May 2025 arXiv papers — page 50

Bipul Thapa, Gabriel Cofre

Classifying customer feedback into distinct emotion categories is essential for understanding sentiment and improving customer experience. In this paper, we classify customer feedback in Spanish into three emotion categories--positive, neutral, and negative--using advanced NLP and ML techniques. Traditional methods translate feedback from widely spoken langu

Carlos Saji, Eduardo Saavedra, Vagson L. Carvalho-Santos, Alvaro S. Nunez

Stable three-dimensional topological skyrmion structures in frustrated magnets are investigated. The texture exhibits a helicoid pattern along the vertical direction, described by a position-dependent helicity, which interpolates between Neel- and hedgehog-like two-dimensional skyrmions, characterized by the Hopf index, and is referred to as "twisted skyrmio

Jihoon Lee, Min Song

Despite significant advancements in Large Vision-Language Models, Object Hallucination (OH) remains a persistent challenge. Building upon prior studies on contrastive decoding that address this issue without requiring additional model training, we introduce RVCD (Retrieval Visual Contrastive Decoding), an advanced method to suppress OH. RVCD leverages both n

Parsa Razmara, Takfarinas Medani, Anand A. Joshi, Majid Abbasi Sisara

0.55T MRI offers advantages compared to conventional field strengths, including reduced susceptibility artifacts and better compatibility with simultaneous EEG recordings. However, reliable task-based fMRI at 0.55T has not been significantly demonstrated. In this study, we establish a robust task-based fMRI protocol and analysis pipeline at 0.55T that achiev

Junhong Liu, Qinfei Long, Rong-Peng Liu, Wenjie Liu

Distributed peer-to-peer (P2P) energy trading mandates an escalating coupling between the physical power network and communication network, necessitating high-frequency sharing of real-time data among prosumers. However, this data-sharing scheme renders the system vulnerable to various malicious behaviors, as Byzantine agents can initiate cyberattacks by inj

Nathan Lane

I study the impact of industrial policies on industrial development by considering an important episode during the East Asian miracle: South Korea's heavy and chemical industry (HCI) drive, 1973--1979. Based on newly assembled data, I use the introduction and termination of industrial policies to study their impacts during and after the intervention period.

Suhana Bedi, Hejie Cui, Miguel Fuentes, Alyssa Unell

While large language models (LLMs) achieve near-perfect scores on medical licensing exams, these evaluations inadequately reflect the complexity and diversity of real-world clinical practice. We introduce MedHELM, an extensible evaluation framework for assessing LLM performance for medical tasks with three key contributions. First, a clinician-validated taxo

Christopher Dobrian, Omar Costa Hamido

Quantum computing can be employed in computer-aided music composition to control various attributes of the music at different structural levels. This article describes the application of quantum simulation to model compositional decision making, the simulation of quantum particle tracking to produce noise-based timbres, the use of basis state vector rotation

Chris Emezue, NaijaVoices Community, Busayo Awobade, Abraham Owodunni

The development of high-performing, robust, and reliable speech technologies depends on large, high-quality datasets. However, African languages -- including our focus, Igbo, Hausa, and Yoruba -- remain under-represented due to insufficient data. Popular voice-enabled technologies do not support any of the 2000+ African languages, limiting accessibility for

Jingjing Liu, Xiansen Ju, Xianchao Xiu, Wanquan Liu

Unsupervised feature selection (UFS) is an important task in data engineering. However, most UFS methods construct models from a single perspective and often fail to simultaneously evaluate feature importance and preserve their inherent data structure, thus limiting their performance. To address this challenge, we propose a novel bi-level unsupervised featur

Walid Shaker, Mustafa Suphi Erden

Robotic surgery represents a major breakthrough in medical interventions, which has revolutionized surgical procedures. However, the high cost and limited accessibility of robotic surgery systems pose significant challenges for training purposes. This study addresses these issues by developing a cost-effective robotic laparoscopy training system that closely

Shenao Zhang, Yaqing Wang, Yinxiao Liu, Tianqi Liu

Large Language Models (LLMs) trained via Reinforcement Learning (RL) have exhibited strong reasoning capabilities and emergent reflective behaviors, such as rethinking and error correction, as a form of in-context exploration. However, the Markovian policy obtained from conventional RL training does not give rise to reflective exploration behaviors since the

José A. Carrillo, Stefano Fronzoni, Yuji Nakatsukasa, Endre Süli

We present a numerical method for the approximation of the inverse of the fractional Laplacian $(-\Delta)^{s}$, based on its spectral definition, using rational functions to approximate the fractional power $A^{-s}$ of a matrix $A$, for $0<s<1$. The proposed numerical method is fast and accurate, benefiting from the fact that the matrix $A$ arises from a fin

Irene Gonzalvez, Alfredo Miranda, Julio D. Rossi, Jorge Ruiz-Cases

In this paper we introduce a new two-player zero-sum game whose value function approximates the level set formulation for the geometric evolution by mean curvature of a hypersurface. In our approach the game is played with symmetric rules for the two players and probability theory is involved (the game is not deterministic).

