arXiv Daily Digest - 2026-03-30

Focal plane arrays (FPAs) promise robust solid-state beam steering for LiDAR and free-space optical communications. However, the need for external collimation lenses hinders chip-scale compactness. Discrete switching between FPA elements further introduces blind spots and limits the number of resolvable points, restricting applications that require continuous tracking. Here, we demonstrate a silicon photonic beam steerer based on a metalens FPA that monolithically integrates the collimation lens on-chip. Thermo-optic prisms enable continuous fine-tuning, eliminating blind spots and tripling the number of resolvable points. Continuous steering over a 62° field of view is achieved while maintaining high beam quality, with an average sidelobe suppression ratio of 19 dB.

Chiral plasmonic nanostructures are rapidly emerging as ideal substrates for enantioselective sensing, chiral near-field engineering, and plasmon-assisted catalysis, owing to their exceptional sensitivity to structural handedness. However, the physical origin of plasmonic chirality, whether intrinsically quantum or primarily governed by collective electrodynamics, remains an open question, limiting the development of predictive theoretical methods for the design of novel chiral plasmonic architectures. Here, we show that a fully atomistic classical electrodynamic model, coupling intraband charge transport and interband polarization, quantitatively reproduces state-of-the-art \textit{ab initio} and experimental chiroptical spectra across the quantum-to-classical regime, from atomistically defined chiral Ag and Au nanostructures to DNA-origami-assembled Au nanorods containing up to $\sim 10^5$ atoms. Our results support a unified electrodynamic origin of plasmonic chirality, providing the missing foundation to connect local structural motifs to chiroptical response and local chiral near fields, and paving the way for the atomistically defined, rational design of chiral plasmonic nanostructures optimized for targeted applications.

Traditional wireless power transfer (WPT) systems are largely limited to 1-D charging pads or 2-D charging surfaces and therefore do not support a truly ubiquitous device-powering experience. Although room-scale WPT based on multimode quasistatic cavity resonance (QSCR) has demonstrated full-volume coverage by leveraging multiple resonant modes, existing high-coverage implementations require obstructive internal conductive structures, such as a central pole. This letter presents a new structure, termed the patched-wall QSCR, that eliminates such internal obstructions while preserving full-volume coverage. By using conductive wall segments interconnected by capacitors, the proposed structure supports two complementary resonant modes that cover both the peripheral and central regions without obstructions within the charging volume. Electromagnetic simulations show that, by selectively exciting these two resonant modes, the proposed structure achieves a minimum power-transfer efficiency of 48.1% across the evaluated 54 m^3 charging volume while preserving an unobstructed interior space.

Ultraweak photon emission is the spontaneous emission of extremely low levels of light from a broad range of biological systems. Recent studies have reported that UPE measured extracranially can serve as a potential non-invasive biomarker of brain activity. Here, we show that this interpretation suffers from serious problems. First, when observed under properly dark conditions, the UPE from the head is much weaker than what is reported in certain papers on 'brain UPE' from human heads. Signals detected in these studies are overwhelmingly dominated by background light. Second, photons at wavelengths < 600 nm are strongly attenuated by scalp and skull tissues, and longer wavelengths fall largely outside the effective spectral sensitivity of the photomultiplier tubes (PMTs) used. As a consequence, even if UPE from the head is detected under properly background-free conditions, it is likely to be dominated by emission from the scalp rather than from the brain, certainly as long as PMTs are used. Our results emphasize the importance of careful experimental design to make genuine progress on this important question.

The nozzle of an aircraft is a major source of radar scattering from the rear aspect of the aircraft, which undergoes higher operational temperatures. In order to reduce the radar scattering of these nozzles, high temperature radar absorbing materials (RAM) are essential. The thickness of these RAM typically increases to attain RCS reduction at lower frequencies, which subsequently leads to a higher weight of the structure. Therefore, this research study investigates the weight advantages of a star auxetic (SA) lattice made up of barium titanate to reduce the RCS of aircraft exhaust nozzles in the frequency range of 8-18 GHz. Modelling of SA with a complicated aircraft structure may lead to complexities in terms of Computer Aided Design and electromagnetic modelling and higher computational time for solving the electromagnetic problem using exact solvers. In order to simplify the computational problem, a homogenization and modified transfer matrix method is used to generate the RL performance. The RL from the proposed in-house tools is also compared with the Floquet port analysis. The RL performance obtained from the proposed method is also validated against experimental data. Comparative analyses are performed between SA and solid pure block (PB) barium titanate samples over 32761 SA and PB thickness combinations. Results show that selected SA samples with the same thickness achieve weight saving of approximately 60%, with 20dB lower RL than PB. The median RCS of the nozzle rear aspect also indicates that the SA-based barium titanate has an advantage in terms of weight penalty with similar or better RCS performance. The study demonstrates that auxetic metamaterials will be a multifunctional, lightweight, thermally stable, and radar absorbent structure for high temperature aircraft applications.

Upscaling unsaturated flow in fractured rock remains challenging because fractures and matrix often exhibit sharply contrasting hydraulic behaviors across saturation states. Here, we demonstrate that unsaturated flow undergoes a transition between matrix- and fracture-dominated regimes. Three-dimensional direct numerical simulations reveal that both relative permeability and capillary pressure curves display a robust two-branch structure. We analytically derive a generalized retention formulation that identifies a critical saturation marking the transition between the two distinct retention regimes and reproduces the two-branch behavior captured in the numerical simulations. An analytical expression for the critical pressure head is further derived to represent the limiting case of fully connected fracture networks, providing a physical explanation for the retention regime shift and showing good agreement with the numerical results for systems above the percolation threshold. Our results provide a mechanistic framework for understanding and upscaling unsaturated flow in fractured rock, with broad implications for hydrology and geophysics.

In a world with ever-growing scientific literature, meaningful classifications are vital to keep on top of the latest results. In this Comment, historian and sociologist Phillip Roth traces the history of preprint classification in physics.

Fluorescence imaging is an essential diagnostic tool in many fields, but diffraction-limited optical imaging at depth is limited by scattering. Here, we present a method based on multiple random illuminations, combined with a computational framework that retrieves high-resolution images by aligning local speckle replicas and computing their pixel-wise variance. We demonstrate its versatility in two regimes: linear wide-field one-photon (1P) fluorescence imaging and nonlinear two-photon (2P) fluorescence imaging where the object is excited by a scanned speckle field and detected with a single-pixel detector. This approach outperforms standard autocorrelation techniques in terms of resolution and convergence.

Political discourse attributes the pressure on European welfare systems to foreign nationals. Yet projections of service demand rarely disaggregate service demand by citizenship status. We develop a structural demographic model and project healthcare, education, and housing demand in Austria through 2050, disaggregated by citizenship status and regions across migration scenarios. We find that migration, ageing, and fertility shape each sector differently. In healthcare, the ageing of Austrian nationals contributes 4.7 times more to demand growth than immigration, with the most acute pressures in rural, low-migration regions. In housing, migration accounts for the entire net growth in demand, concentrated in metropolitan hubs. In education, aggregate demand contracts regardless of migration assumptions, whereas future needs are driven more by the births of foreigners in Austria than by new arrivals. Foreign nationals consume services in proportion to their demographic weight, with deviations explained by age structure rather than over-utilisation. These results show that the drivers of service demand are sector-specific: migration restrictions could ease housing pressure, but would not address ageing-driven healthcare demand and may accelerate contraction in the education system.

