March 2025 arXiv papers — page 87

Changlong Shi, He Zhao, Bingjie Zhang, Mingyuan Zhou

Federated Learning (FL) has emerged as a promising framework for distributed machine learning, enabling collaborative model training without sharing local data, thereby preserving privacy and enhancing security. However, data heterogeneity resulting from differences across user behaviors, preferences, and device characteristics poses a significant challenge

Wenxin Fu, Wenming Hong

In this article, we study the maximal displacement of critical branching random walk in random environment. Let $M_n$ be the maximal displacement of a particle in generation $n$, and $Z_n$ be the total population in generation $n$, $M$ be the rightmost point ever reached by the branching random walk. Under some reasonable conditions, we prove a conditional l

Junle Li, Siqi Chen, Jiakai Li, Meiqi Tian

Converting high-level tasks described by natural language into formal specifications like Linear Temporal Logic (LTL) is a key step towards providing formal safety guarantees over cyber-physical systems (CPS). While the compliance of the formal specifications themselves against the safety restrictions imposed on CPS is crucial for ensuring safety, most exist

Chunpeng Wang, Zihao Zhang

This paper concerns the structural stability of smooth cylindrically symmetric supersonic Euler-Poisson flows in nozzles. Both three-dimensional and axisymmetric perturbations are considered. On one hand, we establish the existence and uniqueness of three-dimensional smooth supersonic solutions to the potential flow model of the steady Euler-Poisson system.

Tarek Mahmud, Bin Duan, Corina Pasareanu, Guowei Yang

Ensemble learning has been widely used in machine learning to improve model robustness, accuracy, and generalization, but has not yet been applied to code generation tasks with large language models (LLMs). We propose an ensemble approach for LLMs in code generation. Instead of relying on the output of a single model, we generate multiple candidate programs

Shangqing Zhao, Yuhao Zhou, Yupei Ren, Zhe Chen

Ancient Chinese text processing presents unique challenges for large language models (LLMs) due to its distinct linguistic features, complex structural constraints, and rich cultural context. While existing benchmarks have primarily focused on evaluating comprehension through multiple-choice questions, there remains a critical gap in assessing models' genera

Philip Huang, Ruixuan Liu, Shobhit Aggarwal, Changliu Liu

Compared to a single-robot workstation, a multi-robot system offers several advantages: 1) it expands the system's workspace, 2) improves task efficiency, and, more importantly, 3) enables robots to achieve significantly more complex and dexterous tasks, such as cooperative assembly. However, coordinating the tasks and motions of multiple robots is challengi

Yiren Lu, Yunlai Zhou, Disheng Liu, Tuo Liang

3D Gaussian Splatting (3DGS) has shown remarkable potential for static scene reconstruction, and recent advancements have extended its application to dynamic scenes. However, the quality of reconstructions depends heavily on high-quality input images and precise camera poses, which are not that trivial to fulfill in real-world scenarios. Capturing dynamic sc

Jordan S. Ellenberg, Will Hardt

In a 1986 paper, Smyth proposed a conjecture about which integer-linear relations were possible among Galois-conjugate algebraic numbers. We prove this conjecture. The main tools (as Smyth already anticipated) are combinatorial rather than number-theoretic in nature. For instance, the question can be reinterpreted as a question about the possible eigenvalues

Zihao Zhang, Haoran Chen, Haoyu Zhao, Guansong Lu

Handling complex or nonlinear motion patterns has long posed challenges for video frame interpolation. Although recent advances in diffusion-based methods offer improvements over traditional optical flow-based approaches, they still struggle to generate sharp, temporally consistent frames in scenarios with large motion. To address this limitation, we introdu

Martin Cole, Yang Xiang, Will Consagra, Anuj Srivastava

Brain networks are typically represented by adjacency matrices, where each node corresponds to a brain region. In traditional brain network analysis, nodes are assumed to be matched across individuals, but the methods used for node matching often overlook the underlying connectivity information. This oversight can result in inaccurate node alignment, leading

Diarmuid Crowley, Sebastian Goette, Thorsten Hertl

Starting from Joyce's generalised Kummer construction, we exhibit non-trivial families of $\mathrm{G}_2$-manifolds over the two dimensional sphere by resolving singularities with a twisted family of Eguchi-Hanson spaces. We establish that the comparison map $\mathcal{G}_2^{\mathrm{tf}}(M) /\!\!/ \mathrm{Diff}(M)_0 \rightarrow \mathcal{G}_2^{\mathrm{tf}}(M) /

