February 2025 arXiv papers — page 28

Yuxiang Wang, Xinnan Dai, Wenqi Fan, Yao Ma

In recent years, large language models (LLMs) have emerged as promising candidates for graph tasks. Many studies leverage natural language to describe graphs and apply LLMs for reasoning, yet most focus narrowly on performance benchmarks without fully comparing LLMs to graph learning models or exploring their broader potential. In this work, we present a com

Wen-Xu Lin, Sheng-Bang Qian, Li-Ying Zhu, Wen-Ping Liao

This study develops a robust framework for exoplanet characterization by leveraging asteroseismic constraints on host stars. Using precise photometric data from missions such as \textit{Kepler} and \textit{TESS}, we derive stellar parameters, including mass, radius, and age, with high accuracy through asteroseismic analysis. These stellar parameters are inco

Weixuan Wang, Alejandro I. Maass, Dragan Nešić, Ying Tan

This paper studies the emulation-based stabilization of nonlinear networked control systems with two time scales. We address the challenge of using a single communication channel for transmitting both fast and slow variables between the plant and the controller. A novel dual clock mechanism is proposed to schedule transmissions for this purpose. The system i

Refik Mert Cam, Junjing Deng, Rajkumar Kettimuthu, Mathew J. Cherukara

Ptychography is a data-intensive computational imaging technique that achieves high spatial resolution over large fields of view. The technique involves scanning a coherent beam across overlapping regions and recording diffraction patterns. Conventional reconstruction algorithms require substantial overlap, increasing data volume and experimental time, reach

Jun Jiang, Wenjun Yu, Yuan Gao, Shugong Xu

In modern wireless communication systems, the effective processing of Channel State Information (CSI) is crucial for enhancing communication quality and reliability. However, current methods often handle different tasks in isolation, thereby neglecting the synergies among various tasks and leading to extract CSI features inadequately for subsequent analysis.

Peter Connick, Shane P. Kelly, Yaroslav Tserkovnyak

We consider two classical macrospins with dynamical (frequency-dependent) coupling, modeled by a generalized Landau-Lifshitz-Gilbert equation. We show that, in the absence of local damping, the resulting dynamics are pseudo-Hermitian. When two precessional modes hybridize near a crossing, the spectral behavior takes the form either of an anticrossing or leve

Zhiyu Tao, Yani Wang, Shuyi He, Jiade Li

Topological physics has evolved from its initial focus on fermionic systems to the exploration of bosonic systems, particularly phononic excitations in crystalline materials. Two-dimensional (2D) topological phonons emerge as promising candidates for future technological applications. Currently, experimental verification of 2D topological phonons has remaine

Feibo Jiang, Wanyun Zhu, Li Dong, Kezhi Wang

Large Language Models (LLMs) possess human-level cognitive and decision-making capabilities, making them a key technology for 6G. However, applying LLMs to the communication domain faces three major challenges: 1) Inadequate communication data; 2) Restricted input modalities; and 3) Difficulty in knowledge retrieval. To overcome these issues, we propose Comm

Nicolas Michel, Maorong Wang, Jiangpeng He, Toshihiko Yamasaki

Continual Learning (CL) aims to learn from a non-stationary data stream where the underlying distribution changes over time. While recent advances have produced efficient memory-free methods in the offline CL (offCL) setting, where tasks are known in advance and data can be revisited, online CL (onCL) remains dominated by memory-based approaches. The transit

Seokhyun Choi, Bo-Hae Im

We prove Larsen's conjecture for elliptic curves over $\mathbb{Q}$ with analytic rank at most $1$. Specifically, let $E/\mathbb{Q}$ be an elliptic curve over $\mathbb{Q}$. If $E/\mathbb{Q}$ has analytic rank at most $1$, then we prove that for any topologically finitely generated subgroup $G$ of $\mathrm{Gal}(\overline{\mathbb{Q}}/\mathbb{Q})$, the rank of $

Akhil Nagariya, Dimitar Filev, Srikanth Saripalli, Gaurav Pandey

In the area of autonomous driving, navigating off-road terrains presents a unique set of challenges, from unpredictable surfaces like grass and dirt to unexpected obstacles such as bushes and puddles. In this work, we present a novel learning-based local planner that addresses these challenges by directly capturing human driving nuances from real-world demon

Xuan Pang, Pingzhi Yuan, Hongjian Li

Permutation polynomials with explicit constructions over finite fields have long been a topic of great interest in number theory. In recent years, by applying linear translators of functions from $\mathbb{F}_{q^n}$ to $\mathbb{F}_q$, many scholars constructed some classes of permutation polynomials. Motivated by previous works, we first naturally extend the

