November 2025 arXiv papers — page 64

Jose R. Palacio, Katherine B. Ensor, Sallie A. Keller, Rebecca Schneider

Wastewater-based epidemiology (WBE) is an effective tool for tracking community circulation of respiratory viruses. We address estimating the effective reproduction number ($R_t$) and the relative number of infections from wastewater viral load. Using weekly Houston data on respiratory syncytial virus (RSV), we implement a parsimonious Bayesian renewal model

Robert S. Coulter, Steven Senger

We show that the graph of a bent function is a Salem set in an appropriate sense. We also establish a simple result that quantifies redundancies in the difference operators of a function, which applies to bent functions over fields of odd characteristic via their equivalence to perfect non-linear functions in that setting. We end by demonstrating, by entirel

Scott C. Chapman, Roger P. Deane, Dazhi Zhou, Manuel Aravena

Following the detection of a radio-loud Active Galactic Nucleus (AGN) in the z=4.3 protocluster SPT2349-56, we have obtained additional observations with MeerKAT in S-band (2.4 GHz) with the aim of further characterizing radio emission from amongst the ~30 submillimeter (submm) galaxies (SMGs) identified in the structure. We newly identify three of the proto

Erkao Bao, Lina Liu

In this note, we construct invariant and coinvariant Morse chain complexes with integer coefficients for any compact effective orbifold. We show that the homologies of these two chain complexes are invariants of the orbifold. We conjecture that the homology of the coinvariant chain complex computes the singular homology of the underlying topological space wi

Thomas Pigeon, Manuel Corral Valero, Pascal Raybaud

This study address the computational determination of catalytic reaction rates by moving beyond traditional Transition State Theory (TST), addressing its limitations in complex systems. The Hill relation framework, integrated with Adaptive Multilevel Splitting (AMS), offers exact rate constants for stochastic dynamics, overcoming TST's assumptions and limita

Cuong Pham, Hoang Anh Dung, Cuong C. Nguyen, Trung Le

Large language models require significant computational resources for deployment, making quantization essential for practical applications. However, the main obstacle to effective quantization lies in systematic outliers in activations and weights, which cause substantial LLM performance degradation, especially at low-bit settings. While existing transformat

Thales Sales Almeida, Ramon Pires, Hugo Abonizio, Rodrigo Nogueira

Large Language Models (LLMs) exhibit significant variations in performance across linguistic and cultural contexts, underscoring the need for systematic evaluation in diverse languages. In this work, we present the most extensive evaluation of LLMs for the Portuguese language to date. Leveraging our newly introduced PoETa v2 benchmark -- a comprehensive suit

Gilberto Aguilar-Pérez, Giovany Cruz, Miguel Cruz, Efraín Rojas

In this work, we explore the effect at cosmological level of the extra contribution arising from the Geodetic Brane Gravity model within a thermodynamical perspective. As already known, the universe seen as an extended object embedded within a higher dimensional space time, modifies the dynamical background equations, which in turn results in correction cont

Ryoma Yataka, Pu Perry Wang, Petros Boufounos, Ryuhei Takahashi

Multi-view indoor radar perception has drawn attention due to its cost-effectiveness and low privacy risks. Existing methods often rely on {implicit} cross-view radar feature association, such as proposal pairing in RFMask or query-to-feature cross-attention in RETR, which can lead to ambiguous feature matches and degraded detection in complex indoor scenes.

Chengan Che, Chao Wang, Xinyue Chen, Sophia Tsoka

Procedural activities, ranging from routine cooking to complex surgical operations, are highly structured sequences of actions performed in a specific temporal order. Despite the success of current self-supervised learning (SSL) methods on static images and short clips, these models often overlook the underlying sequential structure of such activities. We ex

Toni Scarmato

Measuring cometary nuclei is notoriously difficult because they are usually unresolved and embedded within bright comae, which hampers direct size measurements even with space telescopes. We present a practical, instrumental method that, stabilises the inner core through bicubic resampling, performs forward point-spread function PSF+convolution, and separate

Joao Lizárraga, Marcus A. M. de Aguiar

Using a minimal aggregation-based model, we address the efficient information transfer observed in natural flocks during collective turns. In our system, the turning signal emitted by a single individual propagates throughout the collective primarily at a constant speed. To characterize the source of this capability, we analyze the system in the continuum li

Kumar Krishna Agrawal, Longchao Liu, Long Lian, Michael Nercessian

Radiology plays an integral role in modern medicine, yet rising imaging volumes have far outpaced workforce growth. Foundation models offer a path toward assisting with the full spectrum of radiology tasks, but existing medical models remain limited: they process volumetric CT and MRI as low-fidelity 2D slices, discard critical grayscale contrast information

