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Physics Physics

physics Watch Physics

Saturday 20 June

Data Analysis and Statistics · Cross-listed from cond-mat.stat-mech

The Market Crystal: A Spin-Lattice Model for Collective Cryptocurrency States

Hamidreza Oliaei-Moghadam

Collective dynamics in financial markets can emerge through synchronized movements of large groups of assets. Motivated by analogies with interacting many-body systems, we introduce a spin-lattice representation for analyzing collective states in cryptocurrency markets. In this framework, assets are encoded as binary spin variables according to the sign of their returns, while correlations between assets determine effective interaction strengths. A correlation-based breadth-first search (CBFS) procedure embeds 169 cryptocurrencies into a $13 \times 13$ lattice, enabling the construction of an Ising-like Hamiltonian describing the market configuration, which we call the Market Crystal. Macroscopic observables such as magnetization and energy provide a statistical-mechanical characterization of collective market states. The resulting phase-space structure highlights regimes of strong alignment and fragmentation among assets, with an energy–magnetization pattern suggestive of predominantly ferromagnetic interactions. This framework offers a statistical-mechanical viewpoint for studying collective behavior in financial systems.
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Biological Physics · Cross-listed from cond-mat.soft

Generation of two-dimensional pulses in lipid monolayers by rapid photoswitching

Tom Rosenstein, Philipp Zolthoff, Jan Kierfeld et al. (+1 more)

We study pressure pulse generation and propagation in lipid monolayers by an experimental approach employing rapid photoisomerization of photoswitchable lipids (azoPC). This allows us to generate longitudinal surface pressure pulses by optical flash excitation in both free and constrained layer geometries. We compare the observed pulse shapes with a theoretical approach based on a nonlinear fractional wave equation for a surface displacement field, where a fractional time derivative term captures the hydrodynamics of the monolayer subphase. We explore channel geometries of different lengths and widths and find quantitative agreement between theory and experiment regarding pulse speed and pulse shapes. For narrow channels, we employ a one-dimensional version of the fractional wave equation to study pulse propagation without any fit parameters by using the pressure signal at a close pressure sensor as boundary condition to predict the pressure signal at a second far sensor. A full two-dimensional description can capture all effects arising from the channel geometry for wider channels using one common set of fit parameters for the pulse excitation that can be applied to all geometries. The nonlinearity in the fractional wave equation plays no role in explaining the observed pulse shapes because pulse amplitudes generated by azoPC photoswitching remain very small.
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All authors Tom Rosenstein, Philipp Zolthoff, Jan Kierfeld, Matthias F. Schneider

Chemical Physics

The impedance of a charged flat-plate electric double-layer capacitor

Adrian L. Usler, David Fertig and Mathijs Janssen

We calculate the impedance of a flat-plate electric double-layer (EDL) capacitor by means of Finite Element Method simulations of modified Poisson–Nernst–Planck equations. In Nyquist representation, the impedance spectra show a slanted line at intermediate frequencies if the capacitor is biased by a voltage, $U_\mathrm{bias}\neq 0$, or if the cation and anion diffusion coefficients differ, $D_-\neq D_+$. By inspecting the concentration perturbations in the relevant frequency range, we confirm that the slanted line is in both cases related to ambipolar salt diffusion. On the basis of our impedance data, we disprove two previously made claims: 1) that the width $R_\mathrm{sl}$ of the slanted-line region represents an EDL resistance; and 2) that the slope $k_\mathrm{sl}$ of the slanted line is a measure of the ratio of the diffusion and charging time scales, $\tau_\mathrm{diff}/\tau_\mathrm{c}$. For the quantitative analysis of flat-electrode EDL capacitor impedance, we propose instead two equivalent circuits, for the cases $D_-\neq D_+$ and $U_\mathrm{bias}\neq 0$. These two cases give rise to antisymmetric and symmetric salt perturbations, which are best described by a Warburg short and Warburg open element, respectively. From our circuit analysis, we obtain quantitative relations that link the Warburg prefactors to the ambipolar diffusion coefficient, the chemical capacitance of the bulk electrolyte, and the differential charge efficiency of the EDLs. We thus provide a theoretical framework that explains why the width of the slanted line saturates at large biases, why it vanishes for large ion packing fractions, and how the system's overall capacitance is limited by the finite amount of ions in a closed system.
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Geophysics

On the classification of triggers of dynamic processes in geophysics

A.V. Guglielmi, A.D. Zavyalov and O.D. Zotov

In geophysics, the problem of classifying triggers of dynamic processes in the lithosphere, hydrosphere, atmosphere, ionosphere and magnetosphere has arisen and needs to be solved. Triggers that induce catastrophic events, such as destructive earthquakes, require special attention. Classification is necessary in order to express the diversity of triggers in a limited number of ordered and well-identifiable species. This paper proposes a project for collective work on the systematization of triggers, i.e., the formation of a unified classification and nomenclature. A simplified (basic) and extended classification table are proposed. The basic matrix uses the principle of binary opposition. The matrix contains three categories of descending rank: type (natural, artificial), class (endogenous, exogenous) and specie (periodic, aperiodic). Three additional categories have been added to the expanded classification table. This results in 128 trigger varieties. The heuristic significance of the classification of triggers is emphasized. New phenomena discovered during the classification process are indicated: excitation of a strong aftershock by a round-the-world seismic echo, modulation of global seismicity by spheroidal oscillations of the Earth, the weekend effect in earthquake activity. In the magnetosphere, a cause-and-effect chain of triggers has been identified, called a trigger cascade. The existence of so-called antitriggers is indicated, which do not excite, as usually happens, but interrupt the ongoing dynamic process. Key words: systematics, nomenclature, dynamic system, disturbance, lithosphere, hydrosphere, atmosphere, ionosphere, magnetosphere, earthquake, magnetic storm, trigger cascade, antitrigger.
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Optics · Cross-listed from quant-ph

