Optics · Cross-listed from quant-ph
High-Fidelity Teleportation of Continuous-Variable Quantum States Via Non-Ideal Qutrit Entangled Resources
Fatemeh Taghipoor, Mojtaba Golshani, Mostafa Motamedifar et al. (+1 more)
Achieving near-unity fidelity in conventional continuous-variable
quantum teleportation schemes based on two-mode squeezed
vacuum states is fundamentally unattainable. To overcome this limitation, alternative approaches utilizing ensembles of two-dimensional entangled
qubits have been proposed. In this work, we investigate continuous-variable
quantum teleportation employing entangled qutrit resources under realistic noise effects. The results demonstrate that the proposed scheme performs well in both ideal and noisy conditions, enabling high-fidelity teleportation with a reasonable success probability.
Read the full abstract ▾
All authors Fatemeh Taghipoor, Mojtaba Golshani, Mostafa Motamedifar, Khatereh Jafari
Atomic Physics
Rydberg states with a liquid core
Juan Carlos Acosta Matos, P. Giannakeas and Jan M. Rost
We develop a self-consistent approach that provides an explicit potential for a Rydberg electron whose ionic core consists of a polarizable medium, typically realized with superfluid droplets. The electron's motion remains separable in spherical coordinates, but the radial force exerted by the droplet breaks degeneracy of the angular momentum states non-perturbatively. The ensuing electron spectrum reveals intriguing properties dependent on droplet size and electron excitation. Deviations of the polarizable medium from the continuous spherical distribution can be taken into account as a perturbation of this redefined Rydberg dynamics. We discuss specific but paradigmatic examples for superfluid helium and also propose a way to probe droplet properties including its possible crystallized fraction through stimulated transitions of the Rydberg electron.
Read the full abstract ▾
Physics and Society · Cross-listed from cs.CL
The Statistical Signature of LLMs
Ortal Hadad, Edoardo Loru, Jacopo Nudo et al. (+3 more)
Large
language models generate text through probabilistic sampling from high-dimensional distributions, yet how this process reshapes the structural statistical organization of
language remains incompletely characterized. Here we show that lossless compression provides a simple, model-agnostic measure of statistical regularity that differentiates generative regimes directly from surface text. We analyze compression behavior across three progressively more complex information ecosystems: controlled human-LLM continuations, generative mediation of a knowledge infrastructure (Wikipedia vs. Grokipedia), and fully synthetic social interaction environments (Moltbook vs. Reddit). Across settings, compression reveals a persistent structural signature of probabilistic generation. In controlled and mediated contexts, LLM-produced
language exhibits higher structural regularity and compressibility than human-written text, consistent with a concentration of output within highly recurrent statistical patterns. However, this signature shows scale dependence: in fragmented interaction environments the separation attenuates, suggesting a fundamental limit to surface-level distinguishability at small scales. This compressibility-based separation emerges consistently across models, tasks, and domains and can be observed directly from surface text without relying on model internals or semantic evaluation. Overall, our findings introduce a simple and robust framework for quantifying how generative systems reshape textual production, offering a structural perspective on the evolving complexity of communication.
Read the full abstract ▾
All authors Ortal Hadad, Edoardo Loru, Jacopo Nudo, Niccolò Di Marco, Matteo Cinelli, Walter Quattrociocchi
Optics
Nanoscopy of surface polarization with oblique dipole orientations
V. G. M. Duarte, D. A. Miranda, D. F. P. Cunha et al. (+4 more)
The boundary conditions imposed by confined dipoles with arbitrary orientation on surfaces are presented, extending the conventional in-plane (IP) and out-of-plane (OOP) treatments, here applied for planar and cylindrical sheets. Examples include van der Waals
heterostructures, thin films of molecular aggregates, and metal-dielectric interfaces. For large dipole strengths, the reflectance peak associated with the dipole oscillation frequency splits into two, revealing the presence of oblique dipoles. The loss function for the dipole sheet reveals pairs of polaritonic resonances originating from the IP and OOP dipole components, accessible through near-field probes. The point-dipole model for s-SNOM shows two distinct peaks, revealing higher sensitivity to dipole obliqueness than reflectance experiments. We apply the model to monolayer WSe$_2$, showing that the oblique dipole formulation with a dipole angle of $5.7^\circ$ yields significant improvements in the qualitative and quantitative agreement with an experiment reported in the literature. This work proposes a unified
language for the description of two-dimensional materials, thin films, and interfaces with anisotropic dipolar responses and shows that near-field methods, sensitive to high in-plane momenta, are suitable for measuring such oblique dipoles.
