February 2026 arXiv papers — page 5

Rong Shan, Aofan Yu, Bo Chen, Kuo Cai

Recommender systems (RecSys) are increasingly emphasizing scaling, leveraging larger architectures and more interaction data to improve personalization. Yet, despite the optimizer's pivotal role in training, modern RecSys pipelines almost universally default to Adam/AdamW, with limited scrutiny of whether these choices are truly optimal for recommendation. I

Sina Mohammadi, Wayne Wang, Marcus Chen I Wada, Rouzbeh Haghighi

Artificial intelligence (AI) is driving unprecedented growth in data center (DC) scale and power demand. AI workloads impose highly dynamic, difficult-to-forecast power profiles on the utility grid, creating reliability and stability challenges that conventional DC architectures are not designed to address. This paper provides a critical review of energy sto

J. E. Dickinson, J. D. Bray, D. Kenney, T. Booler

We report the design and functionality of the Murchison Widefield Array Particle Detector Array (MWA PDA), an array of eight particle scintillation detectors deployed to Inyarrimanha Ilgari Bundara, the Murchison Radio-astronomy Observatory (MRO). The purpose of the instrument is to identify cosmic ray extensive air showers (EAS) occurring over the core of t

Changpu Li, Shuang Wu, Songlin Tang, Guangming Lu

Reconstructing transparent objects from a set of multi-view images is a challenging task due to the complicated nature and indeterminate behavior of light propagation. Typical methods are primarily tailored to specific scenarios, such as objects following a uniform topology, exhibiting ideal transparency and surface specular reflections, or with only surface

Yuanhao Su, Shaofeng Zhang, Xiaosong Jia, Qi Fan

The development of 3D Vision-Language Models (VLMs), crucial for applications in robotics, autonomous driving, and augmented reality, is severely constrained by the scarcity of paired 3D-text data. Existing methods rely solely on next-token prediction loss, using only language tokens for supervision. This results in inefficient utilization of limited 3D data

Siddharth Barman, Ioannis Caragiannis, Sudarshan Shyam

We study fair division of divisible goods under generalized assignment constraints. Here, each good has an agent-specific value and size, and every agent has a budget constraint that limits the total size of the goods she can receive. Since it may not always be feasible to assign all goods to the agents while respecting the budget constraints, we use the con

Tianyu Cao, Sangyoon Yi, Joshua Habiger

There is recent interest in estimating the false discovery rate (FDR) with published p-values. However, there is little formal research that addresses the manner and extent to which the presumed selection, or publication, bias model impacts the bias and variance of FDR estimators. This manuscript provides general and closed-form expressions for the bias and

Yi Zhang, Youya Xia, Yong Wang, Meng Song

While Multimodal Large Language Models (MLLMs) excel in semantic tasks, they frequently lack the "spatial sense" essential for sophisticated geometric reasoning. Current models typically suffer from exorbitant modality-alignment costs and deficiency in fine-grained structural modeling precision.We introduce SSR, a framework designed for Structured Scene Reas

Liwei Hu, Guangyao Li, Wenyong Wang, Xiaoming Zhang

Euclidean gradient descent algorithms barely capture the geometry of objective function-induced hypersurfaces and risk driving update trajectories off the hypersurfaces. Riemannian gradient descent algorithms address these issues but fail to represent complex hypersurfaces via a single classic manifold. We propose geodesic gradient descent (GGD), a generic a

Tianyou Li, Haifeng Hu, Dapeng Li

Reconfigurable intelligent surface (RIS) constitutes a disruptive technology for enhancing vehicular communication performance through reconfigurable propagation environments. In this paper, we propose an adaptive channel estimation framework and hybrid beamforming optimization strategy for RIS-aided vehicular multiple-input multiple-output (MIMO) systems op

Zenefa Rahaman, Sandip Sen

We propose an agent-based framework for personalized filtering of categorized harassing communication in online social networks. Unlike global moderation systems that apply uniform filtering rules, our approach models user-specific tolerance levels and preferences through adaptive filtering agents. These agents learn from user feedback and dynamically adjust

Maryam Bagherian

We develop a comprehensive axiomatic framework for quantum-inspired distance metrics on projective Hilbert spaces, providing a unified foundation that organizes and generalizes existing measures in quantum information theory. Starting from five fundamental axioms, projective invariance, unitary covariance, superposition sensitivity, entanglement awareness, a

Jiawei Li, Ming Wang, Kai Mu, Zhaodong Ding

Spatial linear instability analysis is employed to investigate the instability of a viscoelastic liquid jet in a co-flowing gas stream. The theoretical model incorporates a non-uniform axial base profile represented by a hyperbolic tangent, capturing the shear layer. The Oldroyd-B model discretized with Chebyshev polynomials is employed, and energy budget an