Cuong Pham, Hoang Anh Dung, Cuong C. Nguyen, Trung Le

Large language models (LLMs) have significantly advanced natural language processing, but their massive parameter counts create substantial computational and memory challenges during deployment. Post-training quantization (PTQ) has emerged as a promising approach to mitigate these challenges with minimal overhead. While existing PTQ methods can effectively q

Isabelle Diana May-Xin Ng, Tharindu Cyril Weerasooriya, Haitao Zhu, Wei Wei

In this paper, we introduce, MultiGA, an optimization framework which applies genetic algorithm principles to address complex natural language tasks and reasoning problems by sampling from a diverse population of LLMs to initialize the population of candidate solutions. MultiGA generates a range of outputs from various parent LLMs and uses a neutral fitness

Ren Deng, Kaya Mori, Eric Miao, Gabriel Bridges

We present X-ray observations of the periodic optical source ZTF J185139.81+171430.3 (hereafter ZTF J1851) by the XMM, NICER and NuSTAR telescopes. The source was initially speculated to be a white dwarf (WD) pulsar system due to its short period ($P\sim12$ min) and highly-modulated optical lightcurves. Our observations revealed a variable X-ray counterpart

Qiang Liu, Wenlong Zhang, Muhui Jiang, Lei Wu

Security bugs in the Linux kernel emerge endlessly and have attracted much attention. However, fixing security bugs in the Linux kernel could be incomplete due to human mistakes. Specifically, an incomplete fix fails to repair all the original security defects in the software, fails to properly repair the original security defects, or introduces new ones. In

L. J. M. Houben, H. Falcke, L. G. Spitler, E. D. Barr

The High Time Resolution Universe (HTRU) survey is an all-sky survey looking for pulsars and other radio transients. A new single-pulse (SP) search pipeline is presented, tailored to the northern part of the HTRU survey collected with the 100m Effelsberg Radio Telescope. In a selection of this data, synthetic SPs are injected with frequency-time structures r

Afsaneh Mahanipour, Hana Khamfroush

Multi-label feature selection (FS) reduces the dimensionality of multi-label data by removing irrelevant, noisy, and redundant features, thereby boosting the performance of multi-label learning models. However, existing methods typically require centralized data, which makes them unsuitable for distributed and federated environments where each device/client

Edgar López-Contreras, José Antonio García-Hernández, Elías Natanael Polanco-Euán, Wolfgang Bietenholz

Despite intense experimental and theoretical research, the QCD phase diagram at finite baryon density remains to a large extent unexplored. From the theoretical side, the obvious non-perturbative approach is lattice QCD simulations, which are, however, obstructed by a severe sign problem. Here we employ the O(4) non-linear $\sigma$-model as an effective theo

Hamoon Soleimani

Since its inception, Bitcoin has been positioned as a revolutionary alternative to national currencies, attracting immense public and academic interest. This paper presents a critical evaluation of this claim, suggesting that Bitcoin faces significant structural barriers to qualifying as money. It synthesizes critiques from two distinct schools of economic t

Shweta Mahajan, Hoang Le, Hyojin Park, Farzad Farhadzadeh

Large Vision-Language Models (VLMs) rely on effective multimodal alignment between pre-trained vision encoders and Large Language Models (LLMs) to integrate visual and textual information. This paper presents a comprehensive analysis of attention patterns in efficient VLMs, revealing that concatenation-based architectures frequently fail to distinguish betwe

Dingrui Wang, Zhihao Liang, Hongyuan Ye, Zhexiao Sun

While recent video world models can generate highly realistic videos, their ability to perform semantic reasoning and planning remains unclear and unquantified. We introduce Target-Bench, the first benchmark that enables comprehensive evaluation of video world models' semantic reasoning, spatial estimation, and planning capabilities. Target-Bench provides 45

Vincent Guillemet, Michael Unser

Multidimensional continuous-domain inverse problems are often solved by the minimization of a loss functional, formed as the sum of a data fidelity and a regularization. In this work, we present a new construction where the regularization is itself built as the sum of two terms: i) the M norm of the regularizing operator L1 b L2, with L1 and L2 being two one

Kaytki Chakankar, Xinhui Tang, Yiguo Zhang

The centipede game is a two-player non-zero-sum game. Each turn, a player can choose whether they want to take or pass a growing reward. The classical, rational solution of this game shows defection in the first round, when in reality, players cooperate much more often. Inspired by prior work employing quantum strategies in the prisoners dilemma, we showed t