Acceleration of Free Electrons by Photonic Time-Crystals

Lior Bar-Hillel, Alexey Gorlach, Ido Kaminer et al. (+1 more)

We study the quantum interaction between free electrons and photons in a time-varying media, and find that periodic modulation exponentially amplifies electron-photon coupling within momentum gaps, enabling arbitrarily large momentum transfer. By preparing the light in a two-mode squeezed vacuum state, the electron momentum grows faster than its spectral spreading, establishing time-modulated photonic media as a platform for accelerating free-electrons and shaping their quantum state.
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All authors Lior Bar-Hillel, Alexey Gorlach, Ido Kaminer, Mordechai Segev

Optics

Beyond Data-Driven: How Physics-Informed Neural Networks are Reshaping Multi-Physics Design and Discovery

Amir H. M. Labeb, Basmala Sallam, Abdelrahman W. Elsayed et al. (+3 more)

Physics-informed neural networks (PINNs) constitute a rapidly maturing class of scientific machine learning models in which the governing equations of a physical system are embedded directly into the training objective as soft constraints. By enforcing partial differential equations (PDEs), conservation laws, and constitutive relationships during optimization, PINNs enable the construction of models that are simultaneously data-efficient, physically consistent, and capable of operating in regimes where measurements are sparse or indirect. In contrast to conventional deep learning, where the loss is typically defined solely in terms of data misfit, the learning task in PINNs is reformulated as a constrained optimization problem in which admissible solutions are confined to the manifold defined by the underlying physics. This review provides a comprehensive assessment of recent developments in physics-informed machine learning with an emphasis on PINN-based formulations for forward modelling, inverse design, and equation discovery across nanophotonics, fluid mechanics, astronomy, and biomedical engineering. Particular attention is devoted to how physical knowledge is injected at different stages of the modelling pipeline, including synthetic data generation, non-dimensionalization and scaling, architecture selection, loss design, and post-training regularization. We highlight emerging strategies for multi-physics coupling, transfer learning across parameter and geometry spaces, and rigorous benchmarking against established numerical solvers. Finally, the review discusses interpretability, uncertainty quantification, and hardware acceleration, and articulates how physics-informed learning is reshaping engineering practice by enabling digital twins and design workflows that combine simulation and data in a unified differentiable framework.
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All authors Amir H. M. Labeb, Basmala Sallam, Abdelrahman W. Elsayed, Islam I. Abdulaal, Amany M. Kamal, Omar A. M. Abdelraouf

Atomic Physics

Capture velocities for direct loading of heavy molecules into conveyor-belt magneto-optical traps

Shoukang Yang, Shuhua Deng, Zixuan Zeng et al. (+1 more)

Conveyor-belt magneto-optical traps (CB-MOTs) use blue-detuned polarization-gradient forces to provide simultaneous cooling, confinement, and loading on type-II molecular transitions. Recent experiments with \baf{138} showed that this mechanism can directly load a slowed molecular beam with an efficiency exceeding that of a conventional red-detuned MOT. Here we use established optical-Bloch-equation force calculations and classical trajectory propagation to ask whether this direct-loading strategy should extend beyond the specific molecule used in the first demonstration. For \baf{138}, the calculation reproduces the experimentally observed trend that the CB-MOT capture velocity increases with laser intensity. We then apply the same framework to two closely related but experimentally distinct cases: \baf{137}, whose dense hyperfine structure complicates a conventional dual-frequency MOT, and \bah{138}, whose narrower linewidth and longer wavelength reduce the available radiative force. In both cases, the CB-MOT retains a broad region of nonzero capture velocity. These results identify the molecular conditions under which direct CB-MOT loading should remain effective and show that the dipole-force-dominated conveyor-belt mechanism provides a practical loading route for heavy laser-coolable molecules whose MOT performance is otherwise limited by photon recoil, scattering rate, or hyperfine complexity.
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All authors Shoukang Yang, Shuhua Deng, Zixuan Zeng, Bo Yan

Fluid Dynamics

Pressure-strain redistribution as the mechanism for dissimilar heat transfer under spanwise wall oscillation waveforms

Lionel Agostini and Cedric Flageul

Spanwise wall oscillation can enhance convective heat transfer disproportionately to its drag penalty, a departure from the Reynolds analogy termed dissimilar heat transfer (DHT). The companion study of Gu'erin et al. (2026) established that an optimised quasi-plateau waveform attains an analogy factor $\overline{A}n \approx 1.09$ at $Pr = 1$ and attributed this preferential thermal enhancement to the absence of a pressure-strain redistribution channel in the temperature variance equation, but the mechanism had not been quantitatively verified. The present study addresses this gap through phase-resolved variance transport budget analysis from direct numerical simulation of turbulent channel flow at $Re\tau = 200$, $Pr = 1$. Two complementary pressure-mediated mechanisms are identified. At the Stokes-strain reversal, the pressure-strain redistribution $\Pi_{uu}$ imposes a pronounced drain on the streamwise velocity variance with no counterpart in the temperature variance equation: the divergence-free constraint redistributes momentum variance among velocity components but has no scalar analogue. During the quasi-steady plateau phases, the pressure-temperature-gradient correlation $\Pi_{v\theta}$ preferentially enhances the wall-normal scalar flux relative to the momentum flux. The concentration of both mechanisms within the reversal and plateau phases, rather than at the Stokes-layer penetration maxima, identifies the duration of the quasi-steady phases as the controlling parameter for DHT enhancement, resolving the paradox whereby increased penetration depth does not produce increased dissimilarity.
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Optics