Austin Ware, Patrick Young

Future direct imaging space telescopes, such as NASA's Habitable Worlds Observatory (HWO), will be the first capable of both detecting and characterizing terrestrial exoplanets in the habitable zones (HZ) of nearby Sun-like stars. Since this will require a significant amount of time and resources for even a single system or exoplanet, the likelihood that a s

McCullen Sandora

The relative abundances of exotic environments provides us with (uninformed) bounds on the habitability of those environments relative to our own, on the basis that our presence here is not too atypical. For instance, since red stars outnumber yellow stars 7 to 3, we can infer that red stars must be less than 8.1 times as habitable as yellow, as otherwise ou

Yinglun Xu, Hangoo Kang, Tarun Suresh, Yuxuan Wan

This work proposes `PET', a novel pessimistic reward fine-tuning method, to learn a pessimistic reward model robust against reward hacking in offline reinforcement learning from human feedback (RLHF). Traditional reward modeling techniques in RLHF train an imperfect reward model, on which a KL regularization plays a pivotal role in mitigating reward hacking

Behnam Esmayli, Riddhi Mishra

We give sufficient geometric conditions, not involving capacities, for a compact null set to be removable for the Sobolev functions on weighted $\mathbb R^n$, defined as the closure of smooth functions in the weighted Sobolev norm. Our porosity conditions are in terms of suitable coverings by cubes. The weights are assumed to be doubling and satisfy a Poinca

Md Mahadi Hasan

I develop a continuous-time model in which an incumbent batch-service provider faces stochastic passenger arrivals and must decide when to dispatch under the threat of customer defection to a faster entrant. The incumbent's problem is formalized as a trade-off between departure frequency and load maximization, with the option to accept mid-route pickups. I c

Luís Carvalho, João L. Costa, José Mourão, Gonçalo Oliveira

We propose a new simple architecture, Zeta Neural Networks (ZeNNs), in order to overcome several shortcomings of standard multi-layer perceptrons (MLPs). Namely, in the large width limit, MLPs are non-parametric, they do not have a well-defined pointwise limit, they lose non-Gaussian attributes and become unable to perform feature learning; moreover, finite

Ernesto Accolti

This manuscript presents initial findings critical for supporting augmented acoustics experiments in custom-made hearing booths, addressing a key challenge in ensuring perceptual validity and experimental rigor in these highly sensitive setups. This validation ensures our proposed methodology is sound, guarantees the reliability of future results, and lays t

José Ignacio Rivero-Wildemauwe

Two agents trade an item in a simultaneous offer setting, where the exchange takes place if and only if the buyer's bid price weakly exceeds the seller's ask price. Each agent is randomly assigned the buyer or seller role. Both agents are characterized by a certain degree of Kantian morality, whereby they pick their bidding strategy behind a Veil of Ignoranc

Pragna Das, Saptarshi Saha

We present a comprehensive study of different phases in the Dicke model incorporating both anisotropy and dissipation. We begin with a concise review of the quantum phase transition in this setting, highlighting how these two parameters shift the critical point. We then perform a detailed investigation of the transition from ergodic to nonergodic phases by a

Julian Mayer-Steudte

We here measure, for the first time, adjoint chromoelectric correlators at finite temperatures that encode the diffusion of quarkonium in the medium. Understanding the dynamics of quarkonium in the QGP plays an essential role in understanding quarkonium suppression and the QGP in general. We perform SU(3) gauge theory calculations and use gradient flow to im

Tadahiro Kimura, Haruka Hoshino, Eiichiro Kokubo, Yuji Matsumoto

In the standard model of terrestrial planet formation, planets are formed through giant impacts of planetary embryos after the dispersal of the protoplanetary gas disc. Traditionally, $N$-body simulations have been used to investigate this process. However, they are computationally too expensive to generate sufficient planetary populations for statistical co

