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Light(science & Applications) 2022年第6期11卷 1040-1058页

作者： Longqian Huang Ruichen Luo Xu Liu Xiang Hao State Key Laboratory of Modern Optical Instrumentation College of Optical Science and TechnologyZhejiang UniversityHangzhou 310027China College of Computer Science and Technology Zhejiang UniversityHangzhou 310027China Jiaxing Key Laboratory of Photonic Sensing&Intelligent Imaging Jiaxing 314000China Intelligent Optics&Photonics Research Center Jiaxing Research Institute Zhejiang UniversityJiaxing 314000China

The goal of spectral imaging is to capture the spectral signature of a *** scanning method for spectral imaging suffers from large system volume and low image acquisition speed for large *** contrast,computational spectral imaging methods have resorted to computation power for reduced system volume,but still endure long computation time for iterative spectral ***,deep learning techniques are introduced into computational spectral imaging,witnessing fast reconstruction speed,great reconstruction quality,and the potential to drastically reduce the system *** this article,we review state-of-the-art deep-learning-empowered computational spectral imaging *** are further divided into amplitude-coded,phase-coded,and wavelength-coded methods,based on different light properties used for *** boost future researches,we've also organized publicly available spectral datasets.

关键词： resort signature boost

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Energy-Efficient Multi-Trip Routing for Municipal Solid Waste Collection by Contribution-Based Adaptive Particle Swarm Optimization

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Complex System Modeling and Simulation 2023年第3期3卷 202-219页

作者： Xiaoning Shen Hongli Pan Zhongpei Ge Wenyan Chen Liyan Song Shuo Wang Collaborative Innovation Center of Atmospheric Environment and Equipment Technology and also with the Jiangsu Key Laboratory of Big Data Analysis TechnologySchool of AutomationNanjing University of Information Science and TechnologyNanjing 210044China School of Automation Nanjing University of Information Science and TechnologyNanjing 210044China Guangdong Provincial Key Laboratory of Brain-Inspired Intelligent Computation the Department of Computer Science and EngineeringSouthern University of Science and TechnologyShenzhen 518055China School of Computer Science University of BirminghamBirminghamB152TTUK

Waste collection is an important part of waste management *** costs and carbon emissions can be greatly reduced by proper vehicle ***,each vehicle can work again after achieving its capacity limit and unloading the *** this,an energy-efficient multi-trip vehicle routing model is established for municipal solid waste collection,which incorporates practical factors like the limited capacity,maximum working hours,and multiple trips of each *** both economy and environment,fixed costs,fuel costs,and carbon emission costs are minimized *** solve the formulated model effectively,contribution-based adaptive particle swarm optimization is *** strategies named greedy learning,multi-operator learning,exploring learning,and exploiting learning are specifically designed with their own searching *** assessing the contribution of each learning strategy during the process of evolution,an appropriate one is selected and assigned to each individual adaptively to improve the searching efficiency of the ***,an improved local search operator is performed on the trips with the largest number of waste sites so that both the exploiting ability and the convergence accuracy of the algorithm are *** of the proposed algorithm is tested on ten waste collection instances,which include one real-world case derived from the Green Ring Company of Jiangbei New District,Nanjing,China,and nine synthetic instances with increasing scales generated from the commonly-used capacitated vehicle routing problem benchmark *** with five state-of-the-art algorithms show that the proposed algorithm can obtain a solution with a higher accuracy for the constructed model.

