Medical Physics
Overcoming data scarcity through multi-center federated learning for organs-at-risk segmentation in pediatric upper abdominal radiotherapy
Mianyong Ding, Maximilian Knoll, Semi Harrabi et al. (+7 more)
Deep learning-based organs/structures-at-risk(OARs) auto-contouring models can improve radiotherapy workflows, but models trained on adult data often underperform in pediatric patients. Developing robust pediatric-specific models is hindered by data scarcity and fragmentation across centers.
Federated learning (FL) enables
privacy-preserving collaborative training without the need for data sharing. We evaluated the feasibility and performance of FL for developing pediatric-specific OAR
segmentation models across two European medical centers. Computed
tomography (CT) images from pediatric patients from Utrecht and Heidelberg with a renal tumor or abdominal neuroblastoma were retrospectively collected and locally processed. An nnU-Net-based framework segmented 19 OARs using local and FL schemes. FL was implemented with secure weight exchange on a cloud storage across institutional firewalls. Performance was assessed using the Dice similarity coefficient (DSC), 95th percentile Hausdorff distance, and mean surface distance. Robustness to patient orientation, false-positive
segmentation of surgically removed kidneys, and failure cases were identified. A total of 310 postoperative CTs from 272 patients (105 renal tumors, 167 neuroblastomas) were included. Local models performed well on their respective center data but showed significantly reduced cross-center performance for four to seven of the nine evaluated OARs (DSC). In contrast, the FL model matched local performance for at least seven of nine OARs and achieved the best cross-center results across three metrics, with DSC gains of 0.003-0.007 over local models. FL also maintained stable performance across patient orientations and reduced false-positive kidney
segmentations. Real-world FL improves cross-center robustness of CT-based OAR
segmentation models in pediatric upper abdominal tumors.
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All authors Mianyong Ding, Maximilian Knoll, Semi Harrabi, Martine van Grotel, Annemieke S. Littooij, Max van Noesel, Jens-Peter Schenk, Marry M. van den Heuvel-Eibrink, Geert O. Janssens, Matteo Maspero
Physics and Society
Persistent Imbalance in Open Networks with Coevolutionary dynamics
S. Arab Mohammadi, H. Jafari, A. Kargaran et al. (+2 more)
Societies are quintessential open systems, shaped by internal dynamics as well as external influences. The question is how these external influences alter the collective behavior and network dynamics. To answer this, we investigate coevolutionary balance dynamics in a system of independent and open networks. Here, the system consists of two interacting networks with directed (asymmetric) coupling: an independent network evolving autonomously and an open (dependent) network whose dynamics are influenced by the former. Using a mean-field framework, we demonstrate a transition temperature: below the transition temperature, the independent network reaches a state of structural balance, while the open network is destabilized by persistent imbalance states and enters a sustained imbalance phase. This coupling also induces a measurable upward shift in the transition temperature. Direct numerical simulations robustly confirm these analytical predictions.
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All authors S. Arab Mohammadi, H. Jafari, A. Kargaran, A. Hosseiny, G. Reza Jafari
Optics
A 0.08 pJ/bit 56 GBaud Monolithic Optical Receiver Front End for IMDD Photonic Links
Robert P. Pesch, Arjun Khurana, Joshua J. Wong et al. (+2 more)
We present the design, fabrication, and measurement of a monolithically integrated optical receiver analog front end, where low power operation is a primary consideration with a goal of supporting 56 Gbaud intensity modulated direct detect transceivers. The need for low-power consumption and low-noise operation motivates a monolithic, layout driven design approach which begins with
circuit topology selection and analysis. Various transistor unit
cell layout configurations are explored, minimizing parasitics, enabling wide analog bandwidth and reduced input referred noise. The post-layout analog front end achieves a 28.9 GHz bandwidth with a low-frequency gain of 61.7 dB{\Omega}. This
circuit was designed within the GlobalFoundries FotonixTM monolithic silicon photonics platform. The fabricated device is characterized by its DC operation, noise characteristics, and time domain behavior. The final design was validated by on-off keyed and PAM-4 electrical eye diagram measurements to 64 GBaud, consuming 9.22 mW of power from a 1.2 V supply with less than 737 nA RMS integrated input referred noise current and 0.08 pJ/bit.
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All authors Robert P. Pesch, Arjun Khurana, Joshua J. Wong, Joel Slaby, Stephen E. Ralph
Instrumentation and Detectors
Exploring the Boundaries of Differentiable Radiation Transport and Detector Simulation
Jeffrey Krupa, Yiyang Zhao, Mihaly Novak et al. (+9 more)
We present an application of automatic differentiation for particle transport through matter using a Geant4-like radiation transport simulation with a full electromagnetic physics model. When differentiating this step-based transport, we observe exploding gradients driven by rare but extreme sensitivities at material boundaries, which propagate through subsequent transport and shower development. To obtain usable derivatives for optimization, we introduce a targeted mitigation strategy that stops gradient propagation through boundary-crossing operations under identifiable unstable conditions while leaving the forward (primal) simulation unchanged. We demonstrate that this enables stable, optimization-ready gradients in a detector-design problem.