Read the full abstract ▾
All authors V. G. M. Duarte, D. A. Miranda, D. F. P. Cunha, M. I. Vasilevskiy, N. Asger Mortensen, A. J. Chaves, N. M. R. Peres
Optics
Gradient-Informed Bayesian and Interior Point Optimization for Efficient Inverse Design in Nanophotonics
Yannik Mahlau, Yannick Augenstein, Tyler W. Hughes et al. (+2 more)
Inverse design, particularly
geometric shape optimization, provides a systematic approach for developing high-performance nanophotonic devices. While numerous optimization
algorithms exist, previous global approaches exhibit slow convergence and conversely local search strategies frequently become trapped in local optima. To address the limitations inherent to both local and global approaches, we introduce BONNI:
Bayesian optimization through neural network ensemble surrogates with interior point optimization. It augments global optimization with an efficient incorporation of gradient information to determine optimal sampling points. This capability allows BONNI to circumvent the local optima found in many nanophotonic applications, while capitalizing on the efficiency of gradient-based optimization. We demonstrate BONNI's capabilities in the design of a distributed Bragg reflector as well as a dual-layer grating coupler through an exhaustive comparison against other optimization
algorithms commonly used in literature. Using BONNI, we were able to design a 10-layer distributed Bragg reflector with only 4.5% mean spectral error, compared to the previously reported results of 7.8% error with 16 layers. Further designs of a broadband waveguide taper and photonic
crystal waveguide transition validate the capabilities of BONNI.
Read the full abstract ▾
All authors Yannik Mahlau, Yannick Augenstein, Tyler W. Hughes, Marius Lindauer, Bodo Rosenhahn
Applied Physics · Cross-listed from cs.LG
Stable Long-Horizon Spatiotemporal Prediction on Meshes Using Latent Multiscale Recurrent Graph Neural Networks
Lionel Salesses, Larbi Arbaoui, Tariq Benamara et al. (+2 more)
Accurate long-horizon prediction of spatiotemporal fields on complex geometries is a fundamental challenge in scientific machine learning, with applications such as additive manufacturing where temperature histories govern defect formation and mechanical properties. High-fidelity simulations are accurate but computationally costly, and despite recent advances, machine learning methods remain challenged by long-horizon temperature and gradient prediction. We propose a deep learning framework for predicting full temperature histories directly on meshes, conditioned on geometry and process parameters, while maintaining stability over thousands of time steps and generalizing across heterogeneous geometries. The framework adopts a temporal multiscale architecture composed of two coupled models operating at complementary time scales. Both models rely on a latent recurrent graph neural network to capture spatiotemporal dynamics on meshes, while a variational graph autoencoder provides a compact latent representation that reduces
memory usage and improves training stability. Experiments on simulated powder bed fusion data demonstrate accurate and temporally stable long-horizon predictions across diverse geometries, outperforming existing baseline. Although evaluated in two dimensions, the framework is general and extensible to physics-driven systems with multiscale dynamics and to three-dimensional geometries.