Spin-orbit interactions of light couple polarization and spatial degrees of freedom, underpinning phenomena such as the spin Hall effect of light. Although widely explored at interfaces and in tightly focused beams, their impact in nominally paraxial confocal systems remains largely unexamined. Here we show that a single quarter-wave plate embedded in a simple confocal geometry between polarizers can strongly reshape the transverse structure of a Gaussian beam. We observe an enhancement of the polarization extinction ratio by more than two orders of magnitude, accompanied by a transformation of the Gaussian intensity profile into a first-order Hermite-Gaussian-like two-lobe mode. The orientation of this pattern is continuously tunable via rotation of the wave plate, evidencing polarization-controlled reorientation of the transverse field. To explain these observations, we introduce a minimal extension of Jones matrix formalism incorporating complex parameters that quantitatively reproduces the measurements. Our results uncover a previously overlooked form of spin-orbit-mediated mode control in standard confocal optics and establish a simple route to on-demand spatial mode engineering for applications in resonant spectroscopy, optical imaging and quantum optics.

Machine learning interatomic potentials (MLIPs) enable large-scale atomistic simulations but remain challenged in describing mixed-valence materials where charge ordering strongly influences thermodynamic stability. Here we investigate the role of electronic entropy in MLIP structural optimization of the battery cathode material \ce{NaFePO4}. We show that conventional MLIPs fail to reproduce the correct stability of intermediate \ce{Na} concentrations because structural optimization leads to incorrect \ce{Fe^{2+}}/\ce{Fe^{3+}} charge assignments, resulting in erroneous energy ordering and convex-hull predictions. Analysis of magnetic moments during structural optimization reveals that MLIPs are unable to capture electronic entropy associated with charge ordering. To address this limitation, we introduce an approach that embeds charge-state information directly into the MLIP representation by distinguishing between \ce{Fe^{2+}} and \ce{Fe^{3+}} environments during training. Retraining CHGNet, cPaiNN, and MACE with this representation enables accurate structural optimization, correct identification of charge ordering, and improved agreement with density functional theory convex hulls. Our results demonstrate that incorporating electronic entropy into MLIP representations is essential for modeling charge-disordered materials and provide a practical framework for extending MLIP simulations to mixed-valence transition-metal systems.

Laser-induced lift-off of functional surface layers is a key process in micro- and nano-fabrication; however, optimization criteria for maximizing the lifted-off area remain insufficiently defined. In analogy to the well-established theory of efficient laser ablation, where the maximum ablated volume per pulse is achieved at a peak fluence of F_0^{\mathrm{opt}} = e^{2} F_{\mathrm{th}}, we develop a theoretical framework for efficient laser lift-off driven by Gaussian beams. By analytically describing the lift-off area as a function of peak fluence, beam radius, and focus position, we demonstrate that the maximum lifted-off area is achieved at a substantially lower optimal fluence, namely F_0^{\mathrm{opt}} = e^{1} F_{\mathrm{th}}. Closed-form expressions for the optimal beam radius, maximal lift-off area, and optimal focus position are derived and validated by numerical modeling. The theory is applied to picosecond laser lift-off of copper oxide from copper, showing excellent agreement between experimental observations and model predictions. The results reveal fundamental differences between ablation- and lift-off-dominated material removal and provide practical guidelines for maximizing process efficiency in laser-assisted delamination, selective coating removal, and surface functionalization.

Cd(Zn)O stands out as probably the best plasmonic material in the mid-IR, but it is usually grown on sapphire or other passive substrates. In this work we introduce SiC as a novel, highly polar, dopable substrate for Cd(Zn)O. The Cd(Zn)O/SiC system is analyzed as a function of the Zn concentration and thin film thickness, and the results are compared to those obtained in the Cd(Zn)O/sapphire system. XRD and reflectance measurements show that the alloy with 10 % Zn nominal concentration has the best crystalline and plasmonic quality, with optical losses as good as 13 % of the plasma frequency. The thin films show two surface polariton modes: a purely plasmonic symmetric mode at higher energies with negligible frequency dispersion and pinning at the plasma frequency for the thinnest films, characteristic of an ideal epsilon-near-zero mode; and a plasmonic-phononic hybridized antisymmetric mode at lower energies, which thanks to the large value of the higher frequency dielectric constant of SiC compared to sapphire, shows much lower frequency dispersion, indicative of the stronger epsilon-near-zero character. Hence, Cd(Zn)O/SiC offers a promising platform for the development of ENZ devices on an active substrate.

Nanoscale surface analysis of 1 micrometer thick high entropy alloys (HEAs) was carried out using nano-IR for hyperspectral imaging and single point spectroscopy in the 700-1700 1/cm spectral range. Nano-IR is based on the detection of scattered light from an oscillating metal coated nano-tip in one of the arms of the Fourier transform infrared spectrometer and has a resolution defined by the tip radius of the probe, 20 nm, regardless of the excitation wavelength. HEA CuPdAgPtAu showed an absorption and reflection increase at 900-1100 1/cm band, which is consistent with Drude-Lorenz modeling of permittivity, however, could also signify oxide formation as tested by X-ray photoelectron spectroscopy of CuPdAgPtAu and CrFeCoNiCuMo. Realization of polarization analysis for nano-IR nano-spectroscopy in the plane perpendicular to the sample's surface is discussed and modeled. The currently available modality of surface analysis with the excitation-detection mode of the p-pol. antenna can be extended to full 3D analysis of the orientational dependencies of local absorbance and refractive index.

The density-based bi-objective topology optimization (TO) has been widely adopted in liquid cooling plate design, where the design domain is treated as porous media with porosity as the design variable. However, conventional TO method struggles to directly optimize the convective heat transfer due to its incapabilities of explicitly depicting the heat transfer area in objective function, which limits the optimization of thermal performance. In this study, a fractal geometry topology optimization (FGTO) method is proposed, which incorporates fractal dimension as an additional design freedom into the density-based TO framework. Different from the conventional TO methods, the FGTO explicitly describes the heat transfer area, and achieves a direct optimization of convective heat transfer through the objective function. Compared to the conventional TO, the FGTO achieves a more complex structural topology in the optimized liquid cooling plate with a 46% improvement in heat transfer area. The fractal dimension is manipulated by varying the input parameter s, and increasing s can improve thermal performance of the FGTO results at the cost of larger pressure drop. Superior thermal-hydraulic performance can be achieved by varying s, with the average and maximum temperatures of the FGTO results reduced by 15.6 K and 16.9 K, respectively, compared with those of the conventional TO results. The integration of fractal geometry into the TO intensifies the difference in objective function sensitivity between solid and liquid phases, which is conducive to facilitating solid-liquid separation and contributes to escape from local optimal solutions.

The product of resting heart rate and maximum lifespan is approximately constant across adult warm-blooded vertebrates, $N^\star = f_H L \approx 10^9$ cardiac cycles, a regularity documented since Rubner (1908) but lacking a thermodynamic derivation. We derive $N^\star$ from the non-equilibrium second law by treating the adult organism as a metabolic non-equilibrium steady state (NESS) and introducing the closure $\dot{e}_p = σ_0 f$, linking entropy production rate to heart rate via a mass-specific parameter $σ_0 \propto M^0$. Integration yields a finite dissipative budget $Σ= σ_0 N^\star$, identifying $N^\star = Σ/σ_0$ as the correct primitive conserved quantity; lifetime energy per unit mass is a derived consequence valid only under simultaneous constancy of body temperature and $σ_0$. Phylogenetically independent contrasts on 112 endotherm species yield a $\log f_H$--$\log L$ slope of $-0.99 \pm 0.04$ ($p=0.84$ against $-1$); the West--Brown--Enquist null of zero inter-clade variation is rejected ($F=12.7$, $p<0.001$). A factored multiplier $Φ_C = Φ_{\mathrm{duty}} \cdot Φ_{\mathrm{thermal}} \cdot Φ_{\mathrm{mito}} \cdot Φ_{\mathrm{haz}}$, calibrated from independently measured physiology, accounts for longevity deviations across four warm-blooded clades. The integral of physiological frequency defines a biological proper time classifying longevity mechanisms as time dilation (reduce $f$) or budget expansion (reduce $σ_0$). The decisive test is calorimetric measurement of $σ_0 = P/(TfM)$ across three body-mass decades.

