Published: 2026-02-20 18:59:36
Authors: Miklos Abert, Adam Arras, Jaelin Kim
Categories: math.PR, math.DG, math.SP
Abstract:
We analyze the existence of Brownian motion tilted by a potential of full support on hyperbolic spaces $\mathbb{H}^d$. On compact spaces, it is classical that these path limits, called Q-processes, exist and can be directly defined using the ground state of the corresponding Schrödinger operator. On non-compact spaces like $\mathbb{H}^d$, the existence fails in general.
We show that for \emph{stationary random} potentials on $\mathbb{H}^d$ with suitable spectral and sup norm bounds, the Q-processes exist a.s. For potentials that are factors of a Poisson point process, the method works up to sup norm $(d-1)^2/8$. In this case, we also show that the path limit can be approximated by periodic potentials.
As a tool, we use the foliated space defined by the point process. It turns out that the global ground state of this foliated space serves as a substitute for the non-existing $L^2$ ground states on the leaves of the foliation. Restricting the global ground state to a leaf gives a generalized eigenwave that can be plugged into the usual machinery to get the Q-process.
Published: 2026-02-20 18:57:44
Authors: Lingxia Shi, Stephen DeWitt, David Montiel, Qianying Shi, John Allison, Katsuyo Thornton
Categories: cond-mat.mtrl-sci
Abstract:
The spatial distribution and morphology of precipitates formed during aging are key factors that determine the precipitation hardening response of various magnesium-rare earth alloys. In recent years, the use of high-performance computing clusters and massively parallel frameworks has enabled quantitative simulations of the evolution of individual and multiple precipitates at relevant length and time scales. However, predictive modeling of precipitate evolution remains challenging, in part because many key thermodynamic and kinetic parameters governing the underlying physics are either unknown or have a high degree of uncertainty. In this work, we developed a workflow in which experimental data were used to parameterize a phase-field model to perform two-dimensional (2D) simulations of concurrent nucleation and evolution of $β_1$ precipitates in magnesium-neodymium alloy during aging. Matrix composition and precipitate number density at different aging times were obtained from atom probe tomography and transmission electron microscopy measurements, respectively. We applied a stereological method to estimate the three-dimensional (3D) number densities from experimental cross-sectional transmission electron micrographs. The estimated 3D number density data were then converted to effective 2D number densities. The effective 2D number density and composition data were used to determine the required model parameters by minimizing the discrepancy between simulation and experimental results. The parameterized model allows for quantitative phase-field simulations of nucleation and growth of $β_1$ precipitates, which can be employed to optimize aging time to achieve a target number density of precipitates. This work highlights an approach to overcome the challenges associated with parameterizing a coupled phase-field and nucleation model.
Published: 2026-02-20 18:48:42
Authors: Péter Madarasi
Categories: math.CO, cs.DM, math-ph, math.OC
Abstract:
We investigate the convex hulls of the eight dihedral symmetry classes of $n \times n$ alternating sign matrices, i.e., ASMs invariant under a subgroup of the symmetry group of the square. Extending the prefix-sum description of the ASM polytope, we develop a uniform core--assembly framework: each symmetry class is encoded by a set of core positions and an affine assembly map that reconstructs the full matrix from its core. This reduction transfers polyhedral questions to lower-dimensional core polytopes, which are better suited to the tool set of polyhedral combinatorics, while retaining complete information about the original symmetry class. For the vertical, vertical--horizontal, half-turn, diagonal, diagonal--antidiagonal, and total symmetry classes, we give explicit polynomial-size linear inequality descriptions of the associated polytopes. In these cases, we also determine the dimension and provide facet descriptions. The quarter-turn symmetry class behaves differently: the natural relaxation admits fractional vertices, and we need to extend the system with a structured family of parity-type Chvátal--Gomory inequalities to obtain the quarter-turn symmetric ASM polytope. Our framework leads to efficient algorithms for computing minimum-cost ASMs in each symmetry class and provides a direct link between the combinatorics of symmetric ASMs and tools from polyhedral combinatorics and combinatorial optimization.
Published: 2026-02-20 18:48:05
Authors: Deniz Qian, Hung-Ting Chen, Eunsol Choi
Categories: cs.CL, cs.IR
Abstract:
Comprehensively retrieving diverse documents is crucial to address queries that admit a wide range of valid answers. We introduce retrieve-verify-retrieve (RVR), a multi-round retrieval framework designed to maximize answer coverage. Initially, a retriever takes the original query and returns a candidate document set, followed by a verifier that identifies a high-quality subset. For subsequent rounds, the query is augmented with previously verified documents to uncover answers that are not yet covered in previous rounds. RVR is effective even with off-the-shelf retrievers, and fine-tuning retrievers for our inference procedure brings further gains. Our method outperforms baselines, including agentic search approaches, achieving at least 10% relative and 3% absolute gain in complete recall percentage on a multi-answer retrieval dataset (QAMPARI). We also see consistent gains on two out-of-domain datasets (QUEST and WebQuestionsSP) across different base retrievers. Our work presents a promising iterative approach for comprehensive answer recall leveraging a verifier and adapting retrievers to a new inference scenario.
Published: 2026-02-20 18:46:27
Authors: Xia Su, Ruiqi Chen, Benlin Liu, Jingwei Ma, Zonglin Di, Ranjay Krishna, Jon Froehlich
Categories: cs.CV, cs.RO
Abstract:
Vision-Language Models (VLMs) have shown remarkable progress in Vision-Language Navigation (VLN), offering new possibilities for navigation decision-making that could benefit both robotic platforms and human users. However, real-world navigation is inherently conditioned by the agent's mobility constraints. For example, a sweeping robot cannot traverse stairs, while a quadruped can. We introduce Capability-Conditioned Navigation (CapNav), a benchmark designed to evaluate how well VLMs can navigate complex indoor spaces given an agent's specific physical and operational capabilities. CapNav defines five representative human and robot agents, each described with physical dimensions, mobility capabilities, and environmental interaction abilities. CapNav provides 45 real-world indoor scenes, 473 navigation tasks, and 2365 QA pairs to test if VLMs can traverse indoor environments based on agent capabilities. We evaluate 13 modern VLMs and find that current VLM's navigation performance drops sharply as mobility constraints tighten, and that even state-of-the-art models struggle with obstacle types that require reasoning on spatial dimensions. We conclude by discussing the implications for capability-aware navigation and the opportunities for advancing embodied spatial reasoning in future VLMs. The benchmark is available at https://github.com/makeabilitylab/CapNav
Published: 2026-02-20 18:31:07
Authors: Jan David Fischbach, Fridtjof Betz, Lukas Rebholz, Puneet Garg, Kristina Frizyuk, Felix Binkowski, Sven Burger, Martin Hammerschmidt, Carsten Rockstuhl
Categories: physics.optics, physics.comp-ph
Abstract:
The transition matrix (T-matrix) is a complete description of an object's linear scattering response. As such, it has found wide adoption for the theoretical and computational description of multiple-scattering phenomena. In its original form, the T-matrix describes the interaction of a scatterer with a monochromatic source. In practice, however, information about the T-matrix is usually needed in an extended spectral domain. To access the frequency-dispersion, one might naively sample T-matrices over a finely resolved set of discrete frequencies and store one T-matrix per frequency. This approach has multiple drawbacks: it is computationally expensive, requires excessive memory, and it disregards the physical origin of the spectral features, weakening physical interpretability. To overcome these major limitations, we leverage a pole-expansion technique to represent the T-matrix with arbitrary frequency resolution within a selected frequency domain via a set of resonant contributions. A matrix-valued variant of the recently established adaptive Antoulas-Anderson (AAA) algorithm for rational approximation enables us to compute the pole-expansion at minimal computational cost using only a small number of direct evaluations. We demonstrate the benefits of such a representation with examples ranging from semi-analytically accessible scatterers to quasi-dual bound states in the continuum. To allow the wider community to capitalize on these findings, we provide open-source tools to perform the presented pole-expansion of the T-matrix.
