Classical Physics · Cross-listed from gr-qc
Gravitating Tubes Beyond World Line Paradigm In General Relativity
Abhi Savaliya
The simplest point-particle description of classical matter is incompatible with Einstein's General Relativity because the stress-energy tensor of a point particle is distributional and concentrated on a one-dimensional worldline. For such higher-codimension sources, smooth spacetime solutions generally do not exist. This obstruction was established by Geroch and Traschen for sources of codimension $\geq2$. Motivated by this result, this thesis proposes codimension-zero tubes as a fundamental description of gravitating matter. Timelike tubes are constructed within the tubular neighbourhood of an auxiliary timelike curve. The tube interior is foliated by timelike codimension-one hypersurfaces whose dynamics are governed by a brane-like action. The resulting collective stress-energy tensor is smooth, unlike that of a point particle. For a broad class of tension and potential profiles, the strong energy condition is violated inside the tube, while the null and weak energy conditions remain satisfied. In the ultraviolet limit, where the tube radius vanishes, an appropriate rescaling of the Lagrangian density reduces the tube action to the point-particle action together with a canonical self-force-like term. The particle's rest mass then emerges as an effective quantity rather than a fundamental localized parameter. Perturbative stability is analysed at two levels. Field perturbations yield an infinite squared sound speed, showing that the foliation-generating scalar is non-dynamical and cuscuton-like. Small deformations of the leaves lead to the Jacobi equation for timelike hypersurface congruences, further constraining admissible tension and potential profiles. These results establish gravitating tubes as a geometrically and dynamically consistent description of matter that respects the Geroch–Traschen obstruction.
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Chemical Physics
Unsupervised Thermodynamics of Molecular Diffusion Models: Action-Operator Semantics and Auditable Free-Energy Readout
Wenjie Xi
Diffusion models are increasingly utilized for modeling molecular structures and conformational ensembles, yet the thermodynamic meaning of their learned representations and scores remains elusive. To resolve this ambiguity, we introduce a mathematically consistent action-operator framework natively compatible with
diffusion models. By defining a fixed molecular environment as a base action $S_0(x)$ and an alchemical perturbation as an operator $O(x)$, standard
diffusion noising induces effective noised actions and operators whose gradients and alchemical derivatives are directly represented by the model's learned fields. This rigorous self-consistency enables a “noisy operator bridge” capable of reading out free-energy differences ($\Delta F$) from endpoint ensembles and per-frame evaluations. In controlled experiments on alanine dipeptide systems, we show that incorporating physical inductive biases enables partial recovery of the base action and perturbation operator. When applied to a challenging C6-H to C6-F ligand-pocket nonbonded perturbation (185L/IND) with negligible phase-space overlap, our supervised bridge estimates the alchemical $\Delta F$ within approximately $1\ k_\mathrm{B}T$ of a stable 19-state MBAR reference. Finally, we demonstrate that endpoint coordinates and binary labels alone are sufficient to partially recover the operator shape and a centered free-energy scale without any force or action supervision. This work provides a rigorous path toward transforming generative molecular
diffusion models from black-box coordinate samplers into auditable thermodynamic
estimators.
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Physics and Society · Cross-listed from econ.GN
Cascading Impacts of the USA–China Trade War on Global Oilseed Supply Chain
Diksha Gupta, Ritwick Mishra, Achla Marathe et al. (+2 more)
Global supply chains are highly interconnected, making them vulnerable to cascading disruptions induced by trade policy shocks. Understanding how such disruptions propagate through production networks, and how mitigation mechanisms such as trade reallocation and production adjustment can alleviate their impacts, remains a central challenge. In this work, we develop a linear programming formulation of an Input-Output (IO) system that captures cascading supply-chain disruptions together with trade reallocation and production expansion. Our formulation yields a system-level
equilibrium characterization that enables the joint analysis of disruption propagation and mitigation within a unified framework. We propose an efficient
algorithm for computing approximate
equilibrium solutions by minimizing total unmet demand in large IO systems. We apply our approach to tariff-induced disruptions in the global oilseeds supply chain arising from the U.S.-China trade war. Our results show that a localized 70% disruption to flows from the U.S. oilseeds sector to China leads to a 3.27% loss in global output, with China experiencing a disproportionate loss of 14.02%. As a counterfactual mitigation strategy, allowing a 20% reallocation from Brazil's oilseed sector to China significantly reduces global output losses to 1.36%, although pressure remains high on final-demand flows. We further investigate production expansion as an additional mitigation mechanism and show that it introduces tradeoffs between reducing global final-demand losses and protecting Brazil's domestic flows. Domestic reallocation disproportionately shifts losses toward smaller economies, while globally sourced expansion redistributes losses more broadly across the network.