A Comparative Evaluation of Sample Selection Algorithms for Multivariate Calibration in Near-Infrared Spectroscopic Analysis of Pharmaceutical Formulations

Aminata Sow, Harouna Sangaré, Fadaba Danioko et al. (+1 more)

The construction of robust multivariate calibration models for near-infrared (NIR) spectroscopic analysis necessitates careful partitioning of samples into training and validation sets. The selection strategy employed fundamentally influences model generalizability and predictive accuracy. This investigation presents a systematic comparative analysis of four established sample selection algorithms-Duplex, Honigs, Kennard-Stone, and Naes-applied to NIR spectral data acquired from 58 commercial paracetamol tablets. Gaussian process regression (GPR) served as the modeling framework, with model performance quantified through the coefficient of determination (R^2) and root mean square error of prediction (RMSEP). The Kennard-Stone algorithm employing the Mahalanobis distance metric demonstrated superior performance, yielding optimal validation statistics (\(R^2 = 0.99999\), RMSEP = \(1.74 \times 10^{-6}\)). Rigorous non-parametric statistical analysis employing Kruskal-Wallis and post-hoc Mann-Whitney U tests with Bonferroni correction confirmed significant performance differences among algorithms (p < 0.001), while revealing statistical equivalence between Kennard-Stone and Honigs methods. Systematic investigation of training set proportions (60-90%) elucidated the monotonic relationship between calibration set size and predictive accuracy. These findings provide evidence-based guidance for optimizing sample selection protocols in pharmaceutical NIR applications and underscore the critical importance of chemometric validation in spectroscopic method development.
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All authors Aminata Sow, Harouna Sangaré, Fadaba Danioko, Tidiane Diallo

Optics

Per-Span Microwave Frequency Fiber Interferometry in Subsea Cables for Scalable Deep-Ocean Geophysical Monitoring

Georgios Aias Karydis, Nicolas L. Celli, David Craig et al. (+6 more)

We demonstrate low-cost microwave frequency fibre interferometry for per-span monitoring of a 1,770 km operational subsea cable connecting Ireland and Iceland. Over four months, this approach successfully resolved tidal variations, storms, and teleseismic earthquakes, highlighting its potential for large-scale, cost-effective ocean monitoring.
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All authors Georgios Aias Karydis, Nicolas L. Celli, David Craig, Örn Jónsson, Andrés Arnar Hlynsson, Eoin Kenny, Charis Mesaritakis, Christopher J. Bean, Adonis Bogris

Applied Physics

Revisiting Theoretical Modeling of Seebeck Coefficient of Semiconductors

Yuchao Hua

We revisit the closed-form models of Seebeck coefficient, and identify the thermodynamic flaws in those extensively-used methodologies, essentially including the confusion of electrochemical potential and electrical potential within the definition, arbitrarily neglecting the dependence on conduction band bottom and Fermi level when calculating distribution function spatial gradient, and the improper heat flux presentation. Here, an alternative methodology is presented, which derives the Seebeck coefficient model based on the drift-diffusion equation with the Soret effect in the open circuit condition. The reversible model that eliminates the electron velocity and relaxation terms in the formulation is recovered for the band term in the near-equilibrium case, and it can give the lower bound of Seebeck coefficient in theory, while the phonon-drag term is of the identical form to the Boltzmann transport equation(BTE)-based one. A case study is performed for highly-n-doped Silicon, where the reversible, ballistic(based on Landauer formulation) and BTE models are compared with each other and with the experimental data. The reversible model gives the fairly-good predictions for the experiments. Given its theoretical-soundness, simple form and low computation cost, the reversible model can serve as a concise alternative for evaluating thermoelectric properties in both the reversible and near-equilibrium cases.
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Physics and Society

Perceiving exposure segregation with open urban imagery

Yunke Zhang, Ruolong Ma, Xin Zhang et al. (+4 more)

Socioeconomic exposure segregation – the lack of daily interaction between income groups – erodes social capital and entrenches inequality, yet the specific physical features that drive these behavioral restrictions remain poorly understood. Prior research has quantified where segregation occurs using mobility data, but has not identified how the built environment facilitates or inhibits these interactions. Here we introduce VISAGE, a large multi-modal model-enabled framework that perceives exposure segregation directly from open satellite and street-level imagery across 10,030 communities in 31 U.S. cities. Moving beyond black-box correlations, we operationalize cross-disciplinary sociological theory into an interpretable visual codebook to detect physical regulators of social mixing. We find that the built environment encodes a legible grammar of segregation: "defensible" architectural forms (e.g., fences, gated enclosures) and monofunctional zoning systematically predict higher social isolation, whereas mixed-use infrastructure fosters interaction, explaining substantial variance in mobility-derived segregation patterns (Pearson $r=0.770$). Crucially, we show that inclusionary housing policies manifest in distinct visual signatures associated with higher mixing, suggesting that policy interventions successfully alter the physical landscape to encourage diversity. Our findings offer a scalable pathway to decipher the social production of space, providing a mechanism-based lens to understand how the built environment shapes social behavior.
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All authors Yunke Zhang, Ruolong Ma, Xin Zhang, Yu Shang, Fengli Xu, Tong Xia, Yong Li