Russell Jay Hendel

This paper presents results on maximal runs, order of squares, palindromes, and unbordered factors of members of the family of binary pattern sequences with the all-one pattern. Restricting ourselves to binary pattern sequences with the all-one pattern with at least three ones, five categories of maximal run lengths and 3 categories of orders of squares are

Meng Lu, Ruochen Zhang, Carsten Eickhoff, Ellie Pavlick

Multilingual large language models (LLMs) often exhibit factual inconsistencies across languages, with significantly better performance in factual recall tasks in English than in other languages. The causes of these failures, however, remain poorly understood. Using mechanistic analysis techniques, we uncover the underlying pipeline that LLMs employ, which i

G. Panotopoulos, A. Rincón, I. Lopes

We investigate anisotropic compact stars comprising two non-interacting fluids: quark matter and condensed dark matter. Using the MIT Bag model equation of state for quark matter and Bose-Einstein Condensate equation of state for dark matter, we numerically compute interior solutions for those two-fluid component spherical configurations. Varying the initial

P. Rezende-Gonçalves, A. Antezak, T. Kato, K. Feng

Compounds of the new materials class LnTAl$_4$X$_2$ (Ln = lanthanide, X = tetrel, T = transition metal) host exotic magnetic phenomena due to geometric frustration induced by their triangular lattice. Complex spin arrangements, magnetic fluctuations and double magnetic transitions have been well observed by means of magneto-transport. Nevertheless, the exper

Mattia Miotto, Giorgio Gosti, Maria Rosito, Michela Dell'Omo

Multicellular rosettes are observed in different situations such as morphogenesis, wound healing, and cancer progression. While some molecular insights have been gained to explain the presence of these assemblies of five or more cells around a common center, what are the tunable, global features that favors/hinders their formation is still largely unknown. H

Tianyu Wang, Akira Horiguchi, Lingyou Pang, Carey E. Priebe

The increasing use of synthetic data from the public Internet has enhanced data usage efficiency in large language model (LLM) training. However, the potential threat of model collapse remains insufficiently explored. Existing studies primarily examine model collapse in a single model setting or rely solely on statistical surrogates. In this work, we introdu

László Molnár, Klára Lelkes

We present the first asteroseismic analysis of the bright, nearby red giant star, HD145250. We calculate the global seismic quantities of the star from single-sector, 2-minute TESS photometry, and determine its mass and radius to be ~1.4 M$_\odot$ and ~16 R$_\odot$ using asteroseismic scaling relations. Our values agree with published non-seismic mass and ra

Md Rashidunnabi, Kailash Hambarde, Hugo Proença

Video-based person re-identification (Re-ID) remains brittle in real-world deployments despite impressive benchmark performance. Most existing models rely on superficial correlations such as clothing, background, or lighting that fail to generalize across domains, viewpoints, and temporal variations. This survey examines the emerging role of causal reasoning

Emily J. Evans, Russell Jay Hendel

Barret, Evans, and Francis conjectured that if $G$ is the straight linear 3-tree with $n$ vertices and $H$ is the straight linear 3-tree with $n+1$ vertices then \[\lim_{n\rightarrow \infty} r_{H} (1, n+1) - r_G(1,n) = \frac{1}{14},\] where $r_G(u,v)$ and $r_H(u,v)$ are the resistance distance between vertices $u$ and $v$ in graphs $G$ and $H$ respectively.

Sebastian Antony Joseph, Syed Murtaza Husain, Stella S. R. Offner, Stéphanie Juneau

Large Language Models (LLMs) are being explored for applications in scientific research, including their capabilities to synthesize literature, answer research questions, generate research ideas, and even conduct computational experiments. Ultimately, our goal is for these to help scientists derive novel scientific insights. In many areas of science, such in

Junxiang Wang, Emek Barış Küçüktabak, Rana Soltani Zarrin, Zackory Erickson

Clear communication of robot intent fosters transparency and interpretability in physical human-robot interaction (pHRI), particularly during assistive tasks involving direct human-robot contact. We introduce CoRI, a pipeline that automatically generates natural language communication of a robot's upcoming actions directly from its motion plan and visual per

Guanhao Zhou, Yuefeng Han, Xiufan Yu

This paper studies the task of estimating heterogeneous treatment effects in causal panel data models, in the presence of covariate effects. We propose a novel Covariate-Adjusted Deep Causal Learning (CoDEAL) for panel data models, that employs flexible model structures and powerful neural network architectures to cohesively deal with the underlying heteroge

