March 2025 arXiv papers — page 208

M. Sharif, Madiha Ajmal

In this study, we investigate the features of a charged gravastar within the framework of $f(\mathbb{Q})$ gravity ($\mathbb{Q}$ represents non-metricity) using the Finch-Skea metric. This metric is applied to both the interior and shell regions of the charged gravastar and the field equations are derived accordingly. For the exterior regions, we consider var

Ce Cai, Yan-Qiu Zhang, Shao-Lin Xiong, Ping Wang

In the era of time-domain, multi-messenger astronomy, the detection of transient events on the high-energy electromagnetic sky has become more important than ever. The Gravitational wave high-energy Electromagnetic Counterpart All-sky Monitor (GECAM) is a dedicated mission to monitor gamma-ray transients, launched in December, 2020. A real-time on-board trig

Jiang Zhang, Rohan Xavier Sequeira, Konstantinos Psounis

Specialized machine learning (ML) models tailored to users needs and requests are increasingly being deployed on smart devices with cameras, to provide personalized intelligent services taking advantage of camera data. However, two primary challenges hinder the training of such models: the lack of publicly available labeled data suitable for specialized task

Idris Assani, Aiden Chester, Alex Paschal

We provide new, elementary proofs that Robin's inequality and the Lagarias inequality hold for almost every number, including all numbers not divisible by one of the prime numbers $2$, $3$, $5$; all primorials; given $k$ a natural number, all sufficiently large numbers of the form $2^kn$ for $n\ge1$ odd; and all $21$-free integers. Additionally, we prove tha

Thanh-Dat Nguyen, Haoye Tian, Bach Le, Patanamon Thongtanunam

Debugging ML software (i.e., the detection, localization and fixing of faults) poses unique challenges compared to traditional software largely due to the probabilistic nature and heterogeneity of its development process. Various methods have been proposed for testing, diagnosing, and repairing ML systems. However, the big picture informing important researc

Jeremy R. Worsfold, Richard G. Morris

The two-state Togashi-Kaneko model demonstrates how, at finite system sizes, autocatalysis can lead to noise-induced bistability between the cellular concentrations of different molecular species. Here, we show that, in the biologically relevant scenario of species-dependent export rates, the nascent stochastic switching between molecular species also drives

Wei-Hao Chen, Weixi Tong, Amanda Case, Tianyi Zhang

Data wrangling is a time-consuming and challenging task in a data science pipeline. While many tools have been proposed to automate or facilitate data wrangling, they often misinterpret user intent, especially in complex tasks. We propose Dango, a mixed-initiative multi-agent system for data wrangling. Compared to existing tools, Dango enhances user communic

C. B. Aberlé, Chris Martens, Frank Pfenning

Ordered, linear, and other substructural type systems allow us to expose deep properties of programs at the syntactic level of types. In this paper, we develop a family of unary logical relations that allow us to prove consequences of parametricity for a range of substructural type systems. A key idea is to parameterize the relation by an algebra, which we e

Dankai Liao, Sicheng Chen, Nuwa Xi, Qiaochu Xue

Pathology image analysis plays a pivotal role in medical diagnosis, with deep learning techniques significantly advancing diagnostic accuracy and research. While numerous studies have been conducted to address specific pathological tasks, the lack of standardization in pre-processing methods and model/database architectures complicates fair comparisons acros

Xiangliu Tu, Chiranjib Saha, Harpreet S. Dhillon

Subset selection is central to many wireless communication problems, including link scheduling, power allocation, and spectrum management. However, these problems are often NP-complete, because of which heuristic algorithms applied to solve these problems struggle with scalability in large-scale settings. To address this, we propose a determinantal point pro

Rylan Schaeffer, Joshua Kazdan, Alvan Caleb Arulandu, Sanmi Koyejo

The proliferation of AI-generated content online has fueled concerns over \emph{model collapse}, a degradation in future generative models' performance when trained on synthetic data generated by earlier models. Industry leaders, premier research journals and popular science publications alike have prophesied catastrophic societal consequences stemming from

YiQiu Guo, Yuchen Yang, Zhe Chen, Pingjie Wang

The reliability of large language models remains a critical challenge, particularly due to their susceptibility to hallucinations and factual inaccuracies during text generation. Existing solutions either underutilize models' self-correction with preemptive strategies or use costly post-hoc verification. To further explore the potential of real-time self-ver

Haiduo Huang, Fuwei Yang, Dong Li, Ji Liu

Designing an efficient and effective neural network has remained a prominent topic in computer vision research. Depthwise onvolution (DWConv) is widely used in efficient CNNs or ViTs, but it needs frequent memory access during inference, which leads to low throughput. FasterNet attempts to introduce partial convolution (PConv) as an alternative to DWConv but