Tsao-Lun Chen, Chien-Liang Liu, Tzu-Ming Harry Hsu, Tai-Hsien Wu

In this study, a novel idea, Uncertainty Structure Estimation (USE), a lightweight, algorithm-agnostic procedure that emphasizes the often-overlooked role of unlabeled data quality is introduced for Semi-supervised learning (SSL). SSL has achieved impressive progress, but its reliability in deployment is limited by the quality of the unlabeled pool. In pract

PARC E16 collaboration, Satomi Nakasuga, Yuhei Morino, Kazuya Aoki

We present the first measurement of the production of the $\phi$ meson in 30 GeV proton-nucleus interactions on carbon and copper targets via the di-electron decay channel. The measurement was conducted at the high-momentum beamline of the J-PARC Hadron Experimental Facility, which was commissioned in 2020. The $e^+e^-$ pairs were detected using the E16 spec

Liam P. McGuinness

Randomness is intrinsic to quantum mechanics; the outcome of a measurement on a quantum state is a random variable. This feature has been applied to randomness certification, where one party must decide whether the data they receive is truly random. However, existing demonstrations are not black-box, to avoid falsely certifying deterministic data, assumption

Tijs Karman, Sebastian Will, Zoe Yan

Ultracold molecules are becoming an increasingly important technology for quantum simulation, computation, and sensing, but their state preparation in large, low-entropy arrays remains a key challenge. We propose to deterministically load single molecules into optical tweezer arrays or lattices from either thermal or degenerate gases, with a high probability

H. Sinan Bank, Casey E. Eaton

Charting the intellectual evolution of a scientific discipline is crucial for identifying its core contributions, challenges, and future directions. The IISE Annual Conference proceedings offer a rich longitudinal archive of the Industrial and Systems Engineering (ISE) community's development, but the sheer volume of scholarship produced over two decades mak

Xiyue Cheng, S. Muthukrishnan, Hanxiang Mi, Shuiquan Deng

The mixing of organic cations represents yet another direction to explore in the field of chiral organic-inorganic hybrid metal halides (OIHMH). Here, we perform structural optimizations, electronic structures, and non-linear optical (NLO) studies using the density functional theory of two recently synthesized chiral OIHMHs, [R-MePEA][C3A]PbBr4, and [R-MePEA

Hongjie Jiang, Di Luo

Accurately solving time-dependent partial differential equations (PDEs) with neural networks remains challenging due to long-time error accumulation and the difficulty of enforcing general boundary conditions. We introduce TENG-BC, a high-precision neural PDE solver based on the Time-Evolving Natural Gradient, designed to perform under general boundary const

Paul Nitschke, Shahriar Talebi

Meta-Reinforcement Learning (Meta-RL) commonly generalizes via smoothness in the task encoding. While this enables local generalization around each training task, it requires dense coverage of the task space and leaves richer task space structure untapped. In response, we develop a geometric perspective that endows the task space with a "hereditary geometry"

Elias Malomgré, Pieter Simoens

Multi-agent systems provide mature methodologies for role decomposition, coordination, and normative governance, capabilities that remain essential as increasingly powerful autonomous decision components are embedded within agent-based systems. While learned and generative models substantially expand system capability, their safety behavior is often entangle

Gabriel Mantegna, Emil Dimanchev, Filippo Pecci, Neha Patankar

Capacity expansion models are frequently used to inform multi-billion dollar grid infrastructure decisions, a context in which there is significant uncertainty surrounding the future need for and performance of such infrastructure. However, despite much academic literature on the topic, virtually no grid planning processes use capacity expansion models that

Zhouyang Ge, Gwynn J. Elfring

We study the energy expenditure and structural correlations in semi-dilute to concentrated suspensions of squirmers using active fast Stokesian dynamics simulations. Specifically, we simulate apolar active suspensions of squirmers, or 'shakers,' and show that shear enhances the total dissipation but reduces the relative viscosity for both puller- and pusher-

Shulin Lyu, Yuanfei Lyu

We study the Hankel determinant for the weight $x^{\alpha}{\rm exp}(-x-t_1/x-t_2/x^2), x\in[0,+\infty)$, with $\alpha>-1,~t_1\in\mathbb{R}\setminus\{0\}, ~t_2>0.$ Compared with the weight $x^{\alpha}{\rm e}^{-x-t_1/x}$ studied in prior work (where $\alpha,t_1>0$), the range of $\alpha$ in our work is extended and the parameter $t_2$ introduces a ``stronger"