Uros Zivanovic, Serafina Di Gioia, Andre Scaffidi, Martín de los Rios

Applying Transformers to irregular time-series typically requires specializations to their baseline architecture, which can result in additional computational overhead and increased method complexity. We present the Rotary Masked Autoencoder (RoMAE), which utilizes the popular Rotary Positional Embedding (RoPE) method for continuous positions. RoMAE is an ex

Emily J. Evans, Russell Jay Hendel

This paper presents an introduction and expository account of a beautiful, current, and active application of recursions to the computation of resistance distance. Resistance distance, also referred to as effective resistance, is a well-known graph metric that arises naturally by considering a graph as an electrical circuit; heuristically resistance distance

Gaurav Saxena, Jack S. Baker, Pablo Díez Valle, William E. Salazar

In celebration of the 2025 International Year of Quantum Science and Technology, this article highlights the pioneering achievements and ongoing innovations in quantum error correction and quantum error mitigation by Canadian institutions, academia and industry alike. Emphasizing Canada's central role in advancing these two related areas, we summarize landma

Julius Richter, Till Svajda, Timo Gerkmann

We present ReverbFX, a new room impulse response (RIR) dataset designed for singing voice dereverberation research. Unlike existing datasets based on real recorded RIRs, ReverbFX features a diverse collection of RIRs captured from various reverb audio effect plugins commonly used in music production. We conduct comprehensive experiments using the proposed da

Andreas S. Kronfeld

These proceedings summarize a newly found connection between the factorial growth of coefficients in perturbative QCD and power corrections to the perturbation series, discussed in refs. [1-4]. The improved convergence is shown for three quantities four which four terms in the series are available: the static energy, the quark pole mass, and the polarized Bj

Yu. Bilu, D. Marques, C. G. Moreira

For every positive integer $m$ LeVeque (1953) defined the $U_m$-numbers as the transcendental numbers that admit very good approximation by algebraic numbers of degree $m$, but not by those of smaller degree. In these terms, Mahler's $U$-numbers are the transcendental numbers which are $U_m$ for some $m$. In 1965 Mahler showed that (properly defined) lacunar

Charles McGhee, Mark J. F. Gales, Kate M. Knill

Acoustic-to-Articulatory Inversion (AAI) attempts to model the inverse mapping from speech to articulation. Exact articulatory prediction from speech alone may be impossible, as speakers can choose different forms of articulation seemingly without reference to their vocal tract structure. However, once a speaker has selected an articulatory form, their produ

Valerie Tsao, Nathaniel W. Chaney, Manolis Veveakis

The large underlying assumption of climate models today relies on the basis of a "confident" initial condition, a reasonably plausible snapshot of the Earth for which all future predictions depend on. However, given the inherently chaotic nature of our system, this assumption is complicated by sensitive dependence, where small uncertainties in initial condit

Soo Go, Victor Y. Pan

A matrix algorithm is said to be superfast (that is, runs at sublinear cost) if it involves much fewer scalars and flops than the input matrix has entries. Such algorithms have been extensively studied and widely applied in modern computations for matrices with low displacement rank and more recently for low-rank approximation of matrices, even though they a

David Aristei, Manuela Gallo

This paper investigates the impact of objective financial knowledge, confidence in one's financial skills, and digital financial literacy on individuals' decisions to seek financial advice from robo-advice platforms. Using microdata from the Bank of Italy's 2023 survey on Italian adults' financial literacy, we find that individuals with greater financial kno

Marta Casanellas, Jennifer Garbett, Roser Homs, Annachiara Korchmaros

Phylogenetic invariants are equations that vanish on algebraic varieties associated with Markov processes that model molecular substitutions on phylogenetic trees. For practical applications, it is essential to understand these equations across a wide range of substitution models. Recent work has shown that, for equivariant models, phylogenetic invariants ca

Aniket Roy, Maitreya Suin, Ketul Shah, Rama Chellappa

Low-Rank Adaptation (LoRA) has gained prominence as a computationally efficient method for fine-tuning generative models, enabling distinct visual concept synthesis with minimal overhead. However, current methods struggle to effectively merge multiple LoRA adapters without training, particularly in complex compositions involving diverse visual elements. We i

Alejandro Hernández-Cano, Dhia Garbaya, Imanol Schlag, Martin Jaggi

Despite the significant potential of FP8 data formats for large language model (LLM) pre-training, their adoption has been limited due to challenges in maintaining stability at scale. Existing approaches often rely on suboptimal fine-grained FP8 kernels or fall back to higher-precision matrix multiplications (GEMMs) in sensitive components, such as attention

