Papers

A high resolution, Diagnostic Instrument Manipulator (DIM)-based x-ray Bragg crystal spectrometer has been calibrated for and deployed at the National Ignition Facility (NIF) to diagnose plasma conditions in ignition capsules near stagnation times. The spectrometer has two conical crystals in the Hall geometry focusing rays from the Kr He$\alpha$, Ly$\alpha$, and He$\beta$ complexes onto a streak camera, with the physics objectives of measuring time-resolved electron density and temperature through observing Stark broadening and the relative intensities of dielectronic satellites. A third von Hámos crystal that time-integrates the Kr He$\alpha$, He$\beta$ and intervening energy range provides in-situ calibration for the streak camera signals. The spectrometer has been absolutely calibrated using a microfocus x-ray source, an array of CCD and single-photon-counting detectors, and multiple K- and L-absorption edge filters at the Princeton Plasma Physics Laboratory (PPPL) x-ray laboratory. Measurements of the integrated reflectivity, energy range, and energy resolution for each crystal are discussed. These calibration data provide absolute x-ray signal levels for NIF measurements, enabling precise filter selection and comparisons to simulations.

Compact representations of rarefied flows must preserve kinetic observables, not only smooth macroscopic fields. We introduce Gaussian kinetic representations for discrete velocity method (DVM)-Shakhov solutions of normal shocks and a lid-driven cavity. A positive log-density phase-space model reconstructs shock velocity distribution functions (VDFs) and their moments, while a moment-field model compresses wall-bounded cavity structure. Log-density training recovers heat flux, stress, and third- and fourth-order shock moments without explicit moment supervision; the cavity representation gives a compact continuous wall-transport map.

The support structure is an integral part of the design of nuclear fusion reactors, especially 3D stellarator devices. A practical reactor's coils and support structures must have three competing qualities: an accurate magnetic field for good confinement, sufficient rigidity to protect the brittle high-temperature superconductor (HTS) from damage, and a simple geometry for low-cost construction. In existing devices, the coil geometry is often optimized without knowledge of the support structures' design and the coils' true stress and deformation. The support structures are then placed by hand through repeated finite element analyses (FEA) until engineering requirements are met. This makes the structural design of stellarator coil systems lengthy and labor-intensive. Using new developments in differentiable structural mechanics, we present coil-fem, an open-source software tool that integrates support differentiable FEA into the stellarator coil optimization loop. It enables the integrated optimization of coil geometry and support clamp locations to simultaneously reduce magnetic field errors and stresses in the coil body. We also present the first combined coil-support optimization in the stellarator literature. Using a penalty term based on coil-fem, we produced a coil set with 2.4x lower RMS von Mises stress and similar field error compared to an unoptimized baseline.

Using 20 OMEGA laser beams at the Laboratory for Laser Energetics, University of Rochester, to irradiate a flat plastic target in a hollow ring configuration, we created supersonic cylindrical stable plasma jets with self-generated megagauss magnetic fields extending out to > 4 mm. These well-collimated magnetized jets possess a number of distinct and novel properties that will allow us to study the dynamics, physical processes and scaling properties of astrophysical jets not feasible with other laboratory settings. The dimensionless parameters of these laboratory jets fall in the same regime as those of YSO jets. They will also provide new versatile laser-based platforms to study magnetized shocks, shear flows and other plasma processes under controllable conditions.

