March 2025 arXiv papers — page 178

Yu Jin, Jingming Liu, Zhexu Luo, Yifei Peng

Visual generative abductive learning studies jointly training symbol-grounded neural visual generator and inducing logic rules from data, such that after learning, the visual generation process is guided by the induced logic rules. A major challenge for this task is to reduce the time cost of logic abduction during learning, an essential step when the logic

Zihao Peng, Xijun Wang, Shengbo Chen, Hong Rao

Diffusion models are powerful generative models that can produce highly realistic samples for various tasks. Typically, these models are constructed using centralized, independently and identically distributed (IID) training data. However, in practical scenarios, data is often distributed across multiple clients and frequently manifests non-IID characteristi

Yuehan Qiao, Zhihao Yao, Meiyu Hu, Qianyao Xu

Deaf and Hard-of-Hearing (DHH) individuals are increasingly participating as livestreamers in China's e-commerce livestreaming industry but face obstacles that limit the scope and diversity of their audience. Our paper examines these challenges and explores a potential solution for connecting the hearing audience to sign language (SL) livestreaming teams wit

Rong Li, Tao Deng, Siwei Feng, He Huang

WiFi-based human activity recognition (HAR) holds significant promise for ubiquitous sensing in smart environments. A critical challenge lies in enabling systems to dynamically adapt to evolving scenarios, learning new activities without catastrophic forgetting of prior knowledge, while adhering to the stringent computational constraints of edge devices. Cur

Alexander Scarlatos, Naiming Liu, Jaewook Lee, Richard Baraniuk

Generative artificial intelligence (AI) has the potential to scale up personalized tutoring through large language models (LLMs). Recent AI tutors are adapted for the tutoring task by training or prompting LLMs to follow effective pedagogical principles, though they are not trained to maximize student learning throughout the course of a dialogue. Therefore,

David A. Meyer, Thomas G. Wong

In this tutorial, which contains some original results, we bridge the fields of quantum computing algorithms, conservation laws, and many-body quantum systems by examining three algorithms for searching an unordered database of size $N$ using a continuous-time quantum walk, which is the quantum analogue of a continuous-time random walk. The first algorithm u

Bing-Rui Ma, Hao Chen, Cheng-Qun Pang

In this paper, the Schr{\"o}dinger equation in a magnetic field is utilized to study the effect of the magnetic field on $B$ mesons. The mass spetrum of $B$ mesons are numerically calculated for different magnetic field strengths by solving the Schr{\"o}dinger equation under the non-relativistic Cornell potential model, incorporating the Zeeman effect and th

Lin Zhang, Yuteng Zhang, Dusit Niyato, Lei Ren

Generative AI (GenAI) has demonstrated remarkable capabilities in code generation, and its integration into complex product modeling and simulation code generation can significantly enhance the efficiency of the system design phase in Model-Based Systems Engineering (MBSE). In this study, we introduce a generative system design methodology framework for MBSE

Weihao Cui, Ziyi Xu, Han Zhao, Quan Chen

Large Language Model (LLM) applications have emerged as a prominent use case for Function-as-a-Service (FaaS) due to their high computational demands and sporadic invocation patterns. However, serving LLM functions within FaaS frameworks faces significant GPU-side cold start. A fundamental approach involves leveraging a template with function state saved on

Debraj Chakraborty, Clemens Dubslaff, Sudeep Kanav, Jan Kretinsky

Safety-critical controllers of complex systems are hard to construct manually. Automated approaches such as controller synthesis or learning provide a tempting alternative but usually lack explainability. To this end, learning decision trees (DTs) have been prevalently used towards an interpretable model of the generated controllers. However, DTs do not expl

Tao Xia, Yudi Zhang, Ting Liu Lei Zhang

Despite the great success of large-scale text-to-image diffusion models in image generation and image editing, existing methods still struggle to edit the layout of real images. Although a few works have been proposed to tackle this problem, they either fail to adjust the layout of images, or have difficulty in preserving visual appearance of objects after t

Dana Lynn Ona-Lansigan Lavacot, Jessie Liu, Brandon E. Morgan, Ali Mani

While recent approaches, such as the macroscopic forcing method (MFM) or Green's function-based approaches, can be used to compute Reynolds-averaged Navier--Stokes closure operators using forced direct numerical simulations, MFM can also be used to directly compute moments of the effective nonlocal and anisotropic eddy diffusivities. The low-order spatial an