Priyanka Patel, Francesc Molina, Albert Guillén i Fàbregas

This paper investigates achievable information rates and error exponents of mismatched decoding when the channel belongs to the class of channels that are close to the decoding metric in terms of relative entropy. For both discrete- and continuous-alphabet channels, we derive approximations of the worst-case achievable information rates and error exponents a

Jian Wang, Boyan Zhu, Chak Tou Leong, Yongqi Li

Large reasoning models (LRMs) have exhibited the capacity of enhancing reasoning performance via internal test-time scaling. Building upon this, a promising direction is to further scale test-time compute to unlock even greater reasoning capabilities. However, as we push these scaling boundaries, systematically understanding the practical limits and achievin

Sajid Hussain, Muhammad Sohail, Nauman Ali Khan

Background: Federated Learning (FL) has emerged as a promising paradigm for training machine learning models while preserving data privacy. However, applying FL to Natural Language Processing (NLP) tasks presents unique challenges due to semantic heterogeneity across clients, vocabulary mismatches, and varying resource constraints on edge devices. Objectives

Ana Rita Ortigoso, Gabriel Vieira, Daniel Fuentes, Luis Frazão

This paper presents Project Riley, a novel multimodal and multi-model conversational AI architecture oriented towards the simulation of reasoning influenced by emotional states. Drawing inspiration from Pixar's Inside Out, the system comprises five distinct emotional agents - Joy, Sadness, Fear, Anger, and Disgust - that engage in structured multi-round dial

Néstor Espinoza, Marshall D. Perrin

The James Webb Space Telescope (JWST) has started a revolution in exoplanetary science. From studying in exquisite detail the chemical inventories and physical processes in gas giant exoplanets, the structure and chemical diversity of the enigmatic sub-Neptune population to even providing constraints on the atmospheric make-up of rocky exoplanets, the observ

Yasamin Panahi, Maria Ciudad Alañón, Daniel Centeno, Ralph Jason Costales

We introduce novel upper bounds on the Hilbert space dimensions required to realize quantum correlations in Bell scenarios. We start by considering bipartite cases wherein one of the two parties has two settings and two outcomes. Regardless of the number of measurements and outcomes of the other party, the Hilbert space dimension of the first party can be li

Goodluck Archibong, Ardiansyah Koeshidayatullah, Umair Waheed, Weichang Li

Seismic interpretation is vital for understanding subsurface structures but remains labor-intensive, subjective, and computationally demanding. While deep learning (DL) offers promise, its success hinges on large, high-quality datasets, often scarce in geophysics. Foundation Models (FMs), which have shown significant success in fields like natural language p

Julien Soulé, Jean-Paul Jamont, Michel Occello, Louis-Marie Traonouez

In cloud-native systems, Kubernetes clusters with interdependent services often face challenges to their operational resilience due to poor workload management issues such as resource blocking, bottlenecks, or continuous pod crashes. These vulnerabilities are further amplified in adversarial scenarios, such as Distributed Denial-of-Service attacks (DDoS). Co

The CBM Collaboration

The Compressed Baryonic Matter (CBM) experiment at the future Facility for Antiproton and Ion Research (FAIR) is a heavy-ion experiment designed to study nuclear matter at the highest baryonic density. For high-statistics measurements of rare probes, event rates of up to 10 MHz are targeted. The experiment, therefore, requires fast and radiation-hard detecto

J. Zak, H. M. J. Boffin, A. Bocchieri, E. Sedaghati

Understanding the diverse formation and migration pathways that shape exoplanetary systems requires characterizing both their atmospheric properties and their orbital dynamics. A key dynamical diagnostic is the projected spin-orbit angle - the alignment between the stellar spin and the planetary orbit-which provides crucial tests for theoretical models. This

Avik Pal, Alan Edelman, Christopher Rackauckas

Despite the promise of scientific machine learning (SciML) in combining data-driven techniques with mechanistic modeling, existing approaches for incorporating hard constraints in neural differential equations (NDEs) face significant limitations. Scalability issues and poor numerical properties prevent these neural models from being used for modeling physica

Alexander Migdal

We formulate a nonsingular loop-space calculus for Yang-Mills (YM) gradient flow directly in terms of Wilson loops. Variations act within the manifold of smooth loops via finite, reparametrization-invariant "dot derivatives," eliminating cusp/backtracking singularities. This yields a closed linear diffusion equation in loop space for Wilson loops. The associ

Wenhao Gu, Li Gu, Ching Yee Suen, Yang Wang

Recent advancements in handwritten text recognition (HTR) have enabled the effective conversion of handwritten text to digital formats. However, achieving robust recognition across diverse writing styles remains challenging. Traditional HTR methods lack writer-specific personalization at test time due to limitations in model architecture and training strateg