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All authors Diksha Gupta, Ritwick Mishra, Achla Marathe, Krista Danielle Yu, Anil Vullikanti
Space Physics · Cross-listed from cs.LG
t-STEP: An interpretable model for Total Electron Content predictions and irregularities estimations
Stephen Tete, Carl Shneider, Maxime Cordy et al. (+3 more)
Earth system infrastructures relying on satellite-based technologies, such as Global Positioning System (GPS) communications, are affected by ionospheric Total Electron Content (TEC) gradients. Modeling these gradients under physical constraints remains challenging due to their dynamic and transient nature. While existing machine learning (ML) models can predict hourly TEC variations, it remains unclear whether their temporal resolution is sufficient to preserve small-scale TEC irregularities within predicted signals. To address this gap, we introduce an interpretable ML-based model, t-STEP, designed to predict TEC at a 30-second resolution and estimate irregularity signatures from the modeled signals. This high cadence enables the derivation of Rate of TEC changes (ROT) and the ROT Index (ROTI) as diagnostic indicators of ionospheric variability. The model is developed using GPS observations from
solar cycle 24 at a station located at 5.49{\deg}S, 47.49{\deg}W. A multi-metric evaluation framework, including dynamic time warping, is used for robustness assessment, while SHAP (SHapley Additive exPlanations) provides insight into feature contributions. The 30-second TEC predictions achieve 91% accuracy with a mean absolute error (MAE) of 4.38 TECU during high
solar activity (2015). Compared with the International Reference Ionosphere (IRI-2020), the hourly model improves accuracy by 35%, reduces absolute errors by 57%, and increases prediction skill by 54%. More importantly, the 30-second model captures TEC irregularity dynamics and morphologies during geomagnetic storms of different intensities, outperforming an
attention-based Long Short-Term
Memory model under the same experimental conditions. This study demonstrates the potential of a single TEC prediction framework for scalable irregularity monitoring without requiring separate models for individual transient events.
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All authors Stephen Tete, Carl Shneider, Maxime Cordy, Claudio Cesaroni, Andreas Hein, Vasily Petrov
Applied Physics
Sub-Terahertz Channel Performance under Snowfall
Kefeng Huang, Jiabiao Zhao, Yuheng Song et al. (+8 more)
The terahertz (THz) band promises terabit-per-second links but is highly sensitive to snowfall. Natural snowflakes are non-spherical. Yet existing THz studies treat them as spheres under Mie theory, and no ITU-R model covers THz snow attenuation. This work combines line-of-sight measurements at 120, 140, and 160 GHz with physics-based scattering modeling. The measured loss is compared against the ITU-R P.1817-1 optical model, Mie models, and a discrete dipole approximation (DDA) for randomly oriented hexagonal-plate ice
crystals, each with the Scott and Gunn-Marshall size distributions. Over the measured band, ITU-R P.1817-1 overestimates and the Mie models underestimate the loss. The shape-aware DDA-Scott model agrees best, with the lowest RMSE at every frequency. From DDA-Scott, we derive a compact modified ITU-R expression in carrier frequency and
liquid-water-equivalent (LWE) rate. It reproduces the reference to within 2.5 dB/km over 100-500 GHz and 0-3 mm/h. A Rician K-factor analysis shows the channel stays LoS-dominated, so snowfall degrades the link mainly through attenuation, not multipath fading. A QPSK/16-QAM link-budget analysis then quantifies the cost of the spherical assumption. Mie-based margins overestimate the tolerable snowfall rate by 3.4 across 120-160 GHz, rising toward 5.8 in the upper transparency windows by model extrapolation. The model is further mapped into snow-limited range and adaptive-modulation switching boundaries. These results support future ITU-R recommendations for THz channels under snowfall.
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All authors Kefeng Huang, Jiabiao Zhao, Yuheng Song, Yapeng Ge, Jie Yang, Wanzhu Chang, Xiaoxiang Li, Wenbo Liu, Peian Li, Hong Liang, Jianjun Ma
Medical Physics
A Mapping Sheath with Thermally Drawn Multi-Electrode Basket for Cardiac Electrophysiological Recording and Ablation Catheter Delivery
Qindong Zheng, Anil Demircali, Jinshi Zhao et al. (+14 more)
Cardiac arrhythmias, particularly atrial fibrillation, represent a major cardiovascular health burden and underscore the need for efficient and integrated strategies for electrical mapping and targeted therapy. Cardiac electrophysiology procedures depend on accurate identification of arrhythmogenic substrates followed by timely catheter ablation, but conventional diagnostic and therapeutic devices remain separate, often requiring repeated catheter exchanges and multiple access routes. Here, we report an adaptable strategy for functionalizing hollow-core sheaths with EP mapping capabilities, integrating multielectrode recording and ablation catheter delivery within a single compact platform. The device leverages thermal drawing to enable complex
geometric fabrication, miniaturization, rapid prototyping, and scalable manufacturing of ultrathin electrode splines arranged circumferentially at the distal end to form an adjustable basket. The mapping sheath exhibited mechanical and electrophysiological properties suitable for intracardiac
navigation and electrogram recording in bench-top evaluations, an in vitro left atrial phantom study, and ex vivo Langendorff-perfused porcine heart testing. In vivo porcine studies further demonstrated translational feasibility through vascular introduction, fluoroscopic visualization, intracardiac deployment, tissue contact, electrogram acquisition, and reconstruction of voltage and activation maps. These results support the development of intracardiac platforms with an adapted manufacturing approach, potentially guiding advances in agile cardiac mapping and ablation.
