November 2025 arXiv papers — page 126

Tasnim Ahmed, Siana Rizwan, Naveed Ejaz, Salimur Choudhury

Building on advancements in Large Language Models (LLMs), we can tackle complex analytical and mathematical reasoning tasks requiring nuanced contextual understanding. A prime example of such complex tasks is modelling resource allocation optimization in networks, which extends beyond translating natural language inputs into mathematical equations or Linear

Fred Heiding, Simon Lermen

We present an end-to-end demonstration of how attackers can exploit AI safety failures to harm vulnerable populations: from jailbreaking LLMs to generate phishing content, to deploying those messages against real targets, to successfully compromising elderly victims. We systematically evaluated safety guardrails across six frontier LLMs spanning four attack

Ravi Singh Adhikari, Aman Gupta, Anju Rani, Xiaoyu Ai

High-precision clock synchronization is essential for a wide range of network-distributed applications. In the quantum space, these applications include communication, sensing, and positioning. However, current synchronization techniques are vulnerable to attacks, such as intercept-resend attacks, spoofing, and delay attacks. Here, we propose and experimenta

Rebecca Dorn, Christina Chance, Casandra Rusti, Charles Bickham

Automated emotion detection is widely used in applications ranging from well-being monitoring to high-stakes domains like mental health and hiring. However, models often rely on annotations that reflect dominant cultural norms, limiting model ability to recognize emotional expression in dialects often excluded from training data distributions, such as Africa

Natan Doubez, Pascal Lenzner, Marcus Wunderlich

Communication networks are essential for our economy and our everyday lives. This makes them lucrative targets for attacks. Today, we see an ongoing battle between criminals that try to disrupt our key communication networks and security professionals that try to mitigate these attacks. However, today's networks, like the Internet or peer-to-peer networks am

Shinnung Jeong, Chihyo Ahn, Huanzhi Pu, Jisheng Zhao

Recent efforts in open-source GPU research are opening new avenues in a domain that has long been tightly coupled with a few commercial vendors. Emerging open GPU architectures define SIMT functionality through their own ISAs, but executing existing GPU programs and optimizing performance on these ISAs relies on a compiler framework that is technically compl

Anna Franziska Frigge, Alexander Medvedev

Deep Brain Stimulation (DBS) is a well-established neurosurgical treatment aiming at symptom alleviation in a range of neurological and psychiatric diseases. Computational models of DBS are widely used to investigate the effects of stimulation on neural tissue, to explore stimulation targets and sweetspots, and ultimately, to aid clinicians in the DBS progra

Alexander W. Goodall, Edwin Hamel-De le Court, Francesco Belardinelli

Many reinforcement learning algorithms, particularly those that rely on return estimates for policy improvement, can suffer from poor sample efficiency and training instability due to high-variance return estimates. In this paper we leverage new results from off-policy evaluation; it has recently been shown that well-designed behaviour policies can be used t

Jugal Gajjar, Kaustik Ranaware, Kamalasankari Subramaniakuppusamy, Vaibhav Gandhi

Knowledge graphs have emerged as fundamental structures for representing complex relational data across scientific and enterprise domains. However, existing embedding methods face critical limitations when modeling diverse relationship types at scale: Euclidean models struggle with hierarchies, vector space models cannot capture asymmetry, and hyperbolic mod

Michael Sun, Weize Yuan, Gang Liu, Wojciech Matusik

Protein structure is central to biological function, and enabling multimodal protein models requires joint reasoning over sequence, structure, and function. A key barrier is the lack of principled protein structure tokenizers (PSTs): existing approaches fix token size or rely on continuous vector codebooks, limiting interpretability, multi-scale control, and

Abir Harrasse, Florent Draye, Punya Syon Pandey, Zhijing Jin

Multilingual Large Language Models (LLMs) can process many languages, yet how they internally represent this diversity remains unclear. Do they form shared multilingual representations with language-specific decoding, and if so, why does performance favor the dominant training language? To address this, we train models on different multilingual mixtures and

Ivan H. C. Shum, Angela Capel

We present hybrid Gibbs sampling algorithms for the stabilizer code Hamiltonians of the rotated surface code and the toric code with only local quantum algorithms, using $\sim L/2$ quantum circuit depth to prepare the Gibbs state of the rotated surface code Hamiltonian, and $L$ quantum circuit depth to prepare the Gibbs state of the toric code Hamiltonian, b

Artem Alexandrov, Georgi S. Medvedev

We study phase transitions in the Ising model on random graphs using graph limits. We show that the critical temperatures are determined by the eigenvalues of the kernel operator associated with the graph limit. Bifurcation diagrams for Erdos-Renyi, small-world, and power-law graphs illustrate the theory. In the small-world case, we identify metastable behav