We propose modeling absorption spectrum measurements as mixtures of Gaussian process experts. This enables us to construct a flexible statistical model for interpolating and extrapolating measurements, facilitating statistical integration of Kramers-Kronig relations to estimate the whole complex refractive index. Additionally, we statistically model the anchoring points used in subtractive Kramers-Kronig relations to account for possible measurement errors of the anchor point. In addition to flexible statistical modeling, the mixtures of Gaussian process formulation enables automatic selection of measurement points to use for extrapolation. We apply the method to experimental absorption spectrum measurements of gallium arsenide, potassium chloride, and transparent wood.

Colloidal plasmonic-photonic crystals represent a class of hybrid materials composed of a dielectric colloidal spheres photonic lattice and a metal plasmonic film. In this work, the optical properties of a linear array colloidal plasmonic-photonic crystal consisting of silver films deposited over linear arrays of polystyrene microspheres are analysed in detail. Experimental and simulated optical transmittance and reflectance spectra both with unpolarized and polarized light are used to investigate the optical response of the linear plasmonic-photonic crystal. Among the various photonic/plasmonic modes observed, the existence of both propagative plasmonic-photonic hybrid mode and localized surface plasmon mode can be mentioned. The spectral tunability of these structures is highlighted by studying the dependence of the optical response on geometrical parameters such as sphere diameter and grating period. Finally, the linear plasmonic-photonic crystal exhibits a polarization-selective surface-enhanced Raman scattering effect, making them of interest for both fundamental studies and development of applications based on surface-enhanced Raman spectroscopy or surface-enhanced fluorescence.

Squeezed states of light are used for precision metrology and quantum-enhanced measurements, with applications spanning communication and sensing. State-of-the-art squeezed-light sources typically rely on optical cavities to achieve high, usable levels of squeezing. Recently, waveguide-based squeezed-light sources have demonstrated significant improvements in achievable squeezing, with performance currently limited by fabrication-induced losses. In this work, we present a detailed analysis of waveguide-based squeezers, examining the effects of phase noise, multiple loss mechanisms, and fundamental light leakage seeding the squeezer. We further investigate a cascaded squeezer architecture, in which a second waveguide operates as a phase-sensitive amplifier to mitigate out-coupling and detection losses. Owing to their ease of integration, robustness to high pump powers, and low intrinsic phase noise, we propose waveguide-based squeezed-light sources as a promising alternative for quantum noise reduction in future gravitational wave detectors, such as the Einstein Telescope.

Metal-coated microsphere monolayers (MCM) are a class of plasmonic crystals consisting of noble metal films over arrays of self-assembled colloidal microspheres. Despite their ease of fabrication and tunable plasmonic response, their optical sensing potential has been scarcely explored. Here, silver coated polystyrene sphere monolayers are proposed as surface plasmon resonance sensors capable of functioning in both transmission (T) and reflection (R) readout modes. An original and key point is the use of ~200 nm colloids, smaller than in MCM studied before. It allowed us to reveal a previously unobserved, additional/secondary Enhanced Optical Transmission band, which can be exploited in sensing, with higher sensitivity than the better-known main transmission band. The reflection configuration however, is almost an order of magnitude more efficient for sensing than the transmission one. We also evidenced a strong impact of the adsorbate location on the metal surface on the sensing efficiency. Electric field distribution analysis is performed to explain these results. Proof-of-concept experiments on the detection of 11-MUA molecular monolayers, performed in both readout modes, confirm the behaviors observed through FDTD simulations. Results in this paper can serve as guidelines for designing optimized sensors based on metal-coated colloidal monolayers, and more generally for plasmonic sensors based on metal nanostructured films.

Structured light carrying orbital angular momentum enables new regimes of nonlinear light-matter interaction. Here we develop a molecular quantum electrodynamics description of third-harmonic generation (THG) driven by focused Laguerre-Gaussian beams in isotropic molecular media. We show that the nonparaxial longitudinal field components of a tightly focused beam permit THG with circularly polarized excitation in an isotropic fluid, a process forbidden for plane waves and paraxial beams. Within the electric-dipole approximation, the resulting emission is independent of the sign of the vortex charge. Including electric-magnetic dipole interference introduces a chiral contribution to the nonlinear response, giving rise to third-harmonic vortex dichroism (THVD). The emitted intensity then acquires a component linear in the topological charge \(\ell\), reversing sign with either the wavefront handedness or molecular chirality. Numerical modelling reveals corresponding spatial asymmetries in the harmonic field. These results establish both an allowed pathway for circularly polarized THG in isotropic fluids and the first chiroptical analogue of THG in such media, identifying orbital angular momentum as a new control parameter for nonlinear chiral spectroscopy.

Braess's paradox -- where adding network capacity increases travel time -- is typically attributed to selfish agents. Although eusocial colonies maximize collective fitness, we find experimentally that \emph{Diacamma indicum} ants exhibit this paradox: Leaders favour the shortest path even when it slows the colony. We present a quantitative model of the exploration-exploitation trade-off, demonstrating that evolutionary forces selecting for shortest-path identification can force suboptimal global states. This proves the paradox can emerge in highly cooperative systems without individual selfishness.

Non-Hermitian systems exhibit anomalous scaling, a striking departure from conventional bulk laws, rooted in the non-Hermitian skin effect (NHSE). Here, we experimentally uncover this scaling and demonstrate its active control in a temporal photonic lattice. By tracking the real-time evolution of all eigenstates as system size varies, we directly observe scaling-driven spectral reshaping and eigenstate localization, revealing phenomena absent in Hermitian or NHSE-free lattices. In a Su-Schrieffer-Heeger lattice, scaling alone can trigger a non-Hermitian topological phase transition, with edge modes remaining protected. Crucially, Kerr interactions open the frontier of nonlinear non-Hermitian physics: weak nonlinearity accelerates or decelerates anomalous scaling, while strong nonlinearity suppresses it entirely. These results establish the first experimental platform for linear and nonlinear anomalous scaling engineering, paving the way for compact non-Hermitian devices and exploration of nonlinear and many-body non-Hermitian phenomena.

The geometric phase (GP) is a fundamental quantum effect arising from conical intersections (CIs), with profound consequences for vibronic energy levels. Standard imaginary-time path integral molecular dynamics (PIMD) based on the Born-Oppenheimer approximation does not account for the GP, potentially leading to significant errors in low-temperature thermodynamic properties. In this Perspective, we demonstrate that the multi-electronic-state path integral (MES-PI) formulation in imaginary time (developed in J. Chem. Phys. 2018, 148, 102319) naturally captures the GP effect through the electronic trace of the product of statistically weighted overlap matrices between successive imaginary-time slices. This crucial capability was already implicit in the benchmark MES-PIMD simulations in that foundational work. To isolate this topological effect from other nonadiabatic effects, we introduce a geometric signature matrix (for the CI) and a winding-number-induced phase factor, constructing an ad hoc GP-excluded MES-PI method. Comparing this ad hoc baseline against the rigorous MES-PI approach allows us to unambiguously quantify the impact of the GP on thermodynamic properties. While simpler approximations exist when only the ground electronic-state is considered, MES-PIMD is the most general and accurate approach applicable to real complex systems where the location and topology of CI seams are often not known a priori.

