March 2024 arXiv papers — page 110

Yuanbo Gao, Peng Lin, Dongyue Wang, Feng Mei

Click-through rate (CTR) prediction is a core task in recommender systems. Existing methods (IDRec for short) rely on unique identities to represent distinct users and items that have prevailed for decades. On one hand, IDRec often faces significant performance degradation on cold-start problem; on the other hand, IDRec cannot use longer training data due to

Masato Minamitsuji, Shinji Mukohyama, Shinji Tsujikawa

We study the linear stability of spontaneously scalarized black holes (BHs) induced by a scalar field $\phi$ coupled to a Gauss-Bonnet (GB) invariant $R_{\rm GB}^2$. For the scalar-GB coupling $\xi(\phi)=(\eta/8) (\phi^2+\alpha \phi^4)$, where $\eta$ and $\alpha$ are constants, we first show that there are no angular Laplacian instabilities of even-parity pe

Jiahao Lyu, Jin Wei, Gangyan Zeng, Zeng Li

Existing scene text spotters are designed to locate and transcribe texts from images. However, it is challenging for a spotter to achieve precise detection and recognition of scene texts simultaneously. Inspired by the glimpse-focus spotting pipeline of human beings and impressive performances of Pre-trained Language Models (PLMs) on visual tasks, we ask: 1)

Wenqi Marshall Guo, Jeffrey Uhlmann

This report evaluates the efficiency of Graph Edit Distance (GED) computation for graph similarity search, comparing Cascading Metric Trees (CMT) with brute-force verification. Despite the anticipated advantages of CMT, our findings indicate it does not consistently outperform brute-force methods in speed. The study, based on graph data from PubChem, suggest

Kunie Ishioka, Oleg V. Misochko

Bismuth, with its rhombohedral crystalline structure and two Raman active phonon modes corresponding to the internal displacement ($A_{1g}$) and shear ($E_{g}$) ionic motions, offers an ideal target for the investigation of the phonon-phonon and electron-phonon couplings under photoexcitation. We perform transient reflectivity measurements of bismuth single

Eric Xue, Yijiang Li, Haoyang Liu, Peiran Wang

Dataset distillation (DD) allows datasets to be distilled to fractions of their original size while preserving the rich distributional information, so that models trained on the distilled datasets can achieve a comparable accuracy while saving significant computational loads. Recent research in this area has been focusing on improving the accuracy of models

Tao Wu, Xuewei Li, Zhongang Qi, Di Hu

Controllable spherical panoramic image generation holds substantial applicative potential across a variety of domains.However, it remains a challenging task due to the inherent spherical distortion and geometry characteristics, resulting in low-quality content generation.In this paper, we introduce a novel framework of SphereDiffusion to address these unique

Jihao Huang, Xuemin Chi, Jun Zeng, Zhitao Liu

Optimization-based approaches are widely employed to generate optimal robot motions while considering various constraints, such as robot dynamics, collision avoidance, and physical limitations. It is crucial to efficiently solve the optimization problems in practice, yet achieving rapid computations remains a great challenge for optimization-based approaches

Yanfei Li, Juejing Liu, Xiaodong Zhao, Wenjun Liu

Traditional analysis of highly distorted micro-X-ray diffraction ({\mu}-XRD) patterns from hydrothermal fluid environments is a time-consuming process, often requiring substantial data preprocessing and labeled experimental data. This study demonstrates the potential of deep learning with a multitask learning (MTL) architecture to overcome these limitations.

Jeongeun Park, Taemoon Jeong, Hyeonseong Kim, Taehyun Byun

This paper presents the design and development of an innovative interactive robotic system to enhance audience engagement using character-like personas. Built upon the foundations of persona-driven dialog agents, this work extends the agent's application to the physical realm, employing robots to provide a more captivating and interactive experience. The pro

Hyungjun Oh, Kihong Kim, Jaemin Kim, Sungkyun Kim

This paper presents ExeGPT, a distributed system designed for constraint-aware LLM inference. ExeGPT finds and runs with an optimal execution schedule to maximize inference throughput while satisfying a given latency constraint. By leveraging the distribution of input and output sequences, it effectively allocates resources and determines optimal execution c

Zhikang Wang, Yumeng Zhang, Yingxue Xu, Seiya Imoto

Histo-genomic multi-modal methods have recently emerged as a powerful paradigm, demonstrating significant potential for improving cancer prognosis. However, genome sequencing, unlike histopathology imaging, is still not widely accessible in underdeveloped regions, limiting the application of these multi-modal approaches in clinical settings. To address this,

Yang Liu, Peiran Wu, Jiayu Huo, Gongyu Zhang

Unsupervised video-based surgical instrument segmentation has the potential to accelerate the adoption of robot-assisted procedures by reducing the reliance on manual annotations. However, the generally low quality of optical flow in endoscopic footage poses a great challenge for unsupervised methods that rely heavily on motion cues. To overcome this limitat

Anna I. Polovinkina, Sergey G. Kolesnikov

This article is the third in the series. It is devoted the calculation of the structure constants for the complex simple Lie algebra of type E_6 and Chevalley commutator formulas.