O. P. Mykhailiv, Yu. A. Mishchenko, A. M. Gavrilik

In this paper, we derive the $\mu$-deformed Einstein field equations from the generalized thermodynamic functions of the $\mu$-deformed analog of Bose gas model, applying the (adapted) Verlinde's approach. The basic role of deformation parameter is shown: it provides the possibility to vary the value of the cosmological constant. Due to this, we suggest an i

Sam Dillavou, Shruti Mishra

Digital computers are power-hungry and largely intolerant of damaged components, making them potentially difficult tools for energy-limited autonomous agents in uncertain environments. Recently developed Contrastive Local Learning Networks (CLLNs) - analog networks of self-adjusting nonlinear resistors - are inherently low-power and robust to physical damage

Steven Young Eulig

The IceCube Neutrino Observatory is a cubic-kilometer Cherenkov detector embedded in the Antarctic ice at the South Pole. Its densely instrumented sub-array and dedicated low-energy analyses provide sensitivity to neutrinos in the 5-100 GeV range, enabling precision studies of neutrino oscillations and searches for new physics. This work focuses specifically

Elizabeth Chen, Andrew Lee, Tanbir Sarowar, Xiaolin Chen

Deterministic Lateral Displacement (DLD) devices are widely used in microfluidics for label-free, size-based separation of particles and cells, with particular promise in isolating circulating tumor cells (CTCs) for early cancer diagnostics. This study focuses on the optimization of DLD design parameters, such as row shift fraction, post size, and gap distan

Matteo Scandola, Silvia Holler, Richard J. G. Loeffler, Martin M. Hanczyc

The study of synthetic active matter systems holds the promise for designing smart materials and devices with emergent characteristics akin to those of living organisms, eventually opening the doors to the realization of artificial life. Such an investigation, however, is challenged by the difficulty inherent in identifying the relationship between the featu

H. S. Parker, S. L. Sims, D. J. Lamphere, A. L. Harris

Circular Rydberg states offer advantages for quantum information and quantum simulation platforms due to their long lifetimes and strong dipole-dipole interactions. Unfortunately, current techniques for the production of these states remain technically challenging. Here we investigate the ability of twisted electron collisions to produce circular Rydberg sta

Lyu Yuhuan

Verifying uniform conditions over continuous spaces through random sampling is fundamental in machine learning and control theory, yet classical coverage analyses often yield conservative bounds, particularly at small failure probabilities. We study uniform random sampling on the $d$-dimensional unit hypercube and analyze the number of uncovered subcubes aft

Jony Karki, Dongzhou Huang, Yunpeng Zhao

Node popularity is recognized as a key factor in modeling real-world networks, capturing heterogeneity in connectivity across communities. This concept is equally important in bipartite networks, where nodes in different partitions may exhibit varying popularity patterns, motivating models such as the Two-Way Node Popularity Model (TNPM). Existing methods, s

Yiwen Kou, Raghu Meka

Agnostic learning of Boolean halfspaces is a fundamental problem in computational learning theory, but it is known to be computationally hard even for weak learning. Recent work [CKKMK24] proposed smoothed analysis as a way to bypass such hardness, but existing frameworks rely on additive Gaussian perturbations, making them unsuitable for discrete domains. W

Kristy Sakano, Jianyu An, Dinesh Manocha, Huan Xu

We present a novel, regulator-driven approach for the temporal verification of black-box autonomous robot policies, inspired by real-world certification processes where regulators often evaluate observable behavior without access to model internals. Central to our method is a regulator-in-the-loop approach that evaluates execution traces from black-box polic

Elijah Pelofske, Pratik Sathe, Cristiano Nisoli, Frank Barrows

Using programmable analog quantum annealing processors, we implement a sampling-based magnetic hysteresis protocol to probe the counterintuitive notion of magnetic memory in antiferromagnetic models. A key component of this protocol responsible for the hysteresis is a transverse field, which enables state transitions, while the longitudinal magnetic field sw

Elchin Hasanalizade, Hua Lin, Greg Martin, Andradis Luna Martínez

Burgess proved that for $χ_q$ a primitive Dirichlet character modulo $q$ with $q$ cubefree, $\sum_{M< n\le M+N}χ_q(n)= O\left(N^{1-\frac{1}{r}}q^{\frac{r+1}{4r^2}+ε}\right)$ for all integers $r\ge1.$ More recently, explicit versions with prime moduli $q$ were computed by Booker, McGown, Treviño, and Francis, with applications to finding the least $k$-th powe