Tangzheng Lian, Oya Celiktutan

Recent studies on fairness have shown that Facial Expression Recognition (FER) models exhibit biases toward certain visually perceived demographic groups. However, the limited availability of human-annotated demographic labels in public FER datasets has constrained the scope of such bias analysis. To overcome this limitation, some prior works have resorted t

Chengyan Wu, Yiqiang Cai, Yang Liu, Pengxu Zhu

While text-based emotion recognition methods have achieved notable success, real-world dialogue systems often demand a more nuanced emotional understanding than any single modality can offer. Multimodal Emotion Recognition in Conversations (MERC) has thus emerged as a crucial direction for enhancing the naturalness and emotional understanding of human-comput

Yuxuan Sun, Yixuan Si, Chenglu Zhu, Kai Zhang

Recent advances in computational pathology have led to the emergence of numerous foundation models. These models typically rely on general-purpose encoders with multi-instance learning for whole slide image (WSI) classification or apply multimodal approaches to generate reports directly from images. However, these models cannot emulate the diagnostic approac

Tony Kim, Haotian Liu, Chiung-Ting Huang, Ingrid Wu

Functional Near-Infrared Spectroscopy (fNIRS) is a non-invasive, real-time method for monitoring brain activity by measuring hemodynamic responses in the cerebral cortex. However, existing systems are expensive, bulky, and limited to clinical or research environments. This paper introduces OpenNIRScap, an open-source, low-cost, and wearable fNIRS system desi

Joann Jasiak, Cheng Zhong

We study the Functional PCA (FPCA) forecasting method in application to functions of intraday returns on Bitcoin. We show that improved interval forecasts of future return functions are obtained when the conditional heteroscedasticity of return functions is taken into account. The Karhunen-Loeve (KL) dynamic factor model is introduced to bridge the functiona

Elhoucine Elfatimia, Recep Eryigitb, Lahcen Elfatimi

Agricultural research has accelerated in recent years, yet farmers often lack the time and resources for on-farm research due to the demands of crop production and farm operations. Seed classification offers valuable insights into quality control, production efficiency, and impurity detection. Early identification of seed types is critical to reducing the co

Elias Arbash, Ahmed Jamal Afifi, Ymane Belahsen, Margret Fuchs

The global challenge of sustainable recycling demands automated, fast, and accurate, state-of-the-art (SOTA) material detection systems that act as a bedrock for a circular economy. Democratizing access to these cutting-edge solutions that enable real-time waste analysis is essential for scaling up recycling efforts and fostering the Green Deal. In response,

Hawau Olamide Toyin, Rufael Marew, Humaid Alblooshi, Samar M. Magdy

We introduce ArVoice, a multi-speaker Modern Standard Arabic (MSA) speech corpus with diacritized transcriptions, intended for multi-speaker speech synthesis, and can be useful for other tasks such as speech-based diacritic restoration, voice conversion, and deepfake detection. ArVoice comprises: (1) a new professionally recorded set from six voice talents w

Md Mahadi Hassan, John Salvador, Akond Rahman, Santu Karmaker

LLMs show promise in code generation, yet their effectiveness for IT automation tasks, particularly for tools like Ansible, remains understudied. Existing benchmarks rely primarily on synthetic tasks that fail to capture the needs of practitioners who use IT automation tools, such as Ansible. We present ITAB (IT Automation Task Benchmark), a benchmark of 126

Peter Holm Nielsen

We propose martingale consumption as a natural, desirable consumption pattern for any given (proportional) investment strategy. The idea is to always adjust current consumption so as to achieve level expected future consumption under the arbitrarily chosen investment strategy. This approach avoids the formulation of an optimization objective based on prefere

Matthew Lisondra, Beno Benhabib, Goldie Nejat

Rapid advancements in foundation models, including Large Language Models, Vision-Language Models, Multimodal Large Language Models, and Vision-Language-Action Models, have opened new avenues for embodied AI in mobile service robotics. By combining foundation models with the principles of embodied AI, where intelligent systems perceive, reason, and act throug

Masoud Rahimi, Reza Karbasi, Abdol-Hossein Vahabie

We introduce an open-source Python framework for generating synthetic ECG image datasets to advance critical deep learning-based tasks in ECG analysis, including ECG digitization, lead region and lead name detection, and pixel-level waveform segmentation. Using the PTB-XL signal dataset, our proposed framework produces four open-access datasets: (1) ECG imag