The statistics of a classical flow is a problem of quantum mechanics in loop space, and not by analogy: the loop functional of any flow is the loop-ordered exponential of the covariant derivative $\partial+\mathrm{i}v/\nu$. We follow this representation from the Navier–Stokes equation to the Euler ensemble, the fixed point of the loop equation regularized by polygons with $N$ sides. Near the turning angle $\beta=\pi$ an exact operator identity, followed by an asymptotic continuum step and without a saddle-point approximation, reduces the ensemble to two quantum pendulums, the Mathieu equation; this supersedes our earlier WKB evaluation. A Monte Carlo simulation up to $N=10^5$ confirms the reduction. Extreme turbulence has singularities at small scales, and, as in quantum field theory, we define it by this regularization and a renormalization: the leading power of $N$ is removed, and the turbulent viscosity $\tilde\nu$ remains as the single physical scale. This is a proposed definition of turbulence at infinite Reynolds number; its continuum spectrum is computed under a stated convergence condition, and its identification with physical turbulence is a hypothesis. The energy spectrum is a function of $\kappa=k\sqrt{\tilde\nu(t+t_0)}$ that falls as $1/k$ and then as $k^{-2}$. At $\kappa=\sqrt2$ it has a weak nonanalyticity: its second derivative diverges logarithmically. At a fixed wave number the decay is therefore singular at the finite time $t_*=2/(\tilde\nu k^2)-t_0$, when $t_*>0$, and the vorticity correlations oscillate in space with the period $\sqrt2\,\pi\sqrt{\tilde\nu(t+t_0)}$. Of the limits of the ensemble with structure, only the pendulum limit is taken at a critical point and has a bulk dissipation that vanishes with the viscosity. A companion paper compares it with simulations and experiments.

Evolution of the hot spot plasma conditions was measured using high-resolution x-ray spectroscopy at the National Ignition Facility (NIF). The capsules were filled with DD gas with trace levels of Kr, and had either a high-density-carbon (HDC) ablator or a tungsten (W)-doped HDC ablator. Time-resolved measurement of the Kr He$\beta$ spectra, absolutely calibrated by a simultaneous time-integrated measurement, allows inference of the electron density and temperature through observing Stark broadening and the relative intensities of dielectronic satellites. By matching the calculated hot spot emission using a collisional-radiative code to experimental observations, the hot spot size and areal density are determined. These advanced spectroscopy techniques further reveal the effect of W dopant in the ablator on the hot spot parameters for their improved implosion performance.

With the detection of the Higgs boson, the standard model is an almost complete theory. Missing from the model is a massless spin-two boson, the graviton. We might reasonably expect that detection of gravitational radiation would include the observation of the graviton as the quanta of the radiation. However, the connection between gravitational radiation observations and the theoretical graviton has yet to be realized due to the physical limitations on single-graviton detections. A more promising approach to the demonstration of the non-classical nature of gravitational radiation is to look at quantum entanglement in bipartite detections.

Discrete global grid systems are conventionally designed over a prior coordinate reference system and inherit its compromises. Hex9 inverts the direction of design: we ask what requirements a hierarchical grid must satisfy to be geometrically coherent – intrinsic orientability, flat mode transport, vertex closure, refinement commutativity – and show that these requirements essentially determine the grid. The admissible cell primitive is the triangle; the admissible seed is the octahedral triangulation of S^2; admissible refinement is uniquely aperture 9, and uniquely up to chirality, with exactly one orientation of the hexagonal dual lattice per chirality – fixed by a machine-verified exhaustive enumeration. The surviving structure is a shifted-aperture-9 hexagonal hierarchy in which every cell carries a unique address derived from the construction alone: truncated at level L, the address is a DGGS zonal identifier in the sense of OGC Topic 21; carried to the limit, a function recovers from it a point on the reference ellipsoid to arbitrary precision. The addressing is quasi-continuous – position is recoverable everywhere except on a measure-zero set of seams – rather than continuous in the strict ISO 19111 sense; the same object serves as both zonal identifier and position-recovery coordinate, with no prior coordinate reference system as input. A separable geometric realisation – an analytical octahedral base projection composed with an optimal-transport-derived area-correcting warp – places the grid on WGS84 with quasi-uniform cell areas: at level 5, 99% of the 708,588 cells lie within 0.005% of ideal area, with residual deviation confined to the six octahedral vertices required by the topology. The combinatorial grid is projection- and ellipsoid-independent; only the warp is specific to the reference body, and is recomputable for any ellipsoid, terrestrial or planetary.