Yuheng Kuang, Zhengning Wang, Jianping Zhang, Zhenyu Shi

The importance of four-dimensional (4D) trajectory prediction within air traffic management systems is on the rise. Key operations such as conflict detection and resolution, aircraft anomaly monitoring, and the management of congested flight paths are increasingly reliant on this foundational technology, underscoring the urgent demand for intelligent solutio

Pan He, Min Zhang, Jin Cao, Jingru Li

The valley Hall effect (VHE) holds great promise for valleytronic applications by leveraging the valley degree of freedom. To date, research on VHE has focused on its linear response to an applied current, leaving nonlinear valley responses undetected and nonlinear valleytronic devices undeveloped. Here, we report the experimental observation of a nonlinear

Zhichao You, Xuewen Dong, Ke Cheng, Xutong Mu

Fine-tuning large language models (LLMs) raises privacy concerns due to the risk of exposing sensitive training data. Federated learning (FL) mitigates this risk by keeping training samples on local devices, while facing the following problems in privacy-preserving federated fine-tuning. (i) Recent studies show that adversaries can still infer private inform

Jiechao Deng, Ning Tan

Deep reinforcement learning has made significant strides in various robotic tasks. However, employing deep reinforcement learning methods to tackle multi-stage tasks still a challenge. Reinforcement learning algorithms often encounter issues such as redundant exploration, getting stuck in dead ends, and progress reversal in multi-stage tasks. To address this

Kairong Yu, Chengting Yu, Tianqing Zhang, Xiaochen Zhao

Spiking Neural Networks (SNNs), inspired by the human brain, offer significant computational efficiency through discrete spike-based information transfer. Despite their potential to reduce inference energy consumption, a performance gap persists between SNNs and Artificial Neural Networks (ANNs), primarily due to current training methods and inherent model l

Zhenning Sun, Tao Wang, Hao Jin, Xinru Li

The interplay between quantum geometry and magnetic order offers a novel strategy for designing next-generation nanodevices. Here, we demonstrate that interlayer magnetic coupling in two-dimensional (2D) CoPSe3 bilayers enables precise control over quantum geometric mechanisms, unlocking dual intrinsic Hall effects. Our first-principles calculations reveal t

Runlong Yu, Shengyu Chen, Yiqun Xie, Xiaowei Jia

Modeling environmental ecosystems is essential for effective resource management, sustainable development, and understanding complex ecological processes. However, traditional methods frequently struggle with the inherent complexity, interconnectedness, and limited data of such systems. Foundation models, with their large-scale pre-training and universal rep

Chun-Wun Cheng, Yining Zhao, Yanqi Cheng, Javier A. Montoya-Zegarra

Image segmentation is a fundamental task in both image analysis and medical applications. State-of-the-art methods predominantly rely on encoder-decoder architectures with a U-shaped design, commonly referred to as U-Net. Recent advancements integrating transformers and MLPs improve performance but still face key limitations, such as poor interpretability, d

Wentai Wu, Ligang He, Saiqin Long, Ahmed M. Abdelmoniem

Increasing legislation and regulations on private and proprietary information results in scattered data sources also known as the "data islands". Although Federated Learning-based paradigms can enable privacy-preserving collaboration over decentralized data, they have inherent deficiencies in fairness, costs and reproducibility because of being learning-cent

Jinwoo Lim, Suhyun Kim, Soo-Mook Moon

Backward error analysis allows finding a modified loss function, which the parameter updates really follow under the influence of an optimization method. The additional loss terms included in this modified function is called implicit regularizer. In this paper, we attempt to find the implicit regularizer for various federated learning algorithms on non-IID d

Alexander Stone-Martinez, Jon A. Holtzman, Yuxi, Lu

Understanding the ages of stars is crucial for unraveling the formation history and evolution of our Galaxy. Traditional methods for estimating stellar ages from spectroscopic data often struggle with providing appropriate uncertainty estimations and are severely constrained by the parameter space. In this work, we introduce a new approach using normalizing

Aaron R. Altman, Felipe H. da Jornada

Photocatalytic reactions often exhibit fast kinetics and high product selectivity, qualities which are desirable but difficult to achieve simultaneously in thermally driven processes. However, photo-driven mechanisms are poorly understood owing to the difficulty in realistically modeling catalysts in optically excited states. Here we apply many-body perturba