Published: 2026-02-20 18:22:13
Authors: Zimo Hao, Khoa Lê, Chengcheng Ling
Categories: math.PR
Abstract:
We study the weak convergence of a generic tamed Euler-Maruyama scheme for kinetic stochastic differential equations (SDEs) with integrable drifts. We show that the marginal density of the considered scheme converges at rate 1/2 to the corresponding marginal density of the SDE. The convergence rate is independent from the criticality gap, which is new compared to previous results.
Published: 2026-02-20 18:01:59
Authors: Ehsan Lari, Reza Arablouei, Stefan Werner
Categories: cs.LG, eess.SP, math.PR, stat.AP, stat.ML
Abstract:
We propose PRISM-FCP (Partial shaRing and robust calIbration with Statistical Margins for Federated Conformal Prediction), a Byzantine-resilient federated conformal prediction framework that utilizes partial model sharing to improve robustness against Byzantine attacks during both model training and conformal calibration. Existing approaches address adversarial behavior only in the calibration stage, leaving the learned model susceptible to poisoned updates. In contrast, PRISM-FCP mitigates attacks end-to-end. During training, clients partially share updates by transmitting only $M$ of $D$ parameters per round. This attenuates the expected energy of an adversary's perturbation in the aggregated update by a factor of $M/D$, yielding lower mean-square error (MSE) and tighter prediction intervals. During calibration, clients convert nonconformity scores into characterization vectors, compute distance-based maliciousness scores, and downweight or filter suspected Byzantine contributions before estimating the conformal quantile. Extensive experiments on both synthetic data and the UCI Superconductivity dataset demonstrate that PRISM-FCP maintains nominal coverage guarantees under Byzantine attacks while avoiding the interval inflation observed in standard FCP with reduced communication, providing a robust and communication-efficient approach to federated uncertainty quantification.
Published: 2026-02-20 17:52:07
Authors: Pinki Pradhan, Anup Bhattacharya, Ragesh Jaiswal
Categories: cs.DS
Abstract:
Bateni et al. has recently introduced the weak-strong distance oracle model to study clustering problems in settings with limited distance information. Given query access to the strong-oracle and weak-oracle in the weak-strong oracle model, the authors design approximation algorithms for $k$-means and $k$-center clustering problems. In this work, we design algorithms with improved guarantees for $k$-means and $k$-center clustering problems in the weak-strong oracle model. The $k$-means++ algorithm is routinely used to solve $k$-means in settings where complete distance information is available. One of the main contributions of this work is to show that $k$-means++ algorithm can be adapted to work in the weak-strong oracle model using only a small number of strong-oracle queries, which is the critical resource in this model. In particular, our $k$-means++ based algorithm gives a constant approximation for $k$-means and uses $O(k^2 \log^2{n})$ strong-oracle queries. This improves on the algorithm of Bateni et al. that uses $O(k^2 \log^4n \log^2 \log n)$ strong-oracle queries for a constant factor approximation of $k$-means. For the $k$-center problem, we give a simple ball-carving based $6(1 + ε)$-approximation algorithm that uses $O(k^3 \log^2{n} \log{\frac{\log{n}}ε})$ strong-oracle queries. This is an improvement over the $14(1 + ε)$-approximation algorithm of Bateni et al. that uses $O(k^2 \log^4{n} \log^2{\frac{\log{n}}ε})$ strong-oracle queries. To show the effectiveness of our algorithms, we perform empirical evaluations on real-world datasets and show that our algorithms significantly outperform the algorithms of Bateni et al.
Published: 2026-02-20 17:51:44
Authors: Dana Zilberberg, Fioralba Cakoni, Michael S. Vogelius
Categories: math.AP
Abstract:
We investigate elliptic fractional equations in the whole space, involving zero order perturbations of the fractional Laplacian $(-Δ)^s$, $0
Published: 2026-02-20 17:48:21
Authors: Mohamed Elgouhary, Amr S. El-Wakeel
Categories: cs.RO, cs.AI, cs.LG, eess.SY
Abstract:
Pure Pursuit (PP) is widely used in autonomous racing for real-time path tracking due to its efficiency and geometric clarity, yet performance is highly sensitive to how key parameters-lookahead distance and steering gain-are chosen. Standard velocity-based schedules adjust these only approximately and often fail to transfer across tracks and speed profiles. We propose a reinforcement-learning (RL) approach that jointly chooses the lookahead Ld and a steering gain g online using Proximal Policy Optimization (PPO). The policy observes compact state features (speed and curvature taps) and outputs (Ld, g) at each control step. Trained in F1TENTH Gym and deployed in a ROS 2 stack, the policy drives PP directly (with light smoothing) and requires no per-map retuning. Across simulation and real-car tests, the proposed RL-PP controller that jointly selects (Ld, g) consistently outperforms fixed-lookahead PP, velocity-scheduled adaptive PP, and an RL lookahead-only variant, and it also exceeds a kinematic MPC raceline tracker under our evaluated settings in lap time, path-tracking accuracy, and steering smoothness, demonstrating that policy-guided parameter tuning can reliably improve classical geometry-based control.
Published: 2026-02-20 17:45:28
Authors: Fotios Zantalis, Evangelos Zervas, Grigorios Koulouras
Categories: cs.LG, cs.AI
Abstract:
Federated Learning (FL) enables distributed model training on edge devices while preserving data privacy. However, clients tend to have non-Independent and Identically Distributed (non-IID) data, which often leads to client-drift, and therefore diminishing convergence speed and model performance. While adaptive optimizers have been proposed to mitigate these effects, they frequently introduce computational complexity or communication overhead unsuitable for resource-constrained IoT environments. This paper introduces Federated Zero Mean Gradients (FedZMG), a novel, parameter-free, client-side optimization algorithm designed to tackle client-drift by structurally regularizing the optimization space. Advancing the idea of Gradient Centralization, FedZMG projects local gradients onto a zero-mean hyperplane, effectively neutralizing the "intensity" or "bias" shifts inherent in heterogeneous data distributions without requiring additional communication or hyperparameter tuning. A theoretical analysis is provided, proving that FedZMG reduces the effective gradient variance and guarantees tighter convergence bounds compared to standard FedAvg. Extensive empirical evaluations on EMNIST, CIFAR100, and Shakespeare datasets demonstrate that FedZMG achieves better convergence speed and final validation accuracy compared to the baseline FedAvg and the adaptive optimizer FedAdam, particularly in highly non-IID settings.