Xuhui Peng, Houqi Su

This paper establishes the ergodicity in $H^\nn,\nn=\lfloor\frac{d}{2}+1\rfloor$ of the viscous scalar conservation laws on torus $\mT^d$ with general polynomial flux and a degenerate noise. The noise could appear in as few as several directions. We introduce a localized framework that restricts attention to trajectories with controlled energy growth, circum

Yongtao Zhan, Zhiyan Ding, Jakob Huhn, Johnnie Gray

Inspired by natural cooling processes, dissipation has become a promising approach for preparing low-energy states of quantum systems. However, the potential of dissipative protocols remains unclear beyond certain commuting Hamiltonians. This work provides significant analytical and numerical insights into the power of dissipation for preparing the ground st

Lina Wang, Bin Wang, Jiyong Li

In this paper, we propose two novel fourth-order integrators that exhibit uniformly high accuracy and long-term near conservations for solving the nonlinear Dirac equation (NLDE) in the nonrelativistic regime. In this regime, the solution of the NLDE exhibits highly oscillatory behavior in time, characterized by a wavelength of O($\varepsilon^{2}$) with a sm

Yuzhou Fang, Chenyu Zhou, Jingbo Wang, Chao Wang

We propose a symbolic execution method for analyzing the safety of software under fault attacks both accurately and efficiently. Fault attacks leverage physically injected hardware faults in an embedded system to break the safety of a software program. While there are existing methods for analyzing the impact of maliciously injected hardware faults on the em

Yuxin Du, Dejian Tian, Hui Zhang

The paper investigates the robust distortion risk measure with linear penalty function under distribution uncertainty. The distribution uncertainties are characterized by predetermined moment conditions or constraints on the Wasserstein distance. The optimal quantile distribution and the optimal value function are explicitly characterized. Our results partia

Matheus F. Reis, José P. Carvalho, A. Pedro Aguiar

Ensuring liveness and safety of autonomous and cyber-physical systems remains a fundamental challenge, particularly when multiple safety constraints are present. This letter advances the theoretical foundations of safety-filter Quadratic Programs (QP) and Control Lyapunov Function (CLF)-Control Barrier Function (CBF) controllers by establishing a unified ana

Bereket Kindo, Arjee Restar, Anh Tran

Alzheimer's disease and related dementias (AD/ADRD) represent a growing healthcare crisis affecting over 6 million Americans. While genetic factors play a crucial role, emerging research reveals that social determinants of health (SDOH) significantly influence both the risk and progression of cognitive functioning, such as cognitive scores and cognitive decl

Yuzhi Zhou, Yaru Fu, Zheng Shi, Howard H. Yang

The digital twin edge network (DITEN) is a significant paradigm in the sixth-generation wireless system (6G) that aims to organize well-developed infrastructures to meet the requirements of evolving application scenarios. However, the impact of the interaction between the long-term DITEN maintenance and detailed digital twin tasks, which often entail privacy

Michael Potter, Michael Everett, Ashutosh Singh, Georgios Stratis

Aggressive behavior, including aggression towards others and self-injury, occurs in up to 80% of children and adolescents with autism, making it a leading cause of behavioral health referrals and a major driver of healthcare costs. Predicting when autistic youth will exhibit aggression can be challenging due to their communication difficulties. Many are mini

Katherine Goldman, Amy Herron

We show that the piecewise Euclidean Moussong metric on the Deligne complex of the Artin group of type $B_3$ is $\mathrm{CAT}(0)$. We do this by establishing a criteria for a complex made of $B_3$ simplices to be $\mathrm{CAT}(1)$ in terms of embedded edge paths, which in particular applies to the spherical Deligne complex of type $B_3$. This provides one mo

Junyi Shen, Tetsuro Miyazaki, Kenji Kawashima

The intrinsic nonlinearities of soft robots present significant control but simultaneously provide them with rich computational potential. Reservoir computing (RC) has shown effectiveness in online learning systems for controlling nonlinear systems such as soft actuators. Conventional RC can be extended into physical reservoir computing (PRC) by leveraging t

Haotian Ma, Lin Gu, Siyi Wu, Yingying Zhu

3D point cloud has been widely used in applications such as self-driving cars, robotics, CAD models, etc. To the best of our knowledge, these applications raised the issue of privacy leakage in 3D point clouds, which has not been studied well. Different from the 2D image privacy, which is related to texture and 2D geometric structure, the 3D point cloud is t

Suryani Lim, Henri Prade, Gilles Richard

AI-driven outcomes can be challenging for end-users to understand. Explanations can address two key questions: "Why this outcome?" (factual) and "Why not another?" (counterfactual). While substantial efforts have been made to formalize factual explanations, a precise and comprehensive study of counterfactual explanations is still lacking. This paper proposes