Runze Wang, Mingqi Yang, Yanming Shen

While Large Language Models (LLMs) have shown exceptional generalization capabilities, their ability to process graph data, such as molecular structures, remains limited. To bridge this gap, this paper proposes Graph2Token, an efficient solution that aligns graph tokens to LLM tokens. The key idea is to represent a graph token with the LLM token vocabulary,

Torin Anderson, Shuo Niu

Generative AI (GenAI) tools enhance social media video creation by streamlining tasks such as scriptwriting, visual and audio generation, and editing. These tools enable the creation of new content, including text, images, audio, and video, with platforms like ChatGPT and MidJourney becoming increasingly popular among YouTube creators. Despite their growing

Wei Zhang, Xinyue Wang, Lan Yu, Shi Li

In the data era, the integration of multiple data types, known as multimodality, has become a key area of interest in the research community. This interest is driven by the goal to develop cutting edge multimodal models capable of serving as adaptable reasoning engines across a wide range of modalities and domains. Despite the fervent development efforts, th

Richard Salami, Luise Poley, Kirsten Affolder, Tony Affolder

The ATLAS experiment at the Large Hadron Collider (LHC) is currently preparing to replace its present Inner Detector (ID) with the upgraded, all-silicon Inner Tracker (ITk) for its High-Luminosity upgrade (HL-LHC). The ITk will consist of a central pixel tracker and the outer strip tracker, consisting of about 19,000 strip detector modules. Each strip module

Awais Nizamani, Hamid Laga, Guanjin Wang, Farid Boussaid

We propose a novel framework for the statistical analysis of genus-zero 4D surfaces, i.e., 3D surfaces that deform and evolve over time. This problem is particularly challenging due to the arbitrary parameterizations of these surfaces and their varying deformation speeds, necessitating effective spatiotemporal registration. Traditionally, 4D surfaces are dis

Linhao Wang, Qichang Zhang, Yifan Yang, Ye Su

As 3D point clouds become the prevailing shape representation in computer vision, generating high-quality point clouds remains a challenging problem. Flow-based models have shown strong potential due to exact likelihood estimation and invertible mappings. However, existing flow-based methods for point clouds typically rely on point-wise feature extractors, w

Thomas Williams, James M. McCaw, James M. Osborne

There is growing recognition in both the experimental and modelling literature of the importance of spatial structure to the dynamics of viral infections in tissues. Aided by the evolution of computing power and motivated by recent biological insights, there has been an explosion of new, spatially-explicit models for within-host viral dynamics in recent year

Shuaishuai Hou, Lin Cheng, Nan Meng, Naixin Liu

We propose a serrated ultra-broadband infrared absorber based on the multi-layer repetitive stacking of indium tin oxide (ITO) and silicon materials. This absorber achieves continuously tunable absorption within a wide infrared light range. By changing the size and materials of the absorber, its average absorption rate can reach a relatively high level. Simu

Shi Li

Deep Learning has emerged as one of the most significant innovations in machine learning. However, a notable limitation of this field lies in the ``black box" decision-making processes, which have led to skepticism within groups like healthcare and scientific communities regarding its applicability. In response, this study introduces a interpretable approach

Chenhui Xu, Dancheng Liu, Jiajie Li, Amir Nassereldine

Recent advancements in cognitive science and multi-round reasoning techniques for Large Language Models (LLMs) suggest that iterative thinking processes improve problem-solving performance in complex tasks. Inspired by this, approaches like Chain-of-Thought, debating, and self-refinement have been applied to auto-regressive LLMs, achieving significant succes

Li Ye, Yisheng Song

It is well-known that a symmetric matrix with its entries $\pm1$ is not positive definite. But this is not ture for symmetric tensors (hyper-matrix). In this paper, we mainly dicuss the positive (semi-)definiteness criterion of a class of $4$th order $3$-dimensional symmetric tensors with entries $t_{ijkl}\in\{-1,0,1\}$. Through theoretical derivations and d

Wei Wang, Brian A. Camley

Groups of cells, including clusters of cancerous cells, multicellular organisms, and developing organs, may both grow and break apart. What physical factors control these fractures? In these processes, what sets the eventual size of clusters? We first develop a one-dimensional framework for understanding cell clusters that can fragment due to cell motility u

Ziying Song, Caiyan Jia, Lin Liu, Hongyu Pan

End-to-end autonomous driving frameworks enable seamless integration of perception and planning but often rely on one-shot trajectory prediction, which may lead to unstable control and vulnerability to occlusions in single-frame perception. To address this, we propose the Momentum-Aware Driving (MomAD) framework, which introduces trajectory momentum and perc