Elyes Hajji, Aymen Bouguerra, Fabio Arnez

Large Language Models (LLMs) are increasingly deployed in safety-critical domains, yet remain susceptible to hallucinations. While prior works have proposed confidence representation methods for hallucination detection, most of these approaches rely on computationally expensive sampling strategies and often disregard the distinction between hallucination typ

Xinle Cheng, Emil Viñas Boström, Frank Y. Gao, Edoardo Baldini

Sub-wavelength cavities have emerged as a promising platform to realize strong light-matter coupling in condensed matter systems. Previous studies are limited to dielectric sub-wavelength cavities, which preserve time-reversal symmetry. Here, we lift this constraint by proposing a cavity system based on magneto-electric materials, which host surface electrom

Domenico Maisto, Davide Nuzzi, Giovanni Pezzulo

Collective behavior pervades biological systems, from flocks of birds to neural assemblies and human societies. Yet, how such collectives acquire functional properties -- such as joint agency or knowledge -- that transcend those of their individual components remains an open question. Here, we combine active inference and information-theoretic analyses to ex

Ansel Kaplan Erol, Seungjun Lee, Divya Mahajan

Low-latency delivery of satellite imagery is essential for time-critical applications such as disaster response, intelligence, and infrastructure monitoring. However, traditional pipelines rely on downlinking all captured images before analysis, introducing delays of hours to days due to restricted communication bandwidth. To address these bottlenecks, emerg

Sanchit Kabra, Shobhnik Kriplani, Parshin Shojaee, Chandan K. Reddy

Equation discovery from data is a central challenge in machine learning for science, which requires the recovery of concise symbolic expressions that govern complex physical and geometric phenomena. Recent large language model (LLM) approaches have shown promise in symbolic regression, yet existing benchmarks predominantly evaluate low-dimensional scalar fun

Zixin Huang, Johannes Jakob Meyer, Theshani Nuradha, Mark M. Wilde

The goal of quantum channel discrimination and estimation is to determine the identity of an unknown channel from a discrete or continuous set, respectively. The query complexity of these tasks is equal to the minimum number of times one must call an unknown channel to identify it within a desired threshold on the error probability. In this paper, we establi

Jiang Yuhan, Matthew Otten

Quantum kernel methods are a promising branch of quantum machine learning, yet their effectiveness on diverse, high-dimensional, real-world data remains unverified. Current research has largely been limited to low-dimensional or synthetic datasets, preventing a thorough evaluation of their potential. To address this gap, we developed an algorithmic framework

Wei Sun, Wanyin Zheng, Xiangyu Wei, David A. Czaplewski

Flow sensing is fundamental to both biological survival and technological innovation. Inspired by biological mechanoreceptors, artificial flow sensors detect subtle fluid motion using slender, viscous-driven structures. Among these, acoustic flow sensors that mimic nature's velocity-sensitive ears have the potential to transform vector sound detection. Yet,

Mikhail Masliaev, Dmitry Gusarov, Ilya Markov, Alexander Hvatov

Although neural operators are widely used in data-driven physical simulations, their training remains computationally expensive. Recent advances address this issue via downstream learning, where a model pretrained on simpler problems is fine-tuned on more complex ones. In this research, we investigate transformer-based neural operators, which have previously

E. Zhixuan Zeng, Yuhao Chen, Alexander Wong

Image generation models frequently encode social biases, including stereotypes tied to gender, race, and profession. Existing methods for analyzing these biases in diffusion models either focus narrowly on predefined categories or depend on manual interpretation of latent directions. These constraints limit scalability and hinder the discovery of subtle or u

Weiheng Bai, Kefu Wu, Qiushi Wu, Kangjie Lu

Directed fuzzing is a useful testing technique that aims to efficiently reach target code sites in a program. The core of directed fuzzing is the guiding mechanism that directs the fuzzing to the specified target. A general guiding mechanism adopted in existing directed fuzzers is to calculate the control-flow distance between the current progress and the ta

Marcin Dziubiński

We study equilibrium strategies and the value of the asymmetric variant of the discrete Colonel Blotto game with $K \geq 2$ battlefields, $B \geq 1$ resources of the weaker player and $A > B$ resources of the stronger player. We derive equilibrium strategies and the formulas for the value of the game for the cases where the number of resources of the weaker