Published: 2026-02-20 17:41:45
Authors: Marek Żukowski, Paweł Cieśliński, Marcin Markiewicz, Konrad Schlichtholz
Categories: quant-ph
Abstract:
Frustrated down conversion is a process in which a quantum superposition of emissions from two separate parametric down-conversion processes gives rise to observable interference. Depending on the phase relation between the probability amplitudes associated with emissions by the first and second crystal, the process can be enhanced or suppressed. This is achieved by aligning the setup so that the signal and idler modes from the first crystal are fed into the second and constitute its signal-idler modes.
In Sci. Adv. 11, 1794 (2025), two-observer interwoven frustrated PDC processes produced interference effects based on path identity [Phys. Rev. Lett. 118, 080401 (2017)]. The signal and idler modes of source crystals I and II are arranged to fully overlap with the emission modes of crystals A and B, which serve as elements of measurement stations controlled by Alice and Bob. In the interwoven configuration, crystal A (B) receives the signal mode of crystal I (II) and the idler mode of crystal II (I), enabling interference between joint emission processes at the sources and at the measurement stations. It was conjectured that such interference may lead to new non-classical phenomena.
In arXiv:2508.19207 it was shown that the process violates the standard Clauser-Horne Bell inequality without additional assumptions, provided suitable measurement settings are used. Here we extend the interference scheme to more than two measurement stations and demonstrate a violation of one of the WWWZB inequalities. This indicates that the proposed approach may provide a general method for revealing non-classicality in a range of phenomena discussed in [Rev. Mod. Phys. 94, 025007 (2022)]. We also present a GHZ/Hardy-type argument that further highlights the paradoxical character of the interference.
Published: 2026-02-20 17:34:57
Authors: Florian Herren, Raynette van Tonder
Categories: hep-ph, hep-ex
Abstract:
Modern measurements in flavour physics rely on accurate simulations of signal and background processes, provided by a wide range of general-purpose and specialised Monte-Carlo event generators. Due to the inclusion of a larger amount of specialised decays of heavy hadrons, EvtGen is often the tool of choice for many scenarios. We investigate the phase-space sampling algorithm of EvtGen and demonstrate that it generates unphysical features in kinematic distributions of semileptonic $B$ decays involving resonances, originating from neglected phase-space factors. We provide a short-term solution to correct the affected simulated samples through reweighting of the hadronic invariant mass distribution.
Published: 2026-02-20 17:27:10
Authors: Kévin Le Balc'h, Jiaqi Yu
Categories: math.AP
Abstract:
In this article, we study the Schrödinger equation posed in the Euclidean space. We prove observability inequalities for measurable sets that are thick with respect to decaying densities. The proof relies on quantitative uncertainty principles adapted to decaying densities, notably those established by Shubin, Vakilian, Wolff, and Kovrijkine.
Published: 2026-02-20 17:26:47
Authors: Benjamin Dowling, Prosanta Gope, Mehr U Nisa, Bhagya Wimalasiri
Categories: cs.CR
Abstract:
LINE has emerged as one of the most popular communication platforms in many East Asian countries, including Thailand and Japan, with millions of active users. Therefore, it is essential to understand its security guarantees. In this work, we present the first provable security analysis of the LINE version two (LINEv2) messaging protocol, focusing on its cryptographic guarantees in a real-world setting. We capture the architecture and security of the LINE messaging protocol by modifying the Multi-Stage Key Exchange (MSKE) model, a framework for analysing cryptographic protocols under adversarial conditions. While LINEv2 achieves basic security properties such as key indistinguishability and message authentication, we highlight the lack of forward secrecy (FS) and post-compromise security (PCS). To address this, we introduce a stronger version of the LINE protocol, introducing FS and PCS to LINE, analysing and benchmarking our results.
Published: 2026-02-20 17:13:19
Authors: Sandip Maiti, Debasish Banerjee, Shailesh Chandrasekharan, Marina K. Marinkovic
Categories: hep-lat, cond-mat.str-el, hep-th
Abstract:
We study the phase structure of a model containing two flavors of massless staggered fermions interacting through two independent four-fermion couplings, UI and UB, formulated on a three-dimensional Euclidean space-time lattice. At UB = 0, this model is known to exhibit a direct second-order quantum phase transition between a massless fermion (MF) phase and a phase in which fermions acquire masses through the mechanism commonly referred to as symmetric mass generation (SMG). We demonstrate that introducing a small nonzero value of UB qualitatively alters this structure: the single exotic transition at UB = 0 splits into two distinct, conventional transitions, separated by an intermediate phase in which fermion masses arise through the standard mechanism of spontaneous symmetry breaking (SSB). The first of these is a Gross-Neveu transition separating the MF phase from the SSB-induced massive phase, while the second is a three-dimensional XY transition between the SSB phase and the SMG phase. Using the fermion-bag Monte Carlo method, we verify that the critical exponents associated with both transitions are consistent with the literature, thereby yielding a quantitative characterization of the resulting phase structure of the model.
Published: 2026-02-20 17:09:52
Authors: Abhishek Ghosh, Kalachand Shuin
Categories: math.CA
Abstract:
In this article, we characterize the range of $α$ for which the helical maximal function is bounded from $L^p(|x|^α)$ to itself for $3
Published: 2026-02-20 16:44:20
Authors: Syeda Farwa Bukhari, Alessandro Magni, Witold Skowroński, Elena Losero, Vittorio Basso, Carlo Appino, Piotr Wiśniowski, Juergen Langer, Berthold Ocker, Dario Daghero, Michaela Kuepferling
Categories: cond-mat.mes-hall, physics.app-ph
Abstract:
Next-generation spintronic sensors aim to overcome the limitations of traditional tunneling-magnetoresistance (TMR) devices, such as complex manufacturing, high $1/f$ noise, and significant offsets. This work presents a comprehensive modeling and experimental validation of a magnetic field sensor based on Spin Hall Magnetoresistance (SMR) in a Wheatstone bridge configuration. Utilizing a multiphysics approach, we simulate the interplay between SMR, Anisotropic Magnetoresistance (AMR), and Spin-Orbit Torque (SOT) using a Stoner-Wohlfarth model complemented by a Fuchs-Sondheimer analysis of current distribution. To account for the presence of magnetic domains, we incorporate a modified Stoner-Wohlfarth framework that considers non-uniform magnetization and domain wall motion through a "truncated astroid" approach, allowing for a statistical distribution of single-domain particles. The model is validated against experimental measurements of Pt/$\text{Fe}_{60}\text{Co}_{20}\text{B}_{20}$ and Ta/$\text{Fe}_{60}\text{Co}_{20}\text{B}_{20}$ bilayers patterned into Hall bars and Wheatstone bridges. The model provides critical design guidelines for optimizing material properties, layer thickness, and device layout to minimize power consumption and maximize sensitivity in SMR-based sensing applications.
Published: 2026-02-20 16:43:23
Authors: Sayantan Chakraborty
Categories: math.DG
Abstract:
We prove new Sobolev type inequalities on compact Kähler manifolds with positive Ricci curvature. A proof of an already existing Sobolev inequality in the classical Bidaut-Véron and Véron approach is also discussed.