Anirudh Jaidev Mahesh, Ben Griffin, Fuat Alican, Joseph Ternasky

Large language models (LLMs) are increasingly used for high-stakes decision-making, yet existing approaches struggle to reconcile scalability, interpretability, and reproducibility. Black-box models obscure their reasoning, while recent LLM-based rule systems rely on per-sample evaluation, causing costs to scale with dataset size and introducing stochastic,

Zheng Li, Xiaojin Liu, Zhi-Qiang You, Jumei Yao

Quantifying the natal kick distribution of pulsars is essential for understanding supernova physics and binary evolution, yet measurements are historically limited by the lack of radial velocity data. Most previous studies rely on transverse velocities under the assumption of spatial isotropy. In this work, we reconstruct the intrinsic three-dimensional (3D)

Vincent Okoth, Cyrielle Opitom, Colin Snodgrass, Brian Murphy

This study presents findings from narrowband imaging of comet C/2006 P1 (McNaught) using the 3.6-metre New Technology Telescope (NTT) at La Silla, Chile. Observations commenced on January 27, 2007, 15 days after perihelion, and continued until February 4, with additional sessions from February 25 to 28. Imaging was conducted using the ESO Multi-Mode Instrume

Jacob Brown, Sang-hoon Kim, Walter Hartung, Ting Xu

We applied heat treatments to 80.5 MHz quarter-wave resonators made from bulk niobium and prepared with buffered chemical polishing BCP. We evaluated their performance at 4.3 K. We found that a 48 hour, 120 C bake-out ("low-temperature bake out") reduces the surface resistance by a factor of 2 to 3, stemming from a reduction in the Bardeen-Cooper-Schrieffer

Daniela Hurtado-Lange, Izzy Grosof

In parallel-server systems with a single stream of arrivals (a.k.a. load balancing), Join-the-Shortest-Queue (JSQ) is a popular routing algorithm. There is extensive literature studying this system in various asymptotic regimes, but all assume constant parameters (arrival and service rates). We study the JSQ system with Markov-modulated parameters and hetero

Hanlong Fang, Alex Massarenti, Xian Wu

Let $\mathbf{LG}(V\oplus V^*)$ and $\mathbf{OG}^+(V\oplus V^*)$ denote the Lagrangian and orthogonal Grassmannians endowed with the natural $\mathbb{G}_m$-actions, respectively. Thaddeus proved that over $\mathbb{C}$, the Hilbert quotients $\mathbf{LG}(V\oplus V^*)\!/\!/\mathbb{G}_m$ and $\mathbf{OG}^+(V\oplus V^*)\!/\!/\mathbb{G}_m$ are isomorphic to the wo

Y. Gómez Maqueo Chew, G. Dransfield, K. Barkaoui, C. Cadieux

We present the detection and validation of a small, temperate transiting exoplanet orbiting TOI-1080 every 3.9652482$^{+0.0000014}_{-0.0000015}$ days. The host is a quiet M4V star at 25.6 pc. The planet signal was first detected by TESS and validated using TESS and ground-based observations. By fitting the available light curves, the planet radius is measure

Ci Zhang, Zhaojun Ding, Chence Yang, Jun Liu

Pruning-based unlearning has recently emerged as a fast, training-free, and data-independent approach to remove undesired concepts from diffusion models. It promises high efficiency and robustness, offering an attractive alternative to traditional fine-tuning or editing-based unlearning. However, in this paper we uncover a hidden danger behind this promising

Gernot Eichmann

The central objects in a quantum field theory are its n-point correlation functions and matrix elements. Their structure is determined by Lorentz invariance and leads to tensor decompositions whose Lorentz-invariant coefficient functions encode the physics of the process. For growing n, the complexity of these objects may increase considerably and make it ch

Shijie Yuan, Amy Cochran, Paul Rathouz

Accurate power and sample size (PSS) calculations are essential for designing studies that use quasi-likelihood (QL) models, which extend generalized linear models (GLMs) to settings where the full distribution of the outcome is not specified. Traditional PSS approaches often rely on restrictive distributional assumptions, limiting their applicability when r

Zhengqiang Li

We introduce a novel deterministic fractal set PF in the unit interval whose construction is driven by the sequence of prime numbers modulo 16. At each step of the recursive construction, two subintervals are retained based on the residues of consecutive primes, yielding a Cantor-like set with a uniform contraction ratio of 1/16 and a branching number of 2.