关键词： municipal solid waste collection energy conservation multi-trip contribution particle swarm optimization

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An Improved Machine Learning Model for Pure Component Property Estimation

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Engineering 2024年第8期39卷 61-73页

作者： Xinyu Cao Ming Gong Anjan Tula Xi Chen Rafiqul Gani Venkat Venkatasubramanian State Key Laboratory of Industrial Control Technology College of Control Science and EngineeringZhejiang UniversityHangzhou 310027China Department of Physics Bard College at Simon’s RockGreat BarringtonMA 01230USA PSE for SPEED Company Charlottenlund DK-2920Denmark Sustainable Energy and Environment Thrust The Hong Kong University of Science and Technology(Guangzhou)Guangzhou 510000China Department of Applied Sustainability Széchenyi István UniversityGyör 9026Hungary Complex Resilient Intelligent Systems Laboratory Department of Chemical EngineeringColumbia UniversityNew YorkNJ 10027USA

Information on the physicochemical properties of chemical species is an important prerequisite when performing tasks such as process design and product ***,the lack of extensive data and high experimental costs hinder the development of prediction techniques for these ***,accuracy and predictive capabilities still limit the scope and applicability of most property estimation *** paper proposes a new Gaussian process-based modeling framework that aims to manage a discrete and high-dimensional input space related to molecular structure representation with the group-contribution approach.A warping function is used to map discrete input into a continuous domain in order to adjust the correlation between different *** selection techniques,including prior elicitation and prior predictive checking,are also applied during the building procedure to provide the model with more information from previous research *** framework is assessed using datasets of varying sizes for 20 pure component *** 18 out of the 20 pure component properties,the new models are found to give improved accuracy and predictive power in comparison with other published models,with and without machine learning.

关键词： Group contribution Gaussian process Warping function Prior predictive checking

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Deep Imputation Bi-stochastic Graph Regularized Matrix Factorization for Clustering Single-cell RNA-sequencing Data

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IEEE/ACM Transactions on Computational Biology and Bioinformatics 2024年 PP卷 1-11页

作者： Lan, Wei Chen, Jianwei Liu, Mingyang Chen, Qingfeng Liu, Jin Wang, Jianxin Chen, Yi-Ping Phoebe Wei Lan is School of Computer Electronic and Information and Guangxi Key Laboratory of Multimedia Communications and Network Technology Guangxi University Nanning Guangxi China School of Computer Electronic and Information Guangxi University Nanning Guangxi China School of Computer Electronic and Information and State Key Laboratory for Conservation and Utilization of Subtropical Agro-bioresources Guangxi University Nanning Guangxi China Hunan Provincial Key Lab on Bioinformatics School of Computer Science and Engineering Central South University Changsha Hunan China Department of Computer Science and Information Technology La Trobe University Melbourne Victoria Australia

By generating massive gene transcriptome data and analyzing transcriptomic variations at the cell level, single-cell RNA-sequencing (scRNA-seq) technology has provided new way to explore cellular heterogeneity and functionality. Clustering scRNA-seq data could discover the hidden diversity and complexity of cell populations, which can aid to the identification of the disease mechanisms and biomarkers. In this paper, a novel method (DSINMF) is presented for single cell RNA sequencing data by using deep matrix factorization. Our proposed method comprises four steps: first, the feature selection is utilized to remove irrelevant features. Then, the dropout imputation is used to handle missing value problem. Further, the dimension reduction is employed to preserve data characteristics and reduce noise effects. Finally, the deep matrix factorization with bi-stochastic graph regularization is used to obtain cluster results from scRNA-seq data. We compare DSINMF with other state-of-the-art algorithms on nine datasets and the results show our method outperformances than other methods. IEEE

关键词： RNA

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Recent Developments in Event-Triggered Control of Nonlinear systems:An Overview

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无人系统（英文） 2023年第1期11卷 27-56页

作者： Pengpeng Zhang Tengfei Liu Jie Chen Zhong-Ping Jiang Department of Control Science & Engineering Frontiers Science Center for Intelligent Autonomous SystemsTongji UniversityShanghai 201804P.R.China State Key Laboratory of Synthetical Automation for Process Industries Northeastern UniversityShenyang 110819P.R.China Department of Electrical and Computer Engineering New York University370 Jay StreetBrooklynNY 11201USA

Event-triggered control is a most popular paradigm for transferring feedback information in an economical"as needed"*** study of event-triggered control can be traced back to the *** significant advances on the topic of control over networks and the topic of nonlinear control systems over the last two decades,event-triggered control has quickly emerged as a major theoretical subject in control *** of event-triggered control are wide-spread ranging from embedded control systems and industrial control processes to unmanned systems and cyber-physical transportation *** this paper,we first review developments in the synthesis of event-triggered sampling *** event triggering mechanisms,such as static event trigger,dynamic event trigger,time-regularized event trigger,and event trigger with positive threshold offsets,are systematically ***,we study how to design a stabilizing controller that is robust with respect to the sampling ***,we review some recent results in the directions of self-triggered control,event-triggered tracking control and cooperative control,and event-triggered control of stochastic systems and partial differential equation *** applications of event-triggered control are also discussed.