Published: 2026-02-20 16:21:15
Authors: Goni Yoeli, Gilad Gour
Categories: quant-ph
Abstract:
We consolidate coherence, athermality, and nonuniformity as sub-resources within an underlying quantum resource theory: instability. We formulate instability axiomatically as the transient information within a decaying physical system. Specifying a decay mechanism (e.g., dephasing, thermalization) recovers these familiar resources as specific manifestations of instability. We compute the one-shot distillation yield and dilution cost in various operational paradigms, and use them to pin down the extremal additive monotones. In the asymptotic regime, we show that all conversion rates are governed by a single additive monotone, and thereby we establish a universal second law for instability.
Published: 2026-02-20 16:20:50
Authors: Ziteng Cui, Shuhong Liu, Xiaoyu Dong, Xuangeng Chu, Lin Gu, Ming-Hsuan Yang, Tatsuya Harada
Categories: cs.CV
Abstract:
High-quality image acquisition in real-world environments remains challenging due to complex illumination variations and inherent limitations of camera imaging pipelines. These issues are exacerbated in multi-view capture, where differences in lighting, sensor responses, and image signal processor (ISP) configurations introduce photometric and chromatic inconsistencies that violate the assumptions of photometric consistency underlying modern 3D novel view synthesis (NVS) methods, including Neural Radiance Fields (NeRF) and 3D Gaussian Splatting (3DGS), leading to degraded reconstruction and rendering quality. We propose Luminance-GS++, a 3DGS-based framework for robust NVS under diverse illumination conditions. Our method combines a globally view-adaptive lightness adjustment with a local pixel-wise residual refinement for precise color correction. We further design unsupervised objectives that jointly enforce lightness correction and multi-view geometric and photometric consistency. Extensive experiments demonstrate state-of-the-art performance across challenging scenarios, including low-light, overexposure, and complex luminance and chromatic variations. Unlike prior approaches that modify the underlying representation, our method preserves the explicit 3DGS formulation, improving reconstruction fidelity while maintaining real-time rendering efficiency.
Published: 2026-02-20 16:18:02
Authors: Maxime Pinson, Adélie André, Yannick Boursier, Mathieu Dupont, Marie-Laure Gallin Martel, Alicia Garnier, Christophe Hoarau, Pavel Kavrigin, Daniel Maneval, Christian Morel, Jean-François Muraz, Marco Pullia, Simone Savazzi, Sara Marcatili
Categories: physics.med-ph, physics.ins-det
Abstract:
In the context of range monitoring for particle therapy, this study presents the first experimental results obtained with the TIARA detector using carbon-ion beams at the CNAO clinical center in Pavia, Italy. TIARA is based on the Prompt Gamma Timing (PGT) technique, which measures the time of flight (TOF) between incident ions and prompt gamma rays (PGs) emitted during nuclear interactions in the target. While the TIARA prototype has previously been validated with protons, carbons present a more challenging scenario due to their higher linear energy transfer, nuclear fragmentation products, and the continuous beam time structure of synchrotron accelerators. Experiments were performed by irradiating PMMA targets of different thicknesses with 200 MeV/u carbon beams. A coincidence time resolution of 279$\pm$35 ps FWHM was achieved, outperforming results previously obtained with protons at the same facility. A range accuracy of 4.74$\pm$0.36 mm at a 2$σ$ confidence level was measured at clinical intensity, when considering 5600 detected PGs, corresponding to the grouping of four irradiation spots of 2.4$\cdot$10$^6$ ions each. Overall, the results demonstrate that PGT-based range monitoring remains viable for carbon-ion beams, although increased background from secondary protons indicates that detector configuration adaptations are required.
Published: 2026-02-20 16:09:02
Authors: Geyang Wang, Alexander Barg, Navin Kashyap
Categories: math.CO, cs.DM, cs.IT, math.PR
Abstract:
In a previous paper (arXiv:2510.19746), we have studied the maximal hard-code model on the square lattice ${\mathbb Z}^2$ from the perspective of recoverable systems. Here we extend this study to the case of the triangular lattice ${\mathbb A}$. The following results are obtained:
(1) We derive bounds on the capacity of the associated recoverable system on ${\mathbb A}$;
(2) We show non-uniqueness of Gibbs measures in the high-activity regime;
(3) We characterize extremal periodic Gibbs measures for sufficiently low values of activity.
Published: 2026-02-20 15:46:53
Authors: Sergey Goncharov, Marco Peressotti, Stelios Tsampas, Henning Urbat, Stefano Volpe
Categories: cs.PL, cs.LO
Abstract:
The bialgebraic abstract GSOS framework by Turi and Plotkin provides an elegant categorical approach to modelling the operational and denotational semantics of programming and process languages. In abstract GSOS, bisimilarity is always a congruence, and it coincides with denotational equivalence. This saves the language designer from intricate, ad-hoc reasoning to establish these properties. The bialgebraic perspective on operational semantics in the style of abstract GSOS has recently been extended to higher-order languages, preserving compositionality of bisimilarity. However, a categorical understanding of bialgebraic denotational semantics according to Turi and Plotkin's original vision has so far been missing in the higher-order setting. In the present paper, we develop a theory of adequate denotational semantics in higher-order abstract GSOS. The denotational models are parametric in an appropriately chosen semantic domain in the form of a locally final coalgebra for a behaviour bifunctor, whose construction is fully decoupled from the syntax of the language. Our approach captures existing accounts of denotational semantics such as semantic domains built via general step-indexing, previously introduced on a per-language basis, and is shown to be applicable to a wide range of different higher-order languages, e.g. simply typed and untyped languages, or languages with computational effects such as probabilistic or non-deterministic branching.
Published: 2026-02-20 15:38:02
Authors: Zhuoran Li, Hai Zhong, Xun Wang, Qingxin Xia, Lihua Zhang, Longbo Huang
Categories: cs.AI
Abstract:
Online Multi-Agent Reinforcement Learning (MARL) is a prominent framework for efficient agent coordination. Crucially, enhancing policy expressiveness is pivotal for achieving superior performance. Diffusion-based generative models are well-positioned to meet this demand, having demonstrated remarkable expressiveness and multimodal representation in image generation and offline settings. Yet, their potential in online MARL remains largely under-explored. A major obstacle is that the intractable likelihoods of diffusion models impede entropy-based exploration and coordination. To tackle this challenge, we propose among the first \underline{O}nline off-policy \underline{MA}RL framework using \underline{D}iffusion policies (\textbf{OMAD}) to orchestrate coordination. Our key innovation is a relaxed policy objective that maximizes scaled joint entropy, facilitating effective exploration without relying on tractable likelihood. Complementing this, within the centralized training with decentralized execution (CTDE) paradigm, we employ a joint distributional value function to optimize decentralized diffusion policies. It leverages tractable entropy-augmented targets to guide the simultaneous updates of diffusion policies, thereby ensuring stable coordination. Extensive evaluations on MPE and MAMuJoCo establish our method as the new state-of-the-art across $10$ diverse tasks, demonstrating a remarkable $2.5\times$ to $5\times$ improvement in sample efficiency.
