October 2025 arXiv papers — page 197

Caitlyn Stone-Whitehead, Israel Hernandez, Connor Bray, Allison Davenport

Superconducting detectors with sub-eV energy resolution have demonstrated success setting limits on Beyond the Standard Model (BSM) physics due to their unique sensitivity to low-energy events. G4CMP, a Geant4-based extension for condensed matter physics, provides a comprehensive toolkit for modeling phonon and charge dynamics in cryogenic materials. This pa

Rohit Chouhan

On modern computers with graphical user interfaces, application windows are managed by a window manager, a core component of the desktop environment. Mainstream operating systems such as Microsoft Windows and Apple's macOS employ window managers, where users rely on a mouse or trackpad to manually resize, reposition, and switch between overlapping windows. T

Sri Durga Sai Sowmya Kadali, Evangelos E. Papalexakis

Jailbreaking large language models (LLMs) has emerged as a pressing concern with the increasing prevalence and accessibility of conversational LLMs. Adversarial users often exploit these models through carefully engineered prompts to elicit restricted or sensitive outputs, a strategy widely referred to as jailbreaking. While numerous defense mechanisms have

Tianyue Xu, Yanlin Wu, Abhai K. Tripathi, Matthew M. Ippolito

Deep learning advances have revolutionized automated digital pathology analysis. However, differences in staining protocols and imaging conditions can introduce significant color variability. In deep learning, such color inconsistency often reduces performance when deploying models on data acquired under different conditions from the training data, a challen

Xin Chen, Jian-Tong Hou, Long Liang, Jie Lu

Two-dimensional (2D) semi-Dirac materials feature a unique anisotropic band structure characterized by quadratic dispersion along one spatial direction and linear dispersion along the other, effectively hybridizing ordinary and Dirac fermions. The anisotropy of energy dispersion can be further modulated through band tilting along either spatial direction of

Ziyuan Huang, DanDan Zheng, Cheng Zou, Rui Liu

Visual tokenization remains a core challenge in unifying visual understanding and generation within the autoregressive paradigm. Existing methods typically employ tokenizers in discrete latent spaces to align with the tokens from large language models, where the quantization errors can limit semantic expressiveness and degrade the capability of vision-langua

Shraddha Biswas, Ing-Guey Jiang, Li-Chin Yeh, Hsin-Min Liu

In this study, we examine the transit timing deviations of the extensively studied hot Jupiter WASP-12 b using a comprehensive dataset of 391 transit light curves. The dataset includes 7 new photometric observations obtained with the 1.3 m Devasthal Fast Optical Telescope, the 0.61 m VASISTHA telescope, and the 0.3 m AG Optical IDK telescope, along with 119

Danush Shekar, Ben Weiss, Morris Swartz, Corrinne Mills

Pixel tracking detectors at upcoming collider experiments will see unprecedented charged-particle densities. Real-time data reduction on the detector will enable higher granularity and faster readout, possibly enabling the use of the pixel detector in the first level of the trigger for a hadron collider. This data reduction can be accomplished with a neural

Jingbo Yang, Bairu Hou, Wei Wei, Shiyu Chang

Large language model (LLM) agents are becoming competent at straightforward web tasks, such as opening an item page or submitting a form, but still struggle with objectives that require long horizon navigation, large scale information extraction, and reasoning under constraints. We present WebDART, a general framework that enables a single LLM to handle such

Alexandre X. Milewski, Charles S. Peskin

The immersed boundary (IB) method has been used as a means to simulate fluid-membrane interactions in a wide variety of biological and engineering applications. Although the numerical convergence of the method has been empirically verified, it is theoretically unproved because of the singular forcing terms present in the governing equations. This paper is mo

Clément Aubert, Jean Krivine

Causality serves as an abstract notion of time for concurrent systems. A computation is causal, or simply valid, if each observation of a computation event is preceded by the observation of its causes. The present work establishes that this simple requirement is equally relevant when the occurrence of an event is invertible. We propose a conservative extensi

Justin Cheung, Samuel Savine, Calvin Nguyen, Lin Lu

If a CT scanner introduces a new artifact not present in the training labels, the model may misclassify the images. Although modern CT scanners include design features which mitigate these artifacts, unanticipated or difficult-to-mitigate artifacts can still appear in practice. The direct solution of labeling images from this new distribution can be costly.

