Papers

Long-range electronic correlation can be represented in terms of coupled fluctuations of atomic charges and higher multipoles, motivating a density-response description beyond local dipolar polarization, particularly in low-dimensional, conducting, and small-gap systems where collective electronic fluctuations become important. We extend the frequency-dependent polarizable force field ACKS2$\omega$ to electronic-structure references containing exact exchange by deriving both the static and additional frequency-dependent exchange contributions, with particular attention to HFsrPBE, which combines long-range Hartree–Fock exchange with short-range PBE exchange and correlation. Numerical validation of ACKS2$\omega$ using dipole polarizabilities and $C_6$ dispersion coefficients for the 27 molecules of the TS27 set, with HF, PBE, B3LYP, and HFsrPBE as the underlying electronic-structure references, shows that including the exchange contributions and enriching the response basis improve agreement with the corresponding orbital-space linear-response calculations, although finite-response-basis errors remain molecule dependent. Long-range correlation energies evaluated through the adiabatic-connection fluctuation-dissipation theorem using the ACKS2$\omega$ parameters reproduce the corresponding HFsrPBE orbital-space results for small test molecules when the response basis is made complete within the chosen orbital basis, while a local multipole decomposition resolves the finite-basis energies into monopole–monopole, mixed, dipole–dipole, and remaining higher-multipole contributions, providing a route to evaluating long-range correlation energies in terms of charge fluctuations within atom-condensed models.

This paper reports a computational scheme which predicts the light-matter interaction in multiple mesoscopic plasmonic nanowires (NWs), each of which includes concentric or eccentric constituent wires. Lying at the core of the proposed scheme is the S-Matrix of each individual cylindrical interface. The optical response of the materials inside and outside each interface can be described by three popular mesoscopic models: the Hydrodynamic Drude model (HDM) and its diffusive variant, namely the Generalized Nonlocal Optical Response (GNOR) model, as well as the Surface Response Model (SRM). To take into account the relative displacements between the cylindrical interfaces, the addition theorem is applied to construct the main equation behind the algorithm, where the possibility of longitudinal waves intrinsic in the HDM and GNOR models is also included. The optical responses calculated by the proposed S Matrix Method are compared with an in-house developed boundary element method (BEM) toolbox for a single gold NW and an eccentric dual-material NW, and excellent agreements are seen. The algorithm is further physically checked for a sodium nanolens trimer featuring large field focusing, and the effects of the HDM and the SRM are compared.

It is well known that quasi-symmetric (QS) stellarators are isomorphic to tokamaks in terms of their neoclassical-transport properties, and the corresponding transport coefficients can be calculated in the same manner as in tokamaks. However, less is known regarding the turbulent-transport properties of QS stellarators, e.g. the transport coefficients from the ion-temperature-gradient (ITG) mode. In this work, a systematic theory of the “equivalent tokamaks” for QS stellarators is presented based on the local gyrokinetic formulation and the near-axis expansion theory. It is shown that to zeroth order in the minor radius, the equivalent tokamaks can be chosen to have circular flux surfaces and can be characterized by three geometric quantities: the aspect ratio, the rotational transform, and the magnetic shear. To achieve first-order accuracy, however, not all QS stellarators have equivalent tokamaks, but good approximations can be found for some cases either as global or local equilibria. Local and global gyrokinetic simulations of ITG transport are performed for a selection of QS configurations, and quantitative agreement in the turbulent transport levels is found between the stellarators and their equivalent tokamaks.

This letter presents a method to compute fundamental bounds on the total efficiency of pixel antennas. Using semidefinite relaxation, we obtain an upper bound on the optimization problem of maximizing the antenna's total efficiency subject to binary-valued pixel states. The bounds are demonstrated for antennas with varying numbers of pixels and compared with realizable solutions obtained using the genetic algorithm.