Yubin Kim, Hyewon Jeong, Shan Chen, Shuyue Stella Li

Hallucinations in foundation models arise from autoregressive training objectives that prioritize token-likelihood optimization over epistemic accuracy, fostering overconfidence and poorly calibrated uncertainty. We define medical hallucination as any model-generated output that is factually incorrect, logically inconsistent, or unsupported by authoritative

Zeyuan Zhou, Siyuan Chen, Xinzhang Wu, Jisen Zhang

Genotype-to-Phenotype prediction can promote advances in modern genomic research and crop improvement, guiding precision breeding and genomic selection. However, high-dimensional nonlinear features often hinder the accuracy of genotype-to-phenotype prediction by increasing computational complexity. The challenge also limits the predictive accuracy of traditi

Mingdai Yang, Zhiwei Liu, Liangwei Yang, Xiaolong Liu

Recommender systems (RS) have become essential tools for helping users efficiently navigate the overwhelming amount of information on e-commerce and social platforms. However, traditional RS relying on Collaborative Filtering (CF) struggles to integrate the rich semantic information from textual data. Meanwhile, large language models (LLMs) have shown promis

Rong Wu, Ziqi Chen, Gen Li, Hai Shu

Motivation: Biomedical studies increasingly produce multi-view high-dimensional datasets (e.g., multi-omics) that demand integrative analysis. Existing canonical correlation analysis (CCA) and generalized CCA methods address at most two of the following three key aspects simultaneously: (i) nonlinear dependence, (ii) sparsity for variable selection, and (iii

Weiming Hu, Haoyan Zhang, Cong Guo, Yu Feng

Large language models (LLMs) are one of the most important killer computer applications. The recent algorithmic advancement proposes a fine-grained group-wise quantization for LLMs, which treats a small set (e.g., 64) of values in a tensor as a compression unit. It effectively preserves the model accuracy without retraining, and has become the standard appro

Yuwei Yan, Yu Shang, Qingbin Zeng, Yu Li

The AgentSociety Challenge is the first competition in the Web Conference that aims to explore the potential of Large Language Model (LLM) agents in modeling user behavior and enhancing recommender systems on web platforms. The Challenge consists of two tracks: the User Modeling Track and the Recommendation Track. Participants are tasked to utilize a combine

Maria Tsampazi, Michele Polese, Falko Dressler, Tommaso Melodia

Reconfigurable Intelligent Surfaces (RISs) pose as a transformative technology to revolutionize the cellular architecture of Next Generation (NextG) Radio Access Networks (RANs). Previous studies have demonstrated the capabilities of RISs in optimizing wireless propagation, achieving high spectral efficiency, and improving resource utilization. At the same t

Indranil Biswas, Buddhadev Hajra

A topological space (not necessarily simply connected) is said to have finite homotopy rank-sum if the sum of the ranks of all higher homotopy groups (from the second homotopy group onward) is finite. In this article, we consider Stein spaces of arbitrary dimension satisfying the above rational homotopy theoretic property, although most of this article focus

Charlie K. Guan, Zhimei Ren, Daniel W. Apley

We propose one-at-a-time knockoffs (OATK), a new methodology for detecting important explanatory variables in linear regression models while controlling the false discovery rate (FDR). For each explanatory variable, OATK generates a knockoff design matrix that preserves the Gram matrix by replacing one-at-a-time only the single corresponding column of the or

Yi Heng San, Vasanthamaran Ravichandram, J-Anne Yow, Sherwin Stephen Chan

Ensuring safe and comfortable bite transfer during robot-assisted feeding is challenging due to the close physical human-robot interaction required. This paper presents a novel approach to modeling physical human-robot interaction in a physics-based simulator (MuJoCo) using soft-body dynamics. We integrate a flexible head model with a rigid skeleton while ac

Yihang Wu, Ahmad Chaddad, Christian Desrosiers, Tareef Daqqaq

Despite the remarkable performance of vision language models (VLMs) such as Contrastive Language Image Pre-training (CLIP), the large size of these models is a considerable obstacle to their use in federated learning (FL) systems where the parameters of local client models need to be transferred to a global server for aggregation. Another challenge in FL is

Shaheer Mohamed, Tharindu Fernando, Sridha Sridharan, Peyman Moghadam

Hyperspectral object tracking using snapshot mosaic cameras is emerging as it provides enhanced spectral information alongside spatial data, contributing to a more comprehensive understanding of material properties. Using transformers, which have consistently outperformed convolutional neural networks (CNNs) in learning better feature representations, would

