Papers

Multistability, the coexistence of multiple stable states, is a cornerstone of nonlinear dynamical systems, governing their equilibrium, tunability, and emergent complexity. Recently, the concept of hidden multistability, where certain stable states evade detection via conventional continuous parameter sweeping, has garnered increasing attention due to its elusive nature and promising applications. In this Letter, we present the first experimental observation of hidden multistability using a programmable acoustic coupled-cavity platform that integrates competing self-focusing and self-defocusing Kerr nonlinearities. Beyond established bistability, we demonstrate semi- and fully-hidden tristabilities by precisely programming system parameters. Crucially, the hidden stable states, typically inaccessible via the traditional protocol, are unambiguously revealed and dynamically controlled through pulsed excitation, enabling flexible transitions between distinct types of stable states. These experimental findings not only offer new insights into the fundamental physics of emerging hidden multistability, but also unlock new avenues for applications in information storage, information encryption, and safety precaution, where multi-state dynamics could enable advanced control techniques.

The recent rapid deployment of datacenter infrastructures for performing large language models (LLMs) and related artificial intelligence (AI) applications in the clouds is predicted to incur an exponentially growing energy consumption in the near-term future. In this paper, we propose and analyze the implementation of the transformer model, which is the cornerstone of the modern LLMs, with novel large-scale optoelectronic neurons (OENs) constructed over a complementary metal-oxide-semiconductor (CMOS) compatible platform. With all of the required optoelectronic devices and electronic circuits integrated in a chiplet only about 2 cm by 3 cm in size, 175 billon parameters in the case of GPT-3 are shown to perform inference at an unprecedented speed of 12.6 POPS using only 40 nm CMOS process node, orchestrated by an optoelectronic version of systolic array with no data skew and negligible propagation delay, along with a high power efficiency of 74 TOPS/W and a high area efficiency of 19 TOPS/mm2. The influence of the quantization formats and the hardware induced errors are numerically investigated, and are shown to have a minimal impact. Our study presents a new yet practical path toward analog neural processing units (NPUs) to complement existing digital processing units.

Predicting phenomena that mix few-photon quantum optics with strong field nonlinear optics is hindered by the use of separate theoretical formalisms for each regime. We close this gap with a unified effective field theory valid for frequencies lower than the material-dependent cutoff set by the band gap, plasma frequency, or similar scale. The action couples the electromagnetic gauge field to vector polarisation modes. An isotropic potential generates the optical susceptibilities, while a higher-dimension axion-like term captures magnetoelectric effects; quantisation on the Schwinger-Keldysh contour with doubled BRST ghosts preserves gauge symmetry in dissipative media. One-loop renormalisation-group equations reproduce the measured dispersion of the third-order susceptibility from terahertz to near-visible frequencies after matching a single datum per material. Real-time dynamics solved with a matrix-product-operator engine yield good agreement with published results for GaAs polariton cavities, epsilon-near-zero indium-tin-oxide films and superconducting quarton circuits. The current formulation is limited to these 1-D geometries and sub-cut-off frequencies; higher-dimensional or above-cut-off phenomena will require additional degrees of freedom or numerical methods.

The diffusion of ideas and language in society has conventionally been described by S-shaped models, such as the logistic curve. However, the role of sub-exponential growth – a slower-than-exponential pattern known in epidemiology – has been largely overlooked in broader social phenomena. Here, we present a piecewise power-law model to characterize complex growth curves with a few parameters. We systematically analyzed a large-scale dataset of approximately one billion Japanese blog articles linked to Wikipedia vocabulary, and observed consistent patterns in web search trend data (English, Spanish, and Japanese). Our analysis of 2,963 items, selected for reliable estimation (e.g., sufficient duration/peak, monotonic growth), reveals that 1,625 (55%) diffusion patterns without abrupt level shifts were adequately described by one or two segments. For single-segment curves, we found that (i) the mode of the shape parameter $\alpha$ was near 0.5, indicating prevalent sub-exponential growth; (ii) the peak diffusion scale is primarily determined by the growth rate $R$, with minor contributions from $\alpha$ or the duration $T$; and (iii) $\alpha$ showed a tendency to vary with the nature of the topic, being smaller for niche/local topics and larger for widely shared ones. Furthermore, a micro-behavioral model of outward (stranger) vs. inward (community) contact suggests that $\alpha$ can be interpreted as an index of the preference for outward-oriented communication. These findings suggest that sub-exponential growth is a common pattern of social diffusion, and our model provides a practical framework for consistently describing, comparing, and interpreting complex and diverse growth curves.

