March 2024 arXiv papers — page 47

Lu Lv, Dongyang Xu, Rose Qingyang Hu, Yinghui Ye

Driven by the ever-increasing requirements of ultra-high spectral efficiency, ultra-low latency, and massive connectivity, the forefront of wireless research calls for the design of advanced next generation multiple access schemes to facilitate provisioning of these stringent demands. This inspires the embrace of non-orthogonal multiple access (NOMA) in futu

Ming Zong, Jiaying Wu, Zhanyu Zhu, Jingen Ni

An efficient and accurate traffic monitoring system often takes advantages of multi-sensor detection to ensure the safety of urban traffic, promoting the accuracy and robustness of target detection and tracking. A method for target detection using Radar-Vision Fusion Path Aggregation Fully Convolutional One-Stage Network (RV-PAFCOS) is proposed in this paper

Dan Dai, Luming Yao, Yu Zhai

In this paper, we investigate a determinantal point process on the interval $(-s,s)$, associated with the confluent hypergeometric kernel. Let $\mathcal{K}^{(\alpha,\beta)}_s$ denote the trace class integral operator acting on $L^2(-s, s)$ with the confluent hypergeometric kernel. Our focus is on deriving the asymptotics of the Fredholm determinant $\det(I-\

Roshna Faeq Kakbra

This factorial experiment was conducted in a greenhouse during the period of May 3, 2021 to August 5, 2021 at the research farm belongs to the Horticulture Department, College of Agricultural Engineering Sciences, University of Sulaimani, Sulaimani, Iraq. The experiment was designed to study the effect of some biostimulants, individually and their combinatio

Suryendu Dalal, Rahul Gangopadhyay, Rajiv Raman, Saurabh Ray

Let $\Gamma$ be an arrangement of Jordan curves in the plane, i.e., simple closed curves in the plane. For any curve $\gamma \in \Gamma$, we denote the bounded region enclosed by $\gamma$ as $\tilde{\gamma}$. We say that $\Gamma$ is non-piercing if for any two curves $\alpha , \beta \in \Gamma$, $\tilde{\alpha} \,\setminus\, \tilde{\beta}$ is connected. A no

Mengyu Sun, Ziyuan Yang, Maosong Ran, Zhiwen Wang

In the fast-evolving field of medical image analysis, Deep Learning (DL)-based methods have achieved tremendous success. However, these methods require plaintext data for training and inference stages, raising privacy concerns, especially in the sensitive area of medical data. To tackle these concerns, this paper proposes a novel framework that uses surrogat

Ruizhe Long, Hu Zhou, Ying-Chang Liang

Active reconfigurable intelligent surface (RIS) has attracted significant attention in wireless communications, due to its reflecting elements (REs) capable of reflecting incident signals with not only phase shifts but also amplitude amplifications. In this paper, we are interested in active RIS-aided interference channels in which $K$ user pairs share the s

D. K. Galloway, A. J. Goodwin, T. Hilder, L. Waterson

Thermonuclear (type-I) bursts exhibit properties that depend both on the local surface conditions of the neutron stars on which they ignite, as well as the physical parameters of the host binary system. However, constraining the system parameters requires a comprehensive method to compare the observed bursts to simulations. We have further developed the bean

Xavier Guidetti, Ankita Mukne, Marvin Rueppel, Yannick Nagel

The quality of 3D prints often varies due to different conditions inherent to each print, such as filament type, print speed, and nozzle size. Closed-loop process control methods improve the accuracy and repeatability of 3D prints. However, optimal tuning of controllers for given process parameters and design geometry is often a challenge with manually tuned

Meng Wei, Zhongnian Li, Yong Zhou, Xinzheng Xu

Long-tailed data is prevalent in real-world classification tasks and heavily relies on supervised information, which makes the annotation process exceptionally labor-intensive and time-consuming. Unfortunately, despite being a common approach to mitigate labeling costs, existing weakly supervised learning methods struggle to adequately preserve supervised in

Shuaishuai Guo, Kaiqian Qu

The design of communication signal sets is fundamentally a sphere packing problem. It aims to identify a set of M points in an N -dimensional space, with the objective of maximizing the separability of points that represent different bits.In contrast, signals used for sensing targets should ideally be asdeterministic as possible. This paper explores the inhe

Nicholas J. O'Neill, Patrick M. Meyers, Andrew Melatos

In the standard two-component crust-superfluid model of a neutron star, timing noise can arise when the two components are perturbed by stochastic torques. Here it is demonstrated how to analyse fluctuations in radio pulse times of arrival with a Kalman filter to measure physical properties of the two-component model, including the crust-superfluid coupling

