March 2025 arXiv papers — page 121

Vu Thi Hue

The Milstein approximation with step size $\Delta t>0$ of the solution $(X, Y)$ to a two-by-two system of linear stochastic differential equations is considered. It is proved that when the solution of the underlying model is exponentially stable or exponentially blowing up at infinite time, these behaviours are preserved at the level of the Milstein approxim

Jerry Liu, Jessica Grogan, Owen Dugan, Ashish Rao

This paper investigates whether sequence models can learn to perform numerical algorithms, e.g. gradient descent, on the fundamental problem of least squares. Our goal is to inherit two properties of standard algorithms from numerical analysis: (1) machine precision, i.e. we want to obtain solutions that are accurate to near floating point error, and (2) num

Olivier Gouvert, Julie Hunter, Jérôme Louradour, Christophe Cerisara

We present both the Lucie Training Dataset and the Lucie-7B foundation model. The Lucie Training Dataset is a multilingual collection of textual corpora centered around French and designed to offset anglo-centric biases found in many datasets for large language model pretraining. Its French data is pulled not only from traditional web sources, but also from

Averi Bates, Ryan Vavricka, Shane Carleton, Ruosi Shao

The Unified Modeling Language is a standardized visual language widely used for modeling and documenting the design of software systems. Although many tools generate UML diagrams from UML code, generating executable UML code from image-based UML diagrams remains challenging. This paper proposes a new approach to generate UML code using a large multimodal lan

Zijin Li, Xinghong Pan

In this paper, we show existence and non-uniqueness on the axially symmetric stationary Navier-Stokes equations in an exterior periodic cylinder. On the boundary of the cylinder, the horizontally swirl velocity is subject to the perturbation of a rotation, the horizontally radial velocity is subject to the perturbation of an interior sink, while the vertical

Chao Li, Minsu Park, Cristina Rossi, Zhuang Li

StyleMamba has recently demonstrated efficient text-driven image style transfer by leveraging state-space models (SSMs) and masked directional losses. In this paper, we extend the StyleMamba framework to handle video sequences. We propose new temporal modules, including a \emph{Video State-Space Fusion Module} to model inter-frame dependencies and a novel \e

Mohamad Alameddine, Olivier Marchal, Nikita Nikolaev, Nicolas Orantin

We prove that the formal $\hbar$-power series solution of the deformed Painlev\'{e} I equation is resurgent, which means it is generically Borel summable and its Borel transform admits endless analytic continuation. In particular, we find that the Borel transform defines a global multivalued holomorphic function on a singular algebraic surface isomorphic to

Fioralba Cakoni, Shixu Meng, Zehui Zhou

In this work, we construct the Born and inverse Born approximation and series to recover two function-valued coefficients in the Helmholtz equation for inverse scattering problems from the scattering data at two different frequencies. An analysis of the convergence and approximation error of the proposed regularized inverse Born series is provided. The resul

Shukai Liu, Arunav Bordoloi, Jacob Issokson, Ido Levy

The gatemon qubits, made of transparent superconducting-semiconducting Josephson junctions, typically have even weaker anharmonicity than the opaque AlOx-junction transmons. However, flux-frustrated gatemons can acquire a much stronger anharmonicity, originating from the interference of the higher-order harmonics of the supercurrent. Here we investigate this

Vaneet Aggarwal, Shweta Jain, Subham Pokhriyal, Christopher John Quinn

We study bi-criteria combinatorial optimization under noisy function evaluations. While resilience and black-box offline-to-online reductions have been studied in single-objective settings, extending these ideas to bi-criteria problems introduces new challenges due to the coupled degradation of approximation guarantees for objectives and constraints. We intr

Krzysztof Byrski, Grzegorz Wilczyński, Weronika Smolak-Dyżewska, Piotr Borycki

Gaussian Splatting (GS) has become one of the most important neural rendering algorithms. GS represents 3D scenes using Gaussian components with trainable color and opacity. This representation achieves high-quality renderings with fast inference. Regrettably, it is challenging to integrate such a solution with varying light conditions, including shadows and

Mengmeng Li, Daniel Kuhn, Tobias Sutter

We study offline reinforcement learning problems with a long-run average reward objective. The state-action pairs generated by any fixed behavioral policy thus follow a Markov chain, and the {\em empirical} state-action-next-state distribution satisfies a large deviations principle. We use the rate function of this large deviations principle to construct an

Liying Han, Gaofeng Dong, Xiaomin Ouyang, Lance Kaplan

Complex events (CEs) play a crucial role in CPS-IoT applications, enabling high-level decision-making in domains such as smart monitoring and autonomous systems. However, most existing models focus on short-span perception tasks, lacking the long-term reasoning required for CE detection. CEs consist of sequences of short-time atomic events (AEs) governed by

