May 2025 arXiv papers — page 103

David Calano, Michele C. Weigle, Michael L. Nelson

Software is often developed using versioned controlled software, such as Git, and hosted on centralized Web hosts, such as GitHub and GitLab. These Web hosted software repositories are made available to users in the form of traditional HTML Web pages for each source file and directory, as well as a presentational home page and various descriptive pages. We e

Gulai Shen, Gurpreet Singh, Ali Mehmani

This paper introduces a novel method for optimizing HVAC systems in buildings by integrating a high-fidelity physics-based simulation model with machine learning and measured data. The method enables a real-time building advisory system that provides optimized settings for condenser water loop operation, assisting building operators in decision-making. The b

Ivan Smirnov, Shangding Gu

Reinforcement learning (RL) has seen significant advancements through the application of various neural network architectures. In this study, we systematically investigate the performance of several neural networks in RL tasks, including Long Short-Term Memory (LSTM), Multi-Layer Perceptron (MLP), Mamba/Mamba-2, Transformer-XL, Gated Transformer-XL, and Gate

Sicheol Sung, Aditi, Dogyu kim, Yo-Sub Han

Automated Test Case Generation (ATCG) is crucial for evaluating software reliability, particularly in competitive programming where robust algorithm assessments depend on diverse and accurate test cases. However, existing ATCG methods often fail to meet complex specifications or generate effective corner cases, limiting their utility. In this work, we introd

Haiyan Zhao, Xuansheng Wu, Fan Yang, Bo Shen

Linear concept vectors effectively steer LLMs, but existing methods suffer from noisy features in diverse datasets that undermine steering robustness. We propose Sparse Autoencoder-Denoised Concept Vectors (SDCV), which selectively keep the most discriminative SAE latents while reconstructing hidden representations. Our key insight is that concept-relevant s

Zherui Fan, Lu-Jing Huang

Consider the critical long-range percolation on $\mathbb{Z}$, where an edge connects $i$ and $j$ independently with probability $1-\exp\{-\beta\int_i^{i+1}\int_j^{j+1}|u-v|^{-2}d ud v\}$ for $|i-j|>1$ for some fixed $\beta>0$ and with probability 1 for $|i-j|=1$. We prove that both the quenched and annealed spectral dimensions of the associated simple random

Kehinde O. Aina, Hosain Bagheri, Daniel I. Goldman

As robots are increasingly deployed to collaborate on tasks within shared workspaces and resources, the failure of an individual robot can critically affect the group's performance. This issue is particularly challenging when robots lack global information or direct communication, relying instead on social interaction for coordination and to complete their t

Runyu Shi, Dan Huang, Yanxi Wang, Qiuju Zhou

Nucleic acids can form diverse non-canonical structures, such as G-quadruplexes (G4s) and i-motifs (iMs), which are critical in biological processes and disease pathways. This study presents an innovative probe design strategy based on groove size differences, leading to the development of BT-Cy-1, a supramolecular cyanine probe optimized by fine-tuning dime

Kaiwen Zha, Zhengqi Gao, Maohao Shen, Zhang-Wei Hong

Reinforcement learning (RL) has recently emerged as a compelling approach for enhancing the reasoning capabilities of large language models (LLMs), where an LLM generator serves as a policy guided by a verifier (reward model). However, current RL post-training methods for LLMs typically use verifiers that are fixed (rule-based or frozen pretrained) or traine

Kehinde O. Aina, Ram Avinery, Hui-Shun Kuan, Meredith D. Betterton

Social organisms which construct nests consisting of tunnels and chambers necessarily navigate confined and crowded conditions. Unlike low-density collectives like bird flocks and insect swarms, in which hydrodynamic and statistical phenomena dominate, the physics of glasses and supercooled fluids is important to understand clogging behaviors in high-density

C. Devon Lin, John Stufken

Orthogonal arrays are arguably one of the most fascinating and important statistical tools for efficient data collection. They have a simple, natural definition, desirable properties when used as fractional factorials, and a rich and beautiful mathematical theory. Their connections with combinatorics, finite fields, geometry, and error-correcting codes are p

Aochuan Chen, Yifan Niu, Ziqi Gao, Yujie Sun

The LifeTime Value (LTV) prediction, which endeavors to forecast the cumulative purchase contribution of a user to a particular item, remains a vital challenge that advertisers are keen to resolve. A precise LTV prediction system enhances the alignment of user interests with meticulously designed advertisements, thereby generating substantial profits for adv