Read the full abstract ▾
All authors Lionel Salesses, Larbi Arbaoui, Tariq Benamara, Arnaud Francois, Caroline Sainvitu
Atomic Physics
The hyperfine interaction as a probe of the microscopic structure of the atomic nucleus
Denis Janković, Jean-Gabriel Hartmann, Johann Bartel et al. (+3 more)
The study of highly charged electronic and muonic hydrogen-like ions, provides an intriguing way to probe the internal structure of their atomic
nuclei. In this work, we use
nuclear structure calculations to accurately calculate the hyperfine splitting of electronic and muonic hydrogen-like ions, focusing in particular on the incorporation of finite-volume corrections, such as Bohr-Weisskopf and Breit-Rosenthal, due to the penetration of the electron and
muon wavefunction into the
nuclear electric charge and
magnetic dipole densities. These corrections are essential for refining our understanding of the
nuclear magnetic dipole and electric quadrupole moments. Our simulations use a Skyrme-Hartree-Fock-BCS model known for its effectiveness in modeling well-deformed
nuclei such as ${}^{159}\mathrm{Tb}^{64+}$ and ${}^{165}\mathrm{Ho}^{66+}$, with particular emphasis on ${}^{161,163}\mathrm{Dy}^{65+}$ isotopes. It can also be generalised to multi-electron ions by studying the hyperfine anomaly between two isotopes.
Read the full abstract ▾
All authors Denis Janković, Jean-Gabriel Hartmann, Johann Bartel, Hervé Molique, Ludovic Bonneau, Paul-Antoine Hervieux
Fluid Dynamics · Cross-listed from cond-mat.soft
Analytical response functions for a compressible thin fluid layer with odd viscosity
Abdallah Daddi-Moussa-Ider, Yuto Hosaka and Shigeyuki Komura
Fluids composed of
chiral active components can exhibit odd viscosity, a property that breaks time-reversal and parity
symmetries. We investigate the hydrodynamic response to monopole and dipole singularities in a compressible thin fluid layer with odd viscosity, supported by a conventional lubrication layer. Using the two-dimensional Green's function in
Fourier space, we derive exact analytical solutions for the flow and pressure fields. These solutions provide a detailed description of the hydrodynamic interactions governing the motion of colloidal particles and microswimmers in confined
chiral fluids, offering insight into the role of odd viscosity in modifying particle dynamics and collective behavior. The derived results are directly applicable to modeling transport, control, and self-organization phenomena in active and
chiral microfluidic systems.
Read the full abstract ▾
Instrumentation and Detectors
Extended X-ray energy characterization of SIDDHARTA-2 large-area Silicon Drift Detectors up to 50 keV
Francesco Clozza, Francesco Sgaramella, Leonardo Abbene et al. (+38 more)
The SIDDHARTA-2 experiment at the DA$\Phi$NE
collider of INFN-LNF performs high precision light kaonic atoms X-ray spectroscopy to investigate the kaon-nucleon(s) strong interaction in the low-energy (O(10 keV)) regime. A large area Silicon Drift Detectors (SDDs) system has been developed to carry out these measurements. The collaboration aims to extend the measurements campaign to higher mass kaonic atoms, which exhibit transition lines at increased X-ray energies. In this context, the spectroscopic response of the SIDDHARTA-2 SDD system was investigated in terms of linearity and energy resolution up to 50 keV. An accuracy of the energy calibration procedure $\Delta E/E < 10^{-3}$ was achieved.