Habitat fragmentation, often driven by human activities, alters ecological landscapes by disrupting connectivity and reshaping species interactions. In such fragmented environments, habitats can be modeled as networks, where individuals disperse across interconnected patches. We consider an intraspecific competition model, where individuals compete for space while dispersing according to a nonlinear random walk, capturing the heterogeneity of the network. The interplay between asymmetric competition, dispersal dynamics, and spatial heterogeneity leads to nonuniform species distribution: individuals with stronger competitive traits accumulate in central (hub) habitat patches, while those with weaker traits are displaced toward the periphery. We provide analytical insights into this mechanism, supported by numerical simulations, demonstrating how competition and spatial structure jointly influence species segregation. In the large-network limit, this effect becomes extreme, with dominant individuals disappearing from peripheral patches and subordinate ones from central regions, establishing spatial segregation. This pattern may create favorable conditions for speciation, as physical separation can reinforce divergence within the population over time.

This study analyzes pass networks in football (soccer) using a stochastic model known as the Pólya urn. By focusing on preferential selection, it theoretically demonstrates that the time evolution of networks can be characterized by a single parameter. Building on this result, a data analysis method is proposed and applied to a large-scale public dataset of professional football matches. The statistical properties of the preferential-selection parameter are examined, demonstrating its correlation with pass accuracy and with mean pass difficulty. This method is applicable to various evolving networks.

Accurately modeling seismic wave attenuation is critical for ground response analyses (GRAs), which aim to replicate local site effects in ground motions. However, theoretical transfer functions (TTFs) from GRAs often overestimate empirical transfer functions (ETFs) when the small-strain damping ratio ($D_{\text{min}}$) is set equal to laboratory measurements. Prior studies addressed this by inflating $D_{\text{min}}$ in one-dimensional (1D) GRAs to account for apparent damping mechanisms such as diffraction and mode conversions that cannot be captured in 1D. Although this approach improved fundamental-mode predictions, it often overdamped higher modes. This study explores more direct modeling of apparent damping using two-dimensional (2D) GRAs at four downhole array sites: Delaney Park (DPDA), I-15 (I15DA), Treasure Island (TIDA), and Garner Valley (GVDA). At each site, three numerical damping formulations, Full Rayleigh, Maxwell, and Rayleigh Mass, were implemented using both conventional $D_{\text{min}}$ and an inflated $D_{\text{min}}$ ($m \times D_{\text{min}}$) obtained from site-specific calibration. Results show that the appropriate $D_{\text{min}}$ multiplier ($m$) correlates with the site's velocity contrast. Using inflated $D_{\text{min}}$, Full Rayleigh and Maxwell damping systematically overdamped higher modes, with Maxwell damping also shifting modal peaks. In contrast, Rayleigh Mass damping consistently achieved the closest match to ETFs at three of the four sites while offering faster computational performance. These findings demonstrate that inflated $D_{\text{min}}$ can represent unmodeled attenuation in 2D GRAs, particularly at sites with low velocity contrast, and that frequency-dependent formulations such as Rayleigh Mass damping can more accurately predict site response than traditional frequency-independent approaches.

The Moon has been long regarded as a natural resonator of gravitational waves (GWs) since 1960, showing great potential to fill the frequency gap left behind GW detections by ground- or space-based laser interferometry. However, the spatial variation of this amplification capacity on the Moon remains unclear. Here, we numerically simulate the lunar response to GWs by fully considering the fluctuant topography and laterally heterogeneous interior structures. Our results show that most regions on the Moon can amplify GWs with a ratio over 2, a finding significantly higher than previous estimations. Particularly, the amplification ratio can even reach factors of tens at the resonant frequency of ~0.015 Hz on the highlands surrounding the South Pole-Aitken (SPA) basin, where the regional crust is the thickest. Our findings establish the thick-crust regions as critical zones of GW amplification, which is essential for future landing site selection and instrumental setting for GW detection on the Moon.