Yuki Kanakubo

Crystal bases are powerful combinatorial tools in the representation theory of quantum groups $U_q(\mathfrak{g})$ for a symmetrizable Kac-Moody algebras $\mathfrak{g}$. The polyhedral realizations are combinatorial descriptions of the crystal base $B(\infty)$ for Verma modules in terms of the set of integer points of a polyhedral cone, which equals the strin

Michelle Vaccaro, Michael Caosun, Harang Ju, Sinan Aral

We conducted an International AI Negotiation Competition in which participants designed and refined prompts for AI negotiation agents. We then facilitated over 180,000 negotiations between these agents across multiple scenarios with diverse characteristics and objectives. Our findings revealed that principles from human negotiation theory remain crucial even

Alex Dolce, Ryan Lavelle, Bernard Scott, Ashlyn Urbanski

The turning distance is a well-studied metric for measuring the similarity between two polygons. This metric is constructed by taking an $L^p$ distance between step functions which track each shape's tangent angle of a path tracing its boundary. In this study, we introduce \textit{turning disorders} for polygonal planar networks, defined by averaging turning

Shanya Baghel, Shuvashree Mondal

Nondestructive one-shot device (NOSD) testing plays a crucial role in engineering, particularly in the reliability assessment of high-stakes systems such as aerospace components, medical devices, and semiconductor technologies. Accurate reliability prognosis of NOSD testing data is essential for ensuring product durability, safety, and performance optimizati

Nguyen Do, Truc Nguyen, Malik Hassanaly, Raed Alharbi

Despite a plethora of anomaly detection models developed over the years, their ability to generalize to unseen anomalies remains an issue, particularly in critical systems. This paper aims to address this challenge by introducing Swift Hydra, a new framework for training an anomaly detection method based on generative AI and reinforcement learning (RL). Thro

Canlun Zheng, Yize Mi, Hanqing Guo, Huaben Chen

MAV-capturing-MAV (MCM) is one of the few effective methods for physically countering misused or malicious MAVs.This paper presents a vision-based cooperative MCM system, where multiple pursuer MAVs equipped with onboard vision systems detect, localize, and pursue a target MAV. To enhance robustness, a distributed state estimation and control framework enabl

Krti Tallam

This paper examines the intricate interplay among AI safety, security, and governance by integrating technical systems engineering with principles of moral imagination and ethical philosophy. Drawing on foundational insights from Weapons of Math Destruction and Thinking in Systems alongside contemporary debates in AI ethics, we develop a comprehensive multi-

Alex Casella, Wayne Wang

The rise of Agentic applications and automation in the Voice AI industry has led to an increased reliance on Large Language Models (LLMs) to navigate graph-based logic workflows composed of nodes and edges. However, existing methods face challenges such as alignment errors in complex workflows and hallucinations caused by excessive context size. To address t

Yoshiaki Uchida, Go Watanabe

This study explores how molecular shape changes influence the phase behavior of liquid crystals, particularly the nematic (N) phase of 5CB, through all-atom molecular dynamics (MD) simulations. The results demonstrate that molecular shape anisotropy increases in the N phase, with molecules adopting more elongated conformations as aggregation occurs. We find

Jonathan H. Manton

The detection of irregularly spaced pulses of non-negligible width is a fascinating yet under-explored topic in signal processing. It sits adjacent to other core topics such as radar and symbol detection yet has its own distinctive challenges. Even modern techniques such as compressed sensing perform worse than may be expected on pulse processing problems. R

Marian Petre, Mary Shaw

Three decades of empirical research in high-performing software development teams provides evidence that creativity can be promoted by an effective, disciplined development culture. This paper describes 'contrasting' as a key driver for creativity; describes creativity moves, tactics used by high-performing teams to produce useful contrasts; and characterize

R. Daniel Murphy, Anthony Mezzacappa, Eric J. Lentz, Pedro Marronetti

We present for the first time an analysis of high-frequency gravitational wave (GW) emission from proto-neutron stars (PNS) in core collapse supernovae (CCSN) that combines spatial decomposition and modal decomposition to both source and characterize the emission using three-dimensional CCSN simulations. We analyze simulations initiated from 15 and 25 solar

Jiachen Luo, Huy Phan, Lin Wang, Joshua Reiss

Multi-modal emotion recognition in conversations is a challenging problem due to the complex and complementary interactions between different modalities. Audio and textual cues are particularly important for understanding emotions from a human perspective. Most existing studies focus on exploring interactions between audio and text modalities at the same rep

Xiaofang Liu, Wentao Liu, Zhilong Liu, Jieci Wang

In this paper, we investigate the effects of Lorentz violation on correlations harvesting, specifically focusing on the harvested entanglement and harvested mutual information between two Unruh-DeWitt detectors interacting with a quantum field in the Lorentz-violating BTZ-like black hole spacetime. Our findings reveal that Lorentz symmetry breaking has contr

