneously. Rather than relying on ultrahigh-$Q$ resonators for self-injection locking, our design employs strong synthetic feedback within a Pockels-tunable, resonator-enhanced distributed Bragg reflector to suppress phase noise. As a proof of concept, we demonstrate a hybrid integrated laser with a short-term linewidth of 29 Hz, realized using a lithium niobate external cavity with a loaded $Q$ of only 0.62 million. The adoption of a moderate resonator $Q$ relaxes the photon-lifetime constraint on tuning speed, enabling sub-exahertz-per-second tuning rates and a chirp nonlinearity as low as 0.14%. Leveraging this laser, we implement a frequency-modulated continuous-wave LiDAR system that achieves a relative ranging precision of $1.7 \times 10^{-4}$ at a measurement rate of $1\,\text{MSa s}^{-1}$, without requiring complex chirp linearization techniques. We further demonstrate fiber-optic acoustic sensing capable of detecting sub-$με$ dynamic strain, underscoring the platform's versatility for high-speed precision optical measurements. Our work provides a route toward cost-effective yet high-performance sensing and metrology systems.

Bound states in the continuum (BICs) are polarization singularities in momentum space whose topological charges (TCs) govern advanced light-matter interactions. While lattice symmetry protects the existence of robust BICs at the $Γ$-point (SP-BICs), it also restricts their TCs to low-order values. Achieving high-order TCs in common crystal lattices, such as $C_4$-symmetric systems, has therefore remained an open question. Here, we systematically demonstrate that high-order TCs that surpass fundamental symmetry bounds can be created through the rich dynamics of a parameter-driven merging process of off-$Γ$ BICs. We introduce a unified geometric framework based on the interplay between Fabry-Pérot interference and guided resonances, which uncovers different types of merging BICs dynamics, including near-isotropic, anisotropic, and cross-merging. Leveraging this mechanism, we realize unconventional TCs of up to $\pm3$ at either a symmetry-protected BIC or a degeneracy point in a simple $C_4$-symmetric photonic crystal slab. We further show that this high-order topology enables the generation of high-quality Bessel OAM beams, providing a physically transparent route toward engineering high-order topological photonics.

Neutron multiplicity counting (NMC) underpins plutonium assay in nuclear safeguards, arms control, and disarmament verification, but existing simulation tools are essentially limited to MCNPX-PoliMi [1] (export-controlled, MCNP license required) and ONMS [2] (open-source but built on Geant4 with no scripting API); other codes (RMC, MCNP-PTA) are institute-internal. We present PyNMC, an open-source, Python-native NMC simulation framework that couples OpenMC for transport with FREYA for event-by-event correlated prompt-neutron emission and ALPHANSO for native ($α$, n)-source estimates, together with collision-level time-tagged event recording and a Python shift-register post-processor cross-validated against ONMS. The framework is validated against the ESARDA Neutron Multiplicity Benchmark on bare $^{252}$Cf (c2-10, c2-100), the low-multiplication Pu metal case c3s (M = 1.12 from an independent k-eigenvalue calculation; ESARDA spec M = 1.08), and a 10 g PuO2 sample with an ($α$, n)-source term (c4s); an internal stress-test extension to a $\approx 100$ g Pu metal sample at M = 1.29 is reported alongside but lies beyond the ESARDA participant range. For c4s, ALPHANSO gives $α$ = 0.78 with modern cross-section data; the reported benchmark comparison rescales the ($α$,n) rate to the ESARDA value $α$ = 0.853. Simulated rates agree with point-model predictions for all cases, and with the published ESARDA participant-code scatter where participant results exist. The framework is shipped as a Docker container under the MIT license and is openly available on GitHub at github.com/cfichtlscherer/nmc.

The elasticity of PDMS has played a central role in advancing important microfluidic technologies, ranging from early valves to sophisticated organ-on-a-chip systems. However, most deformable microfluidic devices are based on geometries that require complex multi-layer PDMS architectures and include thin membranes, leading to difficult microfabrication and poor stability. Recently, Jain, Belkadi et al. (Biofabrication 16.3 (2024): 035010) introduced a single-layer device in which a wide and long microfluidic channel was deformed by controlling the pressure in two independent and adjacent air chambers. While they demonstrated the ability to deform the channel ceiling to compress biological materials, the design parameters remain unexplored. Here, we perform a numerical study on 14,336 variants of this device and identify the height of the PDMS layer, the width of the microchannel and the width of the air chamber as the main features that determine the ceiling deformation. Three deformation modes are observed as the geometrical parameters are varied: A U shape with a central minimum, a W shape with two minima and a central maximum, or an inverse U shape with an upward-bulging single maximum. The numerical results are validated in experiments that reproduce the three shapes for the predicted geometries and demonstrate vertical ceiling deformations ranging from a few microns to the millimeter scale. The generality of this approach is demonstrated for two example applications: A fully closing single-layer microfluidic valve and an optical lens of controllable anisotropy. This work leverages the rapid prototyping enabled by 3D printing or micro-milling to open new perspectives in microfluidic actuation.

Single-layer molybdenum disulfide ($MoS_2$) possesses significant potential for nanoscale optoelectronics, but achieving high-intensity, long-term-stable photoluminescence (PL) emission remains a challenge. In this work, we demonstrate a remarkably robust, more than 8-fold maximum enhancement in the PL intensity of exfoliated and CVD-grown single-layer $MoS_2$ via a non-destructive ultraviolet (UV) laser treatment method. This substantial increase in radiative efficiency is accompanied by a trion-to-neutral exciton transition in the PL signal and a corresponding blue shift of the Raman $E_{2g}^1$ and $A_{1g}$ vibrational modes, signaling successful electron depletion (p-doping) and formation of Mo-O bonds, respectively. Furthermore, we demonstrate precise spatial control over PL properties by confining PL treatment exclusively to the UV laser-treated area. Crucially, the enhanced PL performance shows exceptional longevity; the CVD sample and the exfoliated sample remained stable for the entire monitoring period (72 and 32 days, respectively) under ambient conditions. We further investigated UV laser treatment in a controlled-environment chamber under argon, nitrogen, and oxygen atmospheres, distinguishing the influence of oxygen as the PL treatment agent. These findings establish a reliable pathway for the permanent treatment of single-layer $MoS_2$ PL properties, an essential step toward practical, high-performance nanophotonic devices.

Reliable early detection of lithium-ion battery degradation requires health indicators that are physically interpretable and computable from routine cycler telemetry without access to the degradation region. We introduce \textsc{CausalHealth}, a framework that applies causal graph discovery and $k$-nearest-neighbour transfer entropy to per-cycle voltage, current, temperature, and resistance time series, and organises twelve resulting anomaly scores into three signal-class bundles (Magnitude-shift, Predictive-residual, Complexity-entropy) -- with Isolation Forest reported separately as it falls below the bundle reliability threshold -- to characterise detection sensitivity across ten commissioning fractions (5--30\,\%). The Magnitude-shift class achieves 100\,\% detection across all seven tested cells spanning LFP (MIT--Stanford MATR) and LCO (NASA PCoE, CALCE CS2) chemistries, with a lead time of up to 402 cycles before conventional capacity-threshold failure on gradual-fade cells. A Reliability-Weighted Master Health Index (RWMHI) -- a cross-bundle fusion of five high-reliability detectors weighted by inverse coefficient of variation -- improves lead time by 15--52 cycles over the class median on long-lived cells while maintaining 100\,\% detection. Validation against electrochemical impedance spectroscopy on an NMC prismatic cell provides independent physical grounding: transfer entropy $\mathrm{TE}(R \!\to\! V)$ correlates with charge-transfer resistance $R_{\mathrm{ct}}$ (pooled $r = +0.990$; temperature-controlled partial $r = +0.898$), and an Arrhenius analysis of both quantities yields an activation energy consistent with published NMC charge-transfer kinetics. These results are evaluated on seven cells across three benchmark datasets.