Monika Blue Kwapisz, Yoav Ackerman, Jennifer Nguyen, Prashanth Rajivan

Online proctoring systems (OPS) are technologies and services that are used to monitor students during an online exam to deter cheating. However, OPS often violates student privacy by implementing overly intrusive surveillance to which students cannot consent meaningfully. The technologies used in OPS have been shown to unfairly flag students with disabiliti

D. Elia, A. Coletta, S. Molinari, E. Schisano

Context. The fragmentation of massive molecular clumps into smaller, potentially star-forming cores plays a key role in the processes of high-mass star formation. The ALMAGAL project offers high-resolution data to investigate these processes across various evolutionary stages in the Galactic plane. Aims. This study aims at correlating the fragmentation prope

Inga Girshfeld, Xiaohui Chen

We study the estimation problem of distribution-on-distribution regression, where both predictors and responses are probability measures. Existing approaches typically rely on a global optimal transport map or tangent-space linearization, which can be restrictive in approximation capacity and distort geometry in multivariate underlying domains. In this paper

Tim Randolph, Karol Węgrzycki

We consider exact algorithms for Subset Balancing, a family of related problems that generalizes Subset Sum, Partition, and Equal Subset Sum. Specifically, given as input an integer vector $\vec{x} \in \mathbb{Z}^n$ and a constant-size coefficient set $C \subset \mathbb{Z}$, we seek a nonzero solution vector $\vec{c} \in C^n$ satisfying $\vec{c} \cdot \vec{x

Kota Kondo, Yuwei Wu, Vijay Kumar, Jonathan P. How

Hard-constraint trajectory planners often rely on commercial solvers and demand substantial computational resources. Existing soft-constraint methods achieve faster computation, but either (1) decouple spatial and temporal optimization or (2) restrict the search space. To overcome these limitations, we introduce MIGHTY, a Hermite spline-based planner that pe

Iván Olarte Rodríguez, Maria Laura Santoni, Fabian Duddeck, Carola Doerr

Benchmarking is essential for developing and evaluating black-box optimization algorithms, providing a structured means to analyze their search behavior. Its effectiveness relies on carefully selected problem sets used for evaluation. To date, most established benchmark suites for black-box optimization consist of abstract or synthetic problems that only par

Athanasios G. Tzikas

We study thermodynamic aspects of ordinary and lower dimensional noncommutative black holes within an extended anti-de Sitter phase space by treating the negative cosmological constant and the minimal cut-off length as thermodynamic variables representing the pressure and tension of the system, respectively. In four-dimensional spacetime, the regular black h

Grace Byun, Swati Rajwal, Jinho D. Choi

Large Language Models (LLMs) are increasingly explored for educational tasks such as grading, yet their alignment with human evaluation in real classrooms remains underexamined. In this study, we investigate the feasibility of using an LLM (GPT-4o) to evaluate short-answer quizzes and project reports in an undergraduate Computational Linguistics course. We c

Hisashi Kasuya, Dan Popovici, Luis Ugarte

We continue the study of compact holomorphic $p$-contact manifolds $X$ that we introduced recently by expanding the discussion to include non-K\"ahler hyperbolicity issues and a differential calculus based on what we call the Lie derivative with respect to a $(0,\,q)$-form with values in the holomorphic tangent bundle of $X$. We also propose the notion of $p

Domingos S. P. Salazar

We show that any Petz $f$-divergence (where $f$ is operator convex) between quantum states admits a universal $\chi^2$-mixture representation: the distinguishability of $\rho$ from $\sigma$ is obtained as a positive superposition of quadratic contrasts $\chi^2_\lambda$, with nonnegative weights $w_f(\lambda)$ determined explicitly from the Stieltjes represen

William Harris, Lucas Yager, Syler Sylvester, Elizabeth Peiros

Search and rescue (SAR) robots are required to quickly traverse terrain and perform high-force rescue tasks, necessitating both terrain adaptability and controlled high-force output. Few platforms exist today for SAR, and fewer still have the ability to cover both tasks of terrain adaptability and high-force output when performing extraction. While legged ro

Yuyang Huang, Dante Kalise, Hicham Kouhkouh

We study global optimization of non-convex functions through optimal control theory. Our main result establishes that (quasi-)optimal trajectories of a discounted control problem converge globally and practically asymptotically to the set of global minimizers. Specifically, for any tolerance $\eta > 0$, there exist parameters $\lambda$ (discount rate) and $t

Ibrahim Mbouandi Njiasse, Florent Ouabo Kamkumo, Ralf Wunderlich

Nonlinear filtering problems are encountered in many applications, and one solution approach is the extended Kalman filter, which is not always convergent. Therefore, it is crucial to identify conditions under which the extended Kalman filter provides accurate approximations. This paper generalizes two significant results of Picard (1991) on the efficiency o