Read the full abstract ▾
All authors Francesco Clozza, Francesco Sgaramella, Leonardo Abbene, Francesco Artibani, Massimiliano Bazzi, Giacomo Borghi, Damir Bosnar, Mario Bragadireanu, Antonino Buttacavoli, Marco Carminati, Alberto Clozza, Raffaele Del Grande, Luca De Paolis, Kamil Dulski, Francesco Ficorella, Carlo Fiorini, Ivica Friščić, Carlo Guaraldo, Mihail Iliescu, Masa Iwasaki, Aleksander Khreptak, Simone Manti, Johann Marton, Pawel Moskal, Fabrizio Napolitano, Szymon Niedźwiecki, Hiroaki Ohnishi, Antonino Picciotto, Kristian Piscicchia, Fabio Principato, Alessandro Scordo, Michał Silarski, Diana Sirghi, Florin Sirghi, Magdalena Skurzok, Antonio Spallone, Kairo Toho, Oton Vazquez Doce, Nicola Zorzi, Johann Zmeskal, Catalina Curceanu
Atomic Physics
Time-resolved Electron Momentum Spectroscopy with Ultrashort Electron Pulses: Confined Probing and Effects of Vacuum Dispersion
Pieter Hessel Harkema and Lars Bojer Madsen
Previous theoretical studies have shown that attosecond electron dynamics can, in principle, be captured in electron momentum spectroscopy (EMS) using ultrashort electron pulses. By including further analytical considerations on the scattering probability, we here study the effect of the finite transversal extend of the projectile electron wave packet. We find that in wave packet scattering, the target momentum distribution is probed solely in a finite spatial region. This is evident from a spatially filtering Gabor transform appearing in the scattering probability, replacing the full momentum wave function appearing in the conventional plane wave treatment. In addition, by spatially shifting the target with regard to the wave packet focus, we illustrate the influence of
vacuum dispersion, i.e., the spatial broadening of the wave packet as it propagates. Our findings are significant for the possibility to correctly interpret future attosecond-EMS results and the considered effects reflect fundamental aspects of wave packet scattering. The EMS setup may, therefore, constitute a useful framework for understanding scattering with finite wave packets.
Read the full abstract ▾
History and Philosophy of Physics · Cross-listed from gr-qc
The Emergence of Measured Geometry in Self-Gravitating Systems
Maria I. R. Lourenço, Julian Barbour and Francisco S. N. Lobo
This work investigates the geometrical properties of self-gravitating $N$-body systems from the perspective established by Henri Poincaré and Albert Einstein concerning the operational nature of measured geometry. Utilizing recent numerical analyses of central configurations–special
equilibrium solutions to the Newtonian $N$-body problem–we uncover systematic spatial variations in nearest-neighbor particle separations correlated with the radial distance from the system's center of mass. We argue that these variations reflect a context-dependent, emergent effective geometry shaped by gravitational interactions, in accordance with Poincaré's assertion that measured geometry depends on the forces influencing measuring devices, and Einstein's view that rods and clocks define physical geometry through their local dynamics. By revisiting these foundational insights within a modern computational framework, we provide evidence that geometry in self-gravitating Newtonian systems is not a fixed background, but an emergent construct arising from internal physical interactions.
Read the full abstract ▾
Optics
Cavity Solitons as a Nonlinear Substrate for Photonic Neuromorphic Computing
Amir Arsalan Arabieh, Alessandro Lupo, Simon-Pierre Gorza et al. (+1 more)
Reservoir computing leverages nonlinear dynamics of physical systems to process temporal information with minimal training cost. Here, we demonstrate that cavity
solitons sustained in a fiber optical cavity provide an optical platform for photonic reservoir computing. Our methodology exploits the use of a phase-modulated drive
laser to encode the input, while the reservoir states are accessed through frequency-resolved readout. Numerical simulations indicate that the emission of Kelly waves enriches the dynamics and enhances performance for machine learning tasks. We evaluate the performance of the cavity-
soliton reservoir computer on several standard
benchmark tasks.
Read the full abstract ▾
All authors Amir Arsalan Arabieh, Alessandro Lupo, Simon-Pierre Gorza, Serge Massar
Physics and Society · Cross-listed from cs.SI
Beyond Individual Influence: The Role of Echo Chambers and Community Seeding in the Multilayer three state q-Voter Model
Igor Hołowacz and Piotr Bródka
The
diffusion of complex opinions is severely hindered in multilayer social networks by echo chambers and cognitive consistency mechanisms. We investigate Influence Maximization strategies within the 3-state multilayer q-voter model. Utilizing the mABCD
benchmark, we simulate social environments ranging from integrated Open Worlds to segregated Fortress Worlds. Our results reveal a topological paradox that we term the "Fortress Trap". In highly modular networks, strategies maximizing local density such as Clique Influence Maximization (CIM) and k-Shell fail to trigger global cascades, creating isolated bunkers of consensus due to the Overkill Effect. Furthermore, we identify a Redundancy Trap in perfectly aligned Clan
topologies, where the structural overlap of layers creates a "Perfect Prison," rendering it the most resistant environment to
diffusion. We demonstrate that VoteRank, a strategy that prioritizes diversity of reach over local intensity, consistently outperforms structure-based methods. These findings suggest that, for complex contagion, maximizing topological
entropy is more effective than reinforcing local clusters.