Abduljaleel Adejumo, Faegheh Yeganli, Clifford Broni-bediako, Aoran Xiao

Multimodal Large Language Models (MLLMs) have achieved remarkable success in vision-language tasks but their remote sensing (RS) counterpart are relatively under explored. Unlike natural images, RS imagery presents unique challenges that current MLLMs struggle to handle, particularly in visual grounding and spatial reasoning. This study investigates the limi

Vishnu Asutosh Dasu, Md Rafi ur Rashid, Vipul Gupta, Saeid Tizpaz-Niari

This paper explores pruning attention heads as a post-processing bias mitigation method for large language models (LLMs). Modern AI systems such as LLMs are expanding into sensitive social contexts where fairness concerns become especially crucial. Since LLMs develop decision-making patterns by training on massive datasets of human-generated content, they na

Hannah S. Christie, Adrien Hélias, Matheus do Carmo Carvalho, Pauline Barmby

The beginning of the 21st century marked the "modern era of galaxy surveys" in astronomy. Rapid innovation in observing technology, combined with the base built by galaxy catalogs and atlases dating back centuries, sparked an explosion of new observational programs driven by efforts to understand the different processes driving galaxy evolution. This review

Yi Gao, Kui Wang

We prove a sharp isoperimetric inequality for the harmonic mean of the first $m-1$ nonzero Neumann eigenvalues for bounded Lipschitz domains symmetric about the origin in Gauss space. Our result generalizes the Szeg\"o-Weinberger type inequality in Gauss space, as proved in [8, Theorem 4.1].

Jason Mars

The evolution of programming languages from low-level assembly to high-level abstractions demonstrates a fundamental principle: by constraining how programmers express computation and enriching semantic information at the language level, we can make previously undecidable program properties tractable for optimization. Building on the insight of this undecida

Daniel Tubbenhauer, Victor Zhang

We apply big data techniques, including exploratory and topological data analysis, to investigate quantum invariants. More precisely, our study explores the Jones polynomial's structural properties and contrasts its behavior under four principal methods of enhancement: coloring, rank increase, categorification, and leaving the realm of Lie algebras.

Xuan Gao, Jingtao Zhou, Dongyu Liu, Yuqi Zhou

Recent advances in diffusion models have made significant progress in digital human generation. However, most existing models still struggle to maintain 3D consistency, temporal coherence, and motion accuracy. A key reason for these shortcomings is the limited representation ability of commonly used control signals(e.g., landmarks, depth maps, etc.). In addi

Katie Seaborn

ChatGPT, powered by a large language model (LLM), has revolutionized everyday human-computer interaction (HCI) since its 2022 release. While now used by millions around the world, a coherent pathway for evaluating the user experience (UX) ChatGPT offers remains missing. In this rapid review (N = 58), I explored how ChatGPT UX has been approached quantitative

Cheng Li, Jiexiong Liu, Yixuan Chen, Yanqin Jia

In the field of video-language pretraining, existing models face numerous challenges in terms of inference efficiency and multimodal data processing. This paper proposes a KunLunBaize-VoT-R1 video inference model based on a long-sequence image encoder, along with its training and application methods. By integrating image packing technology, the Autonomy-of-E

Atanas G. Stefanov, P. G. Kevrekidis

In the present work we construct kink solutions for different (parabolic and wave) variants of the fractional $\phi^4$ model, in both the sub-Laplacian and super-Laplacian setting. We establish existence and monotonicity results (for the sub - Laplacian case), along with sharp asymptotics which are corroborated through numerical computations. Importantly, in

Guowang Du, Yehao Cheng, Yuan-Pei Yang, Jun Yang

The interaction between the relativistic jet and the circumburst medium produces a multiwavelength afterglow of a gamma-ray burst (GRBs). In this work, we present multiwavelength properties of GRB~250101A based on the observations of Swift, Fermi and Mephisto. The spectral analysis of Swift/BAT and Fermi/GBM reveals a soft prompt spectrum with a low-energy p

Jie Liu, Yongqiang Wang

By letting local clients perform multiple local updates before communicating with a parameter server, modern federated learning algorithms such as FedAvg tackle the communication bottleneck problem in distributed learning and have found many successful applications. However, this asynchrony between local updates and communication also leads to a ''client-dri

Yu Si, Yueyang Zheng, Jingtao Shi

This paper is concerned with a linear-quadratic partially observed mean field Stackelberg stochastic differential game, which contains a leader and a large number of followers. Specifically, the followers confront a large-population Nash game subsequent to the leader's initial announcement of his strategy. In turn, the leader optimizes his own cost functiona

