October 2025 arXiv papers — page 109

Zhehao Zhang, Weijie Xu, Shixian Cui, Chandan K. Reddy

Recent advances in large reasoning models (LRMs) have enabled remarkable performance on complex tasks such as mathematics and coding by generating long Chain-of-Thought (CoT) traces. In this paper, we identify and systematically analyze a critical vulnerability we term reasoning distraction, where LRMs are diverted from their primary objective by irrelevant

Bhala Ranganathan, Mickey Zhang, Kai Wu

Hyperscale large language model (LLM) inference places extraordinary demands on cloud systems, where even brief failures can translate into significant user and business impact. To better understand and mitigate these risks, we present one of the first provider-internal, practice-based analysis of LLM inference incidents. We developed a taxonomy and methodol

Yizhuo Chen, Xin Liu, Ruijie Wang, Zheng Li

Large language models (LLMs) are typically aligned with population-level preferences, despite substantial variation across individual users. We introduce POPI, a user-level personalization framework that separates the problem into two components connected by a natural-language interface: a shared inference model that distills heterogeneous user signals into

Claire McLean, Makenzie Meendering, Tristan Swartz, Orri Gabbay

The Codec Avatars Lab at Meta introduces Embody 3D, a multimodal dataset of 500 individual hours of 3D motion data from 439 participants collected in a multi-camera collection stage, amounting to over 54 million frames of tracked 3D motion. The dataset features a wide range of single-person motion data, including prompted motions, hand gestures, and locomoti

Chu Fei Luo, Samuel Dahan, Xiaodan Zhu

As language models have a greater impact on society, it is important to ensure they are aligned to a diverse range of perspectives and are able to reflect nuance in human values. However, the most popular training paradigms for modern language models often assume there is one optimal answer for every query, leading to generic responses and poor alignment. In

Cesar Mello Fernando Medina da Cunha

Functional Spectral Imaging (FSI) models image formation as the recovery of tissue surrogates such as density and stiffness from spectral perturbations of a self-adjoint elliptic operator. Rather than relying on reflectivity or relaxation kinetics, FSI tracks shifts of a truncated set of eigenmodes under controlled excitation, providing a non-ionizing and op

Sarah Egler, John Schulman, Nicholas Carlini

Large Language Model (LLM) providers expose fine-tuning APIs that let end users fine-tune their frontier LLMs. Unfortunately, it has been shown that an adversary with fine-tuning access to an LLM can bypass safeguards. Particularly concerning, such attacks may avoid detection with datasets that are only implicitly harmful. Our work studies robust detection m

Alexander Arhangel'skii, Raushan Buzyakova

We study conditions under which a space that has a good property and a courser topology with another good property admits a continuous bijection onto a space with both properties.

Changyu Zhao, Yohan Beugin, Jean-Charles Noirot Ferrand, Quinn Burke

Dynamic program analysis is invaluable for malware detection, debugging, and performance profiling. However, software-based instrumentation incurs high overhead and can be evaded by anti-analysis techniques. In this paper, we propose LibIHT, a hardware-assisted tracing framework that leverages on-CPU branch tracing features (Intel Last Branch Record and Bran

Danil Akhtiamov, Reza Ghane, Babak Hassibi

Recent advances in neural networks have led to significant computational and memory demands, spurring interest in one-bit weight compression to enable efficient inference on resource-constrained devices. However, the theoretical underpinnings of such compression remain poorly understood. We address this gap by analyzing one-bit quantization in the Random Fea

Mojegan Azadi, Belinda Wilkes, Joanna Kuraszkiewicz, Steven. P. Willner

We present bolometric corrections, as a function of wavelength, for powerful radio-loud quasars from the Revised Third Cambridge Catalogue of Radio Galaxies (3CRR) at 1 < z < 2. The bolometric luminosities are derived by integrating the intrinsic accretion disk spectral energy distributions (SEDs) over the range 1{\mu}m-10keV (excluding reprocessed infrared

Mohsen Yarmohammadi, Libor Šmejkal, James K. Freericks

Altermagnets feature antiparallel spin sublattices with $d$-, $g$-, or $i$-wave spin order, yielding nonrelativistic spin splitting without net magnetization. We show that embedding a two-dimensional $d$-wave altermagnet in a driven optical cavity induces a finite, tunable magnetization. Coherent photon driving couples selectively to electronic sublattices,

H. Mozaffari, A. Nahvi

A general and psychologically plausible collision avoidance driver model can improve transportation safety significantly. Most computational driver models found in the literature have used control theory methods only, and they are not established based on psychological theories. In this paper, a unified approach is presented based on concepts taken from psyc

