October 2024 arXiv papers — page 102

Yifu Cai, Arjun Choudhry, Mononito Goswami, Artur Dubrawski

Large Language Models (LLMs) have recently demonstrated a remarkable ability to model time series data. These capabilities can be partly explained if LLMs understand basic time series concepts. However, our knowledge of what these models understand about time series data remains relatively limited. To address this gap, we introduce TimeSeriesExam, a configur

G. A. Bougas, K. Mukherjee, S. I. Mistakidis

Ultracold quantum gases with long-range anisotropic interactions host novel exotic phases of matter, such as supersolids, exhibiting both rigid and superfluid characteristics. The impact of this interplay on the out-of-equilibrium dynamics of dipolar gases, and in particular its connection with universal turbulent behavior, remains highly unexplored. Here, u

Yonghyun Kim, Alexander Lerch

Recent advancements in Automatic Piano Transcription (APT) have significantly improved system performance, but the impact of noisy environments on the system performance remains largely unexplored. This study investigates the impact of white noise at various Signal-to-Noise Ratio (SNR) levels on state-of-the-art APT models and evaluates the performance of th

Van Tuan Nguyen, Razvan Beuran

This paper proposes a novel federated learning approach for improving IoT network intrusion detection. The rise of IoT has expanded the cyber attack surface, making traditional centralized machine learning methods insufficient due to concerns about data availability, computational resources, transfer costs, and especially privacy preservation. A semi-supervi

Mingxiang Cao, Yelong Qiu, Junyong Park

This paper is devoted to the study of the general linear hypothesis testing (GLHT) problem of multi-sample high-dimensional mean vectors. For the GLHT problem, we introduce a test statistic based on $L^2$-norm and random integration method, and deduce the asymptotic distribution of the statistic under given conditions. Finally, the potential advantages of ou

Zi-Min Zhou, Xiang-Gao Wang, En-Wei Liang, Jia-Xin Cao

Power Density Spectrum (PDS) is one of the powerful tools to study light curves of gamma-ray bursts (GRBs). We show the average PDS and individual PDS analysis with {\it Hard X-ray Modulation Telescope} (also named \insighthxmt) GRBs data. The values of power-law index of average PDS ($\alpha_{\bar{P}}$) for long GRBs (LGRBs) vary from 1.58-1.29 (for 100-245

Rachel Ma, Lyndon Lam, Benjamin A. Spiegel, Aditya Ganeshan

It is imperative that robots can understand natural language commands issued by humans. Such commands typically contain verbs that signify what action should be performed on a given object and that are applicable to many objects. We propose a method for generalizing manipulation skills to novel objects using verbs. Our method learns a probabilistic classifie

Shuguang Chu, Zebin Huang, Mingwei Lin, Dejun Li

Reinforcement Learning (RL) is a promising solution, allowing Unmanned Underwater Vehicles (UUVs) to learn optimal behaviors through trial and error. However, existing simulators lack efficient integration with RL methods, limiting training scalability and performance. This paper introduces MarineGym, a novel simulation framework designed to enhance RL train

Min Wen, Chengchang Liu, Ahmed Abdelmoniem, Yipeng Zhou

Bilevel optimization, crucial for hyperparameter tuning, meta-learning and reinforcement learning, remains less explored in the decentralized learning paradigm, such as decentralized federated learning (DFL). Typically, decentralized bilevel methods rely on both gradients and Hessian matrices to approximate hypergradients of upper-level models. However, acqu

Xinyue Evelyn Zhao, Yixiang Wu, Rachel Leander, Wandi Ding

We study the optimal control problem of a free boundary PDE model describing the growth of multilayered tumor tissue in vitro. We seek the optimal amount of tumor growth inhibitor that simultaneously minimizes the thickness of the tumor tissue and mitigates side effects. The existence of an optimal control is established, and the uniqueness and characterizat

Yingdong Wu, Haiyuan Feng, Wei-Qiang Chen

We examine the accretion process in a thin disk surrounding a supermassive black hole within the framework of Einstein-Maxwell-scalar (EMS) gravity. Our investigation aims to elucidate how variations in model parameters affect different physical properties of the disk. When keeping EMS parameters $\beta$ and $q$ constant, we observe a reduction in radiation

Jiang-Chao Wan, Yi Wang, Zhi-Yuan Wang

As a bridge connecting the matching polynomial and the Laplacian matching polynomial of graphs, the subdivision method is expected to be useful for investigating the Laplacian matching polynomial. In this paper, we study applications of the method from three aspects. We prove that the zero sequence of the Laplacian matching polynomial of a graph majorizes it