Ping He, Bo-Qiang Ma

Since the early reports of events beyond the Greisen-Zatsepin-Kuzmin (GZK) cutoff, the investigation of ultrahigh-energy cosmic rays has emerged as a fundamental method for testing Lorentz Invariance violation (LV) effects. Recent advances in observational capabilities have resulted in more stringent constraints on LV parameters. This study delves into the p

ZeYu Li, Xinsheng Zhang, Wang Zhou

Distributed algorithms and theories are called for in this era of big data. Under weaker local signal-to-noise ratios, we improve upon the celebrated one-round distributed principal component analysis (PCA) algorithm designed in the spirit of divide-and-conquer, by introducing a few additional communication rounds of consensus. The proposed shifted subspace

Zichao Li, Xueru Wen, Jie Lou, Yuqiu Ji

Multimodal Reward Models (MM-RMs) are crucial for aligning Large Language Models (LLMs) with human preferences, particularly as LLMs increasingly interact with multimodal data. However, we find that MM-RMs trained on existing datasets often struggle to generalize to out-of-distribution data due to their reliance on unimodal spurious correlations, primarily t

Zanting Ye, Xiaolong Niu, Xu Han, Xuanbin Wu

Organ segmentation in Positron Emission Tomography (PET) plays a vital role in cancer quantification. Low-dose PET (LDPET) provides a safer alternative by reducing radiation exposure. However, the inherent noise and blurred boundaries make organ segmentation more challenging. Additionally, existing PET organ segmentation methods rely on coregistered Computed

Hyunsoo Cho, Eunmi Kim, Ae Ja Yee

The Littlewood decomposition for partitions is a well-known bijection between partitions and pairs of $t$-core and $t$-quotient partitions. This decomposition can be described in several ways, such as the $t$-abacus method of James or the biinfinite word method of Garvan, Kim, and Stanton. In a recent study, Frobenius partitions have proven to be a highly us

Prashant Singh, Anis Biswas, Alexander Thayer, Yaroslav Mudryk

We present a new descriptor, i.e., local lattice distortion, to predict structural phase transformation in inorganic compounds containing lanthanides and transition metals. The descriptor utilizes local lattice and angular distortions obtained from structural optimization of experimentally known crystalline phases within state-of-the-art density-functional t

Jian Gao, Yuxuan Kang, Mingzhe Li, Yeheng Tong

Gravity is identical to curved spacetime. It is manifested by the curvature of a Riemannian spacetime in general relativity but by torsion or non-metricity in teleparallel gravity models. In this paper, we apply these multiple options to the spacetime perturbation theory and seek the possibilities of representing the gravitation of the background and that of

Caishan Yan, Penger Tong, Qin Xu

Solid friction between two rough surfaces is often observed to increase logarithmically over time due to contact creeping. An intriguing question is whether a similar aging effect occurs in contact line (CL) friction over rough substrates. Here, we report a systematic experimental study of CL friction using a hanging-fiber atomic force microscope (AFM) to me

Ali Bereyhi, Chongjun Ouyang, Saba Asaad, Zhiguo Ding

Pinching-antenna systems (PASSs) are a recent flexible-antenna technology that is realized by attaching simple components, referred to as pinching elements, to dielectric waveguides. This work explores the potential of deploying PASS for uplink and downlink transmission in multiuser MIMO settings. For downlink PASS-aided communication, we formulate the optim

Michael A. Fedderke, Junwu Huang, Nils Siemonsen

We study string formation and dynamics in a scalar field theory with a global $U(1)$ symmetry. If a scalar field $\Phi$ is initially displaced from the minimum of a wine-bottle potential, even if uniformly over large spatial patches, small spatial perturbations to $\Phi$ grow via parametric resonance as $\Phi$ oscillates; this occurs for a wide range of init

Kun Yang, Yuxiang Liu, Zeyu Cui, Yu Liu

Thermal infrared imaging offers the advantage of all-weather capability, enabling non-intrusive measurement of an object's surface temperature. Consequently, thermal infrared images are employed to reconstruct 3D models that accurately reflect the temperature distribution of a scene, aiding in applications such as building monitoring and energy management. H

Ayenew Yihune Demeke, Moein Younesi Heravi, Israt Sharmin Dola, Youjin Jang

Highway work zones are critical areas where accidents frequently occur, often due to the proximity of workers to heavy machinery and ongoing traffic. With technological advancements in sensor technologies and the Internet of Things, promising solutions are emerging to address these safety concerns. This paper provides a systematic review of existing studies