Xiaokan Yang, Ding Zhang, Wei Chen, Li Qiu

In this paper, we utilize a variant of the scaled relative graph (SRG), referred to as the $\theta$-symmetric SRG, to develop a graphical stability criterion for the feedback interconnection of a cascade of systems. A crucial submultiplicative property of $\theta$-symmetric SRG is established, enabling it to handle cyclic interconnections for which conventio

Fei Zhang, Rob Chancia, Josie Clapp, Amirhossein Hassanzadeh

Accurate semantic segmentation of terrestrial laser scanning (TLS) point clouds is limited by costly manual annotation. We propose a semi-automated, uncertainty-aware pipeline that integrates spherical projection, feature enrichment, ensemble learning, and targeted annotation to reduce labeling effort, while sustaining high accuracy. Our approach projects 3D

S. Sajad Dabiri, Reza Asgari

We establish a steady-state theory for nonlinear optical conductivity in pseudo-Hermitian systems. We derive compact formulas for the first and second order conductivity tensors in both the velocity and length gauges and prove their exact equivalence through generalized sum rules and Berry connection identities by formulating the nonlinear response in terms

Haofei Yu, Keyang Xuan, Fenghai Li, Kunlun Zhu

Automatic research with Large Language Models (LLMs) is rapidly gaining importance, driving the development of increasingly complex workflows involving multi-agent systems, planning, tool usage, code execution, and human-agent interaction to accelerate research processes. However, as more researchers and developers begin to use and build upon these tools and

Wensi Wu, Matthew Daemer, Jeffrey A. Weiss, Alison M. Pouch

Valvular heart disease is prevalent and a major contributor to heart failure. Valve leaflet strain is a promising metric for evaluating the mechanics underlying the initiation and progression of valvular pathology. However, generalizable methods for noninvasively quantifying valvular strain from clinically acquired patient images remain limited. To address t

Jiaogen Zhang

In this manuscript, we investigate fully nonlinear prescribed curvature problems for the modified Schouten tensor on closed Riemannian manifolds with negative curvature. We prove that whenever the corresponding concave elliptic operator satisfies a structural Condition $T$, which encompasses all $O(n)$-invariant G\r{a}rding-Dirichlet operator, such prescribe

Meng-Sen Ma, Yun He, Xiao-Ming Wang, Huai-Fan Li

We explore the thermodynamic properties of the regular Bardeen-AdS black hole obtained by imposing an additional constraint on a singular "mother" black hole. This constraint eliminates the physical singularity but leads to the breakdown of the standard first law of black hole thermodynamics. The singular black hole exhibits a reentrant phase transition simi

K. Sakanashi, T. Kawabata, S. Adachi, H. Akimune

We measured the $\gamma$-decay probability of the Hoyle state with a new method of triple coincidence detection of a scattered $\alpha$ particle, a recoil $\rm ^{12}C$ nucleus, and a $\gamma$ ray in inelastic alpha scattering on $\rm ^{12}C$. This method successfully enabled a low-background measurement and a precise determination of the $\gamma$-decay proba

Xinyun Cao, Kexin Phyllis Ju, Chenglin Li, Venkatesh Potluri

As virtual 3D environments become more prevalent, equitable access is essential for blind and low-vision (BLV) users, who face challenges with spatial awareness, navigation, and interaction. Prior work has explored supplementing visual information with auditory or haptic modalities, but these methods are static and offer limited support for dynamic, in-conte

S. N. Sajadi, Supakchai Ponglertsakul

To satisfy the Cardy formula for Warped AdS$_3$ (WAdS$_{3}$) solutions in a quadratic ensemble, a specific set of boundary conditions has been proposed in \cite{Aggarwal:2020igb}. In this paper, these boundary conditions have been investigated in the three-dimensional new massive gravity (NMG) framework. The associated solution space, asymptotic symmetries,

Shumon Koga, Miroslav Krstic

This paper presents a safe stabilization of the Stefan PDE model with a moving boundary governed by a high-order dynamics. We consider a parabolic PDE with a time-varying domain governed by a second-order response with respect to the Neumann boundary value of the PDE state at the moving boundary. The objective is to design a boundary heat flux control to sta

Shingo Araki, Hinata Izumida, Kazuto Akiba, Tatsuo C. Kobayashi

PrSb$_2$ exhibits a charge-density wave (CDW) transition at $T_\mathrm{CDW} = 100$ K and antiferromagnetic (AFM) ordering at $T_\mathrm{N} = 5$ K at ambient pressure. Hall resistivity measurements revealed an anomalous feature within the AFM state, which was attributed to the topological Hall effect (THE), ruling out contributions from the ordinary and anoma

Pedro Fellype Pontes, Minbo Yang

In this paper, we study the regularity of solutions to a linear elliptic equation involving a mixed local-nonlocal operator of the form $$Lu - \operatorname{div}\big(a(x)\nabla u(x)\big)= f, \quad \text{in } \Omega \subset \mathbb{R}^n,$$ where $L$ is a general stable L\'{e}vy type operator and $a(\cdot)$ is a positive H\"{o}lder continuous weight. By establ