Yu Qian, Zeyu Yang, Kai Ni, Alptekin Vardar

Computationally challenging combinatorial optimization problems (COPs) play a fundamental role in various applications. To tackle COPs, many Ising machines and Quadratic Unconstrained Binary Optimization (QUBO) solvers have been proposed, which typically involve direct transformation of COPs into Ising models or equivalent QUBO forms (D-QUBO). However, when

Heinz Schmidt, Peter Herrmann, Maria Spichkova, James Harland

Many very large-scale systems are networks of cyber-physical systems in which humans and autonomous software agents cooperate. To make the cooperation safe for the humans involved, the systems have to follow protocols with rigid real-time and real-space properties, but they also need to be capable of making competitive and collaborative decisions with varyin

Lang Cao, Yingtian Zou, Chao Peng, Renhong Chen

Mathematical reasoning has been challenging for large language models (LLMs), and the introduction of step-by-step Chain-of-Thought (CoT) inference has significantly advanced the mathematical capabilities of LLMs. However, current approaches either necessitate extensive inference datasets for training or depend on few-shot methods that frequently compromise

Seong Ho Pahng, Sahand Hormoz

Graph Neural Networks (GNNs) traditionally employ a message-passing mechanism that resembles diffusion over undirected graphs, which often leads to homogenization of node features and reduced discriminative power in tasks such as node classification. Our key insight for addressing this limitation is to assign fuzzy edge directions -- that can vary continuous

Zhonghai Liu, Ziyi Li, Liang Liang, Shoulong Li

Recently, a class of Gauss-Bonnet extended Starobinsky gravity was proposed, allowing black holes to carry ghost-free massive scalar hair for the first time without requiring additional matter fields. This intriguing feature offers a new perspective for understanding higher-curvature pure gravity and highlights the importance of further studying the potentia

Robert Spencer, Surangika Ranathunga, Mikael Boulic, Andries van Heerden

This study investigates the application of Transfer Learning (TL) on Transformer architectures to enhance building energy consumption forecasting. Transformers are a relatively new deep learning architecture, which has served as the foundation for groundbreaking technologies such as ChatGPT. While TL has been studied in the past, prior studies considered eit

Bangti Jin, Qimeng Quan, Wenlong Zhang

In this work, we investigate the inverse problem of recovering a potential coefficient in an elliptic partial differential equation from the observations at deterministic sampling points in the domain subject to random noise. We employ a least squares formulation with an $H^1(\Omega)$ penalty on the potential in order to obtain a numerical reconstruction, an

Hao Sui, Bing Chen, Jiale Zhang, Chengcheng Zhu

Recent studies have revealed that GNNs are highly susceptible to multiple adversarial attacks. Among these, graph backdoor attacks pose one of the most prominent threats, where attackers cause models to misclassify by learning the backdoored features with injected triggers and modified target labels during the training phase. Based on the features of the tri

Tasuku Soma, André Uschmajew

We propose accelerated versions of the operator Sinkhorn iteration for operator scaling using successive overrelaxation. We analyze the local convergence rates of these accelerated methods via linearization, which allows us to determine the asymptotically optimal relaxation parameter based on Young's SOR theorem. Using the Hilbert metric on positive definite

Li Chaorong, Ling Xudong, Yang Qiang, Qin Fengqing

Deep learning models have achieved remarkable progress in precipitation prediction. However, they still face significant challenges in accurately capturing spatial details of radar images, particularly in regions of high precipitation intensity. This limitation results in reduced spatial localization accuracy when predicting radar echo images across varying

Jiale Zhang, Haoxuan Li, Di Wu, Xiaobing Sun

Code Summarization Model (CSM) has been widely used in code production, such as online and web programming for PHP and Javascript. CSMs are essential tools in code production, enhancing software development efficiency and driving innovation in automated code analysis. However, CSMs face risks of exploitation by unauthorized users, particularly in an online e

Shuwei He, Rui Liu

Visual Text-to-Speech (VTTS) aims to take the environmental image as the prompt to synthesize reverberant speech for the spoken content. Previous works focus on the RGB modality for global environmental modeling, overlooking the potential of multi-source spatial knowledge like depth, speaker position, and environmental semantics. To address these issues, we