Sayantan Chakraborty, Rahul Jain, Pranab Sen

Pure states are an important resource in many quantum information processing protocols. However, even making a fixed pure state, say $|0\rangle$, in the laboratory requires a considerable amount of effort. Often one ends up with a mixed state $\rho$ whose classical description is nevertheless known. Hence it is important to develop protocols that extract a f

Francisco E Enríquez-Mier-y-Terán, Andre Z Kyme, Georgios Angelis, Steven R Meikle

Objective: Image reconstruction in high resolution PET scanners with depth of interaction (DOI) capability is computationally challenging due to the high sampling in detector and image space. This study evaluates the use of a virtual cylinder to reduce the number of lines of response (LOR) for DOI-based reconstruction while maintaining uniform sub-millimetre

Takuhiro Kaneko, Hirokazu Kameoka, Kou Tanaka

A generative adversarial network (GAN)-based vocoder trained with an adversarial discriminator is commonly used for speech synthesis because of its fast, lightweight, and high-quality characteristics. However, this data-driven model requires a large amount of training data incurring high data-collection costs. This fact motivates us to train a GAN-based voco

Jiawei Chen, Hongyu Lin, Xianpei Han, Yaojie Lu

Few-shot NER aims to identify entities of target types with only limited number of illustrative instances. Unfortunately, few-shot NER is severely challenged by the intrinsic precise generalization problem, i.e., it is hard to accurately determine the desired target type due to the ambiguity stemming from information deficiency. In this paper, we propose Sup

Cemal Tugrul Yilmaz, Mamadou Diagne, Miroslav Krstic

There have been recent efforts that combine seemingly disparate methods, extremum seeking (ES) optimization and partial differential equation (PDE) backstepping, to address the problem of model-free optimization with PDE actuator dynamics. In contrast to prior PDE-compensating ES designs, which only guarantee local stability around the extremum, we introduce

Yuchen Wang, Man Zhang, Yaoyu Liu, Zhikeng Zheng

Transition-metal-based layered double hydroxides (TM-LDHs) nanosheets are promising electrocatalysts in the renewable electrochemical energy conversion system, which are regarded as alternatives to noble metal-based materials. In this review, recent advances on effective and facile strategies to rationally design TM-LDHs nanosheets as electrocatalysts, such

Yuxin Zhang, Haoyu Chen, Zheng Lin, Zhe Chen

Clustered federated learning (CFL) is proposed to mitigate the performance deterioration stemming from data heterogeneity in federated learning (FL) by grouping similar clients for cluster-wise model training. However, current CFL methods struggle due to inadequate integration of global and intra-cluster knowledge and the absence of an efficient online model

Yunfei Yang, Han Feng, Ding-Xuan Zhou

We study approximation and learning capacities of convolutional neural networks (CNNs) with one-side zero-padding and multiple channels. Our first result proves a new approximation bound for CNNs with certain constraint on the weights. Our second result gives new analysis on the covering number of feed-forward neural networks with CNNs as special cases. The

Changsheng You, Yunlong Cai, Yuanwei Liu, Marco Di Renzo

To accommodate new applications such as extended reality, fully autonomous vehicular networks and the metaverse, next generation wireless networks are going to be subject to much more stringent performance requirements than the fifth-generation (5G) in terms of data rates, reliability, latency, and connectivity. It is thus necessary to develop next generatio

Kevin Exton, Maria Read

To support parallelizable serverless workflows in applications like media processing, we have prototyped a distributed scheduler called Raptor that reduces both the end-to-end delay time and failure rate of parallelizable serverless workflows. As modern serverless frameworks are typically deployed to extremely large scale distributed computing environments b

Grigorios Giotopoulos, Hisham Sati, Urs Schreiber

Flux quantization of the C-field in 11d supergravity is arguably necessary for the (UV-)completion of the theory, in that it determines the torsion charges carried by small numbers of M-branes. However, hypotheses about C-field flux-quantization ("models of the C-field") have previously been discussed only in the bosonic sector of 11d supergravity and ignori

Yuchen Wang, Yaoyu Liu, Zhiyue Zhao, Zhikeng Zheng

Electrocatalytic hydrogenation of nitrobenzene (Ph-NO2) reaction (EHNR) has been considered as a potential alternative to the traditional thermocatalytic process in the production of high-value aniline (Ph-NH2). However, due to the absence of robust catalyst and low surface H* coverage, the EHNR faces the challenges of undesired performance and indetermined