Alyssa R. Jankowski, Juliette Becker, Melinda Soares-Furtado, Andrew Vanderburg

Ultra-short-period (USP) planets are a rare but dynamically significant subset of the exoplanet sample, and understanding their dynamical histories and migration processes is necessary to build a complete picture of the outcomes of planet formation. In this work, we present an analysis of system age constraints and the impact of tidal evolution in the TOI-19

Zhiyu An, Zhibo Hou, Wan Du

We study aleatoric and epistemic uncertainty estimation in a learned regressive system dynamics model. Disentangling aleatoric uncertainty (the inherent randomness of the system) from epistemic uncertainty (the lack of data) is crucial for downstream tasks such as risk-aware control and reinforcement learning, efficient exploration, and robust policy transfe

Jingyun Liu, Daiqin Yang, Zhenzhong Chen

Recovering high-frequency textures in image demosaicking remains a challenging issue. While existing methods introduced elaborate spatial learning methods, they still exhibit limited performance. To address this issue, a frequency enhancement approach is proposed. Based on the frequency analysis of color filter array (CFA)/demosaicked/ground truth images, we

Jacqueline Hidalgo-Jiménez, Taner Akbay, Xavier Sauvage, Tatsumi Ishihara

Methane (CH4) formation from photocatalytic carbon dioxide (CO2) conversion in water is currently of interest because methane is a fuel, and it can also be transformed into other useful hydrocarbons. However, achieving high selectivity to produce methane remains a challenge because of the large number of contributing electrons (eight) in methanation. High-en

Shibo Jie, Yehui Tang, Kai Han, Yitong Li

Mixture-of-Experts (MoE) activates only a subset of experts during inference, allowing the model to maintain low inference FLOPs and latency even as the parameter count scales up. However, since MoE dynamically selects the experts, all the experts need to be loaded into VRAM. Their large parameter size still limits deployment, and offloading, which load expe

Ying Cui, Kongxin Qiu, Shan Gao, Hailong Liu

The key challenge in multispectral radiation thermometry is accurately measuring emissivity. Traditional constrained optimization methods often fail to meet practical requirements in terms of precision, efficiency, and noise resistance. However, the continuous advancement of neural networks in data processing offers a potential solution to this issue. This p

Xinlong Zhai, Chunchen Wang, Ruijia Wang, Jiazheng Kang

Drug-target interaction prediction (DTI) is essential in various applications including drug discovery and clinical application. There are two perspectives of input data widely used in DTI prediction: Intrinsic data represents how drugs or targets are constructed, and extrinsic data represents how drugs or targets are related to other biological entities. Ho

Jae Woo Lee, Min-Ji Jeong, Kyeongsoo Hong

We present results from VLT/UVES spectra and TESS photometric observations of the pulsating EL CVn binary WASP 1021-28, containing a He-core white dwarf precursor (pre-He WD). Double-lined radial velocities were measured with the atmospheric parameters of $T_{\rm eff,A}$ = 7411$\pm40$ K, [M/H] = 0.34$\pm$0.05 dex, and $v_{\rm A}$$\sin i$ = 86.6$\pm$4.0 km s$

Chuanzhi Lv, Hongdan Li, Huanshui Zhang

This paper addresses the optimal control problem of finite-horizon discrete-time nonlinear systems under state and control constraints. A novel numerical algorithm based on optimal control theory is proposed to achieve superior computational efficiency, with the novelty lying in establishing a unified framework that integrates all aspects of algorithm design

Masoud Hashemi, Oluwanifemi Bamgbose, Sathwik Tejaswi Madhusudhan, Jishnu Sethumadhavan Nair

Test-time scaling has significantly improved large language model performance, enabling deeper reasoning to solve complex problems. However, this increased reasoning capability also leads to excessive token generation and unnecessary problem-solving attempts. We introduce Don\'t Reason Bench (DNR Bench), a new benchmark designed to evaluate LLMs ability to r

Christopher Berg Smiet, Ludovic Rais, Joaquim Loizu, Robert Davies

We analyze stochasticity in the magnetic fields of magnetic confinement fusion reactors by calculating the lobe areas of turnstiles - a method developed for characterizing transport into and out of resonance zones in Hamiltonian dynamical systems. We develop an efficient algorithm based on an action principle to calculate this quantity directly from the magn

Yoshinori Namikawa, Yuji Odaka

We show that any symplectic singularity lying on a smoothable projective symplectic variety locally admits a good action of $(\mathbb{C}^*)^r$, which is canonical. Under mild assumptions, we actually prove such singularity germ is the cone vertex over a contact orbifold with weak K\"ahler-Einstein metric, forcing $r=1$. In particular, it admits a (canonical)