Rafael Costa-Amaral, Soungmin Bae, Vu Thi Ngoc Huyen, Yu Kumagai

Two-dimensional (2D) $\beta$-TeO$_2$ has gained attention as a promising material for optoelectronic and power device applications, thanks to its transparency and high hole mobility. However, the underlying mechanism behind its $p$-type conductivity and dopability remains unclear. In this study, we investigate the intrinsic and extrinsic point defects in mon

Haoyu He, Haozheng Luo, Qi R. Wang

Predicting human mobility across multiple cities presents significant challenges due to the complex and diverse spatial-temporal dynamics inherent in different urban environments. In this study, we propose a robust approach to predict human mobility patterns called ST-MoE-BERT. Compared to existing methods, our approach frames the prediction task as a spatia

Zofia Kaczmarek, Peter McGill, Scott E. Perkins, William A. Dawson

There are expected to be millions of isolated black holes in the Galaxy resulting from the death of massive stars. Measuring the abundance and properties of this remnant population would shed light on the end stages of stellar evolution and the evolution paths of black hole systems. Detecting isolated black holes is currently only possible via gravitational

Sida Huang, Yuanting Wu, Dinh Hoa Nguyen

Optical wireless power transfer (OWPT) is a technology that wirelessly transmit light energy from an optical transmitter to an optical receiver, usually a solar cell. In order to achieve the highest transmission efficiency, the solar cell receiver should be accurately aligned with the optical transmitter. Hitherto, only a few works have been existed for sola

Ryo Matsuda

We proved that the Maximal cusp is not dense on the Bers boundary of the Teichm\"uller space of infinite type Riemann surfaces satisfying some analytic conditions. This is a counterexample to the infinite-type case of the McMullen result for finite-type Riemann surfaces. More precisely, we showed that maximal cusps cannot approach the points on the Bers boun

Hong Yuan, Chang-Pu Sun

To address the observation of Max Born (M. Born 1969) that the Newton's second law can emerge from a purely statistical perspective, we derive the evolution equation about the statistical distribution for dilute gas based solely on statistical principles, without invoking Newtonian mechanics, and then obtain the equations of motion for individual particles.

Kosuke Tatsumura, Yohei Hamakawa, Masaya Yamasaki, Koji Oya

A cognitive function of tracking multiple objects, needed in autonomous mobile vehicles, comprises object detection and their temporal association. While great progress owing to machine learning has been recently seen for elaborating the similarity matrix between the objects that have been recognized and the objects detected in a current video frame, less fo

Alberto Del Pia, Dekun Zhou, Yinglun Zhu

Sparse Principal Component Analysis (Sparse PCA) is a pivotal tool in data analysis and dimensionality reduction. However, Sparse PCA is a challenging problem in both theory and practice: it is known to be NP-hard and current exact methods generally require exponential runtime. In this paper, we propose a novel framework to efficiently approximate Sparse PCA

Bharath Muppasani, Protik Nag, Vignesh Narayanan, Biplav Srivastava

In this study, we investigate the under-explored intervention planning aimed at disseminating accurate information within dynamic opinion networks by leveraging learning strategies. Intervention planning involves identifying key nodes (search) and exerting control (e.g., disseminating accurate or official information through the nodes) to mitigate the influe

Xiao Liu, Jingyi Feng, Xinchao Liu

Projection-based model reduction is among the most widely adopted methods for constructing parametric Reduced-Order Models (ROM). Utilizing the snapshot data from solving full-order governing equations, the Proper Orthogonal Decomposition (POD) computes the optimal basis modes that represent the data, and a ROM can be constructed in the low-dimensional vecto

Giorgio Maria Cavallazzi, Luca Guastoni, Ricardo Vinuesa, Alfredo Pinelli

The wall cycle in wall-bounded turbulent flows is a complex turbulence regeneration mechanism that remains not fully understood. This study explores the potential of deep reinforcement learning (DRL) for managing the wall regeneration cycle to achieve desired flow dynamics. We integrate the StableBaselines3 DRL libraries with the open-source DNS solver CaNS

Elena Sierra, Lauren E. Gillespie, Salim Soltani, Moises Exposito-Alonso

Large-scale, volunteer-collected datasets of community-identified natural world imagery like iNaturalist have enabled marked performance gains for fine-grained visual classification of species using machine learning methods. However, such data -- sometimes referred to as citizen science data -- are opportunistic and lack a structured sampling strategy. This

Xinxin Liu, Zhongliang Guo, Siyuan Huang, Chun Pong Lau

Neural networks have achieved remarkable performance across a wide range of tasks, yet they remain susceptible to adversarial perturbations, which pose significant risks in safety-critical applications. With the rise of multimodality, diffusion models have emerged as powerful tools not only for generative tasks but also for various applications such as image