Ashima Sood, Md Sabir Ali, J. K. Singh, Sushant G. Ghosh

We obtain an exact solution of spherically symmetric Letelier AdS black holes immersed in perfect fluid dark matter (PFDM). Considering the cosmological constant as the positive pressure of the system and volume as its conjugate variable, we analyse the thermodynamics of our black holes in the extended phase space. Owing to the background clouds of strings p

Yuto Hama, Hideki Ochiai

For doubly-selective channels, delay-Doppler (DD) modulation, mostly known as orthogonal time frequency space (OTFS) modulation, enables simultaneous compensation of delay and Doppler shifts. However, OTFS modulated signal has high peak-to-average power ratio (PAPR) because of its precoding operation performed over the DD domain. In order to deal with this p

Didarul Islam, Mohammad Abdullah Al Faisal

This study examines the factors influencing the short-term real effective exchange rate (REER) in Uruguay by applying an extended Mundell-Fleming model. Analyzing the impact of the US lending rate (USLR), money supply (M2), inflation (CPI), and the world interest rate (WIR), the paper uses a linear regression model with Newey-West standard errors. Key findin

Xiang-Li Lu, Hwai-Jung Hsu, Che-Wei Chou, H. T. Kung

We describe DeepMachining, a deep learning-based AI system for online prediction of machining errors of lathe machine operations. We have built and evaluated DeepMachining based on manufacturing data from factories. Specifically, we first pretrain a deep learning model for a given lathe machine's operations to learn the salient features of machining states.

Pengna Li, Kangyi Wu, Wenli Huang, Sanping Zhou

Unsupervised person re-identification aims to retrieve images of a specified person without identity labels. Many recent unsupervised Re-ID approaches adopt clustering-based methods to measure cross-camera feature similarity to roughly divide images into clusters. They ignore the feature distribution discrepancy induced by camera domain gap, resulting in the

Shubham Mishra

This study explores the leading contributions at the next-to-leading order for the event shape variable, spherocity. Our investigation is presented through a combination of analytical derivations and graphical representations. Additionally, we delve into the intriguing behavior of the spherocity distribution, mainly as it arises from different regions of the

Alexander Iksanov, Zakhar Kabluchko, Alexander Marynych, Vitali Wachtel

We introduce multinomial and $r$-variants of several classic objects of combinatorial probability, such as the random recursive and Hoppe trees, random set partitions and compositions, the Chinese restaurant process, Feller's coupling, and some others. Just as various classic combinatorial numbers - like Stirling, Eulerian and Lah numbers - emerge as essenti

Dongjun Jang, Sungjoo Byun, Hyopil Shin

This study examines whether the attention scores between tokens in the BERT model significantly vary based on lexical categories during the fine-tuning process for downstream tasks. Drawing inspiration from the notion that in human language processing, syntactic and semantic information is parsed differently, we categorize tokens in sentences according to th

Lei Liu, Xiaoyan Yang, Fangzhou Li, Chenfei Chi

Large language models (LLMs) are gaining increasing interests to improve clinical efficiency for medical diagnosis, owing to their unprecedented performance in modelling natural language. Ensuring the safe and reliable clinical applications, the evaluation of LLMs indeed becomes critical for better mitigating the potential risks, e.g., hallucinations. Howeve

Xianchao Wu

Let $\{u_\lambda\}$ be a sequence of $L^2$-normalized Laplacian eigenfunctions on a compact two-dimensional smooth Riemanniann manifold $(M,g)$. We seek to get an $L^p$ restriction bounds of the Neumann data $ \lambda^{-1} \partial_\nu u_{\lambda}\,\vline_\gamma$ along a unit geodesic $\gamma$. Using the $T$-$T^*$ argument one can transfer the problem to an

Dongjun Jang, Sungjoo Byun, Hyemi Jo, Hyopil Shin

Instruction Tuning on Large Language Models is an essential process for model to function well and achieve high performance in specific tasks. Accordingly, in mainstream languages such as English, instruction-based datasets are being constructed and made publicly available. In the case of Korean, publicly available models and datasets all rely on using the o

Daoguang Zan, Ailun Yu, Wei Liu, Dong Chen

The impressive performance of large language models (LLMs) on code-related tasks has shown the potential of fully automated software development. In light of this, we introduce a new software engineering task, namely Natural Language to code Repository (NL2Repo). This task aims to generate an entire code repository from its natural language requirements. To

Reza Esfandiarpoor, Cristina Menghini, Stephen H. Bach

Recent works often assume that Vision-Language Model (VLM) representations are based on visual attributes like shape. However, it is unclear to what extent VLMs prioritize this information to represent concepts. We propose Extract and Explore (EX2), a novel approach to characterize textual features that are important for VLMs. EX2 uses reinforcement learning