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All authors Jeffrey Krupa, Yiyang Zhao, Mihaly Novak, Max Aehle, Max Sagebaum, Long Chen, Nicolas Gauger, David Lange, Vassil Vassilev, Miaoyuan Liu, Lukas Heinrich, Michael Kagan
Instrumentation and Detectors
Dynamic thermal sensitivity of microwave cryogenic sapphire resonator
Mohamed-Yacine Hachani, Christophe Fluhr, Benoit Dubois et al. (+3 more)
We have discovered a
memory effect in the temperature sensitivity of a cryogenic sapphire microwave resonator, at the heart of the ultra-stable Cryogenic Sapphire Oscillators (CSOs). Such effect is due to the relaxaxtion time of Cr3+
impurities, and results in hysteresis in the frequency vs temperature behavior, These paramagnetic
impurities, always present in synthetic sapphire, produce a temperature turning point which is necessary to achieve ultimate frequency stability. The practical implication on the CSO is that the sapphire resonators's frequency depends on the rate of temperature change. This dynamical thermal sensitivity results in a wide bump in the Allan deviation at 10 s integration time, where the frequency stability is degraded. The actual degradation depends on the specie and on the amount of the dominant paramagnetic
impurity.
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All authors Mohamed-Yacine Hachani, Christophe Fluhr, Benoit Dubois, Guillaume Le Têtu, Gonzalo Cabodevila, Vincent Giordano
Chemical Physics
Development of embedded target detection system based on FPGA and YOLOv3-Tiny
Zihan Jiang, Fanghao Liu, Huawei Wang et al. (+5 more)
Computational complexity and storage requirements are crucial factors influencing the performance and efficiency of convolutional neural networks (CNNs) in resource-constrained environments. This paper presents a high-performance embedded target detection system based on FPGA and YOLOv3-Tiny, specifically designed for embedded artificial intelligence applications. By integrating lightweight CNN optimization techniques with hardware accelerator design, significant improvements are made in both computational efficiency and resource utilization. Key optimizations, including low-bit quantization, batch normalization fusion, and table lookup mapping, reduce model parameters and computational complexity. Additionally, an FPGA hardware accelerator with a pipelined architecture is developed to enhance the efficiency of convolution operations while minimizing off-chip data transmission through modular design and on-chip cache optimization. On the ZYNQ-XC7Z035 platform, the system achieves an inference latency of 0.211 seconds, outperforming comparable designs by 75.58% in speed. The system achieves an power efficiency of 10.11 GOPS/W, surpassing comparable designs by at least 29.45%. Furthermore, hardware resource utilization is reduced by up to 51.94% compared to similar systems. This study offers innovative design methodologies and practical application examples for the efficient deployment of deep learning models on embedded platforms.
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All authors Zihan Jiang, Fanghao Liu, Huawei Wang, Mamataziz Mattohti, Xiangquan Chen, Jingfu Guo, Xiaotian Wu, Yongjun Dong
Atomic Physics · Cross-listed from cond-mat.quant-gas
The Kubo-Thermalization Correspondence
Songtao Huang, Xingyu Li, Jianyi Chen et al. (+5 more)
Quantum thermalization describes how interacting
quantum systems relax toward thermal
equilibrium, a central problem in modern physics. Yet most experimental information on many-body systems comes from short-time transition spectroscopy, typically interpreted within Kubo's linear-response framework. These perspectives-long-time equilibration versus short-time response-seem fundamentally disconnected. Here we establish an exact link between them: the Kubo-Thermalization correspondence, which connects long-time thermalized magnetization under weak driving to short-time linear-response spectra for a
spin coupled to a thermal bath. The correspondence holds even when the steady state differs substantially from the initial state and when each regime is individually difficult to describe theoretically. We experimentally confirm the correspondence using effective
spin-$1/2$
impurities realized with ultracold
fermions in two internal states coupled to a
Fermi sea. Our results provide a rare exact statement about
quantum thermalization and offer a novel route to infer thermalization dynamics from
equilibrium response measurements in strongly interacting
quantum systems, independent of microscopic details of the system–bath coupling.
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All authors Songtao Huang, Xingyu Li, Jianyi Chen, Alan Tsidilkovski, Gabriel G. T. Assumpção, Pengfei Zhang, Hui Zhai, Nir Navon
Fluid Dynamics
Significant heat transfer enhancement via polymer additives in two-dimensional sheared convection
Guanhan Li, Lu Zhu and Rich. R. Kerswell
Heat dissipation is critical in modern engineering systems.