This paper presents an enhanced version of the subgroup method for resonance self-shielding treatment, termed the robust subgroup method, which integrates Robust Estimation (RE) with a Differential Evolution (DE) algorithm. The RE approach is employed to handle model misspecification and data contamination, while the DE algorithm serves as an optimization tool within the RE framework to obtain constrained solutions. Numerical validation against experimental benchmarks shows that the proposed method removes a systematic absorption bias in conventional subgroup fits that would otherwise depress reactivity. This bias appears only in benchmarks sensitive to U-238. Mechanistically, it reflects a threshold-like conditioning failure: strong self-shielding leverage dominates the loss and is magnified by dilution-induced multicollinearity. This adverse conditioning appears to be seeded by a narrow, sparse resonance structure at low energies in fertile even-even nuclides, thereby causing rapid self-shielding response saturation and a weak Doppler broadening. By bounding influence and enforcing feasibility within an RE-DE framework, the inferred subgroup parameters track the underlying physics more faithfully, improving the predictive fidelity of subsequent transport simulations.

We numerically study the fast spatial transport of a trapped Bose-Einstein condensate (BEC) using shortcuts-to-adiabaticity (STA) by counterdiabatic driving (CD). The trapping potential and the required auxiliary potential were simulated as painted potentials. We compared STA transport to transport that follows a constant-acceleration scheme (CA). Experimentally feasible values of trap depth and atom number were used in the 2D Gross-Pitaevskii equation (GPE) simulations. Different transport times, trap depths, and trap lengths were investigated. In all simulations, there exists a minimum amount of time necessary for fast transport, which is consistent with previous results from quantum speed limit studies.

A neural network model based on the Transformer architecture has been developed to predict the nonlinear evolution of optical pulses in Er-doped fiber amplifier under conditions of limited experimental data. To address data scarcity, a two-stage training strategy is employed. In the first stage, the model is pretrained on a synthetic dataset generated through numerical simulations of the amplifier's nonlinear dynamics. In the second stage, the model is fine-tuned using a small set of experimental measurements. This approach enables accurate reproduction of the fine spectral structure of optical pulses observed in experiments across various nonlinear evolution regimes, including the development of modulational instability and the propagation of high-order solitons.

Global gyrokinetic simulations are performed for the first time to investigate cross-scale interactions between electromagnetic ion temperature gradient (ITG) turbulence and fishbone instability in tokamak plasmas. The investigation of fluctuation response in the multiscale simulation including both instabilities indicates a strong impact of fishbone on ITG turbulence. Detailed analysis reveals that fishbone-driven zonal radial electric fields at nonlinear saturation significantly suppress electromagnetic ITG turbulence, reducing ion thermal transport close to the neoclassical level. The simulation results agree well with experimental observations that turbulence suppression during fishbone bursts. These findings advance understanding of multiscale interactions that enhance thermal confinement in fusion plasmas.

In this study, global nonlinear electromagnetic gyrokinetic simulations are conducted to investigate turbulence in the Internal transport barrier (ITB) region of the EAST tokamak discharge with weakly reversed magnetic shear. Linear simulations reveal two dominant ion temperature gradient (ITG) modes: a higher frequency mode at the $q=1$ surface, which dominates in the electrostatic limit, and a lower frequency mode near the $q_{\min}$ surface, which prevails under the experimental $\beta$ (the ratio of plasma pressure to magnetic pressure). Finite $\beta$ effects effectively suppress higher frequency ITG modes, and once $\beta_i$ on axis exceeds 0.5%, this ITG mode is no longer dominant, and the ITG mode near $q_{\min}$ surface becomes the primary instability. Therefore, electromagnetic effects play a crucial role in stabilizing ITG modes, and in causing the transition between the most unstable mode at different radial positions. The linear growth rate of the unstable mode in the electrostatic limit is approximately 1.25 times higher than that of the dominant mode in the electromagnetic case. However, in the electromagnetic nonlinear regime, the thermal ion heat conductivity is reduced by at least a factor of 4. This reduction primarily results from nonlinear electromagnetic effects enhancing the shearing effect of zonal flows, thereby further suppressing microturbulence. Finally, energetic particles exert a slight stabilizing effect on ITG turbulence due to dilution and finite $\beta$ contributions. It is emphasized that the electromagnetic effect on ITG with weak magnetic shear should be included to accurately calculate the transport coefficients.