Chenxi Zhang, Bicheng Zhang, Dingyu Yang, Xin Peng

With the increasing complexity of modern online service systems, understanding the state and behavior of the systems is essential for ensuring their reliability and stability. Therefore, metric monitoring systems are widely used and become an important infrastructure in online service systems. Engineers usually interact with metrics data by manually writing

Tan-Hanh Pham, Jingchen Bi, Rodrigo Mesa-Arango, Kim-Doang Nguyen

Comprehensive forecasts of space tourism demand are crucial for businesses to optimize strategies and customer experiences in this burgeoning industry. Traditional methods struggle to capture the complex factors influencing an individual's decision to travel to space. In this paper, we propose an explainable and trustworthy artificial intelligence framework

Yuchuan Jiang, Chaolong Jia, Yunyi Qin, Wei Cai

The rapid proliferation of the Internet and the widespread adoption of social networks have significantly accelerated information dissemination. However, this transformation has introduced complexities in information capture and processing, posing substantial challenges for researchers and practitioners. Predicting the dissemination of topic-related informat

Wanke Xia, Ruoxin Peng, Haoqi Chu, Xinlei Zhu

Rice is a staple food for a significant portion of the world's population, providing essential nutrients and serving as a versatile in-gredient in a wide range of culinary traditions. Recently, the use of deep learning has enabled automated classification of rice, im-proving accuracy and efficiency. However, classical models based on first-stage training may

Tao Feng, Jie Zhang, Xiangjian Li, Rong Huang

Federated Learning (FL) stands as a prominent distributed learning paradigm among multiple clients to achieve a unified global model without privacy leakage. In contrast to FL, Personalized federated learning aims at serving for each client in achieving persoanlized model. However, previous FL frameworks have grappled with a dilemma: the choice between devel

Steve Andreas Immanuel, Woojin Cho, Junhyuk Heo, Darongsae Kwon

Limited data is a common problem in remote sensing due to the high cost of obtaining annotated samples. In the few-shot segmentation task, models are typically trained on base classes with abundant annotations and later adapted to novel classes with limited examples. However, this often necessitates specialized model architectures or complex training strateg

Weifan Liu

The high cost of sequential time integration is one major constraint that limits the speedup of a time-parallel algorithm like the Parareal algorithm due to the difficulty of coarsening time steps in a stiff numerical problem. To address this challenge, we develop a parallel-in-time approach based on the Parareal algorithm, in which we construct a novel coar

Wenrui Cheng, Tiantian Zhu, Shunan Jing, Jian-Ping Mei

Recently, Provenance-based Intrusion Detection Systems (PIDSes) have been widely used for endpoint threat analysis. These studies can be broadly categorized into rule-based detection systems and learning-based detection systems. Among these, due to the evolution of attack techniques, rules cannot dynamically model all the characteristics of attackers. As a r

Biwei Cao, Qihang Wu, Jiuxin Cao, Bo Liu

With the rapid development of the Internet, the information dissemination paradigm has changed and the efficiency has been improved greatly. While this also brings the quick spread of fake news and leads to negative impacts on cyberspace. Currently, the information presentation formats have evolved gradually, with the news formats shifting from texts to mult

Shujie Li, Yuxia Wu, Yuan Fang, Chuan Shi

Graph neural networks (GNNs) have demonstrated success in modeling relational data primarily under the assumption of homophily. However, many real-world graphs exhibit heterophily, where linked nodes belong to different categories or possess diverse attributes, such as webpages, Wikipedia articles, social networks, and e-commerce platforms. Additionally, nod

Yurui Chang, Bochuan Cao, Lu Lin

While large language models have demonstrated exceptional performance across a wide range of tasks, they remain susceptible to hallucinations -- generating plausible yet factually incorrect contents. Existing methods to mitigating such risk often rely on sampling multiple full-length generations, which introduces significant response latency and becomes inef

Daniel Carrera, Jeonghoon Lim, Linn E. J. Eriksson, Wladimir Lyra

One of the most important open questions in planet formation is how dust grains in a protoplanetary disk manage to overcome growth barriers and form the $\sim$100km planet building blocks that we call planetesimals. There appears to be a gap between the largest grains that can be produce by coagulation, and the smallest grains that are needed for the streami

Jinhui Zheng, Zhiquan Liu, Yain-Whar Si, Jianqing Li

Handwritten Paragraph Text Recognition (HPTR) is a challenging task in Computer Vision, requiring the transformation of a paragraph text image, rich in handwritten text, into text encoding sequences. One of the most advanced models for this task is Vertical Attention Network (VAN), which utilizes a Vertical Attention Module (VAM) to implicitly segment paragr