Oscillator Ising machines (OIMs) are often viewed as physical systems that perform gradient descent on an energy landscape encoding Ising solutions. Here, we show that this interpretation is not generic and breaks down in a broad class of oscillator implementations. We establish that gradient-flow dynamics require a harmonic-by-harmonic quadrature relation between the oscillator waveform and its phase response. Deviations from this condition, which we term harmonic misalignment, introduce even components in the pairwise interaction function, leading to non-conservative phase dynamics and precluding a gradient-flow description. We introduce a normalized metric for this non-gradient contribution and evaluate it across representative oscillator models relevant to OIMs. This metric reveals substantial non-gradient contributions in ring oscillators and across other hardware-realistic oscillator models. These findings identify harmonic misalignment as a fundamental mechanism for the breakdown of energy-based dynamics in OIMs and motivate nonequilibrium analysis and algorithms that explicitly account for and potentially exploit non-gradient behavior.

Ecological communities are often characterized by many weak and few strong interspecific interactions, yet their quantitative structure, generative basis, and links to community-level properties remain poorly understood. Using two empirical datasets of plant--animal networks, we show that both trophic and mutualistic interaction strengths distribute skewed weak and Pareto-strong tails (SWAPS), as quantified by positive skewness and extreme value theory, respectively. We further find that interaction strengths are taxon-specific and largely constrained within taxa. In community assembly simulations based on a generalized Lotka--Volterra model, this taxonomic conservatism, together with multiple interaction types beyond trophic and mutualistic ones, is required for the emergence of SWAPS distribution. Notably, SWAPS distribution emerges not only at the species level but also across lineages, and its emergence accompanies increases in community diversity and complexity. Together, these results identify SWAPS distribution as a previously unrecognized interaction signature of ecological communities and provide a new perspective on the organization of community-level properties.

Molten iron chlorides are central to emerging energy technologies, including electrochemical iron production and redox flow batteries. Optimizing their electrochemical performance and transport properties requires atomic-scale structural understanding, yet detailed data for molten FeCl2 and its differences from FeCl3 remain scarce. Here, we determined the structures of molten FeCl2 and FeCl3 using High Energy X-ray diffraction (HEXRD), Empirical Potential Structure Refinement (EPSR), and molecular dynamics (MD) simulations with machine learning interatomic potentials (MLIPs). HEXRD measurements provided structure factors and total radial distribution functions (RDFs), which were quantitatively reproduced through EPSR refinement directly constrained by experimental data. MD simulations using MACE foundation and fine-tuned models reproduced experimental structure factors as well as total and partial RDFs, capturing key structural differences between the melts. The models resolved the octahedral to tetrahedral coordination transition of Fe upon melting in FeCl3 and predicted a similar transition in FeCl2. Analysis of MD trajectories quantified coordination environments, bridging Cl populations, bond-angle distributions, and connectivity patterns, revealing distinct degrees of polymerization and local geometry. Polymer chain statistics further showed that, contrary to prior reports, both liquids predominantly consist of extended chains containing six or more Fe centers rather than discrete Fe2Cl6 units. Finally, diffusion coefficients of the two melts calculated from the MACE-MD simulations were compared. Together, these results establish atomic-scale structural benchmarks for molten FeCl2 and FeCl3 and demonstrate the reliability of MACE-based MLIPs for predictive modeling of high-temperature molten salts, while providing practical guidance for MLIP development in complex ionic liquids.

The transverse modal identity of a freely propagating optical beam is ordinarily fixed at the point of generation. We show that the conical angular spectrum of a Bessel beam establishes a one-to-one mapping between radial beam position and axial reconstruction distance. This mapping converts the radial aperture of a single static, phase-only spatial light modulator into a programmable longitudinal-mode register. By partitioning the modulator into independent annular regions, we encode discrete transverse modes at preselected axial positions. We demonstrate this principle with programmable ring-lattice fields of axially varying site number, and with passive transitions that sequence through Bessel, Bessel vortex beam, Hermite-Gaussian-Bessel, and Airy caustic modes within a single beam, without dynamic modulation or cascaded optical elements.

Memory systems can store vastly different amounts of information despite similar hardware constraints. Here, we show that superior spatial memory emerges from a discrete stiffening of hippocampal population geometry-a transition from disorganized to crystalline collective coding. Comparing food-caching chickadees to non-caching zebra finches, we found that the caching hippocampus maintains a topologically rigid, "crystalline" geometry with significantly higher geometric stability (Shesha 0.245 v 0.166) and nearly two-fold greater temporal coherence (Shesha 0.393 v 0.209), while the non-caching hippocampus resembles a disorganized "mist." This stability is actively constructed by synergistic circuit dynamics: excitatory neurons form the spatial scaffold while inhibitory populations contribute orthogonal decorrelation, a circuit motif in which excitatory and inhibitory populations occupy largely non-overlapping representational subspaces. A double dissociation with Valiant's Stable Memory Allocator, a model predicting that dedicated neuron ensembles underlie each memory, confirms this advantage reflects continuous topological organization rather than discrete neuron allocation: caching networks exhibit near-zero split-half allocation reliability despite their geometric superiority. Computational modeling across 10k configurations reveals topological rigidity as the mathematical prerequisite for scale: crystalline codes sustain high-fidelity readout beyond M=1k locations while mist codes fail below M=10, a >100-fold capacity advantage. This capacity requires a 169fold representational redundancy: a "geometric tax" stabilizing the manifold against biological noise. These results establish geometric stability as a candidate organizing principle of biological memory: evolution achieves high-capacity memory not by proliferating neurons, but by engineering the geometry of the neural code itself.

This work provides an overview of key historical developments in the formulation of the Second Law of Thermodynamics, focusing on the notorious challenge of ``Maxwell's Demon'', a hypothetical creature who could presumably violate that law. It begins by recalling Maxwell's challenge and discussing the apparent loophole in the Second Law that appears to make such a violation possible. An alternative formulation of the Demon challenge by Szilard is considered, along with his attempted defeat of the Demon through reference to measurement. A similar effort by Brillouin is also analyzed. The proposal of Bennett to defeat the Demon through the requirement of memory erasure is critically discussed. Finally, it is proposed that the Second Law gains a firm foundation through neglected features of quantum theory. In particular, an application of the Heisenberg Uncertainty Principle is shown to decisively defeat the Demon, as well as to serve as justification for Landauer's Principle, albeit in terms distinct from the usual computational formulation.

We describe a high-order ADER-DG solver for the compressible Euler equations within the ExaHyPE framework. The implementation combines a high-order ADER-DG polynomial representation, a local space-time DG predictor, adaptive mesh refinement, and an a posteriori subcell finite-volume limiter. We test the code on a deliberately mixed set of one- and two-dimensional problems: a strong-shock Sod-type problem, the Shu-Osher shock-entropy interaction, the Woodward-Colella blast wave, a contact-driven vortex sheet, and a shock-interface interaction. The one-dimensional cases recover the expected Euler wave patterns and show clear order-dependent gains in smooth and oscillatory regions. The two-dimensional cases probe a different part of the method, namely contact preservation, shear-driven roll-up, baroclinic vorticity deposition, and Richtmyer-Meshkov-type growth. In these tests the high-order update gives the expected resolution away from discontinuities, whereas the subcell limiter keeps the calculation stable near shocks and steep interfaces. The resulting code provides a reproducible ExaHyPE implementation for idealised inviscid, non-relativistic flows in which shocks, contacts, and multidimensional interfaces are the dominant features.

