March 2025 arXiv papers — page 64

Minh-Tuan Tran, Trung Le, Xuan-May Le, Thanh-Toan Do

Dataset distillation has become a popular method for compressing large datasets into smaller, more efficient representations while preserving critical information for model training. Data features are broadly categorized into two types: instance-specific features, which capture unique, fine-grained details of individual examples, and class-general features,

Botao Long, Ghadir Sadeghi

A compact quantum metric space is a unital $C^*$-algebra equipped with a Lip-norm. Let $\{(A_n, L_n)\}$ be a sequence of compact quantum metric spaces, and let $\phi_n:A_n\to A_{n+1}$ be a unital $^*$-homomorphism preserving Lipschitz elements for $n\geq 1$. We show that there exists a compact quantum metric space structure on the inductive limit $\varinjlim

Akaash Vishal Hazarika, Mahak Shah, Swapnil Patil, Pradyumna Shukla

Effective risk management solutions become absolutely crucial when financial markets embrace distributed technology and decentralized financing (DeFi). This study offers a thorough survey and comparative analysis of the integration of artificial intelligence (AI) in risk management for distributed arbitrage systems. We examine several modern caching techniqu

Xiaole He, Yingqing Xiao, Fei Yang

It has been shown that the Sierpi\'nski gasket-like sets can appear as the Julia sets of some geometrically finite rational maps. In this paper we prove that such type of Julia sets can also appear in the rational maps containing Siegel disks, Cremer points or which are infinitely renormalizable. Based on this, we prove the existence of gasket Julia sets wit

Praveen Chopra, Himanshu Kumar, Sandeep Yadav

Fault detection in rotating machinery is a complex task, particularly in small and heterogeneous dataset scenarios. Variability in sensor placement, machinery configurations, and structural differences further increase the complexity of the problem. Conventional deep learning approaches often demand large, homogeneous datasets, limiting their applicability i

S. G. Barwick, Alice M. W. Hui, Wen-Ai Jackson

Let $\phi$ be a collineation of order 3 acting on $PG(2,q^3)$ whose fixed points are exactly an $\mathbb F_q$-plane $\pi_q$. Let $T$ be a point whose orbit under $\phi$ is a triangle and let $S_G$ be the subgroup of $PGL(3,q^3)$ that fixes setwise the $\mathbb F_q$-plane $\pi_q$ and fixes setwise the line $T^\phi T^{\phi^2}$. The point orbits of $S_G$ form a

Christian T. Covington, Jeffrey W. Miller

Models are often misspecified in practice, making model criticism a key part of Bayesian analysis. It is important to detect not only when a model is wrong, but which aspects are wrong, and to do so in a computationally convenient and statistically rigorous way. We introduce a novel method for model criticism based on the fact that if the parameters are draw

Mahak Shah, Akaash Vishal Hazarika, Meetu Malhotra, Sachin C. Patil

Sentiment analysis is a field within NLP that has gained importance because it is applied in various areas such as; social media surveillance, customer feedback evaluation and market research. At the same time, distributed systems allow for effective processing of large amounts of data. Therefore, this paper examines how sentiment analysis converges with dis

Yingli Wang, Zhenyu Cui, Lingjiong Zhu

We study nearly unstable bivariate cumulative heavy-tailed INAR($\infty$) processes and show that, under a one-factor parameterization and a suitable scaling, they converge to the rough Heston model. This yields a discrete-time microstructural route to the joint price-variance dynamics and gives explicit formulas linking the INAR asymmetry parameters to the

Varun Mulchandani, Jung-Eun Kim

Deep neural networks have been shown to learn and rely on spurious correlations present in the data that they are trained on. Reliance on such correlations can cause these networks to malfunction when deployed in the real world, where these correlations may no longer hold. To overcome the learning of and reliance on such correlations, recent studies propose

Chengxiang Zhao, Wenjun Zhang, Haotong Wang, Fangwei Han

We theoretically investigate the electric field-tuning plasmons and plasmon-phonon couplings of two-dimensional (2D) transition metal dichalcogenides (TMDs), such as monolayer MoS2, under the consideration of spin-orbit coupling. It is revealed that the frequencies of plasmons and coupled plasmon-phonon modes originating from electron-electron and electron-p

Xiudi Li, Sijia Li

We propose a general framework for statistical inference on the overall strengths of players in pairwise comparisons, allowing for potential shifts in the covariate distribution. These covariates capture important contextual information that may impact the winning probability of each player. We measure the overall strengths of players under a target distribu