Yujun Shi, Haijiang Ren

We report numerical evidence of Fermi-Pasta-Ulam-Tsingou (FPUT)-like recurrence in weakly damped, periodically driven alpha-FPUT chains. In narrow regions of driving amplitude and damping, the steady-state energy is exchanged among a few low-frequency modes in a quasi-periodic (or highly regular, near-periodic) manner over long timescales. The maximum dampin

Yao Chen, David Ohlssen, Aimee Readie, Gregory Ligozio

Artificial intelligence (AI) holds great promise for supporting clinical trials, from patient recruitment and endpoint assessment to treatment response prediction. However, deploying AI without safeguards poses significant risks, particularly when evaluating patient endpoints that directly impact trial conclusions. We compared two AI frameworks against human

Vincent Lam, Robin Chhabra

The objective of this study is to develop a model-free workspace trajectory planner for space manipulators using a Twin Delayed Deep Deterministic Policy Gradient (TD3) agent to enable safe and reliable debris capture. A local control strategy with singularity avoidance and manipulability enhancement is employed to ensure stable execution. The manipulator mu

Jiuan Zhou, Yu Cheng, Yuan Xie, Zhaoxia Yin

With the rapid progress of LLMs, high quality generative text has become widely available as a cover for text steganography. However, prevailing methods rely on hand-crafted or pre-specified strategies and struggle to balance efficiency, imperceptibility, and security, particularly at high embedding rates. Accordingly, we propose Auto-Stega, an agent-driven

Qiongyang Hu, Wenyang Liu, Wenbin Zou, Yuejiao Su

Existing deep learning approaches for image super-resolution, particularly those based on CNNs and attention mechanisms, often suffer from structural inflexibility. Although graph-based methods offer greater representational adaptability, they are frequently impeded by excessive computational complexity. To overcome these limitations, this paper proposes the

Swathi Chandrasekhar, Shiva Raj Pokhrel, Navneet Singh

Accurate prediction of bond dissociation energies (BDEs) underpins mechanistic insight and the rational design of molecules and materials. We present a systematic, reproducible benchmark comparing quantum and classical machine learning models for BDE prediction using a chemically curated feature set encompassing atomic properties (atomic numbers, hybridizati

Kaiqi Yang, Hang Li, Yucheng Chu, Zitao Liu

Mathematical reasoning serves as a crucial testbed for the intelligence of large language models (LLMs), and math word problems (MWPs) are a popular type of math problems. Most MWP datasets consist of problems containing only the necessary information, while problems with distracting and excessive conditions are often overlooked. Prior works have tested popu

Côme Cattin, Thomas Plé, Olivier Adjoua, Nicolaï Gouraud

We present a distilled multi-time-step (DMTS) strategy to accelerate molecular dynamics simulations using foundation neural network models. DMTS uses a dual-level neural network where the target accurate potential is coupled to a simpler but faster model obtained via a distillation process. The 3.5 \r{A}-cutoff distilled model is sufficient to capture the fa

Masato Nozawa, Takashi Torii

The Benenti-Francaviglia (BF) family of metrics provides the most general form of a spacetime metric that admits two mutually commuting Killing vectors and an irreducible Killing tensor. The geodesic equations for the BF family are thus completely integrable by separation of variables. Within this broad class, we explore the Kerr-Schild transformation of a d

Nguyen Xuan Bach

In this paper, we study Clifford algebra construction from the perspective of adjunctions motivated by the general framework of Krashen and Lieblich. We introduce a category of weighted polynomial laws whose associated Clifford algebra functor is a left adjoint on this category. We introduce a notion of lifting weighted polynomial laws generalizing explicit

Cheonkam Jeong, Sungdo Kim, Jewoo Park

Contemporary language models are fluent yet routinely mis-handle the types of meaning their outputs entail. We argue that hallucination, brittle moderation, and opaque compliance outcomes are symptoms of missing type-theoretic semantics rather than data or scale limitations. Building on Montague's view of language as typed, compositional algebra, we recast a

Jiacong Zhu, Eloy de Lera Acedo, Kaan Artuc, Xuelei Chen

The CosmoCube project plans to deploy a global 21-cm spectrometer with 10-100 MHz observation band in a lunar orbit. The farside part of such an orbit, i.e. the part of orbit behind the Moon, offers an ideal site for accurately measuring the 21-cm signal from the Dark Ages, Cosmic Dawn and Epoch of Reionization, as the effects of the Earth's ionosphere, arti