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All authors Qindong Zheng, Anil Demircali, Jinshi Zhao, Xiaotong Guo, Libaihe Tian, Oliver Jones, Jamie Kay, Shengzhe Li, Alex Ranne, Elaine Lim, Huiyi Wu, Simos Koutsoftidis, Mohamed Abdelaziz, Emmanuel Drakakis, Prapa Kanagaratnam, Nick Linton, Burak Temelkuran
Fluid Dynamics
Kriging and neural network models for pressure losses across perforated plates
Shuai Li
In this paper, two novel data-driven models based on kriging and neural networks (NN) are proposed to predict pressure losses across perforated plates with circular perforations in turbulent flows. The models are developed using two sets of experimental data available in the literature. The predictive performance of the proposed models is assessed and compared against widely used empirical formulae. It is found that the proposed models consistently outperform existing empirical models for most perforated plate configurations contained in the experimental
datasets. Besides, the predicted pressure losses generally show good agreement with experimental measurements, demonstrating that data-driven approaches based on kriging and NN provide a feasible framework for modelling pressure losses across perforated plates. Overall, both approaches are promising, despite being trained on a relatively limited amount of experimental data, owing to the scarcity of measurements reported in the literature. To demonstrate the applicability of the proposed models in numerical simulations, two-dimensional channel flows are simulated using the Reynolds-averaged Navier-Stokes (RANS) equations, in which the new pressure-loss models are implemented as a source term in the momentum equations. The RANS predictions are found to be in excellent agreement with the model predictions, confirming the suitability of the proposed approaches for practical computational fluid dynamics applications.
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Plasma Physics · Cross-listed from stat.ML
Bidirectional Autoregressive Latent Diffusion for Forward and Inverse Magnetohydrodynamics
Alexander Scheinker
This work presents a new bidirectional autoregressive latent
diffusion approach for predicting the
evolution of multiple fields (mass density, pressure, velocity, and
magnetic field components) for magnetohydrodynamics. We show that this bidirectional flow can be used as a self-supervised consistency metric for uncertainty and error estimation, which enables the model to estimate test-time uncertainty and error without access to ground truth, by comparing how closely flowing forwards and backwards in time returns to the same predicted fields. We also demonstrate this methods's potential to serve as a non-invasive
plasma diagnostic, and show how adaptive feedback can be used to make the model more robust based on sparse diagnostics or limited views/measurements.
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Fluid Dynamics
Monolithic kinetic algorithm for heterogeneous porous media systems using a continuous one-domain approach
Nikita O. Gusev and Ilya V. Karlin
We propose a
lattice Boltzmann model (LBM) on standard
lattices for simulating multi-dimensional, weakly compressible, isothermal flows within and around isotropic heterogeneous porous media. The model incorporates Darcy-Forchheimer drag and a Brinkman-like effective viscous stress tensor. In the hydrodynamic limit, it recovers a generalized volume-averaged formulation valid in both free-fluid and porous-medium regions. By relying on a single kinetic equation and a monolithic LBM
algorithm, the formulation provides a one-domain solver for free-fluid/porous-medium interactions. Unlike previous LBM formulations for porous media, the proposed model recovers the correct porosity scaling of both the pressure and convective terms, while preserving the isotropy, and hence the Galilean invariance, of the viscous stress tensor. Linear and nonlinear drag, variable-porosity corrections, and additional body forces are incorporated through a consistent generalized forcing scheme. The model allows the speed of sound to be specified independently thereby improving computational efficiency. In addition, it includes a freely tunable effective bulk viscosity that can be used to enhance numerical stability. Model performance was evaluated using 2D
benchmark flow problems. The ability of the proposed LBM model to simulate transport between free-fluid and heterogeneous porous regions within a one-domain framework enables a broad range of applications, particularly in early-stage, device-scale design studies of engineered porous structures with spatially varying porosity.