This paper provides a rigorous, self-contained investigation of consensus segmentation. We derive the mathematical foundations from first principles – the generative model, EM algorithm, Van Leemput's marginalization analysis, identifiability conditions, Spatial STAPLE, and deep variational formulations – and validate each theoretical prediction through controlled experiments. The central finding is sobering: under common conditions, STAPLE reduces to thresholded majority voting, suffers 95% EM suboptimality, and collapses under class imbalance. These are not edge cases but typical scenarios in medical imaging. Majority voting – simple, non-parametric, and robust – is a surprisingly strong baseline that the field has perhaps too hastily dismissed in favor of more "sophisticated" methods. At the same time, the deep consensus model demonstrates that the consensus problem is not inherently difficult – it becomes tractable when the image is used alongside the labels. And conformal prediction shows that formal uncertainty guarantees are achievable and practical. We hope this work encourages practitioners to critically evaluate their consensus methods rather than applying STAPLE by default, and provides the mathematical and empirical foundation for more principled approaches.

We build a motion suppression pipeline based on Koopman operator theory, which provides a way to turn nonlinear dynamics into linear ones by looking at the data through the right set of mathematical "lenses" (called observables). We test three versions of this idea: plain DMD that works directly on pixel values, an extended version (EDMD) that adds physically motivated features like squared intensities and spatial gradients to better capture how the MRI signal and tissue motion interact, and a neural network version that tries to learn the best features automatically. A key practical contribution is time-course repetition: we tile the entire temporal series multiple times before decomposition, which does not change the underlying dynamics but gives the algorithm more data to work with, fixing a dimensionality bottleneck that otherwise prevents the extra features from helping. The full pipeline works slice by slice, dividing each image into small overlapping blocks, applying the Koopman lifting and DMD to separate slow contrast enhancement from fast motion based on their characteristic frequencies, and blending the corrected blocks back together.

We investigate the collective dynamics of a bundle of charged particles undergoing cyclotron motion in a uniform magnetic field when subjected to a short-duration electric pulse. Using the geometric framework based on the evolution of trajectory congruences, we analyze how the pulse affects the expansion, shear, and rotation of a small family of trajectories. We show that the geometric imprint persists after the pulse has vanished, manifesting as a memory of the transient perturbation. Unlike gravitational memory effects, this does not manifest itself in focusing behaviour of the trajectories, and instead implies a restructuring of the shear component before and after the pulse. We offer direct analytic and regression based arguments for the same.

Animals navigating complex vertical environments must secure stable footholds, a challenge for species without feet. While arboreal climbing has evolved repeatedly in snakes, the physical mechanisms they use to scale broad, nearly flat surfaces remain poorly understood. By measuring three-dimensional body kinematics and per-contact forces on a smooth vertical wall with protruding posts, we show that cornsnakes climb by dynamically balancing forces across a highly redundant network of 5 to 16 simultaneous contacts–far exceeding the three contacts minimally required for physical stability. Using a computational model and a robotic climber, we demonstrate that while simple body undulations and passive friction are mechanically sufficient to climb this terrain, snakes systematically deviate from this passive baseline. While downward climbing relies primarily on friction, ascending snakes actively generate positive mechanical work at their contacts to propel themselves. Furthermore, we found that whenever a snake engages a new contact, it triggers a stereotyped, system-wide redistribution of force that seamlessly integrates the new foothold without disrupting whole-body balance. These results reveal how a continuous, flexible body can transform sparse environmental features into a robust, fault-tolerant network. This mechanism provides a biomechanical framework for understanding the repeated evolution of limbless climbing and offers physical principles for designing agile robots for unstructured terrain.