Ravi Prakash, Vincent Y. Wang, Arpit Mishra, Devi Yuliarti

Robotic laser systems offer the potential for sub-millimeter, non-contact, high-precision tissue resection, yet existing platforms lack volumetric planning and intraoperative feedback. We present RATS (Robot-Assisted Tissue Surgery), an intelligent opto-mechanical, optical coherence tomography (OCT)-guided robotic platform designed for autonomous volumetric

Melika Shirian, Kianoosh Vadaei, Kian Majlessi, Audrina Ebrahimi

We present PrismSSL, a Python library that unifies state-of-the-art self-supervised learning (SSL) methods across audio, vision, graphs, and cross-modal settings in a single, modular codebase. The goal of the demo is to show how researchers and practitioners can: (i) install, configure, and run pretext training with a few lines of code; (ii) reproduce compac

Sandro Rama Fiorini, Leonardo G. Azevedo, Raphael M. Thiago, Valesca M. de Sousa

Agentic frameworks powered by Large Language Models (LLMs) can be useful tools in scientific workflows by enabling human-AI co-creation. A key challenge is recommending the next steps during workflow creation without relying solely on LLMs, which risk hallucination and require fine-tuning with scarce proprietary data. We propose an episodic memory architectu

Salma Mozaffari, Daniel Ruan, William van den Bogert, Nima Fazeli

Fabrication uncertainty arising from tolerance accumulation, material imperfection, and positioning errors remains a critical barrier to automated robotic assembly in construction, particularly for contact-rich manipulation tasks under minimal geometric clearance. This paper investigates the deployment of diffusion policy learning on construction-scale indus

Maria Bou-Sakr-El-Tayar, Jason J. Bramburger, Matthew J. Colbrook

Many data-driven algorithms in dynamical systems rely on ergodic averages that converge painfully slowly. One simple idea changes this: taper the ends. Weighted Birkhoff averages can converge much faster (sometimes superpolynomially, even exponentially) and can be incorporated seamlessly into existing methods. We demonstrate this with five weighted algorithm

E. V. Chen, A. L. Saewert, R. V. Sharankova

The closed-off structure of the Fermilab Drift Tube Linac precludes a robust array of instrumentation from directly monitoring the H- beam that is accelerated from 750 keV to 116 MeV. To improve beam tuning and operational assessment of Drift Tube Linac performance, scintillator-based loss monitors were previously installed along the exterior of the first tw

Daniele Amato, Paolo Facchi, Arturo Konderak

We study the asymptotic dynamics of open quantum systems in the Heisenberg picture. We find an explicit expression for the attractor subspace and the dynamics that takes place in it. We present the relationship between the attractor subspaces in the Schr\"odinger and Heisenberg pictures and, in particular, the connection between their algebraic structures. A

A. Rodríguez-González, P. R. Rivera-Ortiz, Z. Meliani, E. Alquicira-Peláez

There is clear observational evidence that the main Class 0/I stages of the star formation process are associated with powerful collimated outflows (jets), which sometimes propagate up to distances as large as $10^{4-5}$ au scales in molecular clouds. Additionally, intermediate high-mass and low-mass protostars have often been observed to form in crowded clu

Nazir Khan, Rahul Jangid, Taras Stanislavchuk, Aaron Stein

Production and manipulation of orbital angular momentum (OAM) of coherent soft x-ray beams is demonstrated utilizing consecutive diffractive optics. OAM addition is observed upon passing the beam through consecutive fork gratings. The OAM of the beam was found to be decoupled from its spin angular momentum (SAM). Practical implementation of angular momentum

Dominic Mashak, S. A. Alexander

Evolutionary algorithms for molecular design require computationally efficient yet accurate fitness functions. We systematically benchmark Hartree-Fock and density functional theory for predicting molecular first hyperpolarizability ($\beta$), evaluating five functionals (HF, PBE0, B3LYP, CAM-B3LYP, M06-2X) across six basis sets against experimental data fro

Mansur Yerzhanuly

The rapid advancement of generative models such as StyleGAN2 and Stable Diffusion poses a growing threat to the authenticity of satellite imagery, which is increasingly vital for reliable analysis and decision-making across scientific and security domains. While deepfake detection has been extensively studied in facial contexts, satellite imagery presents di

Darren Chiu, Zhehui Huang, Ruohai Ge, Gaurav S. Sukhatme

Nano-UAV teams offer great agility yet face severe navigation challenges due to constrained onboard sensing, communication, and computation. Existing approaches rely on high-resolution vision or compute-intensive planners, rendering them infeasible for these platforms. We introduce LEARN, a lightweight, two-stage safety-guided reinforcement learning (RL) fra