Transport networks must balance viscous pumping losses with the energetic cost of maintaining an operative architecture. This paper formulates that trade-off as an entropy-per-information-cost (EPIC) extremum that prices structural upkeep in calibrated units (joules per bit). An upkeep law r^m distinguishes volume-priced (m = 2) from surface-priced (m = 1) maintenance. In laminar Poiseuille flow, stationarity yields (i) a generalized Murray scaling Q proportional to r^alpha with alpha = (m + 4)/2; (ii) a tariff-weighted vector balance that fixes bifurcation geometry and predicts near-symmetric daughter openings of about 75 degrees for m = 2 and about 97 degrees for m = 1; and (iii) a universal partition of power between pumping and upkeep. Eliminating radii gives a strictly concave flux cost proportional to Q^gamma with gamma = 2m/(m + 4), favoring mergers and deep tree hierarchies, and defines a routing index that induces Snell-like refraction of optimal paths across spatial tariff contrasts. A preregistered, held-out test on retinal bifurcations from the High-Resolution Fundus dataset (N = 19,126) shows sharp vector closure: the median residual is R = 0.232 with a nonparametric 95 percent bootstrap interval [0.229, 0.236], 91 percent of junctions fall under the pre-specified strict threshold, and structure-preserving nulls shift decisively to larger residuals. These results render classical branching relations explicitly unit-bearing (J/bit) and provide falsifiable geometric targets and quantitative design rules for transport networks.

With the rapid development of nanophotonics and cavity quantum electrodynamics, there has been growing interest in how confined electromagnetic fields modify fundamental molecular processes such as electron transfer. In this paper, we revisit the problem of nonadiabatic electron transfer (ET) in confined electromagnetic fields studied in [J. Chem. Phys. 150, 174122 (2019)] and present a unified rate theory based on Fermi's golden rule (FGR). By employing a polaron-transformed Hamiltonian, we derive analytic expressions for the ET rate correlation functions that are valid across all temperature regimes and all cavity mode time scales. In the high-temperature limit, our formalism recovers the Marcus and Marcus-Jortner results, while in the low-temperature limit it reveals the emergence of the energy gap law. We further extend the theory to include cavity loss by using an effective Brownian oscillator spectral density, which enables closed-form expressions for the ET rate in lossy cavities. As applications, we demonstrate two key cavity-induced phenomena: (i) resonance effects, where the ET rate is strongly enhanced at certain cavity mode frequencies, and (ii) electron-transfer-induced photon emission, arising from the population of cavity photon Fock states during the ET process. These results establish a general framework for understanding how confined electromagnetic fields reshape charge transfer dynamics, and suggest novel opportunities for controlling and probing ET reactions in nanophotonic environments.

Soliton microcombs are evolving towards octave-spanning for $f$-$2f$ self-referencing and expanding applications in spectroscopy and timekeeping. As spectra broaden and pulses shorten, the Raman-induced soliton self-frequency shift (SSFS) becomes a principal limitation: it reduces pump-to-comb conversion efficiency, constrains achievable span, and can, in extremes, preclude stationary operation. We develop a complementary theory of SSFS in microresonators that remains valid when the soliton duration $\tau_s$ is shorter than the Raman response timescale. The theory predicts a reduced dependence of the SSFS on $\tau_s$ which also expands the soliton existence range. Such predictions are validated by numerical simulations and by experiments on Si$_3$N$_4$ microresonators. Our results provide practical guidelines for engineering efficient and broadband soliton microcombs.

We report watt-level femtosecond pulses in the 1.75 $\mu$m region using a thulium-doped core, terbium-doped cladding fluoride (Tm:Tb:ZBLAN) fiber laser system. The seed pulse is generated through stimulated Raman scattering in a silica fiber pumped by an erbium-doped fiber laser. The soliton is subsequently amplified through a multi-stage Tm:Tb:ZBLAN amplifier. The tunability of our chirped fiber Bragg grating stretcher, matched with a Treacy compressor, compresses the pulse to 217 fs. Our system generates ~250 nJ of single-pulse energy, with a corresponding average power of ~1 W at a 4 MHz repetition rate. The laser system is suitable for multiphoton microscopy.

We report on R&D study to improve the photon detection efficiency of water Cherenkov detectors by doping ultra-pure water with 4-methylumbelliferone (4-MU), a wavelength shifting additive. Cherenkov light yields from cosmic-ray muons were measured for various 4-MU concentrations and compared with those from pure water. At a concentration of 1 ppm, the detected light yield increased by approximately a factor of three. This enhancement can be attributed to wavelength shifting and improved photon collection efficiency. No noticeable degradation in optical transparency was observed across the tested concentrations of 0.5 and 1 ppm with different concentration of ethanol. These results suggest that 4-MU is a promising additive for improving the performance of water Cherenkov detectors.