Kush Janani

The modern enterprise is facing an unprecedented surge in digital identities, with machine identities now significantly outnumbering human identities. This paper examines the cybersecurity risks emerging from what we define as the "human-machine identity blur" - the point at which human and machine identities intersect, delegate authority, and create new att

Lukas Uzolas, Elmar Eisemann, Petr Kellnhofer

Many 3D tasks such as pose alignment, animation, motion transfer, and 3D reconstruction rely on establishing correspondences between 3D shapes. This challenge has recently been approached by pairwise matching of semantic features from pre-trained vision models. However, despite their power, these features struggle to differentiate instances of the same seman

Tadesse Destaw Belay, Dawit Ketema Gete, Abinew Ali Ayele, Olga Kolesnikova

Developing and integrating emotion-understanding models are essential for a wide range of human-computer interaction tasks, including customer feedback analysis, marketing research, and social media monitoring. Given that users often express multiple emotions simultaneously within a single instance, annotating emotion datasets in a multi-label format is crit

Naoki Fukushima, Kei-Ichi Kondo

The residual gauge symmetry (RGS) is the local gauge symmetry remaining even after imposing the gauge fixing condition. Although this symmetry is ``spontaneously broken'' in the perturbative vacuum, it can be restored in the true confining vacuum of QCD. Therefore, a color confinement criterion is obtained as the condition of restoration of the RGS, namely,

Kuldeep Gautam, S. VenkataKeerthy, Ramakrishna Upadrasta

In recent years, a lot of technological advances in computer science have aided software programmers to create innovative and real-time user-friendly software. With the creation of the software and the urging interest of people to learn to write software, there is a large collection of source codes that can be found on the web, also known as Big Code, which

Jong Myoung Kim, Young-Jun_Lee, Ho-Jin Choi, Sangkeun Jung

Transfer learning leverages the abundance of English data to address the scarcity of resources in modeling non-English languages, such as Korean. In this study, we explore the potential of Phrase Aligned Data (PAD) from standardized Statistical Machine Translation (SMT) to enhance the efficiency of transfer learning. Through extensive experiments, we demonst

Anseong Park, Jaeyune Ryu, Won Bo Lee

Machine learning force fields (MLFFs) are gaining attention as an alternative to classical force fields (FFs) by using deep learning models trained on density functional theory (DFT) data to improve interatomic potential accuracy. In this study, we develop and apply MLFFs for ionic liquids (ILs), specifically PYR14BF4 and LiTFSI/PYR14TFSI, using two differen

G. S. Pogosyan, A. Yakhno

In the present paper we revisit the Helmholtz equation on the Euclidean plane and make some remarks on normalization constants and completeness of wave function sets. The coefficients of interbasis expansions are also reconsidered.

Naoya Arakawa, Kenji Yonemitsu

We show that the driving field of circularly polarized light (CPL) can be used to enhance and reverse the spin current generated in the spin Hall effect for some transition-metal dichalcogenides. This is demonstrated by analyzing the time-averaged spin Hall conductivities in the nonequilibrium steady states of monolayers WS$_{2}$, MoS$_{2}$, MoTe$_{2}$, and

Tadesse Destaw Belay, Israel Abebe Azime, Ibrahim Said Ahmad, David Ifeoluwa Adelani

Language models built from various sources are the foundation of today's NLP progress. However, for many low-resource languages, the diversity of domains is often limited, more biased to a religious domain, which impacts their performance when evaluated on distant and rapidly evolving domains such as social media. Domain adaptive pre-training (DAPT) and task

Feiran Wang, Bin Duan, Jiachen Tao, Nikhil Sharma

Medical image segmentation is crucial for enhancing diagnostic accuracy and treatment planning in Magnetic Resonance Imaging (MRI). However, acquiring precise lesion masks for segmentation model training demands specialized expertise and significant time investment, leading to a small dataset scale in clinical practice. In this paper, we present ZECO, a Zero

Wei Huang, Hanchen Wang, Dong Wen, Wenjie Zhang

The Graph Edit Distance (GED) problem, which aims to compute the minimum number of edit operations required to transform one graph into another, is a fundamental challenge in graph analysis with wide-ranging applications. However, due to its NP-hard nature, traditional A* approaches often suffer from scalability issue, making them computationally intractable

Qianou Ma, Dora Zhao, Xinran Zhao, Chenglei Si

In the era of Large Language Models (LLMs), establishing effective evaluation methods and standards for diverse human-AI interaction systems is increasingly challenging. To encourage more transparent documentation and facilitate discussion on human-AI system evaluation design options, we present an evaluation card SPHERE, which encompasses five key dimension