Polymer additives offer a potential route to improve fluid-based cooling. Here, we study elasticity-enhanced heat transfer in two-dimensional, thermally-stratified Poiseuille flow. At Reynolds numbers, $Re$, $\lesssim 1000$, we observe two types of linearly unstable modes: the recently identified elasticity-induced centre mode (Khalid et al., J. Fluid Mech. 915, 2021) and the classical buoyancy-driven convective mode (Kelly, Adv. Appl. Mech. 31, 35-112, 1994). Direct numerical simulations show that the centre mode develops into a nonlinear `arrowhead' state but yields negligible heat transfer enhancement (typically $\approx 0.03\%$ increase compared to the conductive state). By contrast,
polymers can enhance the heat flux associated with the convective mode by up to $1100\%$. The nonlinear convective-mode states take the form of either periodic orbits or travelling waves, and are dominated by hook-like
polymer-stress structures that can attach to the walls. The unattached hooks act as `speed bumps' that reduced streamwise velocity and promote wall-normal motion, whereas wall-attached hooks form effective `
polymer walls', reorganising the flow into strong counter-rotating rolls and triggering the extreme-enhancement regime. The elasto-buoyant nature of these states is confirmed by perturbation kinetic energy budgets, which show that
polymer and buoyancy sustain the states synergistically. The wall-attached hooks enable rapid thermal equilibration but impose a large hydraulic penalty, making them suitable for process streams requiring fast temperature adjustment. Unattached hooks provide a more thermally efficient regime for heat-transport applications. These results highlight the potential of elastic fluids for future heat transfer enhancement technologies.
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Computational Physics
libwignernj: a reusable C/C++/Fortran/Python library for exact Wigner symbols and related coefficients
Susi Lehtola
We describe libwignernj, a freely available, BSD-licensed library that evaluates Wigner 3j, 6j, and 9j symbols, Clebsch–Gordan, Racah $W$, and Fano $X$ coefficients, and Gaunt coefficients over both complex and real spherical harmonics in standards-compliant C99. libwignernj represents factorials by the vector of their signed
prime-exponent decomposition - a
prime-factorization technique introduced for the angular-momentum coefficients by Dodds and Wiechers (Comput. Phys. Commun. 4, 268 (1972)) and refined in a long line of subsequent work - and combines that representation with the multiword-integer Racah sum of Johansson and Forssén (SIAM J. Sci. Comput. 38, A376 (2016)), under which every intermediate quantity is an exact rational and all rounding is confined to the final floating-point conversion. Single-, double-, and long-double-precision results are correct to the last representable bit, and IEEE 754 binary128 evaluation through libquadmath and arbitrary-precision evaluation through the GNU Multiple-Precision Floating-Point Reliable (MPFR) library are optionally exposed. libwignernj has no mandatory runtime dependencies and no caller-side initialization step, making it easy to embed across the atomic, molecular,
nuclear, and electromagnetic-scattering applications in which these coefficients arise. C++, CPython, and Fortran 90 bindings ship alongside the C library. Half-integer angular momenta are encoded exactly via integer $2j$ arguments throughout the application programming interface (API). CMake-package and pkg-config files ship for drop-in integration into downstream projects, and a continuous-integration (CI) pipeline runs the full test suite on Linux (shared and static), macOS, and Windows on every push.
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Fluid Dynamics
AI CFD Scientist: Toward Open-Ended Computational Fluid Dynamics Discovery with Physics-Aware AI Agents
Nithin Somasekharan, Rabi Pathak, Manushri Dhanakoti et al. (+4 more)
Recent LLM-based
agents have closed substantial portions of the scientific discovery loop in software-only machine-learning research, in chemistry, and in biology. Extending the same loop to high-fidelity physical simulators is harder, because solver completion does not imply physical validity and many failure modes appear only in field-level imagery rather than in solver logs. We present AI CFD Scientist, an open-source AI scientist for computational fluid dynamics (CFD) that, to our knowledge, is the first to span literature-grounded ideation, validated execution,
vision-based physics verification, source-code modification, and figure-grounded writing within a single inspectable workflow. Three coupled pathways cover parameter sweeps within a fixed solver, case-local C++ library compilation for new physical models, and open-ended hypothesis search against a reference comparator, all running on OpenFOAM through Foam-
Agent. At the center of the framework is a
vision-
language physics-verification gate that inspects rendered flow fields before any result is accepted, rerun, or written into a manuscript. On five tasks under a shared GPT-5.5 backbone, AI CFD Scientist autonomously discovers a Spalart-Allmaras runtime correction that reduces lower-wall Cf RMSE against DNS by 7.89% on the periodic hill at Reh=5600; under matched LLM cost, two strong general AI-scientist baselines (ARIS, DeepScientist) execute partial CFD workflows but lack the domain-specific validity gates needed to convert runs into defensible scientific claims; and a controlled planted-failure ablation shows that the
vision-
language gate detects 14 of 16 silent failures missed by solver-level checks. Code,
prompts, and run artifacts are released at https://github.com/csml-rpi/cfd-scientist.