Boyuan Zhang, Bo Fang, Fanjiang Ye, Yida Gu

Full-state quantum circuit simulation requires exponentially increased memory size to store the state vector as the number of qubits scales, presenting significant limitations in classical computing systems. Our paper introduces BMQSim, a novel state vector quantum simulation framework that employs lossy compression to address the memory constraints on graph

Nirav Patel, Payal Prajapati, Maitrik Shah

Generating a concise and informative video summary from a long video is important, yet subjective due to varying scene importance. Users' ability to specify scene importance through text queries enhances the relevance of such summaries. This paper introduces an approach for query-focused video summarization, aiming to align video summaries closely with user

Eric Elmoznino, Tom Marty, Tejas Kasetty, Leo Gagnon

A central goal of machine learning is generalization. While the No Free Lunch Theorem states that we cannot obtain theoretical guarantees for generalization without further assumptions, in practice we observe that simple models which explain the training data generalize best: a principle called Occam's razor. Despite the need for simple models, most current

Toshiyuki Katsura, Shigeyuki Kondō, Matthias Schütt

We show that K3 surfaces in characteristic 2 can admit sets of $n$ disjoint smooth rational curves whose sum is divisible by 2 in the Picard group, for each $n=8,12,16,20$. More precisely, all values occur on supersingular K3 surfaces, with exceptions only at Artin invariants 1 and 10, while on K3 surfaces of finite height, only $n=8$ is possible.

Arnav Agrawal, Ryan J. MacDonald

Atmospheric studies of exoplanets and brown dwarfs are a cutting-edge and rapidly evolving area of astrophysics research. Calculating models of exoplanet or brown dwarf spectra requires knowledge of the wavelength-dependent absorption of light (cross sections) by the molecules and atoms in the atmosphere. Here we introduce Cthulhu, a pure Python package that

Danyal Saqib, Wajahat Hussain

Learning Based Robot Grasping currently involves the use of labeled data. This approach has two major disadvantages. Firstly, labeling data for grasp points and angles is a strenuous process, so the dataset remains limited. Secondly, human labeling is prone to bias due to semantics. In order to solve these problems we propose a simpler self-supervised roboti

Shiqi Huang, Tingfa Xu, Ziyi Shen, Shaheer Ullah Saeed

This paper describes a new spatial correspondence representation based on paired regions-of-interest (ROIs), for medical image registration. The distinct properties of the proposed ROI-based correspondence are discussed, in the context of potential benefits in clinical applications following image registration, compared with alternative correspondence-repres

Fanyu Meng, Jules Larke, Xin Liu, Zhaodan Kong

Machine learning is revolutionizing nutrition science by enabling systems to learn from data and make intelligent decisions. However, the complexity of these models often leads to challenges in understanding their decision-making processes, necessitating the development of explainability techniques to foster trust and increase model transparency. An under-ex

Shangzhe Li, Zhiao Huang, Hao Su

Imitation learning (IL) enables agents to acquire skills directly from expert demonstrations, providing a compelling alternative to reinforcement learning. However, prior online IL approaches struggle with complex tasks characterized by high-dimensional inputs and complex dynamics. In this work, we propose a novel approach to online imitation learning that l

Joan Vendrell, Russell Bent, Solmaz Kia

We consider an optimal flow distribution problem in which the goal is to find a radial configuration that minimizes resistance-induced quadratic distribution costs while ensuring delivery of inputs from multiple sources to all sinks to meet their demands. This problem has critical applications in various distribution systems, such as electricity, where effic

Shunhao Ji, Zhongxiang Wang

We report our study of the field of a $\simeq$0.2 PeV neutrino event IC-190619A. This neutrino belongs to Gold events, which more likely have an astrophysical origin. Among the two $\gamma$-ray sources within the neutrino's positional uncertainty region, we find that one of them, the BL-Lac--type blazar PKS~2254+074, had a $\gamma$-ray flare at the arrival t

Jiehua Chen, Christian Hatschka, Sofia Simola

We survey two key problems-Multi-Winner Determination and Hedonic Games in Computational Social Choice, with a special focus on their parameterized complexity, and propose some research challenges in the field.