Applied Physics

Opportunistic Lower-Terahertz Rainfall Estimation with DSD-Constrained Channel Characterization

Wanzhu Chang, Yuheng Song, Jiabiao Zhao et al. (+8 more)

Rain-induced attenuation and scattering become significant at terahertz (THz) frequencies, and exploiting this rain sensitivity is necessary both to safeguard link reliability and to enable opportunistic environmental sensing without dedicated instrumentation, a capability that remains largely unvalidated on real outdoor channels above 100 GHz. This article investigates opportunistic rainfall estimation using measured lower-terahertz (THz) channels at 140 and 229 GHz. Outdoor measurements over a 41.5-m rain-exposed path are used to characterize rain-induced attenuation and the rainfall dependence of an effective Rician K-factor. Because the path-representative drop-size distribution (DSD) is unavailable, several propagation-model scenarios based on ITU-R P.838-3 and Mie theory with canonical DSDs are employed to quantify model-form sensitivity. These channel characteristics are then used to generate physics-constrained synthetic received-power sequences for training RainFormer, a compact attention-convolution regression network that combines temporal features with explicit attenuation and fluctuation statistics. Under matched synthetic conditions, RainFormer achieves RMSEs of 0.1782 and 0.2925 mm/h at 140 and 229 GHz, respectively, and outperforms the investigated convolutional and Transformer baselines in most metric-frequency combinations. Direct application to the independent measured dataset produces physically consistent rainfall estimates at 140 GHz and demonstrates that received-power fluctuations provide useful information beyond mean attenuation. The results establish a measurement-informed framework for evaluating lower-THz links as opportunistic rainfall sensors while explicitly accounting for propagation-model uncertainty.
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All authors Wanzhu Chang, Yuheng Song, Jiabiao Zhao, Mingxia Zhang, Jie Yang, Kefeng Huang, Kaixin Sun, Hong Liang, Weidong Hu, Fawad Sheikh, Jianjun Ma

Plasma Physics

Plasma Flow Generation and Particle Acceleration from Expanding Magnetic Bubbles

Yang Zhang, Brandon K. Russell, Geoffrey Pomraning et al. (+9 more)

Impulsive plasma dynamics in the laboratory are often driven by rising electric currents, yet their quantitative plasma response has not been well established. By means of fully kinetic particle-in-cell simulations and laser-driven capacitor-coil experiments, we show that a rising current expels plasma, forming an expanding magnetic bubble and accelerating particles. The expansion front velocity scales with the Alfvén speed determined by the magnetic field at its inner edge and the plasma density at its outer edge. This mechanism establishes impulsive current drive as a fundamental way that generates plasma flows and accelerates particles in laboratory plasmas, with potential relevance to astrophysics.
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All authors Yang Zhang, Brandon K. Russell, Geoffrey Pomraning, Lan Gao, Xiaocan Li, Adam Stainer, William Daughton, Chuanfei Dong, Liang Wang, Peiyun Shi, Kian Orr, Hantao Ji

Computational Physics

False Summit and Silent Drift: A Failure Taxonomy and Efficiency Analysis of LLM-Assisted Multiphysics Simulation in an Open-Source Framework

Chi-Chun Chiang and Shih-Ming He

Multiphysics simulation of semiconductor processes requires simultaneous command of governing equations, numerical methods, software implementation, and process physics, creating a substantial entry barrier for newcomers. We investigate whether large language model (LLM) assistance and curated literature guidance can reduce this barrier during the development of a low-pressure chemical vapor deposition (LPCVD) model for graphene growth using the open-source Multiphysics Object-Oriented Simulation Environment (MOOSE). We compare GPT and Claude across direct prompting, staged development without literature, and staged development with literature guidance. Staged development successfully produced converged simulations but revealed two recurring failure classes. We define a false summit as a simulation that converges and appears physically plausible while implementing incorrect physics, and silent drift as the undetected propagation of unverified assumptions through successive development stages. In our case study, a non-conservative species-transport formulation generated spurious methane depletion that was visually indistinguishable from genuine surface consumption, while an omitted self-limiting surface-reaction term produced physically incorrect growth behavior that the LLM repeatedly rationalized as valid. Literature guidance substantially reduced interaction burden during the most challenging development stage, decreasing prompt counts by 91% for GPT and 64% for Claude. Based on these observations, we propose an eight-category failure taxonomy, model-specific human oversight strategies, and a stage-gated workflow to improve the detectability of hidden modeling errors. The results demonstrate that LLMs can lower the barrier to multiphysics simulation development, but rigorous physical validation remains essential because apparently reasonable solutions may conceal critical defects
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Optics