Anya Bardach, Hamilton Murrah

Research on how the popularization of generative Artificial Intelligence (AI) tools impacts learning environments has led to hesitancy among educators to teach these tools in classrooms, creating two observed disconnects. Generative AI competency is increasingly valued in industry but not in higher education, and students are experimenting with generative AI

Katherine Eng, Timothy Harris, Mike Krebs, Mason Meeks

We show that given an arbitrary set of four plane unit vectors $v_1, v_2, v_3, v_4$, the Cayley graph generated by $\{\pm v_1, \pm v_2, \pm v_3, \pm v_4\}$ is always $3$-colorable. Indeed, we show that this is a specific case of a much more general result wherein we determine the chromatic number of an arbitrary abelian Cayley graph generated by a set of fou

François Charton, Ashvni Narayanan

We investigate transformer prediction of long Collatz steps, a complex arithmetic function that maps odd integers to their distant successors in the Collatz sequence ( $u_{n+1}=u_n/2$ if $u_n$ is even, $u_{n+1}=(3u_n+1)/2$ if $u_n$ is odd). Model accuracy varies with the base used to encode input and output. It can be as high as $99.7\%$ for bases $24$ and $

Ran Elgedawy, Sanjay Das, Ethan Seefried, Gavin Wiggins

Operational safety at mission-critical work sites is a top priority given the complex and hazardous nature of daily tasks. This paper presents the Human-Agent Risk Navigation and Event Safety System (HARNESS), a modular AI framework designed to forecast hazardous events and analyze operational risks in U.S. Department of Energy (DOE) environments. HARNESS in

Jiazhou Liang, Hassan Khurram, Scott Sanner

Linear Predictive Clustering (LPC) partitions samples based on shared linear relationships between feature and target variables, with numerous applications including marketing, medicine, and education. Greedy optimization methods, commonly used for LPC, alternate between clustering and linear regression but lack global optimality. While effective for separab

Yihan Hu, Yifei Huang, Weizhao Wang

Seasonality has traditionally shaped the U.S. housing market, with activity peaking in spring-summer and declining in autumn-winter. However, recent disruptions, particularly post-COVID-19, raise questions about shift in these patterns. This study analyzes housing market date (1991-2024) to examine evolving seasonality and regional heterogeneity. Using Housi

Kenneth A. V. Distefano, Sara Shabani, Uwe C. Täuber

Stochastic chemical reaction or population dynamics in finite systems often terminates in an absorbing state. Yet in large spatially extended systems, the time to reach species extinction (or fixation) becomes exceedingly long. Tuning control parameters may diminish the survival probability, rendering species coexistence susceptible to stochastic extinction

Cheng-Liang Wei, Yu Luo, Hao Tian, Ming Li

The Chinese Space Station Survey Telescope (CSST) is a flagship space mission, designed to carry out a large-area sky survey to explore the nature of dark matter and dark energy in the Universe. The onboard multi-band imaging and slitless spectroscopic modules will enable us to obtain photometric data for billions of galaxies and stars, as well as hundreds o

Pål Grønås Drange, Fedor V. Fomin, Petr Golovach, Danil Sagunov

We study a Stackelberg variant of the classical Most Vital Links problem, modeled as a one-round adversarial game between an attacker and a defender. The attacker strategically removes up to $k$ edges from a flow network to maximally disrupt flow between a source $s$ and a sink $t$, after which the defender optimally reroutes the remaining flow. To capture t

R. L. P. G. Amaral, V. E. R. Lemes, O. S. Ventura, L. C. Q. Vilar

This work is divided in two parts. The first three sections review meson physics phenomenology, highlighting the history of pseudoscalar multiplet mass spectra research. We then propose a new approach for the mass mixing problem based exclusively on a second order phase transition principle. This development leads to new relations among the masses of the mes

Renan da Silva Souza, Youjiang Xu, Walter Hofstetter

We numerically study a gas of two-component fermions coupled to a transversely pumped optical cavity and confined to a two-dimensional static square optical lattice. In the dispersive regime, the steady state of the system is described by an extended Hubbard Hamiltonian with cavity-mediated long-range interactions. Using real-space dynamical mean-field theor

Junellie Perez, Laura K. Schaefer, Edward Schwieterman, Kevin B. Stevenson

The TRAPPIST-1 planets have become prime targets for studying the atmospheric and geophysical properties of planets around M-dwarf stars. To effectively identify their atmospheric composition, we first must understand their geological evolution. For this study, we focus on enhancing an existing atmosphere-interior exchange model by incorporating additional g