关键词： Event-triggered control event-triggered sampling mechanism event-triggered controller

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Context-Driven Index Trimming: A Data Quality Perspective to Enhancing Precision of RALMs

Context-Driven Index Trimming: A Data Quality Perspective to...

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2024 Conference on Empirical Methods in Natural Language Processing, EMNLP 2024

作者： Ma, Kexin Jin, Ruochun Wang, Haotian Wang, Xi Chen, Huan Tang, Yuhua Wang, Qian Institute for Quantum Information State Key Laboratory of High Performance Computing China College of Computer Science and Technology National University of Defense Technology Changsha China Intelligent Game and Decision Lab Academy of Military Science Beijing China

ISBN: (纸本)9798891761681

Retrieval-Augmented Large Language Models (RALMs) have made significant strides in enhancing the accuracy of generated responses. However, existing research often overlooks the data quality issues within retrieval results, often caused by inaccurate existing vector-distance-based retrieval methods. We propose to boost the precision of RALMs' answers from a data quality perspective through the Context-Driven Index Trimming (CDIT) framework, where Context Matching Dependencies (CMDs) are employed as logical data quality rules to capture and regulate the consistency between retrieved contexts. Based on the semantic comprehension capabilities of Large Language Models (LLMs), CDIT can effectively identify and discard retrieval results that are inconsistent with the query context and further modify indexes in the database, thereby improving answer quality. Experiments demonstrate average improvement of 3.75% in accuracy on challenging question-answering tasks. Also, the flexibility of CDIT is verified through its compatibility with various language models and indexing methods, which offers a promising approach to bolster RALMs' data quality and retrieval precision jointly. © 2024 Association for Computational Linguistics.

关键词： Data accuracy

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Fix the Tests: Augmenting LLMs to Repair Test Cases with Static Collector and Neural Reranker 35

Fix the Tests: Augmenting LLMs to Repair Test Cases with Sta...

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35th IEEE International Symposium on Software Reliability Engineering, ISSRE 2024

作者： Liu, Jun Yan, Jiwei Xie, Yuanyuan Yan, Jun Zhang, Jian State Key Laboratory of Computer Science Institute of Software China Chinese Academy of Sciences Technology Center of Software Engineering Institute of Software China China UCAS School of Intelligent Science and Technology Hangzhou Institute for Advanced Study China

ISBN: (纸本)9798350353884

During software evolution, it is advocated that test code should co-evolve with production code. In real development scenarios, test updating may lag behind production code changing, which may cause compilation failure or bring other troubles. Existing techniques based on pre-trained language models can be directly adopted to repair obsolete tests caused by such unsynchronized code changes, especially syntactic-related ones. However, the lack of task-oriented contextual information affects the repair accuracy on large-scale projects. Starting from an obsolete test, the key challenging task is precisely identifying and constructing Test-Repair-Oriented Contexts (TROCtxs) from the whole repository within a limited token *** this paper, we propose Synter (SYNtactic-breaking- changes-induced TEst Repair), a novel approach based on LLMs to automatically repair obsolete test cases via precise and concise TROCtxs construction. Inspired by developers' programming practices, we design three types of TROCtx: class context, usage context, and environment context. Given an obsolete test case to repair, Synter firstly collects the related code information for each type of TROCtx through static analysis techniques automatically. Then, it generates reranking queries to identify the most relevant TROCtxs, which will be taken as the repair-required key contexts and be input to the large language model for the final test *** evaluate the effectiveness of Synter, we construct a benchmark dataset that contains a set of obsolete tests caused by syntactic breaking changes. The experimental results show that Synter outperforms baseline approaches both on textual- and intent-matching metrics. With the augmentation of constructed TROCtxs, hallucinations are reduced by 57.1%. © 2024 IEEE.