Daniel Engelsman, Itzik Klein

Linear quadratic Gaussian (LQG) control is a well-established method for optimal control through state estimation, particularly in stabilizing an inverted pendulum on a cart. In standard laboratory setups, sensor redundancy enables direct measurement of configuration variables using displacement sensors and rotary encoders. However, in outdoor environments,

Balázs Maga

For a permuton $μ$ let $H_n(μ)$ denote the Shannon entropy of the sampling distribution of $μ$ on $n$ points. We investigate the asymptotic growth of $H_n(μ)$ for a wide class of permutons. We prove that if $μ$ has a non-vanishing absolutely continuous part, then $H_n(μ)$ has a growth rate $Θ(n \log n)$. We show that if $μ$ is the graph of a piecewise contin

Susan M. Cooper, Sara Faridi, Thiago Holleben, Lisa Nicklasson

The terms "whiskering", and more generally "grafting", refer to adding generators to any monomial ideal to make the resulting ideal Cohen-Macaulay. We investigate the independence complexes of simplicial complexes that are constructed through a whiskering or grafting process, and we show that these independence complexes are (generalized) Bie

Philip Loche, Kevin K. Huguenin-Dumittan, Melika Honarmand, Qianjun Xu

Most atomistic machine learning (ML) models rely on a locality ansatz, and decompose the energy into a sum of short-ranged, atom-centered contributions. This leads to clear limitations when trying to describe problems that are dominated by long-range physical effects - most notably electrostatics. Many approaches have been proposed to overcome these limitati

Johan de Aguas, Johan Pensar, Tomás Varnet Pérez, Guido Biele

Confounding bias and selection bias bring two significant challenges to the validity of conclusions drawn from applied causal inference. The latter can stem from informative missingness, such as in cases of attrition. We introduce the Sequential Adjustment Criteria (SAC), which extend available graphical conditions for recovering causal effects from confound

Jungsoo Lee, Debasmit Das, Munawar Hayat, Sungha Choi

We propose a novel knowledge distillation approach, CustomKD, that effectively leverages large vision foundation models (LVFMs) to enhance the performance of edge models (e.g., MobileNetV3). Despite recent advancements in LVFMs, such as DINOv2 and CLIP, their potential in knowledge distillation for enhancing edge models remains underexplored. While knowledge

Guy Laban, Julie Wang, Hatice Gunes

Emotion regulation is a crucial skill for managing emotions in everyday life, yet finding a constructive and accessible method to support these processes remains challenging due to their cognitive demands. In this study, we explore how regular interactions with a social robot, conducted in a structured yet familiar environment within university halls and dep

Aneesh Vathul, Daniel Lee, Sheryl Chen, Arthi Tasmia

Large Language Models (LLMs) have demonstrated impressive capabilities on a broad array of NLP tasks, but their tendency to produce hallucinations$\unicode{x2013}$plausible-sounding but factually incorrect content$\unicode{x2013}$poses severe challenges in high-stakes domains. Existing hallucination detection methods either bear the computational cost of mul

Xiaodan Shao, Weidong Mei, Changsheng You, Qingqing Wu

Six-dimensional movable antenna (6DMA) is a new and revolutionary technique that fully exploits the wireless channel spatial variations at the transmitter/receiver by flexibly adjusting the three-dimensional (3D) positions and/or 3D rotations of antennas/antenna surfaces (sub-arrays), thereby improving the performance of wireless networks cost-effectively wi

Yun Soo Myung

We perform the thermodynamic and shadow radius analysis of an electrically charged black hole (EC) with electric charge $q$ and coupling constant $\mu$ obtained from the Einstein-Euler-Heisenberg nonlinear electodynamics. For $\mu=0.03$, we have four solution branches of the horizon including low, hot, negative, and cold ones, while for $\mu=0.3,3$ there exi

Harang Ju, Sinan Aral

We examined the mechanisms underlying productivity and performance gains from AI agents using a large-scale experiment on Pairit, a platform we developed to study human-AI collaboration. We randomly assigned 2,234 participants to human-human and human-AI teams that produced 11,024 ads for a think tank. We evaluated the ads using independent human ratings and

Tarun Chitra

Lending within decentralized finance (DeFi) has facilitated over \$100 billion of loans since 2020. A long-standing inefficiency in DeFi lending protocols such as Aave is the use of static pricing mechanisms for loans. These mechanisms have been shown to maximize neither welfare nor revenue for participants in DeFi lending protocols. Recently, adaptive suppl

Hardik A. Jain, Rohitash Chandra

The evaluation of a researcher's performance has traditionally relied on various bibliometric measures, with the h-index being one of the most prominent. However, the h-index only accounts for the number of citations received in a publication and does not account for other factors such as the number of authors or their specific contributions in collaborative