A sensor that can detect the direction of the incoming light plays a crucial role in further enhancing the versatility of the multifunction sensors for future applications, where the sensor can read multiple pieces of information, similar to the biological senses, like skin. A hybrid sensor based on an n-type ZnO micro-rod with p-type optically active organic polymer (PBTTT-C14) is developed for low-cost, large-area piezoelectric and optical sensing applications for future artificial electronic skin. The multi-functionality of the device is achieved due to the heterostructure configuration of vertically aligned piezoelectric ZnO micro rod arrays and PBTTT-C14 polymer between two gold electrodes. The deposition of the top gold electrode also led to the formation of two regions where it forms a continuous film and isolated gold particles (Au NPs). The isolated NPs, when activated, has shown surface plasmon resonance (SPR) and Förster resonance energy transfer (FRET), which generate a potential opposite to the normal working of the device, depending on the number of excited Au NPs by the incident light. The polarity flipping/opposite potential development can be attributed to the rise in electron density near the top Au contact due to the SPR and FRET mechanism of isolated Au NPs over the PBTTT-C14 which depends on the illumination direction. As a result, direction-dependent photo voltage polarity flipping was realized in the device. The device has produced piezoelectric and direction-dependent photovoltage flipping responses, leading the way for a multifunction sensor that can detect the direction of incident light and touch.

Second-harmonic generation in resonant structures is commonly evaluated in terms of intracavity field enhancement at the fundamental and harmonic frequencies. Here, we formulate nonlinear frequency conversion within a symmetry-resolved overlap framework that explicitly separates resonant field buildup from nonlinear mode projection. Using a simple and analytically tractable Fabry--Perot thin-film-on-substrate geometry, we show that, even in the presence of spectrally bright resonances at both $ω$ and $2ω$, the emitted second-harmonic signal can be strongly suppressed when the spatial parity of the pump-induced nonlinear polarization is incompatible with that of the radiating $2ω$ standing-wave mode. This mechanism gives rise to nonlinearity-selective quasi-bound states in the continuum. Beyond providing a compact interpretation of these nonlinear dark states, the framework unifies pump enhancement, harmonic enhancement, and symmetry-controlled modal overlap within a single predictive metric. More broadly, it identifies thickness regimes in which resonant buildup is accompanied by constructive nonlinear coupling, and distinguishes them from regimes in which apparently favorable resonance conditions remain conversion-inactive because the nonlinear source is orthogonal to the radiating harmonic mode.

Flat bands in driven-dissipative systems offer a route to engineer strongly localized, long-lived excitations, yet their selective population via incoherent pumping remains an open challenge. We study a one-dimensional chain of coupled lasing dimers arranged in a cross-stitch geometry and show that the synchronization regime of the individual dimers, controllable through pump intensity or inter-resonator distance, determines the character of the flat band hosted by the chain. In the in-phase (ferromagnetic) regime, the flat band appears as a subdominant, damped mode in the linear excitation spectrum. In the antiphase (antiferromagnetic) regime, by contrast, the dimers decouple and the flat band becomes the dominant, neutrally stable mode: it corresponds to an infinite family of Goldstone modes arising from the independent phase rotations of non-interacting dimers, and its compact localized states are directly observable in the noise response spectrum. Switching between these two regimes via pump control constitutes a pump-induced phase transition of the lasing lattice. Our results establish synchronization engineering as a practical mechanism for selective flat-band population in driven-dissipative optical systems, and open new avenues for studying flat-band physics, including nonlinear effects, Fano resonances, and excitation coherence in experimentally accessible laser and polariton platforms.

Self-organized dynamic patterns in dense active matter are striking manifestations of non-equilibrium physics. A prominent example is the macroscopic elliptical motion observed in quasi-2D bacterial suspensions, which has lacked a physical explanation. Here, we examine a minimal linear response framework coupling bacterial swimming dynamics with fluid flow, treating long-range hydrodynamic interactions as a macroscopic communication channel. We demonstrate that microscopic swim motion, via Jeffery coupling, manifests as a ``phase-leading'' response to local shear flows. System-wide sustained oscillations, on the other hand, require both a critical bacterial density and strict geometric confinement. By analytically predicting the onset cell density and maximum film thickness, our model achieves excellent quantitative agreement with experiments, establishing a unified physical framework for self-organized periodic motion of elongated body in active fluids.

The imaging problem of a specular reflector is revisited. Retaining terms through second order in the reflector surface expansion, we derive the form of the aberration-limiting aperture for arbitrary magnification assuming no bandwidth limitations. A permissible relative aperture size of the reflector is limited by a set relative aberration tolerance and scales with the tangent of the central glancing angle of incidence. These limiting aberrations become practically insignificant near backscattering. The results extend to x-ray diffracting crystals in symmetric Bragg geometry shaped as an ellipsoid of revolution. This geometry permits polychromatic imaging for hard x-rays over a bandwidth defined by the accepted range of Bragg angles, thereby suppressing aberrations of higher orders. We assess ellipsoidal crystal imagers using ray tracing simulations for two high-magnification designs with Bragg angles far from and close to backscattering. In both cases the ellipsoidal crystals produce images of higher quality compared to those formed by equivalent toroidal crystal imagers.

Rhombohedrally stacked transition metal dichalcogenides such as 3R-MoS$_2$ offer an exceptional platform for nonlinear optics, naturally forming Fabry-Pérot (FP) microcavities due to their giant dielectric contrast with the surrounding media. However, rigorously tracking the evolution of multiple harmonic fields within these unpatterned monolithic crystals remains a fundamental challenge. Here, we establish a self-consistent framework, spanning from linear broadband reflectance to second- and third-harmonic generation (SHG and THG), to systematically decode these nonlinear behaviors. Moving beyond conventional models, we demonstrate that the nonlinear emission is dictated by a delicate interplay among the intrinsic material absorption, the FP effects at the fundamental frequency, as well as those at the harmonic frequencies. When harmonic photons lie below the bandgap, weak absorption allows the nonlinear spectra to exhibit a complex modulation driven by the synergistic contribution of FP effects from both fundamental and harmonic waves. In stark contrast, severe intrinsic absorption of higher-energy photons heavily damps the FP effects of the harmonic fields, reducing the nonlinear response to an absorption-limited regime modulated almost exclusively by the FP effects at the fundamental frequency. By successfully decoupling these geometric and material contributions across different harmonic orders, our findings provide a precise design paradigm for engineering next-generation van der Waals photonic architectures.

Strong light-matter coupling gives rise to polaritons -- quasiparticles that combine both photonic and material characteristics. Here, we show that polar nanocrystals exhibit structure- and environment-dependent Raman scattering, enabled by their hybrid phonon polariton nature. Such dispersive behavior enables refractive index sensing in the mid-infrared range via visible-wavelength inelastic spectroscopy and draws parallels with molecular systems under vibrational strong coupling. Crucially, Raman scattering appears only under nanoscale confinement of phonon polaritons. For optimal structures, this leads to self-hybridization between localized phonon modes and surface phonon polaritons hosted by the same nanoparticle.

This paper reports FDTD simulations of optofluidic reconfiguration in two-dimensional silicon photonic crystal waveguides, treating structural plasticity (the creation and destruction of optical pathways) via selective fluid infiltration. Using MPB eigenmode analysis, we decouple bandgap narrowing from defect-mode weakening, showing that defect weakening dominates (2.4 times faster transmission decay than bandgap narrowing at CS_2 indices). Infiltration topology controls signal routing (L-bend selectivity S = 0.98), though modulation depth is weak (Delta varepsilon/ varepsilon_ textSi = 11 %). A phenomenological optothermal feedback model produces self-organized pathways that achieve 63 % of a hand-designed waveguide's bandgap transmission (7.6 times the heavily suppressed empty-crystal baseline). Amplitude competition between counter-propagating sources produces strong, monotonic pathway steering (DeltaCOM_x from +0.03 to +4.92 ;a), while pulsed spike-timing-dependent plasticity yields a predictable null result: the timing-sensitive cross-term is suppressed by >10^2 when pulse delays exceed the temporal pulse width. The results provide benchmarks and identify physical limits for bio-inspired reconfigurable optofluidic photonics.