Read the full abstract ▾
Optics
Rigorous electromagnetic quasinormal-mode method made easy for users
Tong Wu and Philippe Lalanne
Full-wave numerical methods based on quasinormal modes (QNMs) offer valuable physical insights and computational efficiency for analyzing electromagnetic resonators. However, despite their advantages, many researchers in electromagnetism continue to favor real-frequency domain or time-domain approaches, often using finite element or finite-difference time-domain methods. This preference stems from various factors, including the perception that QNM theory is still developing or requires advanced mathematical tools from complex analysis. In this work, we combine numerical techniques with accurate ap-proximations to simplify the computation of QNMs and enable ultrafast reconstructions us-ing QNM expansions. The result is a new approach that is straightforwardly accessible to users familiar with real-frequency methods. We demonstrate the practicality of our ap-proach through an open-source package [Doi: 10.5281/zenodo.18708748] implemented within a widely-used commercial photonics software.
Read the full abstract ▾
Data Analysis and Statistics · Cross-listed from astro-ph.EP
Probabilistic Methods for Initial Orbit Determination and Orbit Determination in Cislunar Space
Ishan Paranjape, Tarun Hejmadi and Suman Chakravorty
In orbital mechanics, Gauss's method for orbit determination (OD) is a popular, minimal assumption solution for obtaining the initial state estimate of a passing resident space object (RSO). Since much of the cislunar domain relies on three-body dynamics, a key assumption of Gauss's method is rendered incompatible, creating a need for a new, minimal assumption method for initial orbit determination (IOD). In this work, we present a framework for short and long term probabilistic target tracking in cislunar space which produces an initial state estimate with as few assumptions as possible. Specifically, we propose an IOD method involving the kinematic fitting of several series of noisy, consecutive ground-based observations. Once a probabilistic initial state estimate in the form of a particle cloud is formed, we apply the powerful Particle Gaussian Mixture (PGM) Filter to reduce the uncertainty of our state estimate over time. This combined IOD/OD framework is demonstrated for several classes of trajectories in cislunar space and compared to better-known filtering frameworks.
Read the full abstract ▾
Physics and Society
Effect of vehicle groups on heterogeneous disordered traffic flow
Akihito Nagahama, Nichika Asai, Claudio Feliciani et al. (+2 more)
In heterogeneous disordered traffic, where various vehicle types operate without strict lane discipline, self-organized vehicle groups often emerge, but their influence remains unclear. This study examines how group prevalence and composition affect flow-density relationships using real-world trajectory data. Three Passenger Car Unit (PCU) estimation methods construct flow-density diagrams accounting for heterogeneity. Group proportion, the share of vehicles classified in groups, has a nonlinear, traffic-situation-dependent impact on flow. Moderate group proportions (30-60%) were more clearly associated with peak-flow or high-flow cases under denser traffic conditions, rather than consistently producing the highest representative flow in the median-based analysis. Comparing vehicle-count-based and PCU-based group proportions shows that normalization affects interpretation, particularly when groups consist mainly of small-PCU vehicles such as motorcycles. Lower group proportions were associated with relatively favorable flow under free-flow conditions, while
entropy-based analysis shows that the association between
entropy alone and speed is not consistent across traffic situations. Contrasting representative trends and extreme high-flow cases suggests that traffic at similar density and group proportion can exhibit low-efficiency and high-efficiency modes. Overall, group prevalence is important in shaping macroscopic traffic dynamics, while linking internal group composition to performance may require richer structural descriptors beyond a single
entropy measure. These results imply condition-dependent bottom-up traffic control: encouraging favorable vehicle formations in denser traffic and preserving fluid leader-follower relations in free-flow traffic may improve heterogeneous traffic flow, especially with minimal infrastructure or driver-assistance technologies.