Feng Chen, Yiran Meng, Kegan Li, Chaoran Yang

Recently, physics-informed neural networks (PINNs) and their variants have gained significant popularity as a scientific computing method for solving partial differential equations (PDEs), whereas accuracy is still its main shortcoming. Despite numerous development efforts, there is no literature demonstrating that these methods surpass classic numerical alg

Adarsh Salagame, Eric Sihite, Milad Ramezani, Alireza Ramezani

This paper presents an optimization-based motion planning methodology for snake robots operating in constrained environments. By using a reduced-order model, the proposed approach simplifies the planning process, enabling the optimizer to autonomously generate gaits while constraining the robot's footprint within tight spaces. The method is validated through

Alexander Coulter, Rebecca Lee, Irina Gaynanova

Distribution-as-response regression problems are gaining wider attention, especially within biomedical settings where observation-rich patient specific data sets are available, such as feature densities in CT scans (Petersen et al., 2021) actigraphy (Ghosal et al., 2023), and continuous glucose monitoring (Coulter et al., 2024; Matabuena et al., 2021). To ac

Mary Shaw, Marian Petre

Many disciplines use standard examples for education and to share and compare research results. The examples are rich enough to study from multiple points of view; they are often called model problems. Software design lacks such a community resource. We propose an activity for Designing 2025 in which participants improve some existing model problem descripti

Haisheng Fu, Jie Liang, Zhenman Fang, Jingning Han

Learned image compression (LIC) methods have recently outperformed traditional codecs such as VVC in rate-distortion performance. However, their large models and high computational costs have limited their practical adoption. In this paper, we first construct a high-capacity teacher model by integrating Swin-Transformer V2-based attention modules, additional

Tao Feng, Yunke Zhang, Huandong Wang, Yong Li

Accurate origin-destination (OD) flow prediction is of great importance to developing cities, as it can contribute to optimize urban structures and layouts. However, with the common issues of missing regional features and lacking OD flow data, it is quite daunting to predict OD flow in developing cities. To address this challenge, we propose a novel Causalit

Yanyu Zhu, Lichen Bai, Jintao Xu, Hai-tao Zheng

Recent advances in diffusion-based lip-syncing generative models have demonstrated their ability to produce highly synchronized talking face videos for visual dubbing. Although these models excel at lip synchronization, they often struggle to maintain fine-grained control over facial details in generated images. In this work, we identify "lip averaging" phen

Tao Feng, Yunke Zhang, Xiaochen Fan, Huandong Wang

To uncover the city's fundamental functioning mechanisms, it is important to acquire a deep understanding of complicated relationships among citizens, location, and mobility behaviors. Previous research studies have applied direct correlation analysis to investigate such relationships. Nevertheless, due to the ubiquitous confounding effects, empirical correl

Tatsuro Inaba, Go Kamoda, Kentaro Inui, Masaru Isonuma

This study explores how bilingual language models develop complex internal representations. We employ sparse autoencoders to analyze internal representations of bilingual language models with a focus on the effects of training steps, layers, and model sizes. Our analysis shows that language models first learn languages separately, and then gradually form bil

Motoki Nakata, Masaaki Imaizumi

We propose a stochastic sampling approach to identify stability boundaries in general dynamical systems. The global landscape of Lyapunov exponent in multi-dimensional parameter space provides transition boundaries for stable/unstable trajectories, i.e., the edge of chaos. Despite its usefulness, it is generally difficult to derive analytically. In this stud

Yanze Zhang, Yiwei Lyu, Siwon Jo, Yupeng Yang

Decentralized safe control plays an important role in multi-agent systems given the scalability and robustness without reliance on a central authority. However, without an explicit global coordinator, the decentralized control methods are often prone to deadlock -- a state where the system reaches equilibrium, causing the robots to stall. In this paper, we p

Tao Feng, Yunke Zhang, Huandong Wang, Yong Li

User consumption behavior data, which records individuals' online spending history at various types of stores, has been widely used in various applications, such as store recommendation, site selection, and sale forecasting. However, its high worth is limited due to deficiencies in data comprehensiveness and changes of application scenarios. Thus, generating

Wenbo Sun, Zhuomin M. Zhang, Zubin Jacob

Enhancement and peaks in near-field radiative heat transfer (NFRHT) typically arise due to surface phonon-polaritons, plasmon-polaritons, and electromagnetic (EM) modes in structured materials. However, the role of material quantum coherence in enhancing near-field radiative heat transfer remains unexplored. Here, we unravel that NFRHT in superconductor-ferr