Chang Sun, Jennifer Ngadiuba, Maurizio Pierini, Maria Spiropulu

We explore the innovative use of MLP-Mixer models for real-time jet tagging and establish their feasibility on resource-constrained hardware like FPGAs. MLP-Mixers excel in processing sequences of jet constituents, achieving state-of-the-art performance on datasets mimicking Large Hadron Collider conditions. By using advanced optimization techniques such as

Weixing Chen, Yang Liu, Binglin Chen, Jiandong Su

Video question grounding (VideoQG) requires models to answer the questions and simultaneously infer the relevant video segments to support the answers. However, existing VideoQG methods usually suffer from spurious cross-modal correlations, leading to a failure to identify the dominant visual scenes that align with the intended question. Moreover, vision-lan

Krishangi Krishna, Jieliyue Sun, Zhaowei Jiang, Alec Mccall

The assembly of arbitrary 3D structures using nano- to micron-scale colloidal building blocks has broad applications in photonics, electronics, and biology. Combining optical tweezers (OT) with two-photon polymerization (TPP) enables 3D selective tweezing and immobilization of colloids (STIC) without requiring specialized particle functionalization. Unlike t

Zelin Meng, Takanori Fukao

Depth estimation in complex real-world scenarios is a challenging task, especially when relying solely on a single modality such as visible light or thermal infrared (THR) imagery. This paper proposes a novel multimodal depth estimation model, RTFusion, which enhances depth estimation accuracy and robustness by integrating the complementary strengths of RGB

Matthew Pearce, Lauren Pearce, Graham White, Csaba Balázs

Curvature perturbations induce gravitational waves (GWs) at second order, contributing to the stochastic gravitational wave background. The resulting gravitational wave spectrum is sensitive to the evolutionary history of the universe and can be substantially enhanced by early matter-dominated (eMD) epochs, particularly if they end rapidly. Such epochs can b

Asma A. Almutairi, David J. LeBlanc, Arpan Kusari

Generating large-scale sensing datasets through photo-realistic simulation is an important aspect of many robotics applications such as autonomous driving. In this paper, we consider the problem of synchronous data collection from the open-source CARLA simulator using multiple sensors attached to vehicle based on user-defined criteria. We propose a novel, on

Diem Thi-Xuan Dang, Dai-Nam Le, Lilia M. Woods

In this paper, we systematically investigate the structural and electronic properties of twisted h-BN bilayers to understand the role of the twisting angle. Using first-principles methods with relaxation taken into account, we simulate h-BN bilayers with commensurate supercells with the smallest angle being $2.88^{\circ}$ until $60^{\circ}$. We find that the

Qiaofeng Liu, Ian Low, Zhewei Yin

In quantum computing, non-stabilizerness -- the magic -- refers to the computational advantage of certain quantum states over classical computers and is an essential ingredient for universal quantum computation. Employing the second order stabilizer R\'enyi entropy to quantify magic, we study the production of magic states in Quantum Electrodynamics (QED) vi

Anshan Yuan, Xinghua Sun, Yayu Gao, Wen Zhan

This paper characterizes and jointly optimizes Age of Information (AoI) and energy efficiency in heterogeneous correlated random access networks, where each sensor adopts a distinct transmission probability and its observations are correlated with those of other sensors. An analytical model is proposed to analyze AoI and energy efficiency for each sensor. Cl

Xia Zhang, Leilei Wei, Ming Liu

In this paper, we first introduce the notion of a (mild) $C$-existence family in complete random normed modules, then we prove that a (mild) $C$-existence family can guarantee the existence of the (mild) solutions of the associated abstract Cauchy problem in the random setting. Second, we investigate several important properties peculiar to locally almost su

Jing-Qiang Peng, Shu Zhang, Qing-Cang Shui, Yu-Peng Chen

We study the spectral properties of the black hole X-ray transient binary 4U 1630--472 during the 2022 and 2023 outbursts with Insight-HXMT observations. We find that the outbursts are in peculiar soft states. The effect of the hardening factor on the disk temperature is taken into account by kerrbb, and the flux and temperature of the disk are found to foll

Shiyuan Zhou, Bingxuan Li, Xiyuan Chen, Zhi Tu

Image labeling is an important task for training computer vision models. In specialized domains, such as healthcare, it is expensive and challenging to recruit specialists for image labeling. We propose HEPHA, a mixed-initiative image labeling tool that elicits human expertise via inductive logic learning to infer and refine labeling rules. Each rule compris