Lin Htoo Zaw

In circuit and cavity quantum electrodynamics devices where control qubits are dispersively coupled to high-quality-factor cavities, characteristic functions of cavity states can be directly probed with conditional displacement (CD) gates. In this Letter, I propose a method to certify non-Gaussian entanglement between cavities using only CD gates and qubit r

Zhiwei Lin, Zhe Liu, Zhongyu Xia, Xinhao Wang

Three-dimensional object detection is one of the key tasks in autonomous driving. To reduce costs in practice, low-cost multi-view cameras for 3D object detection are proposed to replace the expansive LiDAR sensors. However, relying solely on cameras is difficult to achieve highly accurate and robust 3D object detection. An effective solution to this issue i

Xunjiang Gu, Guanyu Song, Igor Gilitschenski, Marco Pavone

High-definition (HD) maps have played an integral role in the development of modern autonomous vehicle (AV) stacks, albeit with high associated labeling and maintenance costs. As a result, many recent works have proposed methods for estimating HD maps online from sensor data, enabling AVs to operate outside of previously-mapped regions. However, current onli

Yosuke Bando, Ramdas Pillai, Atsushi Kajita, Farhan Abdul Hakeem

In voltage imaging, where the membrane potentials of individual neurons are recorded at from hundreds to thousand frames per second using fluorescence microscopy, data processing presents a challenge. Even a fraction of a minute of recording with a limited image size yields gigabytes of video data consisting of tens of thousands of frames, which can be time-

Junkai Chen, Zhiyuan Pan, Xing Hu, Zhenhao Li

Large language models for code (i.e., code LLMs) have shown strong code understanding and generation capabilities. To evaluate the capabilities of code LLMs in various aspects, many benchmarks have been proposed (e.g., HumanEval and ClassEval). Code reasoning is one of the most essential abilities of code LLMs, but existing benchmarks for code reasoning are

Minaoar Hossain Tanzil, Masud Sarker, Gias Uddin, Anindya Iqbal

Context: DevOps practices combine software development and IT operations. There is a growing number of DevOps related posts in popular online developer forum Stack Overflow (SO). While previous research analyzed SO posts related to build/release engineering, we are aware of no research that specifically focused on DevOps related discussions. Objective: To le

Chao-Wei Huang, Yun-Nung Chen

This paper introduces InstUPR, an unsupervised passage reranking method based on large language models (LLMs). Different from existing approaches that rely on extensive training with query-document pairs or retrieval-specific instructions, our method leverages the instruction-following capabilities of instruction-tuned LLMs for passage reranking without any

Jun Matsumoto

We provide a classification of complete improper affine spheres with singularities (say \emph{improper affine fronts}) in unimodular affine three-space $\boldsymbol{R}^3$ whose total curvature is greater than or equal to $-6\pi$, and a partial classification in the case of total curvature $-8\pi$. For the case of total curvature $-8\pi$, we give a complete c

Yongjian Zeng, Lu Lin, Di Hu, Zhiwei Jiang

The hydrodeoxygenation (HDO) of lignin-derived feedstocks into value-added chemicals with high efficiency and selectivity is desirable for the utilization of biomass resource. The complex oxygen-containing groups of lignin-derived substance result in the challenge of the low selectivity toward the required product. In this work, highly dispersed Ru nanoparti

Yue Xu, Wenjie Wang

Prompt-based learning is a new language model training paradigm that adapts the Pre-trained Language Models (PLMs) to downstream tasks, which revitalizes the performance benchmarks across various natural language processing (NLP) tasks. Instead of using a fixed prompt template to fine-tune the model, some research demonstrates the effectiveness of searching

Xiaoxuan Yu, Hao Wang, Weiming Li, Qiang Wang

Point scene understanding is a challenging task to process real-world scene point cloud, which aims at segmenting each object, estimating its pose, and reconstructing its mesh simultaneously. Recent state-of-the-art method first segments each object and then processes them independently with multiple stages for the different sub-tasks. This leads to a comple

Stanley H. Chan, Hashan K. Weerasooriya, Weijian Zhang, Pamela Abshire

Single-photon Light Detection and Ranging (LiDAR) systems are often equipped with an array of detectors for improved spatial resolution and sensing speed. However, given a fixed amount of flux produced by the laser transmitter across the scene, the per-pixel Signal-to-Noise Ratio (SNR) will decrease when more pixels are packed in a unit space. This presents