Read the full abstract ▾
All authors Akihito Nagahama, Nichika Asai, Claudio Feliciani, Xiaolu Jia, Katsuhiro Nishinari
Computational Physics · Cross-listed from cond-mat.mtrl-sci
Lattice and Orbital-Resolved Fermiology of Metallenes
Kameyab Raza Abidi, Mohammad Bagheri and Pekka Koskinen
Atomically thin metallenes have emerged as a new member of the two-dimensional (2D) materials family. Recent experimental realization of metallenes in the Ångström limit has further intensified interest in this class of 2D materials. However, achieving sub-atomic insight into them demands the most detailed and systematic characterization of their electronic structure. Such understanding is essential for the rational design and exploitation of their properties in plasmonics, catalysis, and
quantum optics. Existing electronic-structure studies are either scattered or focus on a few selected systems, and a comprehensive view of their band structures and
Fermi surfaces remains missing. Here, we address this gap by studying 45 elemental metallenes in six monolayer
lattices (honeycomb, square, hexagonal, and their buckled forms) using density-functional theory. We found that
lattice type primarily fixes the shape and radial placement of the
Fermi-lines, while out-of-plane buckling introduces controlled modifications: it shortens long straight
Fermi-line segments, and occasionally creates, removes, or merges small
Fermi-line pockets. The electronic configuration determines which orbital type dominates the
Fermi level. We summarized Fermiology using a single score for each element, termed pocketness, derived from four descriptors that combine element properties (
symmetry, coordination) with electronic characteristics (dispersion,
Fermi-surface
topology). This score enables targeted angle-resolved photoemission spectroscopy (ARPES) tests, controlled Lifshitz transitions, and provides a predictive basis for transport and device applications.
Read the full abstract ▾
Computational Physics · Cross-listed from cs.CE
PINEAPPLE: Physics-Informed Neuro-Evolution Algorithm for Prognostic Parameter Inference in Lithium-Ion Battery Electrodes
Karkulali Pugalenthi, Jian Cheng Wong, Qizheng Yang et al. (+4 more)
Accurate, real-time, yet non-destructive estimation of internal states in lithium-ion batteries is critical for predicting degradation, optimizing usage strategies, and extending operational lifespan. Here, we introduce PINEAPPLE (Physics-Informed Neuro-
Evolution Algorithm for Prognostic Parameter inference in Lithium-ion battery Electrodes), a novel framework that integrates physics-informed neural networks (PINNs) with an evolutionary search
algorithm to enable rapid, scalable, and interpretable parameter inference with potential for application to next-generation batteries. The meta-learned PINN utilizes fundamental physics principles to achieve accurate zero-shot prediction of electrode behavior with test errors below 0.1$\%$ while maintaining an order-of-magnitude speed-up over conventional solvers. PINEAPPLE demonstrates robust parameter inference solely from voltage-time discharge curves across multiple batteries from the open-source CALCE repository, recovering the
evolution of key internal state parameters such as Li-ion
diffusion coefficients across usage cycles. Notably, the inferred cycle-dependent
evolution of these parameters exhibit consistent trends across different batteries without any customized degradation physics-embedded heuristic, highlighting the effective regularizing effect and robustness that can be conferred through incorporation of fundamental physics in PINEAPPLE. By enabling computationally efficient, real-time parameter estimation, PINEAPPLE offers a promising route towards the non-destructive, physics-based characterization of inter-
cell and intra-
cell variability of battery modules and battery packs, thereby unlocking new opportunities for downstream on-the-fly needs in next-generation battery management systems such as individual
cell-scale state-of-health diagnostics.