Published: 2026-02-20 15:32:15
Authors: Said Varlioglu, Nelly Elsayed, Murat Ozer, Zag ElSayed, John M. Emmert
Categories: cs.CR
Abstract:
With the emergence of remote code execution (RCE) vulnerabilities in ubiquitous libraries and advanced social engineering techniques, threat actors have started conducting widespread fileless cryptojacking attacks. These attacks have become effective with stealthy techniques based on PowerShell-based exploitation in Windows OS environments. Even if attacks are detected and malicious scripts removed, processes may remain operational on victim endpoints, creating a significant challenge for detection mechanisms. In this paper, we conducted an experimental study with a collected dataset on detecting PowerShell-based fileless cryptojacking scripts. The results showed that Abstract Syntax Tree (AST)-based fine-tuned CodeBERT achieved a high recall rate, proving the importance of the use of AST integration and fine-tuned pre-trained models for programming language.
Published: 2026-02-20 15:11:04
Authors: Shiyan Du, Conghan Yue, Xinyu Cheng, Dongyu Zhang
Categories: cs.CV
Abstract:
Multi-Instance Generation has advanced significantly in spatial placement and attribute binding. However, existing approaches still face challenges in fine-grained semantic understanding, particularly when dealing with complex textual descriptions. To overcome these limitations, we propose DEIG, a novel framework for fine-grained and controllable multi-instance generation. DEIG integrates an Instance Detail Extractor (IDE) that transforms text encoder embeddings into compact, instance-aware representations, and a Detail Fusion Module (DFM) that applies instance-based masked attention to prevent attribute leakage across instances. These components enable DEIG to generate visually coherent multi-instance scenes that precisely match rich, localized textual descriptions. To support fine-grained supervision, we construct a high-quality dataset with detailed, compositional instance captions generated by VLMs. We also introduce DEIG-Bench, a new benchmark with region-level annotations and multi-attribute prompts for both humans and objects. Experiments demonstrate that DEIG consistently outperforms existing approaches across multiple benchmarks in spatial consistency, semantic accuracy, and compositional generalization. Moreover, DEIG functions as a plug-and-play module, making it easily integrable into standard diffusion-based pipelines.
Published: 2026-02-20 15:10:43
Authors: Timothy W. Carlson, Swathi Kadaba, Motahhare Mirhosseini, Maria Kolesnik-Gray, Gabriel Marcus, Lindsey J. Gray, Anthony Walsh, Vojislav Krstic, D. L. Carroll
Categories: cond-mat.mes-hall, cond-mat.mtrl-sci
Abstract:
Two-dimensional topological insulators are central to our understanding of the connection between topological symmetries in a material and its band electronics. Within this class of materials, a breadth of complex quantum behaviors, such as persistent spin-polarized current states in the presence of a broken time reversal symmetry, and temperature-independent topological protection of quantum states, are thought to exist. However, current studies using photoemission and spectroscopic analyses or transport experiments fail to provide insight into the interplay between the physical 2D manifold and the band topology itself, since they do not provide spatial resolution of the phenomena to be understood. In this work, we develop a methodology for applying magnetic force microscopy to such systems to address this issue. Using well-characterized 2D crystallites of bismuth telluride ($Bi_2$$Te_3$), we image the magnetic signal directly associated with topological edge states. The observed phase contrast is remarkably robust at a temperature of 25°C and occurs across crystallite sizes and shapes. A detailed analysis of the magnetic imaging suggests that the current observed is composed of two parts: the first is a persistent current ($I_{Persistent}$) as predicted by theory, and the second is due to Faraday induction, $I_{Faraday}$. Damping dynamics of the cantilever during imaging further suggest that this Faraday EMF is established by spin accumulation along the 1D edge channel of the crystal, which then converts to a charge current in the presence of time reversal symmetry breaking, creating a novel form of rectification in the channel. This unexpected result can prompt new ideas for topology-based circuit elements with extremely low losses and power consumption.
Published: 2026-02-20 15:02:42
Authors: Finn van der Knaap, Kejiang Qian, Zheng Xu, Fengxiang He
Categories: cs.LG, cs.AI, stat.ML
Abstract:
This work studies heterogeneous Multi-Objective Reinforcement Learning (MORL), where objectives can differ sharply in temporal frequency. Such heterogeneity allows dense objectives to dominate learning, while sparse long-horizon rewards receive weak credit assignment, leading to poor sample efficiency. We propose a Parallel Reward Integration with Symmetry (PRISM) algorithm that enforces reflectional symmetry as an inductive bias in aligning reward channels. PRISM introduces ReSymNet, a theory-motivated model that reconciles temporal-frequency mismatches across objectives, using residual blocks to learn a scaled opportunity value that accelerates exploration while preserving the optimal policy. We also propose SymReg, a reflectional equivariance regulariser that enforces agent mirroring and constrains policy search to a reflection-equivariant subspace. This restriction provably reduces hypothesis complexity and improves generalisation. Across MuJoCo benchmarks, PRISM consistently outperforms both a sparse-reward baseline and an oracle trained with full dense rewards, improving Pareto coverage and distributional balance: it achieves hypervolume gains exceeding 100\% over the baseline and up to 32\% over the oracle. The code is at \href{https://github.com/EVIEHub/PRISM}{https://github.com/EVIEHub/PRISM}.
Published: 2026-02-20 15:01:53
Authors: Michelle Mora, German Chaparro, Juan D. Guerrero, Catalina Alzate, Juan P. Urrego, Jimena Giraldo, Pablo Cuartas-Restrepo, Julian Rodriguez-Ferreira, Oscar Restrepo
Categories: astro-ph.IM
Abstract:
Detecting the global 21 cm signal from the epoch of reionization remains a major observational challenge due to bright foregrounds and instrumental systematics. As part of the Colombian Antarctic Telescopes for 21 cm Absorption during Reionization (CANTAR) initiative, we present a simulation and analysis framework to evaluate antenna chromaticity, optimize instrument design, and assess site suitability for global 21 cm experiments. Using frequency-dependent beam models and Haslam-based sky maps, we compute dynamic spectra for the EDGES blade dipole and a set of dipole and novel monopole antennas optimized via particle swarm optimization. The optimized designs exhibit improved spectral smoothness compared to EDGES, particularly in the 70-120 MHz range. We also evaluate latitude-dependent sky brightness and identify mid-latitude sites (-40° to +5°) as optimal for foreground suppression. We apply Bayesian inference together with posterior predictive model validation to the publicly released EDGES data, assessing statistical consistency rather than hypothesis testing or model comparison. We find that physically motivated foreground and ionospheric models are statistically consistent with the data only when a 21 cm absorption feature is excluded. From the validated posterior, we generate a statistically validated ensemble of foreground corrections for use in beam-sky simulations. These results support a two-phase strategy for CANTAR: Antarctic deployments for calibration and testing, and future science operations at mid-latitude sites. Our framework provides a validated path toward robust foreground modeling, antenna design, and systematics control for global 21 cm signal detection.