Wenqing Wu, Haixu Xi, Chengzhi Zhang

Peer review is vital in academia for evaluating research quality. Top AI conferences use reviewer confidence scores to ensure review reliability, but existing studies lack fine-grained analysis of text-score consistency, potentially missing key details. This work assesses consistency at word, sentence, and aspect levels using deep learning and NLP conference

Yang Qin, Chao Chen, Zhihang Fu, Dezhong Peng

Despite remarkable advancements in text-to-image person re-identification (TIReID) facilitated by the breakthrough of cross-modal embedding models, existing methods often struggle to distinguish challenging candidate images due to intrinsic limitations, such as network architecture and data quality. To address these issues, we propose an Interactive Cross-mo

Qingyu Song, Rui Liu, Wei Lin, Peiyu Liao

Deploying Large Language Models (LLMs) on edge devices enhances privacy but faces performance hurdles due to limited resources. We introduce a systematic methodology to evaluate on-device LLMs, balancing capability, efficiency, and resource constraints. Through an extensive analysis of models (0.5B-14B) and seven post-training quantization (PTQ) methods on c

Xiaocun Zong, Fan Yang, Shenheng Xu, Maokun Li

In this paper, the impacts of spatial quantization and phase quantization on the beam granularity characteristic of reconfigurable reflectarray (RRA) radars are systematically investigated. From the perspective of the difference beam, a theoretical analysis is conducted to derive the factors influencing beam granularity. To validate the theoretical findings,

R Ignace, A G Fullard, G V Panopoulou, D J Hillier

Linear polarimetry of unresolved stars is a powerful method for discerning or constraining the geometry of a source and its environment, since spherical sources produce no net polarization. However, a general challenge to interpreting intrinsic stellar polarization is the contribution to the signal by interstellar polarization (ISP). Here, we review methodol

Nursyazela Badrina Baharin, Affan Adly Nazri, Zulfazli Rosli, Zamri Zainal Abidin

We present a comprehensive study of formaldehyde (H2CO) absorption and radio recombination line (H110a) emission in 215 molecular clouds from the Bolocam Galactic Plane Survey (BGPS), observed using the Nanshan 25-m radio telescope. H2CO was detected in 88 sources (40.93 percent) with 59 being new detections, while H110a emission was found in only 11 sources

Tongmiao Fan, Jihua Zhang, Andrey A. Sukhorukov

The ability to generate quantum entangled photon pairs through spontaneous parametric down conversion (SPDC) is playing a pivotal role in many applications in quantum technologies, including quantum communications, quantum computation, and quantum imaging. Metasurfaces, two-dimensional arrays of nanostructures with subwavelength thickness, have recently show

Ke Ren, Peyman Mohajerin Esfahani, Angelos Georghiou

We study inverse optimization (IO), where the goal is to use a parametric optimization program as the hypothesis class to infer relationships between input-decision pairs. Most of the literature focuses on learning only the objective function, as learning the constraint function (i.e., feasible regions) leads to nonconvex training programs. Motivated by this

John M. Herbert

In a commentary published in mid-2024 (to which the present work is a direct response), a number of scientists argue that U.S. funding agencies have "politicized" the process by which grants are awarded, in service of diversifying the scientific workforce. The commentary in question, however, makes numerous unfounded assertions while recycling citations to a

Furong Jia, David Sontag, Monica Agrawal

Large language models (LLMs) have performed well across various clinical natural language processing tasks, despite not being directly trained on electronic health record (EHR) data. In this work, we examine how popular open-source LLMs learn clinical information from large mined corpora through two crucial but understudied lenses: (1) their interpretation o

David N. Palacio

This dissertation addresses achieving causal interpretability in Deep Learning for Software Engineering (DL4SE). While Neural Code Models (NCMs) show strong performance in automating software tasks, their lack of transparency in causal relationships between inputs and outputs limits full understanding of their capabilities. To build trust in NCMs, researcher

Ya-Yun Huang, Joseph McClernon, Jason A. Oliver, Matthew M. Engelhard

Daily environments have profound influence on our health and behavior. Recent work has shown that digital envirotyping, where computer vision is applied to images of daily environments taken during ecological momentary assessment (EMA), can be used to identify meaningful relationships between environmental features and health outcomes of interest. To systema

Riddhi Ghosh, Alexei Gilchrist, Daniel Burgarth

The indirect estimation of couplings in quantum dynamics relies on the measurement of the spectrum and the overlap of eigenvectors with some reference states. This data can be obtained by local measurements on some sites and eliminates the need for full Hamiltonian tomography. For a 1D chain, access to only one edge site is sufficient to compute all the coup