Hexagonally-packed polystyrene (PS) microsphere lattices coated with tantalum pentoxide (Ta$_2$O$_5$) form scalable dielectric metasurfaces supporting tunable photonic resonances and enhanced local density of optical states (LDOS). Here we combine fabrication, optical and fluorescence spectroscopy, and multi-scale electromagnetic simulations to quantify how the thickness of Ta$_2$O$_5$ shells control far-field resonances and Rhodamine 6G (Rh6G) emission. Experimentally, Ta$_2$O$_5$ shells of 10 - 70 nm deposited on microsphere lattices generate resonances that shift red with the thickness of the shell and systematically enhance the Rh6G fluorescence relative to flat Ta$_2$O$_5$ films. The largest enhancement is obtained for 30 - 50 nm shells, when lattice resonances overlap the Rh6G excitation and emission bands. Finite-cluster finite-difference time-domain simulations reproduce the measured transmittance and reflectance spectra, confirming the assumed geometry of the Ta$_2$O$_5$ shells covering the sphere lattice. Periodic-cell simulations of single electric dipoles yield wavelength-dependent Purcell factors $Fp(λ)$ and directional $β$-factors $β_{top}(λ)$, from which we construct emission-weighted figures of merit that link LDOS modulation to the experimentally accessible top-side fluorescence enhancement. As a complementary test of our emitter-environment model, we compare simulated and measured Purcell factors for PS/Ta$_2$O$_5$ microsphere lattices. A physically motivated averaging that accounts for emitter position, orientation and ensemble spectral smoothing yields very good agreement across all shells. Overall, our results establish Ta$_2$O$_5$-coated microsphere lattices as robust dielectric substrates for surface-enhanced fluorescence and clarify how shell thickness and emitter placement jointly control photonic resonances, LDOS and fluorescence response.

Laser intensity noise limits performance in quantum sensing, metrology, and computing. Existing stabilization methods face a trade-off between bandwidth and complexity: electronic feedback loops are speed-limited, while optical resonators are constrained by narrow linewidths and locking requirements. Here, we demonstrate an all-optical "noise eater" that passively suppresses intensity fluctuations from DC to >10 gigahertz. By leveraging high-efficiency second-harmonic generation in nanophotonic lithium niobate waveguides, we operate at a pump-depletion stationary point where input fluctuations are decoupled from the output to first order. This passive and nonresonant nanophotonic device suppresses relative intensity noise by 25 to 60 dB over the full measurement bandwidth and stabilizes a noisy fiber amplifier output to the shot-noise limit. Our results establish a scalable, wide-bandwidth paradigm for laser stabilization essential for high-throughput quantum technologies and deployable photonic sensing systems.

The identification and reconstruction of charged particles, such as muons, is a main challenge for the physics program of the ATLAS experiment at the Large Hadron Collider. This task will become increasingly difficult with the start of the High-Luminosity LHC era after 2030, when the number of proton-proton collisions per bunch crossing will increase from 60 to up to 200. This elevated interaction density will also increase the occupancy within the ATLAS Muon Spectrometer, requiring more efficient and robust real-time data processing strategies within the experiment's trigger system, particularly the Event Filter. To address these algorithmic challenges, we present two machine-learning-based approaches. First, we target the problem of background-hit rejection in the Muon Spectrometer using Graph Neural Networks integrated into the non-ML baseline reconstruction chain, demonstrating a 15 % improvement in reconstruction speed (from 255 ms to 217 ms). Second, we present a proof-of-concept for end-to-end muon tracking using state-of-the-art Vision Transformer architectures, achieving ultra-fast approximate muon reconstruction in 2.3 ms on consumer-grade GPUs at 98 % tracking efficiency.

In 2025, the French Society for Astronomy \& Astrophysics (SF2A), gave the environmental transition commission the opportunity to share their considerations during a plenary session at the annual SF2A conference. This year, the presentation focused on some of the main results obtained from the survey entitled 'French astronomy and astrophysics research activities in the face of the environmental crisis, from 2019 to 2024'. The survey was initiated in 2019 by the group 'Environnement-Transition' (coordinated by P. Martin) at IRAP, whose results were presented during the SF2A annual conference 2019 in Nice. The survey was updated in 2024 by the CNRS INSU-AA prospective working group 'Climate and ecological challenge' (coordinated by S. Bontemp). The SF2A environmental transition commission took on the survey to the French institutes, sorted the answers and extracted the preliminary results. The full results will be published at the end of 2025 in the final CNRS INSU-AA 2024 prospective document. This publication presents a selection of pieces from the full survey, along with a few of the main discussions it triggers.

We introduce a GPU-accelerated multigrid Gaussian-Plane-Wave density fitting (FFTDF) approach for efficient Fock builds and nuclear gradient evaluations within Kohn-Sham density functional theory, as implemented in the GPU4PySCF module of PySCF. Our CUDA kernels employ a grid-based parallelization strategy for contracting Gaussian basis function pairs and achieve up to 80% of the FP64 peak performance on NVIDIA GPUs, with no loss of efficiency for high angular momentum (up to f-shell) functions. Benchmark calculations on molecules and solids with up to 1536 atoms and 20480 basis functions show up to 25x speedup on an H100 GPU relative to the CPU implementation on a 28-core shared memory node. For a 256-water cluster, the ground-state energy and nuclear gradients can be computed in ~30 seconds on a single H100 GPU. This implementation serves as an open-source foundation for many applications, such as ab initio molecular dynamics and high-throughput calculations.

Fundamental to the analysis of nonlinear relativistic motion is the precise characterization of the induced dynamic Doppler effects. In this work, we analyze the electromagnetic signals observed by non-inertial receivers using two frameworks to describe the relativistic motion. We first consider observer paths described by higher-order kinematic 4 vectors: relativistic acceleration and jolt. The dynamic Doppler effects of relativistic acceleration and jolt are exponential spectral broadening and exponential amplitude growth or decay. We derive compact expressions for the spectrum transformation resulting from relativistic acceleration and jolt. The jolt induces nonlinear skewed chirps in observed signals. Next we consider observer paths described by the 4D Frenet-Serret frame and the curvature and torsion of the observer path. We obtain descriptions of the amplitude and phase fluctuations of the signal in terms of the geometric parameters of curvature and torsion. Concise, interpretable descriptions of non-inertial dynamic Doppler effects provide a useful diagnostic and predictive tool for engineering applications including radar, sensing, and communications systems.

Understanding how weather and climate influence fire risk is important for many purposes, including climate adaptation planning and decision-making in sectors such as emergency management, finance, health and infrastructure (e.g., for energy and water availability). In this study, bias-corrected 20CRv2c reanalysis data are used to investigate the climatology and long-term trends of weather conditions associated with landscape fires in Australia. The McArthur Forest Fire Danger Index (FFDI) is used here as a broad-scale representation of weather conditions known to influence fire behaviour based on wind speed, humidity, temperature and rainfall measures. In particular, using this reanalysis dataset allows analysis over a longer time period than previous studies, from 1876 to 2011. Another novel aspect is that trends are examined using several different approaches, including a method to help account for the influence of interannual drivers of climate variability not previously used for fire weather analysis. Results show increases in mean and extreme seasonal FFDI values throughout Australia in general, with all statistically significant trends being positive in sign for individual climate zones. Humidity and temperature trends, attributable to human-caused climate change, are shown to be the main cause of the increase in dangerous weather conditions for fires. These findings build on previous studies, with the novel data and methods used adding confidence to the overall understanding of fire risk factors in a changing climate.