Jaesang Park, Alireza Askarian, Srinivasa Salapaka

As inverter-based loads and energy sources become increasingly prevalent, accurate estimation of line impedance between inverters and the grid is essential for optimizing performance and enhancing control strategies. This paper presents a non-invasive method for estimating output-line impedance using measurements local to the inverter. It provides a specific

Arip Asadulaev, Rostislav Korst, Vitalii Shutov, Alexander Korotin

Recently, neural network-based methods for computing optimal transport maps have been effectively applied to style transfer problems. However, the application of these methods to voice conversion is underexplored. In our paper, we fill this gap by investigating optimal transport as a framework for voice conversion. We present a variety of optimal transport a

Greg Sallaberry, Zofia Kaczmarek, Peter McGill, Scott E. Perkins

popclass is a python package that provides a flexible, probabilistic framework for classifying the lens of a gravitational microlensing event. popclass allows a user to match characteristics of a microlensing signal to a simulation of the Galaxy to calculate lens type probabilities for an event. Constraints on any microlensing signal characteristics and any

Brianna Mueller, W. Nick Street, Stephen Baek, Qihang Lin

Federated learning (FL) enables multiple clients with distributed data sources to collaboratively train a shared model without compromising data privacy. However, existing FL paradigms face challenges due to heterogeneity in client data distributions and system capabilities. Personalized federated learning (pFL) has been proposed to mitigate these problems,

Mahdi Teimouri

In general, the statistical simulation approaches are referred to as the Monte Carlo methods as a whole. The broad class of the Monte Carlo methods involves the Markov chain Monte Carlo (MCMC) techniques that attract the attention of researchers from a wide variety of study fields. The main focus of this report is to provide a framework for all users who are

Yuxin Wen, Qingqing Cao, Qichen Fu, Sachin Mehta

Recent advancements in vision-language models (VLMs) have expanded their potential for real-world applications, enabling these models to perform complex reasoning on images. In the widely used fully autoregressive transformer-based models like LLaVA, projected visual tokens are prepended to textual tokens. Oftentimes, visual tokens are significantly more tha

Sebin John, Michael E. West

The methodology developed by McNamara and Buland (2004) for computing Power Spectral Densities (PSDs) has gained popularity due to its low computational cost and reduction of spectral variance. This methodology is widely used in seismic noise studies and station performance evaluations and is implemented in tools like ISPAQ, MUSTANG, and PQLX. However, conce

Teerath Kumar, Alessandra Mileo, Malika Bendechache

Geographical, gender and stereotypical biases in computer vision models pose significant challenges to their performance and fairness. {In this study, we present an approach named FaceSaliencyAug aimed at addressing the gender bias in} {Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs). Leveraging the salient regions} { of faces detected by

Arip Asadulaev, Rostislav Korst, Alexander Korotin, Vage Egiazarian

We propose a novel algorithm for offline reinforcement learning using optimal transport. Typically, in offline reinforcement learning, the data is provided by various experts and some of them can be sub-optimal. To extract an efficient policy, it is necessary to \emph{stitch} the best behaviors from the dataset. To address this problem, we rethink offline re

Luis Verde-Star

We obtain some properties of a class $\mathcal{A}$ of $q$-hypergeometric orthogonal polynomials with $q=-1$, described by a uniform parametrization of the recurrence coefficients. We construct a class $\mathcal{C}$ of complementary $-1$ polynomials by means of the Darboux transformation with a shift. We show that our classes contain the Bannai-Ito polynomial

Yuval Ran-Milo, Eden Lumbroso, Edo Cohen-Karlik, Raja Giryes

Structured state space models (SSMs), the core engine behind prominent neural networks such as S4 and Mamba, are linear dynamical systems adhering to a specified structure, most notably diagonal. In contrast to typical neural network modules, whose parameterizations are real, SSMs often use complex parameterizations. Theoretically explaining the benefits of

Faizan Faisal, Zubair Khalid

This paper explores a GIS-based application of the conditional p-median problem (where p = 1) in last-mile delivery logistics. The rapid growth of e-commerce in Pakistan has primarily benefited logistics companies, which face the challenge of resolving inefficiencies in the existing infrastructure and scaling effectively to meet increasing demand. Addressing

Mihail Stoian, Alexander van Renen, Jan Kobiolka, Ping-Lin Kuo

The growing adoption of data lakes for managing relational data necessitates efficient, open storage formats that provide high scan performance and competitive compression ratios. While existing formats achieve fast scans through lightweight encoding techniques, they have reached a plateau in terms of minimizing storage footprint. Recently, correlation-aware