Yilong Wang, Jiahao Zhang, Tianxiang Zhao, Suhang Wang

Despite their impressive predictive performance, GNNs often exhibit poor confidence calibration, i.e., their predicted confidence scores do not accurately reflect true correctness likelihood. This issue raises concerns about their reliability in high-stakes domains such as fraud detection, and risk assessment, where well-calibrated predictions are essential

Shih-Min Yang, Martin Magnusson, Johannes A. Stork, Todor Stoyanov

Soft Actor-Critic (SAC) has achieved notable success in continuous control tasks but struggles in sparse reward settings, where infrequent rewards make efficient exploration challenging. While novelty-based exploration methods address this issue by encouraging the agent to explore novel states, they are not trivial to apply to SAC. In particular, managing th

Khalil Farouqi, Anna Frebel, Friedrich-Karl Thielemann

Low-metallicity stars preserve the signatures of the first stellar nucleosynthesis events in the Galaxy, as their surface abundances reflect the composition of the interstellar medium from which they were born. Aside from primordial Big Bang nucleosynthesis, massive stars, due to their short lifetimes, dominate the ejecta into the interstellar medium of the

Dorina Mitrea, Irina Mitrea, Marius Mitrea

We employ the Riesz transform as a means for describing geometric properties of sets in ${\mathbb{R}}^n$, and study the extent to which they can be used to characterize function spaces defined on said sets. In particular, characterizations of the end-point spaces on the Lebesgue scale $L^p$ with $1<p<\infty$, namely the Hardy space $H^1$ and the John-Nirenbe

Lorenzo Soprani, Andrea Giunchi, Marco Bardini, Quintin N. Meier

Vibrational dynamics governs the fundamental properties of molecular crystals, shaping their thermodynamics, mechanics, spectroscopy, and transport phenomena. However desirable, the first-principles calculation of solid-state vibrations, i.e.\ phonons, stands as a major computational challenge in molecular crystals characterized by many atoms in the unit cel

José Alberto Benítez-Andrades, Camino Prada-García, Nicolás Ordás-Reyes, Marta Esteban Blanco

The study proposes an advanced machine learning approach to predict spine surgery outcomes by incorporating oversampling techniques and grid search optimization. A variety of models including GaussianNB, ComplementNB, KNN, Decision Tree, and optimized versions with RandomOverSampler and SMOTE were tested on a dataset of 244 patients, which included pre-surgi

Ilim Cimdiker, Mariusz P. Dabrowski, Vincenzo Salzano

We present a general formalism for studying generalized Holographic Dark Energy (HDE) models in which we use a dimensionless form of the area-entropy of cosmological horizons. The future event horizon is applied though the formalism can also be applied to any other type of the horizon, too. Then, we use our formalism for nonextensive horizon entropies of sta

Akash Agrawal, Christopher McComb

Multi-fidelity Reinforcement Learning (RL) frameworks efficiently utilize computational resources by integrating analysis models of varying accuracy and costs. The prevailing methodologies, characterized by transfer learning, human-inspired strategies, control variate techniques, and adaptive sampling, predominantly depend on a structured hierarchy of models

Marco Aymone, Ana Paula Chaves, Maria Eduarda Ramos

A modified Dirichlet character $f$ is a completely multiplicative function such that for some Dirichlet character $\chi$, $f(p)=\chi(p)$ for all but a finite number of primes $p\in S$, and for those exceptional primes $p\in S$, $|f(p)|\leq 1$. If $\chi$ is primitive and for each $p\in S$ we have $|f(p)|=1$, we prove that $\sum_{n\leq x}f(n)=\Omega((\log x)^{

Yiheng Zhong, Zihong Luo, Chengzhi Liu, Feilong Tang

Segment Anything Model (SAM) demonstrates powerful zero-shot capabilities; however, its accuracy and robustness significantly decrease when applied to medical image segmentation. Existing methods address this issue through modality fusion, integrating textual and image information to provide more detailed priors. In this study, we argue that the granularity

Gavin Witsken, Igor Crk, Eren Gultepe

We randomly deploy questions constructed with and without use of the LLM tool and gauge the ability of the students to correctly answer, as well as their ability to correctly perceive the difference between human-authored and LLM-authored questions. In determining whether the questions written with the aid of ChatGPT were consistent with the instructor's que

Venkatesh Bollineni, Igor Crk, Eren Gultepe

Accessing and gaining insight into the Rigveda poses a non-trivial challenge due to its extremely ancient Sanskrit language, poetic structure, and large volume of text. By using NLP techniques, this study identified topics and semantic connections of hymns within the Rigveda that were corroborated by seven well-known groupings of hymns. The 1,028 suktas (hym