Suman Kalyan Pradhan, Arnab Bera, Soham Das, Yongli Yu

Spin-phonon interactions in 2D magnetic materials are crucial in advancing next-generation spintronic devices. Therefore, identifying new materials with significant spin-phonon interactions is of great importance. In this context, MnSe, previously recognized as an exemplary non-layered p-type semiconductor emerges in this study as an intriguing material with

Wendi Cui, Zhuohang Li, Hao Sun, Damien Lopez

Recent advances in Large Language Models have led to remarkable achievements across a variety of Natural Language Processing tasks, making prompt engineering increasingly central to guiding model outputs. While manual methods can be effective, they typically rely on intuition and do not automatically refine prompts over time. In contrast, automatic prompt op

Gabriele Tiboni, Raffaello Camoriano, Tatiana Tommasi

Object-Centric Motion Generation (OCMG) plays a key role in a variety of industrial applications$\unicode{x2014}$such as robotic spray painting and welding$\unicode{x2014}$requiring efficient, scalable, and generalizable algorithms to plan multiple long-horizon trajectories over free-form 3D objects. However, existing solutions rely on specialized heuristics

Jeesu Jung, Chanjun Park, Sangkeun Jung

Recent efforts in LLM alignment have focused on constructing large-scale preference datasets via human or Artificial Intelligence (AI) annotators. However, such approaches rely on instance-wise supervision, incurring substantial annotation cost and limited interpretability. In this paper, we propose ZEBRA - a model behavior-wise zero-annotation framework tha

Armin Aminimajd, Joao Maia, Abhinendra Singh

The viscosity of the suspension consisting of fine particles dispersed in a Newtonian liquid diverges close to the jamming packing fraction. The contact microstructure in suspensions governs this macroscopic behavior in the vicinity of jamming through a frictional contact network (FCN). FCN is composed of mechanical load-bearing contacts that lead to the eme

Maryam Asadi Ahmadabadi, Farimehr Zohari, S. Mohammad Razavizadeh

This study examines the secrecy performance of an uplink device-to-device (D2D) communication system enhanced by reconfigurable intelligent surfaces (RIS) while considering the presence of multiple eavesdroppers. RIS technology is employed to improve wireless communication environment by intelligently reflecting signals, thereby improving both capacity and s

Suman Kalyan Pradhan, Tuhin Debnath, Rui Wu

Materials hosting diverse topological spin textures hold significant potential for spintronic applications. In this context, CrTe1.38, a quasi-two-dimensional material, stands out due to its stable N'eel-type skyrmion phase over a wide temperature range, both with and without an applied magnetic field [APL 125, 152402 (2024)]. Thus, it is a promising candida

Shuaiang Rong, Lina He, Salih Furkan Atici, Ahmet Enis Cetin

Power line infrastructure is a key component of the power system, and it is rapidly expanding to meet growing energy demands. Vegetation encroachment is a significant threat to the safe operation of power lines, requiring reliable and timely management to enhance the resilience and reliability of the power network. Integrating smart grid technology, especial

Xian Li, Xuan Liang, A. H. Welsh, Tao Zou

When data are stored across multiple locations, directly pooling all the data together for statistical analysis may be impossible due to communication costs and privacy concerns. Distributed computing systems allow the analysis of such data, by getting local servers to separately process their own statistical analyses and using a central processor to aggrega

Romil Audhkhasi, Ningzhi Xie, Johannes E. Fröch, Arka Majumdar

Real time, singleshot multispectral imaging systems are crucial for environment monitoring and biomedical imaging. Most singleshot multispectral imagers rely on complex computational backends, which precludes real time operations. In this work, we leverage the spectral selectivity afforded by engineered photonic materials to perform bulk of the multispectral

Frederic Gmeiner, Nicolai Marquardt, Michael Bentley, Hugo Romat

Despite Generative AI (GenAI) systems' potential for enhancing content creation, users often struggle to effectively integrate GenAI into their creative workflows. Core challenges include misalignment of AI-generated content with user intentions (intent elicitation and alignment), user uncertainty around how to best communicate their intents to the AI system

Nathalie Riche, Anna Offenwanger, Frederic Gmeiner, David Brown

Chat-based prompts respond with verbose linear-sequential texts, making it difficult to explore and refine ambiguous intents, back up and reinterpret, or shift directions in creative AI-assisted design work. AI-Instruments instead embody "prompts" as interface objects via three key principles: (1) Reification of user-intent as reusable direct-manipulation in

Nicolas Harvey Chapman, Feras Dayoub, Will Browne, Christopher Lehnert

A domain shift exists between the large-scale, internet data used to train a Vision-Language Model (VLM) and the raw image streams collected by a robot. Existing adaptation strategies require the definition of a closed-set of classes, which is impractical for a robot that must respond to diverse natural language queries. In response, we present QueryAdapter;

