May 2025 arXiv papers — page 6

M. A. Bouaicha, G. Destefanis, T. Montanaro, N. Lasla

In online auctions, fraudulent behaviors such as shill bidding pose significant risks. This paper presents a conceptual framework that applies dynamic, behavior-based penalties to deter auction fraud using blockchain smart contracts. Unlike traditional post-auction detection methods, this approach prevents manipulation in real-time by introducing an economic

Chris M. Ward, Josh Harguess

Retrieval-Augmented Generation (RAG) systems, which integrate Large Language Models (LLMs) with external knowledge sources, are vulnerable to a range of adversarial attack vectors. This paper examines the importance of RAG systems through recent industry adoption trends and identifies the prominent attack vectors for RAG: prompt injection, data poisoning, an

Matthew Hull, Haoyang Yang, Pratham Mehta, Mansi Phute

With 3D Gaussian Splatting (3DGS) being increasingly used in safety-critical applications, how can an adversary manipulate the scene to cause harm? We introduce CLOAK, the first attack that leverages view-dependent Gaussian appearances - colors and textures that change with viewing angle - to embed adversarial content visible only from specific viewpoints. W

Boshra Khajehpiri, Eric Granger, Massimiliano de Zambotti, Fiona C. Baker

Despite extensive research on the relationship between sleep and cognition, the connection between sleep microstructure and human performance across specific cognitive domains remains underexplored. This study investigates whether deep learning models can predict executive functions, particularly cognitive adaptability and conceptual reasoning from physiolog

The STAR Collaboration

High-energy, heavy-ion collisions can create local domains of chirality-imbalanced quarks, reflecting the topological features of quantum chromodynamics. The chiral magnetic effect (CME) predicts an electric charge separation of quarks in such topological domains along the magnetic field ($\vec{B}$) generated by the passing of two high-$Z$ nuclei. We use a c

Hans W. A. Hanley, Zakir Durumeric

Contextual large language model embeddings are increasingly utilized for topic modeling and clustering. However, current methods often scale poorly, rely on opaque similarity metrics, and struggle in multilingual settings. In this work, we present a novel, scalable, interpretable, hierarchical, and multilingual approach to clustering news articles and social

Jiawei Fang, Yuxuan Sun, Chengtian Ma, Qiuyu Lu

Robot co-design, jointly optimizing morphology and control policy, remains a longstanding challenge in the robotics community, where many promising robots have been developed. However, a key limitation lies in its tendency to converge to sub-optimal designs due to the use of fixed reward functions, which fail to explore the diverse motion modes suitable for

The STAR Collaboration

The chiral magnetic effect in heavy-ion collisions predicts a charge separation signal along a magnetic field, which indicates local $P$ and $CP$ violations in the quark-gluon plasma. We report measurements of electric charge separation signals perpendicular to the spectator event plane in Au+Au collisions using high-statistics data from RHIC Beam Energy Sca

Jan-Niclas Hilgert, Carlo Jakobs, Michael Külper, Martin Lambertz

Large language models hold considerable promise for supporting forensic investigations, but their widespread adoption is hindered by a lack of transparency, explainability, and reproducibility. This paper explores how the emerging Model Context Protocol can address these challenges and support the meaningful use of LLMs in digital forensics. Through a theore

Tuochao Chen, D Shin, Hakan Erdogan, Sinan Hersek

This paper introduces SoundSculpt, a neural network designed to extract target sound fields from ambisonic recordings. SoundSculpt employs an ambisonic-in-ambisonic-out architecture and is conditioned on both spatial information (e.g., target direction obtained by pointing at an immersive video) and semantic embeddings (e.g., derived from image segmentation

Sathish Govindarajan, Mayuresh Patle, Siddhartha Sarkar

We study the minimum membership geometric set cover, i.e., MMGSC problem [SoCG, 2023] in the continuous setting. In this problem, the input consists of a set $P$ of $n$ points in $\mathbb{R}^{2}$, and a geometric object $t$, the goal is to find a set $\mathcal{S}$ of translated copies of the geometric object $t$ that covers all the points in $P$ while minimi

Chenjunjie Wang, Shashank N. Sridhara, Eduardo Pavez, Antonio Ortega

We present a novel compression framework for 3D Gaussian splatting (3DGS) data that leverages transform coding tools originally developed for point clouds. Contrary to existing 3DGS compression methods, our approach can produce compressed 3DGS models at multiple bitrates in a computationally efficient way. Point cloud voxelization is a discretization techniq