A train of periodic optical pulses gives an optical frequency "comb" that acts as a precise ruler for light measurement due to its equally spaced frequencies. Today, such pulses last millionths of a billionth of a second (Femtoseconds/fs) and associated comb spans billions of frequencies (Terahertz), similar to a discretized rainbow ranging from ultraviolet to infrared light. This is the core technology in many optic-based applications like atomic clocks and secure communication. Despite its obvious value, these remain mostly confined to research labs for being complex, expensive, and power-hungry. One promising solution is to use laser mode-locking: a technique that forces a laser to emit short coherent pulses. While chip-size systems have already been demonstrated, this approach still lacks flexibility and performance in repetition rate and bandwidth simultaneously. This research proposal leverages the industrialization of integrated photonic chips to develop a first-ever all-integrated two-coloured pulsed source with durations of a few hundred fs. It will engineer a novel turn-key device that will 1) pioneer the demonstration of modelocked pulses at two separate central frequencies originating from the same laser, 2) fit on a fingertip, and 3) be compatible with generic foundry processes, and thus, mass-manufacturable. Sustained generation of such pulses is intricate with little knowledge about light-material interaction at this scale. The chip will emit two broadband combs using one control parameter. These combs will have synergetic comb properties and coupled through one gain medium. Thus, we will create a new ultra-broadband comb resulting in unprecedented phase correlation between the two sub-combs. Simultaneously, the device will be significantly smaller, lighter, cheaper, and more power-efficient than its free-space rivals, reducing the gap between lab and market.

Neural-network quantum states offer a flexible route to compact many-electron wave functions, but their practical accuracy depends strongly on how fermionic antisymmetry, electron correlation, and optimization noise are treated. Here we combine an antisymmetrized geminal power (AGP) determinant with feed-forward neural networks that replace conventional basis-set expansions in the geminal and in two Jastrow-factor constructions. The resulting basis-free Jastrow--AGP ansatz is optimized by variational Monte Carlo and is designed to separate two tasks: the AGP part defines the nodal surface, while the neural-network Jastrow factor recovers dynamical correlation at fixed nodes. This separation makes it possible to distinguish errors associated with dynamical correlation from those caused by static, multireference correlation. Applications to the hydrogen molecule and the rectangular hydrogen tetramer show sub-millihartree accuracy when the AGP nodes are adequate, and expose the residual nodal limitation near the large-radius square geometry of the hydrogen tetramer. These results clarify where neural-network building blocks can improve a compact geminal ansatz and where additional nodal flexibility is required.

The function of the zebra's striped coat has been debated since Darwin and Wallace. A growing body of comparative and experimental evidence supports the hypothesis that the stripes act primarily as a defence against visually orienting biting Diptera - in particular tabanids (horse flies), glossinids (tsetse flies) and culicids (mosquitoes). The mechanisms proposed for this protection range from polarotactic disruption and silhouette break-up to motion-based illusions arising in the Reichardt-type motion detectors of the insect visual system. In this work we focus on a complementary, purely optical mechanism: the Moiré interference that arises when a periodic striped stimulus is sampled by the periodic ommatidial lattice of an insect compound eye. We develop a linear, shift-invariant Fourier model of the diptera compound eye, parameterised from published optical data on diurnal Culicidae, and apply it to images of zebra coats observed at biologically relevant viewing. The model predicts that, in a band of approach distances of approximately 1-5 m, the interaction of the stripe pattern with ommatidial sampling generates parasitic spatial frequencies that are absent from the physical stimulus and that fall within the spatial-frequency window most relevant to host fixation and landing control. A post-retinal motion-detector stage demonstrates that these parasitic frequencies translate into spurious local motion vectors, consistent with the empirical observation that tabanid and glossinid flies fail to land cleanly on striped surfaces. Our results are therefore consistent with the biting-fly hypothesis of zebra striping.

We introduce two approaches to modeling the quantum dynamics of optical $χ^{(3)}$ solitons. Taking an open-system viewpoint, we project the underlying quantum field into system (soliton) and residual reservoir components. The reservoir is treated as either (i) a discrete ``Lanczos supermode'' (LSM) expansion which localizes dynamics to a few-supermode basis, or (ii) a non-local environment which can be traced out by deriving a Markovian master equation (ME). Using these methods, we analyze and identify the quantum structure of both the soliton's stability and its hierarchy of perturbations. Through numerical simulations, we confirm both methods effectively capture quantum-induced soliton phase shifts in a concise few-mode (single-mode for ME) basis, and the LSM approach also captures photon loss which arises from non-Markovian dispersive couplings. As neither method is limited to the linearized regime, our approaches provide powerful computational tools to analyze complex non-Gaussian quantum dynamics of solitons where other commonly-used methods fail, providing insight into such non-perturbative regimes. We also investigate radiation that occurs in the presence of higher-order dispersion with ultrashort pulses, deriving a ME that predicts photon loss consistent with classical theory, but find that both classical and ME theory dramatically underestimate the actual amount of dissipation, which we explain in terms of dispersive coupling-induced soliton broadening.

Scintillators convert X-ray energy into visible or near-visible photons, enabling applications in high-energy particle detection and X-ray imaging. Increasing scintillator thickness improves X-ray absorption but degrades spatial resolution due to diffraction-induced lateral spreading of emitted light, resulting in a fundamental trade-off between detection efficiency and image resolution. Here, we propose a class of three-dimensional photonic crystal scintillators that overcomes this limitation through supercollimation, in which light propagates with suppressed diffraction. We develop a multiscale modeling framework that integrates nanophotonic band-structure simulations with Monte Carlo particle transport to quantitatively evaluate the performance of such scintillators. Our results show that supercollimating photonic crystal scintillators can enhance spatial resolution by up to an order of magnitude relative to conventional bulk scintillators of equal thickness. This improvement leads to a substantial increase in detector quantum efficiency (DQE), particularly at high spatial frequencies, enabling fine features to be preserved at reduced X-ray dose. We further demonstrate that comparable image quality can be achieved with approximately an order-of-magnitude lower X-ray dose. By directly engineering light transport within the bulk of the scintillator, this work establishes a nanophotonic route to simultaneously improving resolution and dose efficiency in X-ray imaging.

The finite-temperature spin response of the uniform electron gas (UEG) is a fundamental reference for spin-polarized and magnetized electron liquids, including warm dense matter (WDM), yet it remains far less constrained than charge response. Using variational diagrammatic Monte Carlo, we compute the static spin exchange--correlation (XC) kernel $K_{xc}(q;T)$ of the unpolarized UEG at metallic densities across the quantum-degenerate, warm-dense, and classical regimes. The kernel connects smoothly to zero-temperature spin-response parametrizations at low temperature, while heating suppresses the Fermi-surface-scale spin-correlation structure and weakens the XC-driven Stoner enhancement. Its long-wavelength limit provides a direct response test of the spin stiffness implied by thermal local-spin-density-approximation (LSDA) parametrizations, showing low-temperature consistency while exposing a resolved warm-dense residual in current LSDA parametrizations. In the classical regime, the spin XC kernel becomes nearly local on the Fermi-momentum scale, in sharp contrast to the corresponding charge XC kernel. These results provide a first-principles basis for finite-temperature spin-response theory and magnetized WDM modeling.

Wikipedia hyperlinks have primarily been studied as navigational tools for readers, but their role in how information providers move between articles during editing remains less explored. Here, we combine the hyperlink network among English Wikipedia articles with editorial histories to examine how article-to-article structure is associated with editors' transitions between articles. We first address the temporal aspect of edit transitions by showing that transitions between hyperlinked article pairs have shorter inter-event times (IETs) than those between non-hyperlinked pairs, indicating that connected articles are effectively closer in editing sequences. We then turn to the structural organization of editing behavior by coarse-graining the hyperlink network into 19 topical communities and measuring editors' topical diversity. Finally, we bring the temporal and structural views together by comparing each editor's transition network with the corresponding hyperlink subnetwork using Jaccard similarity. Combining the measures allows us to distinguish three editor types: 'Specialists' are characterized by focused editing within limited topical domains and transition patterns more closely aligned with the hyperlink structure (low topical diversity, shorter mean IETs, and higher Jaccard similarity), whereas 'generalists' cover broader topics and show weaker similarity to the hyperlink structure (high topical diversity, longer mean IETs, and lower Jaccard similarity). 'Bots' show a distinct algorithm-driven behavior, with low Jaccard similarity and the shortest mean IETs, a combination departing from human-editor patterns despite their often high topical diversity. Such findings demonstrate that the hyperlink structure is not just a static scaffold for reader navigation, but is observationally linked to the sequential organization of editorial activity in collaborative knowledge systems.