Massimo Bini, Leander Girrbach, Zeynep Akata

Parameter-Efficient FineTuning (PEFT) methods have recently gained significant popularity thanks to the widespread availability of large-scale pretrained models. These methods allow for quick adaptation to downstream tasks with minimal computational cost. However, popular finetuning methods such as LoRA exhibit limited robustness when it comes to hyperparame

Shubhankar Agarwal, Hamzah I. Khan, Sandeep P. Chinchali, David Fridovich-Keil

Saddle point optimization is a critical problem employed in numerous real-world applications, including portfolio optimization, generative adversarial networks, and robotics. It has been extensively studied in cases where the objective function is known and differentiable. Existing work in black-box settings with unknown objectives that can only be sampled e

Valentin Gabeff, Haozhe Qi, Brendan Flaherty, Gencer Sumbül

Monitoring wildlife is essential for ecology and ethology, especially in light of the increasing human impact on ecosystems. Camera traps have emerged as habitat-centric sensors enabling the study of wildlife populations at scale with minimal disturbance. However, the lack of annotated video datasets limits the development of powerful video understanding mod

Sivaguru S. Sritharan, Saba Mudaliar

In this paper we will develop linear and nonlinear filtering methods for a large class of nonlinear wave equations that arise in applications such as quantum dynamics and laser generation and propagation in a unified framework. We consider both stochastic calculus and white noise filtering methods and derive measure-valued evolution equations for the nonline

Wen-Tse Chen, Minh Nguyen, Zhongyu Li, Guo Ning Sue

This work addresses the challenge of enabling a team of quadrupedal robots to collaboratively tow a cable-connected load through cluttered and unstructured environments while avoiding obstacles. Leveraging cables allows the multi-robot system to navigate narrow spaces by maintaining slack when necessary. However, this introduces hybrid physical interactions

Minoru Kitahara, Yasunori Okumura

We examine a controlled school choice model where students are categorized into different types, and the distribution of these types within a school influences its priority structure. This study provides a general framework that integrates existing controlled school choice models, including those utilizing reserve rules, quota rules, and bonus-point rules. S

Joshua Ofori Boateng, Tianyi Zhang, Guoying Zu, Taimoor Ul Islam

The rapid evolution of wireless technologies has intensified interest in open and fully programmable radio access networks for whole-stack research, innovation, and evaluation of emerging solutions. Large-scale wireless living labs, such as ARA, equipped with real-world infrastructure play a vital role in this evolution by enabling researchers to prototype a

Daniel P. Whitmire

If two physical timescales are independent, i.e. they depend on different physics, then (statistically) there is no reason to believe that their values should be equal, even to an order of magnitude. The timescale for abiogenesis $\tau_{AB}$, which depends primarily on prebiotic-chemistry, is expected to be independent of the planetary habitability timescale

Roberto Garcia, Jerry Liu, Daniel Sorvisto, Sabri Eyuboglu

Large Language Models (LLMs) are computationally intensive, particularly during inference. Neuron-adaptive techniques, which selectively activate neurons in Multi-Layer Perceptron (MLP) layers, offer some speedups but suffer from limitations in modern Transformers. These include reliance on sparse activations, incompatibility with attention layers, and the u

Er-Te Zheng, Hui-Zhen Fu, Xiaorui Jiang, Zhichao Fang

News and social media are widely used to disseminate science, but do they also help raise awareness of problems in research? This study investigates whether high levels of news and social media attention might accelerate the retraction process and increase the visibility of retracted articles. To explore this, we analyzed 15,642 news mentions, 6,588 blog men

George Fletcher, Peter Wood, Nikolay Yakovets

We consider the problem of defining semantic metrics for relational database queries. Informally, a semantic query metric for a query language $L$ is a metric function $\delta:L\times L\to \mathbb{N}$ where $\delta(Q_1, Q_2)$ represents the length of a shortest path between queries $Q_1$ and $Q_2$ in a graph. In this graph, nodes are queries from $L$, and ed

Delower Hossain, Jake Y Chen

Over the last few decades, Artificial Intelligence (AI) scientists have been conducting investigations to attain human-level performance by a machine in accomplishing a cognitive task. Within machine learning, the ultimate aspiration is to attain Artificial General Intelligence (AGI) through a machine. This pursuit has led to the exploration of two distinct