M. Pignatari, S. Amari, P. Hoppe, C. Fryer

Presolar graphite grains carry the isotopic signatures of their parent stars. A significant fraction of presolar graphites shows isotopic abundance anomalies relative to solar for elements such as O, Si, Mg and Ca, which are compatible with nucleosynthesis in core-collapse supernovae (CCSNe). Therefore, they must have condensed from CCSN ejecta before the fo

Antonio Gómez-Bañón, Pantelis Pnigouras, José A. Pons

Light QCD axions, introduced to solve the strong CP problem, may form condensates inside neutron stars, giving rise to a novel ground state of dense matter. We investigate how such axion condensates modify the equilibrium structure and radial oscillation spectrum of neutron stars. Using a realistic neutron star model with the BSk26 equation of state, and sol

Zhe Michelle Dong, Han Lin Shang, Francis Hui, Aaron Bruhn

Understanding and forecasting mortality by cause is an essential branch of actuarial science, with wide-ranging implications for decision-makers in public policy and industry. To accurately capture trends in cause-specific mortality, it is critical to consider dependencies between causes of death and produce forecasts by age and cause coherent with aggregate

Eva Maxfield Brown, Isaac Slaughter, Nicholas Weber

Software development has become essential to scientific research, but its relationship to traditional metrics of scholarly credit remains poorly understood. We develop a dataset of approximately 140,000 paired research articles and code repositories, and a predictive model that matches research article authors with software repository developer accounts. We

Travis S. Metcalfe

The Sun is just one of a hundred billion stars in the Milky Way galaxy. Our front-row seat on Earth allows us to observe it in much greater detail than we can for other stars. However, those observations provide only one snapshot in the life story of stars like the Sun. To piece together the entire tale, astronomers need to study other stars that are younger

Lukas Zbinden, Nigel Nelson, Juo-Tung Chen, Xinhao Chen

The rise of surgical robots and vision-language-action models has accelerated the development of autonomous surgical policies and efficient assessment strategies. However, evaluating these policies directly on physical robotic platforms such as the da Vinci Research Kit (dVRK) remains hindered by high costs, time demands, reproducibility challenges, and vari

Takeshi Kato, Misa Owa, Jyunichi Miyakoshi, Yasuhiro Asa

Reducing wealth inequality and resource waste is a global challenge. A fundamental problem within the capitalist economy, put simply, lies in the enslavement of labor and the colonization of resources. To address these issues, movements promoting digital democracy and cooperative platforms have emerged as viable alternatives to traditional capitalist systems

Stefanos Tsiopelas, Armen Sedrakian, Micaela Oertel

In this work, we investigate hot, isentropic compact stars in the limiting cases of static and maximally rotating configurations, focusing on how variations in the symmetry energy of the equation of state derived from covariant density functional theory affect stellar properties. We consider both nucleonic and hyperonic matter with systematically varied symm

Wangwei Dong, Zezhou Liu, Ruiyao Liu, Deborah Kuchnir Fygenson

DNA nanotechnology uses predictable interactions of nucleic acids to precisely engineer complex nanostructures. Characterizing these self-assembled structures at the single-structure level is crucial for validating their design and functionality. Nanopore sensing is a promising technique for this purpose as it is label-free, solution-based and high-throughpu

Vishal Manikanden, Aniketh Bandlamudi, Daniel Haehn

Oral Cavity Squamous Cell Carcinoma (OCSCC) is the most common type of head and neck cancer. Due to the subtle nature of its early stages, deep and hidden areas of development, and slow growth, OCSCC often goes undetected, leading to preventable deaths. However, properly trained Convolutional Neural Networks (CNNs), with their precise image segmentation tech

Hanane Nour Moussa, Patrick Queiroz Da Silva, Daniel Adu-Ampratwum, Alyson East

As AI tools become increasingly common for research ideation, robust evaluation is critical to ensure the validity and usefulness of generated ideas. We introduce ScholarEval, a retrieval augmented evaluation framework that assesses research ideas based on two fundamental criteria: soundness - the empirical validity of proposed methods based on existing lite

Patricia West, Michelle WL Wan, Alexander Hepburn, Edwin Simpson

Climate change demands effective legislative action to mitigate its impacts. This study explores the application of machine learning (ML) to understand the progression of climate policy from announcement to adoption, focusing on policies within the European Green Deal. We present a dataset of 165 policies, incorporating text and metadata. We aim to predict a