Paola Natalia Cañas, Marcos Nieto, Oihana Otaegui, Igor Rodríguez

In recent years, we have witnessed significant progress in emerging deep learning models, particularly Large Language Models (LLMs) and Vision-Language Models (VLMs). These models have demonstrated promising results, indicating a new era of Artificial Intelligence (AI) that surpasses previous methodologies. Their extensive knowledge and zero-shot capabilitie

Panagiotis Gavriilidis, George C. Alexandropoulos

The convergence of eXtremely Large (XL) antenna arrays and high-frequency bands in future wireless networks will inevitably give rise to near-field communications, localization, and sensing. Dynamic Metasurface Antennas (DMAs) have emerged as a key enabler of the XL Multiple-Input Multiple-Output (MIMO) paradigm, leveraging reconfigurable metamaterials to su

Yanshu Niu, Zhe Yang, Bikash C. Pal

Grid-forming (GFM) inverter-based resources (IBRs) can emulate the external characteristics of synchronous generators (SGs) through appropriate control loop design. However, in systems with GFM IBRs, the apparent impedance trajectory under current limitation differs significantly from that of SG-based systems due to the limited overcurrent capability of powe

Saikat Guha, Tiju Cherian John, Zihao Gong, Prithwish Basu

Augmenting a train of bright phase-modulated laser-light pulses of a coherent communications system with infinitesimally small quantum photons per pulse -- entangled across several time bins -- prepared by splitting squeezed light in a temporal-mode interferometer can dramatically enhance a homodyne receiver's ability to detect a sudden change in the channel

Cordian. Riener, Robin Schabert, Thi Xuan Vu

A semi-algebraic set is a subset of $\mathbb{R}^n$ defined by a finite collection of polynomial equations and inequalities. In this paper, we investigate the problem of determining whether two points in such a set belong to the same connected component. We focus on the case where the defining equations and inequalities are invariant under the natural action

Christine Eagles, Léo Jimenez

This article is concerned with finite rank stability theory, and more precisely two classical ways to decompose a type using minimal types. The first is its domination equivalence to a Morley power of minimal types, and the second its semi-minimal analysis, both of which are useful in applications. Our main interest is to explore how these two decompositions

Siddharth Rout, Eldad Haber, Stéphane Gaudreault

The modeling of dynamical systems is essential in many fields, but applying machine learning techniques is often challenging due to incomplete or noisy data. This study introduces a variant of stochastic interpolation (SI) for probabilistic forecasting, estimating future states as distributions rather than single-point predictions. We explore its mathematica

Michal Ryznar

Getoor in [3] calculated the mean exit time from a ball for the standard isotropic $\alpha$-stable process in $\mathbb{R}^d$ starting from the interior of the ball. The purpose of this note is to show that, up to multplicative constant, the same formula is valid for any symmetric $\alpha$-stable process.

Shufan Li, Konstantinos Kallidromitis, Akash Gokul, Arsh Koneru

The predominant approach to advancing text-to-image generation has been training-time scaling, where larger models are trained on more data using greater computational resources. While effective, this approach is computationally expensive, leading to growing interest in inference-time scaling to improve performance. Currently, inference-time scaling for text

Negar Shahamiri, Moritz Rempe, Lukas Heine, Jens Kleesiek

Automatic tissue segmentation and nuclei detection is an important task in pathology, aiding in biomarker extraction and discovery. The panoptic segmentation of nuclei and tissue in advanced melanoma (PUMA) challenge aims to improve tissue segmentation and nuclei detection in melanoma histopathology. Unlike many challenge submissions focusing on extensive mo

Noujoud Nader, Patrick Diehl, Steve Brandt, Hartmut Kaiser

Large Language Models (LLMs), such as GPT-4 and DeepSeek, have been applied to a wide range of domains in software engineering. However, their potential in the context of High-Performance Computing (HPC) much remains to be explored. This paper evaluates how well DeepSeek, a recent LLM, performs in generating a set of HPC benchmark codes: a conjugate gradient

Huan Jia, Yinhuo Zhang

In this note, we show that every Noetherian graded ring with an affine degree zero part is affine. As a result, a Noetherian graded Hopf algebra whose degree zero component is a commutative or a cocommutative Hopf subalgebra is affine. Moreover, we show that the braided Hopf algebra of a Noetherian graded Hopf algebra is affine.

