Papers

The Mahabharata and the Ramayana constitute the two major Sanskrit epics of ancient India. Previous network analysis revealed substantial structural similarities between their character networks, alongside differences consistent with their contrasting forms of narrative organisation. Building upon these findings, we investigate the representation and structural positions of male and female characters within the two texts. In particular, we examine whether the genders differ systematically in their connectivity, prominence, community participation, and contribution to network cohesion, while accounting for the considerable imbalance between the numbers of male and female characters. We employ methods designed to identify characters who connect otherwise distinct regions of the networks and to assess the structural consequences of character removal. Through these analyses, we ask whether gendered patterns are consistent across the two epics or instead reflect the different narrative structures previously identified. In so doing, we find that male characters are considerably more prevalent among highly central and structurally consequential nodes in both epics, and targeted removal of central male characters produces substantially greater network disruption. However, once baseline structural position is taken into account, we find little residual difference in deletion effects between male and female characters. Furthermore, under selected Girvan-Newman partitions, we find that female characters in the Mahabharata exhibit greater cross-community participation than female characters in the Ramayana.

Deterministic multiscale gas flow simulations have long suffered from the curse of dimensionality: the number of discrete velocities increases dramatically with the velocity space dimension and the Mach number, exhausting available memory and computational resources. To address this issue, this paper proposes a memory-efficient deterministic method based on an ensemble-of-subproblems strategy using stochastic discrete velocities. This strategy transforms the originally computationally expensive problem into a series of independently and efficiently solvable subproblems. To be concrete, the proposed method replaces the conventional large deterministic velocity set with multiple small random velocity sets. Each random set defines a subproblem, which is solved by a deterministic multiscale numerical scheme that computes macroscopic moments via Monte Carlo integration. The final flow field is obtained by arithmetic averaging over all sub-problems. In this work, we employ the discrete unified gas kinetic scheme (DUGKS) for spatial discretization and term the resulting method SDV-DUGKS. To validate the proposed method, several numerical test cases are conducted, including (a) the one-dimensional shock structure, (b) the two-dimensional cavity flow, and (c) supersonic flow around a square cylinder. The results of the one-dimensional shock structure confirm the feasibility of the proposed method. The two-dimensional cases demonstrate that, compared to its deterministic counterpart, the proposed method saves more than 80% of memory usage while maintaining comparable accuracy. These results indicate that the proposed method markedly reduces memory demand for multiscale flow simulations and exhibits strong potential to alleviate the curse of dimensionality that currently hinders deterministic multiscale numerical schemes from being applied to engineering problems.

In this work, a ensemble-of-subproblems strategy with stochastic discrete velocities is extended to deterministic methods for mitigating ray effects in rarefied flow simulations. The strategy involves performing multiple independent subproblems, each using a small set of randomly sampled velocity points, and then averaging their solutions to obtain the final result. The core idea is to ensure that the distribution function at any velocity can contribute to the final result, approximating highly refined velocity-space resolution without increasing the memory requirement in any single subproblem. We incorporate this strategy within the DUGKS framework, and the resulting method is denoted as SDV-DUGKS. To evaluate the performance of the proposed method, we compare SDV-DUGKS with the original DUGKS on several test cases: (a) the Sod shock tube problem, (b) the one-dimensional Riemann problem, (c) the two-dimensional lid-driven cavity flow, and (d) the two-dimensional Riemann problem. The results show that, in the collisionless limit $\mathrm{Kn} \to \infty$: (1) for one-dimensional compressible flows, SDV-DUGKS reduces memory usage by approximately 2/3 compared with that of the original DUGKS while achieving good agreement; (2) for two-dimensional compressible flows, SDV-DUGKS requires one to two orders of magnitude less memory than the original DUGKS while achieving good agreement. Based on these results, it can be concluded that the proposed method serves as a reliable and effective tool for mitigating ray effects in rarefied flow simulations.