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Instrumentation and Detectors · Cross-listed from quant-ph
Gaussian Quantum Metrology with Realistic Linear Sensors
Jacques Ding, James W. Gardner, Tuvia Gefen et al. (+1 more)
Quantum sensing promises enhanced precision, but the usual
quantum Cramer Rao bound can be too optimistic for realistic linear sensors, where squeezing, filtering, and loss reshape
quantum noise. We derive the tight Holevo Cramer Rao bound and show that realistic degradation yields a hierarchy with the usual bound and homodyne readout. This hierarchy already exists in gravitational-wave detectors. We propose a hardware-efficient readout that reaches the Holevo bound without extra signal loss, increasing compact-binary detection rates by up to 25% over the present LIGO homodyne readout.
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All authors Jacques Ding, James W. Gardner, Tuvia Gefen, Yanbei Chen
Atomic Physics · Cross-listed from quant-ph
Physics-Informed Neural Quantum Control for Rovibrational Photoassociation in a Morse Molecular System
Murilo D. Forlevesi, Emanuel Fernandes de Lima and Edson Denis Leonel
We present a Physics-Informed Neural
Quantum Control (PINQC) framework for rovibrational photoassociation in a Morse molecular system. The proposed method combines neural-network-based
laser-field generation with differentiable
quantum propagation, allowing optimized
laser pulses to be obtained directly from the underlying
quantum dynamics without requiring external training data. The optimized control fields efficiently transfer an initially continuum-like Gaussian wave packet into the vibrational ground-state level, promoting continuum-to-bound population transfer through coherent rovibrational dynamics. The resulting photoassociation process involves both vibrational stabilization and rotational redistribution arising naturally from dipole-induced couplings between neighboring rotational channels. A central result of the present work is the successful application of the PINQC framework to extended rovibrational models containing larger rotational levels than those previously accessible in our conventional photoassociation calculations. The optimization remains numerically stable despite the increased complexity of the molecular system, demonstrating that differentiable optimization provides an effective strategy for treating rovibrational models of increased dimensionality. These results establish the PINQC framework as a promising computational tool for molecular photoassociation and motivate future investigations of increasingly complex rovibrational
quantum-control problems.
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Atomic Physics · Cross-listed from cs.LG
STEMGym: Benchmarking Sequential Decision-Making under Dose Budgets in Autonomous Electron Microscopy
Can Polat, Erchin Serpedin, Mustafa Kurban et al. (+1 more)
A central premise of autonomous scientific imaging is that smarter
navigation, whether
Bayesian, RL-based, or otherwise adaptive, is the principal lever for sample-efficient acquisition. We present evidence to the contrary in scanning transmission electron microscopy (STEM), an atomic-resolution imaging modality whose every measurement deposits damaging electron dose. We introduce STEMGym, an open-source Gymnasium
benchmark of 15 physics-simulated STEM worlds spanning five materials, three difficulty levels, and four characterisation tasks, scored by the Dose-Efficiency Curve area (DEC-AUC), a single scalar capturing the information-vs-dose Pareto frontier. Across 33
agent configurations under realistic dose budgets, the dominant determinant of dose efficiency is the analyst (perception) pipeline, not the navigator: pairing a trained CNN analyst with naïve raster scanning raises DEC-AUC by 5.5x over a CNN-free raster baseline (0.287 vs.\ 0.052), while substituting
Bayesian or adaptive finite-state-machine
navigation for raster yields no statistically significant further gain. Production-tier
vision-
language models further underperform task-specific CNNs by {\sim}13x on crystallographic defect analysis. By decoupling perception,
navigation, and planning under a unified dose budget, STEMGym reframes where ML effort should be invested in autonomous electron microscopy and provides the measurement infrastructure to test it.
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All authors Can Polat, Erchin Serpedin, Mustafa Kurban, Hasan Kurban
Applied Physics · Cross-listed from cs.SD
EchoHawk: A Reproducible Acoustic Pipeline for Drone Detection, Classification, and Direction-Finding, with a Cautionary Study of Session-Level Data Leakage
David Shulman
Passive acoustic sensing is an attractive modality for counter-unmanned aerial system (counter-UAS) defence: it is covert, low-cost, and effective against drones with small radar cross-sections or minimal
radio emissions. We present EchoHawk, an open and fully reproducible reference pipeline that detects a drone from its rotor harmonics, estimates its blade-passing frequency, and localises it with a microphone array via classical wideband beamforming (delay-and-sum, MVDR, MUSIC) and time-delay processing (GCC-PHAT, SRP-PHAT), followed by temporal tracking. We evaluate the system on a physically transparent synthetic
benchmark that pits drones against hard low-frequency harmonic confusers, such as ground vehicles, and on real recorded audio. Our central methodological contribution is a documented case of session-level data leakage in a widely used public
dataset: because its recordings are pre-segmented into short clips, naive clip-level splits place adjacent slices of the same continuous recording in both training and test sets, inflating reported performance. Enforcing recording-session-grouped cross-validation reduces, for example, a random-forest baseline's detection probability at a 1% false-alarm rate from 0.796 to 0.745, yielding honest numbers. All code, figures, and a synthetic data generator are released so that every result runs without any download.