Aditi Raju, Jared Ni, William Won, Changhai Man

Large-scale machine learning models necessitate distributed systems, posing significant design challenges due to the large parameter space across distinct design stacks. Existing studies often focus on optimizing individual system aspects in isolation. This work challenges this limitation and introduces COSMIC, a full-stack distributed machine learning syste

Kamiokande Collaboration

This paper describes the analysis to estimate the sensitivity of the Hyper-Kamiokande experiment to long-baseline neutrino oscillation parameters using accelerator (anti)neutrinos. Results are presented for the CPV discovery sensitivity and precision measurements of the oscillation parameters $\delta_{CP}$, $\sin^2\theta_{23}$, $\Delta m^2_{32}$ and $\sin^2\

Dongsheng Ge, Yu Nakayama

The factorization proposal claims that the co-dimension one "pinning defect", on which a local relevant operator is integrated, factorizes the space into two halves in general conformal field theories in the infrared limit. In this letter, we study a two-dimensional long-range Ising model at criticality with a line defect or an interface, which physically co

Shang Ma, Tianyi Ma, Jiahao Liu, Wei Song

Over the years, online scams have grown dramatically, with nearly 50% of global consumers encountering scam attempts each week. These scams cause not only significant financial losses to individuals and businesses, but also lasting psychological trauma, largely due to scammers' strategic employment of psychological techniques (PTs) to manipulate victims. Mea

Guozhi Long, Hui Zeng, Mingxiang Pan, Wenhui Duan

We propose a robust two-terminal electrical readout scheme for detecting the N\'eel vector orientation in antiferromagnetic (AFM) materials by leveraging longitudinal nonreciprocal transport driven by quantum metric dipoles. Unlike conventional readout mechanisms, our approach does not require spin-polarized electrodes, tunneling junctions, or multi-terminal

Longlong Li, Mengyang Zhao, Guanghui Wang, Cunquan Qu

Most Graph Neural Networks (GNNs) propagate messages by treating node embeddings as holistic feature vectors, implicitly assuming uniform relevance across feature dimensions. This limits their ability to selectively transmit informative components, especially when graph structures exhibit distinct frequency characteristics. We propose MSH-GNN (Multi-Scale Ha

Ya-Xiong Wang, Dan-Dan Hu, Wan-Bing Luo, Tao Zhong

The twist-2 distribution amplitude of the $\phi$-meson has attracted considerable interest due to its unique properties. In this work, we construct the transverse leading-twist light-cone distribution amplitude $\phi_{2;\phi}^\bot(x,\mu_0)$ of the $\phi$-meson using the light-cone harmonic oscillator model, in which a parameter $B_{2;\phi}^\bot$ dominantly c

Anupama Sridhar, Alexander Johansen

First-order adaptive optimization methods like Adam are the default choices for training modern deep neural networks. Despite their empirical success, the theoretical understanding of these methods in non-smooth settings, particularly in Deep ReLU networks, remains limited. ReLU activations create exponentially many region boundaries where standard smoothnes

Xinbao Geng, Guanqi Li, Zhongxiang Zhang, Wenjing Hu

Synthetic antiferromagnetic (SAF) skyrmions have emerged as promising candidates for next-generation high-speed and highly integrated spintronic devices, owing to their exceptional properties such as high driving velocity, nanoscale dimensions, and the absence of the skyrmion Hall effect. In this work, we report the observation of the topological Hall effect

Kryspin Varys, Federico Cerutti, Adam Sobey, Timothy J. Norman

Our society is governed by a set of norms which together bring about the values we cherish such as safety, fairness or trustworthiness. The goal of value-alignment is to create agents that not only do their tasks but through their behaviours also promote these values. Many of the norms are written as laws or rules (legal / safety norms) but even more remain

Yuze Wu, Zhichao Han, Xuankang Wu, Yuan Zhou

Drones have become essential in various applications, but conventional quadrotors face limitations in confined spaces and complex tasks. Deformable drones, which can adapt their shape in real-time, offer a promising solution to overcome these challenges, while also enhancing maneuverability and enabling novel tasks like object grasping. This paper presents a

Yejin Lee, Jaehoon Kang, Kyuhong Shim

While persona-driven large language models (LLMs) and prompt-based text-to-speech (TTS) systems have advanced significantly, a usability gap arises when users attempt to generate voices matching their desired personas from implicit descriptions. Most users lack specialized knowledge to specify detailed voice attributes, which often leads TTS systems to misin