In Non-Intrusive Load Monitoring (NILM), adaptive segmentation of electricity consumption time series is critical for appliance recognition. However, prevailing methods face challenges including heuristic parameter tuning, boundary sensitivity, and metric saturation. This paper proposes BayesSeg, a unified framework integrating time-series segmentation, multidimensional evaluation, and automatic parameter optimization. The segmentation layer employs a dual steady-state criterion based on the tail value and mean of preceding subsequences, combined with a sequential extraction and complement-set parsing strategy, to achieve precise unsupervised partitioning of steady-state and transition-state segments. The evaluation layer maps segmentation results to binary state sequences and formulates a composite metric integrating an event-level F1 score (event_F1) with Normalized Mutual Information (NMI). The event_F1 quantifies switching-event precision and recall via tolerance matching, while NMI captures global structural consistency, jointly overcoming the boundary sensitivity and limited discriminability of point-wise metrics. In the optimization layer, the composite score serves as the objective function for Bayesian optimization, which constructs a TPE surrogate model for efficient global parameter-space exploration. Experiments on the SustDataED2 dataset demonstrate that Bayesian optimization requires only ~100 objective evaluations to locate a parameter region within 0.35% deviation of the exhaustive grid-search optimum. The framework achieves a weighted composite score of 0.7149 and an event_F1 of 0.9340 while reducing optimization latency from ~5300 seconds to under 1 second, a speedup exceeding 5700x. BayesSeg automates segmentation configuration and provides a scalable, efficient solution for time-series analysis in NILM and related domains.

The Jiangmen Underground Neutrino Observatory (JUNO) reconstructs each event's vertex and energy with OMILREC, a maximum-likelihood fit that scans all $17{,}612$ large photomultiplier tubes (LPMTs) in every Minuit function evaluation, about $470$ times per event. This inner loop dominates the reconstruction CPU cost. Profiling shows that the production algorithm is latency-bound, sustaining only $9.9%$ of scalar floating-point peak because of virtual-function dispatch, ROOT-histogram pointer chasing, and repeated computation. We apply staged equivalence-preserving optimizations: flattened data layouts, vectorizable geometry, hoisting of Minuit-invariant work, per-event precomputation, fit-phase loop splitting and indexing, and reduced-precision fast paths. Each stage is checked against a frozen reference from the unmodified code. The optimized implementation achieves single-thread speedups of $8.06\times$ ($1524.8 \rightarrow 189.2$~ms/event) on an Intel Xeon Platinum~8358P and $5.22\times$ ($705.1 \rightarrow 134.9$~ms/event) on an AMD~EPYC~9654, increasing to $8.6\times$ ($177.7$~ms/event) after further optimization. The likelihood remains bit-identical through the first seven releases and later agrees within a relative drift of $1.3\times10^{-14}$, below the $10^{-13}$ contract. For typical events, reconstructed vertex and energy agree with the baseline within $4$~mm and $7$~keV; a few boundary cases reach different valid minima owing to an improved minimizer seed. An eight-metric physics-acceptance test also passes on about $861{,}000$ $^{68}$Ge calibration events. Developed with assistance from an AI coding agent operating under these verification gates, this workflow offers a transferable template for accelerating likelihood-based reconstruction in large neutrino and collider detectors without changing physics output.

The development of deep-tissue super-resolution imaging serves as an essential bridge toward non-invasive in vivo optical observation. However, there remain challenges to balance spatial resolution, imaging depth and phototoxicity. Here, we present a nanoscopy namely Image Scanning Emission Saturation (ISES) nanoscopy, achieving a lateral resolution of 37 nm, 1/25th of the excitation wavelength, at an imaging depth of 200 {\mu}m. Using a 976-nm doughnut-shaped excitation beam within an imaging-scanning microscopy configuration, we apply saturation-based point spread function (PSF) engineering and pixel-level confocal-pinhole enhancement to improve spatial resolution. As the high- and low-frequency components of the image OTF are concurrently acquired in a single scan via different camera pixels, Fourier-domain fusion can be employed with a single scanning dataset to further improve image quality. Compared with the traditional doughnut excitation beam-based adaptive pixel reassignment method, our strategy preserves the original frequency distributions and mitigates reconstruction artifacts in complex biological sample imaging. This strategy is generalizable and compatible with a variety of probes displaying saturation behavior. Beyond enabling a versatile and practical approach for deep tissue super-resolution imaging, it also informs the development of next-generation nanoprobes for imaging.

We analyze an impulsive solar energetic particle (SEP) event observed by Solar Orbiter at 0.93 au on 2022 December 24 that exhibits a pronounced intensity dropout between approximately 07:15 and 10:00 UT. Pitch-angle distributions show a near-field-aligned beam before the dropout, which disappears abruptly at the dropout onset. At the same time, a weak 100–200 keV component appears at pitch angles of about 90–180 degrees; no comparable enhancement over this pitch-angle range is present at MeV energies. The dropout onset is not accompanied by an abrupt change in the in situ magnetic field or solar wind plasma. Solar imaging associates the SEP event with a jet from a compact source. Ballistic back mapping, together with Potential Field Source Surface extrapolations and quasi-separatrix-layer proxy diagnostics, indicates that Solar Orbiter nominally connects to the source region, but lies close to strong connectivity gradients where small displacement may shift the connection to neighboring open field lines. The in situ measurements show that the event occurs during a magnetic-cloud passage and that additional dropouts occur later in the event. These results favor an interpretation in which the dropout reflects rapid changes in particle access between adjacent flux tubes, while the magnetic cloud may help preserve the sharp SEP intensity gradients between them.