Avishkar Seth, Alice James, Endrowednes Kuantama, Richard Han

This paper proposes an Emergency Battery Service (EBS) for drones in which an EBS drone flies to a drone in the field with a depleted battery and transfers a fresh battery to the exhausted drone. The authors present a unique battery transfer mechanism and drone localization that uses the Cross Marker Position (CMP) method. The main challenges include a stabl

Zichen Wang, Seth Kurfman, Sarah Ursel, E. Johnston-Halperin

The molecular-based ferrimagnetic insulator V(TCNE)x has gained recent interest for efficient spin-wave excitation due to its low Gilbert damping ratio a=4E-5, and narrow ferromagnetic resonance linewidth f=1Oe. Here we report a clean spin pumping signal detected on V(TCNE)x/metal bilayer structures, free from spin rectification or thermal artifacts. On-chip

Zicong Fan, Takehiko Ohkawa, Linlin Yang, Nie Lin

We interact with the world with our hands and see it through our own (egocentric) perspective. A holistic 3Dunderstanding of such interactions from egocentric views is important for tasks in robotics, AR/VR, action recognition and motion generation. Accurately reconstructing such interactions in 3D is challenging due to heavy occlusion, viewpoint bias, camer

Ziyan Wang, Yingpeng Du, Zhu Sun, Haoyan Chua

Large Language Models (LLMs) are emerging as promising approaches to enhance session-based recommendation (SBR), where both prompt-based and fine-tuning-based methods have been widely investigated to align LLMs with SBR. However, the former methods struggle with optimal prompts to elicit the correct reasoning of LLMs due to the lack of task-specific feedback

Muhammad Umer, Eleftherios Mastorakis, Sofia Evangelou, Dimitris G. Angelakis

A recently proposed variational quantum algorithm has expanded the horizon of variational quantum computing to nonlinear physics and fluid dynamics. In this work, we probe the ability of such approaches to capture the ground state of the nonlinear Schr\"{o}dinger equation for a range of parameters on real superconducting quantum processors. Specifically, we

Gokul B. Nair, Michael Milford, Tobias Fischer

Event cameras are increasingly popular in robotics due to beneficial features such as low latency, energy efficiency, and high dynamic range. Nevertheless, their downstream task performance is greatly influenced by the optimization of bias parameters. These parameters, for instance, regulate the necessary change in light intensity to trigger an event, which

Eric H. C. Chow, TJ Kao, Xiaoli Li

This study delves into the potential use of large language models (LLMs) for generating Library of Congress Subject Headings (LCSH). The authors employed ChatGPT to generate subject headings for electronic theses and dissertations (ETDs) based on their titles and abstracts. The results suggests that LLMs such as ChatGPT have the potential to reduce catalogin

Shingo Takeuchi

This paper describes the canonical quantization of the U(1) gauge field across all four regions in the Rindler coordinates in the Lorentz-covariant gauge. Concretely, in the four regions (future, past, left and right Rindler-wedges) in the Rindler coordinates, the gauge-fixed Lagrangian in the Lorentz-covariant gauge is obtained, which is composed of the U(1

Sanyam Lakhanpal, Shivang Chopra, Vinija Jain, Aman Chadha

Over the past few years, Text-to-Image (T2I) generation approaches based on diffusion models have gained significant attention. However, vanilla diffusion models often suffer from spelling inaccuracies in the text displayed within the generated images. The capability to generate visual text is crucial, offering both academic interest and a wide range of prac

James T. Meech, Vasileios Tsoutsouras, Phillip Stanley-Marbell

This article presents an electron tunneling noise programmable random variate accelerator for accelerating the sampling stage of Monte Carlo simulations. We used the LiteX framework to generate a FemtoRV imfc RISC-V instruction set soft processor and deploy it on a Digilent Arty-100T FPGA development board. The RISC-V soft processor augmented with our progra

Tong Fu, Gang Luo, Jia Cheng Li, Yuan Chao Geng

Optical vortex beam with orbital angular momentum (OAM) has great potential to increase the capacity of optical communication and information processing in classical and quantum regimes. Nevertheless, important challenges that influence the optical data transmission in free space is the existence of diffusers along the optical path, which causes inevitable i

Muhammad Aneeq uz Zaman, Shubham Aggarwal, Melih Bastopcu, Tamer Başar

In this paper, we investigate the impact of introducing relative entropy regularization on the Nash Equilibria (NE) of General-Sum $N$-agent games, revealing the fact that the NE of such games conform to linear Gaussian policies. Moreover, it delineates sufficient conditions, contingent upon the adequacy of entropy regularization, for the uniqueness of the N