An important and unresolved problem in the physical sciences is explaining the predictions made by neural networks. Several explainable artificial intelligence (XAI) methods have been proposed to address this problem, including gradient XAI, Integrated Gradients, and GradientSHAP. We evaluate the baseline XAI methods according to four scores: sensitivity (XAI patterns strongly affect predictions), attribution (XAI patterns reproduce the change in prediction relative to a baseline), robustness (XAI patterns remain stable for nearby inputs), and coherence (XAI patterns are spatially smooth). We also introduce a new method, average gradient outer product (AGOP) XAI, that uses global gradient information to identify an important direction for a specific input. We apply XAI to neural network predictions of the El Niño-Southern Oscillation (ENSO) based on data from the Zebiak-Cane model. AGOP XAI achieves the highest attribution, robustness, and coherence scores in the architecture and lead-time comparisons reported here. Its sensitivity is surpassed by gradient XAI, which is maximally sensitive by definition. Beyond diagnosing neural-network behavior, AGOP XAI can generate candidate hypotheses about physical mechanisms. The method highlights an equatorial thermocline-depth signal consistent with recharge oscillator physics, together with a southeastern-Pacific lobe that may be specific to the Zebiak-Cane model. Finally, we test the physical relevance of AGOP using optimized perturbations that move the Zebiak-Cane model along AGOP explanation coordinates. Such perturbations can suppress the selected extreme events or, from a near-neutral ensemble, generate strong El Niño or La Niña events 10 months later.

Active metasurfaces are a promising platform for spatial light modulators (SLMs) in applications such as holography, beam shaping, and light detection and ranging (LiDAR), as they enable dynamic control over the amplitude and phase of light at subwavelength scales. Among these, transmissive metasurfaces offer a compact alternative to conventional SLMs, since they allow monolithic integration with chip-scale light sources. While independent amplitude and phase modulation have been previously demonstrated in active reflective metasurfaces, this important milestone has not been achieved in transmission due to the inherent challenges in designing active transmissive metasurfaces. Here, we theoretically demonstrate an active transmissive metasurface that enables independent amplitude and phase modulation in the mid-infrared. The proposed metasurface consists of high-Q cuboid germanium (Ge) Mie resonators integrated with graphene stripes. Utilizing the intraband and interband transitions of graphene, electrostatic gating enables modulation of the transmittance from near 0% to 5% at one spectral frequency and achieves 281{\deg} phase-only modulation at another frequency in the same device. To achieve both types of modulation at the same operating wavelength, we use the thermo-optic effect of Ge as a global tuning mechanism to spectrally align the resonant frequencies, enabling both transmittance and phase-only modulation at different base temperatures. The proposed dual-mechanism architecture achieves transmittance modulation efficiency of ~100% and 282{\deg} phase-only modulation. Finally, we demonstrate the efficacy of phase-only modulation by individually addressing each unit cell to realize a beam-steering device with relative diffraction efficiencies over 90%. Our individually addressable active metasurface opens a route toward compact, dynamically reconfigurable metaphotonic devices.

Implicit full waveform inversion (IFWI) reparameterizes the velocity model with a neural network, but it suffers from spectral bias and requires many iterations to converge. Multi-resolution hash encoding uses a learnable feature grid to recover high wavenumber details at multiscales and provides strong local representations. However, activating all resolution levels from the start of the inversion introduces high wavenumber components, which can exacerbate cycle skipping. In this paper, we propose a coarse-to-fine IFWI (C2F-IFWI) that activates the multi-resolution hash levels progressively rather than all at once. Specifically, only a few coarse base levels are kept active at initialization, while the remaining levels are introduced progressively. A frozen hash level contributes no gradient until it is activated, so the coarse levels resolve the long-wavelength background before the finer levels are introduced regardless of the frequency range of the data. A continuous activation index increases with iterations and determines how many levels are active at any point in training. Each newly admitted level is then faded in gradually through a smooth weighting rather than switched on abruptly, which keeps the optimization stable during transition. This scheme allows model capacity to grow synchronously with wavenumber without adding any additional trainable parameters and with negligible computational overhead. Experiments on the synthetic Overthrust and BP 2004 models and on the Viking marine field dataset show that, compared with the all-resolution hash baseline, the coarse-to-fine scheme converges faster, attains higher accuracy, and is markedly more robust to the choice of initial model and to strong data noise.