Jingming Chen, Linyun Yang, Zhen Gao

The discovery of hyperbolic lattice, a discretized regularization of non-Euclidean space with constant negative curvature, has provided an unprecedented platform to extend topological phases of matter from Euclidean to non-Euclidean spaces. To date, however, all previous hyperbolic topological states are limited to conventional type-I hyperbolic lattice with

Jingyuan Yang, Tao Li, Tianyi Wang, Shuangge Ma

Estimation of intracellular gene networks has been a critical component of single-cell transcriptomic data analysis, which can provide crucial insights into the complex interplay between genes, facilitating the discovery of the biological basis of human life at single-cell resolution. Despite notable achievements, existing methodologies often falter in their

Hamdy Arkoub, Daniel Flynn, Adri C. T. van Duin, Miaomiao Jin

The corrosion behavior of NiCr alloys in molten FLiNaK salt is governed by complex Cr-F chemical interactions, necessitating a fundamental understanding for enhancing alloy performance in harsh environments. However, significant gaps remain in our understanding of the dynamic atomic-scale processes driving the progression of molten salt corrosion. This study

Anurag Swarnim Yadav, Joseph N. Wilson

Large Language Models (LLMs) are of great interest in vulnerability detection and repair. The effectiveness of these models hinges on the quality of the datasets used for both training and evaluation. Our investigation reveals that a number of studies featured in prominent software engineering conferences have employed datasets that are plagued by high dupli

Yusuke Terasawa, Yukiyasu Ozeki

To analyze the $\pm J$ XY spin-glass in three dimensions, we verified a method aimed at obtaining a high-precision dynamical exponent $z$ from the correlation length in the nonequilibrium relaxation process. The obtained $z$ yielded consistent and highly accurate results in previous studies for relatively well-studied models---specifically, the three-dimensi

Wei Dai, Peilin Chen, Malinda Lu, Daniel Li

Recent advances in clinical AI have enabled remarkable progress across many clinical domains. However, existing benchmarks and models are primarily limited to a small set of modalities and tasks, which hinders the development of large-scale multimodal methods that can make holistic assessments of patient health and well-being. To bridge this gap, we introduc

Md Yousuf Harun, Christopher Kanan

To adapt to real-world data streams, continual learning (CL) systems must rapidly learn new concepts while preserving and utilizing prior knowledge. When it comes to adding new information to continually-trained deep neural networks (DNNs), classifier weights for newly encountered categories are typically initialized randomly, leading to high initial trainin

Jasel Berra-Montiel, Daniel Contreras-Bear, Alberto Molgado, Mar Sanchez-Cordova

In this paper, we address the Wigner distribution and the star exponential function for a time-dependent harmonic oscillator for which the mass and the frequency terms are considered explicitly depending on time. To such an end, we explore the connection between the star exponential, naturally emerging within the context of deformation quantization, and the

Mimi Dai

The one-dimensional toy models proposed for the three-dimensional electron magnetohydrodynamics in our previous work share some similarities with the original dynamics under certain symmetry. We continue to study the well-posedness issue and explore the potential singularity formation scenario for these models.

Guofeng Zhang, Ruyi Zha, Hao He, Yixun Liang

Sparse-view 3D CT reconstruction aims to recover volumetric structures from a limited number of 2D X-ray projections. Existing feedforward methods are constrained by the scarcity of large-scale training datasets and the absence of direct and consistent 3D representations. In this paper, we propose an X-ray Large Reconstruction Model (X-LRM) for extremely spa

Jinwen Xu, Qin Lu, Yaakov Bar-Shalom

This paper deals with the identification of linear stochastic dynamical systems, where the unknowns include system coefficients and noise variances. Conventional approaches that rely on the maximum likelihood estimation (MLE) require nontrivial gradient computations and are prone to local optima. To overcome these limitations, a sample-efficient global optim

Khang H. N. Vo, Duc P. T. Nguyen, Thong Nguyen, Tho T. Quan

This paper focuses on multimodal alignment within the realm of Artificial Intelligence, particularly in text and image modalities. The semantic gap between the textual and visual modality poses a discrepancy problem towards the effectiveness of multi-modalities fusion. Therefore, we introduce Text-Image Joint Embedding Predictive Architecture (TI-JEPA), an i