Quantum entanglement is a phenomenon in which two physical systems are correlated in such a way that they appear to instantaneously affect one another, regardless of the distance between them. As commonly understood, Bell's Theorem famously demonstrates that any causal explanation of entanglement must discard either locality (the principle that nothing, including information, travels faster than light) or classical notions of realism (or both). Drawing on this concept, several legal scholars have metaphorically described 'entangled' legal concepts. For instance, if a state's highest court redefines the concept of 'foreseeability' in negligence law, this redefinition alters the concept of 'reasonable care' immediately in the eyes of the law. Godfrey (2024) is the first work to mathematically model entangled legal concepts, particularly in the context of legal interpretation. Here, we extend the quantification to the formulation and delineation of law (lawmaking) and the adjudication of law (judgment). In so doing, we connect legal entanglement to Sichelman's (2022) work on legal entropy, complexity, and the informational content of law. In addition to quantifying entanglement across various legal contexts, our approach provides broader insights. For example, it offers a more comprehensive analysis of the uses and limits of 'modularity' in law–specifically, the role legal boundaries (spatial or intangible) play in reducing information costs within legal systems. Moreover, we discuss how our model can improve theories of legal artificial intelligence. Finally, we explore the application of legal theory back to physics. If quantum physical entanglement operates analogously to legal entanglement, it requires discarding both locality and classical realism, though not in the manner commonly imagined.

Over 125 years ago, Henry Selby Hele-Shaw realized that the depth-averaged flow in thin gap geometries can be closely approximated by two-dimensional (2D) potential flow, in a surprising marriage between the theories of viscous-dominated and inviscid flows. Hele-Shaw approximation allows visualization of potential flows over 2D airfoils and also undergirds important discoveries in the dynamics of interfacial instabilities and convection, yet it has found little use in modeling flows in microfluidic devices, although these devices often have thin gap geometries. Here, we derive a Hele-Shaw approximation for the flow in the kinds of thin gap geometries created within microfluidic devices. Using the Method of Weighted Residuals (MWR), we reinterpret the Hele-Shaw approximation as the leading term of an orthogonal polynomial expansion that can be systematically extended to higher-order corrections. The resulting leading-order equation coincides with the previously derived 2D approximations, but our derivation is shorter and more direct. By extending the expansion beyond leading order, we obtain a new reduced model that captures non-parabolic gap-wise velocity profiles and out-of-plane flow effects. We provide substantial numerical evidence showing that approximate equations can successfully model real microfluidic and inertial-microfluidic device geometries. By reducing three-dimensional (3D) flows to 2D models, our validated model will allow for accelerated device modeling and design.

Theoretical predictions of photochemical processes are essential for interpreting and understanding spectral features. Reliable quantum dynamics calculations of vibronic systems require precise modeling of anharmonic effects in the potential energy surfaces and off-diagonal nonadiabatic coupling terms. In this work, we present the n-mode quantization of all vibronic Hamiltonian terms comprised of general high-dimensional model representations. This results in a second-quantized framework for accurate vibronic calculations employing the density matrix renormalization group algorithm. We demonstrate the accuracy and reliability of this approach by calculating the excited state quantum dynamics of maleimide. We analyze convergence and the choice of parameters of the underlying time-dependent density matrix renormalization group algorithm for the n-mode vibronic Hamiltonian, demonstrating that it enables accurate calculations of complex photochemical dynamics.

Quantum imaging is emerging as a transformative approach for biomedical applications, applying nonclassical properties of light, such as entanglement, squeezing, and quantum correlations, to overcome fundamental limits of conventional techniques. These methods promise superior spatial resolution, enhanced signal-to-noise ratios, improved phase sensitivity, and reduced radiation dose, for potentially safer and more precise imaging for delicate biological samples. Here, we present an overview of quantum optical biomedical imaging technologies as well as quantum-inspired imaging methods, including quantum optical coherence tomography, quantum optical microscopy, ghost imaging, multi-parameter quantum imaging, and imaging with quantum-grade cameras. We describe the operating principles, biomedical applications, and unique advantages of each approach, along with the specific challenges for their translation into real-life practice. This review aims to guide future research toward advancing quantum imaging from experimental demonstrations to impactful biomedical tools.