Patryk Michalski, Arturo Konderak, Wojciech Bruzda, Remigiusz Augusiak

Bell inequalities provide a fundamental tool for probing nonlocal correlations, yet their quantum bound, that is, the maximal value attainable through quantum strategies, is rarely accessible analytically. In this work, we introduce a general construction of Bell inequalities for which this bound can be computed exactly. Our framework generalizes both the Cl

P. Frank Winkler, Joseph Putko, William P. Blair

We report a series of images of the Tycho supernova remnant at eight epochs extending over thirty years: 1986-2016. In addition to our H{\alpha} images, we have obtained matched continuum images which we subtract to reveal faint emission, including a far more extensive network of optical knots and filaments than reported previously. The deepest images also s

Arun Kavishwar, William Lotter

Over 1,200 AI-enabled medical devices have received marketing authorization from the U.S. FDA, yet identifying devices suited to specific clinical needs remains challenging because the FDA's databases contain only limited metadata and non-searchable summary PDFs. To address this gap, we developed FDA AI Search, a website that enables semantic querying of FDA

Henning Femmer, Ivan Esau

Context and motivation. Requirements Engineering (RE) quality still lacks empirical evidence on how specific requirement defects affect downstream activities. Problem: However, empirical data on the detailed effects of requirements quality defects is scarce, since it is costly to obtain. Furthermore, with the advent of AI-based development, the requirements

Manuel Kern, Dominik Steffan, Felix Schuster, Florian Skopik

Intrusion Detection Systems (IDS) are critical to defending enterprise and industrial control environments, yet evaluating their effectiveness under realistic conditions remains an open challenge. Existing benchmarks rely on synthetic datasets (e.g., NSL-KDD, CICIDS2017) or scripted replay frameworks, which fail to capture adaptive adversary behavior. Even M

Ashley S. Dale, Kangming Li, Brian DeCost, Hao Wan

Efficiently and meaningfully estimating prediction uncertainty is important for exploration in active learning campaigns in materials discovery, where samples with high uncertainty are interpreted as containing information missing from the model. In this work, the effect of different uncertainty estimation and calibration methods are evaluated for active lea

Artyom Hovhannisyan, Ulrich Nierste

The precise measurement of the width difference ΔΓ_s among the mass eigenstates of the B_s-\bar{B}_s system requires the calculation of the corresponding decay matrix to order α_s/m_b. QCD corrections to power-suppressed terms in the Heavy Quark Expansion involve the renormalization of dimension-7 four-quark operators for which no general methodology is avai

Giancarlo Giannetti, Faisal Z. Qureshi

Hyperspectral images capture rich spectral information that enables per-pixel material identification; however, spectral mixing often obscures pure material signatures. To address this challenge, we propose the Latent Dirichlet Transformer Variational Autoencoder (LDVAE-T) for hyperspectral unmixing. Our model combines the global context modeling capabilitie

Aparup Khatua, David Jurgens, Ingmar Weber

Immigrants bring unique cultural backgrounds to their host countries. Subsequent interplay of cultures can lead to either a melting pot, where immigrants adopt the dominant culture of the host country, or a mosaic, where distinct cultural identities coexist. The existing literature primarily focuses on the acculturation of immigrants, specifically the meltin

Prantik Howlader, Hoang Nguyen-Canh, Srijan Das, Jingyi Xu

Reasoning segmentation seeks pixel-accurate masks for targets referenced by complex, often implicit instructions, requiring context-dependent reasoning over the scene. Recent multimodal language models have advanced instruction following segmentation, yet generalization remains limited. The key bottleneck is the high cost of curating diverse, high-quality pi

Andrew Lee, Mahir Mobarrat, Xiaolin Chen

Deterministic Lateral Displacement (DLD) devices enable liquid biopsy for cancer detection by separating circulating tumor cells (CTCs) from blood samples based on size, but designing these microfluidic devices requires computationally expensive Navier-Stokes simulations and particle-tracing analyses. While recent surrogate modeling approaches using deep lea

Austin M. Wallace, C. David Sherrill, Giri P. Krishnan

Ab initio quantum chemical methods for accurately computing interactions between molecules have a wide range of applications but are often computationally expensive. Hence, selecting an appropriate method based on accuracy and computational cost remains a significant challenge due to varying performance of methods. In this work, we propose a framework based

Sebastian Grieninger, Jake Montgomery, Felix Ringer, Ismail Zahed

Generalized Parton Distribution functions (GPDs) are off-diagonal light-cone matrix elements that encode the internal structure of hadrons in terms of quark and gluon degrees of freedom. In this work, we present the first nonperturbative study of quasi-GPDs in the massive Schwinger model, quantum electrodynamics in 1+1 dimensions (QED2), within the Hamiltoni

Jason DeVito, Joan West

In each dimension of the form $4n-1$ with $n\geq 3$, we construct infinitely many new examples of manifolds admitting metrics with positive sectional curvature almost everywhere. In addition, we show that if $n\geq 6$, infinitely many of our examples are not homotopy equivalent to any homogeneous space, providing the first infinite family of such examples.