Published: 2026-02-20 14:52:54
Authors: Juhan Raidal, Anastasios Avgoustidis, Edmund Copeland, Adam Moss
Categories: astro-ph.CO, gr-qc, hep-ph, hep-th
Abstract:
We present updated constraints on cosmic string and superstring parameters derived from Cosmic Microwave Background (CMB) anisotropies. The constraints are obtained via Markov Chain Monte Carlo (MCMC) analyses of the full \textit{Planck} temperature and polarization data combined with the Atacama Cosmology Telescope (ACT) Data Release 6 (DR6). For ordinary cosmic strings, we constrain the string tension $Gμ$, the string wiggliness parameter $α$, and the self-chopping efficiency $\tilde{c}$. For cosmic superstrings, we constrain the fundamental string tension $Gμ_F$, the string coupling $g_s$, and a parameter $w$ describing the volume of the compact extra dimensions. In both cases, we find significantly tighter bounds on the string tension compared to previous analyses, obtaining $2σ$ upper limits of $Gμ< 3.66\times10^{-8}$ and $Gμ_F < 1.38\times10^{-8}$. We also discuss the significant prior-dependence of these results. The computational pipeline used in this work, including a modified version of \texttt{CAMB} capable of computing CMB anisotropies sourced by any active network described via unequal-time correlators, is released publicly as \texttt{CAMBactive} \cite{Raidal_CAMBactive_CAMB_extension_2026}.
Published: 2026-02-20 14:49:53
Authors: Stefan Wahl, Raphaela Schenk, Ali Farnoud, Jakob H. Macke, Daniel Gedon
Categories: cs.LG
Abstract:
Automated methods for discovering mechanistic simulator models from observational data offer a promising path toward accelerating scientific progress. Such methods often take the form of agentic-style iterative workflows that repeatedly propose and revise candidate models by imitating human discovery processes. However, existing LLM-based approaches typically implement such workflows via hand-crafted heuristic procedures, without an explicit probabilistic formulation. We recast model discovery as probabilistic inference, i.e., as sampling from an unknown distribution over mechanistic models capable of explaining the data. This perspective provides a unified way to reason about model proposal, refinement, and selection within a single inference framework. As a concrete instantiation of this view, we introduce ModelSMC, an algorithm based on Sequential Monte Carlo sampling. ModelSMC represents candidate models as particles which are iteratively proposed and refined by an LLM, and weighted using likelihood-based criteria. Experiments on real-world scientific systems illustrate that this formulation discovers models with interpretable mechanisms and improves posterior predictive checks. More broadly, this perspective provides a probabilistic lens for understanding and developing LLM-based approaches to model discovery.
Published: 2026-02-20 14:47:27
Authors: David Mercier
Categories: cs.DL
Abstract:
Engineering and materials software is increasingly difficult to track in the scholarly and technical literature because publication volume is growing rapidly and software citation practices remain inconsistent. This is particularly true for the Ansys Granta product family, which is used for materials education, materials and process selection, sustainability-driven design, and enterprise materials information management. We present a structured and reproducible framework to consolidate evidence of \emph{operational} Granta usage and to support quantitative monitoring of adoption patterns, application domains, and technical impact. The framework is implemented as a curated reference database in \textit{Ansys Granta MI Enterprise}: bibliographic metadata are ingested semi-automatically (e.g., via DOI and citation-file parsing) and complemented by expert curation of usage descriptors (product, context, application domain, and technical depth), with relational links to authors and institutions. Downstream analytics are performed with Python, dashboards, and bibliometric/network visualization tools to enable reproducible querying and reporting. As of September~2025, the database contains more than 1{,}100 curated records spanning journals, conferences, theses, books, patents, standards, and reports, and supports rapid retrieval of validated case studies, reproducible literature reviews, and technology scouting. Example analyses highlight dominant domains, key institutions, and recurring integrations with CAD/CAE/FEM environments. Overall, the approach converts heterogeneous software-usage evidence into structured, analyzable knowledge to improve visibility of engineering software impact and to support evidence-based assessment and strategic decision-making.
Published: 2026-02-20 14:40:34
Authors: Tongkai Li, Weifeng Zhu, Shuowen Zhang, Jiannong Cao, Shuguang Cui, Liang Liu
Categories: eess.SP, cs.IT
Abstract:
This paper considers a millimeter wave (mmWave) integrated sensing and communication (ISAC) system, where a base station (BS) equipped with a large number of antennas but a small number of radio-frequency (RF) chains emits pencillike narrow beams for persistent tracking of multiple moving targets. Under this model, the tracking lost issue arising from the misalignment between the pencil-like beams and the true target positions is inevitable, especially when the trajectories of the targets are complex, and the conventional Kalman filter-based scheme does not work well. To deal with this issue, we propose a Transformer-based mmWave multi-target tracking framework, namely m3TrackFormer, with a novel re-acquisition mechanism, such that even if the echo signals from some targets are too weak to extract sensing information, we are able to re-acquire their locations quickly with small beam sweeping overhead. Specifically, the proposed framework can operate in two modes of normal tracking and target re-acquisition during the tracking procedure, depending on whether the tracking lost occurs. When all targets are hit by the swept beams, the framework works in the Normal Tracking Mode (N-Mode) with a Transformer encoder-based Normal Tracking Network (N-Net) to accurately estimate the positions of these targets and predict the swept beams in the next time block. While the tracking lost happens, the framework will switch to the Re-Acquisition Mode (R-Mode) with a Transformer decoder-based Re-Acquisition Network (RNet) to adjust the beam sweeping strategy for getting back the lost targets and maintaining the tracking of the remaining targets. Thanks to the ability of global trajectory feature extraction, the m3TrackFormer can achieve high beam prediction accuracy and quickly re-acquire the lost targets, compared with other tracking methods.
Published: 2026-02-20 14:39:50
Authors: Xabier de Zuazo, Vincenzo Verbeni, Eva Navas, Ibon Saratxaga, Mathieu Bourguignon, Nicola Molinaro
Categories: cs.LG
Abstract:
Data-efficient neural decoding is a central challenge for speech brain-computer interfaces. We present the first demonstration of transfer learning and cross-task decoding for MEG-based speech models spanning perception and production. We pre-train a Conformer-based model on 50 hours of single-subject listening data and fine-tune on just 5 minutes per subject across 18 participants. Transfer learning yields consistent improvements, with in-task accuracy gains of 1-4% and larger cross-task gains of up to 5-6%. Not only does pre-training improve performance within each task, but it also enables reliable cross-task decoding between perception and production. Critically, models trained on speech production decode passive listening above chance, confirming that learned representations reflect shared neural processes rather than task-specific motor activity.
Published: 2026-02-20 14:39:17
Authors: Rishika Bhagwatkar, Irina Rish, Nicolas Flammarion, Francesco Croce
Categories: cs.CV, cs.AI
Abstract:
Discrete image tokenizers encode visual inputs as sequences of tokens from a finite vocabulary and are gaining popularity in multimodal systems, including encoder-only, encoder-decoder, and decoder-only models. However, unlike CLIP encoders, their vulnerability to adversarial attacks has not been explored. Ours being the first work studying this topic, we first formulate attacks that aim to perturb the features extracted by discrete tokenizers, and thus change the extracted tokens. These attacks are computationally efficient, application-agnostic, and effective across classification, multimodal retrieval, and captioning tasks. Second, to defend against this vulnerability, inspired by recent work on robust CLIP encoders, we fine-tune popular tokenizers with unsupervised adversarial training, keeping all other components frozen. While unsupervised and task-agnostic, our approach significantly improves robustness to both unsupervised and end-to-end supervised attacks and generalizes well to unseen tasks and data. Unlike supervised adversarial training, our approach can leverage unlabeled images, making it more versatile. Overall, our work highlights the critical role of tokenizer robustness in downstream tasks and presents an important step in the development of safe multimodal foundation models.