Electron microscopy (EM) is a foundational tool for directly assessing the structure of materials. Recent advances in direct electron detectors have improved signal-to noise ratios via single-electron counting. However, accurately counting electrons at high fluence remains challenging. We developed a new method of electron counting for direct electron detectors, Back-Propagation Counting (BPC). BPC uses machine learning techniques designed for mathematical operations on large tensors but does not require large training datasets. In synthetic data, we show BPC is able to count multiple electron strikes per pixel and is robust to increasing occupancy. In experimental data, frames counted with BPC are shown to reconstruct diffraction peaks corresponding to individual nanoparticles with relatively higher intensity and produce images with improved contrast when compared to a standard counting method. Together, these results show that BPC excels in experiments where pixels see a high flux of electron irradiation such as in situ TEM movies and diffraction.

Solenoid-free tokamak startup techniques are essential for spherical tokamaks and offer a pathway to cost reduction and design simplification in fusion energy systems. Local helicity injection (LHI) is one such approach, employing compact edge current sources to drive open field line current that initiates and sustains tokamak plasmas. The recently commissioned Pegasus-III spherical tokamak provides a platform for advancing this and other solenoid-free startup methods. This study investigates the effect of LHI on magnetic topology in Pegasus-III plasmas. A helical filament model represents the injected current, and the linear plasma response to its 3D field is calculated with M3D-C1. Poincaré mapping reveals substantial flux surface degradation in all modeled cases. The onset of overlapping magnetic structures and large-scale surface deformation begins at $\Psi_{N} \approx 0.37$, indicating a broad region of perturbed topology extending toward the edge. In rotating plasmas, both single-fluid and two-fluid models exhibit partial screening of the $n = 1$ perturbation, with two-fluid calculations showing stronger suppression near the edge. In contrast, the absence of rotation leads to strong resonant field amplification in the single-fluid case, while the two-fluid case with zero electron rotation mitigates this amplification and preserves edge screening. Magnetic probe measurements indicate that modeling the stream with spatial spreading$-$representing distributed current and/or oscillatory motion$-$better reproduces measured magnetic power profiles than a rigid filament model. The results underscore the role of rotation and two-fluid physics in screening stream perturbations and point to plasma flow measurements and refined stream models as key steps toward improving predictive fidelity.

The accurate solution of nonlinear hyperbolic partial differential equations (PDEs) remains challenging due to steep gradients, discontinuities, and multiscale structures that make conventional solvers computationally demanding. Physics-Informed Neural Networks (PINNs) embed the governing equations into the learning process, enabling mesh-free solution of PDEs, yet they often struggle to capture steep gradients, discontinuities, and complex nonlinear wave interactions. To address these limitations, we employ the Extended Physics-Informed Neural Network (XPINN) framework to solve the nonlinear Buckley-Leverett equation with a nonconvex flux, modeling immiscible two-phase flow in porous media. The computational domain is dynamically decomposed in space and time into evolving pre-shock and post-shock subdomains, allowing localized subnetworks to efficiently learn distinct flow behaviors, with coupling enforced via the Rankine-Hugoniot jump condition to ensure physically consistent flux continuity. We compare XPINN with standard PINNs and its variants, including PINN with artificial viscosity, PINN with Welge construction, and PINN with the Oleinik entropy condition, and across all cases, XPINN consistently outperforms the other methods, accurately resolving sharp fronts and capturing the correct physical behavior. Importantly, XPINN achieves this using the simpler Adam optimizer, whereas some PINN variants require more complex or higher-order strategies such as L-BFGS to reach comparable accuracy, demonstrating that XPINN is a robust and scalable approach for challenging hyperbolic PDEs without artificial diffusion or entropy corrections. The code is available at github.com/saifkhanengr/XPINN-for-Buckley-Leverett.

Digital network failures stemming from instabilities in measurements of temporal order motivate attention to concurrent events. A century of attempts to resolve the instabilities have never eliminated them. Do concurrent events occur at indeterminate times, or are they better seen as events to which the very concept of temporal order cannot apply? Logical dependencies of messages propagating through digital networks can be represented by marked graphs on which tokens are moved in formal token games. However, available mathematical formulations of these token games invoke "markings" – global snapshots of the locations of tokens on the graph. The formulation in terms of global snapshots is misleading, because distributed networks are never still: they exhibit concurrent events inexpressible by global snapshots. We reformulate token games used to represent digital networks so as to express concurrency. The trick is to replace global snapshots with "local snapshots." Detached from any central clock, a local snapshot records an action at a node during a play of a token game. Assemblages of local records define acyclic directed graphs that we call history graphs. We show how history graphs represent plays of token games with concurrent motions, and, importantly, how history graphs can represent the history of a network operating while undergoing unpredictable changes.