Zhimin Shao, Abhay Yadav, Rama Chellappa, Cheng Peng

Reliable image correspondences form the foundation of vision-based spatial perception, enabling recovery of 3D structure and camera poses. However, unconstrained feature matching across domains such as aerial, indoor, and outdoor scenes remains challenging due to large variations in appearance, scale and viewpoint. Feature matching has been conventionally fo

Lillian I. Payne Torres, Irma Avdic, Anna O. Schouten, Olivia C. Wedig

Quantum phenomena such as entanglement provide powerful resources for enhancing classical sensing. Here, we theoretically show that collective entanglement of spin qubits, arising from a condensation of particle-hole pairs, can strongly amplify transitions between ground and excited spin states, potentially improving signal contrast in optically detected mag

Daniel Myrén, Zeeshan Afzal, Mikael Asplund

Flexible energy resources are increasingly becoming common in smart grids. These resources are typically managed and controlled by aggregators that coordinate many resources to provide flexibility services. However, these aggregators and flexible energy resources are vulnerable, which could allow attackers to remotely control flexible energy resources to lau

Penghao Rao, Runmin Jiang, Min Xu

Approximating training-point influence on test predictions is critical for deploying deep-learning vision models, essential for locating noisy data. Though the influence function was proposed for attributing how infinitesimal up-weighting or removal of individual training examples affects model outputs, its implementation is still challenging in deep-learnin

Theo Demessance, Chongke Bi, Sonia Djebali, Guillaume Guerard

Nowadays, social networks are becoming a popular way of analyzing tourist behavior, thanks to the digital traces left by travelers during their stays on these networks. The massive amount of data generated; by the propensity of tourists to share comments and photos during their trip; makes it possible to model their journeys and analyze their behavior. Predi

Dawid Wolkiewicz, Anastasiya Pechko, Przemysław Spurek, Piotr Syga

The growing adoption of photorealistic 3D facial avatars, particularly those utilizing efficient 3D Gaussian Splatting representations, introduces new risks of online identity theft, especially in systems that rely on biometric authentication. While effective adversarial masking methods have been developed for 2D images, a significant gap remains in achievin

Sharaj Kunjar, Alyssa Hasegawa Smith, Tyler R Mckenzie, Rushali Mohbe

Computational approaches have previously shown various promises and pitfalls when it comes to the reliable identification of media frames. Generative LLMs like GPT and Claude are increasingly being used as content analytical tools, but how effective are they for frame analysis? We address this question by systematically evaluating them against their computat

Robin Khanfir, Béranger Seguin

We study $n$-flimsy spaces, which are the topological spaces that remain connected when removing fewer than $n$ points but become disconnected when removing exactly $n$ points. We show that no such space exists for $n \geq 3$, and that the compact $2$-flimsy spaces are precisely the dense and order-complete cyclically ordered sets equipped with their order t

Jinyi Hao, Jie Wang, Liqin Gao, Tristan T. Hormel

Retinal neovascularization (RNV) is a vision threatening development in diabetic retinopathy (DR). Vision loss associated with RNV is preventable with timely intervention, making RNV clinical screening and monitoring a priority. Optical coherence tomography (OCT) angiography (OCTA) provides high-resolution imaging and high-sensitivity detection of RNV lesion

Malcolm Slutzky, Alice Pelosse, Michael van der Naald, Heinrich M. Jaeger

Impacted with sufficiently large stress, a dense, initially liquid-like suspension can be forced into a solid-like state through the process of shear jamming. While the onset of shear jamming has been investigated extensively, less is known about the resulting solid-like state in the high stress limit and its failure. We experimentally produce such high-stre

Justin Diamond, Markus Lill

We prove that a denoising diffusion sampler equipped with a sequential bias across the batch dimension is exactly an Euler-Maruyama integrator for overdamped Langevin dynamics. Each reverse denoising step, with its associated spring stiffness, can be interpreted as one step of a stochastic differential equation with an effective time step set jointly by the

Vicente Vergara

We present a time-frequency framework adapted to dispersive phase functions via a subdyadic geometry in phase space. On top of this geometry we construct stable Gabor frames with quantitative control of overlap, almost orthogonality, and off-diagonal decay. Based on these frames we introduce modulation spaces consistent with the subdyadic scale and establish

Adrien Grenier, Chris Kapulkin

We show that the categories of directed and undirected reflexive graphs carry exactly two (up to isomorphism) biclosed monoidal structures.