Dennis M. Heim

Can particles at definite positions in real space, obeying Newton's equations under an ordinary interaction force, have a coarse-grained description that is the Schrödinger equation? We present a medium of companion particles on a line under a force of exactly that kind: each companion pays for the mismatch of the signed inverse gaps to its two neighbors

Karol K. Kozlowski, Alex Simon

The integrable bootstrap program allows one to express the tempered distributions associated with the multipoint functions of the integrable 1+1 dimensional Sinh-Gordon quantum field theory by means of explicit series. The convergence of the latter is an open problem that was only solved for the two-point case. In this work, by taking for granted the converg

Fnu Gaurav

This study presents a comprehensive statistical analysis of criminal complaint data from the New York City Police Department (NYPD) spanning 47 years (1963-2025) [1]. Using a dataset of 438,556 complaint records, we employed exploratory data analysis (EDA), descriptive statistics, and multiple statistical hypothesis tests to investigate the spatial, temporal

Manish Dhakal, Venkat R. Dasari, Rajshekhar Sunderraman, Yi Ding

Parameter-efficient fine-tuning (PEFT) significantly reduces computational and memory costs by updating only a small subset of the model's parameters, enabling faster adaptation to new tasks with minimal loss in performance. Previous studies have introduced PEFTs tailored for point cloud data, as general approaches are suboptimal. To further reduce the numbe

Marcus Greiff, Ray Zhang, Takeru Shirasawa, John Subosits

We consider the problem of estimating the parameters of a vehicle dynamics model for predictive control in driving applications. Instead of solely using the instantaneous parameters estimated from the vehicle signals, we combine this with cameras and update a probabilistic map with parameter estimates and semantic information using Bayesian moment matching.

aBa Mbirika

In the \textit{Fibonacci Quarterly} in 1964, C.~R.~Wall gave the following weighted sum of generalized Fibonacci numbers: $\sum_{i=1}^n i G_i = n G_{n+2} - G_{n+3} + G_3$, where $\left(G_n\right)_{n \geq 0}$ is defined by the recurrence $G_n = G_{n-1} + G_{n-2}$ with fixed $G_0, G_1 \in \mathbb{Z}$. In this paper, we generalize Wall's result to the Lucas seq

Guilherme Grancho D. Fernandes, Marco A. Barroca, Mateus dos Santos, Rafael S. Oliveira

This study presents a bidirectional Long Short-Term Memory (LSTM) neural network for classifying transient astronomical object light curves from the Photometric LSST Astronomical Time-series Classification Challenge (PLAsTiCC) dataset. The original fourteen object classes were reorganized into five generalized categories (S-Like, Fast, Long, Periodic, and No

James Hazelden

The Neural Tangent Kernel (NTK) characterizes how a model's state evolves over Gradient Descent. Computing the full NTK matrix is often infeasible, especially for recurrent architectures. Here, we introduce a matrix-free perspective, using trace estimation to rapidly analyze the empirical, finite-width NTK. This enables fast computation of the NTK's trace, F

Juan Límaco, Luis P. Yapu

We prove that a free boundary semilinear heat equation with Stefan boundary condition and radially symmetric data is locally null controllable. The strategy involves reducing the problem to the corresponding one-dimensional formulation and adapting a Carleman inequality in that setting. The local null controllability of the free-boundary problem is then esta

Jourdan Waas, Eric S. Perlman, Manasvi Lingam, Emily Lohmann

While the influence of supermassive black hole (SMBH) activity on habitability has garnered attention, the specific effects of active galactic nucleus (AGN) winds, particularly ultrafast outflows (UFOs), on planetary atmospheres remain largely unexplored. This study aims to fill this gap by investigating the relationship between SMBH mass at the galactic cen

Farhan Sheth, Girish, Mohd Mujtaba Akhtar, Muskaan Singh

In this work, we address the challenge of generalizable audio deepfake detection (ADD) across diverse speech synthesis paradigms-including conventional text-to-speech (TTS) systems and modern diffusion or flow-matching (FM) based generators. Prior work has mostly targeted individual synthesis families and often fails to generalize across paradigms due to ove

Eric Mugford, Jonathan D. Gammell

Searches are conducted to find missing persons and/or objects given uncertain information, imperfect observers and large search areas in Search and Rescue (SAR). In many scenarios, such as Maritime SAR, expected survival times are short and optimal search could increase the likelihood of success. This optimization problem is complex for nontrivial problems g

Carsten Zülch, Konstantin Gaul, Robert Berger

Quasi-forbidden electronic transitions in atoms and vibronic transitions between quasi-degenerate states in molecules serve as powerful probes of hypothetical temporal variations of fundamental constants. Computation of the sensitivity of a transition to a variation of the fine-structure constant is conventionally performed by numerical variation of the spee