Material- and geometry-independent multishell cloaking device

Pattabhiraju C. Mundru, Venkatesh Pappakrishnan and Dentcho A. Genov

In this paper we proposed a multi-shell generic cloaking system. A transparency condition independent of the object's optical and geometrical properties is proposed in the quasi-static regime of operation. The suppression of dipolar scattering is demonstrated in both cylindrically and spherically symmetric systems. A realistic tunable-low loss shell design is proposed based on composite metal-dielectric shell. The effects due to dissipation and dispersion on the overall scattering cross-section are thoroughly evaluated. It is shown that a strong reduction of scattering by a factor of up to 10^3 can be achieved across the entire optical spectrum. Full wave numerical simulations for complex shape particle are performed validating the analytical theory. The proposed design does not require optical magnetism and is generic in the sense that it is independent of the object's material and geometrical properties.
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Physics and Society

Ranking football teams via the higher-order decomposition of performance networks

A. Chacoma, J.I. Perotti and O.V. Billoni

We propose a unified methodological framework to quantify team performance in elite football by combining event-level performance metrics, higher-order network representations, and algebraic ranking methods. Using data from the 2017–2018 season of the five major European leagues, we construct metric-specific weighted graphs in which teams are connected through relative performance indicators. These graphs are analyzed via Hodge decomposition, and the gradient component is used to derive metric-based team ratings. The resulting rankings are systematically compared with the true league standings using Pearson and Kendall correlation measures, revealing strong metric- and league-dependent effects. Furthermore, by analyzing the ratio between solenoidal and total flow energies, we show that local cyclic dynamics structurally limit the gradient component's capacity to reconstruct the ranking. This topological inconsistency acts as a structural fingerprint of each league's “competition style” successfully mapping the studied systems into distinct regimes: highly hierarchical structures (England and Italy), tactical parity driven by generalized loops (Germany), and pockets of localized chaos (France and Spain). Lastly, we introduce a composite rating obtained as a parsimonious linear combination of metric-based ratings, optimized separately for each league. This composite approach significantly improves predictive power and allows the relative importance of different performance indicators to be quantified in a league-specific manner. Our results demonstrate how higher-order network methods provide a flexible and interpretable framework to uncover latent performance structures in football, offering a complementary perspective to outcome-based rankings and a general approach applicable to other oppositional sports.
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Friday 19 June

Physics and Society

Quantum-like Architecture of the Social Atom: Mental Marker Coherence and the Triple-Resonance Mechanism of Cognitive Activation

Andrei Khrennikov, Christian Aspalter and Likan Zhan

Social Laser Theory (SLT) provides a rigorous quantum-like mathematical formalism for modeling collective social activation. While its macroscopic effects mimic laser physics, the fundamental driver of the lasing regime resides in the internal state dynamics of the social atom. In this paper, we propose a quantum-like architecture for the individual cognitive system, formalizing the social atom as a composite quantum system where mental markers - spanning cognitive, affective, and valence dimensions - are represented as internal degrees of freedom in a complex Hilbert space. We show that social activation is not a classical linear response but is governed by a Triple-Resonance Mechanism: the simultaneous quantum-like matching of semantic topic, energetic impact, and valence stance between the agent's internal state and the informational field. We detail how the repeated processing of quantized informational units (infons) leads to a state of cognitive population inversion. We further establish a link to Human Entanglement Theory (HET), suggesting that the emergence of coherent informational fields is rooted in the phase alignment of these internal mental markers across a population. By situating SLT within the broader framework of Social Quantum Field Theory (SQFT), this paper provides a neuro-cognitive foundation for collective phenomena such as emotional contagion and rapid narrative amplification. Our results suggest that the Social Laser is a macroscopic manifestation of quantum-like effects across the human cognitive and neuro-social architecture.
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Instrumentation and Detectors

Icing monitoring with thickness shear acoustic waves

Jaime del Moral, Miguel González del Val, Víctor Rico et al. (+8 more)

Icing monitoring and ice detection are critical challenges across many industries where ice formation can severely degrade performance or compromise safety. Conventional ice sensing approaches, such as those based on surface acoustic waves (SAWs), often suffer from limited selectivity, fast saturation, and slow or energy-consuming recovery. In this work, we propose the use of thickness shear mode bulk acoustic waves (TSM AWs) as a selective and robust alternative for monitoring icing phenomena, ranging from frozen sessile droplets to ice accretion in near-real scenarios within aeronautical envelopes. The sensing platform consists of a LiNbO3 plate activated through lateral field excitation electrodes and driven electronically by a Vector or Scalar Network Analyzer. This system reliably detects both freezing conditions and temperature variations. Tracking the evolution of the magnitude of the reflection coefficient, specifically, the resonance peak and shape and frequency of|S11| minimum, of a strongly shear dominant mode provides a reliable means to characterize and extract both qualitative and quantitative information about icing processes. Finite element simulations further elucidate the physical mechanisms governing the ice monitoring capabilities. These results position TSM AW devices as promising candidates for a new generation of simple, light-weight, highly sensitive, and robust acoustic wave icing sensors.
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All authors Jaime del Moral, Miguel González del Val, Víctor Rico, Juan R. Sánchez-Valencia, Julio Mora, Paloma García Gallego, Francisco Carreño, Andreas Winkler, Agustín R. González-Elipe, Ana Borrás, Stefan Jacob