Rajarshi Guhaniyogi, Laura Baracaldo, Sudipto Banerjee

Varying coefficient models are popular for estimating nonlinear regression functions in functional data models. Their Bayesian variants have received limited attention in large data applications, primarily due to prohibitively slow posterior computations using Markov chain Monte Carlo (MCMC) algorithms. We introduce Bayesian data sketching for varying coeffi

Christian Duffee, Jordan Athas, Andrea Grimaldi, Deborah Volpe

Ising machines can solve combinatorial optimization problems by representing them as energy minimization problems. A common implementation is the probabilistic Ising machine (PIM), which uses probabilistic (p-) bits to represent coupled binary spins. However, many real-world problems have complex data representations that do not map naturally into a binary e

Sidy M. Djitte, Ignace A. Minlend

We use shape derivative approach to prove that balls are the only convex and $C^{1,1}$ regular domains in which the fractional overdetermined problem \begin{equation*} \left\{\begin{aligned} \Ds u&= \lambda_{s, p} u^{p-1}\quad\text{in}\quad\Om \\ u &= 0\quad \text{in}\quad\R^N\setminus \Om\\ u/d^s&=C_0\quad\text{on\;\; $\partial\O$} \end{aligned} \right. \en

Cihan Xiao, Ruixing Liang, Xiangyu Zhang, Mehmet Emre Tiryaki

The success of large language models has driven interest in developing similar speech processing capabilities. However, a key challenge is the scarcity of high-quality spontaneous speech data, as most existing datasets contain scripted dialogues. To address this, we present a novel pipeline for eliciting and recording natural dialogues and release our datase

Tommy Hofmann

We consider the computational problem of determining the unit group of a finite ring, by which we mean the computation of a finite presentation together with an algorithm to express units as words in the generators. We show that the problem is equivalent to the number theoretic problems of factoring integers and solving discrete logarithms in finite fields.

Sariel Har-Peled, Eliot W. Robson

For a graph $G$ spanning a metric space, the dilation of a pair of points is the ratio of their distance in the shortest path graph metric to their distance in the metric space. Given a graph $G$ and a budget $k$, a classic problem is to augment $G$ with $k$ additional edges to reduce the maximum dilation. In this note, we consider a variant of this problem

Eduardo O. Dias

The observable spacetime can be viewed as worldline coincidences (events) between a particle system and the observers of an extended (material) reference frame (ERF). Particle positions are then operationally well defined with respect to that frame. In the ideal regime where the ERF contributes negligibly to the stress--energy tensor, the metric field $g_{ab

Mohammadamin Shafiei, Hamidreza Saffari, Nafise Sadat Moosavi

As Large Language Models are increasingly deployed in high-stakes domains, their ability to detect false assumptions and reason critically is crucial for ensuring reliable outputs. False-premise questions (FPQs) serve as an important evaluation method by exposing cases where flawed assumptions lead to incorrect responses. While existing benchmarks focus on s

Braxton Cuneo, Joanna Piper Morgan, Ilham Variansyah, Kyle E. Niemeyer

Monte Carlo / Dynamic Code (MC/DC) is a portable Monte Carlo neutron transport package for rapid numerical methods exploration in heterogeneous and HPC contexts, developed under the auspices of the Center for Exascale Monte Carlo Neutron Transport (CEMeNT). To support execution on GPUs, MC/DC delegates resource and execution management to Harmonize (another

Alessandro Buldini, Carlo Mazzocca, Rebecca Montanari, Selcuk Uluagac

Self-Sovereign Identity (SSI) is a novel identity model that empowers individuals with full control over their data, enabling them to choose what information to disclose, with whom, and when. This paradigm is rapidly gaining traction worldwide, supported by numerous initiatives such as the European Digital Identity (EUDI) Regulation or Singapore's National D

Xiaochen Wang, Zongyu Wu, Yuan Zhong, Xiang Zhang

Graph retrieval-augmented generation (GRAG) places high demands on graph-specific retrievers. However, existing retrievers often rely on language models pretrained on plain text, limiting their effectiveness due to domain misalignment and structure ignorance. To address these challenges, we propose GPR, a graph-based retriever pretrained directly on knowledg

Aasish Kumar Sharma, Julian Kunkel

Effective resource utilization and decreased makespan in heterogeneous High Performance Computing (HPC) environments are key benefits of workload mapping and scheduling. Tools such as Snakemake, a workflow management solution, employ Integer Linear Programming (ILP) and heuristic techniques to deploy workflows in various HPC environments like SLURM (Simple L

Zhengyang Fan, Wanru Li, Kuo-chu Chang, Ting Yuan

Accurately estimating the remaining useful life (RUL) for degradation systems is crucial in modern prognostic and health management (PHM). Convolutional Neural Networks (CNNs), initially developed for tasks like image and video recognition, have proven highly effectively in RUL prediction, demonstrating remarkable performance. However, with the emergence of