Kanishka Parankusham, Rodrigue Rizk, KC Santosh

Lakota, a critically endangered language of the Sioux people in North America, faces significant challenges due to declining fluency among younger generations. This paper introduces LakotaBERT, the first large language model (LLM) tailored for Lakota, aiming to support language revitalization efforts. Our research has two primary objectives: (1) to create a

Zhengyuan Li, Kai Cheng, Anindita Ghosh, Uttaran Bhattacharya

Text-based 3D human motion editing is a critical yet challenging task in computer vision and graphics. While training-free approaches have been explored, the recent release of the MotionFix dataset, which includes source-text-motion triplets, has opened new avenues for training, yielding promising results. However, existing methods struggle with precise cont

Nitish Dashora, Dibya Ghosh, Sergey Levine

Online reinforcement learning (RL) with sparse rewards poses a challenge partly because of the lack of feedback on states leading to the goal. Furthermore, expert offline data with reward signal is rarely available to provide this feedback and bootstrap online learning. How can we guide online agents to the right solution without this on-task data? Reward sh

Sajjad Khan, Choonkil Park

In this paper, we introduce the idea of $\ast$-homomorphism on a Hilbert $C^{*}$-module. Furthermore, we prove the Hyers-Ulam stability of homomorphisms and $\ast$-homomorphisms on Hilbert $C^{*}$-modules using the fixed point method.

Allen Caldwell, Silvia Dalla Torre, Rolf Ent, Aharon Levy

Deep Inelastic lepton-hadron Scattering (DIS) is a cornerstone of particle physics discovery and the precision measurement of the structure of matter. This document surveys the international DIS landscape, exploring current and future opportunities to continue this rich heritage, leading to new understandings and enabling discoveries. Of immediate relevance

Debarun Paul, Md Riajul Haque, Supratik Pal

We investigate the prospects of detecting a stochastic gravitational wave (GW) background from the primordial black hole (PBH) reheating epoch. If PBHs form during a non-standard cosmological phase prior to the radiation-dominated era, they can dominate the energy density of the Universe before evaporating via Hawking radiation. Such PBHs can generate induce

Sasindu Wijeratne, Sugeet Sunder, Md Abdullah-Al Kaiser, Akhilesh Jaiswal

Photonics-based in-memory computing systems have demonstrated a significant speedup over traditional transistor-based systems because of their ultra-fast operating frequencies and high data bandwidths. Photonic static random access memory (pSRAM) is a crucial component for achieving the objective of ultra-fast photonic in-memory computing systems. In this wo

Michael Temkin

This is a first paper in a project on extending the dream principalization and resolution methods of [ATW24], [McQ20] and [Que22] to quasi-excellent, logarithmic and relative settings. We show that the main results of [ATW24] extend to regular schemes with enough derivations and are functorial with respect to all regular morphisms. This is already strong eno

D. Romash, E. Sevost'yanov

We consider mappings satisfying a certain estimate of the distortion of the modulus of families of paths, similar to the geometric definition of quasiconformal mappings. Under appropriate restrictions, we show that the class of such mappings is uniformly light, i.e., the chordal diameter of the image of continua whose diameter is bounded below is also bounde

Paweł Hatka, Dawid Brząkała, Marcel Garczyk, Paweł Płaczkiewicz

This paper presents the results of reflection characteristics measurements for reconfigurable intelligent surfaces (RIS). The measurements were carried out in a non-ideal environment, i.e. typical for subsequent practical use of RISes. During the experiments, popular matrices implemented in the open-source project OpenSource- RIS were used. The study focused

Yitao He

The interface plays a critical role in electrochemical systems, driving the development of various theories to investigate properties at nanoscale and microscale levels, including the electrictrochemical double layer (EDL) theory and continuum theory. However, the application of EDL theory within the realm of battery electrode materials has not been clearly

Georg Ziegner, Michael Choi, Hung Mac Chan Le, Sahil Sakhuja

Multi-echelon inventory optimization (MEIO) is critical for effective supply chain management, but its inherent complexity can pose significant challenges. Heuristics are commonly used to address this complexity, yet they often face limitations in scope and scalability. Recent research has found deep reinforcement learning (DRL) to be a promising alternative

Chunmei Wang, Shangyou Zhang

In this paper, we propose a simple numerical algorithm based on the weak Galerkin (WG) finite element method for a class of fourth-order problems in fluorescence tomography (FT), eliminating the need for stabilizer terms required in traditional WG methods. FT is an emerging, non-invasive 3D imaging technique that reconstructs images of fluorophore-tagged mol