Huilong Gu, Hangyang Meng, Xiuyun Guo

Let $G$ be a finite group and $p$ be a prime. We denote by $C_p(G)$ the poset of all cosets of $p$-subgroups of $G$. We characterize the homotopy type of the geometric realization $|\Delta C_p(G)|$ for $p$-closed groups $G$, which is motivated by K.S.Brown's Question. We will further demonstrate that $\chi(C_{p}(G)) \equiv |G|_{p'} (\text{mod} p)$ for any fi

Lexin Zhou, Lorenzo Pacchiardi, Fernando Martínez-Plumed, Katherine M. Collins

Ensuring safe and effective use of AI requires understanding and anticipating its performance on novel tasks, from advanced scientific challenges to transformed workplace activities. So far, benchmarking has guided progress in AI, but it has offered limited explanatory and predictive power for general-purpose AI systems, given the low transferability across

Yen-chi Roger Lin, Akihiro Munemasa, Tetsuji Taniguchi, Kiyoto Yoshino

In 2023, Greaves et~al.\ constructed several sets of 57 equiangular lines in dimension 18. Using the concept of switching root introduced by Cao et~al.\ in 2021, these sets of equiangular lines are embedded in a lattice of rank 19 spanned by norm 3 vectors together with a switching root. We characterize this lattice as an overlattice of the root lattice $A_9

Suyash Pradhan, Asil Koc, Kubra Alemdar, Mohamed Amine Arfaoui

Over-the-air federated learning (OTA-FL) offers an exciting new direction over classical FL by averaging model weights using the physics of analog signal propagation. Since each participant broadcasts its model weights concurrently in time and frequency, this paradigm conserves communication bandwidth and model upload latency. Despite its potential, there is

Sonal Kumar, Sreyan Ghosh, Utkarsh Tyagi, Anton Jeran Ratnarajah

Speech enhancement (SE) is the foundational task of enhancing the clarity and quality of speech in the presence of non-stationary additive noise. While deterministic deep learning models have been commonly employed for SE, recent research indicates that generative models, such as denoising diffusion probabilistic models (DDPMs), have shown promise. However,

Jayanth R Taranath

One of the central aims of neuroscience is to reliably predict the behavioral response of an organism using its neural activity. If possible, this implies we can causally manipulate the neural response and design brain-computer-interface systems to alter behavior, and vice-versa. Hence, predictions play an important role in both fundamental neuroscience and

Krzysztof Sienicki

The correspondence principle states that classical mechanics emerges from quantum mechanics in the appropriate limits. However, beyond this heuristic rule, an information-theoretic perspective reveals that classical mechanics is a compressed, lower-information representation of quantum reality. Quantum mechanics encodes significantly more information through

A. R. Soares, C. F. S. Pereira, R. L. L. Vitória, Marcos V. de S. Silva

In the present work, we theoretically investigate light deflection in the weak and strong field regimes for two regular spacetimes with corrections from loop quantum gravity. We treat analytically the expansions for both limits and use them as a basis for investigating gravitational lensing observables. We analyze and provide reasonable values for observable

Yichen Liu, Colin J. Burke, Diego Miura, Xin Liu

We study the black hole mass $-$ host galaxy stellar mass relation, $M_{\rm{BH}}-M_{\ast}$, for a sample of 706 $z \lesssim 1.5$ and $i \lesssim 24$ optically-variable active galactic nuclei (AGNs) in three Dark Energy Survey (DES) deep fields: C3, X3, E2, which partially cover Chandra Deep Field-South, XMM Large Scale Structure survey, and European Large Ar

Jijie Tang, Adrien Bouhon, Yue Shen, Kailun Wang

The study of topological band theory in classical structures has led to the development of novel topological metamaterials with intriguing properties. While single-gap topologies are well understood, recent novel multi-gap phases have garnished increasing interest. These novel phases are characterized by invariants, such as the Euler and second Stiefel-Whitn

Hesam Mosalli, Saba Sanami, Yu Yang, Hen-Geul Yeh

This paper presents a method for load balancing and dynamic pricing in electric vehicle (EV) charging networks, utilizing reinforcement learning (RL) to enhance network performance. The proposed framework integrates a pre-trained graph neural network to predict demand elasticity and inform pricing decisions. The spatio-temporal EV charging demand prediction

Sahar Dastani, Ali Bahri, Moslem Yazdanpanah, Mehrdad Noori

State Space Models (SSMs) have recently emerged as an alternative to Vision Transformers (ViTs) due to their unique ability of modeling global relationships with linear complexity. SSMs are specifically designed to capture spatially proximate relationships of image patches. However, they fail to identify relationships between conceptually related yet not adj