Hemanth Teja Yanambakkam, Rahul Chinthala

Image captioning is a challenging task at the intersection of computer vision and natural language processing, requiring models to generate meaningful textual descriptions of images. Traditional approaches rely on recurrent neural networks (RNNs), but recent advancements in attention mechanisms have demonstrated significant improvements. This study benchmark

Eric Oliver, Sagnik Dakshit

Deep learning's growing prevalence has driven its widespread use in healthcare, where AI and sensor advancements enhance diagnosis, treatment, and monitoring. In mobile health, AI-powered tools enable early diagnosis and continuous monitoring of conditions like stress. Wearable technologies and multimodal physiological data have made stress detection increas

Suman Kalyan Pradhan, Weiqi Liu, Jicheng Wang, Yongli Yu

With a surge of interest in spintronics, the manipulation and detection of colossal magnetoresistance in quasi-two-dimensional layered magnetic materials have become a key focus, driven by their relatively scarce occurrence compared to giant magnetoresistance and tunneling magnetoresistance. This study presents an investigation into the desired colossal magn

Shinnosuke Sawano, Satoshi Kodera

In this study, we investigate the feasibility of using a human-centered artificial intelligence (AI) chat platform where medical specialists collaboratively assess complex cases. As the target population for this platform, we focus on patients with cardiovascular diseases who are in a state of multimorbidity, that is, suffering from multiple chronic conditio

Rania Farjallah, Bassant Selim, Brigitte Jaumard, Samr Ali

The challenge of handling missing data in time series is critical for maintaining the accuracy and reliability of machine learning (ML) models in applications like fifth generation mobile communication (5G) network management. Traditional methods for validating imputation rely on ground truth data, which is inherently unavailable. This paper addresses this l

Xiao-Hui Xu, Qing-Feng Zhu, Xu-Zhi Li, Hang Zheng

Variable stars play a very important role in our understanding of the Milky Way and the universe. In recent years, many survey projects have generated a large amount of photometric data, necessitating classifiers that can quickly identify various types of variable stars. However, obtaining these classifiers often requires substantial manpower and computation

Long Cheng, Qichen Liao, Fan Wu, Junlin Mu

Attention calculation is extremely time-consuming for long-sequence inference tasks, such as text or image/video generation, in large models. To accelerate this process, we developed a low-precision, mathematically-equivalent algorithm called PASA, based on Flash Attention. PASA introduces two novel techniques: online pseudo-average shifting and global recov

Chia-Fu Liu, Ali Mostafavi

Disasters impact communities through interconnected social, spatial, and physical networks. Analyzing network dynamics is crucial for understanding resilience and recovery. We highlight six studies demonstrating how hazards and recovery processes spread through these networks, revealing key phenomena, such as flood exposure, emergent social cohesion, and cri

Xiaohua Wu, Xiaohui Tao, Wenjie Wu, Yuefeng Li

Social surveys in computational social science are well-designed by elaborate domain theories that can effectively reflect the interviewee's deep thoughts without concealing their true feelings. The candidate questionnaire options highly depend on the interviewee's previous answer, which results in the complexity of social survey analysis, the time, and the

Minsung Cho, John Gouwar, Steven Holtzen

Probabilistic inference is fundamentally hard, yet many tasks require optimization on top of inference, which is even harder. We present a new optimization-via-compilation strategy to scalably solve a certain class of such problems. In particular, we introduce a new intermediate representation (IR), binary decision diagrams weighted by a novel notion of bran

Tengyou Zhu

Let $f$ and $g$ be holomorphic or Maass cusp forms for $\rm SL_2(\mathbb{Z})$ and let $\chi$ be a primitive Dirichlet character of prime power conductor $q=p^n$. For any given $\varepsilon>0$, we establish the following subconvexity bound \begin{equation*} L(1/2,f\otimes g \otimes \chi)\ll_{f,g,\varepsilon}q^{9/10+\varepsilon}. \end{equation*} The proof empl

Alireza Daghighfarsoodeh, Chung-Yu Wang, Hamed Taherkhani, Melika Sepidband

Deep learning (DL) has revolutionized areas such as computer vision, natural language processing, and more. However, developing DL systems is challenging due to the complexity of DL workflows. Large Language Models (LLMs), such as GPT, Claude, Llama, Mistral, etc., have emerged as promising tools to assist in DL code generation, offering potential solutions