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Chemical Physics
Geometric Algebra Meets Cartesian Tensors: Higher-Order Equivariance for Interatomic Potentials
Can Polat, Erchin Serpedin, Mustafa Kurban et al. (+1 more)
$\mathrm{Cl}(3,0)$ interatomic potentials, despite their algebraic elegance, predict force magnitudes accurately but force directions poorly. Across ten rMD17 molecules, every $L \leq 1$ baseline in our twelve-model study attains aggregate force-cosine similarity below $0.25$. The cause is structural. The
geometric product of two vectors in $\mathbb{R}^3$ realises only the $L=0$ and $L=1$ components of its irreducible representation content, leaving the symmetric-traceless rank-2 component absent from the per-edge bilinear that drives each message-passing layer. We address this with CliffordSTF, which couples the Clifford multivector to closed-form symmetric-traceless tensor tracks at ranks two and three through bilinear cross-track contractions, using a single learned bilinear and no Clebsch–Gordan tables, Wigner-$D$
matrices, or e3nn calls. On rMD17, CliffordSTF raises aggregate force-cosine similarity from $0.055$ (base Clifford) to $0.551$, an order-of-magnitude relative directional gain, alongside improved magnitude accuracy (force MAE $15.8\%$ lower; energy MAE $10.9\%$ lower). It outperforms all CG-free or body-ordered baselines in our study (all $\leq 0.17$). On catalysis
benchmarks, CliffordSTF achieves the best out-of-distribution S2EF energy MAE on OC22 in our experiments, and the best in-distribution energy MAE among $L \geq 2$ methods on OC22 IS2RE. An eleven-variant ablation shows the two tracks are complementary: neither alone matches the combined model.
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All authors Can Polat, Erchin Serpedin, Mustafa Kurban, Hasan Kurban
Optics
Nanometric voids as optical antennas for rewritable momentum-engineered photonics in silicon
Aleksei I. Noskov, Evan P. Garcia, Haiping Sun et al. (+2 more)
Optical
antennas are widely used to localize electromagnetic fields far below the diffraction limit, enabling enhanced light-matter interactions across nanophotonics. Yet the regime in which optical confinement approaches the electronic de Broglie wavelength in a solid - where the
photon momentum distribution broadens sufficiently to relax optical selection rules - remains largely unexplored. Here we show that nanometric voids embedded within crystalline silicon act as such optical
antennas, dramatically altering the optical response of an indirect
semiconductor without the introduction of any foreign material. Using an electrically induced melt-quench process, we generate nanometric voids throughout bulk silicon, confirmed by high-resolution electron microscopy, diffraction analysis,
Fourier-filtered
lattice reconstruction, elemental mapping, and supported by optical and vibrational spectroscopies. The void-containing silicon exhibits intense broadband photo- and electroluminescence spectrally indistinguishable from that produced by metallic or
semiconductor nanoconfiners of similar dimensions, establishing that dielectric discontinuity, not confiner composition, governs the observed momentum-assisted optical transitions. The luminescence can be repeatedly written, erased, and rewritten through alternating electrical conditioning and optical recrystallization. These findings establish nanometric voids as a previously unexplored platform for extreme optical confinement and demonstrate that photonic functionality can be embedded and reconfigured directly within bulk silicon.
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All authors Aleksei I. Noskov, Evan P. Garcia, Haiping Sun, Eric O. Potma, Dmitry A. Fishman
Chemical Physics · Cross-listed from quant-ph
Quantum simulation of molecular excited-state manifolds and energies using the TEPID-ADAPT-VQE algorithm
Jason Saroni, Bharath Sambasivam, Ayush Asthana et al. (+1 more)
The simulation of molecular excited states is a key challenge in
quantum chemistry and a promising application for
quantum computing. In this work, we investigate the efficacy of the truncated
eigenvalue parametrized initial density adaptive variational
algorithm (TEPID-ADAPT-VQE) for computing low-lying excited states and potential energy surfaces. TEPID-ADAPT variationally diagonalizes a truncated low-temperature Gibbs state, enabling the simultaneous preparation of multiple excited states within a single optimization. We apply the method to H$_2$, LiH, and linear H$_4$ across bond lengths spanning weakly and strongly correlated regimes. The adaptive derivative-assembled problem-tailored (ADAPT) ansatz construction yields compact
circuits suitable for near-term hardware. We also implement a modified version of the MORE-ADAPT-VQE
algorithm for comparison with TEPID-ADAPT. We find that both
algorithms accurately reproduce excited-state spectra and potential energy curves within chemical accuracy for all the molecules and geometries studied. However, TEPID-ADAPT has the advantage of utilizing only a single, physically motivated hyperparameter (temperature) that controls the energy scale at which excited states are targeted, while MORE-ADAPT utilizes multiple hyperparameters whose optimal values depend sensitively on the target problem. These results demonstrate that combining adaptive ansatz construction with density-
matrix-based formulations provides an efficient framework for excited-state
quantum chemistry on near-term devices.