Published: 2026-02-20 14:26:15
Authors: Craig Alexander, Jennifer Gaskell, Vinny Davies
Categories: stat.OT
Abstract:
Keeping pace with rapidly evolving technology is a key challenge in teaching statistics. To equip students with essential skills for the modern workplace, educators must integrate relevant technologies into the statistical curriculum where possible. University-level statistics education has experienced substantial technological change, particularly in the tools and practices that underpin teaching and learning. Statistical programming has become central to many courses, with R widely used and Python increasingly incorporated into statistics and data analytics programmes. Additionally, coding practices, database management, and machine learning now feature within some statistics curricula. Looking ahead, we anticipate a growing emphasis on artificial intelligence (AI), particularly the pedagogical implications of generative AI tools such as ChatGPT. In this article, we explore these technological developments and discuss strategies for their integration into contemporary statistics education.
Published: 2026-02-20 14:26:02
Authors: Seohwa Hwang, Junyong Park
Categories: stat.ME
Abstract:
We propose an online false discovery rate (FDR) controlling method based on conditional local FDR (LIS), designed for infectious disease datasets that are discrete and exhibit complex dependencies. Unlike existing online FDR methods, which often assume independence or suffer from low statistical power in dependent settings, our approach effectively controls FDR while maintaining high detection power in realistic epidemic scenarios. For disease modeling, we establish a Dynamic Bayesian Network (DBN) structure within the Susceptible-Infected-Susceptible (SIS) model, a widely used epidemiological framework for infectious diseases. Our method requires no additional tuning parameters apart from the width of the sliding window, making it practical for real-time disease monitoring. From a statistical perspective, we prove that our method ensures valid FDR control under stationary and ergodic dependencies, extending online hypothesis testing to a broader range of dependent and discrete datasets. Additionally, our method achieves higher statistical power than existing approaches by leveraging LIS, which has been shown to be more powerful than traditional $p$-value-based methods. We validate our method through extensive simulations and real-world applications, including the analysis of infectious disease incidence data. Our results demonstrate that the proposed approach outperforms existing methods by achieving higher detection power while maintaining rigorous FDR control.
Published: 2026-02-20 14:25:09
Authors: Alvise Pizzella, Lennart Wissel, Miguel Dovale-Alvarez, Pablo Martinez Cano, Rodrigo Garcia Alvarez, Christoph Bode, Juan Jose Esteban Delgado, Gerhard Heinzel
Categories: astro-ph.IM
Abstract:
The Laser Interferometer Space Antenna (LISA) mission aims to detect gravitational waves by interferometrically measuring the change of separation between free-falling test masses (TMs). LISA's interferometers must deliver pm/rtHz sensitivity while accommodating beam tilts up to 1 mrad at the photodiodes, which degrade the interferometric amplitude and increase the induced readout noise coupling. This paper uses an analytical framework developed by the authors in a previous work, based on minimal and justified approximations, that relates beam tilt to the resulting heterodyne signal amplitude in a generic two-beam interferometer with circular-area photodiodes (PDs). A set of interferometric topologies is analyzed, all of high relevance for LISA. We derive the exact amplitude response for an infinite detector and a closed-form approximation for finite detectors, and we validate both against numerical simulations and experimental measurements on an ultra-stable LISA-representative testbed. We then use this model to quantify the phase-noise amplification arising from reduced signal-to-noise ratio (SNR) under tilt, showing that curvature mismatches between the interfering beams substantially enhance this effect. Finally, we introduce a compact function that captures the angular dependence of correlated and uncorrelated phase noises in quadrant photodiode (QPD)-based readouts. Here, a new noise feature, caused by wavefront curvature mismatch, is predicted and measured for the first time. These results indicate that controlling wavefront curvature mismatch in the test mass interferometer (TMI) is essential to limit excess phase noise. The models and results derived in this paper, although originating in the context of LISA, are general and can be applied to any interferometric topology undergoing tilts with pivot on the detector plane.
Published: 2026-02-20 14:22:58
Authors: F. S. Gama, J. R. Nascimento, G. Olmo, A. Yu. Petrov, P. Porfírio
Categories: hep-th
Abstract:
In this work, we propose a new three-dimensional nonlocal spinor superfield model. This theory is constructed by introducing form factors in the local spinor superfield action. Then, we couple it minimally to a scalar superfield, for which we calculate the one-loop effective potential as a first constructive example of perturbative calculations in this new theory.
Published: 2026-02-20 14:21:11
Authors: Markus Diehl, Peter Ploessl
Categories: hep-ph
Abstract:
Double parton distributions at small distances between the two partons are dominated by a mechanism in which the two observed partons originate from the splitting of a single parton. This contribution can be computed in terms of single-parton distributions and perturbative splitting kernels. We demonstrate that two-loop corrections to these kernels can have a substantial quantitative impact and considerably improve the stability of predictions for double parton scattering. We also consider the impact of heavy quark masses in the two-loop splitting kernels in an approximate manner.
Published: 2026-02-20 14:12:37
Authors: Ben Kain
Categories: gr-qc, hep-th
Abstract:
We numerically investigate the dynamical evolution of spherically symmetric charge free wormholes. We concentrate on two specific examples, both of which exhibit wormhole expansion and wormhole collapse: the Ellis-Bronnikov wormhole, which is sourced by a real massless ghost scalar field, and the quantum corrected Schwarzschild black hole in semiclassical gravity (which has a wormhole structure and is not a true black hole), which is sourced by a renormalized energy-momentum tensor. Despite their very different sources, we demonstrate that the dynamics of these two wormholes are remarkably similar. Our analysis focuses on diagrams for the areal radius and components of the energy-momentum tensor. This work also serves as a review, offering a detailed description of how to perform a spherically symmetric dynamical evolution using double null coordinates as well as a review of the static solutions for our two examples.
Published: 2026-02-20 14:07:51
Authors: Redwanul Karim, Changhun Kim, Timon Conrad, Nora Gourmelon, Julian Oelhaf, David Riebesel, Tomás Arias-Vergara, Andreas Maier, Johann Jäger, Siming Bayer
Categories: cs.LG
Abstract:
Accurate AC-PF prediction under domain shift is critical when models trained on medium-voltage (MV) grids are deployed on high-voltage (HV) networks. Existing physics-informed graph neural solvers typically rely on full fine-tuning for cross-regime transfer, incurring high retraining cost and offering limited control over the stability-plasticity trade-off between target-domain adaptation and source-domain retention. We study parameter-efficient domain adaptation for physics-informed self-attention based GNN, encouraging Kirchhoff-consistent behavior via a physics-based loss while restricting adaptation to low-rank updates. Specifically, we apply LoRA to attention projections with selective unfreezing of the prediction head to regulate adaptation capacity. This design yields a controllable efficiency-accuracy trade-off for physics-constrained inverse estimation under voltage-regime shift. Across multiple grid topologies, the proposed LoRA+PHead adaptation recovers near-full fine-tuning accuracy with a target-domain RMSE gap of $2.6\times10^{-4}$ while reducing the number of trainable parameters by 85.46%. The physics-based residual remains comparable to full fine-tuning; however, relative to Full FT, LoRA+PHead reduces MV source retention by 4.7 percentage points (17.9% vs. 22.6%) under domain shift, while still enabling parameter-efficient and physically consistent AC-PF estimation.