Dongze Hao, Jian Jia, Longteng Guo, Qunbo Wang

Knowledge-based visual question answering (KB-VQA) is a challenging task, which requires the model to leverage external knowledge for comprehending and answering questions grounded in visual content. Recent studies retrieve the knowledge passages from external knowledge bases and then use them to answer questions. However, these retrieved knowledge passages

Yiheng Li, Hongyang Li, Zehao Huang, Hong Chang

Multi-modal 3D object detection has exhibited significant progress in recent years. However, most existing methods can hardly scale to long-range scenarios due to their reliance on dense 3D features, which substantially escalate computational demands and memory usage. In this paper, we introduce SparseFusion, a novel multi-modal fusion framework fully built

Nikhilesh Maity, Ravi Kashikar, S. Lisenkov, I. Ponomareva

Spin splitting, or removal of spin degeneracy in the electronic energy band/level is often a measure of spin-orbit coupling strength and a way to manipulate spin degrees of freedom. We use first-principles simulations to predict giant spin splitting in methylhydrazinium lead halide (MHyPbX$_3$, MHy = CH$_3$NH$_2$NH$_2$, X = Br and Cl) hybrid organic-inorgani

Kun Yue, Eardi Lila, Ali Shojaie

Motivated by the problem of inferring the graph structure of functional connectivity networks from multi-level functional magnetic resonance imaging data, we develop a valid inference framework for high-dimensional graphical models that accounts for group-level heterogeneity. We introduce a neighborhood-based method to learn the graph structure and reframe t

Nithin Parepally, Ainesh Chatterjee, Auguste Gezalyan, Hongyang Du

There are many structures, both classical and modern, involving convex polygonal geometries whose deeper understanding would be facilitated through interactive visualizations. The Ipe extensible drawing editor, developed by Otfried Cheong, is a widely used software system for generating geometric figures. One of its features is the capability to extend its f

Junpeng Hu, Shi Jin, Nana Liu, Lei Zhang

Quantum computing has emerged as a promising avenue for achieving significant speedup, particularly in large-scale PDE simulations, compared to classical computing. One of the main quantum approaches involves utilizing Hamiltonian simulation, which is directly applicable only to Schr\"odinger-type equations. To address this limitation, Schr\"odingerisation t

Vasudevarao Allu, Amal Shaji

Let $f$ be analytic in the unit disk and $\mathcal{S}$ be the subclass of normalized univalent functions with $f(0) = 0$, and $f'(0) = 1$. Let $F$ be the inverse function of $f$, given by $F(w)=w+\sum_{n=2}^{\infty}A_nw^n$ defined on some disk $|w|\le r_0(f)$. The inverse logarithmic coefficients $\Gamma_n$, $n \in \mathbb{N}$, of $f$ are defined by the equa

Eugene Jang, Jian Cui, Dayeon Yim, Youngjin Jin

Cybersecurity information is often technically complex and relayed through unstructured text, making automation of cyber threat intelligence highly challenging. For such text domains that involve high levels of expertise, pretraining on in-domain corpora has been a popular method for language models to obtain domain expertise. However, cybersecurity texts of

Sanghyeok Lee, Joonmyung Choi, Hyunwoo J. Kim

Vision Transformer (ViT) has emerged as a prominent backbone for computer vision. For more efficient ViTs, recent works lessen the quadratic cost of the self-attention layer by pruning or fusing the redundant tokens. However, these works faced the speed-accuracy trade-off caused by the loss of information. Here, we argue that token fusion needs to consider d

Rui Liu, Zhe Liu, Jianfeng Li, Yinghua Qiu

Solid-state micro/nanopores play an important role in the sensing field because of their high stability and controllable size. Aiming at problems of complex processes and high costs in pore manufacturing, we propose a convenient and low-cost micro/nanopore fabrication technique based on the needle punching method. The thin film is pierced by controlling the