Milad Aghajohari, Kamran Chitsaz, Amirhossein Kazemnejad, Sarath Chandar

Reinforcement learning (RL) has recently become a strong recipe for training reasoning LLMs that produce long chains of thought (LongCoT). Yet the standard RL "thinking environment", where the state is the prompt plus all prior reasoning tokens, makes the state unbounded and forces attention-based policies to pay quadratic compute as thoughts lengthen. We re

Abdelhafid Khazzar, Yassine Sekaki, Yasser Lachhab, Said El-marzouki

The paper explores the transformation of port logistics operations with artificial intelligence during the port transformation into a smart port. The research integrates capabilities-based resource analysis and dynamic capabilities with sociotechnicalimplementations of technologies and resilience approaches of complex systems under disruptions. The system ap

Ruizhi Liu, Jinmin Yi, Liujun Zou

Symmetries and their anomalies are powerful tools to understand quantum matter. In this work, for quantum spin chains, we define twisted locality-preserving automorphisms and their Gross-Nesme-Vogts-Werner indices, which provide a unified framework to describe both unitary and anti-unitary symmetries, on-site and non-on-site symmetries, and internal and tran

Yue Hu, Zanxia Cao, Yingchao Liu

Protein sequence alignment is a cornerstone of bioinformatics, traditionally approached using dynamic programming (DP) algorithms that find an optimal sequential path. This paper introduces UniOTalign, a novel framework that recasts alignment from a fundamentally different perspective: global matching via Optimal Transport (OT). Instead of finding a path, Un

Mingxuan Liu, Yuhe Ke, Wentao Zhu, Mayli Mertens

The integration of large language models (LLMs) into healthcare holds promise to enhance clinical decision-making, yet their susceptibility to biases remains a critical concern. Gender has long influenced physician behaviors and patient outcomes, raising concerns that LLMs assuming human-like roles, such as clinicians or medical educators, may replicate or a

Chad Berner

Frames allow all elements of a Hilbert space to be reconstructed by inner product data in a stable manner. Recently, there is interest in relaxing the definition of frames to understand the implications for stable signal recovery. In this paper, we relax the definition of a frame by allowing the operator in the frame decomposition formula to not be invertibl

Tarek Naous, Philippe Laban, Wei Xu, Jennifer Neville

Conversations with LMs involve two participants: a human user leading the conversation, and an LM assistant responding to the user's request. To satisfy this specific role, LMs are post-trained to be helpful assistants -- optimized to produce exhaustive and well-structured responses, free of ambiguity and grammar errors. User utterances, on the other hand, a

Keith R. Dienes, Lucien Heurtier, Fei Huang, Tim M. P. Tait

A variety of processes in the very early universe can give rise to a population of primordial black holes (PBHs) with an extended mass spectrum. For certain mass spectra of this sort, it has been shown that the evaporation of these PBHs into radiation can drive the universe toward an epoch of cosmological stasis which can persist for a significant number of

Yuwei Xiao, Shuai Ma, Antti Oulasvirta, Eunice Jun

In Bayesian analysis, prior elicitation, or the process of facilitating the expression of one's beliefs to inform statistical modeling, is an essential yet challenging step. Analysts often have beliefs about real-world variables and their relationships. However, existing tools require analysts to translate these beliefs and express them indirectly as probabi

Jonathan Leake, Shayan Oveis Gharan

Let $\mu$ be a probability distribution on a multi-state spin system on a set $V$ of sites; equivalently, a $d$-partite simplicial complex with distribution $\mu$ on maximal faces. For any pair of vertices $u,v\in V$, define the pairwise spectral influence $\mathcal{I}_{u,v}$ as follows. Let $\sigma$ be a choice of spins $s_w\in S_w$ for every $w\in V\setmin

Seng Pei Liew, Takuya Kato

Reusing pretrained base models for further pretraining, such as continual pretraining or model growth, is promising at reducing the cost of training language models from scratch. However, the effectiveness remains unclear, especially when applied to overtrained base models. In this work, we empirically study the scaling properties of model reuse and find tha

Christopher Martin, Apurva Patil, Wei Li, Takashi Tanaka

Roll-to-roll (R2R) manufacturing is a continuous processing technology essential for scalable production of thin-film materials and printed electronics, but precise control remains challenging due to subsystem interactions, nonlinearities, and process disturbances. This paper proposes a Model Predictive Path Integral (MPPI) control formulation for R2R system

Mohammad Nazeri, Sheldon Mei, Jeffrey Watchorn, Alex Zhang

Surface wettability is a critical design parameter for biomedical devices, coatings, and textiles. Contact angle measurements quantify liquid-surface interactions, which depend strongly on liquid formulation. Herein, we present the Robotic Autonomous Imaging Surface Evaluator (RAISE), a closed-loop, self-driving laboratory that is capable of linking liquid f