Guillaume Maitrier, Grégoire Loeper, Jean-Philippe Bouchaud

This paper introduces a novel algorithm for generating realistic metaorders from public trade data, addressing a longstanding challenge in price impact research that has traditionally relied on proprietary datasets. Our method effectively recovers all established stylized facts of metaorders impact, such as the Square Root Law, the concave profile during met

Sasindu Wijeratne, Rajgopal Kannan, Viktor Prasanna

Sparse Matricized Tensor Times Khatri-Rao Product (spMTTKRP) is the bottleneck kernel of sparse tensor decomposition. In tensor decomposition, spMTTKRP is performed iteratively along all the modes of an input tensor. In this work, we propose a mode-specific tensor layout on GPU that uses multiple tensor copies, where each copy is optimized for a specific mod

Ziheng Chen, Jiali Cheng, Hadi Amiri, Kaushiki Nag

With growing emphasis on privacy regulations, machine unlearning has become increasingly critical in real-world applications such as social networks and recommender systems, many of which are naturally represented as graphs. However, existing graph unlearning methods often modify nodes or edges indiscriminately, overlooking their impact on fairness. For inst

Alejandro Illanes, Verónica Martínez-de-la-Vega, Jorge E. Vega

Given a continuum $X$, let $C(X)$ be the hyperspace of all subcontinua of $X$. We consider the hyperspace $NC^{*}(X)=\{A\in C(X):X\setminus A$ is connected$\}$. In this paper we prove that the only locally connected continua $X$ for which $NC^{*}(X)$ is compact are the arcs and the simple closed curves. We also characterize the finite graphs $G$ for which $N

Hongliang Chi, Qiong Wu, Zhengyi Zhou, Yao Ma

Graph Neural Networks (GNNs) have demonstrated remarkable performance in various graph-based machine learning tasks, yet evaluating the importance of neighbors of testing nodes remains largely unexplored due to the challenge of assessing data importance without test labels. To address this gap, we propose Shapley-Guided Utility Learning (SGUL), a novel frame

Michael Wilkinson

This paper considers an Ostwald ripening process in which new droplets are injected at a constant rate, with a fixed distribution of radii, and in which droplets are removed when they grow to a specified maximum radius. This process exhibits a transition from a steady state to a limit cycle as a parameter is varied. The instability is shown to be related to

Katrin Gelfert, Dominik Kwietniak, Yuri Lima

We combine the two classical topological concepts, time-preserving topological factors and synchronizing time-changes of a continuous flow, and explore some of their thermodynamic consequences. Particular focus is put on equilibrium states and, in particular, measures of maximal entropy, with emphasis on geodesic flows on rank-one surfaces of nonpositive cur

Ahmad Sarlak, Rahul Amin, Abolfazl Razi

Autonomous Vehicles (AVs) rely on individual perception systems to navigate safely. However, these systems face significant challenges in adverse weather conditions, complex road geometries, and dense traffic scenarios. Cooperative Perception (CP) has emerged as a promising approach to extending the perception quality of AVs by jointly processing shared came

Haoyu Li, Jingkai Fu, Qing Li, Windsor Hsu

Cloud platforms host thousands of tenants that demand POSIX semantics, high throughput, and rapid evolution from their storage layer. Kernel-native distributed file systems supply raw speed, but their privileged code base couples every release to the kernel, widens the blast radius of crashes, and slows innovation. FUSE-based distributed file systems flip th

Alejandra Castillo, Jamie Haddock, Iryna Hartsock, Paulina Hoyos

The reconstruction of tensor-valued signals from corrupted measurements, known as tensor regression, has become essential in many multi-modal applications such as hyperspectral image reconstruction and medical imaging. In this work, we address the tensor linear system problem $\mathcal{A} \mathcal{X}=\mathcal{B}$, where $\mathcal{A}$ is a measurement operato

Hoang Vu, Nataliia Klievtsova, Henrik Leopold, Stefanie Rinderle-Ma

With the rise of generative AI, industry interest in software agents is growing. Given the stochastic nature of generative AI-based agents, their effective and safe deployment in organizations requires robust governance, which can be facilitated by agentic business process management. However, given the nascence of this new-generation agent notion, it is not

Wouter Jongeneel, Raphaël M. Jungers

A fruitful approach to study stability of switched systems is to look for multiple Lyapunov functions. However, in general, we do not yet understand the interplay between the desired stability certificate, the template of the Lyapunov functions and their mutual relationships to accommodate switching. In this work we elaborate on path-complete Lyapunov functi

Bing-qiang Qiao, Wei Liu, Huirong Yan, Yi-qing Guo

Recent studies suggest that the anisotropy in cosmic-ray arrival directions can provide insight into local acceleration sites and propagation conditions. We developed a unified framework to interpret both the observed energy spectra and the large-scale anisotropy. In this work, we explore the influence of the Sun's motion relative to the local plasma frame -