Rapidly scaling autonomous science is limited not only by algorithms, compute or data volume, but by which physical records a platform exposes before action. We formulate physically accessible decision-making (PADM) and a measurement-access risk frontier: the Bayes-optimal target risk minimized over records realizable under cost, bandwidth, latency, disturbance, memory and actuation constraints. The frontier gives a no-free-autonomy limit: automation cannot collapse decision uncertainty by computation alone; an optimal controller cannot remove target components absent from its record, and closing that gap requires expanded access, auditing, tolerated disturbance, slower operation or restricted deployment. In monitored feedback, displacement-only control remains exposed to a hidden switching force, whereas a finite-bandwidth cue recovers part of the missing projection before action. A chemistry-aware candidate-ranking audit with a 1000-target stress panel, Gaussian sensing, hidden-regime decisions and cost-aware/thermodynamic channel selection provide reproducible checks. PADM identifies target-specific audit value and residual oracle gaps before deployment.

We extend the persistent homology analysis of~\paperone{} to the full Roman–Byzantine trade network (0–1453\,ce), using 2{,}599 nodes and 4{,}503 trimodal edges calibrated against the orbis Geospatial Network Model. Five results are reported. % (i)~The $H_t{=}0$ western sub-network result of~\paperone{} is a data-coverage artifact: with full western representation ($N_{\rm west}=987$, $\beta_1\approx52$ cycles per decade) a baseline East–West entropy gap of $+2.22$ units is present from 0\,ce and grows at $+3.3\times10^{-3}$\,yr$^{-1}$, predating the Theodosian partition by four centuries. % (ii)~A hub-selection artifact in degree-heterogeneous networks can reverse the sign of the inferred Phase~III slope, requiring full-coverage or stratified sampling for reliable structural-break detection. % (iii)~Decomposing Byzantine resilience into geographic ($H_{\rm geo}$) and economic ($H_{\rm eco}$) components reveals a peak decoupling ratio $R_d = H_{\rm eco}/H_{\rm geo} = 47.7$ at 620\,ce, falling to 13.9 at 640\,ce, quantifying the McCormick–Ward-Perkins historiographical debate as a contrast between two network layers operating on different timescales. % (iv)~The inter-decade $W_2$ Wasserstein velocity identifies the Late Roman–Early Byzantine transition (495\,ce) as the highest topological-velocity event of the 1,453-year record; the cross-network Wasserstein ratio increases by $150$–$300\times$ after the Chrysobull of 1082\,ce, providing an independent diagram-space analogue of $R_d$. Both the Western collapse (476\,ce) and the Byzantine endpoint (1453\,ce) occur at $H^{\ast}\approx0.524$, interpreted as a candidate topological percolation threshold.

Efficient concentration and transport of electromagnetic energy through electromagnetically thick structures often requires resonant phenomenon and careful design considerations. Here, we introduce a realistic non-resonant approach based on electromagnetically thick self-dual metasurfaces that can funnel electromagnetic waves through subwavelength regions and without requiring magnetic materials. By satisfying the self-duality condition, the proposed metasurfaces support impedance-matched propagation and enable reflectionless energy transfer regardless of the metasurface thickness or structural details. This mechanism also allows selected control over the internal field while maintaining reasonable operational bandwidth. Metasurface elements are designed individually, and full-wave simulations confirm the predicted behavior in sample representative cases. The proposed framework provides a general strategy for robust electromagnetic energy routing and confinement, with potential impact in nonlinear optics, sensing and particle manipulation, near-field imaging, advanced absorber technologies, and wide-angle antenna systems.