Aziz Amari, Mariem Makni, Wissal Fnaich, Akram Lahmar

In large organizations, the number of financial transactions can grow rapidly, driving the need for fast and accurate multi-criteria invoice validation. Manual processing remains error-prone and time-consuming, while current automated solutions are limited by their inability to support a variety of constraints, such as documents that are partially handwritte

Daryna Chernobrovkina, Steffen Grünewälder

We analyze the prior that a Deep Gaussian Process with polynomial kernels induces. We observe that, even for relatively small depths, averaging effects occur within such a Deep Gaussian Process and that the prior can be analyzed and approximated effectively by means of the Berry-Esseen Theorem. One of the key findings of this analysis is that, in the absence

Reinhard M. Grassmann, Jessica Burgner-Kahrs

In this letter, we demonstrate that previously proposed improved state parameterizations for soft and continuum robots are specific cases of Clarke coordinates. By explicitly deriving these improved parameterizations from a generalized Clarke transformation matrix, we unify various approaches into one comprehensive mathematical framework. This unified repres

Gaurav Duggal, Harish K. Dureppagari, Harpreet S. Dhillon, Jeffrey H. Reed

Effective indoor positioning is critical for public safety, enabling first responders to locate at-risk individuals accurately during emergency scenarios. However, traditional Global Navigation Satellite Systems (GNSS) often perform poorly indoors due to poor coverage and non-line-of-sight (NLOS) conditions. Moreover, relying on fixed cellular infrastructure

Adrian Abac, Raul Abramo, Simone Albanesi, Angelica Albertini

Einstein Telescope (ET) is the European project for a gravitational-wave (GW) observatory of third-generation. In this paper we present a comprehensive discussion of its science objectives, providing state-of-the-art predictions for the capabilities of ET in both geometries currently under consideration, a single-site triangular configuration or two L-shaped

Daniela Florez, Ricardo Cortez, James M. Hyman, Zhuolin Qu

Arboviral diseases remain a major public health concern, particularly in tropical and subtropical regions where mosquito populations thrive. One promising strategy to curb transmission is the release of Aedes aegypti mosquitoes infected with Wolbachia, a bacterium that reduces their ability to spread viruses. However, past large-scale releases have not alway

R. Gnana Praveen, Jahangir Alam, Eric Charton

Audio and visual modalities are two predominant contact-free channels in videos, which are often expected to carry a complementary relationship with each other. However, they may not always complement each other, resulting in poor audio-visual feature representations. In this paper, we introduce Gated Recursive Joint Cross Attention (GRJCA) using a gating me

Josep Cabacas-Maso, Elena Ortega-Beltrán, Ismael Benito-Altamirano, Carles Ventura

We present our contribution to the 8th ABAW challenge at CVPR 2025, where we tackle valence-arousal estimation, emotion recognition, and facial action unit detection as three independent challenges. Our approach leverages the well-known Dual-Direction Attention Mixed Feature Network (DDAMFN) for all three tasks, achieving results that surpass the proposed ba

Zichen Xi, Zengyu Cen, Dongyao Wang, Joseph G. Thomas

Mid-infrared (MIR) detectors find extensive applications in chemical sensing, spectroscopy, communications, biomedical diagnosis and space explorations. Alternative to semiconductor MIR photodiodes and bolometers, mechanical-resonator-based MIR detectors show advantages in higher sensitivity and lower noise at room temperature, especially towards longer wave

Siliang Zeng, Yao Liu, Huzefa Rangwala, George Karypis

One of the challenges of aligning large models with human preferences lies in both the data requirements and the technical complexities of current approaches. Predominant methods, such as RLHF, involve multiple steps, each demanding distinct types of data, including demonstration data and preference data. In RLHF, human preferences are typically modeled thro

Myisha A. Chowdhury, Gift Modekwe, Qiugang Lu

Accurate capacity prediction is essential for the safe and reliable operation of batteries by anticipating potential failures beforehand. The performance of state-of-the-art capacity prediction methods is significantly hindered by the limited availability of training data, primarily attributed to the expensive experimentation and data sharing restrictions. T

Mengyang Gu, Victor De Oliveira

This article discusses prior distributions for the parameters of Gaussian processes (GPs) that are widely used as surrogate models to emulate expensive computer simulations. The parameters typically involve mean parameters, a variance parameter, and correlation parameters. These parameters are often estimated by maximum likelihood (MLE). In some scenarios, h

Sam R. Katiraee-Far, Yuta Matsumoto, Brennan Undseth, Maxim De Smet

Developing optimal strategies to calibrate quantum processors for high-fidelity operation is one of the outstanding challenges in quantum computing today. Here, we demonstrate multiple examples of high-fidelity operations achieved using a unified global optimization-driven automated calibration routine on a six dot semiconductor quantum processor. Within the