Riku Kurama

We study Fourier-Mukai equivalences of (families of) abelian varieties and K3 surfaces in positive and mixed characteristics. We first prove in any characteristics that Fourier-Mukai partners of abelian varieties are again abelian varieties. We subsequently focus on the canonical lifts of ordinary abelian varieties and ordinary K3 surfaces. For such schemes,

Riku Kurama

Anel and To\"en proved that a smooth projective complex variety has only countably many smooth projective Fourier-Mukai partners up to isomorphism. This is generalized in the Stacks Project to the case where the varieties are smooth proper over an arbitrary algebraically closed field. This note will upgrade the proof of the latter reference to show that a sm

Nino Bašić, Ivan Damnjanović, Patrick W. Fowler

A nut graph is a simple graph for which the adjacency matrix has a single zero eigenvalue such that all non-zero kernel eigenvectors have no zero entry. It is known that infinitely many $d$-regular nut graphs exist for $3 \leq d \leq 12$ and for $d \geq 4$ such that $d \equiv 0 \pmod{4}$. Here it is shown that infinitely many $d$-regular nut graphs exist for

Indrajit Kalita, Lucia Vilallonga, Yves Atchade

With a warming planet, tropical regions are expected to experience the brunt of climate change, with more intense and more volatile rainfall events. Currently, state-of-the-art numerical weather prediction (NWP) models are known to struggle to produce skillful rainfall forecasts in tropical regions of Africa. There is thus a pressing need for improved rainfa

Amir Hossein Saberi, Amir Najafi, Ala Emrani, Amin Behjati

The aim of this paper is to address the challenge of gradual domain adaptation within a class of manifold-constrained data distributions. In particular, we consider a sequence of $T\ge2$ data distributions $P_1,\ldots,P_T$ undergoing a gradual shift, where each pair of consecutive measures $P_i,P_{i+1}$ are close to each other in Wasserstein distance. We hav

Hariprasath Govindarajan, Per Sidén, Jacob Roll, Fredrik Lindsten

A prominent self-supervised learning paradigm is to model the representations as clusters, or more generally as a mixture model. Learning to map the data samples to compact representations and fitting the mixture model simultaneously leads to the representation collapse problem. Regularizing the distribution of data points over the clusters is the prevalent

Qiming Guo, Jinwen Tang, Wenbo Sun, Haoteng Tang

Mental health issues significantly impact individuals' daily lives, yet many do not receive the help they need even with available online resources. This study aims to provide diverse, accessible, stigma-free, personalized, and real-time mental health support through cutting-edge AI technologies. It makes the following contributions: (1) Conducting an extens

Yuzhe Yang, Yifei Zhang, Yan Hu, Yilin Guo

This paper introduces the UCFE: User-Centric Financial Expertise benchmark, an innovative framework designed to evaluate the ability of large language models (LLMs) to handle complex real-world financial tasks. UCFE benchmark adopts a hybrid approach that combines human expert evaluations with dynamic, task-specific interactions to simulate the complexities

Seth Lazar

Algorithmic intermediaries govern the digital public sphere through their architectures, amplification algorithms, and moderation practices. In doing so, they shape public communication and distribute attention in ways that were previously infeasible with such subtlety, speed and scale. From misinformation and affective polarisation to hate speech and radica

Jazmin Collins, Kaylah Myranda Nicholson, Yusuf Khadir, Andrea Stevenson Won

The rapid growth of virtual reality (VR) has led to increased use of social VR platforms for interaction. However, these platforms lack adequate features to support blind and low vision (BLV) users, posing significant challenges in navigation, visual interpretation, and social interaction. One promising approach to these challenges is employing human guides

Seth Lazar

A century ago, John Dewey observed that '[s]team and electricity have done more to alter the conditions under which men associate together than all the agencies which affected human relationships before our time'. In the last few decades, computing technologies have had a similar effect. Political philosophy's central task is to help us decide how to live to

Simone Conia, Daniel Lee, Min Li, Umar Farooq Minhas

Translating text that contains entity names is a challenging task, as cultural-related references can vary significantly across languages. These variations may also be caused by transcreation, an adaptation process that entails more than transliteration and word-for-word translation. In this paper, we address the problem of cross-cultural translation on two

Gökhan Göktürk, Kamer Kaya

Spanning Centrality is a measure used in network analysis to determine the importance of an edge in a graph based on its contribution to the connectivity of the entire network. Specifically, it quantifies how critical an edge is in terms of the number of spanning trees that include that edge. The current state-of-the-art for All Edges Spanning Centrality~(AE