In the USA, periodontal diseases, a spectrum of inflammatory conditions, affect 4 out of 10 adults (>=30 years). Current standards of care in clinical assessment of these diseases are invasive, subjective, only semi-quantitative, and primarily detecting later stages. Applications of ultrasonography in periodontology has been emerging in recent years. Despite such growing interest, Quantitative ultrasound (QUS) approaches remain largely unexplored and their utility for longitudinal characterization of oral inflammation has yet to be established. Here, we present one of the early investigations into the potential of QUS techniques for inflammation monitoring. In a staggered study involving a pig cohort (N=8), interdental gingival tissues at three interproximal oral sites from four quadrants were enrolled. The study involved baseline and five inflammation timepoints. Inflammation was induced using two complementary approaches at each site. Two QUS parameters of attenuation coefficient slope (ACS) and backscatter intensity (BSI) were investigated. Sex and oral sites were also used to stratify the longitudinal QUS estimates with inflammation. Results showed that both ACS and BSI were statistically significant from the healthy baseline across all oral sites at week 2 and|or 4 (combined sexes). Overall, inflammation inoculation was associated with a decrease in ACS and an increase in BSI. For BSI, week4 and/or week 2 remained statistically significant across males and females at all oral sites. ACS variations were spread across oral sites and sexes, with PM4-Dis as overall non-significant. The 2D classification (baseline from week 2) accuracy, were 92%, 82%, and 74.2% for M1-Dis, PM4-Dis, and PM3-Dis, respectively This study is among early implementation of QUS approaches for periodontal inflammation characterization.

More than 80 years ago Kramers published a paper calculating how fast a Brownian particle escapes from a potential well over an activation barrier. Since then Kramers' model has been widely adopted by nuclear physics, biophysics and chemical physics communities as a description of activated barrier crossing. From a chemical kinetics perspective, Kramers' theory provides a mapping from continuous dynamics to discrete-state chemical kinetics. Motivated by recent developments, this Perspective provides a rigorous way of performing such a mapping, explaining why and how Kramers' theory works from several points of view. Specifically, we consider transitions of a Brownian particle between two potential wells corresponding to the “reactant” and the “product” of a chemical reaction. A central unifying idea is to divide the equilibrium ensemble of possible states of the system into two sub-ensembles corresponding to the reactant and product states and then to consider fluxes between these sub-ensembles. Importantly, naive separation based on the location measured relative to the barrier top does not result in a mapping that is physically tenable, and instead the past of the trajectory should be considered. Thus constructed reactant and product ensembles provide an internally consistent description of the problem when also viewed from two different perspectives: one based on the definition of the rate as a conditional transition probability per unit time and the other based on the relaxation modes of the time-evolution operator governing the dynamics.

Multiphysics simulation with lattice Boltzmann methods (LBM) requires a scheme hand-derived for each partial differential equation (PDE), a labor-intensive, error-prone bottleneck. We recognize our recently proposed class of LBM schemes as a discrete-kinetic relaxation approximation of conservation laws and generalize its hand derivation to an automated one for systems of hyperbolic, parabolic, and mixed-type conservation laws. The derivation splits into three steps: First, the PDE system is equivalently rearranged into a first-order cascade of conservation laws: every spatial derivative in flux or source becomes an auxiliary variable, recursively for higher derivatives, so all fluxes are algebraic and updates stay local. Second, the augmented system is approximated by a discrete-velocity kinetic relaxation model with linear, constant-coefficient transport: all nonlinearity resides in a local equilibrium embedding the flux exactly in its first moment, trading the low-Mach truncation for an a priori checkable sub-characteristic wave-speed bound. Third, the relaxation system is discretized by a standard LBM, yielding collide-and-stream algorithms running unchanged on existing solvers. A symbolic compiler using a domain-specific language encapsulates these steps: unlike existing LBM code generators, which start from the discrete scheme, it automatically derives equilibrium, gradient-tracking cascade, and grid scaling from the declared PDE alone. We exercise it across twelve PDE systems, including compressible Navier–Stokes–Fourier flow, resistive magnetohydrodynamics, and nonlinear elasticity. Manufactured-solution verification confirms convergence at or near second order in double precision, retained in single precision by a reference- and equilibrium-shifted formulation. Targeting OpenLB, the generated GPU kernels reach up to 96% of the memory-bandwidth roofline.