Jing-Qiang Peng, Shu Zhang, Qing-Cang Shui, Yu-Peng Chen

The systematic properties are largely unknown for the black hole X-ray binary Swift J151857.0--572147 newly discovered in the 2024 outburst. The nature of a black hole can be completely defined by specifying the mass and dimensionless spin parameter. Therefore, accurate measurement of the two fundamental parameters is important for understanding the nature o

J. Q. Peng, S. Zhang, Y. P. Chen, L. D. Kong

By analyzing the data from Insight-HXMT and NICER, we can determine the evolution of the significance of the hard shortage in 4U 1636--536 with its spectral state, as well as the evolution of the fraction of deficit with energy. Additionally, we investigate the possible geometry and evolution of the corona in 4U 1636-536 by combining our findings with the re

Haji Gul, Ajaz Ahmad Bhat, Abdul Ghani Haji Naim

Knowledge graph completion (KGC) seeks to predict missing entities (e.g., heads or tails) or relationships in knowledge graphs (KGs), which often contain incomplete data. Traditional embedding-based methods, such as TransE and ComplEx, have improved tail entity prediction but struggle to generalize to unseen entities during testing. Textual-based models miti

Zhengyang Wang, Hao Jin, Xusheng Du, Yuxiao Ren

Generative Artificial Intelligence (AI) has advanced rapidly, enabling the generation of renderings from architectural sketches. This progress has significantly improved the efficiency of communication and conceptual expression during the early stage of architectural design. However, generated images often lack the structural details from architects' sketche

Luan Hoang, Akif Ibragimov

We generalize Einstein's probabilistic method for the Brownian motion to study compressible fluids in porous media. The multi-dimensional case is considered with general probability distribution functions. By relating the expected displacement per unit time with the velocity of the fluid, we derive an anisotropic diffusion equation in non-divergence form tha

Wenlun Zhang, Yunshan Zhong, Weiqi Yan, Shengchuan Zhang

The Segment Anything Model (SAM) has revolutionized image and video segmentation with its powerful zero-shot capabilities. However, its massive parameter scale and high computational demands hinder efficient deployment on resource-constrained edge devices. While Post-Training Quantization (PTQ) offers a practical solution, existing methods still fail to hand

Suleiman Saka, Sanchari Das

The proliferation of "Internet of Things (IoT)" provides older adults with critical support for "health monitoring" and independent living, yet significant concerns about security and privacy persist. In this paper, we report on these issues through a two-phase user study, including a survey (N = 22) and semi-structured interviews (n = 9) with adults aged 65

Benjamin Eichinger, Milivoje Lukić, Giorgio Young

A new way of encoding a non-self-adjoint Jacobi matrix $J$ by a spectral measure of $|J|$ together with a phase function was described by Pushnitski--\v Stampach in the bounded case. We present another perspective on this correspondence, based on Weyl functions instead of moments, which simplifies some proofs and generalizes the correspondence to the unbound

Yinghang Jiang, Jiguo Wu, Meng Shi, Hanqing Zheng

Rydberg atoms, with their long coherence time and large electric dipole moment, are pivotal in quantum precision measurement. In the process of approaching the standard quantum limit, higher demands are placed on detection schemes. This paper presents a scheme to amplify dispersion signal of Rydberg atomic microwave detection system, using a quantum weak mea

Ashwin Viswanathan Kannan, Johnson P Thomas, Abhimanyu Mukerji

The exponential rise in data generation has led to vast, heterogeneous datasets crucial for predictive analytics and decision-making. Ensuring data quality and semantic integrity remains a challenge. This paper presents a brain-inspired distributed cognitive framework that integrates deep learning with Hopfield networks to identify and link semantically rela

Takayuki Hibi, Adam Van Tuyl

In 2017, Ehrenborg, Govindaiah, Park, and Readdy defined the van der Waerden complex ${\tt vdW}(n,k)$ to be the simplicial complex whose facets correspond to all the arithmetic sequences on the set $\{1,\ldots,n\}$ of a fixed length $k$. To complement a classification of the Cohen--Macaulay van der Waerden complexes obtained by Hooper and Van Tuyl in 2019, a

Lirong Xia

We introduces a general linear framework that unifies the study of multi-winner voting rules and proportionality axioms, demonstrating that many prominent multi-winner voting rules-including Thiele methods, their sequential variants, and approval-based committee scoring rules-are linear. Similarly, key proportionality axioms such as Justified Representation

Ashwin Viswanathan Kannan, Madhumitha Ganesan

Understanding cognitive flexibility and task-switching mechanisms in neural systems requires biologically plausible computational models. This tutorial presents a step-by-step approach to constructing a spiking neural network (SNN) that simulates task-switching dynamics within the cognitive control network. The model incorporates biologically realistic featu