Christopher F. Chyba, Kevin P. Hand, Thomas H. Chyba

Earth rotates through the axisymmetric part of its own magnetic field, but a simple proof shows that it is impossible to use this to generate electricity in a conductor rotating with Earth.However, we previously identified implicit assumptions underlying this proof and showed theoretically that these could be violated and the proof circumvented. This require

Siddharth Iyer

We refine a remark of Steinerberger (2024), proving that for $\alpha \in \mathbb{R}$, there exists integers $1 \leq b_{1}, \ldots, b_{k} \leq n$ such that \[ \left\| \sum_{j=1}^k \sqrt{b_j} - \alpha \right\| = O(n^{-\gamma_k}), \] where $\gamma_{k} \geq (k-1)/4$, $\gamma_2 = 1$, and $\gamma_k = k/2$ for $k = 2^m - 1$. We extend this to higher-order roots. Bu

Yanmei Hu, Yihang Wu, Biao Cai

Interactive networks representing user participation and interactions in specific "events" are highly dynamic, with communities reflecting collective behaviors that evolve over time. Predicting these community evolutions is crucial for forecasting the trajectory of the related "event". Some models for community evolution prediction have been witnessed, but t

Wali Ullah Khan, Chandan Kumar Sheemar, Eva Lagunas, Symeon Chatzinotas

Beyond diagonal reconfigurable intelligent surfaces (BD-RIS) have emerged as a transformative technology for enhancing wireless communication by intelligently manipulating the propagation environment. This paper explores the potential of BD-RIS in improving cognitive radio enabled multilayer non-terrestrial networks (NTNs). It is assumed that a high-altitude

Yin Song, Wenkai Hu, Xin Li

Unfitted mesh formulations for interface problems generally adopt two distinct methodologies: (i) penalty-based approaches and (ii) explicit enrichment space techniques. While Stable Generalized Finite Element Method (SGFEM) has been rigorously established for one-dimensional and linear-element cases, the construction of optimal enrichment spaces preserving

Gaurab Aryal, Anirban Chattopadhyaya, Federico Ciliberto

We bridge quasi-experimental and structural approaches for robust merger evaluation. First, we show that the difference-in-differences (DiD) equation is the "reduced form" of a structural model, where demand and cost parameters identify price effects of mergers even when the DiD approach faces identification challenges. Second, we propose a $\textit{syntheti

Parham Saremi, Amar Kumar, Mohamed Mohamed, Zahra TehraniNasab

Vision-Language Foundation Models (VLFM) have shown a tremendous increase in performance in terms of generating high-resolution, photorealistic natural images. While VLFMs show a rich understanding of semantic content across modalities, they often struggle with fine-grained alignment tasks that require precise correspondence between image regions and textual

Tsunehiko Tanaka, Edgar Simo-Serra

Game Description Generation (GDG) is the task of generating a game description written in a Game Description Language (GDL) from natural language text. Previous studies have explored generation methods leveraging the contextual understanding capabilities of Large Language Models (LLMs); however, accurately reproducing the game features of the game descriptio

Zeyu Xiang, Fanghao Zhang, Bolin Liao

Conventional semiconductors typically have bonding states near the valence band maximum (VBM) and antibonding states near the conduction band minimum (CBM). Semiconductors with the opposite electronic configuration, namely an antibonding VBM and a bonding CBM, are here termed ``anti-gap semiconductors". They have been theoretically proposed to exhibit excell

Yuci Han, Charles Toth, Alper Yilmaz

The aim of this work is to develop an approach that enables Unmanned Aerial System (UAS) to efficiently learn to navigate in large-scale urban environments and transfer their acquired expertise to novel environments. To achieve this, we propose a meta-curriculum training scheme. First, meta-training allows the agent to learn a master policy to generalize acr

Johnny E. Brown

We prove that the Bernardi Integral Operator maps certain classes of bounded starlike functions into the class of convex functions, improving the result of Oros and Oros. We also present a general unified method for investigating various other integral operators that preserve many of the previously studied subclasses of univalent and p-valent functions.