Ion transport controls the macroscopic performance of solid electrolytes, battery electrodes, ion-selective membranes, electrolysis ceramics, mixed conductors, porous sorbents, and biological channels. In each case, ion transport is determined by structural characteristics, namely the available diffusion pathways and the specific bottleneck atoms that limit the ionic jumps. These are intrinsic properties of the crystalline framework, whereas temperature, carrier concentration, and correlated motion govern the transport rate. Ab initio molecular dynamics (AIMD) resolves the mechanism directly but is too costly for materials screening, and most surrogate models give only a single transport number. Here we present a calibration-free method for determining the mechanism, based on Topo-Spectral Percolation Descriptors (TSPD). TSPD obtains the transport mechanism from a single static structure in seconds. It constructs a periodic migration network whose edge barriers are computed from physics-based energetics. We then analyze the barrier-threshold topology of this network and the spectrum of its barrier-weighted graph Laplacian. To establish its validity for computational solid-state ionics, we test TSPD against AIMD ion densities and neutron diffraction data for eight materials, including cathodes and solid electrolytes, whose transport ranges from one- to three-dimensional. TSPD reproduces the established mechanism in every case and correctly identifies structures in which geometric openness does not imply viable ion percolation. The method is most informative for pathway-limited, direction-dependent transport, and it complements full dynamical simulations for strongly cooperative or nearly isotropic transport.

Frequency-domain physical neural networks (PNNs) have emerged as a promising analog computing paradigm, achieving a reduced number of devices, enhanced robustness, and high inference accuracy. However, existing demonstrations across various physical domains, such as optics, acoustics, and electronics, are mostly specific to the target devices or platforms, usually requiring hardware-dependent models and post-fabrication parameter tuning. Here, we demonstrate cross-platform implementations of frequency-domain PNN across different wave-based computing platforms using identical, pre-trained parameters. By exploiting shared second-order nonlinear processes, the PNN model is implemented on optical, microwave, and acoustic-wave platforms without the need of fine tuning the parameters. Evaluated on a unified four-class classification task, the frequency-domain PNN achieves high inference accuracies of 97.6% in optics, 98.4% in electronics, and 98.2% in mechanics. Further, we systematically compare related performance metrics across these three distinct physical domains, paving the way to a universal neural network across different physical platforms.

Three-dimensional (3D) information is encoded in four-dimensional scanning transmission electron microscopy (4D-STEM) through parallax between the virtual images formed at each detector pixel. In this paper we connect the encoding and retrieval of depth-dependent information in 4D-STEM to two other widely-used 3D imaging methods, tomography and light-field photography, and derive its 3D phase contrast transfer function. Viewing the 4D data as a collection of angular slices yields a collection of sinograms, providing an intuitive visual representation of the 3D information transfer. Within the small-angle approximation, tilt-corrected bright-field (tcBF) depth sectioning is equivalent to non-iterative tomographic reconstruction. Alternatively, the 4D data also can be mapped to light-field (or plenoptic) imaging, which acquires spatially resolved diffraction data in parallel using an array of lenslets. Adapting a light-field imaging algorithm, we show that tcBF depth sections can be obtained by extracting two-dimensional slices from a single four-dimensional fast Fourier transform (4D FFT) of the dataset, yielding a substantial speedup when reconstructing a large number of depth slices. These differing perspectives distinguish the geometric aspects of depth resolution from wave-optical effects that are present in phase-contrast imaging. Depth-resolved aberration-corrected bright-field (acBF) imaging yields improved 3D reconstructions compared to tcBF, removing contrast oscillations and reducing the variation in response with depth. We demonstrate acBF depth sectioning using simulated and experimental data and resolve distinct layers in stacked oxide films and nanoparticle assemblies. These non-iterative volumetric reconstructions from a single 4D-STEM dataset show promise for improved imaging of thick, weakly-scattering samples and for reconstructing low-dose acquisitions.

We introduce GeoFWI3D, a large-scale open-source benchmark dataset of geologically plausible 3D subsurface models designed to accelerate deep learning (DL) assisted seismic imaging and full waveform inversion (FWI). FWI is a physics-driven, wave-equation-based optimization technique used in seismic imaging to estimate the subsurface properties. However, traditional FWI is highly non-linear, non-unique, and ill-posed. The iterative process often converges to local minima when the starting model is insufficiently accurate. Most importantly, 3D FWI is prohibitively expensive. With the advent of DL, a direct mapping between the shot gathers and subsurface properties can be established by training a network on realistic models. The primary bottleneck for such approaches is the lack of large-scale, realistic training datasets. GeoFWI3D addresses this gap with 10,000 geologically diverse velocity models at 96x96x96 resolution, spanning four structural complexity classes: pure stratigraphy, faulted networks, salt diapirism, and complex coupled fault-salt systems. Each model is accompanied by co-registered multi-modal labels including compressional velocity (Vp), zero-offset seismic reflectivity, relative geologic time (RGT), and semantic fault/salt masks. To facilitate systematic evaluation, we introduce benchmark tasks covering fault detection, joint salt body segmentation and chronostratigraphy prediction, FWI, wavefield and traveltime surrogates using neural operators, and generative modeling with a 3D diffusion model. We present baseline results for each task to establish reference performance metrics for future users. The dataset is publicly available under Creative Commons Attribution 4.0 International.