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All authors Nithin Somasekharan, Rabi Pathak, Manushri Dhanakoti, Tingwen Zhang, Ling Yue, Andy Zhu, Shaowu Pan
Classical Physics
On the dual nature of a plane angle
M.I. Kalinin
For decades, metrologists have debated heatedly whether a plane angle is a dimensional or dimensionless quantity; whether it is a base quantity in the International System of Units (SI) or a derived quantity. Two main points of view have emerged in the international metrology community. Those who hold the first view believe that a plane angle is a dimensionless derived quantity equal to the ratio of two lengths, and its unit, the radian, is the dimensionless number one (1 rad = 1 m/m = 1). Those who hold the second view believe that a plane angle is a dimensional quantity with its own independent dimension, and its unit, the radian, is not the dimensionless number one, as is currently accepted in the SI. This article demonstrates that, depending on the physical situation, a plane angle is described by either a dimensional or a dimensionless quantity. When measuring, expressing, and communicating an angle's size, physicists use the dimensional quantity plane angle. Its dimension and unit are independent of the dimensions of other quantities and their units. This quantity, plane angle, should be classified as a base quantity, and its unit, radian, should be included in the class of base SI units. In theoretical studies of physical systems with angular quantities, the latter always enter into equations as a dimensionless combination of dimensional plane angles. This dimensionless combination, in turn, is also a physical quantity characterizing the plane angle in question. This new quantity is a dimensionless derived quantity, which physicists also call an angle.
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Optics
A Unified SU(2) Framework for Vector Beam Transformations and Complex Beam Shaping
Gayathri G T and Gururaj Kadiri
We present a constructive framework for designing transformations between structured light fields using birefringent optical elements, formulated in terms of SU(2) operations on polarization. Within this framework, transformations between vector beams are treated as spatially varying SU(2) operations, leading to a direct procedure for designing doubly inhomogeneous waveplates (d-plates) that implement the desired mapping. We identify a condition under which a single element implements a prescribed transformation exactly, including the global phase, and provide an explicit prescription for constructing the corresponding doubly inhomogeneous waveplate (d-plate) when this condition is satisfied, along with its realization using a finite sequence of singly inhomogeneous plates, including a QHQ configuration. Within this formulation, a broad class of problems in structured light can be treated within a single framework, including vector beam transformations,
spin-orbital dynamics, and complex beam shaping. Crucially, the same SU(2) operations directly realize
quantum channels on the orbital angular momentum degree of freedom, with polarization serving as a physical ancilla. These results establish a unified and explicitly constructive route to complex beam shaping and vector beam transformations based on SU(2) parameter synthesis, and provide a systematic foundation for designing next-generation photonic elements for structured light and
spin-orbit information processing.
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Medical Physics
John Equation Constraints for the 3D X-ray Transform under a Cylindrical-Spherical Mixed Parameterization: Theoretical Derivation, Experimental Validation, and Application Analysis
Shaojie Tang, Zhiwei Qiao and Xuanqin Mou
The John equation serves as the mathematical foundation of the X-ray transform, describing the intrinsic compatibility conditions that projection data must satisfy. In this paper, within three-dimensional (3D) Euclidean space, an innovative mixed parameterization scheme is adopted: the source point is represented using cylindrical coordinates a=(s cos{\theta},s sin{\theta},z_0), and the ray direction is represented using spherical coordinates d=\{rho}(-cos\{beta}sin{\alpha},cos\{beta}cos{\alpha},sin\{beta}). The specific form of the John equation under this
geometric parameterization is systematically derived. Through detailed partial differential operator transformations, application of -1 homogeneity, and algebraic simplification, a complete system of constraint equations is obtained. In particular, under the special configurations where the ray direction is perpendicular to the radial direction of the source point in the horizontal plane (i.e., the so-called
alignment condition:{\alpha} = {\theta}) and the ray has no tilt (\{beta} = 0), the constraint equations simplify to differential relations with clear physical meanings. This paper not only establishes a bridge between abstract mathematical theory and concrete imaging geometry, but also provides rigorous mathematical tools for data consistency verification,
geometric parameter calibration, and incomplete-data reconstruction in 3D Computed
Tomography (CT) systems. The research results are of great significance for advancing the mathematical theory and practical applications of CT imaging.