Quan Nguyen, Thanh Nguyen-Tang

We study the approximation capabilities, convergence speeds and on-convergence behaviors of transformers trained on in-context recall tasks -- which requires to recognize the \emph{positional} association between a pair of tokens from in-context examples. Existing theoretical results only focus on the in-context reasoning behavior of transformers after being

HoHyun Sim, Hyeonjoong Cho, Yeonghyeon Go, Sadegh AlMahdi Kazemi Zarkouei

The C programming language has been foundational in building system-level software. However, its manual memory management model frequently leads to memory safety issues. In response, Rust has emerged as a memory-safe alternative. Moreover, automating the C-to-Rust translation empowered by the rapid advancements of the generative capabilities of LLMs is gaini

Alvin Heng, Harold Soh

Selective classification enhances the reliability of predictive models by allowing them to abstain from making uncertain predictions. In this work, we revisit the design of optimal selection functions through the lens of the Neyman--Pearson lemma, a classical result in statistics that characterizes the optimal rejection rule as a likelihood ratio test. We sh

Andrew Brown, Hong Qin

Gap modes in a modified Mathieu equation, perturbed by a Dirac delta potential, are investigated. It is proved that the modified Mathieu equation admits stable isolated gap modes with topological origins in the unstable regions of the Mathieu equation, which are known as Arnold tongues. The modes may be identified as localized electron wavefunctions in a 1D

Phan Quoc Khanh, Le Ba Khiet

In this article, we propose an efficient way to compute equilibria of a general class of set-valued Lur'e dynamical systems, which plays an important role in the asymptotical analysis of the systems. Besides the equilibria computation, our study can be also used to solve a class of quasi-variational inequalities. Some examples of finding Nash quasi-equilibri

Hongrui Kou, Zhouhang Lyu, Ziyu Wang, Cheng Wang

As autonomous driving technology continues to advance, end-to-end models have attracted considerable attention owing to their superior generalisation capability. Nevertheless, such learning-based systems entail numerous safety risks throughout development and on-road deployment, and existing safety-analysis methods struggle to identify these risks comprehens

Jixun Yao, Hexin Liu, Eng Siong Chng, Lei Xie

Emotion plays a significant role in speech interaction, conveyed through tone, pitch, and rhythm, enabling the expression of feelings and intentions beyond words to create a more personalized experience. However, most existing speaker anonymization systems employ parallel disentanglement methods, which only separate speech into linguistic content and speaker

Samuel Fernández-Menduiña, Xin Xiong, Eduardo Pavez, Antonio Ortega

Service providers must encode a large volume of noisy videos to meet the demand for user-generated content (UGC) in online video-sharing platforms. However, low-quality UGC challenges conventional codecs based on rate-distortion optimization (RDO) with full-reference metrics (FRMs). While effective for pristine videos, FRMs drive codecs to preserve artifacts

Fernando Lucatelli Nunes, Gordon Plotkin, Matthijs Vákár

Combinatory Homomorphic Automatic Differentiation (CHAD) was originally formulated as a semantics-driven source-to-source transformation for reverse-mode AD of total (terminating) functional programs. In this work, we extend CHAD to encompass programs featuring constructs such as partial (potentially non-terminating) operations, data-dependent conditionals (

Yujia Zhou, Hexi Wang, Qingyao Ai, Zhen Wu

As large language models (LLMs) increasingly operate as autonomous agents in social contexts, evaluating their capacity for prosocial behavior is both theoretically and practically critical. However, existing research has primarily relied on static, economically framed paradigms, lacking models that capture the dynamic evolution of prosociality and its sensi

Rui Niu, Shuai Wan, Pi-Yu Wang, Rui Ma

Temporal soliton mode-locking in coherently pumped microcavities provides a promising platform for miniaturized frequency comb systems. While significant progress has been made, achieving high conversion efficiency in such microcombs remains a critical challenge. Soliton generation through pulse pumping has emerged as an effective strategy to improve convers

Chengwei Wei, Bin Wang, Jung-jae Kim, Nancy F. Chen

Recent advances in large language models (LLMs) and multimodal LLMs (MLLMs) have led to strong reasoning ability across a wide range of tasks. However, their ability to perform mathematical reasoning from spoken input remains underexplored. Prior studies on speech modality have mostly focused on factual speech understanding or simple audio reasoning tasks, p

Eric Hanchen Jiang, Haozheng Luo, Shengyuan Pang, Xiaomin Li

Large Language Models (LLMs) struggle with reliable mathematical reasoning, and current verification methods are often computationally expensive. This paper introduces the Energy Outcome Reward Model (EORM), a highly efficient, lightweight post-hoc verifier designed to address this challenge. EORM uses an energy-based framework to rank Chain-of-Thought (CoT)