Xin Liu, Ziyue Zhang, Jingxin Nie

Traditional psychological experiments utilizing naturalistic stimuli face challenges in manual annotation and ecological validity. To address this, we introduce a novel paradigm leveraging multimodal large language models (LLMs) as proxies to extract rich semantic information from naturalistic images through a Visual Question Answering (VQA) strategy for ana

Anthony Etim, Jakub Szefer

Adversarial attacks on deep learning models have proliferated in recent years. In many cases, a different adversarial perturbation is required to be added to each image to cause the deep learning model to misclassify it. This is ineffective as each image has to be modified in a different way. Meanwhile, research on universal perturbations focuses on designin

Anand Ithepalli, Amit Rohan Rajapurohita, Arjan Singh, Rishabh Singh

Two single crystal phases of tantalum nitride were stabilized on c-plane sapphire using molecular beam epitaxy. The phases were identified to be $\delta$-TaN with a rocksalt cubic structure and $\gamma$-Ta$_2$N with a hexagonal structure. Atomic force microscopy scans revealed smooth surfaces for both the films with root mean square roughnesses less than 0.3

Young-Ho Song, Myungkuk Kim, Youngman Kim, Kihyeon Cho

We perform \textit{ab initio} nuclear lattice calculations of the neutron-rich carbon and oxygen isotopes using high-fidelity chiral interactions. We find good agreement with the observed binding energies and compute correlations associated with each two-nucleon interaction channel. For the isospin $T=1$ channels, we show that the dependence on $T_z$ provide

Masato Fujita

Consider an expansion $\mathcal R=(R,<,+,\ldots)$ of an ordered divisible Abelian group of finite burden defining no nonempty subset $X$ of $R$ which is dense and codense in a definable open subset $U$ of $R$ with $X \subseteq U$. We further assume that $\mathcal R$ is nonvaluational, that is, for every nonempty definable subsets $A,B$ of $R$ with $A <B$ and

Chen ZF, Luis C. Ho

In active galactic nuclei, jet-driven feedback plays a significant role in influencing the properties of gas within their host galaxy and the circumgalactic medium. By combining observations from the Very Large Array Sky Survey, the Faint Images of the Radio Sky at Twenty-cm, the LOFAR Two Metre Sky Survey, and the Sloan Digital Sky Survey, we assembled a sa

Yuxin Li, Hao Fang, Wen Liu, Chuantong Cheng

Achieving high subject-independent accuracy in functional near-infrared spectroscopy (fNIRS)-based brain-computer interfaces (BCIs) remains a challenge, particularly when minimizing the number of channels. This study proposes a novel feature extraction scheme and a Pearson correlation-based channel selection algorithm to enhance classification accuracy while

Abraham R. Flores, Kenneth M. Nollett, Maria Piarulli

Nuclear physics seeks to describe both bound and unbound states within a unified predictive framework. While coordinate-space Quantum Monte Carlo (QMC) methods have successfully computed bound states for systems with $A \leq 12$, their application to unbound states remains limited. In this work, we extend the QMC approach to enable a broader range of unbound

Haiying Li, Tianshui Ma

In this paper the conditions that when a Lie algebra is Nijenhuis are investigated. Furthermore all the Nijenhuis operators on $\mathfrak{sl}_2$ under the standard Cartan-Weyl basis are given. On the other hand, the relations between the classical Yang-Baxter equation and Nijenhuis operators $N$ on a Lie algebra and $P$ on a Lie coalgebra are derived by mean

Bowen Li, Lukas Palm, Marius Jürgensen, Yiming Cady Feng

From biology and astronomy to quantum optics, there is a critical need for high frame rate, high quantum efficiency imaging. In practice, most cameras only satisfy one of these requirements. Here we introduce interlaced fast kinetics imaging, a technique that allows burst video acquisition at frame rates up to 3.33 Mfps using a commercial EMCCD camera with s

Youngjin Cho, Yili Hong, Pang Du

In a Cox model, the partial likelihood, as the product of a series of conditional probabilities, is used to estimate the regression coefficients. In practice, those conditional probabilities are approximated by risk score ratios based on a continuous time model, and thus result in parameter estimates from only an approximate partial likelihood. Through a rev

Claudia Reyes, Dennis Stello, Marc Hon, Yaguang Li

Sparked by the asteroseismic space revolution, ensemble studies have been used to produce empirical relations linking observed seismic properties and fundamental stellar properties. Cluster stars are particularly valuable because they have the same metallicity, distance, and age, thus reducing scatter to reveal smoother relations. We present the first study

Hernan A. Makse, Paolo Boldi, Francesco Sorrentino, Ian Stewart

A symmetry is a `change without change'. As simple as it sounds, this concept is the fundamental cornerstone that unifies all branches of theoretical physics. Virtually all physical laws -- ranging from classical mechanics and electrodynamics to relativity, quantum mechanics, and the standard model -- can be expressed in terms of symmetry invariances. In thi