In this work, we systematically investigate homoepitaxial $\beta$-Ga$_2$O$_3$ growth by halide vapor phase epitaxy (HVPE) on (010), (001), and ($\bar{2}$01) native substrates over a broad growth parameter space, extending the growth temperature as high as 1150 $^\circ$C. The growth kinetics exhibit strong dependence on precursor supply, temperature, reactor pressure, and Ga-source-to-substrate distance. Increasing the HCl flow from 80 to 300 sccm enhances the growth rate by up to seven-fold, while increasing the temperature from 1050 to 1150 $^\circ$C nearly doubles the deposition rate. At 1150 $^\circ$C, atmospheric-pressure growth yields rates more than 2.5 times higher than those at 100 Torr, while reducing the O$_2$ flow to 5 sccm further increases the growth rate to 14.3 $\mu$m/h, producing films up to 42.9 $\mu$m thick in 3 h. Notably, phase-pure homoepitaxial layers with good crystalline quality are obtained across all three orientations, with low rocking-curve full width at half maximum (FWHM) values of 63.4, 66.6, and 42.8 arcsec for the (010), (001), and ($\bar{2}$01) epilayers, respectively. The surface morphology exhibits a pronounced crystallographic dependence and evolves strongly with the HVPE growth conditions. In particular, the ($\bar{2}$01) surface develops well-defined step-and-terrace morphology with local RMS roughness as low as 0.17 nm. These results extend $\beta$-Ga$_2$O$_3$ HVPE homoepitaxy into a higher-temperature regime and demonstrate a broad process window for high-rate growth of thick, high-quality epilayers with strongly orientation-dependent surface evolution.

Large Language Models (LLMs) are increasingly used as probabilistic generators for simulation, synthetic data generation, and decision support in settings where real-world data are unavailable. Yet, the structure and reliability of the distributions they produce remain understudied. Here, we systematically analyze LLM-generated distributions of preferences for air travel, restaurants, and consumer products. Encouragingly, all models considered in our analysis exhibit self-coherence, with the most probable outcomes stabilizing rapidly under repeated sampling. At the same time, we observe substantial discordance across both model families and scales, with little consensus even among their most probable outcomes. These patterns hold across nine open-weight models, three choice domains, and show robustness under temperature changes, greedy decoding, and perturbations of prompt and ordering. Our findings indicate that outcomes are influenced more by the choice of model than by the wording of the prompt, challenging the common assumption that sufficiently capable LLMs produce similar preference distributions when used as stand-ins for survey respondents.

Spectroscopy reads structure by sending a structured probe into a system and measuring what comes back. Here we apply this principle to animal decision-making. Harmonic Theory casts choice as motion on an angular landscape over heading, whose Fourier components form an animal's decision spectrum. We show that the arrangement of cues enters that landscape as a structure factor, so it factors like a diffraction amplitude, and symmetry imposes selection rules: a p-fold cue array annihilates every harmonic that is not a multiple of p, and sweeping two cues apart draws a notch through each harmonic. Cue geometry becomes a tunable probe: the experimenter sets which harmonics an experiment can see. We read these spectra from public recordings of flies, schooling fish, larval zebrafish, mice and a swimming alga, and from the fly's neural compass, where symmetric scenes suppress the forbidden harmonics and breaking that symmetry restores them. Measuring the compass form factor favours the theory's smooth bump over its square one, and raising cue contrast narrows that bump in every fly. A width fitted to one harmonic then predicts the next. Cue geometry is a spectrometer for decisions, and symmetry sets what it can see.