Guo Chen, Ru Zheng, Jin-Hua Sun, Fengjie Ma

Material with metallic $σ$-bonding bands is expected to be a high-temperature superconductor, due to the sensitivity of $σ$ electrons to lattice vibration. Based on the first-principles calculations, electronic structures of hydrogenated BC$_3$ monolayers (H$_n$-B$_2$C$_6$ with $n$=1-8) are systematically investigated. At high coverage of hydrogen, the monol

Patrick Stricker, Florian Röhrbein, Andreas Knoblauch

Efficient representation learning is essential for optimal information storage and classification. However, it is frequently overlooked in artificial neural networks (ANNs). This neglect results in networks that can become overparameterized by factors of up to 13, increasing redundancy and energy consumption. As the demand for large language models (LLMs) an

Taira Kawamura, Yoji Ohashi

We theoretically study nonequilibrium superconductivity in voltage-biased normal metal-superconductor-normal metal (NSN) junctions, focusing on effects of lead-coupling asymmetry and impurity scattering. Using the Keldysh Green's function technique, we extend the thermal-equilibrium mean-field BCS theory to the case where the system is out of equilibrium

Peter Sun, Corinne E. Isaac, Michael C. Boucher, Eric W. Moore

We present mrfmsim, an open-source Python package that facilitates the design, simulation, and analysis of magnetic resonance force microscopy (MRFM) experiments. MRFM is a scanning-probe technique that detects magnetic resonance from nanoscale ensembles of nuclear or electron spins with a force sensor. Because MRFM experiments are complex and operate at sen

José F. Fontanari, Mauro Santos

The ubiquitous regression to the mean (RTM) effect complicates statistical inference regarding the relationship between baseline levels of a biological variable and its subsequent change. We demonstrate that common RTM correction methods are problematic: the Berry et al. method, popularized by Kelly & Price in The American Naturalist, is unreliable for hypot

Panagiotis Alimisis, Christos Diou

Causal representation learning has attracted significant research interest during the past few years, as a means for improving model generalization and robustness. Causal representations of interventional image pairs (also called ``actionable counterfactuals'' in the literature), have the property that only variables corresponding to scene elements a

Sergey S. Ketkov, Oleg A. Prokopyev

We consider a class of stochastic interdiction games between an upper-level decision-maker (the leader) and a lower-level decision-maker (the follower), where uncertainty lies in the follower's objective function coefficients. Specifically, the follower's profits (or costs) in our model comprise a random vector, whose probability distribution is esti

Federico Ciardo, Pierre Romanet

Simulating long-term, fully dynamic sequences of earthquakes and aseismic slip (SEAS) on geometrically complex fault networks remains computationally demanding due to the cost of resolving elastodynamic interactions. Although high-performance computing improves feasibility, simulations remain expensive, particularly for multicycle evolution, motivating the w

Yujia Wu, Shuoqi Chen, Shiru Wang, Yucheng Tang

Accurate Speed-of-Sound (SoS) reconstruction from acoustic waveforms is a cornerstone of ultrasound computed tomography (USCT), enabling quantitative velocity mapping that reveals subtle anatomical details and pathological variations often invisible in conventional imaging. However, practical utility is hindered by the limitations of existing algorithms; tra

Om Tailor

Colluding language-model agents can hide coordination in messages that remain policy-compliant at the surface level. We present CLBC, a protocol where generation and admission are separated: a message is admitted to transcript state only if a small verifier accepts a proof-bound envelope under a pinned predicate $\Pi$. The predicate binds policy hash, public

Mohammed Adib Oumer, Vishnu Murali, Majid Zamani

The recently introduced notions of ranking functions and closure certificates utilize well-foundedness arguments to facilitate the verification of dynamical systems against $\omega$-regular properties. A ranking function and a closure certificate are real-valued functions defined over states and state pairs of a dynamical system whose zero superlevel sets ar

Quinn Jacobson, Joe Luo, Jingfei Xu, Shanmuga Venkatachalam

NeuroHex is a brain-inspired hexagonal coordinate system designed to support highly efficient world models and reference frames for online adaptive AI systems. Inspired by the hexadirectional firing structure of grid cells in the human brain, NeuroHex adopts a cubic isometric hexagonal coordinate formulation that provides full 60{\deg} rotational symmetry an

Miguel G. Echevarria, Patricia Andrea Gutierrez García, Ignazio Scimemi

The transverse-momentum-dependent (TMD) factorization theorem for dijet production in deep-inelastic scattering is used here to make predictions of the gluon Sivers function. We revise the previously studied unpolarized case and develop the formalism for a transversely polarized target. We study the impact of TMD evolution in two different schemes and we use