M. Hiraishi, H. Okabe, A. Koda, R. Kadono

The magnetic ground state of single crystalline RuO$_2$ was investigated by the muon spin rotation/relaxation ($\mu$SR) experiment. The spin precession signal due to the spontaneous internal magnetic field $B_{\rm loc}$, which is expected in the magnetically ordered phase, was not observed in the temperature range 5--400~K. Muon sites were evaluated by first

Vidar Gudmundsson, Vram Mughnetsyan, Hsi-Sheng Goan, Jeng-Da Chai

We model the equilibrium properties of a two-dimensional electron gas in a square lateral superlattice of quantum dots in a GaAs heterostructure subject to an external homogeneous perpendicular magnetic field and a far-infrared circular cylindrical photon cavity with one quantized mode, the TE011 mode. In a truncated linear basis constructed by a tensor prod

Weijie Xu, Xianxian Jiang, Yelong Bao, Junjia Wang

With the rapid development of large-scale integrated photonics, optical phased array (OPA) is an effective way to realize highly integrated, stable and low-cost beam control system. Achieving a large field of view (FOV) in the longitudinal direction without increasing fabrication cost and system complexity is still a significant challenge in OPA antennas. He

Manoj Gupta, Manodip Routh, Manoranjan Kumar, Tanusri Saha Dasgupta

Employing first-principles modelling and many-body methods, the magnetic properties of spin-1/2 chain compound NaVOPO$_4$ are explored. The extensive first-principles calculations establish an intricate three-dimensionally coupled model that consists of weakly alternating $J$-$J^{\prime}$ antiferromagnetic chains running along cris-cross directions between t

Hao Hao Tan, Kin Wai Cheuk, Taemin Cho, Wei-Hsiang Liao

This paper presents enhancements to the MT3 model, a state-of-the-art (SOTA) token-based multi-instrument automatic music transcription (AMT) model. Despite SOTA performance, MT3 has the issue of instrument leakage, where transcriptions are fragmented across different instruments. To mitigate this, we propose MR-MT3, with enhancements including a memory rete

You-Qi Nie, Hongyi Zhou, Bing Bai, Qi Xu

Quantum randomness relies heavily on the accurate characterization of the generator implementation, where the device imperfection or inaccurate characterization can lead to incorrect entropy estimation and practical bias, significantly affecting the reliability of the generated randomness. Measurement-device-independent (MDI) quantum random number generation

Minyoung Oh, Jae-Young Sim

Lifelong person re-identification (LReID) assumes a practical scenario where the model is sequentially trained on continuously incoming datasets while alleviating the catastrophic forgetting in the old datasets. However, not only the training datasets but also the gallery images are incrementally accumulated, that requires a huge amount of computational comp

Hangjie Yi, Yuhang Ming, Dongjun Liu, Wanzeng Kong

EEG-based brainprint recognition with deep learning models has garnered much attention in biometric identification. Yet, studies have indicated vulnerability to adversarial attacks in deep learning models with EEG inputs. In this paper, we introduce a novel adversarial attack method that jointly attacks time-domain and frequency-domain EEG signals by employi

Yiyang Luo, Ke Lin, Chao Gu, Jiahui Hou

The proliferation of large language models (LLMs) in generating content raises concerns about text copyright. Watermarking methods, particularly logit-based approaches, embed imperceptible identifiers into text to address these challenges. However, the widespread usage of watermarking across diverse LLMs has led to an inevitable issue known as watermark coll

Gregory P. Sercel, Pradip R. Gatkine, Nemanja Jovanovic, Jeffrey B. Jewell

High-resolution astronomical spectroscopy carried out with a photonic Fourier transform spectrograph (FTS) requires long asymmetrical optical delay lines that can be dynamically tuned. For example, to achieve a spectral resolution of R = 30,000, a delay line as long as 1.5 cm would be required. Such delays are inherently prone to phase errors caused by tempe

Atsushi Iwaki, Chisa Hotta

For quantum many-body systems in one dimension, computational complexity theory reveals that the evaluation of ground-state energy remains elusive on quantum computers, contrasting the existence of a classical algorithm for temperatures higher than the inverse logarithm of the system size. This highlights a qualitative difference between low- and high-temper

Ke Hu, Longqing Yi

We propose utilizing a polarization-tailored high-power laser pulse to extract and accelerate electrons from the edge of a solid foil target to produce isolated attosecond electron bunches. The laser pulse consists of two orthogonally-polarized components with a time delay comparable to the pulse duration, such that the polarization in the middle of the puls

Seonghyeon Lee, Sanghwan Jang, Seongbo Jang, Dongha Lee

Auxiliary function is a helpful component to improve language model's code generation ability. However, a systematic exploration of how they affect has yet to be done. In this work, we comprehensively evaluate the ability to utilize auxiliary functions encoded in recent code-pretrained language models. First, we construct a human-crafted evaluation set, call

I-Kun Chen, Chun-Hsiung Hsia, Daisuke Kawagoe, Jhe-Kuan Su

In this article, we study the stationary Boltzmann equation with the incoming boundary condition for the hard potential cases. Assuming the smallness of the domain and a suitable normal curvature condition on the boundary, we find a suitable solution space which is a proper subset of the $W^{1,p}$ space for $1 \leq p <3$.