Previous research based on electronic structure calculations and molecular dynamics (MD) simulations have demonstrated that graphdiyne (GDY) is a very suitable two-dimensional membrane for the separation of small molecules in a gas mixture of different species. However, quantum effects may play a role in the dynamics of these permeation processes when light molecules are the ones involved in the crossing of the GDY subnanometric pores. In this work we report rigorous quantum-mechanical calculations together with equivalent MD simulations of the transport of H2 molecules through a static GDY membrane, as a case study for the validity of the application to these problems of classical dynamics. The force fields employed are based on an improved Lennard-Jones formulation, with parameters optimized by means of accurate ab initio calculations. It is found that, although quantum effects are still significant at the temperatures of interest (between 250 and 350 K), MD simulations are able to reasonably reproduce the dependence of the quantum permeances with the temperature. Moreover, MD permeances computed with quantum corrections through Feynman-Hibbs effective potentials provide a lower bound to quantum permeances, while the pure classical counterpart gives an upper bound, thus leading to a well delimited range of confidence of the permeation results. Furthermore, within MD simulations it is possible to incorporate the thermal motion of the GDY layer and in this situation it is observed an enhancement of the permeances with respect to the fixed membrane case, due to a significant reduction of the permeation barriers when the GDY atoms are allowed to vibrate. It seems apparent therefore, that modeling the membrane motion is crucial to provide reliable simulations of the gas transport features.

Complex systems are difficult to study not only because they are nonlinear, multiscale, and often nonstationary, but because their scientifically relevant organization is often invisible at the level of individual components, pairwise interactions, or low-order summary statistics. This review argues that topology has become valuable in complex-systems science because it provides a mathematical language for representing emergent organization when relevant structure is distributed, relational, and robust across scale. We synthesize work on persistent homology, Mapper, simplicial complexes, hypergraphs, and related operators, while distinguishing invariant-based topological methods from broader topology-inspired representations. We show how persistence formalizes multiscale stability, how higher-order models preserve collective interactions erased by pairwise graphs, and how topological approaches complement rather than replace statistics, graph theory, and geometry. We review applications in nonlinear dynamics, neuroscience, finance, ecology, materials science, and anomaly detection, emphasizing a common logic: topology turns reorganizing structure into measurable signals for regime shifts, state transitions, and early warning. Across domains, these methods are most effective when the scientific target is organizational rather than scalar, when threshold ambiguity is intrinsic to the problem, and when topology functions as a structural diagnostic or feature extractor within a broader analytic pipeline. We conclude by identifying key limitations, including representation dependence, inferential challenges, interpretability, computational scaling, and the narrowness of one-parameter workflows, and by outlining a research agenda linking topology more closely to dynamics, causality, streaming decision support, topology-aware AI, and socio-technical resilience.

This paper presents a novel terahertz (THz) bandpass filter (BPF) based on a spoof surface plasmon polariton (SSPP) waveguide with a center frequency of 1 THz and a 3 dB bandwidth of 0.3 THz. The proposed BPF comprises cascaded high-pass and low-pass elements. The high-pass element is a capacitive gap in the SSPP transition circuit, and the low-pass element is the SSPP waveguide itself. We find that the measurement results, including cut-off frequencies, align well with the theoretical predictions and simulations. To the authors' knowledge, the proposed SSPP BPF is the first of its kind.

Segregation has long been recognized as a driver of environmental inequalities, with disadvantaged groups often living in neighborhoods where heat-related risks are highest. Yet, it remains unclear how daily mobility patterns, embedded within heterogeneous urban heat fields, shape heat exposure inequalities across sociodemographic groups. Using a mobile phone dataset of daily mobility flows and urban temperature fields across 23 Spanish cities, we develop a network-based framework to quantify how different sociodemographic groups experience heat through their daily movements. We find systematic income-related inequalities, with low-income groups consistently experiencing higher exposure than high-income groups, while age-related disparities are smaller in magnitude, with younger individuals slightly more exposed than elderly ones. These inequalities intensify during commuting trips, indicating that routine mobility amplifies spatial heat gradients more than non-routine movements. We further assess whether state-of-the-art population-based mobility models can capture these observed inequalities. The gravity model underestimates income- and age-related exposure differences, whereas the parameter-free radiation model captures most of the observed disparities. This indicates that heat exposure inequalities largely emerge from the interplay between the unequal organization of daily activities across sociodemographic groups and urban heat gradients, rather than from group-specific behavioral differences. Our findings provide a generalizable framework to characterize mobility-driven heat exposure inequalities and inform climate-resilient urban planning and public health strategies as cities face intensifying climate-related risks.

Achromatic flying-focus enables programmable control of intensity peak velocity, with applications in ultrafast optics. However, spatiotemporal coupling inherently elongates ultrashort pulses by introducing frequency-dependent focusing and arrival-time dispersion. We present a theoretical model identifying this pulse-lengthening effect and propose a radially-dependent spectral chirp to compensate for chromatic timing mismatches. Numerical simulations confirm that this approach preserves both pulse duration and programmed flying-focus velocity over extended focal regions. Additionally, dispersive media such as plasmas can naturally mitigate elongation. These results extend achromatic flying-focus techniques to ultrashort pulses, enabling new opportunities in laser--plasma interactions and high-field nonlinear optics.

We model an electorate voting on the funding of a public good in a two-party system in an evolutionary game theory framework. Voters adopt one of four strategies: Consensus-makers, Gridlockers, Party 1 Zealots, and Party 2 Zealots, which they may change via imitation. The public good benefits both individuals locally and those in neighbouring regions due to spillover effects. A system of differential equations governs the spatial movement of individuals and shifts in their voting strategies. Local social interactions drive strategy evolution, while migration occurs toward areas of higher utility, which is a function of both social and economic factors. Our results reveal bistability and significant spatial variations. Locally, populations converge to a politically gridlocked state or a mix of consensus-makers and zealots, determining public good provisioning. We find that public good spillovers generate a free-rider effect and poorly funded regions become spatially tied to, and dependent upon, well-funded ones.

Multi-agent systems powered by large language models (LLMs) are increasingly deployed in settings that shape consequential decisions, both directly and indirectly. Yet it remains unclear whether their outcomes reflect collective reasoning, systematic bias, or mere chance. Recent work has sharpened this question with naming games, showing that even when no individual agent favors any label a priori, populations rapidly break symmetry and reach consensus. Here, we reveal the mechanism by introducing a minimal model, Quantized Simplex Gossip (QSG), and trace the microscopic origin of this agreement to mutual in-context learning. In QSG, agents maintain internal belief states but learn from one another's sampled outputs, so one agent's arbitrary choice becomes the next agent's evidence and can compound toward agreement. By analogy with neutral evolution, we call this sampling-driven regime memetic drift. QSG predicts a crossover from a drift-dominated regime, where consensus is effectively a lottery, to a selection regime, where weak biases are amplified and shape the outcome. We derive scaling laws for drift-induced polarization as a function of population size, communication bandwidth, in-context adaptation rate, and agents' internal uncertainty, and we validate them in both QSG simulations and naming-game experiments with LLM populations. Together, these results provide a framework for studying the collective mechanisms of social representation formation in multi-agent systems.

Raman phonons arise from the inelastic scattering of light and represent quantized molecular motions that mediate a wide range of spectroscopic and nonlinear optical phenomena. In this work, we clarify the physical role of Raman phonons within a previously-developed time-domain framework based on the Raman-induced index modulation, and show that phonons correspond to the oscillatory component of the Raman-induced index modulation. The analysis further reveals a linear phonon-mediated interaction embedded within Raman scattering, in which optical fields couple through wave-vector matching with existing phonons. This mechanism underlies what has long been described as coherent Stokes and anti-Stokes scattering, as well as molecular modulation. Building on this insight, we introduce a phonon-controlled approach that enables efficient conversion into a selected Stokes order by tuning the wave-vector-matching relation between the driven phonons and the targeted Raman process. These results provide a clearer physical interpretation of Raman phonons and its corresponding Raman dynamics and offer new strategies for controlling Raman interactions.