Enrico Sartor, Florian Dörfler, Nicolas Lanzetti

We study sparse optimal control of a non-local continuity equation, where the goal is to steer a distribution via finitely many controllable agents or actuators. This model arises naturally in mean-field multi-agent systems and takes the form of a coupled PDE-ODE system where the PDE describes the evolution of the distribution and the controlled ODE captures

Austin Yunker, Peter Kenesei, Hemant Sharma, Jun-Sang Park

X-ray computed tomography (CT) is a widely used imaging technique that provides detailed examinations into the internal structure of an object with synchrotron CT (SR-CT) enabling improved data quality by using higher energy, monochromatic X-rays. While SR-CT allows for improved resolution, time-resolved experimentation, and reduced imaging artifacts, it als

Masafumi Shimada

Let $G$ be a connected semisimple Lie group, and $G_0$ be its connected split real form. In this paper, we deduce explicit expressions for the heat kernels $\rho^{G_0}_t$ associated with the Laplace--Beltrami operators $\Delta_{G_0}$ and $\Delta_{G}$ respectively, using the algebra of differential operators on an appropriate homogeneous space. These expressi

Pedro Cisneros-Velarde

Consider an organization whose users send requests in natural language to an AI system that fulfills them by carrying out specific tasks. In this paper, we consider the problem of ensuring such user requests comply with a list of diverse policies determined by the organization with the purpose of guaranteeing the safe and reliable use of the AI system. We pr

Hutama Arif Bramantyo, Mukarram Ali Faridi, Rui Chen, Clarissa Harris

In this study, we present a meat freshness classification framework from Red-Green-Blue (RGB) images that supports both packaged and unpackaged meat datasets. The system classifies four in-distribution (ID) meat classes and uses an out-of-distribution (OOD)-aware abstention mechanism that flags low-confidence samples as No Result. The pipeline combines U-Net

Alice Garbagnati

Given a symplectic involution $\iota$ on a K3 surface $X$, the desingularization $Y$ of $X/\iota$ is still a K3 surface, which in general has a different N\'eron--Severi group. Nevertheless, if the involution is induced by the translation by a 2-torsion section on an elliptic fibration (i.e. it is a van Geemen--Sarti involution) and the Picard number is mini

Fruzsina Agocs, Tristan Goodwill, Jeremy G. Hoskins, Peter Nekrasov

We develop a method for computing the scattering of flexural waves off of a periodic wall or a periodic line of scatterers. These waves model the fluctuations of thin plates with periodic clamped, supported, or free edges. We use the Floquet-Bloch transform to convert the problem into a collection of uncoupled quasi-periodic problems. We then solve each quas

Adam Visokay, Laura Boudreau, Rachel M. Heath, Tyler H. McCormick

Surveys are critical inputs for research and policy, yet, enumerating a sampling frame is logistically infeasible or financially nonviable in many circumstances, such as during pandemics, natural disasters, or armed conflict. Respondent Driven Sampling (RDS) does not require a sampling frame, yet non-random peer recruitment often introduces substantial bias,

Athul Radhakrishnan, Siddhant Mohan, Mahima Sachdeva

Large language models such as GPT and Llama are trained with a next-token prediction loss. In this work, we suggest that training language models to predict multiple future tokens at once results in higher sample efficiency. More specifically, at each position in the training corpus, we ask the model to predict the following n tokens using n independent outp

Sourasekhar Banerjee, David Bergqvist, Salman Toor, Christian Rohner

Distribution shifts in attack patterns within RPL-based IoT networks pose a critical threat to the reliability and security of large-scale connected systems. Intrusion Detection Systems (IDS) trained on static datasets often fail to generalize to unseen threats and suffer from catastrophic forgetting when updated with new attacks. Ensuring continual adaptabi

Galen Pogoncheff, Alvin Wang, Jacob Granley, Michael Beyeler

Cortical visual prostheses aim to restore sight by electrically stimulating neurons in early visual cortex (V1). With the emergence of high-density and flexible neural interfaces, electrode placement within three-dimensional cortex has become a critical surgical planning problem. Existing strategies emphasize visual field coverage and anatomical heuristics b

Bernhard K Meister

Financial markets convert the incremental arrival of information into asset price changes. In a sandpile model grains of sand represent bits of data, and the size of an avalanche, governed by a scaling law, is linked to price volatility. While this model of self-organized criticality reproduces stylized facts, it also identifies a structural tension between

Godwin Abuh Faruna

Safety alignment in large language models relies predominantly on English-language training data. When harmful intent is expressed in low-resource languages, refusal mechanisms that hold in English frequently fail to activate. We introduce LSR (Linguistic Safety Robustness), the first systematic benchmark for measuring cross-lingual refusal degradation in We