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Fluid Dynamics
Reduced-Order Modeling of Parameterized Visco-Plastic Shallow Flows
Md Rezwan Bin Mizan, Ilya Timofeyev and Maxim Olshanskii
We propose a non-intrusive reduced-order modeling framework for parametrized visco-plastic free-surface flows governed by a shallow-water formulation of Herschel–Bulkley fluids. These flows exhibit strong nonlinearities, non-smooth rheology, moving fronts, and yield surfaces, making efficient surrogate modeling particularly challenging. To address this challenge, we employ a tensor-based approach in which the solution
manifold is approximated using a low-rank representation obtained via higher-order singular value decomposition of snapshot data over a structured parameter space. The resulting tensorial reduced-order model (TROM) enables rapid online evaluation by directly reconstructing solution trajectories from the compressed representation, thereby avoiding the need to perform time integration of a reduced dynamical system. The proposed non-intrusive framework can be interpreted as an encoder–decoder architecture with a compressed latent representation and efficient multilinear decoding. Numerical experiments demonstrate that the proposed approach accurately captures key flow features, including front propagation, plug and shear regions, and near-stopping dynamics, while achieving substantial computational speedups relative to full-order simulations.
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Chemical Physics
TDDFT Gradients and Nonadiabatic Couplings with Minimal Auxiliary Basis Set Approximation for Fewest-Switches Surface Hopping Dynamics
Cheng Fan, Zhichen Pu, Zehao Zhou et al. (+3 more)
The electronic structure calculations remain a major bottleneck in ab initio nonadiabatic molecular dynamics. We develop an efficient TDDFT-based FSSH implementation in the GPU4PySCF package for medium-sized molecular systems. Our approach combines density fitting, TDDFT with minimal auxiliary basis sets (TDDFT-ris), and an approximate Z-vector solver to reduce the computational cost of TDDFT excited states and derivative coupling calculations. These approximations introduce negligible errors in realistic FSSH workloads while maintaining high computational efficiency.
Benchmark results show that, for 73-atom systems with a triple-$\zeta$ basis set, individual electronic structure calculations are completed within one minute on a single NVIDIA A100 GPU.
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All authors Cheng Fan, Zhichen Pu, Zehao Zhou, Yuanheng Wang, Yi Qin Gao, Qiming Sun
Applied Physics · Cross-listed from cond-mat.stat-mech
Comparative Study of Potts Machine Dynamics and Performance for Max-k-Cut
Bjarke Almer Frederiksen, Robbe De Prins and Peter Bienstman
Combinatorial optimization problems in logistics, finance, energy, and scheduling routinely involve multi-state decision variables. Ising machines (IMs) require binary expansions (e.g., one-hot encoding) to encode such variables, whereas Potts machines (PMs) represent them natively. By doing so, PMs are expected to outperform IMs on multi-state problems. To the best of our knowledge, no systematic study of PM models has yet assessed whether this expectation holds. We therefore
benchmark five representative PMs against a reference IM on Max-3-Cut and Max-4-Cut, using 800-vertex GSet graphs and random graphs of up to 50 vertices. Surprisingly, the reference IM still outperforms every PM, and the IM supremacy increases significantly in going from Max-3-Cut to Max-4-Cut. These results provide clear evidence that current PM dynamics underperform relative to binary approaches, even in regimes where they are presumed advantageous. We provide a way forward by quantifying the underperformance of current PMs, as well as by identifying three dynamical properties that correlate strongly with their performance ranking. Our work stresses the need for more systematic assessments of algorithmic performance in order to guide the design of more effective Potts machines.
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Physics and Society
Cascading disruptions in natural gas, fertilizers, and crops drive structural food supply vulnerabilities globally
Pavel Kiparisov and Christian Folberth
Global food security depends on tightly coupled international supply chains encompassing natural gas, mineral fertilizers, and staple crops. Earlier research has examined the potential consequences of disruptions in each of these domains separately, but not from a systemic perspective. Here we integrate bilateral trade in natural gas, nitrogen, phosphorus, and potassium fertilizers, and eleven staple crops – accounting for approximately 70% of plant-based calories – into a cascading-impact model spanning 208 countries, 20 geopolitical blocs, and the period 1992–2023. Under complete trade isolation, up to 22% of global caloric consumption would be lost, with a peak in the most recently evaluated years. Structural vulnerabilities vary considerably. Regions largely lacking some segments of the supply chain face near-total crop supply collapse, while few countries can cover the entire nexus through domestic resource endowments and production capacities. Temporal trends highlight a substantial increase in vulnerability globally, most prominently in the EU, with a near two-fold increase since the 1990s.
Market power is most concentrated and most volatile in the upstream gas layer and has risen in the fertilizer layers since the 2000s; shocks propagate downstream from these tightening upstream layers, driving the system's fragility. Food stocks provide only limited resilience, with half of humanity living in countries holding stocks lasting fewer than three months. Our results identify upstream supply chains as the structural bottlenecks of the global agrifood system and propose leverage points to enhance resilience.