Aniket Datar, Chenhui Pan, Mohammad Nazeri, Anuj Pokhrel

Wheeled robots have recently demonstrated superior mechanical capability to traverse vertically challenging terrain (e.g., extremely rugged boulders comparable in size to the vehicles themselves). Negotiating such terrain introduces significant variations of vehicle pose in all six Degrees-of-Freedom (DoFs), leading to imbalanced contact forces, varying mome

Antônio Carlos Souza Ferreira Júnior, Thiago Alves Rocha

The increasing advancements in the field of machine learning have led to the development of numerous applications that effectively address a wide range of problems with accurate predictions. However, in certain cases, accuracy alone may not be sufficient. Many real-world problems also demand explanations and interpretability behind the predictions. One of th

Rui Zhong, Yuefeng Xu, Chao Zhang, Jun Yu

In this paper, we borrow the large language model (LLM) ChatGPT-3.5 to automatically and quickly design a new metaheuristic algorithm (MA) with only a small amount of input. The novel animal-inspired MA named zoological search optimization (ZSO) draws inspiration from the collective behaviors of animals for solving continuous optimization problems. Specifica

Lixi Zhu, Xiaowen Huang, Jitao Sang

Conversational Recommender System (CRS) interacts with users through natural language to understand their preferences and provide personalized recommendations in real-time. CRS has demonstrated significant potential, prompting researchers to address the development of more realistic and reliable user simulators as a key focus. Recently, the capabilities of L

Satoshi Itoh, Taro Matsuo, Motohide Tamura

Potentially habitable planets around nearby stars less massive than solar-type stars could join targets of the spectroscopy of the planetary reflected light with future space telescopes. However, the orbits of most of these planets occur near the diffraction limit for 6-m-diameter telescopes. Thus, while securing contrast-mitigation ability under a broad spe

Cui-Wen Zhang, De-Sheng Zhang, Bai-Song Xie

We present the generation of well-collimated $\gamma$-photons and pairs with extrinsic transverse orbital angular momentum (TOAM) through the head-on collision of an intense spatiotemporal optical vortex (STOV) pulse carrying intrinsic TOAM with a high-energy electron beam. It is found that the TOAM of STOV pulse remains almost unchanged, and the TOAM is con

Jiacheng Deng, Jiahao Lu, Tianzhu Zhang

Unsupervised point cloud shape correspondence aims to establish point-wise correspondences between source and target point clouds. Existing methods obtain correspondences directly by computing point-wise feature similarity between point clouds. However, non-rigid objects possess strong deformability and unusual shapes, making it a longstanding challenge to d

Yixiao Ge, Pieter van Goor, Robert Mahony

Stochastic inference on Lie groups plays a key role in state estimation problems such as; inertial navigation, visual inertial odometry, pose estimation in virtual reality, etc. A key problem is fusing independent concentrated Gaussian distributions defined at different reference points on the group. In this paper we approximate distributions at different po

Yijia Guo, Yuanxi Bai, Liwen Hu, Mianzhi Liu

As a neuromorphic sensor with high temporal resolution, spike cameras offer notable advantages over traditional cameras in high-speed vision applications such as high-speed optical estimation, depth estimation, and object tracking. Inspired by the success of the spike camera, we proposed Spike-NeRF, the first Neural Radiance Field derived from spike data, to

Xuelei Chen, Feng Gao, Fengquan Wu, Yechi Zhang

At the Royal Society meeting in 2023, we have mainly presented our lunar orbit array concept called DSL, and also briefly introduced a concept of a lunar surface array, LARAF. As the DSL concept had been presented before, in this article we introduce the LARAF. We propose to build an array in the far side of the Moon, with a master station which handles the

Xuehan Ye, Kaige Qu, Weihua Zhuang, Xuemin Shen

To maintain high perception performance among connected and autonomous vehicles (CAVs), in this paper, we propose an accuracy-aware and resource-efficient raw-level cooperative sensing and computing scheme among CAVs and road-side infrastructure. The scheme enables fined-grained partial raw sensing data selection, transmission, fusion, and processing in per-

Chengxuan Li, Di Huang, Zeyu Lu, Yang Xiao

Video generation is a rapidly advancing research area, garnering significant attention due to its broad range of applications. One critical aspect of this field is the generation of long-duration videos, which presents unique challenges and opportunities. This paper presents the first survey of recent advancements in long video generation and summarises them

Zixin Cui, Xiangling Zhuang, Seul Chan Lee, Jieun Lee

The development of a reliable and valid assessment tool of human-automation trust is an important topic. This study aimed to develop a Chinese version of human-automation trust scale (C-HATS) with reasonable reliability and validity based on Lee and See (2004)'s trust model. After three phases of assessments including exploratory factor analysis, item analys