This note presents an axisymmetric model of a tornado-like vortex with internal vertical wind shear. By employing a prescribed incompressible circulation-strain flow that captures the horizontal variation of the vertical wind, we show that the model admits an exact viscous Feynman-Kac representation for the tornado structure. In the presence of vortex vertical shear, the tilting term induces radial phase mixing that rapidly deforms the vortex structure, thus causing the radius of the maximum azimuthal wind to broaden and shift upward. At the long-time limit, the vortex approaches the Burgers radial scale while both the wind and vorticity decay exponentially. This class of solutions may help explain why tornadoes often weaken rapidly once an internal vertical shear structure emerges after touchdown.

Bound states in the continuum (BICs) are localized eigenmodes with their frequencies in the radiation continuum of scattering states. The existence of a BIC implies the loss of uniqueness for scattering problems with given incident waves. Perturbed wave systems close to the ideal ones with a BIC exhibit strong resonance effects that are essential to numerous practical applications. A question of fundamental importance is whether a BIC is robust, i.e., whether it can continue its existence when the structure is slightly perturbed. In an earlier work [Yuan and Lu, Optics Letters, Vol.~42, pp.~4490-4493, 2017], for a class of BICs governed by the two-dimensional (2D) Helmholtz equation, which are not trivially protected by symmetry, we uncovered the conditions that ensure robustness and formally constructed the BIC in perturbed systems using a perturbation method. In this paper, we present a rigorous theory on the robustness of BICs in 2D dielectric structures with a single periodic direction. Specifically, we analyze the solvability and provide estimates for each order in the perturbation series, and prove the convergence of the series.

The transition from order to disorder is conventionally regarded as detrimental to solid-state heat transfer in classical wave and quasiparticle systems. In striking contrast, we show that in near-field thermal radiation, breaking long-range order-shifting from periodic to quasiperiodic configurations-induces a counterintuitive enhancement of energy transport. This effect arises from delocalized interactions within quasiperiodic elements, where this quasiperiodicity relays and amplifies thermal electromagnetic energy transfer across large spatial separations, surpassing even corresponding near-field scenarios. The extraordinary transport properties induced by quasidisorder effect of near-field thermal radiation could unlock exciting opportunities for heat flow manipulation, offering transformative implications for thermal science and advancing the fundamental understanding of collective excitations in non-ordered systems.

Reduced density matrix functional theory (RDMFT) provides a rigorous framework for treating strong electron correlation, yet its application to periodic systems has long been hindered by two fundamental challenges: the lack of transferable approximate functionals and the poor efficiency of orbital-occupation optimization. Here we show that these two longstanding obstacles can be overcome simultaneously. We develop a periodic implementation of RDMFT based on a coupled optimization framework that enables the efficient simultaneous optimization of natural orbitals and occupation numbers under periodic boundary conditions. Using this implementation, we demonstrate that physically motivated short-range screening transforms the Power functional into a transferable functional applicable to both molecules and periodic solids. Remarkably, short-range screening is found to play a dual role: besides substantially improving energetic accuracy, it fundamentally reshapes the optimization landscape, producing robust optimization step sizes and dramatically accelerating convergence. The coupled optimization framework consistently requires substantially fewer optimization iterations than conventional decoupled optimization for periodic calculations, while the screened $\omega$P22 functional outperforms semilocal and hybrid density functionals in predicting representative surface reaction barriers. These results establish a computationally efficient periodic RDMFT framework and identify short-range screening as a promising design principle for developing transferable one-body reduced-density-matrix functionals.