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All authors Jason Saroni, Bharath Sambasivam, Ayush Asthana, Edwin Barnes
Data Analysis and Statistics · Cross-listed from cs.LG
Anti-Collapse Dynamics and the Emergence of Multi-Time-Scale Learning in Recurrent Neural Networks
Lorenzo Livi
Long-range learning is hard for recurrent networks trained with
stochastic gradient descent, because the influence of a past input fades with the lag $\ell$, and if it fades too fast the dependence cannot be learned from finite data. This fade is captured by an envelope $f(\ell)$. An exponential fade makes the data needed to learn a lag-$\ell$ dependence grow exponentially, putting long horizons out of reach; a power-law fade keeps the cost
polynomial. We show that the asymptotic decay behavior of $f(\ell)$ is not fixed by the architecture. Instead, it emerges from the coupling between the state dynamics and parameter dynamics, settling into either a collapsed regime (fast, exponential forgetting) or an extended, anti-collapsed regime (slow, power-law forgetting). The intuition is a competition within these coupled dynamics. Training drives the network's effective time scales toward short ones, while rare, heavy-tailed fluctuations of the learning dynamics push a few of them to very long values. Along the route studied here, the extended regime survives only when these heavy-tailed pushes are strong enough to balance the pull. We make this mathematically precise with a coarse-grained
stochastic process and derive an explicit threshold at which this route to the extended regime becomes available. A single exponent, the spectral exponent~$\beta$, then governs both the spread of time scales and how slowly the network forgets. Realizing the regime in practice needs one more ingredient: the joint action of the architecture and the optimizer must be able to hold such a broad spread. A network whose capacity to generate broad time-scale spectra is severely constrained still collapses, even when supplied with strong heavy-tailed forcing. Heavy-tailed fluctuations thus act not as noise to be suppressed, but as the mechanism that sustains long-range learning.
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Applied Physics · Cross-listed from cond-mat.soft
Emergence of beating in a magnetic flagellum consisting of active bots
Francisca Guzmán-Lastra, Daniel Hernández, Nicolás Quintriqueo et al. (+2 more)
We investigate the emergence of flagellar beating in chains of
magnetic self–propelled particles (MSPPs) built from centimeter–scale vibrating
robots (Hexbugs) with embedded neodymium dipoles. When one end of the chain is anchored and self–propulsion is activated, longitudinal stress accumulates along the chain until it overcomes the
magnetic bending stiffness, triggering a buckling instability that drives sustained flagellar beating. Using a combination of experiments and numerical simulations, we identify three distinct dynamical regimes straight chain, stable flagellar beating, and fission governed by the competition between active force, chain length, and
magnetic bending stiffness. The onset of beating requires a seed misalignment set by the balance between
magnetic torques and rotational noise, and we show that the transition corresponds to a supercritical
Hopf bifurcation. A kinematic model reproduces the observed orientation dynamics with excellent agreement. The
magnetic bending stiffness, which arises directly from dipole–dipole interactions, is fully tunable via dipole strength and chain length, offering independent experimental control over both activity and
rigidity. Our results establish a macroscopic platform for studying force-induced buckling and self–oscillations in active filaments, with direct connections to flagellar motion in biological and synthetic microswimmers.
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All authors Francisca Guzmán-Lastra, Daniel Hernández, Nicolás Quintriqueo, Enkeleida Lushi, Erick Burgos
Fluid Dynamics
Single-point statistical moments of the nonhomogeneous stochastic advection equation in the small correlation length limit
Keiko Kircher, Cristian Proistosescu and Ryan L. Sriver
This paper presents the derivation of closed-form expressions of the single-point statistical moments of a solution to a nonhomogeneous
stochastic advection equation with a linear relaxation. While analytical solutions exist for homogeneous systems, nonhomogeneous cases have traditionally relied on intensive numerical simulations. Here, we provide an analytical framework for calculating single-point statistical moments by first obtaining the solution to the
stochastic advection equation via the method of characteristics, from which the moments are derived. Explicit, closed-form expressions for the first four moments are derived as functions of the characteristic length scale of the
stochastic velocity field and the spatial derivatives of time-mean profile of the field. The analytical results are validated against numerical simulations, demonstrating excellent agreement across a range of physical parameters. The resulting theory acts as a generalized “equation-of-state" style approach for predicting variability and non-Gaussian statistical behavior directly from the macroscopic mean state, providing applicability across transport systems with a wide range of time and length scales, including geology, hydrology, and atmospheric sciences.