Jacek Karwowski, Raymond Douglas

We investigate mathematically the notion of incoherence: a structural issue with reinforcement learning policies derived by naive goal-conditioning of autoregressive models. We focus on the process of re-training models on their own actions, that is, fine-tuning offline-learned policies with online RL. We prove that it decreases incoherence and leads to an i

Xutao Mao, Ke Li, Cameron Baird, Ezra Xuanru Tao

The rapid advancement of fake voice generation technology has ignited a race with detection systems, creating an urgent need to secure the audio ecosystem. However, existing benchmarks suffer from a critical limitation: they typically aggregate diverse fake voice samples into a single dataset for evaluation. This practice masks method-specific artifacts and

José Gregorio Rodríguez-Nieto, Olga Patricia Salazar-Díaz, Andrés Sarrazola-Alzate, Raúl Velásquez

The study of derivations and their generalizations on non-associative algebras has proven to be fundamental in understanding the internal symmetries and algebraic dynamics of such structures. In this paper, we investigate derivations and diderivations of tensor product dialgebras arising from the combination of a perm algebra and a unital associative algebra

René Carmona, Ludovic Tangpi, Kaiwen Zhang

Conditional McKean-Vlasov control problems involve controlling McKean-Vlasov diffusions where the interaction occurs through the law of the state process conditionally on it staying in a domain. Introduced by Lions in his 2016 lectures at the Coll\`ege de France, these problems have notable applications, particularly in systemic risk. We establish well-posed

Hiroshi Inazawa

This paper presents a neural network model (associative memory model) for memory and recall of images. In this model, only a single neuron can memorize multi-images and when that neuron is activated, it is possible to recall all the memorized images at the same time. The system is composed of a single cluster of numerous neurons, referred to as the "Cue Ball

Nicholas M. Kroeger, Vincent Bindschaedler

While modern deep neural networks achieve impressive performance in vision tasks, they remain opaque in their decision processes, risking unwarranted trust, undetected biases and unexpected failures. We propose cluster paths, a post-hoc interpretability method that clusters activations at selected layers and represents each input as its sequence of cluster I

Ameya Anjarlekar, Rasoul Etesami, R Srikant

The continuous nature of belief states in POMDPs presents significant computational challenges in learning the optimal policy. In this paper, we consider an approach that solves a Partially Observable Reinforcement Learning (PORL) problem by approximating the corresponding POMDP model into a finite-state Markov Decision Process (MDP) (called Superstate MDP).

Zui Tao, Jonah B. Haber, Jeffrey B. Neaton

We introduce the Wannier function decomposition of excitons (WFDX) method to quantify exciton localization in solids within the ab initio Bethe-Salpeter equation framework. By decomposing each Bloch exciton wavefunction into products of single-particle electron and hole maximally localized Wannier functions, this real-space approach provides well-defined orb

Jiajie Li, Huayi Zhang, Peng Lin, Jinjun Xiong

We present a novel framework that improves the reliability of LLM judges by selectively augmenting LLM with auxiliary evaluation dimensions. Existing LLM judges often miss crucial evaluation dimensions because they fail to recognize the implicit standards underlying human assessments. To address this challenge, we propose the Auto-Prompt Ensemble (APE), an a

Morgan McErlean, Cella M. Sum, Sukrit Venkatagiri, Sarah E. Fox

As panoptical, AI-driven surveillance becomes a norm, everyone is impacted. In a reality where all people fall victim to these technologies, establishing links and solidarity is essential to fighting back. Two groups facing rising and targeted surveillance are workers and individuals impacted by the carceral system. Through preliminary data collection from a

Jack Vanlyssel, Enrique Sobrados, Ramsha Anwar, Gruia-Catalin Roman

Small satellites are integral to scientific, commercial, and defense missions, but reliance on commercial off-the-shelf (COTS) hardware broadens their attack surface. Although supply chain threats are well studied in other cyber-physical domains, their feasibility and stealth in space systems remain largely unexplored. Prior work has focused on flight softwa

Jiahe Jin, Abhijay Paladugu, Chenyan Xiong

Agentic search requires large language models (LLMs) to perform multi-step search to solve complex information-seeking tasks, imposing unique challenges on their reasoning capabilities. However, what constitutes effective reasoning for agentic search and how it can be learned remains unclear. In this work, we first investigate the reasoning behaviors that en