Published: 2026-02-20 13:52:42
Authors: Renzo Bruera
Categories: math.AP
Abstract:
In this paper, we study planar nonlocal Delaunay sets. That is, open sets in $\mathbb{R}^2$ with constant nonlocal mean curvature that are periodic in $x_1$, and even in $x_1$ and in $x_2$. Using bifurcation analysis and fine explicit computations, we prove that every sufficiently $C^{1,β}$-flat nonlocal Delaunay set in $\mathbb{R}^2$ that is not a straight band is unstable with respect to volume-preserving periodic variations.
Our results support the conjecture that, as in the local case, in the range of large areas, minimizers of the periodic nonlocal isoperimetric problem -- also known as the nonlocal liquid drop problem with prescribed area between two parallel hyperplanes -- are all straight bands.
Published: 2026-02-20 13:52:17
Authors: Max Kämper, Christoph Schumacher, Fabian Schwarzenberger, Ivan Veselic
Categories: math.ST, math-ph, math.SP
Abstract:
The integrated density of states (IDS) is a fundamental spectral quantity for quantum Hamiltonians modeling condensed matter systems, describing how densely energy levels are distributed. It can be interpreted as a volume-averaged spectral distribution. Hence, there are two equivalent definitions of the IDS related by the Pastur-Shubin formula: an operator-theoretic trace formula and a limit of normalized eigenvalue counting functions on finite volumes. We study a discrete random Schrödinger operator with bounded random potentials of finite-range correlations and prove a quantitative concentration inequality ensuring, with explicit high probability, that the empirical IDS (normalized eigenvalue counting function) uniformly approximates the abstract IDS trace formula within a prescribed error, thereby implying confidence regions for the IDS.
Published: 2026-02-20 13:51:29
Authors: Gia-Wei Chern, Yunhao Fan, Sheng Zhang, Puhan Zhang
Categories: cond-mat.str-el, cs.LG, physics.comp-ph
Abstract:
We review recent advances in machine learning (ML) force-field methods for Landau-Lifshitz-Gilbert (LLG) simulations of itinerant electron magnets, focusing on scalability and transferability. Built on the principle of locality, a deep neural network model is developed to efficiently and accurately predict the electron-mediated forces governing spin dynamics. Symmetry-aware descriptors constructed through a group-theoretical approach ensure rigorous incorporation of both lattice and spin-rotation symmetries. The framework is demonstrated using the prototypical s-d exchange model widely employed in spintronics. ML-enabled large-scale simulations reveal novel nonequilibrium phenomena, including anomalous coarsening of tetrahedral spin order on the triangular lattice and the freezing of phase separation dynamics in lightly hole-doped, strong-coupling square-lattice systems. These results establish ML force-field frameworks as scalable, accurate, and versatile tools for modeling nonequilibrium spin dynamics in itinerant magnets.
Published: 2026-02-20 13:35:57
Authors: Rezvi Shahariar, Chris Phillips
Categories: cs.NI
Abstract:
Vehicular Ad Hoc Networks (VANETs) support the information dissemination among vehicles, Roadside Units (RSUs), and a Trust Authority (TA). A trust model evaluates an entity or data or both to determine truthfulness. A security model confirms authentication, integrity, availability, non repudiation issues. With these aspects in mind, many models have been proposed in literature. Furthermore, many information dissemination approaches are proposed. However, the lack of a model that can manage traffic incidents completely inspires this work. This paper details how and when a message needs to be generated and relayed so that the incidents can be reported and managed in a timely manner. This paper addresses this challenge by providing a traffic incident management model to manage several traffic incidents efficiently. Additionally, we simulate this model using the VEINS simulator with vehicles, RSUs, and a TA. From the experiments, we measure the average number of transmissions required for reporting a single traffic incident while varying the vehicle density and relaying considerations. We consider two types of relaying. In one series of experiments, messages from regular vehicles and RSUs are relayed up to four hops. In another series of experiments, messages from the regular vehicles and RSUs are relayed until their generation time reaches sixty seconds. Additionally, messages from the official vehicles are relayed when they approach an incident or when the incident is cleared. Results from the simulations show that more vehicles are informed with four-hop relaying than sixty-second relaying in both cases.
Published: 2026-02-20 13:35:46
Authors: Matthew R. Standing, John R. Barnes, Carole A. Haswell, Adam T. Stevenson, João P. Faria, Erwan Quintin, Zachary O. B. Ross, Luca Fossati, James S. Jenkins, Douglas Alves, Daniel Staab
Categories: astro-ph.EP, astro-ph.IM, astro-ph.SR
Abstract:
DMPP is a radial-velocity survey that aims to detect planets around stars exhibiting anomalous activity signatures, consistent with the presence of close-in evaporating planets. Here, we report the discovery of 7 new planetary signals in 5 different systems: DMPP-2c & d, HD67200/DMPP-6b & c, HD118006/DMPP-7b, HD191122/DMPP-8b, and HD200133/DMPP-9b. We update the orbital parameters of the DMPP-1, DMPP-2, and DMPP-3 systems, along with those of the planetary systems orbiting HD181433, HD39194, and HD89839. We derive detection limits for all 24 targets in our sample with adequate observational coverage, and test the DMPP hypothesis by calculating the occurrence rates for planets in this configuration. We find that the occurrence rates of planets in our sample with orbital periods shorter than $50~\mathrm{d}$ and masses in the range $3$-$10$ M$_\oplus$ are $83.0^{+27.1}_{-24.4}\%$, for $10$-$30$ M$_\oplus$ are $27.0^{+15.0}_{-11.2}\%$, and for $30$-$100$ M$_\oplus$ are $13.9^{+11.8}_{-7.5}\%$. This is significantly higher than the occurrence rates reported by other radial velocity surveys, providing strong support for the DMPP hypothesis.
Published: 2026-02-20 13:34:43
Authors: Jiayi Wu, Zhengyu Wu, Xunkai Li, Ronghua Li, Guoren Wang
Categories: cs.IR
Abstract:
Most implicit collaborative filtering (CF) models are trained with negative sampling, where existing work designs sophisticated strategies for high-quality negatives while largely overlooking the exploration of positive samples. Although some denoising recommendation methods can be applied to implicit CF for denoising positive samples, they often sparsify positive supervision. Moreover, these approaches generally overlook user activity bias during training, leading to insufficient learning for inactive users. To address these issues, we propose a simple yet effective negative sampling plugin, PSP-NS, from the perspective of enhancing positive supervision signals. It builds a user-item bipartite graph with edge weights indicating interaction confidence inferred from global and local patterns, generates positive sample pairs via replication-based reweighting to strengthen positive signals, and adopts an activity-aware weighting scheme to effectively learn inactive users' preferences. We provide theoretical insights from a margin-improvement perspective, explaining why PSP-NS tends to improve ranking quality (e.g., Precision@k/Recall@k), and conduct extensive experiments on four real-world datasets to demonstrate its superiority. For instance, PSP-NS boosts Recall@30 and Precision@30 by 32.11% and 22.90% on Yelp over the strongest baselines. PSP-NS can be integrated with various implicit CF recommenders or negative sampling methods to enhance their performance.