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Fluid Dynamics · Cross-listed from math-ph
Dynamical cooling driven by self-similar fronts in the 2D nonlinear Schrödinger model
Jason Laurie, Simon Thalabard and Sergey Nazarenko
We analyze the dynamics towards partial thermalization and subsequent cooling in the defocusing two-dimensional nonlinear Schrödinger model, using direct simulations and insights from the wave-kinetic equations (WKE) and a fourth-order differential approximation model (DAM). We show that the evolving WKE spectrum exhibits two distinct similarity ranges–the quasi-thermal core and the ultraviolet tail–whereas in the DAM, an additional range of infrared self-similarity appears. By stretching the quasi-thermal region, the self-similar fronts drive an effective dynamical cooling process towards the formal but ill-defined
equilibrium state at vanishing temperature–analogous to an ultraviolet catastrophe in a system of classical waves.
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Computational Physics · Cross-listed from math.NA
A penalty-free bulk–surface CutFEM stabilized by lattice Green's function extensions
Qing Xia
We introduce a penalty-free cut finite element method for surface
elliptic problems coupled to a harmonic bulk field on a Cartesian grid. Instead of adding a stabilization term, the method restricts the active finite element space by a discrete bulk harmonic extension represented with the
lattice Green's function, together with a local extrapolation near the interface. The resulting method is a symmetric Galerkin scheme posed on a reduced space embedded in the standard active-mesh finite element space. Under stated
geometric, regularity, and approximation assumptions, we establish optimal $\mathcal{O}(h)$ and $\mathcal{O}(h^2)$ surface convergence rates, as well as robust, cut-independent algebraic conditioning. Furthermore, a density formulation based on
lattice layer potentials is shown to act as an operator preconditioner; the single-layer parametrization yields an $\mathcal{O}(1)$ algebraically well-conditioned system without introducing any tunable parameters. Two-dimensional experiments on circular, deformed, and smooth nonconvex interfaces confirm the predicted convergence and robustness under changes in the cut position. Finally, a three-dimensional
torus experiment demonstrates the identical construction using a seven-point bulk stencil and trilinear surface traces, exhibiting the expected optimal error rates.
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Physics and Society · Cross-listed from q-bio.PE
Higher-order interactions in ecology can be hidden in plain sight
Violeta Calleja-Solanas, Santiago Lamata-Otín, Carlos Gómez-Ambrosi et al. (+2 more)
Higher-order interactions are increasingly recognized as a key component of ecological dynamics. However, we show that higher-order Lotka-Volterra dynamics can, in some scenarios, be accurately reproduced by effective pairwise models fitted to the same abundance time series. Consequently, higher-order interactions cannot, in general, be inferred from time-series data alone. We further identify a fundamental problem of mechanistic identifiability, whereby distinct interaction mechanisms generate nearly indistinguishable dynamics, potentially leading to accurate yet misleading ecological interpretations. Our results highlight the need to complement time-series data with additional ecological information to infer interaction structure reliably.
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All authors Violeta Calleja-Solanas, Santiago Lamata-Otín, Carlos Gómez-Ambrosi, Jesús Gómez-Gardeñes, Sandro Meloni
Optics · Cross-listed from quant-ph
Macroscopic entanglement between two magnon modes via two-tone driving of a superconducting qubit
Rong-Can Yang, Gang Liu, Gen Li et al. (+1 more)
The cavity-mediated coupling between
magnons in an yttrium-iron-garnet (YIG) sphere and a superconducting
qubit has recently been demonstrated as a new platform for preparing macroscopic
quantum states. Here, based on this system, we propose to entangle two
magnon modes in two YIG spheres by driving the
qubit with a two-tone field and by appropriately choosing the frequencies and strengths of the two driving fields. We show that strong
entanglement can be achieved with fully feasible parameters. We further provide a detection scheme for experimentally verifying the
entanglement. Our results indicate that macroscopic
entanglement between two
magnon modes in two millimeter-sized YIG spheres, involving more than $10^{18}$
spins, can be realized using currently available parameters, which finds promising applications in fundamental studies, such as macroscopic
quantum mechanics and the test of unconventional decoherence theories.
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All authors Rong-Can Yang, Gang Liu, Gen Li, Jie Li
Optics
From flat to narrow bands: Engineering quantum emission in a one-dimensional Lieb lattice
Zhiyong Liu, Yue Sun and Ying Hu
We develop a comprehensive theoretical framework that unifies
quantum emission dynamics in one-dimensional Lieb
lattices, bridging the gap between ideal flat-band coherence and realistic narrow-band dissipation. By coupling an emitter to sublattices with finite flat-band wavefunction overlap, we activate a collective, size-independent interaction fundamentally distinct from dispersive-band processes. Controllably breaking
lattice symmetry transforms the flat band into a narrow dispersive band, enabling a continuous crossover from non-Markovian to Markovian dynamics governed by the competition between coupling strength and engineered bandwidth. Crucially, we derive explicit scaling laws that provide a quantitative blueprint for tuning spontaneous emission from coherent trapping to Markovian decay. Our work provides a unified framework that connects idealized flat-band physics to emerging narrow-band platforms such as moir$\rm\acute{e}$ photonic
crystals, offering a practical toolkit for interpreting experiments and engineering
quantum emission in structured photonic environments.