We consider the dynamics of inertial particles in unsteady fluid flows. At low Reynolds numbers, where the drag force is linear in the relative velocity, it is well-known that the dynamics admit an attracting, invariant, slow manifold which emerges as the perturbation of a normally hyperbolic critical manifold. However, at high Reynolds numbers, where the drag force is quadratic in the relative velocity, the critical manifold is no longer normally hyperbolic, and therefore its persistence has remained an open problem. Here, we resolve this issue by a particular application of the blowup method, which transforms the equations of motion to a generalized weighted cylindrical coordinate system, thereby desingularizing the dynamics on the critical manifold. We subsequently prove that the critical manifold persists under sufficiently small perturbations and derive the reduced equations of motion on the perturbed slow manifold to arbitrary accuracy. Our reduced equation differs from its linear-drag counterpart in its asymptotic expansion as well as its convergence rate. Using two examples, we demonstrate the validity of our slow manifold reduction. We also showcase an application of the reduced equations to the problem of source inversion in a turbulent dispersion model.

Global Navigation Satellite Systems (GNSS), best known for positioning, also serve weather science, as atmospheric water vapour delays their signals. This delay, the Zenith Wet Delay (ZWD), is a direct, all-weather measure of column moisture. Although assimilated into numerical weather prediction for decades, ZWD is not yet used by leading Machine Learning Weather Models (MLWM), despite addressing a known deficiency: the underestimation of severe precipitation. Here we present the first integration of GNSS-derived ZWD into Aurora, a state-of-the-art weather foundation model. Our extended Aurora learns ZWD with skill comparable to its pretrained variables. More importantly, including ZWD systematically improves forecasts when fine-tuning for 6-hour accumulated precipitation. Gains grow with severity, reaching an 8.8% increase in Equitable Threat Score at the 99th percentile, while the precipitation power spectrum becomes more realistic at synoptic and planetary scales. GNSS observations therefore encode information that MLWM can exploit for high-impact precipitation.

Deep-learning-based climate downscaling aims to learn relationships from historical low-resolution (LR) and high-resolution (HR) climate data to generate HR climate projections. However, this setting faces a temporal out-of-distribution (OOD) challenge: models trained on historical data are commonly applied to future projections whose distributions may differ substantially from the training period. This study investigates temporal OOD shift for daily temperature downscaling over the Continental United States using paired LR-HR model simulations. We propose a temporal domain-adaptive downscaling framework that combines supervised HR reconstruction on historical data with domain alignment between historical and future climate distributions. Experiments across future validation periods show that the proposed domain-adaptive model consistently outperforms statistical and deep-learning-based bias-correction methods, with the largest gains occurring when the temporal distribution shift is strongest. Spatial analyses indicate stronger improvements over high-elevation and topographically complex regions, along with higher spatiotemporal correlation with the HR target. The extreme analysis shows that domain adaptation also reduces upper-tail temperature bias relative to the non-adaptive model. These results demonstrate that temporal domain adaptation can improve the robustness of HR climate projections under non-stationary climate conditions.

In this work we design a multi-layer model of composite contact lens drug release. Such lenses have been designed by encapsulating drug-polymer films in contact lens hydrogels. Composite lenses can promote sustained discharge of drug and achieve near zero-order release kinetics, thus surpassing other ocular delivery methods that are limited by short residence times and an undesirable initial burst release. Our model is informed by in vivo data, and includes three coupled partial differential equation layers to simulate the composite lens. We mathematically investigate the effect of composite contact lens design characteristics on the time to $50\%$ therapeutic drug release ($t_{50}$) in the vial, eye, and blister pack settings. In the eye setting, we incorporate our prior model that considers the effect of many blinks on the pre- and post-lens tear film drug concentrations. We simulate drug cumulative release profiles and study the variability of $t_{50}$ across: (1) the ratio of the drug-polymer film to hydrogel diffusion coefficients, (2) the centerline of the polymer film within the hydrogel, and (3) the polymer film thickness. In the blister pack setting, we study storage questions that may inform future commercial design. This work may help medical professionals better understand the mechanics of contact lens drug delivery and predict targeted tissue transport of ophthalmic drugs.