Chenlu Ju, Jiaxin Liu, Shobhit Sinha, Hao Xue

This work leverages Large Language Models (LLMs) to simulate human mobility, addressing challenges like high costs and privacy concerns in traditional models. Our hierarchical framework integrates persona generation, activity selection, and destination prediction, using real-world demographic and psychological data to create realistic movement patterns. Both

Liang Song, Huohao Zhang

Let $L=-\sum_{i,j=1}^n a_{ij}D_iD_j$ be the elliptic operator in non-divergence form with smooth real coefficients satisfying uniformly elliptic condition. Let $W$ be the global nonnegative adjoint solution. If $W\in A_2$, we prove that the Riesz transforms $\nabla L^{-\frac{1}{2}}$ is of weak type $(1,1)$ with respect to the measure $W(x)dx$. This, together

Ronald Orozco López

In this paper, we use the effect of the $q$-differential and deformed $q$-exponential operators on basic hypergeometric series to find new $q$-identities from the $q$-Gauss sum, the $q$-Chu-Vandermonde's sum, and Jackson's transformation formula.

Chaitanya Kapoor, Sudhanshu Srivastava, Meenakshi Khosla

Understanding convergent learning -- the degree to which independently trained neural systems -- whether multiple artificial networks or brains and models -- arrive at similar internal representations -- is crucial for both neuroscience and AI. Yet, the literature remains narrow in scope -- typically examining just a handful of models with one dataset, relyi

Yuki Shibukawa, Taira Tsuchiya, Shinsaku Sakaue, Kenji Yamanishi

Online structured prediction is a task of sequentially predicting outputs with complex structures based on inputs and past observations, encompassing online classification. Recent studies showed that in the full-information setting, we can achieve finite bounds on the \textit{surrogate regret}, \textit{i.e.,}~the extra target loss relative to the best possib

Vittorio Del Tatto, Debarshi Banerjee, Ali Hassanali, Alessandro Laio

The ability to distinguish between correlation and causation of variables in molecular systems remains an interesting and open area of investigation. In this work, we probe causality in a molecular system using two independent computational methods that infer the causal direction through the language of information transfer. Specifically, we demonstrate that

Haonan Xiong, Jiahui Wang, Juan Song, Jize Yang

Quantum error correction codes with non-local connections such as quantum low-density parity-check (qLDPC) incur lower overhead and outperform surface codes on large-scale devices. These codes are not applicable on current superconducting devices with nearest-neighbor connections. To rectify the deficiency in connectivity of superconducting circuit system, w

Yao Feng, Jun Zhu, André Platzer, Jonathan Laurent

A major challenge to deploying cyber-physical systems with learning-enabled controllers is to ensure their safety, especially in the face of changing environments that necessitate runtime knowledge acquisition. Model-checking and automated reasoning have been successfully used for shielding, i.e., to monitor untrusted controllers and override potentially uns

Nakyung Lee, Małgorzata Sójka, Annie La, Syna Sharma

Creating superior lanthanide-activated inorganic phosphors is pivotal for advancing energy-efficient LED lighting and backlit flat panel displays. The most fundamental property these luminescent materials must possess is effective absorption/excitation by a blue InGaN LED for practical conversion into white light. The 5$d_1$ excited state energy level of lan

Jia Boon Chin, Diane Prato, Alexander Ling

Photon-pair sources are useful for entanglement distribution. The most mature of these are spontaneous parametric downconversion (SPDC) sources, most of which achieve phasematching via engineering the domains in poled crystals or the angle between the optic axis and the pump beam. For multi-channel entanglement distribution of photon pairs, where one photon

Reza Hamzehofi

In this research, we investigate the distribution of entanglement within two entangled $n$-qubit systems using the one-tangle and $π$-tangle. Our analysis reveals that for certain quantum states such as the generalized $W$ state, where the probability coefficients depend on the number of qubits, increasing the system size causes these traditional measures to

J. M. Campbell, M. Diefenthaler, T. J. Hobbs, S. Höche

We provide an overview of the status of Monte-Carlo event generators for high-energy particle physics. Guided by the experimental needs and requirements, we highlight areas of active development, and opportunities for future improvements. Particular emphasis is given to physics models and algorithms that are employed across a variety of experiments. These co

Loïc Michel

The model-free control approach is an advanced control law that requires few information about the process to control. Since its introduction in 2008, numerous applications have been successfully considered, highlighting attractive robustness properties towards tracking efficiency and disturbance rejection. In this work, a variational approach of the model-f