Akitoshi Kawamura, Yusuke Kobayashi

In the covering version of the pinwheel scheduling problem, a daily task must be assigned to agents under the constraint that agent $i$ can perform the task at most once in any $a_i$-day interval. In this paper, we determine the optimal constant $α^* = 1.264\ldots$ such that every instance with $\sum_{i} 1 / a_i \ge α^*$ is schedulable. This resolves an open

Junhao Chen, Yifan Zhou, Hanqi Jiang, Yi Pan

Quantum compute is scaling fast, from cloud QPUs to high throughput GPU simulators, making it timely to prototype quantum NLP beyond toy tasks. However, devices remain qubit limited and depth limited, training can be unstable, and classical attention is compute and memory heavy. This motivates compact, phase aware quantum token mixers that stabilize amplitud

Mao Lin

The minimum weight matching (MWM) and maximum likelihood decoding (MLD) are two widely used and distinct decoding strategies for quantum error correction. For a given syndrome, the MWM decoder finds the most probable physical error corresponding to the MWM of the decoding graph, whereas MLD aims to find the most probable logical error. Although MLD is the op

Yuhan Liu, Yihua Cheng, Jiayi Yao, Yuwei An

KV cache has traditionally been stored in GPU memory to accelerate the decoding phase of large language model (LLM) inference. However, it is increasingly necessary to move KV caches outside GPU devices, to enable cache reuse across different queries and inference engines. Our real-world usage statistics confirm this trend: over time, the total KV cache stor

Thusitha Dayaratne, Ngoc Duy Pham, Viet Vo, Shangqi Lai

The quality and experience of mobile communication have significantly improved with the introduction of 5G, and these improvements are expected to continue beyond the 5G era. However, vulnerabilities in control-plane protocols, such as Radio Resource Control (RRC) and Non-Access Stratum (NAS), pose significant security threats, such as Blind Denial of Servic

Xiangyi Chen, Théophane Vallaeys, Maha Elbayad, John Nguyen

Recent advances in Vision-Language Models (VLMs) have enabled unified understanding across text and images, yet equipping these models with robust image generation capabilities remains challenging. Existing approaches often rely on reconstruction-oriented autoencoders or complex bridging mechanisms, leading to misalignment between understanding and generatio

Mingyang Yao, Ke Chen, Shlomo Dubnov, Taylor Berg-Kirkpatrick

Automatic chord recognition (ACR) via deep learning models has gradually achieved promising recognition accuracy, yet two key challenges remain. First, prior work has primarily focused on audio-domain ACR, while symbolic music (e.g., score) ACR has received limited attention due to data scarcity. Second, existing methods still overlook strategies that are al

John Dunbar, Scott Aaronson

We establish that randomly initialized neural networks, with large width and a natural choice of hyperparameters, have nearly independent outputs exactly when their activation function is nonlinear with zero mean under the Gaussian measure: $\mathbb{E}_{z \sim \mathcal{N}(0,1)}[\sigma(z)]=0$. For example, this includes ReLU and GeLU with an additive shift, a

Siiri Leppälampi, Sonja M. Hyrynsalmi, Erno Vanhala

Generative AI offers vast opportunities for creating visualisations, such as graphics, videos, and images. However, recent studies around AI-generated visualisations have primarily focused on the creation process and image quality, overlooking representational biases. This study addresses this gap by testing representation biases in AI-generated pictures in

Marko Tesanovic, Daniel M. Markiewitz, Marcus L. Popp, Martin Z. Bazant

Magnetic particles underpin a broad range of technologies, from water purification and mineral processing to bioseparations and targeted drug delivery. The dynamics of magnetic particles in high-gradient magnetic fields-encompassing both their transport and eventual capture-arise from the coupled interplay of field-driven drift, fluid advection, and particle

Dominik Strutz, Tjeerd Kiers, Andrew Curtis

We present a systematic approach to optimise distributed acoustic sensing (DAS) fibre-optic cable layouts using global optimisation techniques. Our method represents cable geometries using splines, enabling efficient exploration of layouts while respecting physical deployment constraints. The use of evolutionary algorithms enables single and multi-objective

Girolamo Oddo, Roberto Nuca, Matteo Parsani

Developing a dynamic model for a high-performance vehicle is a complex problem that requires extensive structural information about the system under analysis. This information is often unavailable to those who did not design the vehicle and represents a typical issue in autonomous driving applications, which are frequently developed on top of existing vehicl

Jinny Cha, Wilfred Kwabena Darko, Jeremy C. Palmer, Michael P. Howard

Multiparticle collision dynamics (MPCD) is a mesoscale simulation technique that uses a simplified solvent to model hydrodynamic interactions. Rather than interact through pairwise forces, MPCD solvent particles undergo momentum-exchanging collisions within spatially localized cells according to prescribed rules. The conventional MPCD algorithm employs cubic

Ian Cavey, Eugene Gorsky, Alexei Oblomkov, Joshua P. Turner

We study sections of line bundles on the nested Hilbert scheme of points on the affine plane. We describe the spaces of sections in terms of certain ideals introduced by Haiman, and find explicit bases for them by analyzing the trailing terms in some monomial order. As a consequence, we compute the Newton-Okounkov bodies for nested Hilbert schemes.