Abdelrahman Elewah, Khalid Elgazzar

The Internet of Things (IoT) has enabled diverse devices to communicate over the Internet, yet the fragmentation of IoT systems limits seamless data sharing and coordinated management. We have recently introduced SensorsConnect, a unified framework to enable seamless content and sensor data sharing in collaborative IoT systems, inspired by how the World Wide

Nam Nguyen, Alex Taekyung Lee, Anh T. Ngo, Hyowon Park

Na$_{3}$Co$_{2}$SbO$_6$ is a promising candidate to realize the Kitaev spin liquid phase since the large Kitaev spin exchange interaction is tunable via the change in electronic structure, such as the trigonal crystal field splitting ($\Delta_{TCF}$). Here, we show that the uncorrelated electronic structure of Na$_{3}$Co$_{2}$SbO$_6$ is rather insensitive to

Matt Gottsacker, Nels Numan, Anthony Steed, Gerd Bruder

When collaborating relative to a shared 3D virtual object in mixed reality (MR), users may experience communication issues arising from differences in perspective. These issues include occlusion (e.g., one user not being able to see what the other is referring to) and inefficient spatial references (e.g., "to the left of this" may be confusing when users are

Linzhi Yu, Haobijam J. Singh, Jesse Pietila, Humeyra Caglayan

All-optical image processing offers a high-speed, energy-efficient alternative to conventional electronic systems by leveraging the wave nature of light for parallel computation. However, traditional optical processors rely on bulky components, limiting scalability and integration. Here, we demonstrate a compact metasurface-based platform for analog optical

J. W. Zhou, Pavel Kroupa, Sami Dib

Many studies have revealed that the core mass function (CMF) in high-mass star-forming regions is top-heavy. In this work, we start from the canonical initial mass function (IMF) to inversely synthesize the observed CMFs of high-mass star formation regions, taking into account variations in multiplicity and mass conversion efficiency from core to star ($\eps

Torbjørn Smith, Olav Egeland

A nonparametric adaptive controller is proposed for crane control where the payload tracks a desired trajectory with feedback from the payload position. The controller is based on a novel version of partial feedback linearization where the unactuated crane load dynamics are controlled with the position of the actuated crane dynamics instead of the accelerati

Boyu Chen, Ameenat L. Solebo, Daqian Shi, Jinge Wu

Anterior Segment Optical Coherence Tomography (AS-OCT) is an emerging imaging technique with great potential for diagnosing anterior uveitis, a vision-threatening ocular inflammatory condition. A hallmark of this condition is the presence of inflammatory cells in the eye's anterior chamber, and detecting these cells using AS-OCT images has attracted research

Nilupulee A Gunathilake, Owen Lo, William J Buchanan, Ahmed Al-Dubai

Side-channel vulnerabilities pose an increasing threat to cryptographically protected devices. Consequently, it is crucial to observe information leakages through physical parameters such as power consumption and electromagnetic (EM) radiation to reduce susceptibility during interactions with cryptographic functions. EM side-channel attacks are becoming more

Jiabei Zhu, Tongyu Li, Hao Wang, Yi Shen

Diffraction tomography (DT) has been widely explored in transmission-mode configurations, enabling high-resolution, label-free 3D imaging. However, industrial metrology applications, such as semiconductor inspection, typically involve opaque or highly reflective substrates (e.g., silicon or metal), necessitating a reflection-mode imaging configuration. In th

SK Ramjan, Asi Khandelwal, L. S. Sharath Chandra, M. K. Chattopadhyay

Few concentrated disordered binary metallic alloys show a negative temperature coefficient of resistivity (TCR), which is quite unusual. V$_{0.3}$Ti$_{0.7}$ is one such alloy that shows resistivity exceeding 100 $\mu\Omega$ cm and exhibits negative TCR. The addition of ferromagnetic rare-earth Gd, which is insoluble in the body-centered cubic V-Ti matrix, ch

Binghui Zhang, Luis Mares De La Cruz, Binghui Wang

Federated Learning (FL) is an emerging decentralized learning paradigm that can partly address the privacy concern that cannot be handled by traditional centralized and distributed learning. Further, to make FL practical, it is also necessary to consider constraints such as fairness and robustness. However, existing robust FL methods often produce unfair mod

Giorgio Panichi, Sebastiano Corli, Enrico Prati

Quantum Physics-Informed Neural Networks (QPINNs) integrate quantum computing and machine learning to impose physical biases on the output of a quantum neural network, aiming to either solve or discover differential equations. The approach has recently been implemented on both the gate model and continuous variable quantum computing architecture, where it ha