Elizaveta Pertseva, Alex Ozdemir, Shankara Pailoor, Alp Bassa

This paper presents a new refutation procedure for multimodular systems of integer constraints that commonly arise when verifying cryptographic protocols. These systems, involving polynomial equalities and disequalities modulo different constants, are challenging for existing solvers due to their inability to exploit multimodular structure. To address this i

Moussa Koulako Bala Doumbouya, Dan Jurafsky, Christopher D. Manning

Work in psychology has highlighted that the geometric model of similarity standard in deep learning is not psychologically plausible because its metric properties such as symmetry do not align with human perception of similarity. In contrast, Tversky (1977) proposed an axiomatic theory of similarity with psychological plausibility based on a representation o

Jeong Rae Kim, Sandra Glotzer, Evan Krysko, Matthew R. Barone

We report molecular beam epitaxy synthesis of vacancy-ordered rocksalt NbO thin films which display superconductivity. A comparative study of substrates identifies Al$_2$O$_3$ (0001) as the optimal platform for realizing high-quality, single-phase films when growing at temperatures exceeding 1000 $^\circ$C. The controlled NbO films exhibit superconductivity

Zixuan Ke, Austin Xu, Yifei Ming, Xuan-Phi Nguyen

Multi-agent systems (MAS) leveraging the impressive capabilities of Large Language Models (LLMs) hold significant potential for tackling complex tasks. However, most current MAS depend on manually designed agent roles and communication protocols. These manual designs often fail to align with the underlying LLMs' strengths and struggle to adapt to novel tasks

Pengfei Li, Jun Ruan, Shilong Liu, Dumitru Mihalache

Domain walls (DWs) are topological defects produced by symmetry-breaking phase transitions. Although DWs have been the subject of much work due to their fundamental physical properties, they have not been explored in optical systems with higher-order dispersion. Recent experimental and theoretical works have demonstrated that pure-quartic (PQ) solitons, with

Mingchen Zheng, Chenguang Liang, Shu Chen, Xin Zhang

Spin helix states-characterized by their spatially modulated spin textures-are exact eigenstates of the one-dimensional anisotropic spin-$\frac{1}{2}$ Heisenberg model under specific parameter conditions. In this work, we extend this framework by constructing exact spin helix eigenstates for the fully anisotropic XYZ Heisenberg model with arbitrary spatial d

Matthew Jagielski, Daniel Escudero, Rahul Rachuri, Peter Scholl

Secure multiparty computation (MPC) allows data owners to train machine learning models on combined data while keeping the underlying training data private. The MPC threat model either considers an adversary who passively corrupts some parties without affecting their overall behavior, or an adversary who actively modifies the behavior of corrupt parties. It

Aneesh Komanduri, Karuna Bhaila, Xintao Wu

Large language models (LLMs) have shown remarkable ability in various language tasks, especially with their emergent in-context learning capability. Extending LLMs to incorporate visual inputs, large vision-language models (LVLMs) have shown impressive performance in tasks such as recognition and visual question answering (VQA). Despite increasing interest i

Mihály Petreczky, Ziad Alkhoury, Guillaume Mercère

We propose a transformation algorithm for a class of Linear Parameter-Varying (LPV) systems with functional affine dependence on parameters, where the system matrices depend affinely on nonlinear functions of the scheduling varable, into Linear Fractional Transformation (LFT) systems. The transformation preserves input-output behavior and minimality, and the

Zhihao Wen, Sheng Liang, Yaxiong Wu, Yongyue Zhang

Information extraction (IE) plays a crucial role in natural language processing (NLP) by converting unstructured text into structured knowledge. Deploying computationally intensive large language models (LLMs) on resource-constrained devices for information extraction is challenging, particularly due to issues like hallucinations, limited context length, and

Anand Deopurkar

Let X be an analytic K3 surface with Pic X = 0. We describe the closure of the Bridgeland stability manifold of X obtained using the masses of semi-rigid objects.