Read the full abstract ▾
All authors Karkulali Pugalenthi, Jian Cheng Wong, Qizheng Yang, Pao-Hsiung Chiu, My Ha Dao, Nagarajan Raghavan, Chinchun Ooi
Optics
Limits and Trade-Offs of Shift-Invariant Meta-Optical Encoders for Image Compression
Yubo Zhang, Rui Chen, Zhihao Zhou et al. (+1 more)
Meta-optical encoders can reduce image data before electronic readout or transmission, but engineered point-spread functions (PSFs) do not automatically outperform conventional imaging. We study scene-agnostic, shift-invariant, linear optical encoders using both a fixed total-variation (TV) reconstruction backend and a learned YOLOv8 detection backend. Under a measurement-budget definition of compression ratio that counts all sensed samples across all channels, we compare lens imaging with spatial binning, positive random multi-channel PSFs, signed random kernels, and orthogonal multi-channel kernels. In the low-noise regime, lens-binning gives the highest reconstruction fidelity and strongest YOLOv8 detection metrics at the same compression ratio. Multi-channel encoders, however, degrade more slowly under measurement noise because the measurements are distributed across complementary channels. These results show that, for scene-agnostic incoherent imaging, engineered convolutional PSFs should be justified primarily by robustness, multiplexing, or downstream system constraints, rather than by an expectation that generic wavefront coding will outperform lens-based binning.
Read the full abstract ▾
All authors Yubo Zhang, Rui Chen, Zhihao Zhou, Arka Majumdar
Biological Physics
Decoding cell signaling via optimal transport and information theory
Mintu Nandi and Sosuke Ito
Cellular signal processing performs reliably despite molecular noise. Mutual information (MI) is widely used to quantify signaling fidelity, capturing how well outputs discriminate input states. However, it fails to capture whether the output preserves the statistical structure of the input, a property crucial in morphogen patterning and dose-dependent signaling. To address this gap, we introduce the 2-Wasserstein (2-WD) distance, which provides a
geometric basis for comparing input and output distributions. We define MI as informational fidelity (INF) and the inverse of the 2-WD as
geometric fidelity (GMF). Applying this dual-fidelity framework to canonical regulatory motifs under Gaussian channel approximation reveals
topology-dependent trade-offs: coherent feed-forward loops can perform well in both dimensions, whereas feedback architectures reduce INF to enhance GMF. Experimental analysis of tumor necrosis factor signaling reveals dual-fidelity behavior qualitatively consistent with feedback regulation. RAS-MAPK data analysis further shows that jointly considering INF and GMF better characterizes intracellular signal relay than INF alone. Our results thus indicate that these signaling behaviors are not fully characterized by MI alone; instead, distributional correspondence provides a complementary dimension of signaling fidelity. Our study provides a practical framework for analyzing natural networks and guiding the design of task-specific synthetic
circuits.
Read the full abstract ▾
Fluid Dynamics
Manifestation of spurious currents and interface regularization in wind turbulence over fast-propagating waves
Hanul Hwang and Catherine Gorle
Accurate simulation of wind
turbulence over fast-propagating waves requires interface-capturing methods that suppress numerical artifacts while accurately resolving momentum transfer across the interface. In high wave-age regimes, numerical errors at the air-water interface can reach magnitudes comparable to the physical flow, directly affecting predicted
turbulence statistics. This study examines widely used interface-capturing techniques to evaluate how
curvature estimation and flux discretization influence wind-wave simulations through the resulting spurious currents and interface regularization. A systematic assessment is performed using static and translating droplet
benchmarks, together with solitary and monochromatic wave cases, to identify and quantify the dominant numerical error mechanisms. In addition, comparison with experimental measurements reveals how these primary error sources manifest in coupled wind-wave simulations. These findings clarify the numerical origin of the observed discrepancies and underscore the importance of accurate
curvature and flux treatment in high wave-age regimes, without which numerical artifacts risk being misattributed to genuine wind-wave physics.