We present a unified, memory-bounded GPU realization of equation-of-motion coupled-cluster (EOM-CC) excited-state gradients and interstate nonadiabatic couplings (NACMEs) on a single 8\,GB consumer GPU. Both are built from one contraction directed acyclic graph: the EOM-CC relaxation is the reverse-mode transpose of the forward density build rather than a per-state re-derivation, and an atomic-orbital-direct Laplace-transform $J/K$ kernel, made non-symmetric ($J^x(A,B)\neq J^x(B,A)$) by the transition densities, resolves every energy denominator with no four-index molecular-orbital tensor; a two-sided Davidson returns both eigenvectors from one device-resident, spin-pure solve. The pipeline is validated end to end at small scale: gradients and NACMEs match finite differences across four spin multiplicities and full configuration interaction to $<\!10^{-12}$ for two electrons, and the excited-state gradient matches the independent Psi4 code to $\le\!4.6\times10^{-7}~E_h/a_0$ from \ce{H2O} to aromatic benzene. The kernels and the ground-state solve reach chromophores ($\le\!730$ AO) in 8\,GB, and a frozen-natural-virtual compression lets the eigensolver execute a complete excited-state gradient and $Q$–$B$ NACME of the chlorophyll-core chromophore \ce{Mg}-porphine (def2-SVP, $439$ AO) on the card. We present that run as a capability demonstration – executed and translationally invariant to machine zero, but anchored only piece-wise and bounded by a direct convergence study at ${\sim}10^{-2}~E_h/a_0$ – not a converged spectroscopic result. The validated small-scale capability and the memory-bounded implementation are the contribution.

For many synchrotrons which cross $\gamma_t$ transition, a high intensity threshold has been identified beyond which significant beam losses are observed. These losses are due to a high-intensity mode coupling instability, which can be suppressed by adjusting the chromaticity curve to cross zero slightly after $\gamma_t$ transition.

Collective actions emerge through the interplay between social influence and awareness. We introduce a nonlinear opinion-dynamics framework on networks in which social influence, shaped by individual interactions and community structure, promotes collective action, while awareness regulates the amount of reinforcement required for adoption. Using a degree-based mean-field reduction, we show that the competition between effective social influence and abandonment controls both the onset and persistence of collective action. Changes in awareness modify the nonlinear adoption mechanism itself, enabling populations to transition between highly responsive and weakly responsive collective states. This generates discontinuous transitions, bistability, and hysteresis, allowing collective action to persist even after the conditions that initially promoted it have weakened. We illustrate these effects through coupled disease-mitigation and resource-consumption dynamics, where external pressures act by reshaping awareness rather than social influence. More broadly, our results identify awareness as a fundamental link between environmental conditions, social interactions, and the emergence and persistence of collective action.

Rules, regulations, and regulatory systems are central to societies, institutions, and organisms, yet surprisingly little is known about their evolution over long timescales. The Laws of Cricket, the world's second most popular sport, offer a unique insight into this fundamental question. Their 268-year history constitutes the longest continuous rule-set record yet assembled. Our quantitative analysis reveals generic features including rule-book size growing exponentially in time but scaling sublinearly with matches played; new situations stimulate new rules, but at a decelerating rate; regulatory structures exhibit abrupt phase transitions, increasing rule specificity, interconnectivity and complexity with central rules shifting from gameplay to officiating. These provide a framework for understanding how governance evolves from simple collections of rules to complex regulatory architectures across social, legal, and biological domains.

Assuming the existence of axion-like particles (ALPs), the standard Quantum Electrodynamics (QED) amplitudes for the linear Breit-Wheeler (BW) pair production process are shown to be supplemented by an additional ALP-mediated amplitude. We compute the coherently combined cross section and show that, for pseudoscalar ALPs, interference with QED produces an asymmetric Fano-type resonance. We propose experimental observation of the BW process with real photons as a complementary probe of MeV-scale ALPs, coupling jointly with photons and electron-positron pairs, on the basis that it occupies a kinematic region not readily accessible to other ALP search methods.