Aras Bacho, Jonghyeon Lee, Houman Owhadi

We propose KROM, a kernel-based reduced-order framework for fast solution of nonlinear partial differential equations. KROM formulates PDE solution as a minimum-norm (Gaussian-process) recovery problem in an RKHS, and accelerates the resulting kernel solves by sparsifying the precision matrix via sparse Cholesky factorization. A central ingredient is an empi

Kyle Elliott Mathewson

Do neural machine translation models learn language-universal conceptual representations, or do they merely cluster languages by surface similarity? We investigate this question by probing the representation geometry of Meta's NLLB-200, a 200-language encoder-decoder Transformer, through six experiments that bridge NLP interpretability with cognitive science

Alokesh Manna, William Snyder, Whitney Tabor

Linguistic insights may help make Large Language Model (LLM) training more efficient. We trained Meta's OPT model on the 100M word BabyLM dataset, and evaluated it on the BLiMP benchmark, which consists of 67 classes, each defined by sentence pairs that differ in a targeted syntactic or semantic rule violation. We tested the model's preference for grammatica

Jiří Hejtmánek, Kyo-Hoon Ahn, Zdeněk Jirák, Petr Levinský

It is unambiguously demonstrated that the low temperature magnon specific heat in a ferromagnet varies as T$^{3/2}$ and the magnon thermal conductivity, due to T$^{1/2}$ - dependent effective velocity of magnons, as T$^{2}$. The confirmation of these model comportments is based on the experimental study of chalcospinel CdCr$_{2}$Se$_{4}$, which represents re

William J. Cunningham

Multi-tenant AI inference platforms must balance resource utilization against service-level guarantees under variable demand. Conventional approaches fail to achieve this balance: dedicated endpoints strand capacity on idle models, while rate limits ignore the heterogeneous cost of inference requests. We introduce \emph{token pools}, a control-plane abstract

Yishan Wang, Tsai-Ning Wang, Mathias Funk, Aaqib Saeed

Listening to heart and lung sounds - auscultation - is one of the first and most fundamental steps in a clinical examination. Despite being fast and non-invasive, it demands years of experience to interpret subtle audio cues. Recent deep learning methods have made progress in automating cardiopulmonary sound analysis, yet most are restricted to simple classi

Stanislaw Mrowczynski

For a long time studies of femtoscopic correlations have provided information about space-time characteristics of particle sources in high-energy collisions. Recently, the correlation functions have been also used to determine interaction parameters of correlated particles which is especially important for short-lived particles, for which scattering experime

Gil Alon, Doron Puder

Aldous' spectral gap conjecture, proven by Caputo, Liggett and Richthammer, states the following: for any set of transpositions in the symmetric group $\mathrm{Sym}(n)$, the spectral gap of the corresponding random walk on the group -- an $n!$-state process -- coincides with that of the corresponding random walk of a single element -- an $n$-state process. T

Lion Weber, Theodor Wienert, Martin Splettstößer, Alexander Koenig

Universal jamming grippers excel at grasping unknown objects due to their compliant bodies. Traditional tactile sensors can compromise this compliance, reducing grasping performance. We present acoustic sensing as a form of morphological sensing, where the gripper's soft body itself becomes the sensor. A speaker and microphone are placed inside the gripper c

Antonio de Sousa Leitão Filho, Allan Kardec Duailibe Barros Filho, Fabrício Saul Lima, Selby Mykael Lima dos Santos

The prevailing paradigm in artificial intelligence research equates progress with scale: larger models trained on broader datasets are presumed to yield superior capabilities. This assumption, while empirically productive for general-purpose applications, obscures a fundamental epistemological tension between breadth and depth of knowledge. We introduce the

Nick Polson, Vadim Sokolov

Bayesian inference in generalized linear models requires a prior on the coefficient vector $\beta$. Practitioners naturally reason about response probabilities at specific covariate values, not about abstract log-odds parameters. We develop synthetic priors: informative Bayesian priors for GLMs grounded in Good's device of imaginary observations -- the princ

S. Bromley, E. Garbe, N. McElroy, C. Ballance

Kilonovae, the electromagnetic transients produced from two merging neutron stars, exhibit evolving spectral signatures in ultraviolet, visible, and infrared radiation. Starting around one week post-merger, equilibrium assumptions describing the local ionization balance and atomic level populations in the ejecta come into question, and non-equilibrium modeli

Dayeon Kang, Jade Sheffey, Mingshi Wu, Pubali Datta

With the increase in Internet censorship globally, various circumvention tools have been designed and developed. However, the monetary cost of these tools deeply impacts both user choice and the sustainability of provider operations. Recent developments in censorship circumvention research attempted to achieve cost efficiency by utilizing Infrastructure-as-a