Girish, Mohd Mujtaba Akhtar, Farhan Sheth, Muskaan Singh

In this work, we address the problem of finegrained traceback of emotional and manipulation characteristics from synthetically manipulated speech. We hypothesize that combining semantic-prosodic cues captured by Speech Foundation Models (SFMs) with fine-grained spectral dynamics from auditory representations can enable more precise tracing of both emotion an

Yuchen Wang, Irma Avdic, Michael Rose, Lillian I. Payne Torres

Classical shadow tomography offers a scalable route to estimating properties of quantum states, but the resulting reduced density matrices (RDMs) often violate constraints that ensure they represent $N$-electron states -- known as $N$-representability conditions -- because of statistical and hardware noise. We present a correlated purification framework base

Chao Wu, Baoheng Li, Mingchen Gao, Yu Tian

Recent advances in large language models (LLMs) have made reasoning a central benchmark for evaluating intelligence. While prior surveys focus on efficiency by examining how to shorten reasoning chains or reduce computation, this view overlooks a fundamental challenge: current LLMs apply uniform reasoning strategies regardless of task complexity, generating

Guilherme Biava Rodrigues, Franciele Beal, Marlon Marcon, Alinne Cristinne Corrêa Souza

Students often report difficulties in accessing day-to-day academic information, which is usually spread across numerous institutional documents and websites. This fragmentation results in a lack of clarity and causes confusion about routine university information. This project proposes the development of a chatbot using Generative Artificial Intelligence (G

Kirill Khoruzhii, Patrick Gelß, Sebastian Pokutta

We give explicit low-rank bilinear non-commutative schemes for multiplying structured $n \times n$ matrices with $2 \leq n \leq 5$, which serve as building blocks for recursive algorithms with improved multiplicative factors in asymptotic complexity. Our schemes are discovered over $\mathbb{F}_2$ or $\mathbb{F}_3$ and lifted to $\mathbb{Z}$ or $\mathbb{Q}$.

Isadora Cristina, Ramon Gonze, Jônatas Santos, Julio Reis

The fragmentation of public data in Brazil, coupled with inconsistent standards and limited interoperability, hinders effective research, evidence-based policymaking and access to data-driven insights. To address these issues, we introduce Brazil Data Commons, a platform that unifies various Brazilian datasets under a common semantic framework, enabling the

Tara Murphy, David L. Kaplan

Astronomical objects that change rapidly give us insight into extreme environments, allowing us to identify new phenomena, test fundamental physics, and probe the Universe on all scales. Transient and variable radio sources range from the cosmological, such as gamma-ray bursts, to much more local events, such as massive flares from stars in our Galactic neig

Philip Dalladay-Simpson, Guglielmo Marchese, Zi-Yu Cao, Paolo Barone

The discovery of high-temperature superconductivity in hydrogen-rich compounds under extreme pressures has prompted great excitement, intense research, but also debate over the past decade. Electrical transport has been the primary diagnostic tool for identifying superconductivity in these systems, whereas complementary probes, including magnetic, spectrosco

Aaron Scher, David Abecassis, Peter Barnett, Brian Abeyta

Many experts argue that premature development of artificial superintelligence (ASI) poses catastrophic risks, including the risk of human extinction from misaligned ASI, geopolitical instability, and misuse by malicious actors. This report proposes an international agreement to prevent the premature development of ASI until AI development can proceed without

Milagros Colazo, Alvaro Alvarez-Candal

We present a comprehensive photometric study of trans-Neptunian objects (TNOs) by combining data from SDSS, Col-OSSOS, DES, and the recent Rubin First Look (RFL) data. Our database comprises 43 677 measurements in the u, g, r, i, and z filters, from which we derived 2 193 phase curves for 781 unique objects. From these data, we computed 2 542 absolute color

Md Ariful Islam Malik, Jeffrey C. Carver, Nasir U. Eisty

Background: Research software is crucial for enabling research discoveries and supporting data analysis, simulation, and interpretation across domains. However, evolving requirements, complex inputs, and legacy dependencies hinder the software quality and maintainability. While peer code review can improve software quality, its adoption by research software

Fethi Bougares, Salima Mdhaffar, Haroun Elleuch, Yannick Estève

In this paper, we introduce TEDxTN, the first publicly available Tunisian Arabic to English speech translation dataset. This work is in line with the ongoing effort to mitigate the data scarcity obstacle for a number of Arabic dialects. We collected, segmented, transcribed and translated 108 TEDx talks following our internally developed annotations guideline