Leonardo Scabini, Kallil M. Zielinski, Emir Konuk, Ricardo T. Fares

Texture recognition has recently been dominated by ImageNet-pre-trained deep Convolutional Neural Networks (CNNs), with specialized modifications and feature engineering required to achieve state-of-the-art (SOTA) performance. However, although Vision Transformers (ViTs) were introduced a few years ago, little is known about their texture recognition ability

Jiaming Zhang, Wenxuan Song, Hanhao Li, Zhiye Kuang

We investigate, both experimentally and theoretically, the eigenmodes of an electronic circuit in which gain and loss $RLC$ resonators are coupled through a capacitor. Due to the unavoidable magnetic loss in the inductors, we find that the eigenmode coalescence no longer emerges in contrast to the conventional non-Hermitian systems with the spontaneous $\cal

Herman Chau, Helen Jenne, Davis Brown, Jesse He

With recent dramatic increases in AI system capabilities, there has been growing interest in utilizing machine learning for reasoning-heavy, quantitative tasks, particularly mathematics. While there are many resources capturing mathematics at the high-school, undergraduate, and graduate level, there are far fewer resources available that align with the level

Si-Hai Zhang, Tao Zhong, Hai-Bing Fu, Ya-Xiong Wang

In this paper, we carry on an investigation of the semileptonic decays $B_s\to D_s^*\ell \bar\nu_{\ell}$. Firstly, we derive the moments of the $D_s^*$-meson longitudinal leading-twist light-cone distribution amplitude (LCDA) based on QCD sum rules within background field theory framework. Considering the contributions of the vacuum condensates up to dimensi

Chen Liu, Tobias Ritschel

We propose a novel generative video model to robustly learn temporal change as a neural Ordinary Differential Equation (ODE) flow with a bilinear objective which combines two aspects: The first is to map from the past into future video frames directly. Previous work has mapped the noise to new frames, a more computationally expensive process. Unfortunately,

Yunkai Wang, Sisi Zhou

Imaging thermal sources naturally yields Gaussian states at the receiver, raising the question of whether Gaussian measurements can perform optimally in quantum imaging. In this work, we establish no-go theorems on the performance of Gaussian measurements for imaging thermal sources in the limit of mean photon number per temporal mode $\epsilon \to 0$ or sou

Umberto Cappellazzo, Minsu Kim, Stavros Petridis

Audio-Visual Speech Recognition (AVSR) leverages audio and visual modalities to improve robustness in noisy environments. Recent advances in Large Language Models (LLMs) show strong performance in speech recognition, including AVSR. However, the long speech representations lead to high computational costs for LLMs. Prior methods compress inputs before feedin

Yanbin Zheng, Meiying Zhang, Yanjin Ding, Zhengbang Zha

Many-to-one mappings and permutation polynomials over finite fields have important applications in cryptography and coding theory. In this paper, we study the many-to-one property of a large class of polynomials such as $f(x) = h(a x^q + b x + c) + u x^q + v x$, where $h(x) \in \mathbb{F}_{q^2}[x]$ and $a$, $b$, $c$, $u$, $v \in \mathbb{F}_{q^2}$. Using a co

T. V. Obikhod, Ie. A. Petrenko

The discovery of the Higgs boson made it possible not only to study its physical properties and update its search strategies, but also to apply new theoretical constructs to explain its properties. Within the framework of the VBF Higgs boson production process in association with two, Hjj and three jets, Hjjj, we modeled its kinematic properties and calculat

Sobhi Saeed, Mehmet Müftüoglu, Glitta R. Cheeran, Thomas Bocklitz

The intrinsic complexity of nonlinear optical phenomena offers a fundamentally new resource to analog brain-inspired computing, with the potential to address the pressing energy requirements of artificial intelligence. We introduce and investigate the concept of nonlinear inference capacity in optical neuromorphic computing in highly nonlinear fiber-based op

Michał Balcerek, Adrian Pacheco-Pozo, Agnieszka Wyłomańska, Diego Krapf

Heterogeneous diffusion processes are prevalent in various fields, including the motion of proteins in living cells, the migratory movement of birds and mammals, and finance. These processes are often characterized by time-varying dynamics, where interactions with the environment evolve, and the system undergoes fluctuations in diffusivity. Moreover, in many

Ömer Veysel Çağatan, Ömer Faruk Tal, M. Emre Gürsoy

Self-supervised learning (SSL) has advanced significantly in visual representation learning, yet comprehensive evaluations of its adversarial robustness remain limited. In this study, we evaluate the adversarial robustness of seven discriminative self-supervised models and one supervised model across diverse tasks, including ImageNet classification, transfer