Interfacing integrated photonics with solid-state spin defects holds great promise for future quantum networks, but the scaling of spin-photon architectures is hindered by frequency mismatches arising from fabrication-induced variations in photonic cavity resonances and the inhomogeneous optical transition frequencies of individual spins. These challenges call for a photonic platform with deterministic and wide-range tunability. Here, we demonstrate a hybrid nanophotonic platform based on direct bonding of silicon carbide photonic crystal nanocavity arrays onto thin-film lithium niobate on insulator, enabling deterministic electrical tuning of multiple SiC nanocavities into mutual spectral resonance. By exploiting the strong electro-optic response of lithium niobate, we achieve continuous and wide-range cavity tuning of 380 GHz ($\sim$1.1 nm), sufficient to compensate both cavity disorder and spin inhomogeneity. The nanocavities balance strong optical confinement with electrical tunability, exhibiting a theoretical Purcell factor of approximately 400. This hybrid platform enables electrically reconfigurable spin-photon interfaces for large-scale integrated quantum photonics.

Borrowed from non-equilibrium statistical mechanics, the generalized Langevin equation (GLE) is imported as a framework for stochastic volatility to address the structural limitations of fractional Brownian motion (fBm), the standard engine of rough volatility. The fBm forces a single parameter to set two logically independent properties at once: how volatility scales and how it remembers. The GLE separates them using a memory kernel $K$, a potential $U$, and a noise covariance $C$. Memory becomes a measurable object, and an asymmetric potential supplies a lever on variance skew that the price-variance correlation cannot reach. Physical-measure tests on public datasets decisively reject two constrained corners of the class, a memoryless leverage effect and time-reversal symmetry, while the central rough scaling constraint is left identification-limited rather than refuted. The paper reports these limits honestly, and validation on industry-grade data remains a valuable direction. The risk-neutral construction and the joint SPX–VIX calibration will be developed in a companion paper.

Early-warning signals are usually computed from one or a few observables, although the underlying dynamics are high dimensional. Projection creates a structural ambiguity: the same scalar record may reflect spectral loss of stability, hidden-state memory, nonnormal amplification, a turning point of a physical balance, or a combination of these. We develop a unified operator theory that makes these alternatives explicit. For a resolved-hidden block linearization we derive exact Volterra reductions, finite and diffusive Markovian liftings, and a Schur-resolvent factorization of the stability problem, distinguishing three notions of memory: realized pathwise, structural Schur return, and finite-horizon projected. A covariance theorem gives sufficient conditions for a scalar to inherit critical slowing down from a real multiplier approaching the unit circle: spectral separation, bounded eigenvector conditioning, observability of the critical mode, stochastic excitation of that mode, and quasi-stationary forcing. We then prove failure mechanisms: a critical mode can be invisible or unexcited; an oscillatory crossing need not raise lag-one correlation; a nonnormal family can produce diverging scalar variance with a fixed stable spectrum; a physics-based turning point need not coincide with a spectral boundary; and finite-horizon data cannot identify long memory uniquely. The theory is illustrated with a 4400-year CESM AMOC hysteresis experiment. EOF1 alone detects altered susceptibility, but EOF1-EOF2 is the smallest resolved space revealing changed Schur return and projected memory after recovery; F_ovS reaches its minimum 26 years before collapse while the modal margin does not decrease toward zero: the two diagnostics probe complementary mechanisms. The application is mechanistic and single-trajectory; no validated early-warning signal is claimed.

A new kinetic equation and Lagrangian of a massive and charged spin-one vector boson is outlined in this paper. Those quantities are derived by exploiting directly the Lorentz group generators including the boson spin. Thereby, the chiral symmetry of the Lorentz transformation is guiding the algebraic calculations. Two associated 4\,x\,4 spin and helicity matrices can be defined, the sum of which give the rotation operator that yields in the presence of an electromagnetic field the boson-spin coupling to the magnetic field. The four polarization vectors of the boson field are calculated and discussed. Moreover, the current density is derived and used to reveal the bosonic nature of the kinetic equations. The energetic stability of the vector boson quantum field is shown. A Pauli equation for the vector boson is derived.