Panagiotis Theodoropoulos, Nikolaos Komianos, Vincent Pacelli, Guan-Horng Liu

Recent advancements in diffusion bridges for distribution transport problems have heavily relied on matching frameworks, yet existing methods often face a trade-off between scalability and access to optimal pairings during training. Fully unsupervised methods make minimal assumptions but incur high computational costs, limiting their practicality. On the oth

Yufeng Yang, Erin Tripp, Yifan Sun, Shaofeng Zou

Recent studies have shown that many nonconvex machine learning problems satisfy a generalized-smooth condition that extends beyond traditional smooth nonconvex optimization. However, the existing algorithms are not fully adapted to such generalized-smooth nonconvex geometry and encounter significant technical limitations on their convergence analysis. In thi

Fateh Bezaz, Chad C. Nelmes, Marta P. Estarellas, Timothy P. Spiller

In recent years, significant progress has been made in the field of state transfer in spin chains, with the aim of achieving perfect state transfer for quantum information processing applications. Previous research has mainly focused on manipulating inter-site couplings within spin chains; here, we investigate in detail the potential of modifying on-site ene

Alireza Rezazadeh, Zichao Li, Wei Wei, Yujia Bao

Recent advancements in large language models have significantly improved their context windows, yet challenges in effective long-term memory management remain. We introduce MemTree, an algorithm that leverages a dynamic, tree-structured memory representation to optimize the organization, retrieval, and integration of information, akin to human cognitive sche

F. Caro-Perez, M. P. Garcia del Moral, A. Restuccia

Higher Forms Symmetries (HFS) of a closed bosonic M2-brane theory formulated on a compactified target space $\mathcal{M}_9 \times T^2$ are obtained. We show that the cancellation of the 't Hooft anomaly present in the theory is related to a 3-form flux with $\mathcal{G}_1^{\nabla}$-gerbe structure associated to the world-volume flux quantization condition. A

Qi Cheng, Mert İnan, Rahma Mbarki, Grace Grmek

Understanding uncertainty plays a critical role in achieving common ground (Clark et al.,1983). This is especially important for multimodal AI systems that collaborate with users to solve a problem or guide the user through a challenging concept. In this work, for the first time, we present a dataset annotated in collaboration with developmental and cognitiv

Chuhong Mai, Ro-ee Tal, Thahir Mohamed

In-context learning (ICL) is a powerful paradigm where large language models (LLMs) benefit from task demonstrations added to the prompt. Yet, selecting optimal demonstrations is not trivial, especially for complex or multi-modal tasks where input and output distributions differ. We hypothesize that forming task-specific representations of the input is key.

Hauke Sandhaus, Quiquan Gu, Maria Teresa Parreira, Wendy Ju

Generative Artificial Intelligence (GenAI) is transforming Human-Computer Interaction (HCI) education and technology design, yet its impact remains poorly understood. This study explores how graduate students in an applied HCI course used GenAI tools during interactive device design. Despite no encouragement, all groups integrated GenAI into their workflows.

Gökhan Göktürk, Kamer Kaya

Influence Maximization (IM) aims to find a given number of "seed" vertices that can effectively maximize the expected spread under a given diffusion model. Due to the NP-Hardness of finding an optimal seed set, approximation algorithms are often used for IM. However, these algorithms require a large number of simulations to find good seed sets. In this work,

Runshi Tang, Tamara Kolda, Anru R. Zhang

This paper presents a canonical polyadic (CP) tensor decomposition that addresses unaligned observations. The mode with unaligned observations is represented using functions in a reproducing kernel Hilbert space (RKHS). We introduce a versatile loss function that effectively accounts for various types of data, including binary, integer-valued, and positive-v

Bolin Lai, Sam Toyer, Tushar Nagarajan, Rohit Girdhar

Predicting future human behavior is an increasingly popular topic in computer vision, driven by the interest in applications such as autonomous vehicles, digital assistants and human-robot interactions. The literature on behavior prediction spans various tasks, including action anticipation, activity forecasting, intent prediction, goal prediction, and so on

Naghmeh Farzi, Laura Dietz

Traditional evaluation of information retrieval (IR) systems relies on human-annotated relevance labels, which can be both biased and costly at scale. In this context, large language models (LLMs) offer an alternative by allowing us to directly prompt them to assign relevance labels for passages associated with each query. In this study, we explore alternati

Zefang Liu, Yinzhu Quan

Retrieving temporal event sequences from textual descriptions is crucial for applications such as analyzing e-commerce behavior, monitoring social media activities, and tracking criminal incidents. To advance this task, we introduce TESRBench, a comprehensive benchmark for temporal event sequence retrieval (TESR) from textual descriptions. TESRBench includes