A. Savchenko, A. Zabrodin

Using the free fermions technique and non-abelian bosonization rules we introduce the multi-component Pfaff-Toda hierarchy. The tau-function is defined as vacuum expectation value of a Clifford group element of the algebra of Fermi-operators. A generating bilinear integral equation for the tau-function is obtained. A number of bilinear functional relations f

Mitia Duerinckx, Corentin Le Bihan

We study the long-time dynamics of a tagged particle coupled to a background of $N$ other particles, all interacting through long-range pairwise forces in the mean-field scaling, with the background initially at thermal equilibrium. Starting from the $N$-particle BBGKY hierarchy, we introduce a simplified (truncated) hierarchical model and show, in sufficien

Xiaxin Li, Arya Mazumdar

One-bit compressed sensing (1bCS) addresses the recovery of sparse signals from highly quantized measurements, retaining only the sign of each linear measurement. From a data compression perspective, the one-bit measurements form a compact binary representation of sparse signals. The support recovery problem seeks to recover the support of an unknown signal

Yuta Kawakami, Jin Tian

Counterfactual decision-making in the face of uncertainty involves selecting the optimal action from several alternatives using causal reasoning. Decision-makers often rank expected potential outcomes (or their corresponding utility and desirability) to compare the preferences of candidate actions. In this paper, we study new counterfactual decision-making r

Fletcher T. Chapin, Akshay K. Rao, Adhithyan Sakthivelu, Carson I. Tucker

Electrification is contributing to substantial growth in U.S. commercial and industrial loads, but the cost and Scope 2 carbon emission implications of this load growth are opaque for both power consumers and utilities. This work describes a unique spatiotemporally resolved data set of U.S. electricity costs and emissions and applies time series approximatio

Junjie Zhang, Feng Zhao, Hanqiang Liu, Jun Yu

The booming remote sensing (RS) technology is giving rise to a novel multimodality generalization task, which requires the model to overcome data heterogeneity while possessing powerful cross-scene generalization ability. Moreover, most vision-language models (VLMs) usually describe surface materials in RS images using universal texts, lacking proprietary li

Marek Janasz, Grzegorz Malara, Halszka Tutaj-Gasińska

Unexpected hypersurfaces arise when vanishing in points of a set $Z$ and higher-order vanishing along a general linear subspace fails to impose the expected number of independent conditions on forms of a fixed degree. The phenomenon was first observed for planar curves by Cook, Harbourne, Migliore and Nagel. This paper shows a syzygy-based construction of, p

Le Liu, Yu Kawano, Ming Cao

For systems whose states implicate sensitive information, their privacy is of great concern. While notions like differential privacy have been successfully introduced to dynamical systems, it is still unclear how a system's privacy can be properly protected when facing the challenging yet frequently-encountered scenario where an adversary possesses prior

Stanislav Selitskiy

A novel Transformer variation architecture is proposed in the implicit sparse style. Unlike "traditional" Transformers, instead of attention to sequential or batch entities in their entirety of whole dimensionality, in the proposed Batch Transformers, attention to the "important" dimensions (primary components) is implemented. In such a way, the "important"

Samson Oruma, Ricardo Colomo-Palacios, Vasileios Gkioulos

As social robots increasingly enter public environments, their acceptance depends not only on technical reliability but also on ethical integrity, accessibility, and user trust. This paper reports on a pilot deployment of an ARI social robot functioning as a university receptionist, designed in alignment with the SecuRoPS framework for secure and ethical soc

Akram Aldroubi, Carlos Cabrelli, Ilya Krishtal, Ursula Molter

Dynamical sampling refers to a class of problems in which space-time samples are taken from a signal evolving under an underlying dynamical system. The goal is to use these samples to recover relevant information about the system, such as the initial state, the evolution operator, or the sources and sinks driving the dynamics. These problems are tightly conn

Ajwad Abrar, Nafisa Tabassum Oeshy, Prianka Maheru, Farzana Tabassum

Summarizing consumer health questions (CHQs) can ease communication in healthcare, but unfaithful summaries that misrepresent medical details pose serious risks. We propose a framework that combines TextRank-based sentence extraction and medical named entity recognition with large language models (LLMs) to enhance faithfulness in medical text summarization.