Chenyu Zhang, Navid Azizan

Multi-agent learning faces a fundamental tension: leveraging distributed collaboration without sacrificing the personalization needed for diverse agents. This tension intensifies when aiming for full personalization while adapting to unknown heterogeneity levels -- gaining collaborative speedup when agents are similar, without performance degradation when th

Bihao Zhang, Davood Soleymanzadeh, Xiao Liang, Minghui Zheng

Intelligent robotic disassembly of end-of-life (EOL) products has been a long-standing challenge in robotics. While machine learning techniques have shown promise, the lack of specialized hardware limits their application in real-world scenarios. We introduce DeGrip, a customized gripper designed for the disassembly of EOL computer desktops. DeGrip provides

Nicholas V. Nardelli, Dileep V. Reddy, Michael Grayson, Daniel Sorensen

We demonstrate the distribution of single-photon-level pulses from a mode-locked laser source over a phase-stable fiber link, achieving an optical timing jitter of less than 100 as over 10 minutes of data accumulation. This stability enables a fidelity greater than 0.998 between two stabilized 2.1 km long deployed fiber links. Building on time and frequency

Vienna Li, Justin Villa, Dan Diessner, Jayson Clifford

GPS spoofing poses a growing threat to aviation by falsifying satellite signals and misleading aircraft navigation systems. This paper demonstrates a proof-of-concept spoofing detection strategy based on analyzing satellite Carrier-to-Noise Density Ratio (C/N$_0$) variation during controlled static antenna orientations. Using a u-blox EVK-M8U receiver and a

Jennifer Hu, Ethan Gotlieb Wilcox, Siyuan Song, Kyle Mahowald

What have language models (LMs) learned about grammar? This question remains hotly debated, with major ramifications for linguistic theory. However, since probability and grammaticality are distinct notions in linguistics, it is not obvious what string probabilities can reveal about an LM's underlying grammatical knowledge. We present a theoretical analysis

C. Reichhardt, C. J. O. Reichhardt

A binary system of particles that move in opposite directions under an applied field can exhibit disordered states as well as laned states where the particles organize into oppositely moving high-mobility lanes to reduce collisions. Previous studies of laning transitions generally focused on particles with purely repulsive interactions. Here, we examine lani

Jiahe Shen

In this note, we study the distribution of the rational canonical form of a random matrix over the finite field $\mathbb{F}_p$, whose entries are independent and $ε$-balanced with $ε\in(0,1-1/p]$. We show that, as the matrix size tends to infinity, the statistics converge to independent Cohen-Lenstra distributions, demonstrating the universality of this asym

Zhongjun Qu, Wendun Wang, Xiaomeng Zhang

A rich set of frequentist model averaging methods has been developed, but their applications have largely been limited to point prediction, as measuring prediction uncertainty in general settings remains an open problem. In this paper we propose prediction intervals for model averaging based on conformal inference. These intervals cover out-of-sample realiza

Kate Glazko, Anika Arugunta, Janelle Chan, Nancy Jimenez-Garcia

In this paper, we present a case study exploring the potential use of Generative Artificial Intelligence (GAI) to address the real-world need of making the design of embroiderable art patterns more accessible. Through an auto-ethnographic case study by a disabled-led team, we examine the application of GAI as an assistive technology in generating embroidery

Jocelyn L. Mendes, Srijan Bhattacharyya, Chengye Huang, Jonathan M. Michelsen

Small polarons remain a significant bottleneck in the realization of efficient devices using transition metal oxides. Routes to engineer small polaron coupling to electronic states and lattice modes to control carrier localization remain unclear. Here, we measure the formation of small polarons in CuFeO$_{2}$ using transient extreme ultraviolet reflection sp

Qinshuang Wei, Vaibhav Srivastava, Vijay Gupta

While sequential task assignment for a single agent has been widely studied, such problems in a multi-agent setting, where the agents have heterogeneous task preferences or capabilities, remain less well-characterized. We study a multi-agent task assignment problem where a central planner assigns recurring tasks to multiple members of a team over a finite ti

Djamel Eddine Boukhari

Facial Beauty Prediction (FBP) is a complex and challenging computer vision task, aiming to model the subjective and intricate nature of human aesthetic perception. While deep learning models, particularly Convolutional Neural Networks (CNNs), have made significant strides, they often struggle to capture the global, holistic facial features that are critical

Yang Feng, Xudong Pan

Malicious agents pose significant threats to the reliability and decision-making capabilities of Multi-Agent Systems (MAS) powered by Large Language Models (LLMs). Existing defenses often fall short due to reactive designs or centralized architectures which may introduce single points of failure. To address these challenges, we propose SentinelNet, the first