The rapid growth of artificial intelligence (AI) and data-centric computing is driving exabyte-scale data transfer, pushing conventional interconnect technologies toward fundamental bandwidth and energy limits. Although optical interconnects provide high-capacity and long-reach communication, their complexity and energy overhead limit scalability in short-reach chiplet-based and on-chip systems. Terahertz (THz) silicon interconnects offer a promising alternative by bridging electronics and photonics in compact, complementary metal-oxide-semiconductor (CMOS)-compatible platforms capable of high bandwidth and low latency. However, practical THz interconnects require simultaneous multi-band operation, dual-polarization support, low propagation loss, low group-velocity dispersion (GVD), and terabit-per-second throughput, while avoiding Bragg-induced stopbands and dispersion penalties at high frequencies. Here, we demonstrate a CMOS-compatible, centimetre-scale, multi-band on-chip THz data link achieving an aggregate throughput of 1.004 Tbps. The performance is enabled by suppressing Bragg-induced stopbands using dispersion-engineered, effective-medium-supported unclad silicon waveguides, resulting in flat transmission and low-ripple group delay across multiple THz bands. The waveguide platform operates from 220 to 500 GHz and supports both transverse-electric (TE) and transverse-magnetic (TM) polarizations with low path loss, low bending loss, and low GVD. Fourteen channels in a straight waveguide and twelve channels in a 90$^\circ$ bend achieve aggregate data rates of 1.004 Tbps and 0.895 Tbps, respectively, with GVD as low as 0.15 ps$^2$/mm over the full operating band. These results establish a scalable and energy-efficient THz interconnect platform for high-density on-chip and chip-to-chip communication fabrics targeting next-generation AI systems and emerging 6G technologies.

We have experimentally investigated the spectral characteristics and spatial structure of femtosecond pulses from a titanium:sapphire laser during filamentation in an optical cell filled with argon at pressures up to 40 atm under pressure shock-drop conditions. This leads to the development of strong jet flows and vortex gas turbulence, which in turn triggers the early onset of multiple filamentation of the optical pulse and largescale broadening of its spectrum throughout the entire duration of the pressure drop. The magnitude of this spectrum broadening can reach 80 nm and is proportional to the initial gas pressure. Using computational fluid dynamics simulations, we studied the dynamics of the emergence, development, and relaxation of stimulated turbulence in compressed gas in the region of the cell outlet valve and assessed the effect it exerts on the propagating femtosecond pulse. The revealed regularities may serve as the basis for developing an effective method of controlling the spectrum of supercontinuum radiation via filamentation of highpower ultrashort laser pulses in gas cells under shock pressure release and rise conditions.

Scientific and technological frontiers advance through punctuated dynamics, yet the principles governing these dynamics remain poorly understood. Here we collect and analyze datasets tracking the evolution of frontiers across 9 different domains, spanning materials discovery, structural biology, AI, computational biomedicine, data science, theoretical computer science, Formula-1 racing, and physical wheel building. Analyzing 6.8M solutions to 6.7K tasks, we uncover three universal patterns: (1) waiting times between new frontiers are heavy-tailed, with most attempts concentrated in long stasis; (2) frontier records accumulate at a sublinear rate, faster than logarithmic yet slower than linear growth; (3) record-breaking events are temporally correlated, generating short-term predictability yet long-term unpredictability. Despite the differences in the scale, scope, and definition of the settings, these patterns are remarkably consistent across all domains we study, and are not captured by models from complex systems, record statistics, economics of innovation, and cultural evolution. We trace the missing ingredient to the distinction between radical and incremental innovation, and develop a minimal, analytically solvable model incorporating both radical resets that restructure what is achievable and incremental refinements that exploit the current frontier. The simple model reproduces all three empirical regularities. Remarkably, the leading-order predictions are parameter-independent, identifying a new universality class governing punctuated progress and yielding testable predictions about how openness and access to frontier solutions shape the pace of advance. Overall, these results reveal universal dynamics governing punctuated progress and identify the interplay between radical resets and incremental refinements as the key driver of how scientific and technological frontiers advance.

Open microcavities provide a powerful platform for studying cavity quantum electrodynamics in solid-state systems. However, operating open microcavities at cryogenic temperatures, as required for many solid-state quantum emitters, typically demands bulky and cryostat-specific vibration-mitigation setups. Here we report a compact, robust, and tunable mechanical host for an open microcavity. The complete mechanical assembly fits within a footprint of $1'' \times 1'' \times 0.5''$. Using this mechanical host, we observe no vibration-induced cavity broadening for an open microcavity with finesse exceeding 1,000 without cryostat customization or active locking. The assembly also enables in situ tuning of the cavity resonance over 3 nm, and the resonance of the same cavity remains within this range across multiple cooldowns. To further showcase the capability of this assembly, we demonstrate coupling between an InGaAs quantum dot and an open microcavity with a cooperativity exceeding unity. This platform provides a versatile testbed for fundamental cavity quantum electrodynamics and a scalable route to portable quantum light sources and spin-photon interfaces for quantum repeaters, quantum networks, and photonic quantum computing.

Many biological processes, from cell division to viral lysis, are triggered when an internal stochastic variable reaches a threshold. Here we introduce Branching under First-Passage Resetting, a general framework in which replication events arise endogenously from first-passage dynamics rather than from externally imposed lifetime clocks. We show that the resulting population dynamics obey an exact renewal equation linking single-trajectory first-passage statistics to the population growth rate. This mapping shows that, for fixed offspring number and fixed mean replication time, stochastic timing fluctuations necessarily enhance growth relative to a deterministic clock. When offspring yield depends on the first-passage time, however, fluctuations have non-trivial effects and expose a fundamental yield-delay trade-off: waiting longer can increase the number of descendants, but delays all future lineages. Our framework allows us to address this optimization problem analytically, and upon application to bacteriophage lysis, gives an optimal lysis time and growth rate consistent with empirical data.

Reduced density-matrix functional theory (RDMFT) provides a variational route to electronic correlations beyond conventional density-functional approximations, but explicit evaluations of density-matrix functionals still scale exponentially with the number of active one-particle states. We formulate and assess a real-space-decomposed density-functional plus reduced-density-matrix-functional (DF+RDMF) scheme in which the Coulomb interaction is partitioned locally in real space and the RDMF correction is evaluated only for the strongly correlated part of the interaction. The resulting local density-matrix functionals are further compressed using the adaptive cluster approximation (ACA), which performs a unitary rotation of the bath subspace before truncation and therefore preserves the local interaction while reducing the number of explicitly correlated bath states. As a molecular test case, we consider the bending potential of carbon suboxide, C$_3$O$_2$. While semilocal PBE favors a linear molecule, the DF+RDMF/ACA correction stabilizes a bent configuration in qualitative agreement with the quasilinear behavior inferred from spectroscopy. The approach provides a systematic embedding hierarchy for combining density functionals with explicitly correlated density-matrix corrections in extended or spatially inhomogeneous systems.

The single-turn coil method is a destructive pulsed magnet for generating over 100 T with a few $μ$-second pulse duration, and it inevitably causes the coil to explode. The temporal and spatial distributions of the electric current and magnetic field are highly inhomogeneous, arising from the skin effect, rapid temperature rise, and coil deformation. To grasp the dynamic phenomena in the single-turn coil, we conducted a finite element analysis using multiphysics simulation. We employed finite element method calculations using a fully 3D model of the single-turn coil with broken cylindrical symmetry. The calculated result revealed highly nonlinear diffusion of electric current, temperature, and magnetic fields, which are the sources of the inhomogeneous magnetic fields inside the single-turn coil in time and space.