Mohammad Shifat E Rabbi, Naqib Sad Pathan, Shiying Li, Yan Zhuang

Learning from point sets is an essential component in many computer vision and machine learning applications. Native, unordered, and permutation invariant set structure space is challenging to model, particularly for point set classification under spatial deformations. Here we propose a framework for classifying point sets experiencing certain types of spati

Haoyu Wang, Jiazhao Wang, Demin Gao, Wenchao Jiang

Cross-technology communication(CTC) enables seamless interactions between diverse wireless technologies. Most existing work is based on reversing the transmission path to identify the appropriate payload to generate the waveform that the target devices can recognize. However, this method suffers from many limitations, including dependency on specific technol

Jun Liu, Yiming Meng, Maxwell Fitzsimmons, Ruikun Zhou

In this paper, we describe a lightweight Python framework that provides integrated learning and verification of neural Lyapunov functions for stability analysis. The proposed tool, named LyZNet, learns neural Lyapunov functions using physics-informed neural networks (PINNs) to solve Zubov's equation and verifies them using satisfiability modulo theories (SMT

Qi Jiang, Zhonghua Yi, Shaohua Gao, Yao Gao

Relying on paired synthetic data, existing learning-based Computational Aberration Correction (CAC) methods are confronted with the intricate and multifaceted synthetic-to-real domain gap, which leads to suboptimal performance in real-world applications. In this paper, in contrast to improving the simulation pipeline, we deliver a novel insight into real-wor

Anu V. S. Nath, Anubhab Roy

We investigate the dynamics of heavy inertial particles in a flow field due to an isolated, non-axisymmetric vortex. For our study, we consider a canonical elliptical vortex - the Kirchhoff vortex and its strained variant, the Kida vortex. Contrary to the anticipated centrifugal dispersion of inertial particles, which is typical in open vortical flows, we ob

The LHAASO Collaboration, Zhen Cao, F. Aharonian, Q. An

We present the measurements of all-particle energy spectrum and mean logarithmic mass of cosmic rays in the energy range of 0.3-30 PeV using data collected from LHAASO-KM2A between September 2021 and December 2022, which is based on a nearly composition-independent energy reconstruction method, achieving unprecedented accuracy. Our analysis reveals the posit

Zhennong Chen, Sekeun Kim, Hui Ren, Quanzheng Li

Accurate myocardium segmentation across all phases in one cardiac cycle in cine cardiac magnetic resonance (CMR) scans is crucial for comprehensively cardiac function analysis. Despite advancements in deep learning (DL) for automatic cine CMR segmentation, generalizability on unseen data remains a significant challenge. Recently, the segment-anything-model (

Hideki Deguchi, Kazuki Shibata, Shun Taguchi

In this paper, a method for generating a map from path information described using natural language (textual path) is proposed. In recent years, robotics research mainly focus on vision-and-language navigation (VLN), a navigation task based on images and textual paths. Although VLN is expected to facilitate user instructions to robots, its current implementa

Jun Liu, Yiming Meng, Maxwell Fitzsimmons, Ruikun Zhou

While there has been increasing interest in using neural networks to compute Lyapunov functions, verifying that these functions satisfy the Lyapunov conditions and certifying stability regions remain challenging due to the curse of dimensionality. In this paper, we demonstrate that by leveraging the compositional structure of interconnected nonlinear systems

Avishek Choudhury, Zaria Chaudhry

This paper explores the evolving relationship between clinician trust in LLMs, the transformation of data sources from predominantly human-generated to AI-generated content, and the subsequent impact on the precision of LLMs and clinician competence. One of the primary concerns identified is the potential feedback loop that arises as LLMs become more reliant

Zheng Fang, Fucai Ke, Jae Young Han, Zhijie Feng

This study addresses the challenge of forming effective groups in collaborative problem-solving environments. Recognizing the complexity of human interactions and the necessity for efficient collaboration, we propose a novel approach leveraging graph theory and reinforcement learning. Our methodology involves constructing a graph from a dataset where nodes r