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Instrumentation and Detectors · Cross-listed from cs.LG
Self-Supervised Calibration of Scientific Instruments Using Physical Consistency Constraints
M. Rejmund and A. Lemasson
Calibration remains one of the principal obstacles to the deployment of machine learning in scientific instrumentation because it typically relies on expert intervention, dedicated procedures, and manually labelled data. We introduce a physics-informed self-supervised framework that jointly learns latent detector calibration parameters and task-specific predictions directly from raw measurements without requiring pre-calibrated signals or external labels. The method exploits known physical constraints to generate pseudo-labels iteratively, transforming calibration into a self-supervised optimization problem. The approach is demonstrated for ionic charge-state determination in the VAMOS++
magnetic spectrometer, where the calibration of a segmented ionization chamber and the inference of ionic charge states are learned simultaneously. Starting from a weak prior on the mean ionic charge state, the model progressively refines its predictions through iterative fractional pseudo-labelling driven by the discrete nature of atomic masses. Beyond accurate ionic charge-state reconstruction, the inferred calibration coefficients provide a compact representation of the detector state that enables automated monitoring of gain drifts, pressure variations, and detector aging. The resulting labels can subsequently be transferred to specialized models that quantify detector imperfections and track their spatial and temporal
evolution. These results establish a general paradigm for self-calibrating and self-monitoring scientific instruments and represent a step toward intelligent experimental systems capable of autonomous calibration, analysis, and performance optimization.
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Biological Physics
Entropic Time, Psychophysics, and Deformed Neural Dynamics: A Unified Physical Theory for Human Time Perception
José Weberszpil and Oscar Sotolongo-Costa
We present a unified physical theory demonstrating that human subjective time perception does not track
geometric coordinate time $t$, but instead emerges from a local metric mutation driven by macroscopic physical
entropy production. By establishing the Nonextensive Troika – a closed, mutually dependent algebraic triplet linking the phase-space fractal dimension $D$, the conformable derivative order $\alpha$, and the Tsallis nonextensive parameter $q$ – we eliminate independent phenomenological fitting constants. We prove that the local time metric inherently scales as $t^{\alpha}$, deriving the conformable operator as a necessary kinetic consequence. Furthermore, we derive the $q$-index from the equiprobable monofractal Tsallis
entropy $S_q$. This structural closure unifies anomalous neural dissipative transport within a deformed leaky integrate-and-fire framework and analytically predicts macroscopic psychophysical response transitions, providing a clear thermodynamic basis for time dilation in psychedelic states (the REBUS model) and temporal compression during cognitive aging.
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Medical Physics
Pawel Moskal, Deepak Kumar, Sushil Sharma et al. (+37 more)
Annihilation
photons are
quantum-entangled in polarization, a phenomenon that has not been exploited in medical diagnostics so far. We present the first in vivo imaging of the degree of
quantum entanglement of
photons originating from positron-electron annihilation within a human subject. This study utilized the Jagiellonian Positron Emission
Tomography (J-PET) scanner, constructed from plastic scintillators. In plastics, annihilation
photons interact primarily via the Compton effect, which provides simultaneous information regarding the
photon interaction position and time, as well as the
photon polarization plane. The patient was injected with a DOTA-TATE radiopharmaceutical labeled with the $^{68}$Ga radionuclide. Using the J-PET scanner, we determined the image of the radiopharmaceutical uptake and, simultaneously, the image of the degree of
quantum entanglement. The latter was determined from the relative angle between the polarization planes of the annihilation
photons. The values of the degree of
quantum entanglement extracted for the liver and the spleen are smaller than those predicted for maximally entangled two-
photon states, yet larger than expected for separable
photons. This demonstration opens new perspectives for the application of
quantum entanglement in clinical diagnostics.