Spin-photon interfaces, realized by coupling optically active spin systems to photonic cavities, are essential for quantum networking and quantum information processing. Implementing such an interface for polarization-encoded photons requires a cavity that supports arbitrary polarization, provides efficient optical access through its far-field mode, and maintains sufficiently high quality factors to enable high cooperativity with the system's optical transitions. However, inherent trade-offs between the Q-factor and far-field emission mode make the simultaneous optimization of these parameters toward the realization of spin-photon interfaces challenging. In this work, we implement a gradient-based inverse-design framework using guided-mode expansion with automatic differentiation to obtain the geometrical features of a circular ring cavity that supports arbitrary polarization while simultaneously optimizing the cavity quality factor and far-field mode profile. The resulting optimized non-periodic cavity achieves a quality factor of approximately $9,000$, about an order-of-magnitude higher than that of a periodic ("bullseye") cavity while preserving a Gaussian-like far-field emission pattern. Furthermore, by varying the cavity geometry within a $\pm 6$ nm fabrication tolerance, we demonstrate the robustness of the design against fabrication errors and identify the innermost ring width and central disk radius as the parameters with the greatest impact on the quality factor and far-field mode. These results establish guided mode expansion-based inverse design as a powerful and computationally efficient approach for developing high-cooperativity spin-photon interfaces for quantum photonic applications.

We study the metastability properties of a simple prototypical bistable system using the formalism of the Koopman operator. Instead of studying noise-induced transitions by following the trajectories of the system, we track them by studying the time evolution and the decay rate of the subdominant mode of the Koopman operator, thus in a geometry-agnostic framework. We find agreement with the predictions - both the exponential and subexponential ones - of large deviation theory in the weak-noise limit for the statistics of escape time, both in equilibrium and nonequilibrium conditions. The subdominant Koopman mode also allows for an accurate reconstruction of the competing basins of attraction. Going deeper in the Koopman spectrum, we are able to recognise modes that are associated with intrawell variability as well as with the escape of trajectories from the saddle towards the attractor, both in the equilibrium and nonequilibrium case. Our methodology, being grounded in purely data-driven techniques, could be helpful for studying high-dimensional metastable systems.

The Berry phase is a geometric phase acquired during adiabatic evolution over a closed loop in parameter space. It plays an essential role in geometric quantum gates and other phase-based protocols. In non-Hermitian systems, the Berry phase is complex, introducing fundamentally new geometric effects, including state amplification. In this work, we report experimental measurement of both the real and imaginary components of a Berry phase in a fully quantum system using a superconducting transmon circuit with engineered dissipation. We also demonstrate the path-dependent effects of the imaginary part on the dissipation and its utility in the implementation of non-unitary quantum control. These findings establish a clear geometric distinction between the real and imaginary components of the Berry phase and experimentally confirm the unique adiabatic behavior of non-Hermitian quantum systems.

The determination of crystallographic texture through elastic wave propagation offers a cost-effective, nondestructive means of obtaining through-thickness information with minimal sample preparation. Existing ultrasonic approaches rely on either bulk-wave or guided-wave velocity measurements for texture inversion. These strategies impose geometric constraints: bulk-wave methods become impractical for thin specimens, whereas guided-wave techniques are limited to relatively small thicknesses. Furthermore, many formulations assume orthotropic symmetry of the aggregate, thereby restricting their applicability to materials with higher anisotropy. In this work, a full-field wave fitting strategy is developed in which the transmitted ultrasonic field is simulated and directly compared to experimental measurements. Because the approach does not rely on bulk-wave or plate-wave approximations, it remains applicable across a broad range of specimen thicknesses. Furthermore, no macroscopic symmetry assumptions are imposed on the aggregate, enabling the characterization of generally anisotropic materials. The effective elastic response is computed using a Hashin-Shtrikman homogenization framework, which provides tighter bounds than classical Voigt-Reuss-Hill averages and constrains the admissible search space during optimization, thereby improving convergence. The nonlinear inverse problem is solved using a GPU-accelerated optimization scheme. The methodology is validated on materials with hexagonal and cubic crystal symmetry over a range of specimen thicknesses. The inferred texture coefficients show consistent agreement with independent diffraction measurements. Additionally, textures with weak elastic anisotropy are successfully recovered, demonstrating the robustness and versatility of the proposed method. Complete measurement and inversion are achieved within approximately 10 minutes.

We present vega-mir, an open-source Python library that bundles nine information-theoretic and statistical metrics for the analysis of symbolic music corpora behind a small, tested, citable API, and demonstrates two of them at corpus scale in case studies not addressed by the upstream Cygnus paper. Of the nine metrics, three (Shannon entropy, Kullback-Leibler divergence, Zipfian fits) were deployed in the companion Cygnus arXiv preprint; two (network analysis on chord-transition graphs and spectral analysis of rubato curves) are deployed in full case studies here; the four remaining (multi-dimensional Gini, chi-squared stationarity, Higuchi fractal dimension, interval distribution) are validated against analytic anchors and exercised as sanity checks on a bundled 8-composer dataset. The two case studies yield two main observations. First, on the fourteen MAESTRO composers with N >= 10 pieces, the PageRank value of the gravity-centre node correlates with the marginal Kullback-Leibler distance at rho = 0.61 (Spearman, composer-level jackknife N = 14); the categorical gravity-centre identity takes five distinct values across the corpus but is not itself correlated with marginal KL (rho = 0.13, p = 0.21). Second, on the 247-piece Bach multi-master corpus (Schiff, Gould, Richter), Gould holds the highest periodicity ratio of the three performers, not the lowest, inverting the cliché that low scalar rubato reads as "metronomic": Gould's rubato is small in amplitude but structured in time, with a median dominant period of 66 beats against Schiff's 102 and Richter's 104.

The radioactive decay from long-lived radioactive isotopes produced by cosmogenic activation can be an important background in direct-detection dark matter and neutrino experiments. In general, activation of materials located above ground is dominated by nuclear spallation due to energetic neutrons produced as secondary particles from primary cosmic ray interactions in the atmosphere. As experiments become larger and strive for greater sensitivity to rare events, it is increasingly important to store, assemble, and even fabricate the detector materials underground to mitigate cosmogenic activation. There has been no study of cosmogenic activation in detector materials at shallow depths (< 100 meter-water-equivalent). Unlike at aboveground or at deep depths, where neutrons are the major contributors to activation in materials, there are multiple competing physical processes that contribute to the activation in materials at shallow depths. We present a detailed calculation of the production of tritium in Ge and Si, as well as the production of 60Co in Cu, at shallow depths. We also obtain cosmogenic activation suppression factors and tritium production at several shallow-depth sites including the Stanford Underground Facility (SUF), where the SuperCDMS collaboration stored Ge, Si, and Cu detector materials for a substantial period of time.

We propose a natural resolution to the fine-tuning problem inherent in the freeze-in dark matter paradigm by embedding a sterile singlet within a 3-3-1 electroweak extension. By imposing an exact $Z_{13}$ discrete gauge symmetry, we formally suppress all low-dimensional portals to ensure that the dark sector communicates with the Standard Model (SM) exclusively through a dimension-six operator. This theoretical structure allows the extraordinarily small coupling required for dark matter production to emerge naturally from the profound hierarchy between the electroweak scale and the ultra-high Peccei-Quinn symmetry breaking scale. Detailed numerical integration of the Boltzmann equations demonstrates that the sterile singlet can be produced via the infrared freeze-in mechanism to match the observed relic abundance of $Ω_S h^2 = 0.12$. The resulting keV-scale warm dark matter candidate remains consistent with stringent Lyman-alpha forest constraints while offering a viable solution to galactic-scale discrepancies such as the cusp-core and missing satellites problems. Ultimately, this framework provides a self-consistent unification of dark matter genesis and the strong CP solution that is completely independent of ad hoc parameter adjustments.