Md. Niamul Islam Sium, Mohammad Hridoy Patwary

In observational studies, causal inference becomes difficult when confounders are missing-not-at-random (MNAR), particularly where the missingness depends on the confounder's own unreported value (self-masking). Existing methods for handling MNAR confounders often rely on strong, unverifiable assumptions, leading to biased estimates. We propose a simple appr

Yiwei Fu, Tianhao Wang, Varun Chandrasekaran

Data valuation methods quantify how individual training examples contribute to a model's behavior, and are increasingly used for dataset curation, auditing, and emerging data markets. As these techniques become operational, they raise serious privacy concerns: valuation scores can reveal whether a person's data was included in training, whether it was unusua

Pawan Giri

The Askaryan Radio Array (ARA) is a neutrino experiment at the South Pole, designed to detect radio-frequency emissions produced by interactions of ultra-high energy (UHE) neutrinos with the Antarctic ice. The array consists of five autonomous stations, each equipped with deep in-ice antennas sensitive to both vertically and horizontally polarized radio sign

Yuandong Zhang, Othmane Echchabi, Tianshu Feng, Wenyi Zhang

Transportation mode detection is an important topic within GeoAI and transportation research. In this study, we introduce SpeedTransformer, a novel Transformer-based model that relies solely on speed inputs to infer transportation modes from dense smartphone GPS trajectories. In benchmark experiments, SpeedTransformer outperformed traditional deep learning m

Jamel Benameur, Chokri Elhechmi, Gmar Benhenda

In this paper, we establish a new result for the Laplace problem with exponential Robin boundary conditions posed on the unit disk in $\R^2$. More precisely, we prove the existence and uniqueness of a solution under suitable smallness assumptions on the boundary data. Our approach relies on an iterative method combined with periodic Sobolev embedding results

Erina Yamaguchi, Ryan M. Bena, Gilbert Bahati, Aaron D. Ames

This paper presents a general end-to-end framework for constructing robust and reliable layered safety filters that can be leveraged to perform dynamic collision avoidance over a broad range of applications using only local perception data. Given a robot-centric point cloud, we begin by constructing an occupancy map which is used to synthesize a Poisson safe

Xuanshuo Fu, Lei Kang, Javier Vazquez-Corral

Low-light images often suffer from low contrast, noise, and color distortion, degrading visual quality and impairing downstream vision tasks. We propose a novel conditional diffusion framework for low-light image enhancement that incorporates a Structured Control Embedding Module (SCEM). SCEM decomposes a low-light image into four informative components incl

Dylan L. Jow, Calvin Leung

The small-scale properties of circumgalactic gas in ordinary galaxies drive its bulk properties: the mass loading of cold neutral gas in galactic outflows affects their bulk momentum; gas cooling processes on small scales affect the spatial distribution of gas in the cool (T~$10^4$K) circumgalactic medium (CGM). However, hydrodynamical simulations have yet t

Shirsh Lata Soni, Anwesha Maharana, Sanchita Pal, Stefaan Poedts

Interacting coronal mass ejections (CMEs) result in complex heliospheric structures that can dramatically enhance their geoeffectiveness compared to isolated events. A striking example of such complex structures is that of the Mothers Day event, which occurred during 10-14 May 2024, leading to the strongest geomagnetic storm in decades. It was driven by at l

Enrique Junchaya, Alberto Alexandre Assis Miranda, Cláudio L. Lucchesi

The number of perfect matchings of a $k$-pfaffian graph can be counted by computing a linear combination of the pfaffians of $k$ matrices. The pfaffian number of a graph $G$ is the smallest integer $k$ such that $G$ is $k$-pfaffian. We present the first known lower bounds for the pfaffian number of graphs. As an intermediate step, we prove an upper bound for

Rahul Baxi

AI agents are increasingly granted economic agency (executing trades, managing budgets, negotiating contracts, and spawning sub-agents), yet current frameworks gate this agency on capability benchmarks that are empirically uncorrelated with operational robustness. We introduce the Comprehension-Gated Agent Economy (CGAE), a formal architecture in which an ag

Weiping Shen, Linglingzhi Zhu, Yaohua Hu, Chong Li

This paper investigates numerical solution methods for the Schatten-$p$ quasi-norm regularized problem with $p \in [0,1]$, which has been widely studied for finding low-rank solutions of linear inverse problems and gained successful applications in various mathematics and applied science fields. We propose a dynamic proximal gradient algorithm that, through