Michael F. Zhang, Seth Davidovits, Nathaniel J. Fisch

We study the amplification of isotropic, incompressible turbulence through multiple planar, collisional shocks, using analytical linear theory. There are two limiting cases we explore. The first assumes shocks occur rapidly in time such that the turbulence does not evolve between shocks. Whereas the second case allows enough time for turbulence to isotropize

Cheng Zhang, Neill Lambert, Xin-Qi Li, Mauro Cirio

The theory of purified pseudomodes [arXiv:2412.04264 (2024)] was recently developed to provide a numerical tool for the analysis of the properties of a quantum system and the environment it couples to via linear system-bath interactions. Here we extend this theory to allow for the description of general nonlinear system-bath interactions. We demonstrate the

Shahrzad Kiani, Nupur Kulkarni, Adam Dziedzic, Stark Draper

Federated learning (FL) with differential privacy (DP) provides a framework for collaborative machine learning, enabling clients to train a shared model while adhering to strict privacy constraints. The framework allows each client to have an individual privacy guarantee, e.g., by adding different amounts of noise to each client's model updates. One underlyi

Yue Fu, Samuel Schwamm, Amanda Baughan, Nicole M Powell

Social online games like Minecraft and Roblox have become increasingly integral to children's daily lives. Our study explores how children aged 8 to 13 create and customize avatars in these virtual environments. Through semi-structured interviews and gameplay observations with 48 participants, we investigate the motivations behind children's avatar-making. O

Angela Busheska, Vikram Iyer, Bruno Silva, Peder Olsen

Understanding land use over time is critical to tracking events related to climate change, like deforestation. However, satellite-based remote sensing tools which are used for monitoring struggle to differentiate vegetation types in farms and orchards from forests. We observe that metrics such as the Normalized Difference Vegetation Index (NDVI), based on pl

Abhay Gupta, Jacob Cheung, Philip Meng, Shayan Sayyed

The diversity of human language, shaped by social, cultural, and regional influences, presents significant challenges for natural language processing (NLP) systems. Existing benchmarks often overlook intra-language variations, leaving speakers of non-standard dialects underserved. To address this gap, we introduce EnDive (English Diversity), a benchmark that

Mintong Kang, Vinayshekhar Bannihatti Kumar, Shamik Roy, Abhishek Kumar

Text-to-image diffusion models often exhibit biases toward specific demographic groups, such as generating more males than females when prompted to generate images of engineers, raising ethical concerns and limiting their adoption. In this paper, we tackle the challenge of mitigating generation bias towards any target attribute value (e.g., "male" for "gende

Nilay Yilmaz, Maitreya Patel, Yiran Lawrence Luo, Tejas Gokhale

Multimodal Large Language Models (MLLMs) have become a powerful tool for integrating visual and textual information. Despite their exceptional performance on visual understanding benchmarks, measuring their ability to reason abstractly across multiple images remains a significant challenge. To address this, we introduce VOILA, a large-scale, open-ended, dyna

James M. Cline

It was recently proposed (arxiv:2502.08887) that the time crystal Lagrangian introduced by Shapere and Wilczek in 2012 could be a model of dark energy. I point out that the model has an instability that drives its energy density to negative values, which may render it unsuitable as a model of dark energy.

Amin Shokrollahi, Syeda Sara Mobasher, Kofi Ohemeng Kyei Prempeh, Sara Borazjani

Accurate prediction of long-term CO2 plume migration beneath seals is crucial for the viability of CO2 storage in deep saline aquifers. Groundwater counterflow and chemical reactions between CO2, brine, and rock significantly influence plume dynamics, dispersion, and boundary evolution. This study develops a novel analytical framework by deriving governing e

Zihan Wang, Ziqi Zhao, Yougang Lyu, Zhumin Chen

Zero-shot named entity recognition (NER) aims to develop entity recognition systems from unannotated text corpora. This task presents substantial challenges due to minimal human intervention. Recent work has adapted large language models (LLMs) for zero-shot NER by crafting specialized prompt templates. It advances model self-learning abilities by incorporat

Yaman Yu, Bektur Ryskeldiev, Ayaka Tsutsui, Matthew Gillingham

Online interactions and e-commerce are commonplace among BLV users. Despite the implementation of web accessibility standards, many e-commerce platforms continue to present challenges to screen reader users, particularly in areas like webpage navigation and information retrieval. We investigate the difficulties encountered by screen reader users during onlin

Pece Trajanovski, Irina Petreska, Katarzyna Gorska, Ljupco Kocarev

A space fractional diffusion-like equation is introduced, which embodies the nonlocality in time, represented by the memory kernel and the non-locality in space. A specific example of the nonlocal term is considered in combination with three different forms of the memory kernel. To analyse the probability density function, we utilize the subordination approa