Stratospheric aerosol injection (SAI) is a solar radiation modification technique, proposed as an interim measure to offset warming while greenhouse gas (GHG) emissions are reduced. This paper discusses a possible SAI implementation route - an alternative to sulfate aerosols formed in situ - based on engineered solid particles having dedicated properties such as size, composition, surface chemistry, and traceable origin, supporting safety, controllability, and functionality needed for SAI systems. These engineered properties also open up options for any future multi-state coordination of SAI through two technical building blocks: (1) the SAI-induced radiative forcing (SRF) - the magnitude of the cooling effect attributable specifically to the SAI layer - as an operator-independent quantity, derivable from direct aerosol-layer measurements; and (2) particle traceability through identifying signatures embedded at production. Both could feed into a shared, publicly accessible monitoring database open to independent interrogation, addressing several governance challenges by anchoring compliance assessments in measurable parameters. Drawing on precedents from the Montreal Protocol, IAEA safeguards, and other regimes, we show that shared technical metrics have historically enabled multi-state cooperation, and we argue the same could apply to SAI. We describe a phased pathway in which the technical capabilities and coordination practices that would use them are developed and tested together, at scales orders of magnitude below operational deployment. To be clear - we regard SAI deployment as premature; the conditions under which it might be considered have not been met. The paper does not propose a governance framework; rather, it identifies technical infrastructure that could support a wide range of such frameworks.

The rapid growth of artificial intelligence, coupled with the slowing of Moore's law, is straining computing infrastructure, as CMOS electronics face inherent limits in bandwidth, energy efficiency, and parallelism. Integrated photonic computing encodes and processes information using the phase, amplitude, spatial modes, wavelength channels, and polarisation of guided optical fields, offering a scalable and energy-efficient route beyond charge-based signalling. Here, we review on-chip photonic computing, emphasising the progression from low-dimensional to high-dimensional architectures. At the foundational level, low-dimensional approaches manipulate the phase and amplitude of guided light through Mach-Zehnder interferometers, diffractive structures, microring resonators, and absorptive elements, forming a programmable basis for optical matrix-vector multiplication. Crucially, high-dimensional architectures exploit spatial modes and wavelength channels to carry multiple independent data streams through a single waveguide, achieving higher throughput with moderate hardware overhead. Practical deployment, however, demands more than device innovation. We examine how system-level techniques, from time-wavelength interleaving to hardware-aware training, address energy efficiency, precision, and algorithm-hardware co-design. Five challenges nevertheless remain: electro-optic conversion efficiency, computing parallelism, spatial integration, reconfigurability, and robustness. We highlight emerging topological structures, such as optical skyrmions, as a promising route to fault-tolerant, topologically protected encoding that exploits the largely untapped polarisation degree of freedom. We argue that, by embracing the higher dimensionality of light, photonic computing can offer not merely an incremental improvement but a new paradigm for high-performance, energy-efficient information processing.

Contemporary computational neuroscience features two prominent modeling traditions. Bottom-up whole-brain modeling (WBM) builds biophysically detailed simulations of brain structure and dynamics, whereas top-down neuroconnectionism optimizes deep neural networks for functional performance. Each has achieved remarkable success yet remains incomplete with WBMs lacking functional competence and neuroconnectionist models showing limited biological grounding. Here we propose functional whole-brain models (fWBMs) as a unified modeling paradigm that integrates structural and dynamical realism with task-performing capacity. fWBMs are defined by four minimal criteria: structural grounding in empirical connectomes and regional biology, continuous-time dynamical realism, functional competence across cognitive domains, and mappable observables to neuroimaging, electrophysiologcal and behavioral data. To formalize this integration, we establish a three-pillar roadmap across short-, mid-, and long-term horizons, and outline the scientific and clinical opportunities this paradigm enables. We argue that the disciplined pursuit of this integrative vision will generate the tools, common language, and cross-scale hypotheses needed to advance our understanding of the brain.

Dengue fever poses a persistent public health challenge in rapidly urbanizing Indian cities such as Ahmedabad, where spatial heterogeneity and seasonal variability complicate forecasting and control. In this study, we develop a data-driven compartmental framework to simulate transmission dynamics, generate forecasts, and evaluate intervention strategies across the Ahmedabad Municipal Corporation (AMC). We employ a Mechanistic Seasonal Dengue Transmission (M-SDT) model that incorporates symptomatic and asymptomatic infections. We calibrated the proposed model using zone-wise dengue case data during 2020--2024. Parameter uncertainty is rigorously quantified using a bootstrap sampling framework with negative binomial noise. The calibrated model reveals pronounced spatial heterogeneity across AMC zones, with persistent hotspots and distinct transmission regimes. Forecasts for 2026--2028 indicate continued endemic circulation with moderate inter-annual variability. Sensitivity analysis identifies the mosquito biting rate and vector mortality as dominant drivers of long-term disease burden, highlighting the central role of vector ecology in shaping epidemic outcomes. Evaluating seasonal vector control strategies shows a notable difference in operation; periodic fogging has a cumulative effect over the years, while sustained residual spraying can quickly curb outbreaks and decrease incidence by over 80%. The zone-wise analysis reveals that the mosquito-to-human ratio governs not only the baseline outbreak potential but also each zone's responsiveness to control strategies. Overall, the M-SDT modelling framework enables reconstruction of unobserved dynamics, rigorous uncertainty quantification, and evaluation of targeted, zone-specific interventions, underscoring the importance of integrating fine-scale surveillance data with mechanistic modelling for adaptive urban dengue control.

AlphaFold3 introduces a diffusion-based architecture that elevates protein structure prediction to all-atom resolution with improved accuracy. This state-of-the-art performance has established AlphaFold3 as a foundation model for diverse generation and design tasks. However, its iterative design substantially increases inference time, limiting practical deployment in downstream settings such as virtual screening and protein design. We propose DCFold, a single-step generative model that attains AlphaFold3-level accuracy. Our Dual Consistency training framework, which incorporates a novel Temporal Geodesic Matching (TGM) scheduler, enables DCFold to achieve a 15x acceleration in inference while maintaining predictive fidelity. We validate its effectiveness across both structure prediction and binder design benchmarks.

Despite similarities between models exhibiting absorbing phase transitions (APTs) and those showing Kardar-Parisi-Zhang (KPZ) growth, the relationship between these universal fluctuations has remained elusive. We numerically study (1+1)-dimensional interfaces of (2+1)-dimensional models showing APTs of directed percolation (DP) and compact directed percolation (CDP) classes with an active boundary, finding a universal crossover from short-time APT-governed fluctuations to long-time KPZ fluctuations. Upon rescaling time and length by the APT correlation time and length, the cumulants of the interface height distributions collapse onto a single scaling function. The fluctuation properties of the discrete Domany-Kinzel model and the continuum stochastic Fisher-Kolmogorov-Petrovsky-Piskunov (sFKPP) equation coincide, indicating that the KPZ growth parameters are determined solely by fundamental properties of the APT. For the CDP sFKPP equation, a dimensionless parameter tunes both the critical interface distribution and the KPZ parameters, with the interface properties of the biased voter model recovered in a limiting case. These results uncover a universal crossover in which KPZ fluctuations emerge from APT fluctuations at long times, linking paradigmatic universality classes of nonequilibrium scale-invariant phenomena.

Large-scale hypothesis testing is central to modern science, where controlling the False Discovery Rate (FDR) has become the standard approach to managing false positives across many simultaneous tests. Hypotheses rarely exist in isolation; they often exhibit structure through proximity, connectivity, or hierarchy. This structure represents both a challenge and an opportunity: while classical methods treat these dependencies as obstacles requiring conservative correction, leveraging them can substantially increase discovery power. Here, we reframe structured FDR control as a regularized learning problem. By optimizing within a suitable Reproducing Kernel Hilbert Space (RKHS), we introduce a framework that unifies continuous domains, graphs, and hierarchies under a single algorithm through kernel choice alone. This formulation enables smooth solutions in place of the piecewise-constant fits of prior methods, principled likelihood-based hyperparameter selection rather than heuristic tuning, and inference at unobserved locations which in turn supports sample-efficient experimental design. Building on this estimator, we provide two decision rules which we prove to control the FDR. We validate our method on two sources: spatial locations derived from high-dimensional real-world datasets, and a differential gene expression task utilizing protein-protein interaction graphs.