Haoxuan Ma, Xishun Liao, Yifan Liu, Qinhua Jiang

Human mobility modeling is critical for urban planning and transportation management, yet existing approaches often lack the integration capabilities needed to handle diverse data sources. We present a foundation model framework for universal human mobility patterns that leverages cross-domain data fusion and large language models to address these limitation

Boshra Khalili, Andrew W. Smyth

Evaluating vision-language models (VLMs) in urban driving contexts remains challenging, as existing benchmarks rely on open-ended responses that are ambiguous, annotation-intensive, and inconsistent to score. This lack of standardized evaluation slows progress toward safe and reliable AI for urban mobility. We introduce AutoDrive-QA, the first benchmark that

Joanikij Chulev, Angela Mladenovska

Clustering high-dimensional data is a critical challenge in machine learning due to the curse of dimensionality and the presence of noise. Traditional clustering algorithms often fail to capture the intrinsic structures in such data. This paper explores a combination of clustering methods, which we called Line Space Clustering (LSC), a representation that tr

Alice Y. Chen, Phil Halper, Niayesh Afshordi

The purpose of this survey is to take a snapshot of the attitudes of physicists working on some of the most pressing questions in modern physics, which may be useful to sociologists and historians of science. For this study, a total of 85 completed surveys were returned out of 151 registered participants of the ``Black holes Inside and out'' conference, held

Zhili Lin

A general algorithm for calculating the reflection and refraction of nonuniform plane waves from an arbitrarily oriented and charged planar interface between two lossy isotropic media is proposed based on the decomposition of the complex wave vector and complex wave numbers with respect to the unit normal vector of the interface. According to the complex vec

Nathan Valadez, Iftakhar Bin Elius, Dante James, Peter Radanovich

Lanthanide (Ln) based LnSbTe materials have garnered significant attention due to rich interplay of long range magnetic ordering and topological properties, driven by unique crystalline symmetry, 4f electron interactions, and pronounced spin-orbit coupling (SOC) effects. DySbTe, as a heavier lanthanide-based member of the LnSbTe family, stands out with its S

L. Dunn, A. Melatos, P. Clearwater, S. Suvorova

A search is performed for continuous gravitational waves emitted by unknown neutron stars in five nearby globular clusters using data from the third Laser Interferometer Gravitational-Wave Observatory (LIGO) observing run, over the frequency range $100$--$800\,\mathrm{Hz}$. The search uses a hidden Markov model to track both the frequency and phase of the co

Vishisht Rao, Aounon Kumar, Himabindu Lakkaraju, Nihar B. Shah

The integrity of peer review is fundamental to scientific progress, but the rise of large language models (LLMs) has introduced concerns that some reviewers may rely on these tools to generate reviews rather than writing them independently. Although some venues have banned LLM-assisted reviewing, enforcement remains difficult as existing detection tools cann

Thomas Erlebach, Othon Michail, Nils Morawietz

A temporal graph $\mathcal{G}=(G,\lambda)$ can be represented by an underlying graph $G=(V,E)$ together with a function $\lambda$ that assigns to each edge $e\in E$ the set of time steps during which $e$ is present. The reachability graph of $\mathcal{G}$ is the directed graph $D=(V,A)$ with $(u,v)\in A$ if only if there is a temporal path from $u$ to $v$. W

Seungwoo Jung, Yeonho Yoo, Gyeongsik Yang, Chuck Yoo

Blockchain is increasingly offered as blockchain-as-a-service (BaaS) by cloud service providers. However, configuring BaaS appropriately for optimal performance and reliability resorts to try-and-error. A key challenge is that BaaS is often perceived as a ``black-box,'' leading to uncertainties in performance and resource provisioning. Previous studies attem

Takeru Goto, Yuki Kizumi, Shun Iwasaki

This paper proposes a design scheme of reward function that constantly evaluates both driving states and actions for applying reinforcement learning to automated driving. In the field of reinforcement learning, reward functions often evaluate whether the goal is achieved by assigning values such as +1 for success and -1 for failure. This type of reward funct

Alexandra DeLucia, Mark Dredze

Abstractive multi-document summarization (MDS) is the task of automatically summarizing information in multiple documents, from news articles to conversations with multiple speakers. The training approaches for current MDS models can be grouped into four approaches: end-to-end with special pre-training ("direct"), chunk-then-summarize, extract-then-summarize

Naiyuan J. Zhang, Pavel A. Nosov, Ophelia Evelyn Sommer, Yibang Wang

Superconductivity in strongly correlated electron systems frequently exhibits broken rotational symmetry, raising fundamental questions about the underlying order parameter symmetry. In this work, we demonstrate that electronic nematicity--driven by Coulomb-mediated rotational symmetry breaking--serves as a crucial link to understanding the nature of superco

Micky C. Nnamdi, J. Ben Tamo, Benoit Marteau, Wenqi Shi

Problem-Based Learning (PBL) has significantly impacted biomedical engineering (BME) education since its introduction in the early 2000s, effectively enhancing critical thinking and real-world knowledge application among students. With biomedical engineering rapidly converging with artificial intelligence (AI), integrating effective AI education into establi