Yasir Mahmood, Markus Hecher, Johanna Groven, Johannes K. Fichte

Structural measures of graphs, such as treewidth, are central tools in computational complexity resulting in efficient algorithms when exploiting the parameter. It is even known that modern SAT solvers work efficiently on instances of small treewidth. Since these solvers are widely applied, research interests in compact encodings into (Q)SAT for solving and

Pooja P Jain, Pietro Mascagni, Giuseppe Massimiani, Nabani Banik

Minimally invasive colorectal surgery is characterized by procedural variability, a difficult learning curve, and complications that impact quality and outcomes. Video-based assessment (VBA) offers an opportunity to generate data-driven insights to reduce variability, optimize training, and improve surgical performance. However, existing tools for workflow a

Mehdi Khaleghi, Nastaran Khaleghi, Sobhan Sheykhivand, Sebelan Danishvar

Systematic logistics, conveyance amenities and facilities as well as warehousing information play a key role in fostering profitable development in a supply chain. The aim of transformation in industries is the improvement of the resiliency regarding the supply chain. The resiliency policies are required for companies to affect the collaboration with logisti

Yilber Fabian Bautista, Andrew Robertson, Laura Sagunski, Adam Smith-Orlik

The Jeans model is a semi-analytical approach to modeling self-interacting dark matter (SIDM) that works remarkably well to reproduce the spherically-averaged halo profiles from observations and simulations of relaxed galaxies and galaxy clusters. However, SIDM halos are not spherically symmetric in general since they respond to nonspherical baryon distribut

Pierce Giffin, Benjamin Lillard, Pankaj Munbodh, Tien-Tien Yu

The interactions of dark matter with Standard Model particles can be systematically studied in the language of effective field theories. We investigate dark matter interactions with Standard Model particles, including spin-dependent interactions, for direct detection experiments and demonstrate that, although the scattering rate generally depends on multiple

Nikolaos Tsagkas, Andreas Sochopoulos, Duolikun Danier, Sethu Vijayakumar

The adoption of pre-trained visual representations (PVRs), leveraging features from large-scale vision models, has become a popular paradigm for training visuomotor policies. However, these powerful representations can encode a broad range of task-irrelevant scene information, making the resulting trained policies vulnerable to out-of-domain visual changes a

Andrin Rehmann, Nolan Black, Josiah Bjorgaard, Alessandro Angioi

Gradient-based optimization of engineering designs is limited by non-differentiable components in the typical computer-aided engineering (CAE) workflow, which calculates performance metrics from design parameters. While gradient-based methods could provide noticeable speed-ups in high-dimensional design spaces, codes for meshing, physical simulations, and ot

Emad Haque, Pragnya Sudershan Nalla, Jeff Zhang, Sachin S. Sapatnekar

The scaling of advanced packaging technologies provides abundant interconnection resources for 2.5D/3D heterogeneous integration (HI), thereby enabling the construction of larger-scale VLSI systems with higher energy efficiency in data movement. However, conventional input/output (I/O) circuitry, including electrostatic discharge (ESD) protection and signali

Joseph Paul MacManus

Suppose that $X$ is an infinite, connected, locally finite, quasi-transitive graph with the property that every bi-infinite quasi-geodesic uniformly coarsely separates $X$ into exactly two deep pieces. We show that such an $X$ is quasi-isometric to either the Euclidean plane or the hyperbolic plane. In particular, if $X$ is a Cayley graph of a finitely gener

Julia K. Brynildsen, Panagiotis Fotiadis, Karol P. Szymula, Jason Z. Kim

Primates utilize distributed neural circuits to learn habits in uncertain environments, but the underlying mechanisms remain poorly understood. We propose a formal theory of network energetics explaining how brain states influence sequential behavior. We test our theory on multi-unit recordings from the caudate nucleus and cortical regions of macaques perfor

Jonathan Teo, Lee Xin Wei, Hoong Chuin Lau

The Equivariant Quantum Circuit (EQC) for the Travelling Salesman Problem (TSP) has been shown to achieve near-optimal performance in solving small TSP problems (up to 20 nodes) using only two parameters at depth 1. However, extending EQCs to larger TSP problem sizes remains challenging due to the exponential time and memory for quantum circuit simulation, a

Luchang Niu, Saleem Iqbal, Yang Xu, Robert W. Boyd

Understanding the influence of atmospheric turbulence on the propagation of entangled biphoton states is essential for free-space quantum communication protocols. Using the extended Huygens-Fresnel principle and the Kolmogorov turbulence model, we derive an analytical expression for the combined density operator of the signal and idler fields generated via S

Jiamin Li, Lei Qu, Tao Zhang, Grigory Chirkov

This document presents a vision for a novel AI infrastructure design that has been initially validated through inference simulations on state-of-the-art large language models. Advancements in deep learning and specialized hardware have driven the rapid growth of large language models (LLMs) and generative AI systems. However, traditional GPU-centric architec