Hongjie Fang, Chenxi Wang, Yiming Wang, Jingjing Chen

Scaling up robotic imitation learning for real-world applications requires efficient and scalable demonstration collection methods. While teleoperation is effective, it depends on costly and inflexible robot platforms. In-the-wild demonstrations offer a promising alternative, but existing collection devices have key limitations: handheld setups offer limited

Ambrish Pandey, Katepalli R. Sreenivasan

We simulate thermal convection in a two-dimensional square box using the no-slip condition on all boundaries, and isothermal bottom and top walls and adiabatic sidewalls. We choose 0.1 and 1 for the Prandtl number $Pr$ and vary the Rayleigh number $Ra$ between $10^6$ and $10^{12}$. We particularly study the temporal evolution of integral transport quantities

Martin G. Herold, Danupon Nanongkai, Joachim Spoerhase, Nithin Varma

Geometric data structures have been extensively studied in the regime where the dimension is much smaller than the number of input points. But in many scenarios in Machine Learning, the dimension can be much higher than the number of points and can be so high that the data structure might be unable to read and store all coordinates of the input and query poi

Ryan Banks, Vishal Thengane, María Eugenia Guerrero, Nelly Maria García-Madueño

This study proposes a deep learning framework and annotation methodology for the automatic detection of periodontal bone loss landmarks, associated conditions, and staging. 192 periapical radiographs were collected and annotated with a stage agnostic methodology, labelling clinically relevant landmarks regardless of disease presence or extent. We propose a h

Federico A. Fogantini, Federico García, Mariano Méndez, Ole König

Cygnus X-1 is a high-mass black hole binary extensively studied since its discovery in 1964. Its rapid X-ray variability provides insights into accretion physics. Unlike other black hole X-ray binaries, its power spectra are generally featureless and modeled with two broad Lorentzians, without requiring narrow quasi-periodic oscillations. We investigate the

Jiawei Xu, Thong Vu, Diego S. D'Antonio, David Saldaña

Testing aerial robots in tasks such as pickup-and-delivery and surveillance significantly benefits from high energy efficiency and scalability of the deployed robotic system. This paper presents MochiSwarm, an open-source testbed of light-weight robotic blimps, ready for multi-robot operation without external localization. We introduce the system design in h

Samit Ganguly, Goutam Manna, Debashis Gangopadhyay, Eduardo Guendelman

We present a new avenue of the Raychaudhuri Equation (RE) by introducing a non-affine parametrization within the k-essence framework. This modification accounts for non-geodesic flow curves, leading to emergent repulsive effects in cosmic evolution. Using a DBI-type k-essence Lagrangian, we derive a modified RE and demonstrate its ability to address the Hubb

Haowei Shi, Christopher M. Jones, Mengjie Yu, Zheshen Zhang

Quantum physics has brought enhanced capability in various sensing applications. Despite challenges from noise and loss in the radio-frequency (RF) domain, [Phys. Rev. Lett. 124, 150502 (2020)] demonstrates a route for enhanced RF-receiver empowered by quantum squeezing and entanglement. In this work, we further explore the quantum advantage of imaging in th

Katharina Winter, Mark Azer, Fabian B. Flohr

Autonomous driving has the potential to set the stage for more efficient future mobility, requiring the research domain to establish trust through safe, reliable and transparent driving. Large Language Models (LLMs) possess reasoning capabilities and natural language understanding, presenting the potential to serve as generalized decision-makers for ego-moti

Zhepu Xu, Ziyi Song, Yupu Dong, Peiyan Chen

As China's road network enters the maintenance era, work zones will become a common sight on the roads. With the development of automated driving, vehicles equipped with Level 2/3 automated driving capabilities will also become a common presence on the roads. When these vehicles pass through work zones, automated driving may disengage, which can have complex

Callan M. Wood, James C. A. Miller-Jones, Arash Bahramian, Steven J. Tingay

High angular resolution radio observations of relativistic jets are necessary to understand the causal connection between accretion and jet ejection in low mass X-ray binaries. Images from these observations can be difficult to reconstruct due to the rapid intra-observational motion and variability of transient jets. We have developed a time-dependent visibi

David Sharp, Abhinav Kala, Hannah Rarick, Hao A. Nguyen

Colloidal quantum dots (QDs) are an attractive medium for nonlinear optics and deterministic heterogeneous integration with photonic devices. Their intrinsic nonlinearities can be strengthened further by coupling QDs to low mode-volume photonic nanocavities, enabling low-power, on-chip nonlinear optics. In this paper, we demonstrated cavity-enhanced second h