T. Zhang, E. Charles, J. F. Crenshaw, S. J. Schmidt

We present the first systematic analysis of photometric redshifts (photo-z) estimated from the Rubin Observatory Data Preview 1 (DP1) data taken with the Legacy Survey of Space and Time (LSST) Commissioning Camera. Employing the Redshift Assessment Infrastructure Layers (RAIL) framework, we apply eight photo-z algorithms to the DP1 photometry, using deep ugr

M. AbuGhanem

The Mølmer-Sørensen gate, a cornerstone entangling operation in trapped-ion systems, represents a promising alternative to standard entangling gates in superconducting quantum architectures. However, its performance on superconducting hardware has remained unverified. In this work, we present a hardware-efficient implementation of the Mølmer-Sørensen gate an

Andrei Galiautdinov

We introduce a coordinate system that complements the Kruskal--Szekeres extension. Like the standard construction, it covers the maximally extended Schwarzschild manifold in its entirety, while offering an additional advantage of expressing the areal radius as an explicit function of the new coordinates. Its main limitation, however, is that radial null geod

Heming Wang, Janet Zhong, Shanhui Fan

The generalized Brillouin zones (GBZs) are integral in the analysis of non-Hermitian band structures. Conventional wisdom suggests that the GBZ should be connected, where each point can be indexed by the real part of the wavevector, similar to the Brillouin zone. Here we demonstrate rich topological features of the GBZs in generic non-Hermitian one-dimension

Marcel Hinsche, Zongbo Bao, Philippe van Dordrecht, Jens Eisert

We consider the problem of Clifford testing, which asks whether a black-box $n$-qubit unitary is a Clifford unitary or at least $\varepsilon$-far from every Clifford unitary. We give the first 4-query Clifford tester, which decides this problem with probability $\mathrm{poly}(\varepsilon)$. This contrasts with the minimum of 6 copies required for the closely

Karim El Khoury, Maxime Zanella, Christophe De Vleeschouwer, Benoit Macq

Remote Sensing Vision-Language Models (RSVLMs) have shown remarkable potential thanks to large-scale pretraining, achieving strong zero-shot performance on various tasks. However, their ability to generalize in low-data regimes, such as few-shot learning, remains insufficiently explored. In this work, we present the first structured benchmark for evaluating

Tony Zhang, Burak Kantarci, Umair Siddique

Ensuring the safety of self-driving cars remains a major challenge due to the complexity and unpredictability of real-world driving environments. Traditional testing methods face significant limitations, such as the oracle problem, which makes it difficult to determine whether a system's behavior is correct, and the inability to cover the full range of s

Alessandra Colla, Bassano Vacchini, Andrea Smirne

Understanding how to assign internal energy, heat, and work in quantum systems beyond weak coupling remains a central problem in quantum thermodynamics, particularly as the difference between competing definitions becomes increasingly relevant. We identify two common sets of definitions for first-law quantities that are used to describe the thermodynamics of

Elvar Atlason

Until recently, the simplest known flexible polyhedron embedded in Euclidean three-space was Steffen's polyhedron on nine vertices. However, in 2024, an embedded flexible polyhedron on eight vertices was discovered. It attains the known lower bound for the number of vertices, showing that the simplest embedded flexible polyhedron has eight vertices. We i

Vidar Gudmundsson, Vram Mughnetsyan, Hsi-Sheng Goan, Jeng-Da Chai

In a two-dimensional electron gas (2DEG) in a periodic lateral superlattice subjected to an external homogeneous magnetic field and in a cylindrical far-infrared photon cavity we search for effects of broken symmetries: Static ones, stemming from the unit cell of the system, and the external magnetic field together with the dynamic ones caused by the vector

Marcos Romero-Madroñal, María de los Remedios Sillero-Denamiel, María Dolores Jiménez-Gamero

The comparison of a parameter in $k$ populations is a classical problem in statistics. Testing for the equality of means or variances are typical examples. Most procedures designed to deal with this problem assume that $k$ is fixed and that samples with increasing sample sizes are available from each population. This paper introduces and studies a test for t