Peter Sharpe, Rishikesh Ranade, Kaustubh Tangsali, Mohammad Amin Nabian

Transient computational fluid dynamics (CFD) simulations are essential for many industrial applications, but suffer from high compute costs relative to steady-state simulations. This is due to the need to: (a) reach statistical steadiness by physically advecting errors in the initial field sufficiently far downstream, and (b) gather a sufficient sample of fl

Bouchra Bensiali, Stefan Sauter

In this paper, we develop a numerical method for the computation of (quasi-)resonances in spherical symmetric, heterogeneous Helmholtz problems with piecewise smooth refractive index. Our focus lies in resonances very close to the real axis, which characterize the so-called whispering gallery modes. Our method involves a modal equation incorporating fundamen

Yong Xiao, Guangming Shi, Ping Zhang

The promising potential of AI and network convergence in improving networking performance and enabling new service capabilities has recently attracted significant interest. Existing network AI solutions, while powerful, are mainly built based on the close-loop and passive learning framework, resulting in major limitations in autonomous solution finding and d

Huan Lei

Neural surface reconstruction has been dominated by implicit representations with marching cubes for explicit surface extraction. However, those methods typically require high-quality normals for accurate reconstruction. We propose OffsetOPT, a method that reconstructs explicit surfaces directly from 3D point clouds and eliminates the need for point normals.

Elena Malnatsky, Shenghui Wang, Koen V. Hindriks, Mike E. U. Ligthart

Dialogic learning fosters motivation and deeper understanding in education through purposeful and structured dialogues. Foundational models offer a transformative potential for child-robot interactions, enabling the design of personalized, engaging, and scalable interactions. However, their integration into educational contexts presents challenges in terms o

Mir Mohammad Khaleghi, Mehran Safayani, Abdolreza Mirzaei

We present GraPLUS (Graph-based Placement Using Semantics), a novel framework for plausible object placement in images that leverages scene graphs and large language models. Our approach uniquely combines graph-structured scene representation with semantic understanding to determine contextually appropriate object positions. The framework employs GPT-2 to tr

Anup Pradhan Sakhya, Brenden R. Ortiz, Barun Ghosh, Milo Sprague

Recent reports on a family of kagome metals of the form LnTi3Bi4 (Ln = Lanthanide) has stoked interest due to the combination of highly anisotropic magnetism and a rich electronic structure. The electronic structure near the Fermi level is proposed to exhibit Dirac points and van Hove singularities. In this manuscript, we use angle resolved photoemission spe

Venmugil Elango

Transformer-based models have emerged as a leading architecture for natural language processing, natural language generation, and image generation tasks. A fundamental element of the transformer architecture is self-attention, which allows the model to capture intricate dependencies within the data. However, the self-attention mechanism also incurs significa

Erekle Khurodze, Leigh A Roberts

Since its introduction in 1950, Fisher's dispersion test has become a standard means of deciding whether or not count data follow the Poisson distribution. The test is based on a characteristic property of the Poisson distribution, and discriminates well between the Poisson and the natural alternative hypotheses of binomial and negative binomial distribution

Jeffrey Z. Song, Hyunsoo Ha, Wen Wei Ho, Vir B. Bulchandani

A recent work [Mierzejewski et al., Phys. Rev. B 107, 045134 (2023)] observed "quasiballistic spin transport" - long-lived and transiently ballistic modes of the magnetization density - in numerical simulations of infinite-temperature XXZ chains with power-law exchange interactions. We develop an analytical theory of such quasiballistic spin transport. Previ

R. Sinaasappel, K. R. Prathyusha, H. Tuazon, E. Mirzahossein

Biological and robotic systems often operate in confined environments where material must be gathered without centralized control. Inspired by the effective collection strategies of aquatic worms (Lumbriculus variegatus and Tubifex tubifex), we investigate how active filaments autonomously aggregate dispersed particles. We study this process across four plat

Andy Zhou, Kevin Wu, Francesco Pinto, Zhaorun Chen

As large language models (LLMs) become increasingly capable, security and safety evaluation are crucial. While current red teaming approaches have made strides in assessing LLM vulnerabilities, they often rely heavily on human input and lack comprehensive coverage of emerging attack vectors. This paper introduces AutoRedTeamer, a novel framework for fully au

Uddhav Bhattarai, Rajkishan Arikapudi, Chen Peng, Steven A. Fennimore

High-resolution yield maps for manually harvested crops are impractical to generate on commercial scales because yield monitors are available only for mechanical harvesters. However, precision crop management relies on accurately determining spatial and temporal yield variability. This study presents the development of an integrated system for precision yiel