Indranil Ghosh, David J. W. Simpson

Periodic orbits of systems of ordinary differential equations can be found and continued numerically by following fixed points of Poincar\'e maps. However, this often fails near grazing bifurcations where a periodic orbit collides tangentially with a boundary of phase space. Failure occurs when the map contains a square-root singularity and the root-finding

Christopher Palmer, Braden Kronheim

High statistical precision is critical for Monte Carlo (MC) samples in high energy physics and is degraded by negatively weighted events. This paper investigates a procedure to learn the relationship between the negative and positive weight distributions of any sample, allowing the reduction of statistical uncertainty by reweighting kinematically equivalent

Maxim V. Polyakov, Illarion E. Popov

This paper presents a comprehensive computer simulation of thermal processes in multilayered biological tissues for the analysis of luminance temperature distributions recorded by microwave radiometry. A mathematical model combining the bioheat transfer equation with the electrodynamic description of electromagnetic field propagation in an inhomogeneous medi

Renée-Claude Bider, Cristian Ciobanu, Jorge Campos Pazmiño, Véronique Fortier

Purpose: To improve on the original form of Fat DESPOT, a multiparametric mapping technique that returns the fat- and water-specific estimates of $R_1$ ($R_{1f}$, $R_{1w}$), $R_2^*$ , and proton density fat fraction (PDFF) by upgrading the fat-water separation method used for selection of initial parameter guesses, and by introducing explicit model sensitivi

N. Kaiser

In these notes the Born series for the $s$-wave scattering $a_0$ is calculated for a class of central potentials $V(r)$ up to sixth order in a dimensionless coupling strength $g$. Examples of exponentially decaying potentials as well truncated potentials involving a single length-scale $a$ are considered. In certain favorable cases the exact result for the $

Henrique Pickler, Jorge K. S. Kamassury, Danilo Silva

Label noise is a common problem in real-world datasets, affecting both model training and validation. Clean data are essential for achieving strong performance and ensuring reliable evaluation. While various techniques have been proposed to detect noisy labels (or label errors), there is no clear consensus on optimal approaches. We perform a comprehensive be

N. M. Murillo, C. M. Fuchs, D. Harsono, T. -H. Hsieh

Protostellar multiplicity is a common outcome of the star formation process. To fully understand the formation and evolution of these systems, the physical parameters of the molecular gas together with the dust must be systematically characterized. Using observations of molecular gas tracers, we characterize the physical properties of cloud cores in the Pers

Nyle Siddiqui, Rohit Gupta, Sirnam Swetha, Mubarak Shah

State space models (SSMs) have emerged as a competitive alternative to transformers in various tasks. Their linear complexity and hidden-state recurrence make them particularly attractive for modeling long sequences, whereas attention becomes quadratically expensive. However, current training methods for video understanding are tailored towards transformers

Sunmook Choi, Yahya Sattar, Yassir Jedra, Maryam Fazel

We study a nonstationary bandit problem where rewards depend on both actions and latent states, the latter governed by unknown linear dynamics. Crucially, the state dynamics also depend on the actions, resulting in tension between short-term and long-term rewards. We propose an explore-then-commit algorithm for a finite horizon $T$. During the exploration ph

Mateus Pinto da Silva, Sabrina P. L. P. Correa, Hugo N. Oliveira, Ian M. Nunes

Mapping agriculture in tropical areas through remote sensing presents unique challenges, including the lack of high-quality annotated data, the elevated costs of labeling, data variability, and regional generalisation. This paper advocates a Data-Centric Artificial Intelligence (DCAI) perspective and pipeline, emphasizing data quality and curation as key dri

Alex Zhavoronkov, Dominika Wilczok, Roman Yampolskiy

Since the rapid expansion of large language models (LLMs), people have begun to rely on them for information retrieval. While traditional search engines display ranked lists of sources shaped by search engine optimization (SEO), advertising, and personalization, LLMs typically provide a synthesized response that feels singular and authoritative. While both a

João Carlos Virgolino Soares, Gabriel Fischer Abati, Claudio Semini

Visual SLAM in dynamic environments remains challenging, as several existing methods rely on semantic filtering that only handles known object classes, or use fixed robust kernels that cannot adapt to unknown moving objects, leading to degraded accuracy when they appear in the scene. We present VAR-SLAM (Visual Adaptive and Robust SLAM), an ORB-SLAM3-based s

Sophie Rosu, Jesús Maíz Apellániz, Luca Sciarini, Roberto C. Gamen

{\tau} Canis Majoris (CMa) is an intriguing system that has captured astronomers' attention for more than a century. The two main components Aa and Ab are two evolved O stars on a 350 years orbit. Aa is itself a SB1 with a 155-days period and a 0.3 eccentricity. Since Hipparcos, we know that a 1.28-days period eclipsing binary (EB) is hidden somewhere in Aa