Mumuksh Tayal, Manan Tayal, Ravi Prakash

Ensuring safety in robotic systems remains a fundamental challenge, especially when deploying offline policy-learning methods such as imitation learning in dynamic environments. Traditional behavior cloning (BC) often fails to generalize when deployed without fine-tuning as it does not account for disturbances in observations that arises in real-world, chang

Yuheng Jiang, Zhehao Shen, Chengcheng Guo, Yu Hong

Human-centric volumetric videos offer immersive free-viewpoint experiences, yet existing methods focus either on replaying general dynamic scenes or animating human avatars, limiting their ability to re-perform general dynamic scenes. In this paper, we present RePerformer, a novel Gaussian-based representation that unifies playback and re-performance for hig

Sogol Cyrusian, Alex Domat, Christopher O'Neill, Vadim Ponomarenko

A numerical semigroup $S$ is a cofinite, additively-closed subset of $\mathbb Z_{\ge 0}$ that contains 0, and a factorization of $x \in S$ is a $k$-tuple $z = (z_1, \ldots, z_k)$ where $x = z_1a_1 + \cdots + z_ka_k$ expresses $x$ as a sum of generators of $S = \langle a_1, \ldots, a_k \rangle$. Much~of the study of non-unique factorization centers on factori

Xing Wang, Ivan Yotov

We study a mathematical model of fluid -- poroelastic structure interaction and its numerical solution. The free fluid region is governed by the unsteady incompressible Navier-Stokes equations, while the poroelastic region is modeled by the Biot system of poroelasticity. The two systems are coupled along an interface through continuity of normal velocity and

Soufiane Bacha, Huansheng Ning, Belarbi Mostefa, Doreen Sebastian Sarwatt

Accurate illness diagnosis is vital for effective treatment and patient safety. Machine learning models are widely used for cancer diagnosis based on historical medical data. However, data imbalance remains a major challenge, leading to hindering classifier performance and reliability. The SMOTEBoost method addresses this issue by generating synthetic data t

V Varagapriya

We examine a constrained Markov decision process under uncertain transition probabilities, with the uncertainty modeled as deviations from observed transition probabilities. We construct the uncertainty set associated with the deviations using polyhedral and second-order cone constraints and employ a robust optimization framework. We demonstrate that each in

Luis Arenas-Carmona, Marco Godoy

We study the extent to which the quotient of the Bruhat-Tits tree at one place $Q$, associated to a genus of orders of maximal rank, can be computed from the analogous quotient at a different place $P$. We show that this computation can be carried out, except for a small set of vertices depending on $P$, but not on $Q$. We give some geometrical conditions on

Zhen Huang, Bodhisattva Sen

This paper reviews recent advancements in the application of optimal transport (OT) to multivariate distribution-free nonparametric testing. Inspired by classical rank-based methods, such as Wilcoxon's rank-sum and signed-rank tests, we explore how OT-based ranks and signs generalize these concepts to multivariate settings, while preserving key properties, i

L. P. Chitta, D. I. Pontin, E. R. Priest, D. Berghmans

Solar flares are the most powerful, magnetically driven, explosions in the heliosphere. The nature of magnetic energy release in the solar corona that heats the plasma and accelerates particles in a flare, however, remains poorly understood. Here, we report high-resolution coronal observations of a flare by the Solar Orbiter mission that reveal initially wea

Ahmad Maqboul, Bakheet Elsadek

ICAM-1 (intercellular adhesion molecule 1) and MPZ (myelin protein zero) are thought to be a factor in the integrity of nerve tissues. In this report, we attempted to trace the expression of ICAM-1, responsible for cell-to-cell adhesion, and of MPZ, the main constituent of myelin sheath, in malignant tissues of the sciatic nerve (SN) in inbred male Copenhage

Han Xiao, Xiaoyan Hu, Ang Li, Wenjie Wang

A robust full-space physical layer security (PLS) transmission scheme is proposed in this paper considering the full-space wiretapping challenge of wireless networks supported by simultaneous transmitting and reflecting reconfigurable intelligent surface (STAR-RIS). Different from the existing schemes, the proposed PLS scheme takes account of the uncertainty

Yead Rahman, Prerna Dua

Tobacco use remains a leading preventable contributor to serious health conditions in the United States, notably chronic obstructive pulmonary disease (COPD) and severe COVID-19 complications. Within Louisiana's Medicaid population, tobacco use prevalence is particularly high compared to privately insured groups, yet its full impact on long-term outcomes is