Zahir Alsulaimawi

The advent of Federated Learning (FL) as a distributed machine learning paradigm has introduced new cybersecurity challenges, notably adversarial attacks that threaten model integrity and participant privacy. This study proposes an innovative security framework inspired by Control-Flow Attestation (CFA) mechanisms, traditionally used in cybersecurity, to ens

Xiangtian Xue, Jiasong Wu, Youyong Kong, Lotfi Senhadji

We present a novel image editing scenario termed Text-grounded Object Generation (TOG), defined as generating a new object in the real image spatially conditioned by textual descriptions. Existing diffusion models exhibit limitations of spatial perception in complex real-world scenes, relying on additional modalities to enforce constraints, and TOG imposes h

Pagnarasmey Pit, Xingjun Ma, Mike Conway, Qingyu Chen

Large Language Models (LLMs) have gained significant popularity for their application in various everyday tasks such as text generation, summarization, and information retrieval. As the widespread adoption of LLMs continues to surge, it becomes increasingly crucial to ensure that these models yield responses that are politically impartial, with the aim of pr

Dan-Dan Hu, Xing-Gang Wu, Long Zeng, Hai-Bing Fu

In the present paper, we study the properties of $\phi$-meson longitudinal leading-twist light-cone distribution amplitude $\phi_{2;{\phi}}^{\|}(x,\mu)$ by starting from a light-cone harmonic oscillator model for its wavefunction. To fix the input parameters, we derive the first ten $\xi$-moments of $\phi_{2;{\phi}}^{\|}(x,\mu)$ by using the QCD sum rules ap

Chong Zhang, Min Dong, Ben Liang, Ali Afana

Next-generation wireless networks need to handle massive user access effectively. This paper addresses the problem of joint group scheduling and multicast beamforming for downlink transmission with many active user groups. Aiming to maximize the minimum user throughput, we propose a three-phase approach to tackle this difficult joint optimization problem eff

Jingyi Xu, Weidong Yang, Lingdong Kong, Youquan Liu

Unsupervised domain adaptation (UDA) is vital for alleviating the workload of labeling 3D point cloud data and mitigating the absence of labels when facing a newly defined domain. Various methods of utilizing images to enhance the performance of cross-domain 3D segmentation have recently emerged. However, the pseudo labels, which are generated from models tr

Zahir Alsulaimawi

In the evolving landscape of Federated Learning (FL), the challenge of ensuring data integrity against poisoning attacks is paramount, particularly for applications demanding stringent privacy preservation. Traditional anomaly detection strategies often struggle to adapt to the distributed nature of FL, leaving a gap our research aims to bridge. We introduce

Maksym Mohorian, Devika Kamath, Meghna Menon, Paolo Ventura

Dusty post-red giant branch (post-RGB) stars are low- and intermediate-mass stars where the RGB evolution was prematurely terminated by a poorly understood binary interaction. These binary stars are considered to be low-luminosity analogues of post-asymptotic giant branch (post-AGB) binary stars. In this study, we investigated the chemical composition of two

Bo Li

This paper provides the first causal evidence that credit supply expansion caused the 1999-2010 U.S. business cycle mainly through the channel of household leverage (debt-to-income ratio). Specifically, induced by net export growth, credit expansion in private-label mortgages, rather than government-sponsored enterprise mortgages, causes a much stronger boom

Zhixing Hou, Yuzhang Shang, Yan Yan

This paper presents a novel Fully Binary Point Cloud Transformer (FBPT) model which has the potential to be widely applied and expanded in the fields of robotics and mobile devices. By compressing the weights and activations of a 32-bit full-precision network to 1-bit binary values, the proposed binary point cloud Transformer network significantly reduces th

Xiang Dai, Sarvnaz Karimi, Nathan O'Callaghan

Electronic health records include information on patients' status and medical history, which could cover the history of diseases and disorders that could be hereditary. One important use of family history information is in precision health, where the goal is to keep the population healthy with preventative measures. Natural Language Processing (NLP) and mach

Yu Du, Yu Song, Ce Guo, Xiaojing Tian

Due to their complex spatial structure and diverse geometric features, achieving high-precision and robust point cloud registration for complex Die Castings has been a significant challenge in the die-casting industry. Existing point cloud registration methods primarily optimize network models using well-established high-quality datasets, often neglecting pr

Paul Alexander Helminck

Let $p\neq{2,3}$ be a prime number and let $\Gamma \subset \mathrm{SL}_{2}(\mathbb{Z})$ be a congruence subgroup with modular curve $X_{\Gamma}/K$ and Jacobian $J(X_{\Gamma})$. In this paper we give an explicit group-theoretic description of the semistable toric rank and component group of $J(X_{\Gamma})$ at the finite places of $K$ lying over $p$. We first