Debatri Chattopadhyay, Kyle A. Rocha, Seth Gossage, Vicky Kalogera

We model the evolution of 21 Gaia neutron star (NS)-main-sequence binaries (orbital period $P_{\mathrm{orb}}\sim200$--$1000$ days, eccentricity $e\gtrsim0.2$) using binary evolution with \texttt{MESA}. We examine eccentric mass transfer and models assuming prior circularization. All systems end as NS-white dwarf (WD) binaries, but transfer modes yield distin

Lorenz Mohr, Michael Döbereiner, Steffen Schieler, Joerg Robert

Integrated sensing and communications (ISAC), radar, and beamforming require real-time, high-resolution estimation algorithms to determine delay-Doppler values of specular paths within the wireless propagation channel. Our contribution is the measurement-based performance comparison of the delay-Doppler estimation between three different algorithms, comprisi

Menglong Zhao, Xueqiao Xu, Ben Zhu, Thomas Rognlien

A large-scale database of two-dimensional UEDGE simulations has been developed to study detachment physics in KSTAR and to support surrogate models for control applications. Nearly 70,000 steady-state solutions were generated, systematically scanning upstream density, input power, plasma current, impurity fraction, and anomalous transport coefficients, with

Mohamed Gamil, Abdelrahman Elsayed, Abdelrahman Lila, Ahmed Gad

Despite recent advances in AI, multimodal culturally diverse datasets are still limited, particularly for regions in the Middle East and Africa. In this paper, we introduce EgMM-Corpus, a multimodal dataset dedicated to Egyptian culture. By designing and running a new data collection pipeline, we collected over 3,000 images, covering 313 concepts across land

Daniel Messenger, Daniel Serino, Balu Nadiga, Marc Klasky

Efficient modeling of the Richtmyer-Meshkov instability (RMI) is essential to many engineering tasks, including high-speed combustion and drive and capsule geometry optimization in Inertial Confinement Fusion (ICF). In the latter, RMI causes the ablator and fuel to mix, introducing cold spots into the fuel and lowering performance; controlling RMI is thus a

Anjali Nambrath

The two-point energy-energy correlator (EEC) is a novel jet substructure observable probing the correlation of energy flow within jets. In these proceedings, three EEC measurements performed by the ALICE Collaboration are reported. First is a finalized measurement in proton-proton collisions, where the angular dependence of the EEC cross-section shows a sepa

Guanchen Wu, Zuhui Chen, Yuzhang Xie, Carl Yang

Protected health information (PHI) de-identification is critical for enabling the safe reuse of clinical notes, yet evaluating and comparing PHI de-identification models typically depends on costly, small-scale expert annotations. We present TEAM-PHI, a multi-agent evaluation and selection framework that uses large language models (LLMs) to automatically mea

Elija Perrier

Corporate responsibility turns on notions of corporate \textit{mens rea}, traditionally imputed from human agents. Yet these assumptions are under challenge as generative AI increasingly mediates enterprise decision-making. Building on the theory of extended cognition, we argue that in response corporate knowledge may be redefined as a dynamic capability, me

Kye Shimizu, Minghan Gao, Ananya Ganesh, Pattie Maes

This study investigated auditory self-recognition boundaries using AI voice morphing technology, examining when individuals cease recognizing their own voice. Through controlled morphing between participants' voices and demographically matched targets at 1% increments using a mixed-methods design, we measured self-identification ratings and response times am

Salvador E. Ayala-Raggi, Manuel Rendón-Marín

The perimeter of an ellipse has no exact closed-form expression in terms of elementary functions, and numerous approximations have been proposed since the eighteenth century. Classical formulas by Fagnano, Euler, and Ramanujan, as well as modern refinements such as Cantrell and Koshy methods, aim to reduce the approximation error while maintaining computatio

Zhicheng Chen, Elizabeth Denne, Kyle Patterson, Timi Patterson

Given a thin strip of paper, tie a knot, connect the ends, and flatten into the plane. This is a physical model of a folded ribbon knot in the plane, first introduced by Louis Kauffman. We study the folded ribbonlength of these folded ribbon knots, which is defined as the knot's length-to-width ratio. The {\em ribbonlength problem} asks to find the infimal f