Ishika Agarwal, Nimet Beyza Bozdag, Nisval Patel, Dilek Hakkani-Tür

Often, multilingual language models are trained with the objective to map semantically similar content (in different languages) in the same latent space. In this paper, we show a nuance in this training objective, and find that by changing the language of the input query, we can improve the question answering ability of language models. We make two main cont

Jingguang Tian, Haoqin Sun, Xinhui Hu, Xinkang Xu

Discrete audio representations, termed audio tokens, are broadly categorized into semantic and acoustic tokens, typically generated through unsupervised tokenization of continuous audio representations. However, their applicability to automated audio captioning (AAC) remains underexplored. This paper systematically investigates the viability of audio token-d

BESIII Collaboration, M. Ablikim, M. N. Achasov, P. Adlarson

The non-locality of quantum correlations is a fundamental feature of quantum theory. The Bell inequality serves as a benchmark for distinguishing between predictions made by quantum theory and local hidden variable theory (LHVT). Recent advancements in photon-entanglement experiments have addressed potential loopholes and have observed significant violations

Anderson O. Calixto, Bernardo Freitas Paulo da Costa, Glauco Valle

We study a multiscale stochastic optimal control problem subject to state constraints on the slow variable. To address this class of problems, we develop a rigorous theoretical framework based on singular perturbation analysis, tailored to settings with constrained dynamics. Our approach relies on the theory of viscosity solutions for degenerate Hamilton-Jac

Meenal Parakh, Alexandre Kirchmeyer, Beining Han, Jia Deng

Generalizing control policies to novel embodiments remains a fundamental challenge in enabling scalable and transferable learning in robotics. While prior works have explored this in locomotion, a systematic study in the context of manipulation tasks remains limited, partly due to the lack of standardized benchmarks. In this paper, we introduce a benchmark f

Jing Shang, Sourav Chatterjee, Trevor Hastie, Robert Tibshirani

Pre-validation is a way to build prediction model with two datasets of significantly different feature dimensions. Previous work showed that the asymptotic distribution of the resulting test statistic for the pre-validated predictor deviates from a standard Normal, hence leads to issues in hypothesis testing. In this paper, we revisit the pre-validation proc

Adarsh Singh, Kushal Raj Bhandari, Jianxi Gao, Soham Dan

Open-Domain Table Question Answering (TQA) involves retrieving relevant tables from a large corpus to answer natural language queries. Traditional dense retrieval models such as DTR and DPR incur high computational costs for large-scale retrieval tasks and require retraining or fine-tuning on new datasets, limiting their adaptability to evolving domains and

Zahra Zahedi, Shashank Mehrotra, Teruhisa Misu, Kumar Akash

For future human-autonomous vehicle (AV) interactions to be effective and smooth, human-aware systems that analyze and align human needs with automation decisions are essential. Achieving this requires systems that account for human cognitive states. We present a novel computational model in the form of a Dynamic Bayesian Network (DBN) that infers the cognit

Faysal Ahamed, Tanushree Roy

Sustainability-targeting attacks (STA) are a growing threat to cyber-physical system (CPS)-based infrastructure, as sustainability goals become an integral part of CPS objectives. STA can be especially disruptive if it impacts the long-term sustainability cost of CPS, while its performance goals remain within acceptable parameters. Thus, in this work, we pro

Dragi Karevski, Gunter M Schütz, Ali Zahra

We consider annihilating random walks on the finite one-dimensional integer torus with deposition of pairs of particles, conditioned on an atypical jump activity. All cumulants of the activity, defined as the number of particle jumps up to some time t, are obtained in closed form to leading order in system size L at the critical point, where in the thermodyn

Emanuele Peschiera, Youssef Agram, François Quitin, Liesbet Van der Perre

What is the optimal base station (BS) resource allocation strategy given a measurement-based power consumption model and a fixed target user rate? Rush-to-sleep in time, rush-to-mute in space, awake-but-whisper in power, or a combination of them? We propose in this paper an efficient solution to the problem of finding the optimal number of active time slots,

Yu Zhang, Linyu Peng, Bing-Zhao Li

Graph signals are widely used to describe vertex attributes or features in graph-structured data, with applications spanning the internet, social media, transportation, sensor networks, and biomedicine. Graph signal processing (GSP) has emerged to facilitate the analysis, processing, and sampling of such signals. While kernel methods have been extensively st

Giovanni Micheli, Maria Teresa Vespucci, Alessia Cortazzi, Cinzia Puglisi

A two-stage multi-period mixed-integer linear stochastic programming model is proposed to assist qualified operators in long-term generation and transmission expansion planning of electricity and gas systems to meet policy objectives. The first-stage decisions concern investments in new plants, new connections in the electricity and gas sectors, and the deco

Zengrui Han

We study the relationship between solutions to better-behaved GKZ hypergeometric systems near different large radius limit points, and their geometric counterparts given by the $K$-groups of the associated toric Deligne-Mumford stacks. We prove that the $K$-theoretic Fourier-Mukai transforms associated to toric wall-crossing coincide with analytic continuati