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Chemical Physics · Cross-listed from cond-mat.soft
Solvent-induced memory effects in a model electrolyte
Sleeba Varghese, Benjamin Rotenberg and Pierre Illien
The fluctuations of ions in polar solvents remain poorly understood theoretically due to the complex coupling between ionic motion and solvent polarization. Indeed, while all-atom resolution can be achieved in numerical simulations, analytical approaches require suitable levels of coarse-graining. In this work, we describe ions and solvent molecules as interacting Brownian particles and use
stochastic density functional theory to derive a generalized Langevin equation for the ionic charge density, explicitly accounting for solvent-mediated
memory effects. In the regime where there is a clear timescale separation between fast solvent and slow ion dynamics, we obtain simple expressions for dynamical charge structure factors, which are validated by BD simulations. For slow solvents, we predict an emerging two-step relaxation in ionic dynamics. These results provide a mesoscopic approach for ion-solvent dynamics and open pathways to study fluctuation-induced phenomena in electrolytes.
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Optics
Elastic and structural anisotropy in silica thin films for gravitational-wave detectors
Brenda Bracco, Michele Magnozzi, Stefano Colace et al. (+11 more)
The thermal noise of mirror coatings for gravitational-wave detectors critically depends on the elastic properties of the constituent materials. Data analyses and theoretical models typically assume each material is homogeneous and isotropic, but isotropy has never been explicitly verified. Using Brillouin light scattering (BLS), we demonstrate for the first time that ion-beam-sputtered SiO2 – a material still viable for future mirror coatings – exhibits cylindrical elastic
symmetry, with in-plane isotropy but a notable 6% compressive anisotropy along the film normal. This anisotropy remains unchanged after the post-deposition heat treatment currently used in ground-based detectors (500 $^\circ$C, 10 h) but is nearly eliminated at 900 $^\circ$C. Infrared reflectivity experiments support these findings by directly revealing heterogeneities in the distribution of bridging and non-bridging oxygen structures along the growth axis. While BLS measures the real part of the elastic constants at GHz frequencies, the data reveal negligible contributions from mechanical relaxations in the kHz-GHz range, making BLS a valid substitute for low-frequency properties obtained from standard anisotropy-insensitive techniques. Our results highlight that restoring isotropy through heat treatment – by softening the material, enabling more than 7% out-of-plane expansion, and smoothing out structural heterogeneities – may play a key role in reducing thermal noise. This proof-of-concept study extends beyond silica, providing critical insights for the design of future coatings.
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All authors Brenda Bracco, Michele Magnozzi, Stefano Colace, Maurizio Canepa, Giulio Favaro, Marco Bazzan, Massimo Granata, David Hofman, Alessandro Di Michele, Laura Silenzi, Gianpietro Cagnoli, Giovanni Carlotti, Paola Sassi, Silvia Corezzi
Space Physics · Cross-listed from astro-ph.CO
Revisiting the Constancy of the Speed of Light: Galaxy Cluster Mass Bias Implications
R. F. L. Holanda, Marcelo Ferreira, Javier E. Gonzalez et al. (+1 more)
In recent years, improvements in
galaxy cluster observations have enabled a variety of tests of fundamental physics using these systems. In this work, we test the constancy of the speed of light, $c$, by combining X-ray gas mass fraction measurements from
galaxy clusters with SNe Ia luminosity distance measurements from Pantheon+. We adopt the SH0ES prior on $H_0$ and the $\Omega_b/\Omega_m$ ratio from
galaxy clustering observations, thereby minimizing the dependence of our analysis on any specific cosmological model. We explore different assumptions for the cluster mass calibration (mass bias), including CLASH, CCCP, and Planck-based estimates. We find no deviation from a constant $c$ when adopting CLASH or CCCP priors, while Planck-based calibration yields a mild tension, with the hypothesis of constant $c$ being only marginally consistent at the $2\sigma$ level, indicating a non-negligible sensitivity of the results to the adopted calibration scheme.
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All authors R. F. L. Holanda, Marcelo Ferreira, Javier E. Gonzalez, S. H. Pereira
Geophysics
Synthetic Well Log Generation with Preserved Multivariate Correlations and Vertical Facies Stacking Patterns
Josue Fonseca and Marcus Saraiva
We present a novel procedure for generating synthetic well logs that simultaneously preserves multivariate correlations among petrophysical properties (Density, P-Sonic, S-Sonic) and vertical stacking patterns of electrofacies. The methodology integrates Markov chain models, autoencoder-based dimensionality reduction, and Markov chain Monte Carlo (MCMC) sampling in latent space. Application to a real turbidite reservoir
dataset demonstrates that the framework successfully sustains fundamental rock physics relationships and generates geologically realistic vertical heterogeneity consistent with actual well log measurements. This technique addresses critical data scarcity in machine learning applications for seismic interpretation while enabling credible synthetic seismogram generation for scenario testing and uncertainty quantification in petroleum exploration and field development.