Physics and Society

Self-replicating seedbox servers using programmable money

Matei Dogariu and Johan Pouwelse

Centralized content distribution makes availability depend on a single operator's survival and willingness to serve. EternalSeedBox replaces the operator and network with inherited economic parameters: each node is a VPS that seeds media over BitTorrent, holds a Bitcoin wallet, and autonomously decides every twelve hours whether to renew its lease, spawn a child, or sweep its funds to a healthier peer before expiring. A single genesis node seeds the fleet, and every node thereafter is provisioned, funded, and retired autonomously. We validate the design against faithful replicas of both the Bitcoin payment network and the SporeStack VPS marketplace by running the unmodified node code. A lump sum of EUR 10,000 grew the fleet to 33 nodes before capital exhausted at day 153. With simulated income, the fleet held 40–80 live nodes across 510 days, recording 268 births and 190 deaths. A heritable caution trait was introduced to diverge across generations: low-caution lineages reproduced faster during high-income phases, while the survival advantage expected of high-caution lineages during income pauses did not appear, leaving selection in favor of low-caution nodes. The fleet tolerates high node turnover because reproduction depends on any node holding a surplus, not any single node surviving. EternalSeedBox shows that a content distribution network can lease, pay for, and replenish its own hardware without a human operator after genesis, provided income exceeds per-node rent.
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Data Analysis and Statistics · Cross-listed from q-bio.NC

A Tensor Network Framework for Interpretable Graph Analysis of Brain Networks

Domenico Pomarico, Giuseppe Magnifico, Alessandro Grecucci et al. (+10 more)

Identifying robust neurobiological signatures of brain disorders requires machine learning approaches that combine predictive performance with interpretable representations of feature interactions. Here we introduce a quantum-inspired framework based on tensor network machine learning that learns distributed representations of gray-matter features encoded in a Matrix Product State representation, a variational ansatz originally developed for quantum many-body systems. The trained model is then used not only as a classifier but to extract quantum connected correlations between features, which encode higher-order feature interactions and which we map onto a weighted graph. This construction allows us to track, within a single representation, both: (i) the global spectral properties of the network (capturing collective learning dynamics), and (ii) node-level centrality measures (providing interpretable signatures of individual brain regions). Using repeated train-test sampling schemes, we analyze two classification tasks on structural MRI data as examples of complex brain disorders: healthy controls versus schizophrenia and versus bipolar disorder. Node-level analysis identifies a stable set of gray-matter features, most prominently Heschl gyrus, insular cortex, and frontal regions, that act as hubs across multiple centrality measures and across resamplings. These centralities display lower variability across resamplings than Shapley values, supporting the interpretive value of the network representation. The bipolar feature set emerges as a subset of the schizophrenia one, consistent with the hierarchically organized neuroanatomical alterations reported in neuroimaging studies and offering a network-based characterization of this hierarchy.
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All authors Domenico Pomarico, Giuseppe Magnifico, Alessandro Grecucci, Loredana Bellantuono, Jesus M. Cortes, Marianna La Rocca, Alfonso Monaco, Marlis Ontivero-Ortega, Alessandro Scarano, Massimo Stella, Roberto Bellotti, Sebastiano Stramaglia, Nicola Amoroso

Classical Physics

Fermi–Born–Infeld electrodynamics: a nonlinear theory with physical gauge

Renato Vieira dos Santos

We construct a nonlinear extension of Fermi's electrodynamics by incorporating a Born–Infeld structure that depends directly on the four-potential $A_\mu$ rather than on the field strength $F_{\mu\nu}$. The resulting theory, which we call Fermi–Born–Infeld (FBI) electrodynamics, eliminates the $U(1)$ gauge redundancy by elevating the Lorenz gauge to a dynamical condition. The Lagrangian is built from the determinant of a metric-like tensor $g_{\mu\nu} = \eta_{\mu\nu} + 2\kappa\, \partial_{(\mu} A_{\nu)}$, ensuring that the canonical energy–momentum tensor and the spin density remain unique and free of gauge ambiguities. We derive the field equations, which reduce to $\partial_\nu(\sqrt{-g}\, g^{\mu\nu}) = 0$, and show that the Lorenz condition $\partial_\mu A^\mu = 0$ emerges dynamically from retarded boundary conditions and the requirement of a positive-energy spectrum. The nonlinearities modify the propagation of longitudinal modes; we argue, via a Vainshtein-like mechanism, that the nonlinear self-interactions may stabilize the longitudinal mode, opening the possibility of a stable massive scalar photon under extreme field conditions. We also compute the spin density from the Noether current and discuss its properties. The FBI theory preserves the physical gauge of Fermi's original formulation while incorporating the regularization features of Born–Infeld electrodynamics, making it a candidate for describing electromagnetic phenomena in strong-field regimes.
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General Physics

Quantum-Conditioned Curvatures in Spacetime Surrounding Kerr-Newmann Black Hole

A. Tawfik, Saleh O. Allehabi, M. Ur Rehman et al. (+1 more)

This research examines the possibility whether the curvatures found in conventional General Relativity (GR) are the only existing ones, using both analytical and numerical techniques. To this end, we introduce a thorough investigation of Riemann curvatures in the spacetime surrounding a Kerr-Newmann black hole, which is distinguished by its specific electric charges and rotational dynamics. We apply a geometric quantization ansatz that centers on the quantization of the metric tensor, from which the complete set of field equations can be derived. The conformal transformation of the standard metric tensor upholds all the principles of GR while also extending its applicability to lower (quantum) scales. We recognize two types of Riemann curvatures. In addition to the positive curvatures present in classical GR formulations, we also find significant negative curvatures at lower (quantum) scales. This may indicate quantum sources of gravitation that classical GR does not seem equipped to explore.
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All authors A. Tawfik, Saleh O. Allehabi, M. Ur Rehman, A. A. Alshehri