Qianqi Yan, Hongquan Li, Shan Jiang, Yang Zhao

Multimodal large language models (MLLMs) are increasingly deployed in open-ended, real-world environments where inputs are messy, underspecified, and not always trustworthy. Unlike curated benchmarks, these settings frequently involve instructions that refer to missing objects or contradictory facts, rely on ambiguous references, or request infeasible action

Sirui Lin, Zijun Gao, Jose Blanchet, Peter Glynn

We study the estimation of causal estimand involving the joint distribution of treatment and control outcomes for a single unit. In typical causal inference settings, it is impossible to observe both outcomes simultaneously, which places our estimation within the domain of partial identification (PI). Pre-treatment covariates can substantially reduce estimat

Mahammed Kamruzzaman, Gene Louis Kim

As large language models (LLMs) become increasingly integrated into hiring processes, concerns about fairness have gained prominence. When applying for jobs, companies often request/require demographic information, including gender, race, and disability or veteran status. This data is collected to support diversity and inclusion initiatives, but when provide

Yesenia Bravo, Inácio Rabelo, Agustín Romano-Velázquez

We study the topology of real polynomial maps $\mathbb{R}^{4n} \longrightarrow \mathbb{R}^{4}$ expressed in terms of bicomplex variables and their conjugates, which we refer to as bicomplex mixed polynomials. We introduce the notion of polar weighted homogeneity, a property that generalizes the concept of weighted homogeneity in the complex setting. This lea

Kelsi N. Singer, Oliver L. White, Sarah Greenstreet, Jeffrey M. Moore

Pluto's surface displays a wide variety of geologic units from smooth plains to extremely rugged mountainous expanses. These terrains range in age from young, actively resurfaced regions (no observable craters even in the highest-resolution New Horizons images) to old, heavily cratered, eroded regions. Here we expand upon the crater data analysis and the ind

Lihao Sun, Chengzhi Mao, Valentin Hofmann, Xuechunzi Bai

Although value-aligned language models (LMs) appear unbiased in explicit bias evaluations, they often exhibit stereotypes in implicit word association tasks, raising concerns about their fair usage. We investigate the mechanisms behind this discrepancy and find that alignment surprisingly amplifies implicit bias in model outputs. Specifically, we show that a

Sammy Khalife, Andrea Lodi

In the recent years, branch-and-cut algorithms have been the target of data-driven approaches designed to enhance the decision making in different phases of the algorithm such as branching, or the choice of cutting planes (cuts). In particular, for cutting plane selection two score functions have been proposed in the literature to evaluate the quality of a c

Moon Kim, Avinash Malik, Partha Roop

Hybrid systems are mostly modelled, simulated, and verified in the time domain by computer scientists. Engineers, however, use both frequency and time domain modelling due to their distinct advantages. For example, frequency domain modelling is better suited for control systems, using features such as spectra of the signal. Considering this, we introduce, fo

Aniketh Garikaparthi, Manasi Patwardhan, Aditya Sanjiv Kanade, Aman Hassan

There has been a surge of interest in harnessing the reasoning capabilities of Large Language Models (LLMs) to accelerate scientific discovery. While existing approaches rely on grounding the discovery process within the relevant literature, effectiveness varies significantly with the quality and nature of the retrieved literature. We address the challenge o

Po-Jui Chen, Piers Coleman

We construct an exactly solvable model of a four-dimensional Kitaev spin liquid. The lattice structure is orthorhombic and each unit-cell contains six sublattice degrees of freedom. We demonstrate that the Fermi surface of the model is made up of two-dimensional surfaces. Additionally, we evaluate the energy cost of creating visons using scattering theory. T

Aasish Kumar Sharma, Sanjeeb Prashad Pandey, Julian M. Kunkel

Convolutional Neural Networks (CNNs) are pivotal in computer vision and Big Data analytics but demand significant computational resources when trained on large-scale datasets. Conventional training via back-propagation (BP) with losses like Mean Squared Error or Cross-Entropy often requires extensive iterations and may converge sub-optimally. Quantum computi

Dang Nguyen, Ali Payani, Baharan Mirzasoleiman

Hallucination in large language models (LLMs) can be detected by assessing the uncertainty of model outputs, typically measured using entropy. Semantic entropy (SE) enhances traditional entropy estimation by quantifying uncertainty at the semantic cluster level. However, as modern LLMs generate longer one-sentence responses, SE becomes less effective because