Conventional non-invasive photothermal imaging techniques are fundamentally constrained by the diffusive nature of heat transport, which causes severe energy dissipation during subsurface reconstruction. Although modulation-based approaches partially mitigate this limitation by encoding depth information into phase delay and amplitude attenuation, they remain inherently restricted by repeated temporal excitation, long acquisition times, and stitching artifacts in large-area inspection. In this work, we propose a structured illumination scanning thermography (SISTER) framework that replaces conventional temporal modulation with continuous spatial scanning under static structured illumination. The key theoretical insight is that heat diffusion is governed by a Markov semigroup, while sample motion transforms static spatial illumination into an equivalent temporal excitation through a Galilean coordinate transformation. This formulation enables dynamic-to-static reconstruction without repeated temporal modulation and provides a unified interpretation of spatial scanning and conventional signal modulation. A scanning system is integrated to implement the proposed framework together with a dynamic-to-static reconstruction algorithm for continuous subsurface defect inspection. Both numerical simulations and experimental results demonstrate that the proposed method significantly improves spatial continuity, signal-to-noise ratio, and detection capability while effectively eliminating stitching artifacts and reducing acquisition complexity. The proposed SISTER framework establishes a unified theoretical foundation for scanning photothermal imaging and provides a practical paradigm for high-efficiency, large-scale industrial non-destructive testing.

Separating homogeneous and inhomogeneous contributions to a spectral linewidth is essential for connecting optical spectra to microscopic dephasing and disorder. We quantitatively compare linewidth separation by one-dimensional Voigt analysis and two-dimensional coherent spectroscopy (2DCS). Using photoluminescence and 2DCS measurements of the same hBN-encapsulated monolayer MoSe2 sample, we construct uncertainty-weighted ḩi}^2 landscapes for the Gaussian inhomogeneous width and Lorentzian homogeneous half-width. Although a Voigt profile accurately reproduces the photoluminescence spectrum, the two widths are strongly anticorrelated and therefore poorly determined independently. By contrast, joint fitting of diagonal and cross-diagonal 2DCS slices produces more compact confidence regions with reduced parameter correlation and elongation. These results quantitatively demonstrate the improved linewidth separation provided by 2DCS and illustrate the limitations of assigning homogeneous and inhomogeneous contributions from one-dimensional Voigt fits alone.

Foundation machine learning force fields (MLFFs) such as MACE-MP-0 and UMA cover broad chemical space at near density functional theory (DFT) accuracy. However, they assume equilibrium ground-state physics and do not natively handle externally induced changes to the electronic state, such as charging, applied fields, or electronic excitation, which limits their use for driven processes such as photoexcitation and charge injection. We propose EquiFiLM, a lightweight extension that adds continuous external conditioning to any equivariant foundation MLFF via a per-layer Feature-wise Linear Modulation (FiLM) block, learning externally driven changes to the potential energy surface from minimal training data. The block modulates only scalar channels and preserves E(3)-equivariance exactly. We demonstrate the recipe on charged liquid water with the foundation model MACE-MatPES as the backbone, yielding E-MACE. On the four training charges, E-MACE delivers a $3.1\times$ reduction in force RMSE ($21.3$ to $6.96$ meV/$\mathring{A}$) and a $61\times$ reduction in per-atom energy RMSE ($6.1$ to $0.1$ meV/atom) over a baseline without EquiFiLM trained on the same data, at indistinguishable inference cost. Across seven held-out interpolation and extrapolation charges, force RMSE stays within $18-61$ meV/$\mathring{A}$ and energy RMSE within $0.7-5.4$ meV/atom. The model runs stable molecular dynamics across the full range tested and predicts the charge-dependent first-shell response of the reduced pair distribution function probed by ultrafast electron diffraction. Adding this conditioning axis to the foundation requires only a few thousand DFT-labeled frames, against the $\approx 10^8$ structures of a charge-aware foundation trained from scratch. The recipe is backbone- and conditioning-agnostic: it applies without architectural change to any equivariant MLFF with scalar interaction-layer channels.