High-dimensional, low-sample-size bulk gene expression data poses a fundamental challenge in transcriptomics, which typically includes tens of thousands of genes but relatively few samples, leading to overfitting and unstable selection for key features as biomarkers. We propose a regression by community centers (RCC) framework tailored for such data. RCC converts gene expression data into a feature proximity network and leverages the phase transition of the giant connected component to identify a critical distance threshold that yields a sparse yet maximally informative network representation, indicated by a minimum normalized shortest compression length. Intuitively, at the criticality, the network balances fragmentation and over-connectivity, allowing meaningful gene-gene associations communities to emerge while filtering noises, such that community-centers capture the most informative and non-redundant signals. These representative genes are then fed to a simple ordinary least squares (OLS) model for downstream predictions. Across five bulk gene expression datasets, RCC displays exceptional prediction performance, clearly outperforming benchmark methods, remains robust to missing data and noises, and does not rely on external biological knowledge. These results suggest that network-based representations provide an effective and general framework for high-dimensional, low-sample-size prediction tasks beyond transcriptomics and illustrate how network-science ideas can support robust learning in data-scarce regimes.

Tropical cyclone landfall angle strongly modulates storm-surge potential, but how it changes with climate remains unknown. From 1980 to 2024, the global fraction of near-perpendicular landfalls shows a weak, statistically insignificant increase, with no significant basin-scale trends. PepC-Global simulations driven by reanalyses reproduce observed landfall-angle and pre-landfall approach-angle distributions and broad geographic variations in the near-perpendicular fraction. When driven by output from 14 CMIP6 models, the simulations show a similarly weak and statistically insignificant increase during 1980-2024, suggesting a forced contribution not yet detectable above internal variability. Under high emissions, however, the global near-perpendicular fraction increases significantly through 2099, with the strongest and most spatially coherent changes along the eastern North Pacific, whereas no significant global trend emerges under intermediate emissions. The projected increase arises mainly from changes in near-perpendicular fractions at individual coastal locations rather than from a redistribution of landfalls. These results identify landfall angle as an overlooked dimension of changing tropical cyclone coastal risk, one whose evolution this century depends on the emissions pathway.

Science teachers frequently search for documentary excerpts not by describing what appears on screen, but by querying the abstract concepts they intend to teach. This use case exposes a limitation of existing language-based video moment retrieval methods, which typically assume that queries describe observable events, whereas instructional search requires retrieving concrete visual phenomena that instantiate an underlying scientific principle. We study this setting as concept-to-example video retrieval, an abstract-needle-in-a-haystack problem where compact curriculum concepts must be grounded in temporally sparse documentary evidence. To bridge this abstraction gap, we propose Concept-Driven Domain Adaptation (CDDA), a three-stage framework for adapting two-tower vision-language models to concept-level retrieval. CDDA treats concepts as intermediate semantic anchors: it first structures the textual embedding space with textbook and teacher-handbook example-concept pairs, then transfers this concept-aware geometry to documentary visuals under a frozen visual encoder, and finally jointly adapts both encoders with sparse visual concept supervision. From a geometric perspective, this staged alignment reduces text-concept and vision-concept angular gaps, thereby encouraging concept-level adaptation while preserving the pretrained model's concrete image description alignment. On a curated middle-school physics retrieval benchmark, CDDA achieves stronger pedagogically oriented concept retrieval than several competitive multimodal baselines, including Qwen3-VL-Embedding-2B, while maintaining concrete image-text matching after adaptation.

We study a real-valued max-plus particle system on a finite periodic lattice. Its three-dimensional fundamental diagram relates the mean flow to two densities: the conserved particle density and a second density defined by a local state function. We prove that the deviation from the diagram tends to zero for arbitrary initial data with all state values in [0,1]. The proof uses two nonnegative local quantities and their evolution relations. For rational initial data with all state values in $[0,1]$, the orbit is eventually periodic, and the fundamental-diagram relation holds exactly on the periodic orbit.

We present CAFES, a correlation-aware agentic framework for electronic-structure simulations of strongly correlated systems. CAFES addresses two challenges: the fragmented software landscape for many-body calculations and the difficulty of selecting appropriate methods across diverse correlation regimes. It combines correlation diagnostics, adaptive method selection, and large language model (LLM) assistance for molecular, crystalline, and model-Hamiltonian systems. A study-task architecture separates study-level planning from task-level execution, while a curated scientific knowledge layer provides reusable guidance for method selection, workflow design, and result interpretation. We demonstrate CAFES through three research-level studies: calculating the low-lying electronic states of lutein using DMRG-CASSCF, probing phase competition in the extended honeycomb Hubbard model using DMET, and generating a quantum-chemical dataset with CCSD labels. These calculations demonstrate the potential of agentic workflows for strongly correlated electronic-structure problems, a regime that has received limited attention in existing agentic computational frameworks.