Pintu Kumar, Nandyala Hemachandra

Noisy labelled datasets are generally inexpensive compared to clean labelled datasets, and the same is true for graph data. In this paper, we propose a denoising technique DeGLIF: Denoising Graph Data using Leave-One-Out Influence Function. DeGLIF uses a small set of clean data and the leave-one-out influence function to make label noise robust node-level pr

Thales Azevedo, Fabricio A. Barone, Carlos Farina, Reinaldo de Melo e Souza

In this work, we investigate the influence of a perfectly conducting plate on the Uehling potential of Quantum Electrodynamics (QED), corresponding to the first loop correction to the classical Coulomb potential in that situation. We use the method of images adapted to the photon propagator, extending the method beyond the standard (classical) tree level cal

Shuai Feng, Wei-Chuang Chan, Srishti Chouhan, Junior Francisco Garcia Ayala

The integration of large language models (LLMs) into global applications necessitates effective cultural alignment for meaningful and culturally-sensitive interactions. Current LLMs often lack the nuanced understanding required for diverse cultural contexts, and adapting them typically involves costly full fine-tuning. To address this, we introduce a novel s

Menglin Zhao, Zhuorui Yong, Ruijia Guan, Kai-Wei Chang

Serious Illness Conversations (SICs), discussions about values and care preferences for patients with life-threatening illness, rarely occur in Emergency Departments (EDs), despite evidence that early conversations improve care alignment and reduce unnecessary interventions. We interviewed 11 ED providers to identify challenges in SICs and opportunities for

Juan P. Farias, Stella S. R. Offner, Ronan Kerr, Michael Y. Grudić

Recent studies suggest that most star-forming regions in our Galaxy form stellar associations rather than bound clusters. We analyse models from the STARFORGE simulation suite, a set of magneto-hydrodynamical simulations that include all key stellar feedback and radiative processes following star formation through cloud dispersal. We create synthetic observa

Dewei Feng, Wei Dai, Carol Li, Alistair Pernigo

The ability of AI to sense and identify various substances based on their smell alone can have profound impacts on allergen detection (e.g. smelling gluten or peanuts in a cake), monitoring the manufacturing process, and sensing hormones that indicate emotional states, stress levels, and diseases. Despite these broad impacts, there are few standardized datas

Ehsan Karimi, Maryam Rahnemoonfar

Natural disasters usually affect vast areas and devastate infrastructures. Performing a timely and efficient response is crucial to minimize the impact on affected communities, and data-driven approaches are the best choice. Visual question answering (VQA) models help management teams to achieve in-depth understanding of damages. However, recently published

Yuki Fujimoto, Kenji Fukushima, Yoshimasa Hidaka, Larry McLerran

Lattice-QCD simulations and theoretical arguments hint at the existence of an intermediate phase of strongly interacting matter between a confined hadron gas and a deconfined Quark-Gluon Plasma (QGP). We qualitatively and semi-quantitatively explore and differentiate the phase structures in the temperature window from the QCD pseudo-critical temperature $T_c

Babak Barazandeh, Subhabrata Majumdar, Om Rajyaguru, George Michailidis

Parameter-efficient fine-tuning (PEFT) methods, such as LoRA, offer compact and effective alternatives to full model fine-tuning by introducing low-rank updates to pre-trained weights. However, most existing approaches rely on global low rank structures, which can overlook spatial patterns spread across the parameter space. In this work, we propose Localized

Yexiao He, Ang Li, Boyi Liu, Zhewei Yao

Healthcare decision-making represents one of the most challenging domains for Artificial Intelligence (AI), requiring the integration of diverse knowledge sources, complex reasoning, and various external analytical tools. Current AI systems often rely on either task-specific models, which offer limited adaptability, or general language models without groundi

Antonio Michele Miti, Leonid Ryvkin

The purpose of this paper is to present a fully algebraic formalism for the construction and reduction of $L_\infty$-algebras of observables inspired by multisymplectic geometry, using Gerstenhaber algebras, BV-modules, and the constraint triple formalism. In the "geometric case", we reconstruct and conceptually explain the recent results of arXiv:2206.03137

Aasish Kumar Sharma, Dimitar Kyosev, Julian Kunkel

Artificial Intelligence (AI) is transforming sectors such as healthcare, finance, and autonomous systems, offering powerful tools for innovation. Yet its rapid integration raises urgent ethical concerns related to data ownership, privacy, and systemic bias. Issues like opaque decision-making, misleading outputs, and unfair treatment in high-stakes domains un

Ruofan Wu, Youngwon Lee, Fan Shu, Danmei Xu

Retrieval-Augmented Generation (RAG) systems are increasingly diverse, yet many suffer from monolithic designs that tightly couple core functions like query reformulation, retrieval, reasoning, and verification. This limits their interpretability, systematic evaluation, and targeted improvement, especially for complex multi-hop question answering. We introdu