Xiao Pu, Tianxing He, Xiaojun Wan

Prompt compression condenses contexts while maintaining their informativeness for different usage scenarios. It not only shortens the inference time and reduces computational costs during the usage of large language models, but also lowers expenses when using closed-source models. In a preliminary study, we discover that when instructing language models to c

Eric Yang, Tomas Garcia, Hannah Williams, Bhawesh Kumar

Effective management of cardiometabolic conditions requires sustained positive nutrition habits, often hindered by complex and individualized barriers. Direct human management is simply not scalable, while previous attempts aimed at automating nutrition coaching lack the personalization needed to address these diverse challenges. This paper introduces a nove

Theodoros Kyriopoulos

Let M, N be free modules over a Noetherian commutative ring R and let F be a field such that card(F) does not exceed the continuum. Then : (1) The assertion that [Any two F-vector spaces with isomorphic duals are isomorphic] is equivallent to the ICF (Injective continium function) hypothesis and it is a non-decidable statement in ZFC. (2) If the dual of M is

Deepak Sah, Manoranjan P. Singh

Pair creation by a spatially homogeneous, time-dependent electric field is studied within the framework of scalar quantum electrodynamics. We employ the standard Bogoliubov transformation approach to compute the single-particle distribution function in an adiabatic basis. We analyzed the distribution function of created particles in two different adiabatic b

Shashank Hegde, Satyajeet Das, Gautam Salhotra, Gaurav S. Sukhatme

With the increasing availability of open-source robotic data, imitation learning has become a promising approach for both manipulation and locomotion. Diffusion models are now widely used to train large, generalized policies that predict controls or trajectories, leveraging their ability to model multimodal action distributions. However, this generality come

Egor Voronetsky

We begin to study Steinberg groups associated with a locally isotropic reductive group $G$ over a arbitrary ring. We propose a construction of such a Steinberg group functor as a group object in a certain completion of the category of presheaves. We also show that it is a crossed module over $G$ in a unique way, in particular, that the $\mathrm K_2$-functor

Bryan L. M. de Oliveira, Luana G. B. Martins, Bruno Brandão, Murilo L. da Luz

Effective visual representation learning is crucial for reinforcement learning (RL) agents to extract task-relevant information from raw sensory inputs and generalize across diverse environments. However, existing RL benchmarks lack the ability to systematically evaluate representation learning capabilities in isolation from other learning challenges. To add

Wilder Schaaf, Stephen R. Sharpe

We present an implementation of the three-neutron quantization condition (QC) derived in previous work. We construct the matrices appearing in the QC and determine solutions numerically. The symmetries of the QC allow the projection onto irreducible representations of the appropriate little group (depending on frame momentum), restricting the size of the mat

Abhishek Agarwal, Dimitra Karabali, V. P. Nair

Generalizing from previous work on the integer quantum Hall effect, we construct the effective action for the analog of Laughlin states for the fractional quantum Hall effect in higher dimensions. The formalism is a generalization of the parton picture used in two spatial dimensions, the crucial ingredient being the cancellation of anomalies for the gauge fi

Xiang Zhang, Kai Wan, Hua Sun, Shiqiang Wang

Secure aggregation is concerned with the task of securely uploading the inputs of multiple users to an aggregation server without letting the server know the inputs beyond their summation. It finds broad applications in distributed machine learning paradigms such as federated learning (FL) where multiple clients, each having access to a proprietary dataset,

Batu Güneysu, Jonas Miehe

Given a self-adjoint operator $H\geq 0$ and (appropriate) densely defined and closed operators $P_{1},\dots, P_{n}$ in a Hilbert space $\mathscr{H}$, we provide a systematic study of bounded operators given by iterated integrals \begin{align}\label{oh} \int_{\{ 0\leq s_1\leq \dots\leq s_n\leq t\}}\mathrm{e}^{-s_1H}P_{1}\mathrm{e}^{-(s_2-s_1)H}P_{2}\cdots \ma

Danilo Monteiro Ribeiro, Felipe Vasconcelos Melo, Vitor Negromonte, Gabriel Walisson Matias

Autism Spectrum Disorder (ASD) is a neurodivergent condition with a wide range of characteristics and support levels. Individuals with ASD can exhibit various combinations of traits such as difficulties in social interaction, communication, and language, alongside restricted interests and repetitive activities. Many adults with ASD live independently due to