Justin Yirka

We give a corrected proof that if PP $\subseteq$ BQP/qpoly, then the Counting Hierarchy collapses, as originally claimed by [Aaronson 2006 arXiv:cs/0504048]. This recovers the related unconditional claim that PP does not have circuits of any fixed size $n^k$ even with quantum advice. We do so by proving that YQP*, an oblivious version of (QMA $\cap$ coQMA),

Zhiqiang Pang, Hong Wang, Qi Xie, Deyu Meng

Exploring and modeling rain generation mechanism is critical for augmenting paired data to ease training of rainy image processing models. Against this task, this study proposes a novel deep learning based rain generator, which fully takes the physical generation mechanism underlying rains into consideration and well encodes the learning of the fundamental r

Inhyeok Choi

Let $G$ be a countable group whose action on a metric space $X$ involves a contracting isometry. This setting naturally encompasses groups acting on Gromov hyperbolic spaces, Teichm{\"u}ller space, Culler-Vogtmann Outer space and CAT(0) spaces. We discuss continuity and differentiability of the escape rate of random walks on $G$. For relatively hyperbolic gr

Hai Xue, Yun Xia, Neal N. Xiong, Di Zhang

Mobile edge computing (MEC) paves the way to alleviate the burden of energy and computation of mobile users (MUs) by offloading tasks to the network edge. To enhance the MEC server utilization by optimizing its resource allocation, a well-designed pricing strategy is indispensable. In this paper, we consider the edge offloading scenario with energy harvestin

Yihuai Gao, Yukai Tang, Han Qi, Heng Yang

We investigate uncertainty quantification of 6D pose estimation from learned noisy measurements (e.g. keypoints and pose hypotheses). Assuming unknown-but-bounded measurement noises, a pose uncertainty set (PURSE) is a subset of SE(3) that contains all possible 6D poses compatible with the measurements. Despite being simple to formulate and its ability to em

Chenjie Fan, Rowan Killip, Monica Visan, Zehua Zhao

We prove dispersive decay, pointwise in time, for solutions to the mass-critical nonlinear Schr\"odinger equation in spatial dimensions $d=1,2,3$.

Usama Ali, Lan Wu, Adrian Mueller, Fouad Sukkar

Human-robot collaborative applications require scene representations that are kept up-to-date and facilitate safe motions in dynamic scenes. In this letter, we present an interactive distance field mapping and planning (IDMP) framework that handles dynamic objects and collision avoidance through an efficient representation. We define interactive mapping and

Xin Sun, Rongjun Ma, Xiaochang Zhao, Zhuying Li

People increasingly rely on online sources for health information seeking due to their convenience and timeliness, traditionally using search engines like Google as the primary search agent. Recently, the emergence of generative Artificial Intelligence (AI) has made Large Language Model (LLM) powered conversational agents such as ChatGPT a viable alternative

Maya De Los Santos, Kimberly Do, Michael Muller, Saiph Savage

As independently-contracted employees, gig workers disproportionately suffer the consequences of workplace surveillance, which include increased pressures to work, breaches of privacy, and decreased digital autonomy. Despite the negative impacts of workplace surveillance, gig workers lack the tools, strategies, and workplace social support to protect themsel

Tsuyoshi Miezaki, Akihiro Munemasa, Yusaku Nishimura, Tadashi Sakuma

In the present paper, we introduce the concept of universal graph series. We then present four invariants of graphs and discuss some of their properties. In particular, one of these invariants is a generalization of the chromatic symmetric function and a complete invariant for graphs.

Xiaotian Hou, Linjun Zhang, Peng Wang, Min-ge Xie

This paper presents a novel method to make statistical inferences for both the model support and regression coefficients in a high-dimensional logistic regression model. Our method is based on the repro samples framework, in which we conduct statistical inference by generating artificial samples mimicking the actual data-generating process. The proposed meth

Junlong Yang

Simultaneous wireless information and power transfer (SWIPT) is an effective energy-saving technology, but its efficiency is hindered by environmental factors. The introduction of reconfigurable intelligent surfaces (RIS) has alleviated this issue, although it still faces significant constraints due to geographical limitations. This paper proposes a scheme t

Ryoichi Saito, Takashi Mukaiyama

Interferometers, which are built using spatially propagating light or matter waves, are commonly used to measure physical quantities. These measurements are made possible by exploiting the interference between waves traveling along different paths. This study introduces a novel approach to sensing of the Aharonov--Bohm phase, an ion matter-wave interferomete