Magnetic field-line transport in magnetically confined plasmas is commonly modeled as diffusion through stochastic magnetic layers generated by resonant perturbations. However, experimentally relevant magnetic configurations frequently exhibit transport rates that are substantially slower than predicted by fully stochastic models, indicating the presence of unresolved dynamical barriers. In this work, we investigate the role of nested resonant island chains and their associated invariant manifolds in regulating transport within the edge magnetic topology of DIII-D tokamak discharge #171491 subjected to strong n=3 resonant magnetic perturbations. Using field-line tracing, Poincare analysis, and manifold calculations, we identify a hierarchy of homoclinic and heteroclinic tangles associated with both period-one and period-ten hyperbolic points. A systematic construction based on primary intersection points is developed to extract partial transport barriers from the full manifold geometry and divide the chaotic region into dynamically distinct domains. Monte Carlo simulations reveal that field-line escape exhibits a bi-exponential character when only the outermost period-one boundary is considered, reflecting the presence of a long-lived "sticky" region associated with the period-ten island chain. When the inner period-ten boundary is explicitly incorporated into the Monte Carlo, the escape process becomes approximately single exponential and is accurately described by turnstile lobe transport. The measured escape rate agrees with predictions obtained from the turnstile lobe area and the symplectic structure of the field-line map. These results demonstrate that nested tangles provide a quantitative transport skeleton governing magnetic-field-line escape and establish a framework for analyzing transport barriers, cantori, and lobe dynamics in realistic fusion-plasma magnetic configurations.

Rare-earth (RE) atoms, such as erbium (Er), have inner-shell optical transitions in the near-infrared, some of which are in the telecom bands. These transitions are largely insensitive to the host environment due to effective shielding by the filled outer shells. Cryogenic rare-gas (RG) solids, such as solid neon (Ne) and argon (Ar), combine high chemical purity with a nearly nuclear-spin-free environment, making them attractive solid hosts for preserving optical and spin coherence. In this work, we isolate neutral Er atoms in solid Ne and Ar at 6 K and characterize their emission by steady-state and time-resolved photoluminescence (PL) spectroscopy. In both hosts, we observe several near-infrared transitions between 1000–1500 nm, including inner-shell emission in the telecom O and E bands. The $4f \rightarrow 5d$ transition near 1299 nm forms a multiplet of narrow components, consistent with crystal-field splitting, and decays on a microsecond timescale. The $4f \rightarrow 4f$ transition near 1437 nm shows a matrix shift below 1 cm$^{-1}$ in both hosts. In solid Ne, its inhomogeneous linewidth is bounded by the spectrometer resolution to < 22 GHz, and its PL lifetime reaches up to 78.9 ms, close to the estimated radiative limit. Thermal annealing extends the 1437 nm lifetime in Ne and suppresses the phonon sidebands relative to the zero-phonon lines in Ar. These results identify RG solids as promising hosts for long-lived telecom-band Er emission and provide a foundation for future spin-photon interfaces and hybrid quantum architectures.

Understanding how macroscopic wealth distributions emerge from microscopic transaction rules among agents is a central problem in econophysics. While many traditional models restrict agents' wealth to non-negative values, debt is an essential feature of realistic economic systems. In this paper, we introduce a stochastic transaction mechanism into the agent-based model with a central-bank-mediated collective debt limit, as studied in [14]. In our model, each agent probabilistically determines whether to transfer a coin, depending on their current asset or debt level. From a probabilistic perspective, the resulting system can be formulated as a zero-range process with $\mathbb{Z}$-valued site occupation numbers, where negative values represent debt. We prove that, under an appropriate scaling, the wealth distribution in the stationary state converges to either a two-sided Gamma distribution or a Gamma distribution, depending on the asymptotic behavior of the function governing coin transfers. Our results rigorously identify these previously unconsidered limiting distributions and provide a mathematical framework for understanding how stochastic monetary exchanges give rise to macroscopic wealth distributions.