Xin Wang, Misbah Mubarak, Yao Kang, Robert B. Ross

With the rapid growth of the machine learning applications, the workloads of future HPC systems are anticipated to be a mix of scientific simulation, big data analytics, and machine learning applications. Simulation is a great research vehicle to understand the performance implications of co-running scientific applications with big data and machine learning

Kaikang Zhao, Xi Chen, Wei Huang, Liuxin Ding

The integration of an ensemble of deep learning models has been extensively explored to enhance defense against adversarial attacks. The diversity among sub-models increases the attack cost required to deceive the majority of the ensemble, thereby improving the adversarial robustness. While existing approaches mainly center on increasing diversity in feature

Yu Nakanishi, Kazuhiro Hiwada, Yosuke Bando, Tomoya Suzuki

Locality sensitive hashing (LSH) is one of the widely-used approaches to approximate nearest neighbor search (ANNS) in high-dimensional spaces. The first work on LSH for the Euclidean distance, E2LSH, showed how ANNS can be solved efficiently at a sublinear query time in the database size with theoretically-guaranteed accuracy, although it required a large h

Kangfa Cheng, Xiaohong Zhao, Jirong Mao, Zhifu Chen

Aims. We aim to provide an explanation for the PA rotation in GRBs and find the physical conditions that lead to the rotation by 90 degrees in the toroidal magnetic-field (MF) model. Moreover, we present some observable polarization properties in the MF model that can be tested in the future. Results. We find that the PA rotation in the toroidal MF is primar

Qi Li, Ye Shi, Yuning Jiang, Yuanming Shi

The integration of various power sources, including renewables and electric vehicles, into smart grids is expanding, introducing uncertainties that can result in issues like voltage imbalances, load fluctuations, and power losses. These challenges negatively impact the reliability and stability of online scheduling in smart grids. Existing research often add

Hannah Schieber, Shiyu Li, Niklas Corell, Philipp Beckerle

In medical and industrial domains, providing guidance for assembly processes can be critical to ensure efficiency and safety. Errors in assembly can lead to significant consequences such as extended surgery times and prolonged manufacturing or maintenance times in industry. Assembly scenarios can benefit from in-situ augmented reality visualization, i.e., au

Mark Fackrell, Hritika Gupta, Peter G. Taylor

This paper addresses a fundamental and practically significant problem in call centre operations -- determining optimal call allocation policies that meet client service targets while minimising staffing costs. Motivated by a problem presented by an industry partner, we examine a real-world setting involving a relatively small call centre with hierarchical s

Xinting Liao, Weiming Liu, Chaochao Chen, Pengyang Zhou

Federated learning achieves effective performance in modeling decentralized data. In practice, client data are not well-labeled, which makes it potential for federated unsupervised learning (FUSL) with non-IID data. However, the performance of existing FUSL methods suffers from insufficient representations, i.e., (1) representation collapse entanglement amon

Xiaojie Li, Songyang Zhang, Hang Li, Xiaoyang Li

Multi-band radiomap reconstruction (MB-RMR) is a key component in wireless communications for tasks such as spectrum management and network planning. However, traditional machine-learning-based MB-RMR methods, which rely heavily on simulated data or complete structured ground truth, face significant deployment challenges. These challenges stem from the diffe

Wenhao Huang, Qianyu He, Zhixu Li, Jiaqing Liang

Definition bias is a negative phenomenon that can mislead models. Definition bias in information extraction appears not only across datasets from different domains but also within datasets sharing the same domain. We identify two types of definition bias in IE: bias among information extraction datasets and bias between information extraction datasets and in

Xinglong Sun, Haijiang Sun, Shan Jiang, Jiacheng Wang

Classification-regression prediction networks have realized impressive success in several modern deep trackers. However, there is an inherent difference between classification and regression tasks, so they have diverse even opposite demands for feature matching. Existed models always ignore the key issue and only employ a unified matching block in two task b

Yingshan Chang, Yasi Zhang, Zhiyuan Fang, Yingnian Wu

The literature on text-to-image generation is plagued by issues of faithfully composing entities with relations. But there lacks a formal understanding of how entity-relation compositions can be effectively learned. Moreover, the underlying phenomenon space that meaningfully reflects the problem structure is not well-defined, leading to an arms race for larg

Jinhua Zhu, Javier Conde, Zhen Gao, Pedro Reviriego

The wide adoption of Large language models (LLMs) makes their dependability a pressing concern. Detection of errors is the first step to mitigating their impact on a system and thus, efficient error detection for LLMs is an important issue. In many settings, the LLM is considered as a black box with no access to the internal nodes; this prevents the use of m