Rico Huhnstock, Lukas Paetzold, Piotr Kuswik, Arno Ehresmann

A common issue faced by magnetic particle-based Lab-on-a-chip systems, e.g, for medical diagnostics, is the intrinsic fabrication-related polydispersity in particle sizes and magnetic properties. Therefore, to reduce this variation, it is prudent to integrate a pre-separation procedure for the particles into the overall workflow of the system. In this work,

Fateme Golivand Darvishvand, Hikaru Shindo, Sahil Sidheekh, Kristian Kersting

Reinforcement learning (RL) has experienced a second wind in the past decade. While incredibly successful in images and videos, these systems still operate within the realm of propositional tasks ignoring the inherent structure that exists in the problem. Consequently, relational extensions (RRL) have been developed for such structured problems that allow fo

Rupal Nigam, Niket Parikh, Hamid Osooli, Mikihisa Yuasa

Real-world multi-agent systems may require ad hoc teaming, where an agent must coordinate with other previously unseen teammates to solve a task in a zero-shot manner. Prior work often either selects a pretrained policy based on an inferred model of the new teammates or pretrains a single policy that is robust to potential teammates. Instead, we propose to l

Reinaldo Valeris, Yacrianny Belandria

This article develops an algebraic-geometric theoretical framework for the study of central, axial, and rotational symmetries in R2 and R3, with applications in the classification of conic and quadric surfaces through transformation groups. An analytical methodology based on group theory and geometric invariants is employed, complemented by numerical modelin

Yiming Zhang, Yuejia Xu, Ziyao Wang, Xin Yan

Globally, disparities in healthcare infrastructure remain stark, leaving countless communities without access to even basic services. Traditional infrastructure planning is often slow and inaccessible, and although many architects are actively delivering humanitarian and aid-driven hospital projects worldwide, these vital efforts still fall far short of the

Daniel Donnelly, Angelo Ferrando, Francesco Belardinelli

A key challenge in reinforcement learning (RL) is reward (mis)specification, whereby imprecisely defined reward functions can result in unintended, possibly harmful, behaviours. Indeed, reward functions in RL are typically treated as black-box mappings from state-action pairs to scalar values. While effective in many settings, this approach provides no infor

Ismail Belgacem, Franck Delaplace

Binarization of gene expression data is a \textbf{critical prerequisite} for the synthesis of Boolean gene regulatory network (GRN) models from omics datasets. Because Boolean networks encode gene activity as binary variables, the accuracy of binarization directly conditions whether the inferred models can faithfully reproduce biological experiments, capture

Jan Rozendaal

We observe that, for $r>1$, $s$ in an $r$-dependent interval, $p$ a homogeneous pseudodifferential symbol of order $m$ having $C^{r}$ regularity in space, and $u\in H^{s+m-r}(\mathbb{R}^{n})$ such that $p(x,D)u\in H^{s}(\mathbb{R}^{n})$, each point in the $H^{s+m-1}$ wavefront set of $u$ lies on a maximally extended null bicharacteristic of $p$ which is cont

Bastien Faucard

In this paper, I present a natural generalization of all the results from [6] to LVMB manifolds: to summarize, very few LVMB manifolds are lck, and none are lck with potential except for diagonal Hopf manifolds. Moreover, if $N$ is an LVMB manifold with a sufficient number of indispensable coordinates, and under a certain assumption $(H)$ (which may be artif

Xavier Giro-i-Nieto, Nefeli Andreou, Anqi Liang, Manel Baradad

Deep generative models have shown impressive progress in recent years, making it possible to produce high quality images with a simple text prompt or a reference image. However, state of the art technology does not yet meet the quality standards offered by traditional photographic methods. For this reason, production pipelines that use generated images often

Juliana Silva Canella, Norai Romeu Rocco

In this paper, we investigate the group $\nu(G)$, an extension of the non-abelian tensor square $G$ by the direct product $G\times G$, in order to determine a presentation of $G \otimes G$ when $G$ is a general finite metacyclic group, $G=g(a,b; m,n, r, s)$, with $m$ odd. A presentation of $\nu(G)$ is obtained from that of $G$ and, consequently, we describe

Eric J. Hanson, Nathan Reading

Noncrossing partition posets in a Coxeter group $W$ can fail to be lattices when $W$ is not finite. When the lattice property fails for $W$ of affine type, McCammond and Sulway's construction provides a larger lattice that contains the noncrossing partition poset and that furthermore is a combinatorial Garside structure. We construct a lattice, isomorphic to

Soumak Bhattacharjee, Stephen M. de Bruyn Kops, Andrew D. Bragg

Previous studies have shown that at the small-scales of stably stratified turbulence, the scale-dependent buoyancy flux reverses sign, such that there is a conversion of turbulent potential energy (TPE) back into turbulent kinetic energy (TKE) at these scales. Moreover, the magnitude of the reverse flux becomes stronger with increasing Prandtl number $Pr$. U