Souichi Ishikawa

The electric multipole strength distributions for transitions from the ${}^{12}\mathrm{C}(0_1^+)$ ground state to $3\alpha$ ($0^+$, $1^-$, $2^+$, and $3^-$) continuum states are studied in terms of $3\alpha$ model. Several sets of the $3\alpha$ Hamiltonian are introduced phenomenologically with conventional $\alpha$-$\alpha$ interaction potentials and $3\alp

Ivan V. Bajić

Increasingly, visual signals such as images, videos and point clouds are being captured solely for the purpose of automated analysis by computer vision models. Applications include traffic monitoring, robotics, autonomous driving, smart home, and many others. This trend has led to the need to develop compression strategies for these signals for the purpose o

Forrest Mozer, Oleksiy Agapitov

The operating principles of a DC and low frequency electric field detector are developed, after which, examples of earlier important electric field measurements are presented, including, the first observation of parallel electric fields in the auroral acceleration region, the first observation of time domain structures in space, the first experimental verifi

Minjae Kwon, Tyler Ingebrand, Ufuk Topcu, Lu Feng

Unseen shifts in environment dynamics, driven by hidden parameters such as friction or gravity, create a challenge for maintaining safety. We address this challenge by proposing Adaptive Shielding, a framework for safe reinforcement learning in constrained hidden-parameter Markov decision processes. A function encoder infers a low-dimensional representation

Ghasem Pasandi, Kishor Kunal, Varun Tej, Kunjal Shah

This paper presents JARVIS, a novel multi-agent framework that leverages Large Language Models (LLMs) and domain expertise to generate high-quality scripts for specialized Electronic Design Automation (EDA) tasks. By combining a domain-specific LLM trained with synthetically generated data, a custom compiler for structural verification, rule enforcement, cod

A. R. Vernon, C. L. Binnersley, R. F. Garcia Ruiz, K. M. Lynch

We employed laser spectroscopy of atomic transitions to measure the nuclear charge radii and electromagnetic properties of the high-spin isomeric states in neutron-rich indium isotopes (Z = 49) near the closed proton and neutron shells at Z = 50 and N = 82. Our data reveal a reduction in the nuclear charge radius and intrinsic quadrupole moment when protons

Roozbeh Aghili, Xingfang Wu, Foutse Khomh, Heng Li

Software logs are messages recorded during the execution of a software system that provide crucial run-time information about events and activities. Although software logs have a critical role in software maintenance and operation tasks, publicly accessible log datasets remain limited, hindering advance in log analysis research and practices. The presence of

John L. Zhou, Jonathan C. Kao

Offline goal-conditioned reinforcement learning (GCRL) is a promising approach for pretraining generalist policies on large datasets of reward-free trajectories, akin to the self-supervised objectives used to train foundation models for computer vision and natural language processing. However, scaling GCRL to longer horizons remains challenging due to the co

Daniel Pimbi, Yi Sun, Roy Zektzer, Xiyuan Lu

Photonic crystal microrings (PhCRs) have emerged as powerful and versatile platforms for integrated nonlinear photonics, offering precise control over frequency and phase matching while maintaining high optical quality factors. Through grating-mediated mode coupling, PhCRs enable advanced dispersion engineering, which is critical for wideband nonlinear proce

Jae-Il Jang, Chang-Hun Lee

This paper presents a customized second-order cone programming (SOCP) solver tailored for embedded real-time optimization, which frequently arises in modern guidance and control (G&C) applications. The solver employs a practically efficient predictor-corrector type primal-dual interior-point method (PDIPM) combined with a homogeneous embedding framework for

Haoyi Qiu, Kung-Hsiang Huang, Ruichen Zheng, Jiao Sun

Large vision-language models (LVLMs) are increasingly deployed in globally distributed applications, such as tourism assistants, yet their ability to produce culturally appropriate responses remains underexplored. Existing multimodal safety benchmarks primarily focus on physical safety and overlook violations rooted in cultural norms, which can result in sym

Prashanth Vijayaraghavan, Soroush Vosoughi, Lamogha Chiazor, Raya Horesh

Recent advancements in large language models (LLMs) have revolutionized natural language processing (NLP) and expanded their applications across diverse domains. However, despite their impressive capabilities, LLMs have been shown to reflect and perpetuate harmful societal biases, including those based on ethnicity, gender, and religion. A critical and under

Xiaoyin Chen, Jiarui Lu, Minsu Kim, Dinghuai Zhang

Reinforcement learning (RL) has proven effective for fine-tuning large language models (LLMs), significantly enhancing their reasoning abilities in domains such as mathematics and code generation. A crucial factor influencing RL fine-tuning success is the training curriculum: the order in which training problems are presented. While random curricula serve as