Pavel N. Tsarev, Yakov V. Fominov

Synchronization between the internal dynamics of the superconducting phase in a Josephson junction (JJ) and an external ac signal is a fundamental physical phenomenon, manifesting as constant-voltage Shapiro steps in the current-voltage characteristic. Mathematically, this phase-locking effect is captured by the Resistively Shunted Junction (RSJ) model, an i

Rohan Balasubramanian, Nitish Gokulakrishnan, Syeda Jannatus Saba, Steven Skiena

Lipograms are a unique form of constrained writing where all occurrences of a particular letter are excluded from the text, typified by the novel Gadsby, which daringly avoids all usage of the letter 'e'. In this study, we explore the power of modern large language models (LLMs) by transforming the novel F. Scott Fitzgerald's The Great Gatsby into a fully 'e

Naba Rizvi, Harper Strickland, Saleha Ahmedi, Aekta Kallepalli

Large language models (LLMs) are increasingly used in decision-making tasks like r\'esum\'e screening and content moderation, giving them the power to amplify or suppress certain perspectives. While previous research has identified disability-related biases in LLMs, little is known about how they conceptualize ableism or detect it in text. We evaluate the ab

Tatyana Barron

We set up a mathematical model for a DC current in a human tissue that shows an attenuation effect in an extended circuit. We give a positive lower bound on the time duration over which this is guaranteed to happen in terms of the parameters of the model. We also discuss shielding and coupling in the context of electrical aspects of biological processes.

Dongyu Luo, Kelin Yu, Amir-Hossein Shahidzadeh, Cornelia Fermüller

Vision-based tactile sensing has been widely used in perception, reconstruction, and robotic manipulation. However, collecting large-scale tactile data remains costly due to the localized nature of sensor-object interactions and inconsistencies across sensor instances. Existing approaches to scaling tactile data, such as simulation and free-form tactile gene

Asif Shahriar, Rifat Shahriyar, M Saifur Rahman

Encoder transformer models compress information from all tokens in a sequence into a single [CLS] token to represent global context. This approach risks diluting fine-grained or hierarchical features, leading to information loss in downstream tasks where local patterns are important. To remedy this, we propose a lightweight architectural enhancement: an ince

Paweł Pilarczyk, Michał Palczewski, Stefano Luzzatto

We introduce an effective algorithmic method for the computation of a lower bound for uniform expansion in one-dimensional dynamics. The approach employs interval arithmetic and thus provides a rigorous numerical result (computer-assisted proof). The method uses efficient graph algorithms and an iterative approach for optimal performance. A software implemen

George Grispos, Logan Mears, Larry Loucks

The increasing digitization of agricultural operations has introduced new cybersecurity challenges for the farming community. This paper introduces an educational intervention called Cybersecurity Improvement Initiative for Agriculture (CIIA), which aims to strengthen cybersecurity awareness and resilience among farmers and food producers. Using a case study

Jianhao Gao, Fuhua Zheng, Chaofeng Wang, Haoran Meng

This study presents a time-resolved analysis of coseismic lateral surface rupture along the Sagaing Fault during the Mw 7.7 Mandalay, Myanmar earthquake on March 28, 2025. Leveraging a publicly available Closed-Circuit Television (CCTV) footage alongside on-site measurements, we show the first in-situ high sampling rate direct measurement of a coseismic slip

Alexandros Gazis, Eleftheria Katsiri

E-polis is a serious digital game designed to gamify sociological surveys studying young people's political opinions. In this platform game, players navigate a digital world, encountering quests posing sociological questions. Players' answers shape the city-game world, altering building structures based on their choices. E-polis is a serious game, not a gove

Thomas Führer, Norbert Heuer, Torsten Linß

We present a tensor-based finite element scheme for a smectic-A liquid crystal model. We propose a simple C\'ea-type finite element projection in the linear case and prove its quasi-optimal convergence. Special emphasis is put on the formulation and treatment of appropriate boundary conditions. For the nonlinear case we present a formulation in two space dim

Amarendra Sharma

This paper explores an innovative financial concept--Academic Research Output Futures (AROFs)--which aim to bring the tools of market-based finance into the realm of academic research. At the heart of this idea lies a cash-settled futures contract tethered to a composite Research Output Index (ROI), a metric designed to quantify the research productivity of