We present a derivation showing that classical spacetime geometry is a gravitational condensate within the Unified Standard Model with Emergent Gravity–Effective Field Theory (USMEG-EFT): the background metric $\barg_{\mu\nu}$ is the vacuum expectation value of the quantum metric operator, existing as an ordered phase below the gravitational breakdown scale $\Lgrav \sim 10^{18}$\,GeV. The condensed phase is characterized by a nondegenerate metric expectation value, a diffeomorphism-invariant criterion whose controlled realization is confined to scales below $\Lgrav$. Three convergent quantum-field-theoretic analyses within the framework, namely canonical covariance breakdown \cite{Chishtie23}, one-loop renormalization group analysis \cite{Chishtie25CJP}, and conditional BRST closure \cite{ChishtieSymmetry26}, identify this scale as the boundary of the controlled geometric description. We show that the condensate order parameter, defined via the Legendre transform of the Lagrange multiplier path integral \cite{BrandtFrenkelMcKeon20,McKeonBrandt25}, satisfies a quantum-corrected saddle-point equation whose one-loop correction grows to the size of the tree term at $\Lgrav$, beyond which the framework supplies no controlled nondegenerate solution; we interpret this boundary as the onset of condensate dissolution. Because the Lagrange multiplier constraint acts on virtual graviton fluctuations and terminates the gravitational sector exactly at one loop, this boundary arises from a finite, closed set of quantum corrections, in contrast to the gradual failure of the derivative expansion in the standard effective field theory treatment of general relativity \cite{Donoghue94}. This mechanism is fundamentally distinct from, and resolves known inconsistencies of, Verlinde's entropic gravity programme.

Fast and nonuniform translocation through solid-state nanopores limits both the detection of small molecular labels and their precise localization along molecular carriers. In this work we report the detection and localization performance of nucleotide-based molecular labels along double-stranded DNA scaffolds using solid-state nanopores in thin planar membranes. For small labels that are challenging to resolve individually, we introduce an anchoring strategy where readily detectable bulky labels serve as reference points to align multiple translocation events, enabling population-based detection and localization of smaller molecular features. The measured anchor positions constrain a probabilistic model of translocation velocity, identifying the most probable velocity profile for each event and enabling nonlinear trace "unwarping" for improved multi-event alignment. A complementary window-based evidence aggregation procedure accumulates weak but consistent label signatures across events, enabling detection of features that are individually masked by noise. These approaches enable robust recovery of single-dumbbell labels (DB1) on the order of 28 nucleotides and reduce mean localization errors to as low as 10 base pairs for DB3 labels and 40 base pairs for DB1 labels when averaging over multiple events. Stronger fractional DNA-associated current blockades, used as proxy for smaller pore geometries, are additionally associated with improved detection and lower localization error across membrane-based nanopore fabrication techniques. Overall, anchor-guided alignment provides a route to higher-density molecular information readout without compromising throughput via controlled translocation approaches.

Compound drought-to-extreme-precipitation (CDEP) events are recognized in climate science as a growing driver of extreme impact, but whether this recognition carries over into real-world early warning and post-event documentation is unknown, so a meteorologically real CDEP event may pass with neither advance warning nor any later record. Here we present CDEP Agent, an auditable LLM-agent framework that tests this mismatch directly by linking CDEP candidates detected from meteorological reanalysis to real-world hazard and impact evidence across sources with different spatial scales, temporal resolutions, and reporting conventions. Using California as a case study, we identify 408 candidate CDEP events from ERA5 observations during 2021-2025 and evaluate each against the U.S. Drought Monitor, NOAA Storm Events, and public webpages along five dimensions: antecedent drought, extreme rainfall, local impact, hazard-impact attribution, and explicit drought-to-rainfall linkage. Only 34.3% of candidates are corroborated on both hazard components, and just 1.5% are ever explicitly linked to their antecedent drought, indicating that most meteorologically detected CDEP events go undocumented and their compound nature almost never enters the record at all. Our framework gives climate scientists a way to test physical event definitions against what actually gets documented, and gives social scientists, economists, and disaster-response agencies a provenance-linked evidence base for compound events that current warning and reporting systems largely fail to capture.

This paper presents the systematic fabrication and characterization of cryogenic thermometers based on neutron transmutation-doped germanium (NTD-Ge). High-purity (10N) germanium samples were irradiated by thermal neutrons with different fluences at the China Advanced Research Reactor (CARR). After irradiation and a six-month cooling-down period, positron annihilation lifetime spectroscopy and temperature-dependent Hall effect measurements were performed to characterize irradiation-induced defects and carrier concentrations in the NTD-Ge samples. Utilizing standard semiconductor fabrication techniques, point electrodes were deposited onto the processed samples to fabricate functional NTD-Ge cryogenic thermometers. The low-temperature resistance performance of the devices was characterized down to 20 mK on a millikelvin range cryogenic test platform. The measured temperature dependence of resistance follows Mott's law, showing excellent agreement across the full measured range. The extracted T0 is consistent with expectations. These results collectively verified both the applicability of the thermometers in cryogenic system and the reliability of the fabrication procedure.