Yan Jiang, Hao Yu, Mengting Wei, Zhaodong Sun

Visible-infrared person re-identification (VI-ReID) is a challenging task that aims to match pedestrian images captured under varying lighting conditions, which has drawn intensive research attention and achieved promising results. However, existing methods adopt the centralized training, ignoring the potential privacy concerns as the data is distributed acr

Umar Muhammad Dauda, Lawal Ja'afaru

This study employs spectral methods to capture the behaviour of wave equation with dispersive-nonlinearity. We describe the evolution of hump initial data and track the conservation of the mass and energy functionals. The dispersive-nonlinearity results to solution in an extended Schwartz space via analytic approach. We construct numerical schemes based on s

Yihao Wang, Raphael Memmesheimer, Sven Behnke

The availability of large language models and open-vocabulary object perception methods enables more flexibility for domestic service robots. The large variability of domestic tasks can be addressed without implementing each task individually by providing the robot with a task description along with appropriate environment information. In this work, we propo

William Louis Rothman, Yasuyuki Matsushita

Finding a balance between artistic beauty and machine-generated imagery is always a difficult task. This project seeks to create 3D models that, when illuminated, cast shadows resembling Kanji characters. It aims to combine artistic expression with computational techniques, providing an accurate and efficient approach to visualizing these Japanese characters

Yihong Jin, Ze Yang, Xinhe Xu, Yihan Zhang

With the rapid evolution of Large Language Models (LLMs) and their large-scale experimentation in cloud-computing spaces, the challenge of guaranteeing their security and efficiency in a failure scenario has become a main issue. To ensure the reliability and availability of large-scale language models in cloud computing scenarios, such as frequent resource f

Alexander Maloney, Viraj Meruliya, Mark Van Raamsdonk

Euclidean wormholes have played a key role in the recent ``disorder averaged" approaches to quantum gravity and holography, but are typically only considered in somewhat special theories of gravity, such as theories in low dimensions or theories with exotic matter content (such as axions). These exotic theories have advantage that both the matter and gravita

Ze Yang, Yihong Jin, Yihan Zhang, Juntian Liu

The fast development of large language models (LLMs) and popularization of cloud computing have led to increasing concerns on privacy safeguarding and data security of cross-cloud model deployment and training as the key challenges. We present a new framework for addressing these issues along with enabling privacy preserving collaboration on training between

Rinku Dewri

This article explores the gaps that can manifest when using a large language model (LLM) to obtain simplified interpretations of data practices from a complex privacy policy. We exemplify these gaps to showcase issues in accuracy, completeness, clarity and representation, while advocating for continued research to realize an LLM's true potential in revolutio

Mohammed Alnajjar, Khalid Alnajjar, Mika Hämäläinen

This study examines AI adoption among Finnish healthcare SMEs through semi-structured interviews with six health-tech companies. We identify three AI engagement categories: AI-curious (exploring AI), AI-embracing (integrating AI), and AI-catering (providing AI solutions). Our proposed threefold model highlights key adoption barriers, including regulatory com

Natinael Solomon Neggatu, Jeremie Houssineau, Giovanni Montana

Offline reinforcement learning (RL) looks at learning how to optimally solve tasks using a fixed dataset of interactions from the environment. Many off-policy algorithms developed for online learning struggle in the offline setting as they tend to over-estimate the behaviour of out of distributions actions. Existing offline RL algorithms adapt off-policy alg

Tetiana Parshakova, Yicheng Bai, Garrett van Ryzin, Stephen Boyd

Dual methods are useful for distributed optimization because they allow agent-level subproblems to be solved in parallel. However, achieving primal feasibility with dual methods is a challenge; it can take many iterations to find prices that recover primal feasibility, and even with optimal dual prices primal feasibility is not guaranteed unless special cond

Yunbo Long, Liming Xu, Ge Zheng, Alexandra Brintrup

Federated learning (FL) enables retailers to share model parameters for demand forecasting while maintaining privacy. However, heterogeneous data across diverse regions, driven by factors such as varying consumer behavior, poses challenges to the effectiveness of federated learning. To tackle this challenge, we propose Privacy-Adaptive Clustered Federated Le

Chengxuan Qian, Kai Han, Jianxia Ding, Chongwen Lyu

Deep learning has shown remarkable success in medical image analysis, but its reliance on large volumes of high-quality labeled data limits its applicability. While noisy labeled data are easier to obtain, directly incorporating them into training can degrade model performance. To address this challenge, we propose a Mean Teacher-based Adaptive Label Correct