Von Economo neurons (VENs) are selectively lost in behavioural-variant frontotemporal dementia (bvFTD) and reduced in autism spectrum conditions (ASC), yet their computational role in social learning remains unexplained. We train a spiking neural network (the VENCircuit) embedding VEN-like projection neurons (K=40, 2% of total) in a recurrent pyramidal circuit across 50 matched random initialisations with and without VENs. The network is trained on a controlled binary classification task; we make no claim to model social cognition directly. VEN-intact networks converged in 49/50 cases (98%) versus 35/50 (70%) for VEN-ablated networks (Fisher's exact OR=21.0, 95% CI 2.7-167, p=8.7e-5). Failed ablated networks showed complete absence of learning, inconsistent with a speed-of-learning account. Phase-ablation experiments show VEN removal is most disruptive during mid-training (epochs 5-25), when a co-adaptive dependency forms in the pyramidal circuit. We derive a formal account showing VENs provide a direct gradient pathway immune to Jacobian instabilities affecting the recurrent circuit. Inference-time VEN ablation caused a significant performance drop (Wilcoxon p=0.022), ranging from no change (16/20 networks) to catastrophic collapse (0.989 to 0.620). VENs function as acquisition scaffolds whose developmental absence produces stochastic learning failure - a computational analogue of variable social skill acquisition in ASC - with falsifiable predictions for organoid and electrophysiology studies.

We consider an SLIARS mathematical epidemiology model including intervention in the form of vaccination and treatment. Contrary to classical models, it is assumed that treatment doses can be limited in availability. Mathematically, we show that most scenarios actually reduce to classic well-known scenarios: having an unreplenished number of doses is akin to having none, while being able to restore stocks is (often) equivalent to the classic situation with vaccination and treatment. We also perform a computational analysis, illustrating some of the transient and stochastic dynamics that diverge from deterministic long-term behaviour, as well as the impact of budgetary constraints.

Adaptive therapy (AT) is designed to postpone the emergence of drug resistance by exploiting evolutionary competition among tumor subclones. Most mathematical models of AT assume a binary population structure of drug-sensitive and drug-resistant cells, which neglects the continuous nature of phenotypic plasticity. In this study, we propose a mathematical model that integrates a continuous drug susceptibility index with a probabilistic inheritance function to describe clonal dynamics under therapy. The resulting integro-differential system generalizes traditional two-type competition models and captures both heterogeneity and plasticity of tumor cells. Analytical and numerical studies show that (i) continuous therapy drives rapid expansion of resistant clones, (ii) adaptive therapy maintains long-term tumor control by dynamically regulating sensitive populations, and (iii) high phenotypic plasticity accelerates phenotype switching, leading to earlier tumor relapse following continuous therapy. These results identify critical parameter regimes where adaptive therapy outperforms fixed regimens and highlight the essential role of plasticity in shaping treatment outcomes. The proposed framework provides a more realistic mathematical foundation for the design of clinically relevant adaptive therapy strategies.

To be useful for downstream applications, vision decoding models that are trained to reconstruct seen images from human brain activity must be able to generalize to internally generated visual representations, i.e., mental images. In an analysis of the recently released NSD-Imagery dataset, we demonstrated that while some modern vision decoders can perform quite well on mental image reconstruction, some fail, and that state-of-the-art (SOTA) performance on seen image reconstruction is no guarantee of SOTA performance on mental image reconstruction. Motivated by these findings, we developed MIRAGE, a method explicitly designed to train on vision datasets and cross-decode mental images from brain activity. MIRAGE employs a linear backbone and multi-modal text and image features as input to a diffusion model. Feature metrics and human raters establish MIRAGE as SOTA for mental image reconstruction on the NSD-Imagery benchmark. With ablation analysis we show that mental image reconstruction works best when decoders use image features with relatively few dimensions and include guidance from text-based and both high- and low-level image-based features. Our work indicates that--given the right architecture--existing large-scale datasets using external stimuli are viable training data for decoding mental images, and warrant optimism about the future success and utility of mental image reconstruction.

Countably infinite systems of linear ODEs arise as forward equations for many continuous-time Markov processes. The standard recipe -- truncate to a finite cap N and exponentiate -- pays cubic cost in N and a time-growing boundary-feedback bias. We identify a structural condition on the rates, L_{n+r,n} = alpha_r n + beta_r ("linear-rate"), under which the generating function G(z,t) = sum_n x_n(t) z^n satisfies a first-order linear PDE in z, and the method of characteristics yields a composition-multiplier representation G(z,t) = K_t(z) G(Phi_t(z), 0). The Taylor coefficients of Phi_t and K_t on any output window {0,...,N} are determined exactly by a closed lower-triangular polynomial ODE on R^{2(N+1)}, independent of any coefficients above N. Truncation enters only through the support M_0 of the initial law, set independently of N. For binary birth-death the closure collapses to the geometric tail p_n(t) = p_1(t) rho(t)^{n-1} with rho(t) = lambda(1 - e^{-(mu-lambda)t})/(mu - lambda e^{-(mu-lambda)t}). The linear-rate class spans Markov branching with immigration, multi-type branching, matrix-valued telegraph and G/R elongation, and signed or non-stochastic hierarchies. When the generator decomposes as L = A + B with A linear-rate and B non-affine (Schlogl bistable, predator-prey, lattice reaction-diffusion), we pair the closure with Strang splitting on B; Richardson extrapolation lifts the time order to Delta-t^4 at ~3x wall clock. On the Schlogl problem at V=500, N=8,000, the split runs 6.3x faster than dense Pade and 20x faster than sparse Krylov expv. For the stationary regime, a closure-Strang power iteration extends the same machinery to multi-dimensional product-state-space generators where sparse LU hits OOM/OOT or boundary-projection bias at usable caps. Numerical experiments locate where each route wins and where it is dominated by standard tools.

We present a methodology providing a one-directional link from within-host individual heterogeneity to population-level disease transmission dynamics. The methodology works in several steps. A within-host model is investigated numerically to determine pathogen and immunological parameters leading to the largest variation of model responses. These key parameters are used to generate a synthetic population of individuals whose temporal immunological response profiles are recorded. These responses are ranked in terms of the severity of experienced outcomes, from mild infections to death, as a function of time since infection. This is used to parametrise an age-of-infection structured epidemiological model to study the transmission dynamics of the disease at the population level. The approach is illustrated using a within-host model describing SARS-CoV-2 infection and an SIR population-level model.

Large Reasoning Models (LRMs) generate chain-of-thought traces whose length tracks human reaction times across cognitive tasks, but recent debate questions whether this alignment reflects genuine computational structure or surface verbosity. We test whether the alignment varies with inference-time reasoning effort. Across GPT-OSS-20B and GPT-OSS-120B, three effort levels, and six reasoning tasks, within-task and cross-task alignment remain invariant: Bayes Factors lean toward the null, and mean alignment is numerically near-identical across conditions. A manipulation check reveals that the effort parameter sets an upper budget on generation rather than driving real-time allocation, suggesting that the allocation policy is crystallized at training time. Arithmetic complexity contrasts further show that token allocation tracks fine-grained, format-dependent human difficulty patterns, with model scale improving the match. Cognitive cost alignment between LRMs and humans appears to be a training-time achievement, robust to inference-time perturbations, supporting a compiled rather than online account of LRM problem-solving.