V. K. Kalantarov, A. A. Namazov, E. S. Titi

We prove global stabilization of the marine riser models using a feedback controller that depend on finitely many finite-volume elements and finitely many nodal observables. Our approach is based on a feedback control design for dissipative nonlinear partial differential equations, inspired by the methodology introduced in [Evol. Equ. Control Theory, Vol. 3

Hima Mynampaty, Nathania Josephine, Katherine E. Isaacs, Andrew M. McNutt

READMEs shape first impressions of software projects, yet what constitutes a good README varies across audiences and contexts. Research software needs reproducibility details, while open-source libraries might prioritize quick-start guides. Through a design probe, LintMe, we explore how linting can be used to improve READMEs given these diverse contexts, aid

A. Rososhek, C. E. Seyler, E. S. Lavine, D. A. Hammer

In this paper, we compare experimental and numerical simulation results to benchmark the PERSEUS code against gas-puff $Z$-pinch implosions on COBRA. We then use the code to investigate the structure of the plasma sheath. To this end, we study the morphology of the implosion, focusing on non-magnetohydrodynamical (MHD) effects such as electron drifts governe

Valerie Bürger, Marlie Besouw, Jana Fehr, Riana Minocher

Meta-research and Trustworthy AI (TAI) share common goals, namely improving evidence, robustness, and transparency, yet there is very little interplay between the two fields. To investigate the potential benefits of closer collaboration between the domains of TAI in healthcare and meta-research, we convened an interdisciplinary workshop funded by the Volkswa

Paul Justice, Emily Marshman, Chandralekha Singh

Physics instructors need support to successfully adopt and adapt evidence-based active engagement (EBAE) approaches because improving teaching and learning is a process and support is needed to ensure that they do not get disheartened if a particular EBAE approach does not produce the desired outcome. The instructors not only need support to refine their imp

Jae Hyeok Lee, Taekang Hwang, Changhyun Kwon

The asymptotic behavior of the optimal TSP tour length is well known from the classical Beardwood--Halton--Hammersley theorem. We extend this result to the Traveling Salesman Problem with Drone (TSPD), a cooperative routing problem in which a truck and a drone jointly serve customers. Using a nonmonotone subadditive Euclidean functional framework, we establi

Casey Rodriguez, Francesco dell'Isola

The relationship between balance laws and the Principle of Virtual Work as well as the structure of contact interactions in continua remain foundational issues in Mechanics. In this work, we revisit these issues within the distributional framework emphasized by Paul Germain. We show that while the Principle of Virtual Work implies balance of forces and momen

Ariel Lubonja, Jungsang Yoon, Haoyin Xu, Yue Wan

Classification using sparse oblique random forests provides guarantees on uncertainty and confidence while controlling for specific error types. However, they use more data and more compute than other tree ensembles because they create deep trees and need to sort or histogram linear combinations of data at runtime. We provide a method for dynamically switchi

Amit Shivam, Manuel C. R. M. Fernandes, Sergio Vinha, Fernando A. C. C. Fontes

This paper introduces inspection through GLASS, a Geometric Look-Angle Shaping Strategy for enclosed regions using unmanned aerial vehicles. In doing so, the vehicles guidance command is constructed through a bounded, geometry-consistent shaping of the look angle relative to a desired standoff path. By embedding a smooth, hyperbolic-tangent-type shaping func

Andrei Khrennikov, Felix Benninger, Oded Shor

Over the past two decades, quantum-like modeling (QLM) has emerged as a powerful framework for describing non-classical features of cognition and decision-making. Rather than assuming physical quantum processes in the brain, QLM employs the Hilbert space formalism to model contextuality, incompatibility of mental observables, and entanglement-like correlatio

Arya Fayyazi, Haleh Akrami

We present Proof-of-Perception (PoP), a tool-using framework that casts multimodal reasoning as an executable graph with explicit reliability guarantees. Each perception or logic node outputs a conformal set, yielding calibrated, stepwise uncertainty; a lightweight controller uses these certificates to allocate compute under a budget, expanding with extra to

Tinglin Huang, Bo Chen, Xiao Zhang, Kai Shen

Interpreting and following human instructions is a critical capability of large language models (LLMs) in automatic programming. However, synthesizing large-scale instruction-paired coding data remains largely unexplored and is particularly challenging when ensuring logical compatibility among multiple constraints. In this study, we propose IFCodeEvolve, an

Manisha Garg, Jeremy T. Tyson

We study how analytic functions, and more generally quasiregular mappings, distort Nagata dimension. Quasiconformal mappings of domains preserve the Nagata dimension of compact subsets, in view of a result of Lang and Schlichenmaier. We establish the same conclusion for analytic functions defined on general planar domains. On the other hand, polynomials (and