Jake D. Turner, Jean-Mathias Grießmeier, Philippe Zarka, Xiang Zhang

Context. Observing the radio emission from exoplanets is among the most promising methods to detect their magnetic fields and a measurement of an exoplanetary magnetic field will help constrain the planet's interior structure, star-planet interactions, atmospheric escape and dynamics, and habitability. Recently, circularly polarized bursty and slow emission

Hikaru Hoshino, Yorie Nakahira

Accurate risk quantification and reachability analysis are crucial for safe control and learning, but sampling from rare events, risky states, or long-term trajectories can be prohibitively costly. Motivated by this, we study how to estimate the long-term safety probability of maximally safe actions without sufficient coverage of samples from risky states an

Yinhui Guo, XinDa Zhou, Jie Li, Rongsheng Ba

This paper proposed a novel and temporal phase-shift digital shearography system for simultaneous measurement of first order displacement derivative in orthogonal shear directions. Dual lasers with wavelengths of 532nm and 637nm, three splitter prism structure, two dichroic mirrors with different response wavelength, and the color CMOS are used in the system

Jordan M. R. Fox, Kyle A. Wendt

We introduce a novel method for studying systematic trends in nuclear reaction data using generative adversarial networks. Libraries of nuclear cross section evaluations exhibit intricate systematic trends across the nuclear landscape, and predictive models capable of reproducing and analyzing these trends are valuable for many applications. We have develope

Matteo Capucci, Bruno Gavranović, Abdullah Malik, Francisco Rios

Categories of lenses/optics and Dialectica categories are both comprised of bidirectional morphisms of basically the same form. In this work we show how they can be considered a special case of an overarching fibrational construction, generalizing Hofstra's construction of Dialectica fibrations and Spivak's construction of generalized lenses. This constructi

Xunpeng Yi, Han Xu, Hao Zhang, Linfeng Tang

Image fusion aims to combine information from different source images to create a comprehensively representative image. Existing fusion methods are typically helpless in dealing with degradations in low-quality source images and non-interactive to multiple subjective and objective needs. To solve them, we introduce a novel approach that leverages semantic te

Zhixuan Chen, Luyang Luo, Yequan Bie, Hao Chen

Medical report generation has achieved remarkable advancements yet has still been faced with several challenges. First, the inherent imbalance in the distribution of normal and abnormal cases may lead models to exhibit a biased focus on normal samples, resulting in unreliable diagnoses. Second, the frequent occurrence of common template sentences in the repo

Zhuowan Li, Bhavan Jasani, Peng Tang, Shabnam Ghadar

Understanding data visualizations like charts and plots requires reasoning about both visual elements and numerics. Although strong in extractive questions, current chart visual question answering (chart VQA) models suffer on complex reasoning questions. In this work, we address the lack of reasoning ability by data augmentation. We leverage Large Language M

Jintong Hu, Hui Che, Zishuo Li, Wenming Yang

Ultrasound imaging is crucial for evaluating organ morphology and function, yet depth adjustment can degrade image quality and field-of-view, presenting a depth-dependent dilemma. Traditional interpolation-based zoom-in techniques often sacrifice detail and introduce artifacts. Motivated by the potential of arbitrary-scale super-resolution to naturally addre

Koretaka Yuge, Yutaro Sakamoto

For classical discrete systems under constant composition (specifically substitutional alloys), canonical average acts as a map from a set of many-body interatomic interactions to a set of configuration in thermodynamic equilibrium, which is generally nonlinear. In terms of the configurational geometry (i.e., information about configurational density of stat

Paul I. Jaffe, Gustavo X. Santiago-Reyes, Robert J. Schafer, Patrick G. Bissett

Evidence accumulation models (EAMs) are the dominant framework for modeling response time (RT) data from speeded decision-making tasks. While providing a good quantitative description of RT data in terms of abstract perceptual representations, EAMs do not explain how the visual system extracts these representations in the first place. To address this limitat

Fabian M. Faulstich, Yuehaw Khoo, Kangbo Li

We propose to improve the convergence properties of the single-reference coupled cluster (CC) method through an augmented Lagrangian formalism. The conventional CC method changes a linear high-dimensional eigenvalue problem with exponential size into a problem of determining the roots of a nonlinear system of equations that has a manageable size. However, cu

Zhongshuo Lin, Haochen Liu, Hehu Xie

In this paper, we introduce a type of tensor neural network based machine learning method to solve elliptic multiscale problems. Based on the special structure, we can do the direct and highly accurate high dimensional integrations for the tensor neural network functions without Monte Carlo process. Here, with the help of homogenization techniques, the multi