Accurate long-horizon glucose forecasting is critical for automated insulin delivery systems, which help people with type 1 diabetes (T1D) manage their glucose and avoid dangerous hypoglycemia. However, standard recursive long short-term memory (LSTM) networks suffer from systematic negative bias at longer horizons due to error compounding, while purely mechanistic ordinary differential equation (ODE) models fail to generalize across individuals when parameterized at the population level. We propose PhysioSeq2Seq, a hybrid architecture that combines patient-specific physiological modeling with a sequence-to-sequence (Seq2Seq) LSTM. For each glucose segment, twin matching searches a population of 300 parameterized digital twins to identify the best-fitting physiological match from a 3-hour continuous glucose monitoring (CGM) history. The 10 internal ODE state variables of the matched twin are injected as exogenous covariates into both the encoder and decoder of the Seq2Seq LSTM. This simultaneous 48-step prediction strategy eliminates recursive error compounding, while the ODE features provide a physics-grounded constraint that bounds long-horizon drift within physiologically plausible ranges. PhysioSeq2Seq was trained on CGM and insulin data from 348 participants in the Type 1 Diabetes Exercise Initiative (T1DEXI) dataset and evaluated on 74 held-out participants. At the 240-minute horizon, PhysioSeq2Seq achieves a mean absolute error of 39.28 mg/dL and a mean error of -10.62 mg/dL, reducing bias by 13.89 mg/dL over the recursive LSTM and reducing mean absolute error by 28.62 mg/dL over the ODE-based digital twin. These results show that eliminating architectural feedback and injecting patient-matched physiological states is an effective and clinically meaningful strategy for long-horizon glucose forecasting in T1D.

Aging research has produced powerful explanatory frameworks -- evolutionary theories, the Hallmarks of Aging, SENS, geroscience, hyperfunction, and information-loss models -- yet none provides quantitative rules for determining which intervention, in which biological state, at what dose, time, and sequence, will safely restore function. Existing frameworks characterize what changes during aging; they do not define equations of motion, safety constraints, or optimality conditions for drug discovery. We propose a control-theoretic framework in which aging is defined as progressive loss of safe controllability: the increasing cost and decreasing feasibility of returning a biological system to a functional viability set. Biological age is the minimum safe control cost required to restore or maintain function. Drugs are vector fields on biological state space; targets are ranked by expected cost reduction; combinations by their expansion of the reachable safe set; sequences matter because intervention vector fields do not commute. We provide a five-dimensional ODE model with analytic Lie-bracket derivation of order dependence; a modality-aware control layer distinguishing small molecules, biologics, gene therapy, and reprogramming by reversibility and safety envelope; three translational case studies (thymus, sarcopenia, ovarian aging); an implementation architecture with power analysis; and empirical scoring of 110 aging interventions across five biological epochs. The framework generates twenty falsifiable predictions. The central empirical claim is that control-value reduction predicts translational success better than Hallmark annotation or static biomarker reversal.

Temporal Interference Stimulation (TIS) is a non-invasive neuromodulation technique in which two high-frequency sinusoidal currents with slightly different frequencies generate a low-frequency envelope that can activate deep neural structures. This study investigates the conditions under which TIS elicits action potentials in a single neuron modeled by the FitzHugh-Nagumo system. This research integrates phase-plane analysis and geometric singular perturbation to develop a mathematical framework for analyzing TIS. By combining a mathematical analysis of differential equations with computer simulations, the study elucidates how the amplitudes and beat frequency jointly determine whether the neuron remains quiescent, exhibits only transient responses, or undergoes persistent (tonic) firing.

What determines whether an organism or collective will survive under particular conditions? This question is asked across the life sciences when determining adaptive fit, developing efficacious treatments for diseases, and assessing the risks posed by ecological shifts. To aid their investigations, researchers employ models of agents which must respect particular constraints to remain alive. By constraining the dynamics of these agents to bounded viability regions, these models form a class of extended dynamical systems where transient dynamics can lead to death, making traditional attractors and separatrices insufficient for characterizing the global space of possible behaviors. To remedy this, we develop viability space decomposition, an analysis framework for ordinary differential equation models of agents with viability constraints. We first introduce the general theory, revealing how several new classes of manifolds (mortality, ordering, and collapse) permit a complete decomposition of state space into regions of qualitatively similar survival outcomes: a viability portrait. We then demonstrate the method by completely analyzing the global behavior of three models: a subcellular network, a behaving cell with the same physiology, and two coupled cell networks. Finally, we finish by discussing how the framework scales and future directions for its development and application.

Decoding visual experience from brain activity has advanced substantially, but cur- rent brain-to-text systems largely recover semantic content while discarding affect. Additionally, language models can generate emotional text when prompted with categorical labels, but such labels collapse rich inter-subject variability into coarse discrete bins. We present EmoMind, the first end-to-end pipeline for decoding affective captions directly from fMRI signals. EmoMind first retrieves a semanti- cally grounded neutral scene description from brain-decoded visual features, then rewrites it using a continuous 34-dimensional emotion vector decoded from the same fMRI recording. To control the balance between content preservation and affective expression, we train the rewriter with classifier-free guidance against an identity-preserving null branch, enabling smooth interpolation between semantic fidelity and affective expressivity. We evaluate affective caption generation with a three-axis validation framework spanning subject-specificity, structural geometry, and causal control. We further augment this framework with a synthetic-brain substitution test that probes robustness to the measurement apparatus, and we benchmark each axis against GPT-4 prompted with brain-decoded top-5 emotion labels as a strong discrete baseline. Across two independent emotion fMRI datasets, EmoMind significantly outperforms label-prompted GPT-4 on all three axes, with the largest gains on metrics that require person-specific affective structure rather than population-level emotion aggregation. These results establish continuous brain-decoded affect as a viable control signal for individualized affective cap- tion generation and open new directions for studying individual affective brain organisation.

Models (i.e., governing equations) are fundamental to science and engineering. Advances in data acquisition now make it possible to extract interpretable, low dimensional descriptions from high dimensional observations. However, existing approaches sacrifice either interpretability for reconstruction accuracy or infer symbolic dynamics without relating latent coordinates to physically meaningful observables. Here we present Edwin (maximum entropy driven compression with interpretable nonlinear model discovery), a unified framework that simultaneously performs dimensionality reduction using the dynamic maximum entropy (DME) principle and discovers sparse symbolic models governing latent dynamics, as well as the coupling between learned features and external metadata. We validate Edwin on diverse simulated systems, including stochastic diffusion, the Ornstein-Uhlenbeck process, self assembling particles, spiking neural populations, and low rank recurrent neural networks, as well as on a noisy experimental time series of aggregating RNA-liposome complexes. Across all systems, Edwin recovers low dimensional symbolic models that are physically interpretable and generalize to unseen conditions. Together, these results establish Edwin as a powerful framework for inferring interpretable, low dimensional dynamics directly from high dimensional data.

A central goal in understanding human vision is to uncover the visual features that drive neuronal activity. A growing body of work has used artificial neural networks as encoding models to predict cortical responses to natural images, revealing the visual content that activates category-selective regions. However, existing approaches are largely correlational and treat the encoder as a black box, leaving open which image features drive each voxel's response. We introduce Mechanistically Interpretable Neural Encoding (MINE), a framework that opens this black box by applying mechanistic-interpretability tools to localize the features within natural images that drive millimeter-scale (voxel-level) activity. MINE predicts each voxel's response using language-aligned image representations, and produces semantically interpretable descriptions of the features critical for the voxel's activation. We further generalize these per-image features into per-voxel functional profiles. To validate the per-image descriptions, we show they are sufficient to generate images that elicit voxel responses matching the responses to the original images, more accurately than images generated from random or low-attribution controls. Moreover, counterfactually inserting or removing the predicted features from images shifts activation in the expected direction, providing causal evidence. Counterfactual editing guided by the per-voxel activation profiles produces even stronger activation shifts, indicating that the profiles faithfully capture each voxel's selectivity. Finally, we apply MINE to well-studied category-selective brain regions, showing it recovers their known categorical preferences while revealing fine-grained unique voxel structure within each region. Overall, our results establish mechanistic interpretability as a path to discover and causally validate fine-grained hypotheses about neural function.