Zhiqi Li, Yiming Chen, Lingzhe Zhao, Peidong Liu

While text-to-3D and image-to-3D generation tasks have received considerable attention, one important but under-explored field between them is controllable text-to-3D generation, which we mainly focus on in this work. To address this task, 1) we introduce Multi-view ControlNet (MVControl), a novel neural network architecture designed to enhance existing pre-

Ananditha Raghunath, Alexander Metzger, Hans Easton, XunMei Liu

Although farmers in Sub-Saharan Africa are accessing feature phones and smartphones at historically high rates, they face challenges finding a robust network of agricultural contacts. With collaborators, we conduct a quantitative survey of 1014 agricultural households in Kagera, Tanzania to characterize technology access, use, and comfort levels in the regio

Jie Wang, Qian Zhang, Ya-Feng Jiao, Sheng-Dian Zhang

Cavity optomechanical (COM) sensors, featuring efficient light-motion couplings, have been widely used for ultra sensitive measurements of various physical quantities ranging from displacements to accelerations or weak forces. Previous works, however, have mainly focused on reciprocal COM systems. Here, we propose how to further improve the performance of qu

Balint Pato, Theerapat Tansuwannont, Kenneth R. Brown

A fault-tolerant error correction (FTEC) protocol with a high error suppression rate and low overhead is very desirable for the near-term implementation of quantum computers. In this work, we develop a distance-preserving flag FTEC protocol for the [[49,1,9]] concatenated Steane code, which requires only two ancilla qubits per generator and can be implemente

Xiaohuan Pei, Tao Huang, Chang Xu

Prior efforts in light-weight model development mainly centered on CNN and Transformer-based designs yet faced persistent challenges. CNNs adept at local feature extraction compromise resolution while Transformers offer global reach but escalate computational demands $\mathcal{O}(N^2)$. This ongoing trade-off between accuracy and efficiency remains a signifi

Yucen Wang, Shenghua Wan, Le Gan, Shuai Feng

Model-based methods have significantly contributed to distinguishing task-irrelevant distractors for visual control. However, prior research has primarily focused on heterogeneous distractors like noisy background videos, leaving homogeneous distractors that closely resemble controllable agents largely unexplored, which poses significant challenges to existi

Yi Xu, Kunyu Peng, Di Wen, Ruiping Liu

Understanding human actions from body poses is critical for assistive robots sharing space with humans in order to make informed and safe decisions about the next interaction. However, precise temporal localization and annotation of activity sequences is time-consuming and the resulting labels are often noisy. If not effectively addressed, label noise negati

Enguang Wang, Zhimao Peng, Zhengyuan Xie, Fei Yang

Given unlabelled datasets containing both old and new categories, generalized category discovery (GCD) aims to accurately discover new classes while correctly classifying old classes. Current GCD methods only use a single visual modality of information, resulting in a poor classification of visually similar classes. As a different modality, text information

Xiaohang Yu, Zhengxian Yang, Shi Pan, Yuqi Han

We have built a custom mobile multi-camera large-space dense light field capture system, which provides a series of high-quality and sufficiently dense light field images for various scenarios. Our aim is to contribute to the development of popular 3D scene reconstruction algorithms such as IBRnet, NeRF, and 3D Gaussian splitting. More importantly, the colle

Jinxia Xie, Bineng Zhong, Zhiyi Mo, Shengping Zhang

The rich spatio-temporal information is crucial to capture the complicated target appearance variations in visual tracking. However, most top-performing tracking algorithms rely on many hand-crafted components for spatio-temporal information aggregation. Consequently, the spatio-temporal information is far away from being fully explored. To alleviate this is

Moxin Li, Wenjie Wang, Fuli Feng, Fengbin Zhu

Self-detection for Large Language Models (LLMs) seeks to evaluate the trustworthiness of the LLM's output by leveraging its own capabilities, thereby alleviating the issue of output hallucination. However, existing self-detection approaches only retrospectively evaluate answers generated by LLM, typically leading to the over-trust in incorrectly generated an

Siyu Teng, Xuan Li, Yucheng Li, Zhe Xuanyuan

In recent years, open-pit mining has seen significant advancement, the cooperative operation of various specialized machinery substantially enhancing the efficiency of mineral extraction. However, the harsh environment and complex conditions in open-pit mines present substantial challenges for the implementation of autonomous transportation systems. This res

Weixiang Sun, Yixin Liu, Zhiling Yan, Kaidi Xu

The rapid expansion of AI in healthcare has led to a surge in medical data generation and storage, boosting medical AI development. However, fears of unauthorized use, like training commercial AI models, hinder researchers from sharing their valuable datasets. To encourage data sharing, one promising solution is to introduce imperceptible noise into the data