Asit Mishra, Dusan Stosic, Simon Layton, Paulius Micikevicius

Using fewer bits to represent model parameters and related tensors during pre-training has become a required technique for improving GPU efficiency without sacrificing accuracy. Microscaling (MX) formats introduced in NVIDIA Blackwell generation of GPUs represent a major advancement of this technique, making it practical to combine narrow floating-point data

Niraj Gohil, Nawshad Haque, Amgad Elgowainy, Amro M. Farid

Life cycle analysis (LCA) has emerged as a vital tool for assessing the environmental impacts of products, processes, and systems throughout their entire lifecycle. It provides a systematic approach to quantifying resource consumption, emissions, and waste, enabling industries, researchers, and policymakers to identify hotspots for sustainability improvement

Zosia Krusberg

Generative AI tools like ChatGPT are rapidly reshaping how students and instructors engage with course material -- and how they think about academic integrity. This paper presents a classroom activity designed to help physics students critically examine the ethical and educational implications of using AI in coursework. Through a structured sequence of scena

Rebekah A. Gelpí, Yibing Ju, Ethan C. Jackson, Yikai Tang

We introduce Sorrel (https://github.com/social-ai-uoft/sorrel), a simple Python interface for generating and testing new multi-agent reinforcement learning environments. This interface places a high degree of emphasis on simplicity and accessibility, and uses a more psychologically intuitive structure for the basic agent-environment loop, making it a useful

Anthony Gosselin, Ge Ya Luo, Luis Lara, Florian Golemo

Video diffusion techniques have advanced significantly in recent years; however, they struggle to generate realistic imagery of car crashes due to the scarcity of accident events in most driving datasets. Improving traffic safety requires realistic and controllable accident simulations. To tackle the problem, we propose Ctrl-Crash, a controllable car crash v

Oldemar Rodríguez

This paper proposes an innovative extension of Principal Component Analysis (PCA) that transcends the traditional assumption of data lying in Euclidean space, enabling its application to data on Riemannian manifolds. The primary challenge addressed is the lack of vector space operations on such manifolds. Fletcher et al., in their work {\em Principal Geodesi

Liyan Chen, Huangying Zhan, Hairong Yin, Yi Xu

Effective robotic autonomy in unknown environments demands proactive exploration and precise understanding of both geometry and semantics. In this paper, we propose ActiveSGM, an active semantic mapping framework designed to predict the informativeness of potential observations before execution. Built upon a 3D Gaussian Splatting (3DGS) mapping backbone, our

Bernardo Subercaseaux, Ethan Mackey, Long Qian, Marijn J. H. Heule

We present a computational methodology for obtaining rotationally symmetric sets of points satisfying discrete geometric constraints, and demonstrate its applicability by discovering new solutions to some well-known problems in combinatorial geometry. Our approach takes the usage of SAT solvers in discrete geometry further by directly embedding rotational sy

Mohammad Saleh Hasankhani

Accurate prediction of molecular solubility is a cornerstone of early-stage drug discovery, yet conventional machine learning models face significant challenges due to limited labeled data and the high-dimensional nature of molecular descriptors. To address these issues, we propose LatMixSol, a novel latent space augmentation framework that combines autoenco

Ghada Sokar, Gintare Karolina Dziugaite, Anurag Arnab, Ahmet Iscen

Continual learning is conventionally tackled through sequential fine-tuning, a process that, while enabling adaptation, inherently favors plasticity over the stability needed to retain prior knowledge. While existing approaches attempt to mitigate catastrophic forgetting, a bias towards recent tasks persists as they build upon this sequential nature. In this

Mario Figueira, David Conesa, Antonio López-Quílez, Håvard Rue

Integrating heterogeneous data sources and expert knowledge is essential for overcoming data scarcity and enhancing estimation accuracy. Two main frameworks naturally arise to perform the integration of these multiple sources: sequential Bayesian inference and integrated models. The first one consists of updating posterior information in a sequential data an

Xingru Zhou, Sadanand Modak, Yao-Cheng Chan, Zhiyun Deng

The rapid growth of AI in robotics has amplified the need for high-quality, reusable datasets, particularly in human-robot interaction (HRI) and AI-embedded robotics. While more robotics datasets are being created, the landscape of open data in the field is uneven. This is due to a lack of curation standards and consistent publication practices, which makes