18F-fluorodeoxyglucose ([18F]FDG) positron emission tomography (PET), combined with compartmental modeling, is a powerful non-invasive imaging method for assessing cellular metabolism. However, classical two- and three-tissue compartment models assume homogeneous [18F]FDG distribution within the tissue, which needs justification, and the definition and interpretation of rate constants across these models is not always consistent. To address these issues, we develop a finite difference solver to simulate [18F]FDG transport and metabolism within a 1 mm3 tissue volume, representing the smallest volume resolvable by PET. Our simulations reveal sub-millimeter heterogeneity in [18F]FDG distribution and show that the measured PET signal is dependent not only on cellular metabolic activity but also on interstitial [18F]FDG diffusivity, vascular permeability, and vascular architecture. We further demonstrate that our finite-difference simulation reduces to a three-tissue compartment model when interstitial [18F]FDG concentration is homogeneous. Furthermore, this simplified model itself reduces to the two-tissue compartment model when vascular permeability is sufficiently high. This work quantitatively links vascular permeability, vascular architecture, cellular uptake kinetics, [18F]FDG diffusivity, and acquisition time. It also unifies the two- and three-tissue compartment models and identifies their applicable regimes. These findings deepen our understanding of [18F]FDG transport kinetics and enhance the interpretability of dynamic [18F]FDG-PET imaging.

AI weather models are often reported to underestimate extremes, but most evidence concerns deterministic regression models verified against reanalysis. We evaluate twelve physical and AI forecast models against ECMWF IFS using ten months of European station observations. The evaluation covers 10 m wind, 2 m temperature, solar radiation, and precipitation within regimes defined from a fixed ERA5 1991-2020 climatology. We find no uniform AI-specific deficit in the tails. Several AI models remain more accurate than IFS under extreme conditions, while others deteriorate markedly; comparable variation occurs among physical models. Every model nevertheless exhibits a common conditional-error pattern, overpredicting low observations and underpredicting high observations. Attenuation of extreme values therefore does not imply a uniform loss of relative skill: tail performance depends on the model, variable, and evaluation setting rather than on whether the forecast is produced by AI or physical numerical modelling.

We develop a mechanically faithful reduced model for a two-dimensional topological phononic crystal composed of rigid square masses connected by slender elastic ligaments. Exploiting Euler Bernoulli beam theory, we derive a Hermitian 12 degree of freedom dynamical matrix that retains in-plane translations, rotations, and ligament eccentricity. This reduction captures effects that are absent from scalar mass spring SSH models while remaining computationally much more tractable and more easily interpretable than full finite element simulations. Dimerizing the ligament widths produces a mechanical 2D SSH lattice with a full band gap and a quantized bulk polarization. The sign of the dimerization controls the transition from trivial to non trivial phases, while ligament eccentricity provides an additional purely geometric mechanism for changing the topology. Ribbon and finite cell calculations predict in gap edge and corner modes, quantified by localization measures and confirmed by finite element simulations. Measurements on 3D printed samples show an evanescent response in the trivial structure and enhanced boundary/corner response in the non trivial structure within the predicted gap. The results provide a validated route for designing topological elastic metamaterials using a continuum informed discrete model rather than either idealized mass spring networks or brute force numerical optimization.

Accelerating scientific discovery is among the most consequential applications of AI, and computational biomolecular simulation stands out as a particularly promising target within this broader effort. Coding agents promise to automate significant portions of this workflow, yet their reliability on realistic molecular dynamics (MD) tasks remains poorly characterized. To address this issue, we introduce MDArena, a benchmark of 50 containerized tasks drawn from active biomolecular simulation projects, spanning 29 molecular systems and 14 broad research protocols, including trajectory analysis, complex system preparation, free-energy protocols, and enhanced sampling. We evaluate six model/harness configurations spanning Codex and OpenCode. Among the evaluated configurations, Codex GPT-5.5 at extra-high reasoning effort performs best, reaching 24/50 Strict-Pass@1 successes (48%), followed by Codex GPT-5.5 Medium with 21/50, and OpenCode Gemini Flash 3.5 with 20/50. Average correctness and process rewards are substantially higher than strict success rates across all configurations, indicating that agents frequently make meaningful partial progress but fail on the fine-grained details required for reproducible scientific workflows. Hard tasks remain largely unsolved, particularly membrane-protein system preparation and alchemical free-energy setup, both unsolved or near-unsolved by every evaluated configuration. MDArena thus exposes a substantial gap between the usefulness of coding agents as supervised assistants and their reliability as autonomous MD researchers, while providing a reproducible and extensible platform for tracking progress toward closing it.