Pablo Samuel Castro

The last decade has seen an upswing in interest and adoption of reinforcement learning (RL) techniques, in large part due to its demonstrated capabilities at performing certain tasks at "super-human levels". This has incentivized the community to prioritize research that demonstrates RL agent performance, often at the expense of research aimed at understandi

Chad Schafer, Larry Wasserman, Mikael Kuusela

A recurring challenge in high energy physics is inference of the signal component from a distribution for which observations are assumed to be a mixture of signal and background events. A standard assumption is that there exists information encoded in a discriminant variable that is effective at separating signal and background. This can be used to assign a

Aria Pessianzadeh, Naima Sultana, Hildegarde Van den Bulck, David Gefen

The rise of generative AI (GenAI) has impacted many aspects of human life. As these systems become embedded in everyday practices, understanding public trust in them is also essential for responsible adoption and governance. Prior work on trust in AI has largely drawn from psychology and human-computer interaction, but there is a lack of computational, large

Jérome Eertmans, Sophie Lequeu, Benoît Legat, Laurent Jacques

We present a fast, differentiable, GPU-accelerated optimization method for ray path tracing in environments containing planar reflectors and straight diffraction edges. Based on Fermat&#39;s principle, our approach reformulates the path-finding problem as the minimization of total path length, enabling efficient parallel execution on modern GPU architectures

Longwei Wang, Ifrat Ikhtear Uddin, KC Santosh, Chaowei Zhang

Adversarial examples reveal critical vulnerabilities in deep neural networks by exploiting their sensitivity to imperceptible input perturbations. While adversarial training remains the predominant defense strategy, it often incurs significant computational cost and may compromise clean-data accuracy. In this work, we investigate an architectural approach to

Paolo Lipari

The study of the cosmic ray positron flux has attracted intense attention in recent years, especially because the observations suggest that it could receive contributions from sources such as pulsars or the self--annihilation or decay of dark matter particles. The main known source of relativistic positrons, that form the background to possible additional co

Valerie A. Rapson, Alex Pietrow, Robert J. Cumming, Jill Burns

Solar eclipses offer unparalleled opportunities for public engagement in astronomy. Large groups of people often gather to view eclipses, and these events require affordable and easy to use tools to safely observe the Sun. One unique way to observe a solar eclipse is by using a disco ball. Here, we present an analysis of the experiences of educators who used

Ahmed Fouad Kadhim Koysha, Aytug Boyaci, Rafet Akdeniz

Web Real-Time Communication (WebRTC) enables real-time peer-to-peer communication, but its Interactive Connectivity Establishment (ICE) process can unintentionally expose internal and public IP addresses as metadata. This paper presents a cross-platform measurement study of WebRTC metadata leakage using current (2025) builds of Chrome, Brave, Firefox, and To

Archie Chaudhury

Reinforcement Learning frameworks, particularly those utilizing human annotations, have become an increasingly popular method for preference fine-tuning, where the outputs of a language model are tuned to match a certain set of behavioral policies or guidelines. Reinforcement Learning through Human Feedback (RLHF) is perhaps the most popular implementation o

Daniel Csillag, Pedro Dall'Antonia, Claudio José Struchiner, Guilherme Tegoni Goedert

Prediction-powered inference is a recent methodology for the safe use of black-box ML models to impute missing data, strengthening inference of statistical parameters. However, many applications require strong properties besides valid inference, such as privacy, robustness or validity under continuous distribution shifts; deriving prediction-powered methods

Jackson D. Taylor, Emmanuel Fonseca, Lankeswar Dey, Sergey Zharikov

Trojan asteroids are found in the equilateral triangle Lagrange points of the Sun-Jupiter system in great number, though they also exist less prolifically in other parts of the Solar System. Despite up to planetary mass Trojans being predicted in extrasolar systems (i.e. exotrojans), they remain unconfirmed, although strong candidate evidence has emerged rec

Mawgan A. Smith, Ryan D. McKenzie, Alban Joseph, Robert L. Stamps

Driven-dissipative systems provide a natural setting for the emergence of exceptional points -- i.e. non-Hermitian degeneracies where eigenmodes coalesce. These points are important for applications such as sensing, where enhanced sensitivity is required, and exhibit interesting and useful phenomena that can be controlled with experimentally accessible param