Machine-learned interatomic potentials are commonly fitted by weighted-sum scalarization, which combines energy and force errors in a single loss. A nominal weight, however, identifies a potential only relative to the complete fitting protocol. We therefore treat energy–force balancing as a protocol-dependent problem of physical model selection. For fixed-basis atomic cluster expansion models of molten LiCl, liquid H$_2$O, and Si, the resulting scalarization paths contain dominated states at extreme weights. Their nondominated subsets depend on the solver, and the out-of-distribution Si path is nonmonotone. We replace direct weight selection by minimizing the regularized energy objective subject to an upper bound on the normalized force loss. Projected dual ascent adjusts the Lagrange multiplier from the force-constraint residual. A frozen-multiplier limited-memory quasi-Newton refinement then returns the final model. Across the three systems, the constrained procedure reaches the solver-matched low-error region and gives measured speedups of $6.7$–$8.9$ over completed scalarization scans under the stated timing convention. Physical-property calculations show that first-shell geometry is comparatively insensitive to the selected balance, whereas transport and solid-state observables vary more strongly. These results identify the fitted model–protocol pair as the relevant object of energy–force model selection and support force-constrained fitting as an explicit selection rule.

Machine learning models are increasingly used in feedback control loops for nuclear fusion, where inference speed and predictable timing are critical. We summarize lessons from models deployed for control on the DIII-D tokamak and develop a benchmark to compare inference backends across ten neural networks and model components from fusion control and diagnostic pipelines. For models greater than five million parameters, the CPU backends take tens to thousands of milliseconds, while GPU inference is substantially faster, suggesting an upper limit on CPU-oriented development for control. These results show why the deployment backend must be selected together with the model and its control-cycle budget.

SIREN (Ship Inviscid Resistance and Elevation eNgine) is a Rankine-source panel solver for the steady wave resistance of a ship, in the tradition of Dawson's method. The free-surface condition is linearised about a base flow, and the linearisation is iterated. In the present formulation the base flow is the solution of a Neumann problem on the actual boundary: sources on the entire hull, wet and dry, and on the free surface panels, with impermeability imposed on both, so that the free surface acts as a rigid lid at the position it currently occupies. This replaces the classical double-body flow, whose image about z=0 is meaningful only for a flat surface and a hull cut at z=0, and it removes the need for desingularised free-surface panels. In the non-linear iteration the free-surface panels are moved rigidly in the vertical to the relaxed wave elevation at their centroid, the hull panels are marked wet or dry against the moved surface, and the base flow is recomputed on the moved geometry; impermeability applies to the whole hull regardless of this marking, which is used only to select which panels enter the force integral, and under-relaxation is confined to the passes where the non-linear correction accumulates without being reset. The radiation condition is imposed by a one-row upstream shift of the collocation points, and the wave resistance is obtained by pressure integration and, independently, by transverse wave-cut analysis.

Quantum modifications of black hole spacetimes can lead to observable departures from the predictions of classical general relativity, particularly in the strong-gravity regime near the event horizon. In this context, the coherent quantum black hole model introduces a sine-integral correction to the gravitational potential, characterized by a coherence scale \(R_s\), which softens the central singularity and alters the near-horizon geometry. Motivated by the growing precision of black hole imaging and strong-field observations, we explore how these quantum-induced effects influence photon motion and the resulting shadow structure. Considering representative values within the interval \(R_s \in [0.5M, 1.2M]\), we show that the photon sphere shifts inward while the critical impact parameter decreases, reaching values such as \(b_{\rm crit}\approx 4.764M\) for \(R_s=0.5M\). Consequently, the shadow radius becomes systematically smaller and develops a more intricate structure than in the Schwarzschild case (\(b_{\rm crit}=3\sqrt{3}M \approx 5.196M\)). The analysis of the corresponding intensity distributions also reveals a pronounced enhancement of radiation near the shadow boundary, especially for intermediate coherence scales. This feature can be attributed to photons remaining longer within the modified near-horizon region before escaping toward distant observers. Such an increase in brightness may therefore provide a possible observational signature of quantum corrections at horizon scales. Furthermore, although the geometry allows radiation and energy to accumulate around an effective horizon surface, its response to perturbations differs from that expected for compact objects with stable light rings. Instead of contracting under backreaction effects, the accumulation surface shifts outward.