We present SU3HOB-ladder, a Fortran 2008 package computing the general Talmi-Moshinsky harmonic-oscillator transformation brackets for an arbitrary mass-ratio parameter d in the SU(3) basis. The bracket reduces to a single sum over Wigner d-functions of the reflection-containing Talmi-Moshinsky matrix, weighted by products of SU(3) > SO(3) isofactors of the chain U(6) > U(3)xU(2); the isofactors are d-independent, so they are built once per block (E,L) and reused for every d. We obtain them from the U(2) pseudo-spin generators of that same chain: because the U(3) and U(2) labels inside [E] of U(6) are complementary, the highest-weight states are the null space of the raising operator J_+ = a^dag(1).a(2) and the remaining members of each multiplet follow by lowering, so that no SU(3) recoupling machinery, K-matrix or outer-multiplicity resolution is required at any point. We compare this construction with two alternatives that produce the same brackets - the same scheme with isofactors taken from the vector-coherent-state library Su3cgvcs, and the classical Talmi-Moshinsky sum of Kamuntavicius et al. - over every block up to E=50. Evaluating the classical sum with its factorials in logarithmic form rather than as tabulated binomials removes the range limit that otherwise caps it near E ~ 12, and makes it an absolute check on the bracket values over the whole range where its cost is bearable. Against that check the two routes agree to within a factor of a few at every shell, while the ladder construction is faster by three orders of magnitude; it is also the only one of the three usable over the whole range, retaining max|HOB.HOB^T - I| <~ 2x10^-6 at E=50, where the library route has become unusable and the classical route is prohibitively slow.

Conformer assignment is meaningful only when the measurement can distinguish the candidate structures. We formulate conformer spectroscopy as a measurement-model-dependent identifiability problem in which each modality defines an observation law with explicit experimental and theoretical uncertainty. Equality of observation laws defines exact classes that refine as modalities are added, whereas finite-resolution ambiguity is non-transitive and is represented by a Bayes-error graph. We also give rank and constrained-Fisher criteria for population recovery from unnormalized additive spectra. The framework is applied to audited B3LYP-D3(BJ)/def2-TZVP ensembles of 1,2-difluoroethane, ethylene glycol, and n-pentane. Under the declared shared-covariance working model, IR separates all non-mirror pairs, leaving only mirror pairs that are exactly degenerate under the achiral observation maps. This within-method result does not imply general IR sufficiency: in a six-case PBE0-to-B3LYP diagnostic, fixed calibration recovers one intended representative, whereas scale-and-shift profiling with a conservative cross-method covariance recovers five representatives and all six achiral classes. Under a combined stress-test covariance, n-pentane develops one non-mirror quotient ambiguity. Three individual Raman windows remove it, while no tested IR window does. Mirror-collapsed class populations remain identifiable and well conditioned under the calibrated-scale working model, whereas separate mirror-partner populations are exactly unidentifiable from achiral additive spectra.

Read noise in infrared sensor arrays remains a major obstacle for ground- and space-based astronomy. It has long been recognized that the upcoming extremely large telescopes cannot meet their full potential unless read noise is significantly improved, and it is also a prohibitive constraint on the Habitable Worlds Observatory, a space telescope with the goal of detection and characterization of nearby Earth-like exoplanets. The main strategy for lowering read noise is averaging through multiple non-destructive reads. However, this typically results in less noise reduction than the 1/$\sqrt{N}$ scaling predicted by theory. In this work, we show the poor averaging behavior can largely be explained by readout glow, photon emission from the sensor electronics that generates photoelectrons in the pixels during readout. Because glow accumulates with reads rather than averaging, this imposes a fundamental noise floor of \sigma_min ~ 1.5 sigma_RN^(1/2)G^(1/4). This limits averaging in HxRG-like sensors to about 2-3 e- of noise, and linear-mode avalanche photodiodes (LmAPDs) to about 0.5 e-. We present laboratory data using both sensor architectures, with the LmAPD following the predicted noise value to within 0.1 e- over two decades of averaging.