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All authors Pawel Moskal, Deepak Kumar, Sushil Sharma, Ermias Y. Beyene, Neha Chug, Catalina Curceanu, Eryk Czerwiński, Atharva Dalvi, Manish Das, Alicja Hubalewska-Dydejczyk, Sharareh Jalali, Krzysztof Kacprzak, Tevfik Kaplanoglu, Łukasz Kapłon, Kamila Kasperska, Aleksander Khreptak, Grzegorz Korcyl, Tomasz Kozik, Sumit Kumar Kundu, Anoop Kunimmal Venadan, Bartosz Leszczyński, Edward Lisowski, Filip Lisowski, Justyna Mędrala-Sowa, Simbarashe Moyo, Wiktor Mryka, Szymon Niedźwiecki, Marta Opalińska, Anand Pandey, Piyush Pandey, Alessio Porcelli, Bartłomiej Rachwał, Magdalena Skurzok, Anna Sowa-Staszczak, Tomasz Szumlak, Satyam Tiwari, Pooja Tanty, Keyvan Tayefi Ardebili, Kavya Valsan Eliyan, Ewa Ł. Stepień
Instrumentation and Detectors
All-directional gamma-ray imaging using a NaI(Tl) scintillator with double-sided SiPM readout
Anzori Sh. Georgadze
Gamma-ray imaging systems capable of determining the direction of incident radiation are essential for homeland security,
nuclear non-proliferation, environmental monitoring, and radiological emergency response. This work presents a compact, high-efficiency omnidirectional gamma-ray imaging concept based on a monolithic cylindrical 2 x 2 inch NaI(Tl) scintillation
crystal coupled to dual-ended 16 x 16 Silicon Photomultiplier (SiPM)
matrices. The system exploits scintillation light distributions collected from both
crystal faces to reconstruct three-dimensional interaction positions. GEANT4 Monte Carlo simulations incorporating full optical
photon transport were performed for 662 keV gamma rays from a ^137Cs source. The simulated energy resolution is 6.69% +- 0.31% FWHM at the photopeak. A hybrid directional reconstruction framework is implemented, combining volumetric self-attenuation (active masking) for robust low-energy localization with intra-
crystal Compton imaging for higher energies. With approximately 40,000 accumulated photopeak counts, the active-masking
algorithm achieves angular resolutions of FWHM_rm elev 5.7^degrees and FWHM_rm az 3.7^degrees. The system fully complies with the EN IEC 62327 standard for handheld radionuclide identification devices. Under the required 120-second acquisition window, it suppresses terrestrial background (NORM) from the lower hemisphere by a factor of ~320, improving to ~980 with 300-second integration. These results demonstrate that a monolithic dual-ended NaI(Tl) detector can transform a conventional scalar spectrometer into a sensitive, real-time directional imaging instrument suitable for portable field use and automated cargo inspection.
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Biological Physics
Flow-polarity decoupling and universal mobility enhancement in dense bacterial active fluids with mesoscale order
Yuhao Wang, Premkumar Leishangthem, Yiming Ding et al. (+2 more)
Active fluids consisting of living
cells or synthetic microswimmers display rich emergent behavior and nonequilibrium mechanical properties, which not only shed light on various biological processes but also inform the engineering of autonomous fluidics and self-driven materials. The individual behavior of microswimmers and their interaction with self-generated mesoscale solvent flows underlie the emergent properties of active fluids. Here we studied the microscopic dynamics in dense 3D bacterial active fluids by simultaneous imaging of
cell body, flagella, and flow field. A surprising finding is that the polarity of
cells was randomly distributed in mesoscale flow regimes, and yet the system displays mesoscale order in the self-generated solvent flows. Despite the apparent flow-polarity decoupling, the motion of
cells relative to local solvent flows predominantly navigated upstream, with the self-advection speed universally enhanced by a flow-controlled constant. Numerical modeling with full hydrodynamic interactions reveals that the observed flow-polarity decoupling arises from the breakdown of the commonly held force-dipole assumption for anisotropic microswimmers: in the presence of flow gradient and near-field hydrodynamic interactions, the direction of total active forcing exerted by a swimming bacterium to the surrounding fluid no longer aligns with its polarity. The simulations suggest that near-field interactions serve as a new type of emergent, configuration-dependent active forcing, which profoundly impact self-organization and transport in dense bacterial suspensions. Taken together, our work establishes fundamental knowledge for faithfully understanding the collective behavior of dense polar active fluids.
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All authors Yuhao Wang, Premkumar Leishangthem, Yiming Ding, Xinliang Xu, Yilin Wu
Optics
Coupling Efficiency and Laser-Induced Damage Threshold Characterization of an End-Capped Optical Fiber with a Sub-Nanosecond Pulsed Laser
George Stavrakakis, Kostas G Mavrakis, Mikis Mylonakis et al. (+1 more)
This report presents experimental measurements of coupling efficiency and
laser-induced damage thresholds for a polarization-maintaining (PM) fiber patchcord with integrated end caps, evaluated at 532 nm using a compact actively Q-switched DPSS
laser with sub-nanosecond pulses. It also describes the development of a custom free-space coupling array through which the problem of low coupling efficiency was identified and successfully addressed. No instantaneous damage was observed at peak power densities exceeding 10 GW/cm$^2$. Sustained operation at 30 kHz was maintained over extended durations (>5 h) at peak power densities of ~13 GW/cm$^2$, while prolonged 1 kHz operation led to gradual degradation at peak power densities of ~24 GW/cm$^2$. The broader context of this work is the investigation of stimulated Raman scattering (SRS) in optical fibers for nonlinear frequency conversion. This process requires the efficient delivery of high-peak-power pulses into the fiber, which serves as the nonlinear medium. In the course of these experiments, a substantial
dataset was accumulated on fiber coupling performance and damage thresholds under repeated high-intensity illumination at 532 nm. These characterization data offer practical insight into the operational limits of end-capped PM fibers in demanding pulsed
laser applications.
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All authors George Stavrakakis, Kostas G Mavrakis, Mikis Mylonakis, Giannis Zacharakis