The requirement of generating predictions that exactly fulfill the fundamental symmetry of the corresponding physical quantities has profoundly shaped the development of machine-learning models for physical simulations. In many cases, models are built using constrained mathematical forms that ensure that symmetries are enforced exactly. However, unconstrained models that do not obey rotational symmetries are often found to have competitive performance, and to be able to \emph{learn} to a high level of accuracy an approximate equivariant behavior with a simple data augmentation strategy. In this paper, we introduce rigorous metrics to measure the symmetry content of the learned representations in such models, and assess the accuracy by which the outputs fulfill the equivariant condition. We apply these metrics to two unconstrained, transformer-based models operating on decorated point clouds (a graph neural network for atomistic simulations and a PointNet-style architecture for particle physics) to investigate how symmetry information is processed across architectural layers and is learned during training. Based on these insights, we establish a rigorous framework for diagnosing spectral failure modes in ML models. Enabled by this analysis, we demonstrate that one can achieve superior stability and accuracy by strategically injecting the minimum required inductive biases, preserving the high expressivity and scalability of unconstrained architectures while guaranteeing physical fidelity.

Background: The identification of optimal signal detection methods is hindered by the lack of reliable reference datasets. Existing datasets do not capture when adverse events (AEs) are officially recognized by regulatory authorities, preventing restriction of analyses to pre-confirmation periods and limiting evaluation of early detection performance. This study addresses this gap by developing a time-indexed reference dataset for the European Union (EU), incorporating the timing of AE inclusion in product labels along with regulatory metadata. Methods: Current and historical Summaries of Product Characteristics (SmPCs) for all centrally authorized products (n=1,513) were retrieved from the EU Union Register of Medicinal Products (data lock: 15 December 2025). Section 4.8 was extracted and processed using DeepSeek V3 to identify AEs. Regulatory metadata, including labelling changes, were programmatically extracted. Time indexing was based on the date of AE inclusion in the SmPC. Results: The database includes 17,763 SmPC versions spanning 1995-2025, comprising 125,026 drug-AE associations. The time-indexed reference dataset, restricted to active products, included 1,479 medicinal products and 110,823 drug-AE associations. Most AEs were identified pre-marketing (74.5%) versus post-marketing (25.5%). Safety updates peaked around 2012. Gastrointestinal, skin, and nervous system disorders were the most represented System Organ Classes. Drugs had a median of 48 AEs across 14 SOCs. Conclusions: The proposed dataset addresses a critical gap in pharmacovigilance by incorporating temporal information on AE recognition for the EU, supporting more accurate assessment of signal detection performance and facilitating methodological comparisons across analytical approaches.

Multicellular self-organization drives development in biological organisms, yet a comprehensive theory is lacking as basic properties of cells can complicate common approaches. Framing such properties by dynamic graphs led to new theoretical propositions for multicellular self-organization in Escherichia coli. Here, corresponding ideas are developed from biologically-general first principles. The resulting perspective could aid both experimental and computational approaches to multicellular biology as well as efforts to control and engineer it.

Connectivity structure shapes neural computation, but inferring this structure from population recordings is degenerate: multiple connectivity structures can generate identical dynamics. Recent work uses low-rank recurrent neural networks (lrRNNs) to infer low-dimensional latent dynamics and connectivity structure from observed activity, enabling a mechanistic interpretation of the dynamics. However, standard approaches for training lrRNNs can recover spurious structures irrelevant to the underlying dynamics. We first characterize the identifiability of connectivity structures in lrRNNs and determine conditions under which a unique solution exists. Then, to find such solutions, we develop an inference framework based on maximum entropy and continuous normalizing flows (CNFs), trained via flow matching. Instead of estimating a single connectivity matrix, our method learns the maximally unbiased distribution over connection weights consistent with observed dynamics. This approach captures complex yet necessary distributions such as heavy-tailed connectivity found in empirical data. We validate our method on synthetic datasets with connectivity structures that generate multistable attractors, limit cycles, and ring attractors, and demonstrate its applicability in recordings from rat frontal cortex during decision-making. Our framework shifts circuit inference from recovering connectivity to identifying which connectivity structures are computationally required, and which are artifacts of underconstrained inference.

Biological systems are governed by coupled interactions between intracellular metabolism and bioreactor operation that span multiple time scales. Constraint-based metabolic models are widely used to describe intracellular metabolism, but repeatedly solving the optimization problem at each time step in dynamic models introduces numerical challenges related to infeasibility and computational efficiency. This work presents a multi-scale modeling framework that integrates genome-scale, constraint-based metabolic models with dynamic bioreactor simulations. Intracellular metabolism is described using positive flux variables in a parsimonious flux balance analysis, and the resulting embedded optimization problem is replaced by a neural network surrogate. The surrogate provides a smooth approximation of the embedded optimization mapping and eliminates repeated linear program solves during simulation. The approach is demonstrated for fed-batch fermentation of Escherichia coli, in which the surrogate model yields intracellular fluxes under substrate-limited conditions, whereas the underlying linear program would otherwise be infeasible. The framework provides a continuous representation of intracellular metabolism suitable for dynamic simulation of genome-scale models in bioreactor configurations.

This work analyzes the RAID dataset to evaluate human responses to affine image distortions, including rotation, translation, scaling, and Gaussian noise. Using Mean Squared Error (MSE), the study establishes human detection thresholds for these distortions, enabling comparison across types. Statistical analysis with ANOVA and Tukey Kramer tests reveals that observers are significantly more sensitive to Gaussian noise, which consistently produced the lowest detection thresholds. Fourier analysis further shows that high-frequency components act as a visual mask for Gaussian noise, demonstrating a strong correlation between high frequency energy and detection thresholds. Additionally, spectral orientation influences the perception of rotation. Finally, the study employs the PixelCNN model to show that image probability significantly correlates with detection thresholds for most distortions, suggesting that statistical likelihood affects human visual tolerance.

The rapid scaling of Protein Language Models (PLMs) has unlocked unprecedented accuracy in protein structure prediction and design, but the quadratic memory growth of the Key-Value (KV) cache during inference remains a prohibitive barrier for single-GPU deployment and high-throughput generation. While 8-bit quantization is now standard, 3-bit quantization remains elusive due to severe numerical outliers in activations. This paper presents TurboESM, an adaptation of Google's TurboQuant to the PLM domain. We solve the fundamental incompatibility between Rotary Position Embeddings (RoPE) and orthogonal transformations by deriving a RoPE-first rotation pipeline. We introduce a head-wise SVD calibration method tailored to the amino acid activation manifold, a dual look-up table (LUT) strategy for asymmetric K/V distributions, and a 1-bit Quantized Johnson-Lindenstrauss (QJL) residual correction. All experiments are conducted on ESM-2 650M, where our implementation achieves a 7.1x memory reduction (330 MB to 47 MB) while maintaining cosine similarity > 0.96 in autoregressive decoding across diverse protein families, including short peptides, transmembrane helices, enzyme active site fragments, and intrinsically disordered regions. We further implement a Triton-based fused decode attention kernel that eliminates intermediate dequantization memory allocations, achieving a 1.96x speedup over the PyTorch two-step path for the KV fetch operation alone; however, TurboESM incurs a prefill overhead of 21-27 ms relative to the original model due to KV quantization and packing, making it most suitable for memory-bound scenarios rather than latency-critical short-sequence workloads. Analysis reveals that PLMs exhibit sharper outlier profiles than large language models (LLMs) due to amino acid vocabulary sparsity, and our method effectively addresses these distributions.

Predicting whether someone with mild cognitive impairment (MCI) will progress to Alzheimer's disease (AD) is crucial in the early stages of neurodegeneration. This uncertainty limits enrollment in clinical trials and delays urgent treatment. The Boundary Sharpness Coefficient (BSC) measures how well-defined the gray-white matter boundary looks on structural MRI. This study measures how BSC changes over time, namely, how fast the boundary degrades each year works much better than looking at a single baseline scan for predicting MCI-to-AD conversion. This study analyzed 1,824 T1-weighted MRI scans from 450 ADNI subjects (95 converters, 355 stable; mean follow-up: 4.84 years). BSC voxel-wise maps were computed using tissue segmentation at the gray-white matter cortical ribbon. Previous studies have used CNN and RNN models that reached 96.0% accuracy for AD classification and 84.2% for MCI conversion, but those approaches disregard specific regions within the brain. This study focused specifically on the gray-white matter interface. The approach uses temporal slope features capturing boundary degradation rates, feeding them into Random Survival Forest, a non-parametric ensemble method for right-censored survival data. The Random Survival Forest trained on BSC slopes achieved a test C-index of 0.63, a 163% improvement over baseline parametric models (test C-index: 0.24). Structural MRI costs a fraction of PET imaging ($800--$1,500 vs. $5,000--$7,000) and does not require CSF collection. These temporal biomarkers could help with patient-centered safety screening as well as risk assessment.