Thermospheric mass density governs aerodynamic drag in low Earth orbit and is a primary source of uncertainty in orbit prediction and conjunction assessment, particularly during geomagnetic disturbances. We present AETHER-P3 (Accelerometer-driven Estimation of THERmospheric density-A Physics-Informed Probabilistic Prediction Platform), a machine-learning-based global thermospheric density forecasting model that provides multi-step forecasts up to 6 hr ahead using a 3-hr input window, with predictive uncertainty estimates. AETHER-P3 formulates thermospheric density forecasting as a sequence-to-sequence regression task conditioned on recent space weather evolution and a user-specified sequence of future times and locations. To enhance physical consistency and generalization, AETHER-P3 incorporates JB2008 and NRLMSISE-00 density estimates evaluated at future locations, along with solar, geomagnetic, and solar-wind drivers. The network employs dual recurrent encoders and an evidential Normal-Gamma output head to jointly estimate forecast mean and uncertainty. The model is evaluated using independent satellite test cases spanning quiet, moderate, and extreme geomagnetic conditions. During quiet periods, AETHER-P3 achieves high forecast skill (R=0.95). Under moderate activity, strong skill is retained (R=0.93), with reduced physical-domain errors than empirical baseline models. During extreme storm conditions, deterministic forecast skill degrades as expected yet remains robust (R=0.89-0.90). Predictive uncertainty remains well calibrated across all regimes. These results establish AETHER-P3 as a practical, low-latency, uncertainty-aware capability for thermospheric density forecasting that supports orbit prediction, drag-risk assessment, and operational decision-making over its validated altitude range of approximately 300-520 km, with highest confidence in the data-rich 400-520 km region.

The finite transverse granularity of a segmented electromagnetic calorimeter fundamentally limits the precision with which the observables of a shower can be reconstructed, among them its position, its lateral profile, and the direction of the incident particle. We show that a substantial fraction of the information suppressed by the segmentation can be inferred under physical constraints, and that it propagates to downstream physics quantities. The reconstruction is cast as an inverse problem and solved with a generative model constrained by the low-order spatial moments of the shower, driving the solution toward physically consistent energy distributions rather than image similarity alone. Using the undoped CsI calorimeter of the KOTO experiment as a reference system, the reconstruction reduces the per-event residual of these moments with respect to the truth-level reference by roughly 40-62% in a representative 1 GeV bin. The inferred morphology also generalizes beyond the training objective: on a $K_{L}\to\pi^{0}\nu\bar\nu$ Monte Carlo sample it improves the reconstruction of the photon incident angle, a quantity never used during training, and of the $\pi^{0}$ decay vertex. These results indicate that finite segmentation is better viewed as a limit on what a calorimeter measures directly than as an absolute limit on the physics information it retains, with the recoverable fraction depending on the observable and growing with the shower energy.

Flying insect swarms exhibit cohesion without the local velocity alignment observed in flocks of birds or schools of fish. The interaction rules and channels of communication between insects that lead to collective behavior have not yet been determined. We propose a theoretical model based on acoustic communication between swarm members, where each individual is attracted to a weighted mean field of its neighbors. We demonstrate that this simple framework can describe a wide range of swarming dynamics, including a phenomenon where pairs of insects break free from the swarm in a helical orbit around each other. Counterintuitively, our numerical model suggests that stochastic noise enhances the stability of an insect swarm, as it interferes with pair formation. Finally, we demonstrate that a specific species of malarial mosquito produces swarms that reside on the edge of a transition to instability. Our study shows that fairly simple local interaction rules can be sufficient to describe the collective behavior of swarming biological systems.

Mineral precipitation and dissolution in rocks alter pore structure, stress, and hydraulic properties, producing tightly coupled chemo-hydro-mechanical processes that are central to subsurface systems. Yet, existing continuum-scale simulators lack a generalizable, mathematically tractable, and physically grounded chemo-mechanics formulation for modeling these coupled processes. To address this gap, this work presents a poromechanics-based framework for chemo-mechanics coupling and integrates it into coupled reactive transport and geomechanics simulations. Building upon classical poromechanics theory, the framework provides distinct descriptions for the stress states of the host rock and pore minerals, in addition to pore fluid pressure, enabling a rigorous and flexible treatment of their individual behavior and mechanical interactions during precipitation and dissolution. As demonstrated by numerical examples, the poromechanics-based method captures the expected mechanical response by translating pore-scale mineral growth into mineralization pressure. By accounting for mineral compressibility, the method also supports more physically realistic representations of deformation and induced stress. Numerical comparisons further highlight the unique capability of the poromechanics-based approach to model effective stress evolution and fracture development without explicitly representing pore geometry or other microstructural features. Overall, this framework offers a versatile, physics-based predictive approach to modeling coupled chemo-mechanical processes in reactive porous rocks and supports future reservoir-scale analyses of engineered subsurface systems.