Alexander Graening, Jonti Talukdar, Saptadeep Pal, Krishnendu Chakrabarty

With the increasing prevalence of chiplet systems in high-performance computing applications, the number of design options has increased dramatically. Instead of chips defaulting to a single die design, now there are options for 2.5D and 3D stacking along with a plethora of choices regarding configurations and processes. For chiplet-based designs, high-impac

Yutong Xie, Qiaozhu Mei, Walter Yuan, Matthew O. Jackson

By varying prompts to a large language model, we can elicit the full range of human behaviors in a variety of different scenarios in classic economic games. By analyzing which prompts elicit which behaviors, we can categorize and compare different strategic situations, which can also help provide insight into what different economic scenarios induce people t

Sébastien Thévenin, Benoît-Joseph Gréa

This article sheds light on the late-time influence of initial conditions in Boussinesq Rayleigh-Taylor turbulence using an approach combining direct numerical simulations, machine learning and theory. The initial conditions are characterized by four non-dimensional numbers describing the statistical properties of random-phase multi-mode perturbations of an

Michal Hrbek, Sergio Pavon, Jorge Vitória

Mutations occur in multiple algebraic contexts, often enjoying good combinatorial properties. In this paper we study mutations of pure-injective cosilting objects in compactly generated triangulated categories from a topological point of view. We consider the topologies studied by Gabriel, Burke and Prest on the set of indecomposable injective objects in a G

William Balderrama, Yueshi Hou, Shangjie Zhang

Let $G$ be a cyclic $p$-group or generalized quaternion group, $X\in π_0 S_G$ be a virtual $G$-set, and $V$ be a fixed point free complex $G$-representation. Under conditions depending on the sizes of $G$, $X$, and $V$, we construct a self map $v\colonΣ^V C(X)_{(p)}\rightarrow C(X)_{(p)}$ on the cofiber of $X$ which induces an equivalence in $G$-equivariant

Matteo Polimeno, Changho Kim, François Blanchette, Ishan Srivastava

It is essential that mesoscopic simulations of reactive systems reproduce the correct statistical distributions at thermodynamic equilibrium. By considering a compressible fluctuating hydrodynamics (FHD) simulation method of ideal gas mixtures undergoing reversible reactions described by the chemical Langevin equations, we show that thermodynamic consistency

Jinyi Ye, Luca Luceri, Emilio Ferrara

Approximately 50% of tweets in X's user timelines are personalized recommendations from accounts they do not follow. This raises a critical question: What political content are users exposed to beyond their established networks, and what implications does this have for democratic discourse online? In this paper, we present a six-week audit of X's alg

Jonathan Chapman, Sam Chow, Philippa Holdridge

We characterise partition regularity for linear equations over the Piatetski-Shapiro numbers $\lfloor n^c \rfloor$ when $1 < c < c^†(s)$, where $s \geqslant 3$ is the number of variables. Here $c^†(3) = 12/11$ and $c^†(4) = 7/6$, while $c^†(s) = 2$ for $s \geqslant 5$. We also establish density results with quantitative bounds. Following recent developments,

Qian Cao, Nianjia Zhang, Andy Chong, Qiwen Zhan

Photonic quantum emulator utilizes photons to emulate the quantum physical behavior of a complex quantum system. Recent study in spatiotemporal optics has enriched the toolbox for designing and manipulating complex spatiotemporal optical wavepackets, bringing new opportunities in building such quantum emulators. In this work, we demonstrate a new type of pho

Mattia Benini, Andrei Shumilin, Rajib Kumar Rakshit, Antarjami Sahoo

We show that, upon the chemisorption of organic molecules, Co thin films display a number of unique magnetic properties, including the giant magnetic hardening and the violation of the Rayleigh law in magnetization reversal. These novel properties originate from the modification of the surface magnetic anisotropy induced by the molecule/film interaction: the

Lisa Jin, Jianhao Ma, Zechun Liu, Andrey Gromov

We develop a principled method for quantization-aware training (QAT) of large-scale machine learning models. Specifically, we show that convex, piecewise-affine regularization (PAR) can effectively induce the model parameters to cluster towards discrete values. We minimize PAR-regularized loss functions using an aggregate proximal stochastic gradient method

Sylwia Cichacz, Claire Hilaire, Tomáš Masařík, Jana Masaříková

The path eccentricity of a connected graph $G$ is the minimum integer $k$ such that $G$ has a path such that every vertex is at distance at most $k$ from the path. A result of Duffus, Jacobson, and Gould from 1981 states that every connected $\{\text{claw}, \text{net}\}$-free graph $G$ has a Hamiltonian path, that is, $G$ has path eccentricity $0$. Several m