Optics

Residual pump diagnostics of lasing-state absorption in multipass pumped Yb:YAG thin-disk lasers

Hanjin Jo, Jiří Mužík, Pawel Sikocinski et al. (+4 more)

Residual pump power was used as a spatially integrated diagnostic of inversion-dependent absorption in 32-pass pumped Yb:YAG thin-disk lasers. A quasi-three-level rate-equation model was coupled to accumulated pump flux, intracavity signal flux, and an effective ASE-induced depletion term. Two disks were tested under non-lasing and multimode lasing conditions. The analysis reproduced residual pump fractions within 2.4% in non-lasing and 1.8% in multimode operation, and output power within 1.3% relative error. Stimulated emission reduced the steady-state inversion and increased the effective pump-band absorption relative to the non-lasing case, while the absorption remained below the room-temperature small-signal value. The same absorption state was then used to estimate volumetric heat-load trends and to assess pump-pass number and output-coupler transmission for high-power thin-disk scaling.
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All authors Hanjin Jo, Jiří Mužík, Pawel Sikocinski, Michal Chyla, Yoann Levy, Martin Smrž, Tomáš Mocek

Fluid Dynamics · Cross-listed from math.AP

Geometric Structures of Pseudo-Sonic Curves in Self-Similar Solutions of the Euler Equations for Potential Flow

Gui-Qiang G. Chen, Mikhail Feldman, Xin Gao et al. (+1 more)

We are concerned with the geometric structures of pseudo-sonic curves in two-dimensional self-similar solutions for the Euler equations for potential flow, allowing for non-uniform supersonic states. Mathematically, the governing second-order potential flow equation is of mixed hyperbolic-elliptic type, with degeneracy occurring along the pseudo-sonic curve. In this paper, we develop rigorous analytical approaches to analyze the geometric structures of pseudo-sonic curves in such self-similar solutions. We first show that the pseudo-sonic curve is necessarily a circle if the pseudo-velocity at each point is a normal to the curve. We then analyze the general case in which the pseudo-velocity on the pseudo-sonic point is not a normal to the curve, and study the geometric properties of streamlines in a neighborhood of the pseudo-sonic curve. Next, we establish two theorems that provide sufficient conditions ensuring that the pseudo-velocity at a pseudo-sonic point is normal to the curve, under natural assumptions on the local behavior of the solution. These results yield a precise characterization of the geometry of pseudo-sonic curves. Finally, we apply the developed theory to the shock reflection-diffraction problem with non-uniform incoming flow. We prove that the pseudo-sonic curve must be an arc if the solution is a $C^2$-small perturbation, either in the pseudo-supersonic or pseudo-subsonic region, of a solution with uniform incoming flow. In particular, the density and velocity must be constant, corresponding to the radius and the center of the pseudo-sonic arc, respectively. Moreover, we prove that the solution is $C^{2,\alpha}$-regular in the pseudo-subsonic region up to the sonic arc (except at point $P_1$). The techniques and ideas developed in this paper are expected to be applicable to other nonlinear problems involving similar mixed-type degeneracies.
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All authors Gui-Qiang G. Chen, Mikhail Feldman, Xin Gao, Wei Xiang

Data Analysis and Statistics · Cross-listed from cs.LG

What Do Lorentz-Equivariant Jet Taggers Learn?

Jay Agarwal, Siddharth Khare and Dhruv Kumar

We study what Lorentz-equivariant jet taggers learn internally, using equivariance tests, linear probes and grade ablations across five models including L-GATr, L-GATr-slim and LLoCa-T. Linear probes show that equivariant models suppress frame-dependent pseudorapidity to zero while encoding jet mass and N-subjettiness strongly. Grade ablations on L-GATr reveal that bivector channels are negligible for top-quark tagging while vector-like channels are dominant but seed variable, consistent with the network exploiting multiple representational pathways. These results characterize which physical features and algebraic grade structures carry discriminative information in equivariant taggers and may inform future development of such models.
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Geophysics

Bayesian three-dimensional seismic travel-time tomography for active- and passive-source seismic data using physics-informed neural network

Ryoichiro Agata, Kazuya Shiraishi, Gou Fujie et al. (+1 more)

Accurate 3D seismic velocity modeling through seismic travel-time tomography using both active- and passive-source data provides critical underpinning models for seismicity monitoring and hazard assessment. Because travel-time tomography is an inherently ill-posed inverse problem, UQ of the estimated models using Bayesian methods is also important for reliable downstream interpretations and analyses. However, Bayesian inference for 3D tomography based on conventional grid-based representations faces the “curse of dimensionality” and severe computational bottlenecks. Consequently, rigorous Bayesian UQ for margin-wide 3D travel-time tomography has remained largely unexplored. In this study, we propose a meshless 3D Bayesian travel-time tomography method that combines PINNs with a neural representation of the velocity structure, enabling tractable and data-efficient Bayesian inference through function-space particle-based variational inference. To efficiently integrate passive-source data into the Bayesian estimation of the velocity structure, we conduct analytical marginalization treating uncertain source parameters as nuisance parameters, with passive-source relocation carried out in post-processing. We validated the capability of our approach for 3D problems through synthetic experiments. Furthermore, we applied the method to a real-world dataset from marine active-source surveys and natural earthquakes off the Kii Peninsula, Nankai Trough. Our probabilistic 3D ensemble successfully resolves key geological features and provides data-consistent uncertainty maps. The posterior mean hypocenters shifted mainly in the vertical direction by 10-15 km, consistent with a previous relocation result. Finally, the neural representation drastically reduces storage requirements for the entire ensemble velocity model, highlighting the scalability and data efficiency of the proposed framework.
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All authors Ryoichiro Agata, Kazuya Shiraishi, Gou Fujie, Dan Bassett