Tristen Shields, Marcia Rieke, Kevin Hainline, Jakob M. Helton

Low surface brightness galaxies (LSBs) are an important class of galaxies that allow us to broaden our understanding of galaxy formation and test various cosmological models. We present a survey of low surface brightness galaxies at $0.4 < z_{\rm phot} < 0.8$ in the GOODS-S field using JADES data. We model LSB surface brightness profiles, identifying those w

João M. Silva, Alba Soto-Ontoso

Spin correlations are required to reproduce the correct azimuthal dependence of matrix elements for successive branchings at disparate angles in QCD jets. In this paper, we study modifications to this, $\cos(2\psi_{12})$, azimuthal pattern in the presence of a quark-gluon plasma. To that end, we consider a simplified setup in which a narrow and energetic QCD

H. R. Paz

Social integration theories suggest that students embedded in supportive peer networks are less likely to drop out. In learning analytics, this has motivated the use of social network analysis (SNA) from institutional co-enrolment data to predict attrition. This study tests whether such administrative network features add predictive value beyond a leakage-aw

Samuel Stevens, Jacob Beattie, Tanya Berger-Wolf, Yu Su

Scientific archives now contain hundreds of petabytes of data across genomics, ecology, climate, and molecular biology that could reveal undiscovered patterns if systematically analyzed at scale. Large-scale, weakly-supervised datasets in language and vision have driven the development of foundation models whose internal representations encode structure (pat

Marcio Pohlmann, Alex Severo, Gefté Almeida, Diego Kreutz

SOCs and CSIRTs face increasing pressure to automate incident categorization, yet the use of cloud-based LLMs introduces costs, latency, and confidentiality risks. We investigate whether locally executed SLMs can meet this challenge. We evaluated 21 models ranging from 1B to 20B parameters, varying the temperature hyperparameter and measuring execution time

Javier de Lucas, Xavier Rivas, Tomasz Sobczak

This paper introduces a new class of Lie systems that are Hamiltonian relative to a $k$-contact manifold. We show that a recent distributional approach to $k$-contact manifolds along with a related $k$-contact Hamiltonian vector field notion allow us to understand relevant Lie systems as Hamiltonian relative to a $k$-contact manifold. Our procedure is more g

Tristan Mott, Caleb Bradshaw, David Grimsman, Christopher Archibald

Baseball is a game of strategic decisions including bullpen usage, pinch-hitting and intentional walks. Managers must adjust their strategies based on the changing state of the game in order to give their team the best chance of winning. In this thesis, we investigate how matchup models -- tools that predict the probabilities of plate appearance outcomes --

Birendra Dhanasingham, Francis-Yan Cyr-Racine, Daniel Gilman

Strong gravitational lensing is a powerful probe for studying the fundamental properties of dark matter on sub-galactic scales. Detailed analyses of galaxy-scale lenses have revealed localized gravitational perturbations beyond the smooth mass distribution of the main lens galaxy, largely attributed to dark matter subhalos and intervening line-of-sight halos

Zeinab Nezami, Shehr Bano, Abdelaziz Salama, Maryam Hafeez

Generative multiagent systems are rapidly emerging as transformative tools for scalable automation and adaptive decisionmaking in telecommunications. Despite their promise, these systems introduce novel risks that remain underexplored, particularly when agents operate asynchronously across layered architectures. This paper investigates key safety pathways in

Aldo Canfora, Eleonora Bergamaschi, Riccardo Mioli, Federico Battini

Urban Building Energy Modeling (UBEM) plays a central role in understanding and forecasting energy consumption at the city scale. In this work, we present a UBEM pipeline that integrates EnergyPlus simulations, high-performance computing (HPC), and open geospatial datasets to estimate the energy demand of buildings in Bologna, Italy. Geometric information in

Yang Zhou, Mingyu Zhao, Zhenting Wang, Difei Gu

We present M^3-Bench, the first benchmark for evaluating multimodal tool use under the Model Context Protocol. The benchmark targets realistic, multi-hop and multi-threaded workflows that require visual grounding and textual reasoning, cross-tool dependencies, and persistence of intermediate resources across steps. We introduce a similarity-driven alignment

Chandrasekhar Gokavarapu, D. Madhusudhana Rao

Binary semirings such as the tropical, log, and probability semirings form a core algebraic tool in classical and modern neural inference systems, supporting tasks like Viterbi decoding, dynamic programming, and probabilistic reasoning. However, these structures rely on a binary multiplication operator and therefore model only pairwise interactions. Many sym