Xiajun Jiang, Sumeet Vadhavkar, Yubo Ye, Maryam Toloubidokhti

Personalized virtual heart models have demonstrated increasing potential for clinical use, although the estimation of their parameters given patient-specific data remain a challenge. Traditional physics-based modeling approaches are computationally costly and often neglect the inherent structural errors in these models due to model simplifications and assump

Mengying Lin, Shugao Liu, Dingxi Zhang, Yaran Chen

Object-goal navigation requires mobile robots to efficiently locate targets with visual and spatial information, yet existing methods struggle with generalization in unseen environments. Heuristic approaches with naive metrics fail in complex layouts, while graph-based and learning-based methods suffer from environmental biases and limited generalization. Al

Eric Gaidos, Thanawuth Thanathibodee, Andrew Hoffman, Joel Ong

Transition disks, with inner regions depleted in dust and gas, could represent later stages of protoplanetary disk evolution when newly-formed planets are emerging. The PDS 70 system has attracted particular interest because of the presence of two giant planets at tens of au orbits within the inner disk cavity, at least one of which is itself accreting. Howe

Siyu Teng, Xuan Li, Yuchen Li, Lingxi Li

One critical bottleneck that impedes the development and deployment of autonomous transportation in open-pit mines is guaranteed robustness and trustworthiness in prohibitively extreme scenarios. In this research, a novel scenarios engineering (SE) methodology for the autonomous mining truck is proposed for open-pit mines. SE increases the trustworthiness an

Jinluan Yang, Ruihao Zhang, Zhengyu Chen, Teng Xiao

This paper studies the problem of distribution shifts on non-homophilous graphs Mosting existing graph neural network methods rely on the homophilous assumption that nodes from the same class are more likely to be linked. However, such assumptions of homophily do not always hold in real-world graphs, which leads to more complex distribution shifts unaccounte

Xiaocai Zhang, Xiuju Fu, Zhe Xiao, Haiyan Xu

This paper investigates the prediction of vessels' arrival time to the pilotage area using multi-data fusion and deep learning approaches. Firstly, the vessel arrival contour is extracted based on Multivariate Kernel Density Estimation (MKDE) and clustering. Secondly, multiple data sources, including Automatic Identification System (AIS), pilotage booking in

Rui-Jing Lu, Wen-Hao Chen, Wen-Qiang Liang, Cheng-Feng Peng

The properties of the progenitors of gamma-ray bursts (GRBs) and of their environment are encoded in their luminosity function and cosmic formation rate. They are usually recovered from a flux-limited sample based on Lynden-Bell's $c^{-}$ method. However, this method is based on the assumption that the luminosity is independent of the redshift. Observational

Minseok Kim, Namjo Ahn, Song Min Kim

Metasurface has recently emerged as an economic solution to expand mmWave coverage. However, their pervasive deployment remains a challenge, mainly due to the difficulty in reaching the tight 260ns NR synchronization requirement and real-time wireless reconfiguration while maintaining multi-year battery life. This paper presents NR-Surface, the first real-ti

Rakshak Adhikari, Govind Menon, Mikhail V. Medvedev

Force-free electrodynamics is the theoretical paradigm used to describe electromagnetic fields in a region where the inertia of plasma is negligible compared to the strength of the electromagnetic field. While these fields are studied extensively around accreting black holes in an attempt to describe energy extraction, force-free fields also routinely appear

Adrià Tauste Campo, Antonio Zainos, Yuriria Vázquez, Raul Adell Segarra

The brain is hierarchically organized to process sensory signals. But, to what extent do functional connections within and across areas shape this hierarchical order? We addressed this problem in the thalamocortical network, while monkeys judged the presence or absence of a vibrotactile stimulus. We quantified the variability by means of intrinsic timescales

Zixin Yang, Richard Simon, Kelly Merrell, Cristian. A. Linte

In image-guided liver surgery, 3D-3D non-rigid registration methods play a crucial role in estimating the mapping between the preoperative model and the intraoperative surface represented as point clouds, addressing the challenge of tissue deformation. Typically, these methods incorporate a biomechanical model, represented as a finite element model (FEM), in

Ziyang Xu, Keqin Peng, Liang Ding, Dacheng Tao

Recent research shows that pre-trained language models (PLMs) suffer from "prompt bias" in factual knowledge extraction, i.e., prompts tend to introduce biases toward specific labels. Prompt bias presents a significant challenge in assessing the factual knowledge within PLMs. Therefore, this paper aims to improve the reliability of existing benchmarks by tho