Read the full abstract ▾
Computational Physics · Cross-listed from gr-qc
Observer-robust energy condition verification for warp drive spacetimes
An T. Le
Energy-condition tests for warp-drive spacetimes must account for all admissible causal directions at each point. We use the classical S-lemma to express the null, weak, and strong conditions as $4\times4$ linear
matrix inequalities; the dominant condition requires two such tests. These criteria require neither Hawking-Ellis classification nor a rapidity cutoff. For the null condition in a fixed orthonormal tetrad, the optimized multiplier margin equals half the minimum normalized null-energy contraction. Interval evaluation of the metric and
curvature provides pointwise certificates and global bounds on its minimum over the bubble wall. On flat unit-lapse slices, the momentum constraint relates Eulerian momentum to shift vorticity. For smooth shifts on all of Euclidean space with bounded vorticity, momentum vanishes identically precisely for a gradient plus rigid rotation. For shifts linear in speed, integrated negative Eulerian energy scales exactly quadratically when finite, with a fixed profile and integration domain on these slices. We implement the tests in Warpax, a JAX toolkit, and compare four warp-drive geometries at matched parameters across subluminal and superluminal speeds. At the reference parameters, the global bounds establish null-energy violation in all four bubble walls. The ideal irrotational Rodal profile has zero Eulerian momentum and is everywhere Type I; Type-IV regions are detected in the other sampled walls. For Rodal, the Eulerian reading misses about $73\%$ of the sampled wall weak-energy violations. These numerical type labels and sampled fractions are distinct from the interval bounds. Finite-segment null-geodesic integrals and flat-space
quantum-inequality estimates supplement the pointwise analysis.
Read the full abstract ▾
Optics
Dynamic topological exciton-polaritons enabling ultrafast logic operations
Feng Jin, Hao Zheng, Zhe Zhang et al. (+7 more)
Topological active materials have emerged as powerful paradigm bridging the discovery of exotic topological phases of matter with the development of functional topological devices. The recent extension of these material systems into dynamic regime, where topological properties can be actively manipulated at ultrafast timescales, promises unprecedented control over topological states and their functionalities. However, translating the static topological lasing signals into high-performance logic functions remain highly challenging, which imposes a far more stringent set of materials attributes. Here, leveraging the strong nonlinearity and pronounced spectral isolation of perovskite
exciton-polaritons embedded in a Dirac
vortex microcavity, we experimentally demonstrate the dynamic topological Majorana-like state polariton condensation with its ultrafast logic operations at room temperature. By actively coordinating pump and control beams in both spectral and temporal domain, we dynamically steer the topological polariton condensation process and demonstrate AND and NOT logic operations, achieving record extinction ratio (~20 dB), extremely low control fluence (~0.2 nJ/cm2) and sub-picosecond response time (~500 fs). Our results expand the frontier of dynamic
topology and establish a novel pathway towards robust, ultrafast, and reconfigurable on-chip polaritonic logic
circuits.
Read the full abstract ▾
All authors Feng Jin, Hao Zheng, Zhe Zhang, Jiaohao Ren, Yuxi Liu, Qing Zhang, Daniele Sanvitto, Timothy C. H. Liew, Baile Zhang, Rui Su
Plasma Physics
Nonlinear Saturation of Ballooning Modes in Stellarators
X. Chu, S. C. Cowley, N. Ferraro et al. (+2 more)
Ballooning mode saturation is investigated in realistic stellarator configurations using the flux tube approach of Ham et. al. [1] [2]. The method is adapted to account for the lack of exact force balance in stellarator
equilibrium solvers that assume existence of nested flux surfaces. A variational approach for calculating flux tube energy is developed to overcome this force error problem in stellarator numerical
equilibria. Saturated (
equilibrium) flux tube states that cross 10-20% of the
plasma minor radius are shown to exist for linearly ballooning unstable profiles. It is shown that several features of the displaced flux tube structure in a full nonlinear MHD simulation of Wendelstein 7X are reproduced by our model. Saturated states are found in a compact stellarator
equilibrium close but below the marginal ballooning linear instability, i.e. the unperturbed
equilibrium is metastable. This suggests that Edge-Localized-Mode-like explosive MHD behavior may be possible in stellarators.
Read the full abstract ▾
All authors X. Chu, S. C. Cowley, N. Ferraro, Y. Zhou, F. I. Parra