Mayank Kumar, Jiaqi Xue, Mengxin Zheng, Qian Lou

Fully Homomorphic Encryption over the torus (TFHE) enables computation on encrypted data without decryption, making it a cornerstone of secure and confidential computing. Despite its potential in privacy preserving machine learning, secure multi party computation, private blockchain transactions, and secure medical diagnostics, its adoption remains limited d

David H. Smith, Max Fowler, Paul Denny, Craig Zilles

Reading and understanding code are fundamental skills for novice programmers, and especially important with the growing prevalence of AI-generated code and the need to evaluate its accuracy and reliability. ``Explain in Plain English'' questions are a widely used approach for assessing code comprehension, but providing automated feedback, particularly on com

Amulya Reddy Maligireddy, Manohar Reddy Uppula, Nidhi Rastogi, Yaswanth Reddy Parla

The increasing frequency of firearm-related incidents has necessitated advancements in security and surveillance systems, particularly in firearm detection within public spaces. Traditional gun detection methods rely on manual inspections and continuous human monitoring of CCTV footage, which are labor-intensive and prone to high false positive and negative

Sharmita Dey, Sarath Ravindran Nair

We present a mutually aligned diffusion framework for cross-modal biomechanical motion generation, guided by a dynamical systems perspective. By treating each modality, e.g., observed joint angles ($X$) and ground reaction forces ($Y$), as complementary observations of a shared underlying locomotor dynamical system, our method aligns latent representations a

Ruyu Wang, Xuefeng Hou, Sabrina Schmedding, Marco F. Huber

In layout-to-image (L2I) synthesis, controlled complex scenes are generated from coarse information like bounding boxes. Such a task is exciting to many downstream applications because the input layouts offer strong guidance to the generation process while remaining easily reconfigurable by humans. In this paper, we proposed STyled LAYout Diffusion (STAY Dif

Nadia Saeed

The PerAnsSumm 2025 challenge focuses on perspective-aware healthcare answer summarization (Agarwal et al., 2025). This work proposes a few-shot learning framework using a Snorkel-BART-SVM pipeline for classifying and summarizing open-ended healthcare community question-answering (CQA). An SVM model is trained with weak supervision via Snorkel, enhancing zer

Elaheh Karooby, Jiazhen Li, Amit Agrawal, Qing Gu

Diffraction gratings integrated into an atomic, molecular, and optical (AMO) setup offer a compact and efficient route toward atom cooling and trapping, thus preparing magneto-optical traps (MOT) for insertion into future scalable quantum systems. Here, we propose and numerically validate a two-dimensional (2D) diffraction grating that satisfies the required

Nir Ailon, Akhiad Bercovich, Yahel Uffenheimer, Omri Weinstein

Modern AI relies on huge matrix multiplications (MatMuls), whose computation poses a scalability problem for inference and training. We propose an alternative, GPU native bilinear operator to MatMuls in neural networks, which offers a three-way tradeoff between: speed, accuracy and parameter count. In particular, this operator requires substantially fewer FL

Sigurd B. Angenent, Dan Knopf

Regarding Ricci flow as a dynamical system, we derive sufficient conditions for noncompact stationary (Ricci-flat) solutions to possess infinite-dimensional unstable manifolds, and provide examples satisfying those criteria that have uncountably many unstable perturbations.

Christian Weiss

The instanton vacuum provides an effective description of chiral symmetry breaking by local topological fluctuations of the gauge fields, as observed in lattice QCD simulations. The resulting effective dynamics at momenta below $1/\bar\rho \approx$ 0.6 GeV explains the basic features of light-quark correlation functions and is used extensively in studies of

David H. Smith, Max Fowler, Paul Denny, Craig Zilles

"Explain in Plain English" (EiPE) questions are widely used to assess code comprehension skills but are challenging to grade automatically. Recent approaches like Code Generation Based Grading (CGBG) leverage large language models (LLMs) to generate code from student explanations and validate its equivalence to the original code using unit tests. However, th

Ans Munir, Faisal Z. Qureshi, Muhammad Haris Khan, Mohsen Ali

Contrastive Language-Image Pretraining (CLIP) has shown impressive zero-shot performance on image classification. However, state-of-the-art methods often rely on fine-tuning techniques like prompt learning and adapter-based tuning to optimize CLIP's performance. The necessity for fine-tuning significantly limits CLIP's adaptability to novel datasets and doma

Yuan-An Xiao, Weixuan Wang, Dong Liu, Junwei Zhou

Fixing static analysis alerts in source code with Large Language Models (LLMs) is becoming increasingly popular. However, LLMs often hallucinate and perform poorly for complex and less common alerts. Retrieval-augmented generation (RAG) aims to solve this problem by providing the model with a relevant example, but existing approaches face the challenge of un