Mehran Soleimani, Kimmo Koponen, Nils Tilton, Amneet Pal Singh Bhalla

The one-dimensional (1D) Stefan problem is a prototypical heat and mass transfer problem that analyzes the temperature distribution in a material undergoing phase change. In addition, it describes the evolution of the phase change front within the phase change material (PCM). Analytical solutions to the two-phase Stefan problem that describe melting of a sol

Zhong-Peng Zhou

By applying inter-universal Teichm\"uller theory and its slight modification over the rational number field, we prove new Diophantine results towards effective abc inequalities and the generalized Fermat equations. For coprime integers $a, b, c$ satisfying $a + b = c$ and $\log(|abc|) \geq 700$, we prove $$ \log|abc| \leq 3\log\mathrm{rad}(abc) + 8\sqrt{\log

Yudong Mao, Dandan Zhang

Magnetic micro-robots have demonstrated immense potential in biomedical applications, such as in vivo drug delivery, non-invasive diagnostics, and cell-based therapies, owing to their precise maneuverability and small size. However, current micromanipulation techniques often rely solely on a two-dimensional (2D) microscopic view as sensory feedback, while tr

Idan Shenfeld, Felix Faltings, Pulkit Agrawal, Aldo Pacchiano

Modern large language models (LLMs) are optimized for human-aligned responses using Reinforcement Learning from Human Feedback (RLHF). However, existing RLHF approaches assume a universal preference model and fail to account for individual user preferences, limiting their effectiveness in personalized applications. We introduce a framework that extends RLHF

David Tlusty

Relativistic heavy ions are sources of strong electromagnetic fields which produce photon-induced interactions. These interactions are usually studied in ultra-peripheral collisions (UPCs) of the relativistic heavy ions. The UPCs can produce di-lepton or di-hadron pairs via the $\gamma-\gamma$ interactions or produce vector mesons via the $\gamma-$nuclear in

Enis Belgacem

We derive analytically some general features of the power-law sensitivity curve. They include an exact parametric equation, a formula for the peak sensitivity and a proof of convexity in log-log plot. A few conceptual points are also clarified.

Jonas Olivier Brown, Taosha Guo, Fabio Pasqualetti, Alexander A. Balandin

Many combinatorial optimization problems fall into the non-polynomial time NP-hard complexity class, characterized by computational demands that increase exponentially with the size of the problem in the worst case. Solving large-scale combinatorial optimization problems efficiently requires novel hardware solutions beyond the conventional von Neumann archit

Keito Inoshita, Kota Nojiri, Haruto Sugeno, Takumi Taga

Scientific names of organisms consist of a genus name and a species epithet, with the latter often reflecting aspects such as morphology, ecology, distribution, and cultural background. Traditionally, researchers have manually labeled species names by carefully examining taxonomic descriptions, a process that demands substantial time and effort when dealing

Mitsuyasu Hashimoto, Xi Tang

Let $R$ be a commutative noetherian ring, and let $\mathscr{S}$(resp. $\mathscr{L}$) be a Serre(resp. localizing) subcategory of the category of $R$-modules. If $\Bbb F$ is an unbounded complex of $R$-modules Tor-perpendicular to $\mathscr{S}$ and $d$ is an integer, then $\HH{i\geqslant d}{S\otimes_R \Bbb F}$ is in $\mathscr{L}$ for each $R$-module $S$ in $\

Di Kevin Gao, Sudip Mittal, Jiming Wu, Hongwei Du

Artificial Intelligence (AI) has made remarkable progress in the past few years with AI-enabled applications beginning to permeate every aspect of our society. Despite the widespread consensus on the need to regulate AI, there remains a lack of a unified approach to framing, developing, and assessing AI regulations. Many of the existing methods take a value-

Chirag Shah, Hideo Joho, Kirandeep Kaur, Preetam Prabhu Srikar Dammu

Recent advancements in generative AI have significantly increased interest in personalized agents. With increased personalization, there is also a greater need for being able to trust decision-making and action taking capabilities of these agents. However, the evaluation methods for these agents remain outdated and inadequate, often failing to capture the dy

Xiao Yue, Guangzhi Qu, Lige Gan

One significant challenge of exploiting Graph neural networks (GNNs) in real-life scenarios is that they are always treated as black boxes, therefore leading to the requirement of interpretability. To address this, model-level interpretation methods have been developed to explain what patterns maximize probability of predicting to a certain class. However, e