Victor Li, Naveenraj Kamalakannan, Avinash Parnandi, Heidi Schambra

Vision-language models (VLMs) have demonstrated remarkable performance across a wide range of computer-vision tasks, sparking interest in their potential for digital health applications. Here, we apply VLMs to two fundamental challenges in data-driven stroke rehabilitation: automatic quantification of rehabilitation dose and impairment from videos. We formul

Subhash Sethumurugan, Hari Cherupalli, Kangjie Lu, John Sartori

Recent work has shown that out-of-order and speculative execution mechanisms used to increase performance in the majority of processors expose the processors to critical attacks. These attacks, called Meltdown and Spectre, exploit the side effects of performance-enhancing features in modern microprocessors to expose secret data through side channels in the m

Jose A. Ordoñez, Tsung-I Lin, Victor H. Lachos, Luis M. Castro

We propose a new Bayesian approach for spatiotemporal areal data with censored and missing observations. The method introduces a flexible random effect that combines the spatial dependence structures of the Simultaneous Autoregressive (SAR) and Directed Acyclic Graph Autoregressive (DAGAR) models with a temporal autoregressive component. We demonstrate that

Xiaoxuan Yang, Peilin Chen, Tergel Molom-Ochir, Yiran Chen

Transformers have become central to natural language processing and large language models, but their deployment at scale faces three major challenges. First, the attention mechanism requires massive matrix multiplications and frequent movement of intermediate results between memory and compute units, leading to high latency and energy costs. Second, in long-

Mohammad Atwany, Mojtaba Lashgari, Robin P. Choudhury, Vicente Grau

Cardiovascular diseases are the leading cause of death globally, with X-ray Coronary Angiography (XCA) as the gold standard during real-time cardiac interventions. Segmentation of coronary vessels from XCA can facilitate downstream quantitative assessments, such as measurement of the stenosis severity and enhancing clinical decision-making. However, developi

Saurav Sengupta, Nazanin Moradinasab, Jiebei Liu, Donald E. Brown

Recent research suggests that Vision Language Models (VLMs) often rely on inherent biases learned during training when responding to queries about visual properties of images. These biases are exacerbated when VLMs are asked highly specific questions that require selective visual attention, a demand that mirrors cognitive challenges observed in human enumera

Shreya Sinha-Roy, Richard G. Everitt, Christian P. Robert, Ritabrata Dutta

Data assimilation is a fundamental task in updating forecasting models upon observing new data, with applications ranging from weather prediction to online reinforcement learning. Deep generative forecasting models (DGFMs) have shown excellent performance in these areas, but assimilating data into such models is challenging due to their intractable likelihoo

Sean Cowan, Pietro Fanti, Leon B. S. Williams, Chit Hong Yam

Private lunar missions are faced with the challenge of robust autonomous navigation while operating under stringent constraints on mass, power, and computational resources. This work proposes a motion-field inversion framework that uses optical flow and rangefinder-based depth estimation as a lightweight CPU-based solution for egomotion estimation during lun

Mátyás Domokos, Barna Schefler

Let $G$ be a finite group and $K$ a field containing an element of multiplicative order $|G|$. It is shown that if $G$ has a cyclic subgroup of index at most $2$, then the separating Noether number over $K$ of $G$ coincides with the Noether number over $K$ of $G$. The same conclusion holds when $G$ is the direct product of a dihedral group and the $2$-elemen

Nazanin Mirhosseini, Jie Luo

We consider a two-user random access system in which each user independently selects a coding scheme from a finite set for every message, without sharing these choices with the other user or with the receiver. The receiver aims to decode only user 1 message but may also decode user 2 message when beneficial. In the synchronous setting, the receiver employs t

Mykhaylo Plotnykov, Diana Valencia, Alejandra Ross, Henrique Reggiani

The relationship between the composition of rocky exoplanets and their host stars is fundamental to understanding planetary formation and evolution. However, previous studies have been limited by inconsistent datasets, observational biases and methodological differences. This study investigates the compositional relationship between rocky exoplanets and thei

E. Dyachenko

We consider the problem of representing the fraction $5/P$ as a sum of three distinct unit fractions $1/A+1/B+1/C$ with $A<B<C$ and $A,B,C\in\mathbb{N}$. The case of primes $P\equiv 1 \pmod{5}$ is analyzed, where two constructive types of solutions arise: ED1 (exactly one denominator divisible by $P$, namely $C=cP$) and ED2 (exactly two denominators divisibl