Nicolas Echevarrieta-Catalan, Ana Ribas-Rodriguez, Francisco Cedron, Odelia Schwartz

Machine learning models achieve high precision, but their decision-making processes often lack explainability. Furthermore, as model complexity increases, explainability typically decreases. Existing efforts to improve explainability primarily involve developing new eXplainable artificial intelligence (XAI) techniques or incorporating explainability constrai

Yangchao Wu, Zongyue Qin, Alex Wong, Stefano Soatto

Speculative decoding is a technique to leverage hardware concurrency in order to enable multiple steps of token generation in a single forward pass, thus improving the efficiency of large-scale autoregressive (AR) Transformer models. State-space models (SSMs) are already more efficient than AR Transformers, since their state summarizes all past data with no

Ching-Chi Lin, Mario Günzel, Jian-Jia Chen

Age-of-information (AoI) is a critical metric that quantifies the freshness of data in communication systems. In the era of the Internet of Things (IoT), data collected by resource-constrained devices often need to be transmitted to a central server to extract valuable insights in a timely manner. However, maintaining a stable and direct connection between a

Fangzhen Zhao, Chenyi Zhang, Naipeng Dong, Ming Li

Deep neural networks (DNNs) are notoriously hard to understand and difficult to defend. Extracting representative paths (including the neuron activation values and the connections between neurons) from DNNs using software engineering approaches has recently shown to be a promising approach in interpreting the decision making process of blackbox DNNs, as the

Ben Pineau, Mitchell A. Taylor

In this article we are concerned with evolution equations of the form \begin{equation*} \partial_tu-A(D)u=F(u,\overline{u},\nabla u, \nabla \overline{u}) \end{equation*} where $A(D)$ is a Fourier multiplier of either dispersive or parabolic type and the nonlinear term $F$ is of limited regularity. Our objective is to develop a robust set of principles which

Chang Eun Song, Priyansh Bhatnagar, Zihan Xia, Nam Sung Kim

Transformers, while revolutionary, face challenges due to their demanding computational cost and large data movement. To address this, we propose HyFlexPIM, a novel mixed-signal processing-in-memory (PIM) accelerator for inference that flexibly utilizes both single-level cell (SLC) and multi-level cell (MLC) RRAM technologies to trade-off accuracy and effici

Mert Sehri, Merve Ertagrin, Ozal Yildirim, Ahmet Orhan

Convolutional Neural Networks (CNNs) are used to evaluate accelerometer and microphone data for bearing and induction motor diagnosis. A Long Short-Term Memory (LSTM) recurrent neural network is used to combine sensor information effectively, highlighting the benefits of data fusion. This approach encourages researchers to focus on multi model diagnosis for

Wenxuan Xie, Imran Mirza, John C Schotland

We consider the problem of two-photon cooperative emission in systems of two-level atoms. Two physically distinct regimes are analyzed. First, we investigate the case of a small number of atoms. We study the evolution of two-photon super- and sub-radiant states and associated two-photon spectra. Second, we investigate the problem of a constant density of ato

Dave Cook, Tim Klawa

AI systems in high-consequence domains such as defense, intelligence, and disaster response must detect rare, high-impact events while operating under tight resource constraints. Traditional annotation strategies that prioritize label volume over informational value introduce redundancy and noise, limiting model generalization. This paper introduces smart-si

Zoher Kachwala, Danishjeet Singh, Danielle Yang, Filippo Menczer

Traditional supervised methods for detecting AI-generated images depend on large, curated datasets for training and fail to generalize to novel, out-of-domain image generators. As an alternative, we explore pre-trained Vision-Language Models (VLMs) for zero-shot detection of AI-generated images. We evaluate VLM performance on three diverse benchmarks encompa

Shan Chen, Pedro Moreira, Yuxin Xiao, Sam Schmidgall

Large language models (LLMs) are increasingly envisioned as decision-support tools in clinical practice, yet safe clinical reasoning demands integrating heterogeneous knowledge bases -- trials, primary studies, regulatory documents, and cost data -- under strict accuracy constraints. Existing evaluations often rely on synthetic prompts, reduce the task to si

Yasuyuki Hatsuda, Tadashi Okazaki

We analyze the supersymmetric defect indices of $\mathcal{N}=4$ super Yang Mills theories which are simultaneously decorated by the BPS line operators and the boundary conditions. We demonstrate that the two-point functions of the boundary 't Hooft lines of magnetic charges associated with the minuscule representations in the presence of the regular Nahm pol