Alina Wernick, Alan Medlar, Sofia Söderholm, Dorota Głowacka

International air travel is highly surveilled. While surveillance is deemed necessary for law enforcement to prevent and detect terrorism and other serious crimes, even the most accurate algorithmic mass surveillance systems produce high numbers of false positives. Despite the potential impact of false positives on the fundamental rights of millions of passe

Ignacio Arretche, Mohammad Tanver Hossain, Ramdas Tiwari, Abbie Kim

Although the inverted vial test has been widely used as a qualitative method for estimating fluid viscosity, quantitative rheological characterization has remained limited due to its complex, uncontrolled flow - driven by gravity, surface tension, inertia, and initial conditions. Here, we present a computer vision (CV) viscometer that automates the inverted

A. Eugene DePrince, Stephen H. Yuwono

First-order electric dipole response functions are implemented for cavity quantum electrodynamics (QED) generalizations of Hartree-Fock (HF) and Kohn-Sham density functional theory (DFT) in order to assess the degree to which static molecular response properties are impacted by interactions between electronic degrees of freedom and an optical cavity mode. Is

Victor Luder, Lukas Schulthess, Silvano Cortesi, Leyla Rivero Davis

Accurate localization is essential for a wide range of applications, including asset tracking, smart agriculture, and animal monitoring. While traditional localization methods, such as Global Navigation Satellite System (GNSS), Wi-Fi, and Bluetooth Low Energy (BLE), offer varying levels of accuracy and coverage, they have drawbacks regarding power consumptio

Madhu Babu Sikha, Lalith Appari, Gurudatt Nanjanagudu Ganesh, Amay Bandodkar

Diabetic foot ulcers (DFUs), a class of chronic wounds, affect ~750,000 individuals every year in the US alone and identifying non-healing DFUs that develop to chronic wounds early can drastically reduce treatment costs and minimize risks of amputation. There is therefore a pressing need for diagnostic tools that can detect non-healing DFUs early. We develop

Ethan Decker, Erik Gustafson, Evan McKinney, Alex K. Jones

Quantum simulation of the interactions of fermions and bosons -- the fundamental particles of nature -- is essential for modeling complex quantum systems in material science, chemistry and high-energy physics and has been proposed as a promising application of fermion-boson quantum computers, which overcome the overhead encountered in mapping fermions and bo

Abhijeet Vishwasrao, Sai Bharath Chandra Gutha, Andres Cremades, Klas Wijk

Rapid urbanization demands accurate and efficient monitoring of turbulent wind patterns to support air quality, climate resilience and infrastructure design. Traditional sparse reconstruction and sensor placement strategies face major accuracy degradations under practical constraints. Here, we introduce Diff-SPORT, a diffusion-based framework for high-fideli

Jefferson Delgado-Quesada, Edgar A. Rojas-González

In an array of nonlinear waveguides, quantum states can be generated from classical states through spontaneous parametric down-conversion of photons. This work simulates and analyzes the effect of disorder on light propagation and its quantum correlations, implementing disorder in three system parameters: coupling, injection amplitude, and injection phase. I

Cristopher Moore, Catherine Gill, Nadya Bliss, Kevin Butler

Artificial intelligence (AI) is increasingly being adopted in most industries, and for applications such as note taking and checking grammar, there is typically not a cause for concern. However, when constitutional rights are involved, as in the justice system, transparency is paramount. While AI can assist in areas such as risk assessment and forensic evide

Na Xue, Xidong Mu, Yue Chen, Yuanwei Liu

A novel uniform circular array (UCA) based near-field (NF) integrated sensing and communication (ISAC) framework is proposed, where the Cylindrical coordinate is invoked to evaluate the joint positioning performance. The joint squared position error bound (SPEB) of the sensing target (ST) is derived for the coplanar and non-coplanar cases. For the coplanar c

Ziji Zhang, Michael Yang, Zhiyu Chen, Yingying Zhuang

Accurate intent classification is critical for efficient routing in customer service, ensuring customers are connected with the most suitable agents while reducing handling times and operational costs. However, as companies expand their product lines, intent classification faces scalability challenges due to the increasing number of intents and variations in

Liwen Sun, Hao-Ren Yao, Ophir Frieder, Xiang Qian

Population health management requires scalable methods to identify individuals at risk of chronic diseases such as cardiovascular conditions and cancer, yet existing approaches rely on fragmented data and resource-intensive screening. We present Scaling Electronic Health Record Foundation Models for Population Health Management, an Electronic Health Record F

Anoop Kini, Andreas Jansche, Timo Bernthaler, Gerhard Schneider

FastCAR is a novel task consolidation approach in Multi-Task Learning (MTL) for a classification and a regression task, despite the non-triviality of task heterogeneity with only a subtle correlation. The approach addresses the classification of a detected object (occupying the entire image frame) and regression for modeling a continuous property variable (f