In this communication, we discuss results obtained with the numerical in-house PHP simulation code CASCO (French abbreviation for Advanced PHP simulation code). The aim is to study the impact of bubble generation (boiling) on the established oscillation of a PHP with a high turn number (10-turn water-copper tubular PHP with independently heated turns in the evaporator region). We vary the nucleation barrier value, i.e. the local superheating $\Delta$Tnucl at which the nucleation occurs and the heat load Pe; other simulation parameters are kept constant. The PHP heat transfer performance is evaluated with the PHP thermal resistance Rth for both horizontal and vertical favorable PHP orientation. Since the bubble generation plays a positive role for the PHP functioning, one expects that the function Rth($\Delta$Tnucl) monotonously increases. This is true for high $\Delta$Tnucl. For small $\Delta$Tnucl, the Rth($\Delta$Tnucl) function is decreasing, thus a minimum occurs. It appears because of a transition between the regimes of stable PHP functioning. The regimes strongly differ by the average penetration of liquid plugs into the evaporator. For horizontal PHP, these regimes correspond to the intermittent and continuous oscillations. The regimes are discussed and the regime map in the coordinates Pe -$\Delta$Tnucl is obtained.

Gas bubbles impose large penalties in water electrolysis at industrially relevant rates. Capillary-fed electrolyzers greatly mitigate them by wicking electrolyte to electrodes and venting gas from the backside, but only below a threshold current density. We show that the separator pore size sets this threshold through two opposing effects: smaller pores raise the supersaturation required for nucleation but reduce the permeability for electrolyte replenishment. A coupled electrochemical-transport model, anchored to a single measured onset, quantifies both mechanisms across 0.1-8 $\mu$m pores. Experimentally, reducing pore size from 8 to 1.2 ${\mu}$m raises the bubble onset limit from 0.18 to 1.73 A cm$^{-2}$ and lowers cell voltage by 0.11 V at 1 A cm$^{-2}$, primarily by suppressing bubble-induced overpotential. At 0.45 ${\mu}$m and below, the replenishment limit takes over and the cells dry out before bubbles form. Our findings establish the bubble-free operation limit as designable rather than purely empirical.

Time-dependent, three-dimensional (3D) oceanic multi-variables define coherent states of the evolving ocean to facilitate ocean diagnosis and advance ocean science to better inform environmental and hazard management. However, extracting quantitative evidence from these variables requires substantial and complex analytical effort. We introduce OceanMind, a multi-agent AI system that directly couples large language models (LLMs) with comprehensive time-dependent 3D ocean states for swift and effective diagnosis. OceanMind organizes the analytical process into four coordinated complexity stages: Query Routing, Skill-based Planning, Tool Execution, and Evidence-based Summary Generation. Specialized agents interpret user requests, construct and execute multi-step computational workflows, and synthesize quantitative evidence. To ensure reliable workflow construction, 63 reusable ocean-specific analysis skills serve as procedural manuals that guide the LLM agent in selecting data, conducting diagnostics, and applying analytical tools. With reflection and replanning mechanisms that use execution feedback to repair invalid plans, OceanMind ensures reliable analysis workflows across diverse needs. On a benchmark of 240 computational-workflow queries spanning the four stages, OceanMind outperformed general ReAct agents with the same registered tool pool, achieving relative improvements of 41.2% in effectiveness and 21.5% in efficiency. Beyond the benchmark, OceanMind reproduced published oceanic diagnostics, validated hypotheses, and supported environmental decision-making over global oceans. Overall, OceanMind advances LLMs by integrating them with time-dependent 3D ocean analysis, enabling scientific interpretation and enhancing formulation of environmental policies based on quantitative ocean evidence.

In general, measurement error constrains the performance of information engines. However, this may not hold universally. In this work, we propose a class of two-reservoirs information engines (TRIEs) with a two-level system and demonstrate that the monotonic relationships between the work output, the efficiency, as well as the work fluctuations of the TRIEs and measurement error are determined by the demon's control parameters. Utilizing this, we establish the conditions that allow the TRIEs to generate useful work under any level of measurement error, and identify the regimes in which these performance parameters can be optimized simultaneously by the measurement error. Furthermore, we demonstrate that when the demon's energy cost is supplied solely by the cold reservoir, the efficiency that accounts for this cost can surpass the standard Carnot bound without violating the second law. Finally, we design a dual Otto cycles TRIE and provide an experimental scheme for its implementation based on a trapped $^{40}\mathrm{Ca}^+$ ion. Significantly, our results can be applied to the multi-level system information engines.