Rasha Karakchi

Designing and optimizing FPGA overlays is a complex and time-consuming process, often requiring multiple trial-and-error iterations to determine a suitable configuration. This paper presents an AI-driven approach to optimizing FPGA overlay configurations, specifically focusing on the NAPOLY+ automata processor implemented on the ZCU104 FPGA. By leveraging ma

Vasily Melnikov

The convergence of stochastic integrals is essential to stochastic analysis, especially in applications to mathematical finance, where they model the gains associated with a self-financing strategy. However, Fatou convergence of $(X^{n})_{n=1}^{\infty}$ $\unicode{x2014}$a notion introduced for its amenability to compactness principles$\unicode{x2014}$implies

Devin Murphy, Yichen Li, Crystal Owens, Layla Stanton

Resistive tactile sensing gloves have captured the interest of researchers spanning diverse domains, such as robotics, healthcare, and human-computer interaction. However, existing fabrication methods often require labor-intensive assembly or costly equipment, limiting accessibility. Leveraging flexible printed circuit board (FPCB) technology, we present an

Ashwin Pillay

Real-time computer-based accompaniment for human musical performances entails three critical tasks: identifying what the performer is playing, locating their position within the score, and synchronously playing the accompanying parts. Among these, the second task (score following) has been addressed through methods such as dynamic programming on string seque

Vikas Dwivedi, Bruno Sixou, Monica Sigovan

This paper presents two novel, physics-informed extreme learning machine (PIELM)-based algorithms for solving steady and unsteady nonlinear partial differential equations (PDEs) related to fluid flow. Although single-hidden-layer PIELMs outperform deep physics-informed neural networks (PINNs) in speed and accuracy for linear and quasilinear PDEs, their exten

Maarten Grachten, Javier Nistal

Generative systems of musical accompaniments are rapidly growing, yet there are no standardized metrics to evaluate how well generations align with the conditional audio prompt. We introduce a distribution-based measure called "Accompaniment Prompt Adherence" (APA), and validate it through objective experiments on synthetic data perturbations, and human list

Theodore Knoll, Amna Liaqat, Andrés Monroy-Hernández

We present ARctic Escape, a co-located augmented reality (AR) escape room designed to promote collaboration between dyads through play. While physical escape rooms provide groups with fun, social experiences, they require a gameplay venue, props, and a game master, all of which detract from their ease of access. Existing AR escape rooms demonstrate that AR c

Robert Ganian, Liana Khazaliya, Fionn Mc Inerney, Mathis Rocton

We study the classical and parameterized complexity of computing the positive non-clashing teaching dimension of a set of concepts, that is, the smallest number of examples per concept required to successfully teach an intelligent learner under the considered, previously established model. For any class of concepts, it is known that this problem can be effor

Francisco C. Caramello, Henrique A. Puel Martins, Ivan P. Costa e Silva

We introduce and investigate a novel notion of transversely affine foliation, comparing and contrasting it to the previous ones in the literature. We then use it to give an extension of the classic Hadamard's theorem from Riemannian geometry to this setting. Our main result is a transversely affine version of a well-known "Hadamard-like" theorem by J. Hebda

Samuel Garcin, Trevor McInroe, Pablo Samuel Castro, Prakash Panangaden

Extracting relevant information from a stream of high-dimensional observations is a central challenge for deep reinforcement learning agents. Actor-critic algorithms add further complexity to this challenge, as it is often unclear whether the same information will be relevant to both the actor and the critic. To this end, we here explore the principles that

Fateme Nateghi Haredasht, Fatemeh Amrollahi, Manoj Maddali, Nicholas Marshall

The Antibiotic Resistance Microbiology Dataset (ARMD) is a de-identified resource derived from electronic health records (EHR) that facilitates research in antimicrobial resistance (AMR). ARMD encompasses big data from adult patients collected from over 15 years at two academic-affiliated hospitals, focusing on microbiological cultures, antibiotic susceptibi

Qizhe Wu, Huawen Liang, Yuchen Gui, Zhichen Zeng

General matrix-matrix multiplication (GEMM) is a cornerstone of AI computations, making tensor processing engines (TPEs) increasingly critical in GPUs and domain-specific architectures. Existing architectures primarily optimize dataflow or operand reuse strategies. However, considering the interaction between matrix multiplication and multiply-accumulators (

Jiawen Wang, Samin Karim, Yuan Hong, Binghui Wang

Diffusion models are powerful generative models in continuous data domains such as image and video data. Discrete graph diffusion models (DGDMs) have recently extended them for graph generation, which are crucial in fields like molecule and protein modeling, and obtained the SOTA performance. However, it is risky to deploy DGDMs for safety-critical applicati