Recent advances in neurotechnologies and decades of scientific and clinical research have made closed-loop electrical neuromodulation one of the most promising avenues for the treatment of drug-resistant epilepsy (DRE), a condition that affects over 15 million individuals globally. Yet, with the existing clinical state of the art, only 18% of patients with DRE who undergo closed-loop neuromodulation become seizure-free. In a recent study, we demonstrated that a simple proportional feedback policy based on the framework of passivity-based control (PBC) can significantly outperform the clinical state of the art. However, this study was purely numerical and lacked rigorous mathematical analysis. The present study addresses this gap and provides the first rigorous analysis of PBC for the closed-loop control of epileptic seizures. Using the celebrated Epileptor neural mass model of epilepsy, we analytically demonstrate that (i) seizure dynamics are, in their standard form, neither passive nor passivatable, (ii) epileptic dynamics, despite their lack of passivity, can be stabilized by sufficiently strong passive feedback, and (iii) seizure dynamics can be passivated via proper output redesign. To our knowledge, our results provide the first rigorous passivity-based analysis of epileptic seizure dynamics, as well as a theoretically-grounded framework for sensor placement and feedback design for a new form of closed-loop neuromodulation with the potential to transform seizure management in DRE.

Phylogenetic comparative methods (PCMs) are widely used to study trait evolution. However, many evolutionary histories involve reticulate evolutionary scenarios, such as hybridization, that violate core assumptions of these methods. In this study, we evaluate how such violations affect the performance of PCMs. In particular, we focus on the ancestral character estimation, evolutionary rate estimation, and model selection. We simulate continuous trait evolution on various phylogenetic network topologies and assess the performance of PCMs that assume a bifurcating tree (i.e., major tree of the network) as the underlying model of evolution. We found that the performance of the tested PCMs was suboptimal. Using random forest, generalized linear models, and model-based clustering, we identified key factors contributing to these inaccuracies. Our results show that frequent and/or recent hybridization accompanied by one ore more transgressive events and rapidly evolving traits (i.e., high evolutionary rate) lead to significant estimation error, especially with respect to rate estimation and model choice. These factors substantially shift trait values away from tree-based model expectations, leading to overall increased error in parameter estimates. Our study demonstrates cases in which PCMs that rely on trees are likely to misinterpret biological histories and offers recommendations for researchers studying systems with complex evolutionary histories.

Protein structural ensembles from NMR spectroscopy capture biologically important conformational heterogeneity, but it remains difficult to determine whether observed variation reflects coordinated motion or noise-like artifacts. We evaluate the Spectral Coherence Index (SCI), a model-free, rotation-invariant summary derived from the participation-ratio effective rank of the inter-model pairwise distance-variance matrix. Under grouped primary analysis of a Main110 cohort of 110 NMR ensembles (30--403 residues; 10--30 models per entry), SCI separated experimental ensembles from matched synthetic incoherent controls with AUC-ROC $= 0.973$ and Cliff's $δ= -0.945$. Relative to an internal 27-protein pilot, discrimination softened modestly, showing that pilot-era thresholds do not transfer perfectly to a larger, more heterogeneous cohort: the primary operating point $τ= 0.811$ yielded 95.5\% sensitivity and 89.1\% specificity. PDB-level sensitivity remained nearly unchanged (AUC $= 0.972$), and an independent 11-protein holdout reached AUC $= 0.983$. Across 5-fold grouped stratified cross-validation and leave-one-function-class-out testing, SCI remained strong (AUC $= 0.968$ and $0.971$), although $σ_{R_g}$ was the stronger single-feature discriminator and a QC-augmented multifeature model generalized best (AUC $= 0.989$ and $0.990$). Residue-level validation linked SCI-derived contributions to experimental RMSF across 110 proteins and showed broad concordance with GNM-based flexibility patterns. Rescue analyses showed that Main110 softening arose mainly from size and ensemble normalization rather than from loss of spectral signal. Together, these results establish SCI as an interpretable, bounded coherence summary that is most useful when embedded in a multimetric QC workflow for heterogeneous protein ensembles.

When designing control strategies for an infectious disease it is critical to identify the key pathways of transmission. Data on infected hosts - when they were born, where they lived and with whom they interacted - can help infer sources of infection and transmission clusters. However such data are generally not powerful enough to identify infector-infectee pairs with any certainty. Whole-genome sequencing data of the underlying pathogen, on the other hand, can serve as a powerful adjoint to these data as they can be used to estimate a time to a most recent common ancestor between two infected hosts. and in turn their relative proximity in the transmission tree. A statistical model that explains the genetic distance between different host pathogens and associated risk factors can therefore inform key risk factors for transmission itself. We show how graph neural networks (GNNs) are a powerful and natural modelling architecture for such a problem. By treating the epidemiological dataset as a graph where infected hosts are nodes and edges are weighted by the genetic distance between different host pairs, we show how a GNN can be fit to predict the genetic distance between known hosts and new, unsequenced hosts. Comparisons with other established approaches show that GNNs have useful performance advantages albeit with greater computational cost.

Quantifying human movement (kinematics) and musculoskeletal forces (kinetics) at scale, such as estimating quadriceps force during a sit-to-stand movement, could transform prediction, treatment, and monitoring of mobility-related conditions. However, quantifying kinematics and kinetics traditionally requires costly, time-intensive analysis in specialized laboratories, limiting clinical translation. Scalable, accurate tools for biomechanical assessment are needed. We introduce OpenCap Monocular, an algorithm that estimates 3D skeletal kinematics and kinetics from a single smartphone video. The method refines 3D human pose estimates from a monocular pose estimation model (WHAM) via optimization, computes kinematics of a biomechanically constrained skeletal model, and estimates kinetics via physics-based simulation and machine learning. We validated OpenCap Monocular against marker-based motion capture and force plate data for walking, squatting, and sit-to-stand tasks. OpenCap Monocular achieved low kinematic error (4.8° mean absolute error for rotational degrees of freedom; 3.4 cm for pelvis translations), outperforming a regression-only computer vision baseline by 48% in rotational accuracy (p = 0.036) and 69% in translational accuracy (p < 0.001). OpenCap Monocular also estimated ground reaction forces during walking with accuracy comparable to, or better than, our prior two-camera OpenCap system. We demonstrate that the algorithm estimates important kinetic outcomes with clinically meaningful accuracy in applications related to frailty and knee osteoarthritis, including estimating knee extension moment during sit-to-stand transitions and knee adduction moment during walking. OpenCap Monocular is deployed via a smartphone app, web app, and secure cloud computing (https://opencap.ai), enabling free, accessible single-smartphone biomechanical assessments.

Machine learning models of chemical bioactivity are increasingly used for prioritizing a small number of compounds in virtual screening libraries for experimental follow-up. In these applications, assessing model accuracy by early hit enrichment such as Positive Predicted Value (PPV) calculated for top N hits (PPV@N) is more appropriate and actionable than traditional global metrics such as AUC. We present KANEL, an ensemble workflow that combines interpretable Kolmogorov-Arnold Networks (KANs) with XGBoost, random forest, and multilayer perceptron models trained on complementary molecular representations (LillyMol descriptors, RDKit-derived descriptors, and Morgan fingerprints).