Tianze Wang, Dongnan Gui, Yifan Hu, Shuhang Lin

Reinforcement Learning from Human Feedback (RLHF) has shown promise in aligning large language models (LLMs). Yet its reliance on a singular reward model often overlooks the diversity of human preferences. Recent approaches address this limitation by leveraging multi-dimensional feedback to fine-tune corresponding reward models and train LLMs using reinforce

Gavin Brown, Lydia Zakynthinou

Mean estimation is a fundamental task in statistics and a focus within differentially private statistical estimation. While univariate methods based on the Gaussian mechanism are widely used in practice, more advanced techniques such as the exponential mechanism over quantiles offer robustness and improved performance, especially for small sample sizes. Tuke

Robert Marlin, Raja Jurdak, Alsharif Abuadbba

The widespread adoption of Electric Vehicles (EVs) poses critical challenges for energy providers, particularly in predicting charging time (temporal prediction), ensuring user privacy, and managing resources efficiently in mobility-driven networks. This paper introduces the Hierarchical Federated Learning Transformer Network (H-FLTN) framework to address th

Christos Papandreou, Michail Mathioudakis, Theodoros Stouraitis, Petros Iatropoulos

The deployment of autonomous navigation systems on ships necessitates accurate motion prediction models tailored to individual vessels. Traditional physics-based models, while grounded in hydrodynamic principles, often fail to account for ship-specific behaviors under real-world conditions. Conversely, purely data-driven models offer specificity but lack int

Konstantina Palla, José Luis Redondo García, Claudia Hauff, Francesco Fabbri

Content moderation plays a critical role in shaping safe and inclusive online environments, balancing platform standards, user expectations, and regulatory frameworks. Traditionally, this process involves operationalising policies into guidelines, which are then used by downstream human moderators for enforcement, or to further annotate datasets for training

Fanyu Wang, Chetan Arora, Chakkrit Tantithamthavorn, Kaicheng Huang

Automated software testing has significant potential to enhance efficiency and reliability within software development processes. However, its broader adoption faces considerable challenges, particularly concerning alignment between test generation methodologies and software requirements. REquirements-Driven Automated Software Testing (REDAST) addresses this

Lucas Brown, Stefano Profumo, Aditi Gangadharan, Zeynep Su Koç

A strong first-order phase transition in a dark sector may produce all or part of the low-frequency gravitational wave signal recently reported by the NANOGrav Collaboration and other pulsar timing arrays. Here we point out, with a simple toy model, that even if the amplitude of the gravitational wave background from the dark phase transition is insufficient

Yangyang Shen, Xu Zhang, Jun Liang, Anton Artemyev

Auroral streamers are important meso-scale processes of dynamic magnetosphere-ionosphere coupling, typically studied using imagers sensitive to energetic (>1 keV) electron precipitation, such as all-sky imagers (ASIs). This paper reports streamer-like red-line auroras, representing low-energy (<1 keV) precipitation, observed poleward of a black aurora and an

Saorj Kumar, Prince Asiamah, Oluwatoyin Jolaoso, Ugochukwu Esiowu

Convolutional Neural Networks (CNNs) serve as the workhorse of deep learning, finding applications in various fields that rely on images. Given sufficient data, they exhibit the capacity to learn a wide range of concepts across diverse settings. However, a notable limitation of CNNs is their susceptibility to overfitting when trained on small datasets. The a

Tanvi Sattiraju, Jonathan Tennyson

An analysis of the measured rovibrational transitions is carried out for the 12C32S2 isotopologue of carbon disulfide. Data from 21 sources is extracted and validated using a consistent set of standard linear molecule quantum numbers. A corrected list of 8714 CS2 transitions forms the input to a Measured Active Rotational-Vibrational Energy Levels (MARVEL) p

Daniel Weiner, Raj Korpan

In role-playing games (RPGs), the level of immersion is critical-especially when an in-game agent conveys tasks, hints, or ideas to the player. For an agent to accurately interpret the player's emotional state and contextual nuances, a foundational level of understanding is required, which can be achieved using a Large Language Model (LLM). Maintaining the L

Clayton Bromley, Alexander Moore, Amar Saini, Doug Poland

Video object segmentation (VOS) is a critical task in the development of video perception and understanding. The Segment-Anything Model 2 (SAM 2), released by Meta AI, is the current state-of-the-art architecture for end-to-end VOS. SAM 2 performs very well on both clean video data and augmented data, and completely intelligent video perception requires an u