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Chemical Physics
Pushing the accuracy of on-top functionals with agent-driven supervised learning
Yuhao Chen, Dayou Zhang, Donald G. Truhlar et al. (+1 more)
Multiconfiguration pair-density functional theory (MC-PDFT) combines multiconfigurational reference wave functions with on-top functionals to provide an efficient electronic-structure method for strongly correlated molecular systems. We developed a large
language model-based
agent for functional development and combined multiple databases into the large, chemically diverse MCDDB26 database. We then used MCDDB26 to develop two on-top functionals, MC26 and COF26. MC26 retains an existing analytical form but substantially improves performance by refitting its linear parameters with optimised weights for the training
datasets. Building on this result, we developed COF26, a general hybrid on-top functional with a new analytical form. COF26 provides greater overall accuracy for multireference chemistry than existing on-top functionals. Applications across MCDDB26 show that COF26 accommodates both multiconfigurational and single-configuration densities. It therefore performs well for barrier heights, isomerisation energies, thermochemistry, non-covalent interactions, radical and non-radical chemistry, single-reference and multireference systems, and transition-metal chemistry.
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All authors Yuhao Chen, Dayou Zhang, Donald G. Truhlar, Xiao He
Physics and Society
Two-mode geometry controls multiscale organization in bipartite systems
Ottavia Falconi, Giulio Cimini and Pablo Villegas
Many complex systems are organized around complementary roles and naturally described as bipartite networks. Unveiling their multiscale structure presents a fundamental challenge because coarse-graining procedures must preserve role separation, whereas standard approaches collapse it via one-mode projections. Here we introduce a
Laplacian-based renormalization framework that operates directly on the bipartite architecture, enabling scale transformations while retaining role differentiation. Using controlled bipartite ensembles at criticality, we show that structural imbalance systematically reshapes organization across scales while leaving scaling properties invariant, revealing a separation between universality and geometry. Applying the coarse-graining framework to empirical bipartite networks, we uncover nontrivial multiscale hierarchies for both roles. In contrast, renormalization performed after one-mode projection – which truncates
diffusion paths to nearest neighbors – yields qualitatively different structures. Our results identify two-mode geometry as a fundamental constraint for revealing multiscale organization in systems with role separation.
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Computational Physics
Data-driven reconstruction of band dispersion and quantum geometry via Koopman dynamical mode decomposition
Yiming Pan, Jinze He, Jiapeng Yang et al. (+1 more)
We present a data-driven framework for reconstructing band structures using Koopman operator analysis and dynamic mode decomposition (Koopman-DMD). Instead of deriving spectra from an explicit
Hamiltonian, the approach reconstructs band dispersion and modal dynamics directly from spatiotemporal data, including wavefunctions and observables. This framework establishes a correspondence between
Hamiltonian Floquet-Bloch decomposition and Koopman-DMD, whereby the extracted DMD modes encode frequencies, decay or growth rates, spatial profiles and projection weights. These quantities allow the reconstruction of spectral functions, local density of states, and delocalized-to-localized measures such as the inverse participation ratio. Also, these extended DMD modes enable inference of
quantum-
geometric and topological properties, including the
quantum metric, Berry
curvature and
geometric phases. Applications to prototypical one- and two-dimensional tight-binding models, including disordered Su-Schrieffer-Heeger model and its Floquet and non-Hermitian variants,
graphene and Haldane models, demonstrate that Koopman-DMD provides a unified route for the data-driven analysis of wave propagation, localization, and topological phases in condensed matter, photonics, and related fields.
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All authors Yiming Pan, Jinze He, Jiapeng Yang, Zhiwei Fan
Fluid Dynamics
Mixing of miscible liquids: Dimensionless scaling for intermediate-to-large density differences in a stirred tank
Michael R. Wagner, Manuela Dubacher, Nikoletta Patsaki et al. (+7 more)
Mixing of miscible
liquids is an essential process in multiple industrial settings, usually with the intent to homogenize the product. This seemingly simple process is in fact a complex hydrodynamic problem that has a direct impact on the product quality. In this study, numerical simulations of a stirred tank were performed with a 50/50 ratio of
liquids and systematically varied the Reynolds and Richardson numbers. A positive correlation between the mixing time and the Richardson number was observed, as reported in the literature. The influence of the Reynolds number was not as pronounced and clear. Based on the Power, Froude and Richardson numbers, we were able to derive an exponential scaling for the dimensionless mixing time that collapsed all our data onto one master curve.
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All authors Michael R. Wagner, Manuela Dubacher, Nikoletta Patsaki, Philipp Eibl, Peter Varun Dsouza, Michael Dekner, Christian Witz, Johan Remmelgas, Stefan Reimann-Zitz, Johannes Khinast