Ali Sarabadani, Kheirolah Rahsepar Fard

The advent of large language models (LLMs) has revolutionized the integration of knowledge graphs (KGs) in biomedical and cognitive sciences, overcoming limitations in traditional machine learning methods for capturing intricate semantic links among genes, diseases, and cognitive processes. We introduce MultiCNKG, an innovative framework that merges three ke

Babak Vakili

We study the dynamics of the Bianchi~I cosmological model in the presence of both polymer quantization effects and an exponential deformation of the Poisson algebra. Starting from the Hamiltonian formulation, we derive the polymer-deformed equations of motion and analyze their solutions for the contracting branch of the model. In contrast with the undeformed

Ané Kritzinger, George O. Dwapanyin, Ralf Mouthaan, Graham D. Bruce

Food and beverage contamination poses a persistent global threat. A prime example is the presence of methanol in counterfeit or illicit spirits, causing severe and often fatal poisoning worldwide. Rapid, non-destructive, and on-site screening methods capable of molecular analysis directly through commercial packaging are therefore urgently needed for quality

James B. Dent, Barry A. Friedman, Jayden L. Newstead, Subir Sabharwal

Low-threshold dark matter detectors, in particular cryogenic detectors based on dielectric materials, are among the best tools for probing sub-GeV dark matter masses. In the coming years detectors of this type will become sensitive to solar neutrino scattering. Previous work has shown that, for dark matter scattering at very low recoil energies, one must inc

Jordan M. Thomas, Andrew R. Cameron, Akil Pathiranage, Si Xie

Filtering is commonly used in quantum optics to reject noise photons, and also to enable interference between independent photons. However, filtering the joint spectrum of photon pairs can reduce the inherent coincidence probability or loss-independent heralding efficiency. Here, we investigate filtering for multiphoton applications based on entanglement and

Gabriel Spadon, Oladapo Oyebode, Camilo M. Botero, Tushar Sharma

This paper presents an overview of a human-centered initiative aimed at strengthening climate resilience along Nova Scotia's Eastern Shore. This region, a collection of rural villages with deep ties to the sea, faces existential threats from climate change that endanger its way of life. Our project moves beyond a purely technical response, weaving togeth

Askin Altinoklu, Leila Musavian

Dynamic metasurface antennas (DMAs) are promising alternatives to fully digital (FD) architectures, enabling hybrid beamforming via low-cost reconfigurable metasurfaces. In DMAs, holographic beamforming is achieved through tunable elements by Lorentzian-constrained holography (LCH), significantly reducing the need for radio-frequency (RF) chains and analog c

Lluís Torres-Hugas, Jordi Duch, Sergio Gómez

Models of network diffusion typically rely on the Laplacian matrix, capturing interactions via direct connections. Beyond direct interactions, information in many systems can also flow via indirect pathways, where influence typically diminishes over distance. In this work, we analyze diffusion dynamics incorporating such indirect connections using the $d$-pa

Niclas Götting, Steffen Wilksen, Alexander Steinhoff, Frederik Lohof

The fading memory property is a key requirement for reservoir computers -- a specific type of recurrent neural network with fixed internal weights. While mostly undesired in gate-based quantum computing, dissipation due to material imperfections or coupling to the environment acts as a natural mechanism intrinsically providing fading memory to reservoir comp

Som Sagar, Aditya Taparia, Harsh Mankodiya, Pranav Bidare

Black box neural networks are an indispensable part of modern robots. Nevertheless, deploying such high-stakes systems in real-world scenarios poses significant challenges when the stakeholders, such as engineers and legislative bodies, lack insights into the neural networks' decision-making process. Presently, explainable AI is primarily tailored to nat

Junyi Ji, Derek Gloudemans, Yanbing Wang, Gergely Zachár

Analyzing stop-and-go waves at the scale of miles and hours of data is an emerging challenge in traffic research. The past 5 years have seen an explosion in the availability of large-scale traffic data containing traffic waves and complex congestion patterns, making existing approaches unsuitable for repeatable and scalable analysis of traffic waves in these

Camille Horbez, Jingyin Huang

Let $G$ be a right-angled Artin group with $|\mathrm{Out}(G)|<+\infty$. We prove that if a countable group $H$ with bounded torsion is measure equivalent to $G$, with an $L^1$-integrable measure equivalence cocycle towards $G$, then $H$ is finitely generated and quasi-isometric to $G$. In particular, through work of Kleiner and the second-named author, $H$ a

Mohammad Ful Hossain Seikh

We propose a geometric hypothesis for neutrino mixing: twice the sum of the three mixing angles equals $180^\circ$, forming a Euclidean triangle. This condition leads to a predictive relation among the mixing angles and, through trigonometric constraints, enables reconstruction of the mass-squared splittings. The hypothesis offers a phenomenological resoluti