This work investigates the two-dimensional thermal behavior of a bilayer medium subject to both internal and external heat sources. The model incorporates diffusion, advection, and temperature-dependent volumetric heat generation or absorption in each layer, as well as general convective conditions on the external boundaries. The influence of interfacial thermal resistance between the two materials is also considered. An analytical solution is developed using Fourier-based techniques, and a stable and convergent finite difference method is proposed to analyze particular scenarios. The theoretical results are validated against known solutions and numerical simulations, demonstrating consistency with the expected physical behavior. The findings contribute to a deeper understanding of heat transfer phenomena in layered systems and offer potential insights for optimizing thermal performance in engineering applications involving composite materials.

The interstellar comet 3I/ATLAS displayed a small but statistically significant non-gravitational acceleration during its passage through the inner Solar System. Using a thermophysical model coupled with stochastic sampling of jet configurations, we investigate whether standard volatile-driven activity can account for the observed acceleration. The model includes diurnal and obliquity-averaged energy balance, empirical vapour-pressure relations, and collimated outflow from localized active areas. We find that CO-dominated activity can reproduce the magnitude of the acceleration inferred from the Marsden non-gravitational parameters for nucleus radii between 0.5 and 3 km with active-area fractions that are substantial but thermodynamically plausible. Less volatile species, including NH_3 and CH_4, contribute less efficiently and cannot provide the required recoil when acting alone, while CO_2 remains radiatively dominated and dynamically ineffective over the heliocentric-distance range relevant to the observations. These results show that the measured acceleration of 3I/ATLAS is consistent with ordinary CO-driven outgassing and does not require unusual physical properties. The analysis delineates the thermophysical conditions under which interstellar comets can exhibit measurable deviations from purely gravitational motion.

This study presents a new high-fidelity multi-modal dataset containing 16000+ geometric variants of automotive hoods useful for machine learning (ML) applications such as engineering component design and process optimization, and multiphysics system surrogates. The dataset is centered on a practical multiphysics problem-hood deformation from fluid entrapment and inertial loading during rotary-dip painting. Each hood is numerically modeled with a coupled Large-Eddy Simulation (LES)-Finite Element Analysis (FEA), using 1.2M cells in total to ensure spatial and temporal accuracy. The dataset provides time-resolved physical fields, along with STL meshes and structured natural language prompts for text-to-geometry synthesis. Existing datasets are either confined to 2D cases, exhibit limited geometric variations, or lack the multi-modal annotations and data structures - shortcomings we address with AutoHood3D. We validate our numerical methodology, establish quantitative baselines across five neural architectures, and demonstrate systematic surrogate errors in displacement and force predictions. These findings motivate the design of novel approaches and multiphysics loss functions that enforce fluid-solid coupling during model training. By providing fully reproducible workflows, AutoHood3D enables physics-aware ML development, accelerates generative-design iteration, and facilitates the creation of new FSI benchmarks. Dataset and code URLs in Appendix.

The supercritical state of matter is usually described as a continuous phase without sharp boundaries between liquid and gas regions. However, under non-equilibrium conditions, this view breaks down. Here we report an experimental investigation of non-equilibrium fluctuations in supercritical carbon dioxide (CO2) subjected to a stabilising temperature gradient. Using shadowgraphy, we reveal spontaneous stratification of the fluid into different layers, separated by transition regions, where thermodynamic properties vary dramatically. These signatures are particularly evident when the system crosses the Widom lines, loci of the extrema of the response function in the supercritical domain. The analysis of the intermediate scattering function of temperature fluctuations highlights the presence of Brunt-Vaisala oscillations within the fluid at multiple frequencies. These oscillations arise from the coupling of thermal and viscous modes under gravity and are a clear signature of the layered structure of the fluid. Our approach enables systematic exploration of a wide range of thermodynamic conditions in a single experiment. These findings suggest that the Widom region cannot be described as a homogeneous phase, but rather as a dynamic assembly of phase-like behaviours, challenging the applicability of classical thermodynamics in non-equilibrium supercritical regimes.