Elena Gaburro, Mario Ricchiuto, Michael Dumbser

In this work, we introduce a framework to design multidimensional Riemann solvers for nonlinear systems of hyperbolic conservation laws on general unstructured polygonal Voronoi-like tessellations. In this framework we propose two simple but complete solvers. The first method is a direct extension of the Osher-Solomon Riemann solver to multiple space dimensi

Ahmad Haboub, Aris Kartsaklas, Vasilis Sarafidis

We hypothesize that portfolio sorts based on the V/P ratio generate excess returns and consist of companies that are undervalued for prolonged periods. Results, for the US market show that high V/P portfolios outperform low V/P portfolios across horizons extending from one to three years. The V/P ratio is positively correlated to future stock returns after c

Junze Deng, Qinhang Wu, Peizhong Ju, Sen Lin

Rehearsal-based methods have shown superior performance in addressing catastrophic forgetting in continual learning (CL) by storing and training on a subset of past data alongside new data in current task. While such a concurrent rehearsal strategy is widely used, it remains unclear if this approach is always optimal. Inspired by human learning, where sequen

Linyuan Gong, Alvin Cheung, Mostafa Elhoushi, Sida Wang

Fill-in-the-Middle (FIM) is a common pretraining method for code LLMs, where models complete code segments given surrounding context. However, existing LLMs treat code as plain text and mask random character spans. We propose and evaluate AST-FIM, a pretraining strategy that leverages Abstract Syntax Trees (ASTs) to mask complete syntactic structures at scal

Omid Reza Abbasi, Franz Welscher, Georg Weinberger, Johannes Scholz

As large language models (LLMs) continue to evolve, questions about their trustworthiness in delivering factual information have become increasingly important. This concern also applies to their ability to accurately represent the geographic world. With recent advancements in this field, it is relevant to consider whether and to what extent LLMs' representat

Matthew Kam, Cody Miller, Miaoxin Wang, Abey Tidwell

Generative AI is showing early evidence of productivity gains for software developers, but concerns persist regarding workforce disruption and deskilling. We describe our research with 21 developers at the cutting edge of using AI, summarizing 12 of their work goals we uncovered, together with 75 associated tasks and the skills & knowledge for each, illustra

Arun Ganesh, Brendan McMahan, Milad Nasr, Thomas Steinke

We consider the problem of secret protection, in which a business or organization wishes to train a model on their own data, while attempting to not leak secrets potentially contained in that data via the model. The standard method for training models to avoid memorization of secret information is via differential privacy (DP). However, DP requires a large l

Johannes Moll, Louisa Fay, Asfandyar Azhar, Sophie Ostmeier

Radiology reports are critical for clinical decision-making but often lack a standardized format, limiting both human interpretability and machine learning (ML) applications. While large language models (LLMs) have shown strong capabilities in reformatting clinical text, their high computational requirements, lack of transparency, and data privacy concerns h

Braden J. Larsen, Hagan Hensley, Gabriela D. Martinez, Alexander Staron

Room temperature thermal atoms have proven to be a powerful resource for magnetometry, electrometry, atom-entanglement generation, and robust atomic clocks. Recent efforts have sought to realize compact and highly manufacturable atomic vapors and atomic beams for chip-scale magnetometry and atomic clocks. Here, we show that a chip-scale rubidium beam source

Srivathsan Badrinarayanan, Rishikesh Magar, Akshay Antony, Radheesh Sharma Meda

The discovery of Metal-Organic Frameworks (MOFs) with application-specific properties remains a central challenge in materials chemistry, owing to the immense size and complexity of their structural design space. Conventional computational screening techniques such as molecular simulations and density functional theory (DFT), while accurate, are computationa

Xinyue Shen, Yun Shen, Michael Backes, Yang Zhang

Knowledge files have been widely used in large language model (LLM) agents, such as GPTs, to improve response quality. However, concerns about the potential leakage of knowledge files have grown significantly. Existing studies demonstrate that adversarial prompts can induce GPTs to leak knowledge file content. Yet, it remains uncertain whether additional lea

Yuge Ye, Qingna Li

This paper investigates a general class of problems in which a lower bounded smooth convex function incorporating $\ell_{0}$ and $\ell_{2,0}$ regularization is minimized over a box constraint. Although such problems arise frequently in practical applications, their inherent non-convexity poses significant challenges for solution methods. In particular, we fo