Chun Ho Lau, Taige Wang

In this paper, we would establish the existence and stability of periodic solutions to the Benjamin-Bona-Mahony-Burgers (BBM-Burgers) equation in $H^1_0([0, 1])$, whose medium interior is applied with time-periodic force $f(x, t)$ with period $\theta$. High regularity analysis has been conducted in Hilbert spaces $H^\ell, \ell>1$. We also consider periodic s

Ankitkumar Joshi, Milos Hauskrecht

Modeling irregularly sampled multivariate time series is a persistent challenge in domains like healthcare and sensor networks. While recent works have explored a variety of complex learning architectures to solve the prediction problems for irregularly sampled time series, it remains unclear what the true benefits of some of these architectures are, and whe

Ahmad Arrabi, Jay Hwasung Jung, Jax Luo, Nathan Franssen

Accurate and reliable C-arm positioning is essential for fluoroscopy-guided interventions. However, clinical workflows rely on manual alignment that increases radiation exposure and procedural delays. In this work, we present a pipeline that autonomously navigates the C-arm to predefined anatomical landmarks utilizing X-ray images. Given an input X-ray image

M. A. Maldonado, Yang Li, James A. McKelvy, Andrey Matsko

We demonstrate a suppression of the light shift in a Coherent-Population-Trapping (CPT) atomic magnetometer by using linearly polarized light and a differential measurement between magnetic resonances. The radio frequency that creates the optical sidebands for CPT quickly switches between two magnetic sensitive transitions and the magnetic field is extrapola

Peter Risse, Nasim Derakhshanian, Tomas Jezo, Karol Kovarik

We present an analysis of parton distribution functions (PDFs) of the proton using Markov Chain Monte Carlo (MCMC) methods. The MCMC approach naturally implements Bayes' theorem and thus provides a means to directly sample the underlying probability distribution - in this case the probability distribution of the PDF parameters. This allows for a straightforw

Xuchen Gong, Tian Li

Classic zeroth-order optimization approaches typically optimize for a smoothed version of the original function, i.e., the expected objective under randomly perturbed model parameters. This can be interpreted as encouraging the loss values in the perturbation set to be small on average. Popular sharpness-aware minimization (SAM) objectives, however, typicall

Yueqian Lin, Zhengmian Hu, Jayakumar Subramanian, Qinsi Wang

Effective human-AI collaboration on complex reasoning tasks requires that users understand and interact with the model's process, not just receive an output. However, the monolithic text from methods like Chain-of-Thought (CoT) prevents this, as current interfaces lack real-time verbalization and robust user barge-in. We present AsyncVoice Agent, a system wh

Nathan Mclane, LeAnh Duckett, Leah G. Dodson

Nuclear-spin conversion in molecular hydrogen is governed by strict symmetry rules that typically require magnetic fields or catalytic surfaces to break. Here we demonstrate that the intrinsic tensor composition of a non-magnetic molecular crystal field can impose and relax these rules without external fields. High-resolution infrared spectra of H$_2$ in cry

Alessio Oliviero, Simone Cacace, Giuseppe Visconti

We investigate the use of multi-agent systems to solve classical image processing tasks, such as colour quantization and segmentation. We frame the task as an optimal control problem, where the objective is to steer the multi-agent dynamics to obtain colour clusters that segment the image. To do so, we balance the total variation of the colour field and fide

Robert Dougherty-Bliss, Natalya Ter-Saakov, Doron Zeilberger

In the March 2025 issue of Pour la Science, Jean-Paul Delahaye described a wonderful solution to the following problem: How many ways can you divide a 3 by 2n rectangle into two connected, congruent pieces? We show that this problem can be solved by the transfer matrix method, and demonstrate this by computing the generating function for the number of ways t

Aida Abiad, Cristina Dalfó, Miquel Àngel Fiol

For a graph $G$, its $k$-th graph power $G^k$ is constructed by placing an edge between two vertices if they are within distance $k$. We consider the problem of deriving upper bounds on the Shannon capacity of graph powers by using spectral graph theory and linear optimization methods. First, we use the so-called ratio-type bound to provide an alternative an

Neda Ebrahimian

Considering ultracold spin-imbalanced Fermi-Fermi mixtures with different spin up and down masses, the absorbed power, subject to an external perturbation with low frequency, has been calculated. The system is composed of spin-up quasiparticles and spin-down quasiholes. The average chemical potential and energy gap have also been numerically calculated via a

Alessandro Berti, Francesco Ghisoni

The preparation of data in quantum states is a critical component in the design of quantum algorithms. The cost of this step can significantly limit the realization of quantum advantage in domains such as machine learning, finance, and chemistry. One of the main approaches to achieve efficient state preparation is through the use of Quantum Random Access Mem