Fluid Dynamics

Physics-Preserving Latent Compression for Zero-Shot Resolution Transfer in 3D Turbulence

Yilong Dai, Yiming Sun, Yiheng Chen et al. (+4 more)

High-resolution turbulence modeling is essential for scientific computing, but remains constrained by the cost of direct numerical simulation and the scarcity of full-resolution data. Existing scientific compressors reduce storage but typically operate on per-frame representations, whereas learned compressors yield compact latents that are often resolution-dependent and weakly aligned with the physics of turbulence. This raises the need for a compression framework that reduces data size, preserves physical diagnostics, and transfers from low-resolution training fields to high-resolution test fields without retraining. In this paper, we propose Physics-Preserving Latent Compression (PPLC), a patch-local latent compressor for three-dimensional turbulence. Motivated by inertial-range scale similarity, PPLC treats fixed-size patches as transferable units and applies a shared variational autoencoder independently of the global grid size. It combines exact mean preservation, zero-mean fluctuation encoding, an invertible Haar wavelet front-end, shift-consistency regularization, and overlap-aware reconstruction. Instantiated on forced isotropic turbulence, PPLC is trained only on stride-downsampled 256^3 fields and transfers zero-shot to 1024^3 fields. Experiments show that PPLC improves the balance between reconstruction accuracy and physical fidelity over classical and learned baselines, keeping diagnostics such as dissipation, enstrophy, energy spectra, and incompressibility closer to the ground truth. Beyond turbulence compression, PPLC offers a general strategy for physics-preserving latent representations that support data-efficient scientific surrogate modeling.
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All authors Yilong Dai, Yiming Sun, Yiheng Chen, Ziyi Wang, Shengyu Chen, Xiaowei Jia, Runlong Yu

Applied Physics · Cross-listed from cond-mat.mes-hall

Solid-state transcapacitor, a new gain element for logic, memory and interconnects

Amrita Mathuriya, Roza Kotlyar, Neal Reynolds et al. (+9 more)

Today's transistors dictate the voltage and charge scales for both logic and memory. While AI systems are recognized to be limited by memory energy, the dominant share of the energy is expended in the intrachip interconnects whose voltage and charge scales are set by transistors. The energy scaling challenges of transistors can be attributed to simultaneously meeting high current density, high current/impedance modulation, and the inability to lower voltages. Hence, a new logic element that lowers the voltage and charge needs is a priority, not only for lowering logic power but also memory access power. Here, we propose a novel 3-terminal logic element for low energy computing, a solid-state transcapacitor (TCAP). A TCAP is a solid state displacement current modulator realized by a gate which controls the charge-voltage relationship of the channel. Unlike transistors, TCAPs eliminate the dissipative transport current, are not bound by the Boltzmann current modulation limit, and operate with displacement currents limited only by the polarization response and contact resistance. Hence, TCAP circuits may simultaneously overcome the voltage, current density, and current modulation limits of CMOS. We describe a solid state TCAP using a piezoelectric transcapacitor in which a gate-controlled stressor modulates the capacitance of a polar channel via electromechanical coupling. This device achieves inversion and gain, essential for logic, and is functionally equivalent to a 1T-1C memory cell, enabling dense memory. Using voltage scaling, capacitive energy recovery, and high polarization densities of polar materials, the logic based on TCAP offers a pathway to 100 fold lower energy consumption with a delay comparable to ultimately scaled CMOS devices. This approach provides a new potential pathway for low-energy computing beyond the limits of transistors using electro-mechanics and multiferroics.
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All authors Amrita Mathuriya, Roza Kotlyar, Neal Reynolds, Rafael Rios, Alan Kalitsov, Peter B. Meisenheimer, James Clarkson, Noriyuki Sato, Tanay Gosavi, Ramamoorthy Ramesh, Dmitri E. Nikonov, Sasikanth Manipatruni

Data Analysis and Statistics · Cross-listed from quant-ph

Common causes for quantum identical particles

A. Hovhannisyan, S. Weigert and A. E. Allahverdyan

Violations of Bell's inequalities imply that joint probabilities generated by non-commutative measurements on two (non-identical) quantum particles do not have a single common cause. But joint probabilities generated for such non-identical particles via commutative measurements do have non-trivial common cause variables. We focus on commutative measurements and consider two identical quantum particles, whose density matrices and observables (hermitian operators) are necessarily permutation-symmetric. It is natural to demand that the common cause describing joint probabilities is also permutation symmetric, i.e., it acts symmetrically on both particles. Looking at various ways of defining joint probabilities from the same measurement data, we conclude that either symmetric common causes need not exist (i.e., that the particles can be hiddenly distinguishable), or that symmetric screening variables exist, but they are trivial, i.e., no single common cause can explain all single-measurement correlations.
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