Filomene Roquefort, Alexandre Ducorroy, Rachid Riad

Early suicide risk detection in adolescents is critical yet hindered by scalability challenges of current assessments. This paper presents our approach to the first SpeechWellness Challenge (SW1), which aims to assess suicide risk in Chinese adolescents through speech analysis. Due to speech anonymization constraints, we focused on linguistic features, lever

Paolo Leonetti

We show that an ideal $\mathcal{I}$ on $\omega$ is meager if and only if the set of sequences $(x_n)$ taking values in a Polish space $X$ for which all elements of $X$ are $\mathcal{I}$-cluster points of $(x_n)$ is comeager. The latter condition is also known as $\nu$-maldistribution, where $\nu: \mathcal{P}(\omega)\to \mathbb{R}$ is the $\{0,1\}$-valued sub

Anne Kétri Pasquinelli da Fonseca, Edson Denis Leonel

We investigate symmetry breaking in a time-dependent billiard that undergoes a continuous phase transition when dissipation is introduced. The system presents unlimited velocity, and thus energy growth for the conservative dynamics. When inelastic collisions are introduced between the particle and the boundary, the velocity reaches a plateau after the crosso

Roi Cohen, Russa Biswas, Gerard de Melo

Factual completeness is a general term that captures how detailed and informative a factually correct text is. For instance, the factual sentence ``Barack Obama was born in the United States'' is factually correct, though less informative than the factual sentence ``Barack Obama was born in Honolulu, Hawaii, United States''. Despite the known fact that LLMs

M. Gorgone, F. Oliveri

In this paper, within the framework of the consistent approach recently introduced for approximate Lie symmetries of differential equations, we consider approximate Noether symmetries of variational problems involving small terms. Then, we state an approximate Noether theorem leading to the construction of approximate conservation laws. Some illustrative app

Allan Zapata

Ultrasonic sensors are devices that use sound waves to measure distances. They are useful in various applications, such as robotics or factory automation. In this report, the use of these devices for the study of kinematics is presented, specifically to measure the speed of a remote control toy car. The calibration of the sensor achieving a mean absolute err

Abhijit Chunduru, Majid Morafah, Mahdi Morafah, Vishnu Pandi Chellapandi

The inevitable presence of data heterogeneity has made federated learning very challenging. There are numerous methods to deal with this issue, such as local regularization, better model fusion techniques, and data sharing. Though effective, they lack a deep understanding of how data heterogeneity can affect the global decision boundary. In this paper, we br

CMS Collaboration

A measurement is presented of the cross section in proton-proton collisions for the production of two W bosons and one Z boson. It is based on data recorded by the CMS experiment at the CERN LHC at center-of-mass energies $\sqrt{s}$ = 13 and 13.6 TeV, corresponding to an integrated luminosity of 200 fb$^{-1}$. Events with four charged leptons (electrons or m

Vibhor Agarwal, Arjoo Gupta, Suparna De, Nishanth Sastry

Understanding online conversations has attracted research attention with the growth of social networks and online discussion forums. Content analysis of posts and replies in online conversations is difficult because each individual utterance is usually short and may implicitly refer to other posts within the same conversation. Thus, understanding individual

Berat Kutay Uğraş, Ömer Nezih Gerek, İbrahim Talha Saygı

Accurate ECG interpretation is vital, yet complex cardiac data and "black-box" AI models limit clinical utility. Inspired by Transformer architectures' success in NLP for understanding sequential data, we frame ECG as the heart's unique "language" of temporal patterns. We present CardioPatternFormer, a novel Transformer-based model for interpretable ECG clas

Hui Zheng, Hai-Teng Wang, Yi-Tao Jing, Pei-Yang Lin

Decoding speech directly from neural activity is a central goal in brain-computer interface (BCI) research. In recent years, exciting advances have been made through the growing use of intracranial field potential recordings, such as stereo-ElectroEncephaloGraphy (sEEG) and ElectroCorticoGraphy (ECoG). These neural signals capture rich population-level activ

Mentzelos Melistas

In this article with study Tamagawa numbers of elliptic curves defined over $\mathbb{Q}$ that have isogenies or torsion points. More precisely, our aim is either to bound the set of primes primes that can divide their Tamagawa numbers or, when such a bound is not possible, to find infinite subfamilies whose Tamagawa numbers are as small as possible. Finally,

Alexandre Ducorroy, Rachid Riad

Automatic Speech Recognition (ASR) has advanced with Speech Foundation Models (SFMs), yet performance degrades on dysarthric speech due to variability and limited data. This study as part of the submission to the Speech Accessibility challenge, explored model merging to improve ASR generalization using Whisper as the base SFM. We compared fine-tuning with si