Yuhao Zhang, Keisuke Okumura, Heedo Woo, Ajay Shankar

This work presents an optimization method for generating kinodynamically feasible and collision-free multi-robot trajectories that exploits an incremental denoising scheme in diffusion models. Our key insight is that high-quality trajectories can be discovered merely by denoising noisy trajectories sampled from a distribution. This approach has no learning c

L. E. Golub, L. Šmejkal

It is shown that the flow of electric current in an altermagnet results in the formation of a homogeneous electron spin orientation in the sample. The spin of the conduction electrons generated in altermagnets with $d$-wave spin-momentum couplings, is quadratic in the current magnitude, varies as the second angular harmonic under variation of the current dir

David W. Kribs, Rajesh Pereira, Mukesh Taank

Isoclinic subspaces have been studied for over a century. Quantum error correcting codes were recently shown to define a subclass of families of isoclinic subspaces. The Knill-Laflamme Theorem is a seminal result in the theory of quantum error correction, a central topic in quantum information. We show there is a generalized version of the Knill-Laflamme res

Yu Fang, Yue Yang, Xinghao Zhu, Kaiyuan Zheng

Vision-language-action (VLA) models present a promising paradigm by training policies directly on real robot datasets like Open X-Embodiment. However, the high cost of real-world data collection hinders further data scaling, thereby restricting the generalizability of VLAs. In this paper, we introduce ReBot, a novel real-to-sim-to-real approach for scaling r

Mark Saaltink

Given an indexed family ${\cal A} = (A_1, A_2, \dotsc, A_n)$ of subsets of some given set $S$, a \emph{transversal} is a set of distinct elements $x_1, x_2, \dotsc, x_n$ with each $x_i \in A_i$. Transversals have been studied since 1935 and have many attractive properties, with a deep connection to matroids. A $q$-analog is formed by replacing the notion of

Nabajyoti Medhi, Kumarjit Ray, Rajdeep Ghosh, Dilip Kumar Saikia

Shipping container based modular architectures provide design flexibility in data centers with building blocks to expand the network as and when needed. In this paper, high capacity Modular Data Center (MDC) network architecture with Rich Inter Connections named MODRIC is proposed. MODRIC is a cost-effective switch-centric network design which allows buildin

Luoyin Zhao, Zheping Yan, Yuqing Wang, Raye Chen-Hua Yeow

A novel local interaction control method (LICM) is proposed in this paper to realize the formation control of multi-agent system (MAS). A local interaction leader follower (LILF) structure is provided by coupling the advantages of information consensus and leader follower frame, the agents can obtain the state information of the leader by interacting with th

S. -P. Alex Chang, William Detmold, Anthony V. Grebe, Issaku Kanamori

The pseudoscalar meson light-cone distribution amplitudes (LCDAs) are essential non-perturbative inputs for a range of high-energy exclusive processes in quantum chromodynamics. In this proceedings, progress towards a determination of the low Mellin moments of the pion and kaon LCDAs by the HOPE Collaboration is reported.

Andrew Cupo, Shuanglong Liu, Silas Hoffman, X. -G. Zhang

We theoretically study Floquet engineering of magnetic molecules via a time-periodic magnetic field that couples to the emergent total electronic spin of the metal center. By focusing on the low-lying energy levels using an $S = 1$ spin Hamiltonian containing the zero-field and Zeeman terms, we demonstrate their continuous tunability under the Floquet field.

Sibin Lü, Jun Hu

The exploration of topological phases remains a cutting-edge research frontier, driven by their promising potential for next-generation electronic and quantum technologies. In this work, we employ first-principles calculations and tight-binding modeling to systematically investigate the topological properties of freestanding two-dimensional (2D) honeycomb Bi

Alexander Bonilla, Alessandro Santoni, Rafael C. Nunes, Jackson Levi Said

Very Special Linear Gravity (VSL-Gravity) is an alternative model of linearized gravity that incorporates massive gravitons while retaining only two physical degrees of freedom thanks to gauge invariance. Recently, the gravitational period-decay dynamics of the model has been determined using effective field theory techniques. In this study, we conduct a com

Yogendra Limbu, Hari Paudyal, Michael E. Flatté, Durga Paudyal

The chemical, structural, mechanical, and dynamical stabilities of the $\alpha$- and $\beta$-Ga$_2$O$_3$ are confirmed from respective negative formation energies, negative cohesive energies, favorable elastic constants, and positive phonon frequencies. The phonon dispersions indicate that the Ga-O bonds are uniform in the $\alpha$-phase, while they vary in