Mingqian Zheng, Wenjia Hu, Patrick Zhao, Motahhare Eslami

Current LLMs are trained to refuse potentially harmful input queries regardless of whether users actually had harmful intents, causing a tradeoff between safety and user experience. Through a study of 480 participants evaluating 3,840 query-response pairs, we examine how different refusal strategies affect user perceptions across varying motivations. Our fin

Stéphane Vinet, Duncan England, Chang-qing Xu, Thomas Jennewein

We experimentally demonstrate a reconfigurable quantum network architecture suitable for integrating satellite links in metropolitan quantum networks. The network architecture is designed such that once a satellite is in range, it is configured in a multipoint-to-point topology where all ground nodes establish entanglement with the satellite receiver using t

S. Weeden, D. C. Harrison, S. Patel, M. Snyder

Decoherence in superconducting qubits is dominated by defects that reside at amorphous interfaces. Interaction with discrete defects results in dropouts that complicate qubit operation and lead to nongaussian tails in the distribution of qubit energy relaxation time $T_1$ that degrade system performance. Spectral diffusion of defects over time leads to fluct

Na Xue, Xidong Mu, Yue Chen, Yuanwei Liu

A simultaneously transmitting and reflecting surface (STARS) assisted near-field (NF) integrated sensing and communication (ISAC) framework is proposed, where the radio sensors are installed on the STARS to directly conduct the distance-domain sensing by exploiting the characteristic spherical wavefront. A new squared position error bound (SPEB) expression i

Jiawei Chen, Lusi Li, Daniel Takabi, Masha Sosonkina

Heterogeneous Graph Neural Networks (HGNNs) excel in modeling complex, multi-typed relationships across diverse domains, yet their vulnerability to backdoor attacks remains unexplored. To address this gap, we conduct the first investigation into the susceptibility of HGNNs to existing graph backdoor attacks, revealing three critical issues: (1) high attack b

Rafaela Filippozzi, Everton Boos, Douglas S. Gonçalves, Fermin S. V. Bazán

Recently, in Applied Mathematics and Computation 474 (2024) 128688, a Levenberg-Marquardt method (LMM) with Singular Scaling was analyzed and successfully applied in parameter estimation problems in heat conduction where the use of a particular singular scaling matrix (semi-norm regularizer) provided approximate solutions of better quality than those of the

Di Zhang, Weida Wang, Junxian Li, Xunzhi Wang

This paper target in addressing the challenges of underthinking and overthinking in long chain-of-thought (CoT) reasoning for Large Reasoning Models (LRMs) by introducing Reasoning Control Fields (RCF)--a novel test-time approach that injects structured control signals to guide reasoning from a tree search perspective. RCF enables models to adjust reasoning

Zheng Cao, Wanchaloem Wunkaew, Helyette Geman

This paper introduces the Hype Index as a novel metric to quantify media attention toward large-cap equities, leveraging advances in Natural Language Processing (NLP) for extracting predictive signals from financial news. Using the S&P 100 as the focus universe, we first construct a News Count-Based Hype Index, which measures relative media exposure by compu

Jila Amini, Mojtaba Alaei, Stefano de Gironcoli

Providing highly simplified models of strongly correlated electronic systems that challenge {\it ab initio} calculations can serve as a valuable testing ground to improve these methods. In this study, we present a comprehensive study of the structural, magnetic, and electronic properties of one-dimensional transition metal mono-oxide chains (VO, CrO, MnO, Fe

Roberto Alvarenga, Leonardo Moço

In this article, we investigate Hecke modifications of vector bundles on a smooth projective curve $X$ defined over an arbitrary field. We obtain structural results that allow us to reduce the classification problem of Hecke modifications to the case of vector bundles of lower rank. Moreover, when the base field is a finite field and $X$ is the projective li

Lilit Grigoryan, Vladimir Bataev, Andrei Andrusenko, Hainan Xu

Transducer models have emerged as a promising choice for end-to-end ASR systems, offering a balanced trade-off between recognition accuracy, streaming capabilities, and inference speed in greedy decoding. However, beam search significantly slows down Transducers due to repeated evaluations of key network components, limiting practical applications. This pape

Robert Penna

The stationary, axisymmetric sector of vacuum general relativity (with zero cosmological constant) enjoys an $\mathrm{SL}(2,\mathbb{R})$ symmetry called the Matzner-Misner group. We study the action of the Matzner-Misner group on the Kerr black hole. We show that the group acts naturally on a three parameter generalization of the usual two parameter Kerr sol

Sebastián Barbieri, Leo Poirier

We show that, for every finitely generated group with decidable word problem and undecidable domino problem, there exists a sequence of effective subshifts whose inverse limit is not the topological factor of any effective dynamical system. This follows from considerations on the universality under topological factors for this class of dynamical systems.