Brenner S. Rego, Daniel N. Cardoso, Marco. H. Terra, Guilherme V. Raffo

This paper proposes a joint state-parameter observer-based controller for trajectory tracking of an octocopter unmanned aerial vehicle (OUAV), for transportation of a heavy load with unknown mass and size. The multi-body dynamic model of the OUAV with a rigidly attached load is obtained, effectively considering the effects of the load parameters into the dyn

R. E. Kastner

I dispute the conventional claim that the second law of thermodynamics is saved from a "Maxwell's Demon" by the entropy cost of information erasure, and show that instead it is measurement that incurs the entropy cost. Thus Brillouin, who identified measurement as savior of the second law, was essentially correct, and putative refutations of his view, such a

Spyridon Evangelatos, Eleni Veroni, Vasilis Efthymiou, Christos Nikolopoulos

Counterfactual explanations have emerged as a prominent method in Explainable Artificial Intelligence (XAI), providing intuitive and actionable insights into Machine Learning model decisions. In contrast to other traditional feature attribution methods that assess the importance of input variables, counterfactual explanations focus on identifying the minimal

Kiran S. Kedlaya, Yutaro Mikami

We establish a relative version of the Nullstellensatz for algebras topologically of finite type over a given Banach Tate ring $A$, under the assumption that the corresponding statement holds for rational localizations of $A$. This applies in particular to pseudoaffinoid algebras and to the coordinate rings of affinoid subspaces of a Fargues--Fontaine curve.

Divya Patel, Vansh Parikh, Om Patel, Agam Shah

In this work, we apply topic modeling using Non-Negative Matrix Factorization (NMF) on the COVID-19 Open Research Dataset (CORD-19) to uncover the underlying thematic structure and its evolution within the extensive body of COVID-19 research literature. NMF factorizes the document-term matrix into two non-negative matrices, effectively representing the topic

Seyed Ali Sadegh-Zadeh, Alireza Soleimani Mamalo, Mahsa Behnemoon, Masoud Ojarudi

This study investigates long-term cardiovascular complications in COVID-19 patients using advanced clustering techniques. The objective was to analyse ECG parameters, demographic data, comorbidities, and hospitalization details to identify patterns in cardiovascular health outcomes. We applied K-means clustering and identified three distinct clusters: Cluste

Edgar Torres, Jonathan Schiefer, Mathias Niepert

Physics-informed neural networks (PINNs) have emerged as a promising approach to solving partial differential equations (PDEs) using neural networks, particularly in data-scarce scenarios, due to their unsupervised training capability. However, limitations related to convergence and the need for re-optimization with each change in PDE parameters hinder their

Le Yao, Yantong Liu

This study specifically investigates the initiation phase of EFL learners' engagement with AI tools, focusing on how technology acceptance constructs perceived usefulness (PU), perceived ease of use (PEOU), and perceived self-efficacy (PSE) influence learning resilience. Drawing on an optimized Technology Acceptance Model (TAM) and integrating constructs fro

Xiaojie Yang, Zipei Fan, Hangli Ge, Takashi Michikata

Human mobility data are fused with multiple travel patterns and hidden spatiotemporal patterns are extracted by integrating user, location, and time information to improve next location prediction accuracy. In existing next location prediction methods, different causal relationships that result from patterns in human mobility data are ignored, which leads to

Ossama Shafiq, Bahman Ghiassi, Alessio Alexiadis

Engineers widely rely on simulation platforms like COMSOL or ANSYS to model and optimise processes. However, setting up such simulations requires expertise in defining geometry, generating meshes, establishing boundary conditions, and configuring solvers. This research aims to simplify this process by enabling engineers to describe their setup in plain langu

Azmine Toushik Wasi, Mahfuz Ahmed Anik, Abdur Rahman, Md. Iqramul Hoque

Supply chain management is growing increasingly complex due to globalization, evolving market demands, and sustainability pressures, yet traditional systems struggle with fragmented data and limited analytical capabilities. Graph-based modeling offers a powerful way to capture the intricate relationships within supply chains, while Digital Twins (DTs) enable

Gulnaz Gimaletdinova, Dim Shaiakhmetov, Madina Akpaeva, Mukhammadmuso Abduzhabbarov

The increasing number of autonomous vehicles and the rapid development of computer vision technologies underscore the particular importance of conducting research on the accuracy of traffic sign recognition. Numerous studies in this field have already achieved significant results, demonstrating high effectiveness in addressing traffic sign recognition tasks.