关键词： Syntactics

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MultiJSQ:Direct joint segmentation and quantification of left ventricle with deep multitask-derived regression network

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CAAI Transactions on Intelligence technology 2025年第1期10卷 175-192页

作者： Xiuquan Du Zheng Pei Ying Liu Xinzhi Cao Lei Li Shuo Li Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education Anhui UniversityHefeiChina School of Computer Science and Technology Anhui UniversityHefeiChina Department of Neurology Shuyang Affiliated Hospital of Nanjing University of Traditional Chinese MedicineSuqianChina Department of Biomedical Engineering Case Western Reserve UniversityClevelandOhioUSA

Quantitative analysis of clinical function parameters from MRI images is crucial for diagnosing and assessing cardiovascular ***,the manual calculation of these parameters is challenging due to the high variability among patients and the time-consuming nature of the *** this study,the authors introduce a framework named MultiJSQ,comprising the feature presentation network(FRN)and the indicator prediction network(IEN),which is designed for simultaneous joint segmentation and *** FRN is tailored for representing global image features,facilitating the direct acquisition of left ventricle(LV)contour images through pixel ***,the IEN incorporates specifically designed modules to extract relevant clinical *** authors’method considers the interdependence of different tasks,demonstrating the validity of these relationships and yielding favourable *** extensive experiments on cardiac MR images from 145 patients,MultiJSQ achieves impressive outcomes,with low mean absolute errors of 124 mm^(2),1.72 mm,and 1.21 mm for areas,dimensions,and regional wall thicknesses,respectively,along with a Dice metric score of *** experimental findings underscore the excellent performance of our framework in LV segmentation and quantification,highlighting its promising clinical application prospects.

关键词： global image features joint segmentation and quantification left ventricle(LV) multitask-derived regression network

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When LoRa meets distributed machine learning to optimize the network connectivity for green and intelligent transportation system

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Green Energy and intelligent Transportation 2024年第3期3卷 24-41页

作者： Malak Abid Ali Khan Hongbin Ma Arshad Farhad Asad Mujeeb Imran Khan Mirani Muhammad Hamza National Key Lab of Autonomous Intelligent Unmanned Systems School of AutomationBeijing Institute of TechnologyBeijing 100081China Department of Computer Science Namal UniversityPakistan Department of Electrical Engineering Tsinghua UniversityBeijing 100084China Faculty of Information Technology Beijing University of TechnologyBeijing 100124China School of Mechanical Engineering Beijing Institute of TechnologyBeijing 100081China

LoRa technology contributes to green energy by enabling efficient,long-range communication for the Internet of Things(IoT).This paper addresses the challenges related to coverage range in outdoor monitoring systems utilizing LoRa,where the network performance is affected by the density of gateways(GWs)and end devices(EDs),as well as environmental *** mitigate interference,data throughput losses,and high-power consumption,the proposed spreading factor(SF)and hybrid(data rate|SF)models dynamically adjust the transmission *** orchestration of concurrent data modifications within the network server(NS)is crucial for uninterrupted communication between GWs and EDs,especially in monitoring electric vehicle(EV)stations to reduce traffic congestion and *** K-means and density-based spatial clustering of applications with noise(DBSCAN)algorithms optimizes ED allocation,averts data congestion,and improves the signal-tointerference noise ratio(SINR).These methods ensure seamless information reception by meticulously allocated EDs across various GW *** estimate the free-space losses(FSL),a log-distance path loss model(log-PL)is *** various bandwidths(BWs),bidirectional communications,and duty cycles(DCs)helps to prevent saturation,thus prolonging the operational lifespan of *** findings reveal a notable packet rejection rate(PRR)of 0%for the DBSCAN(hybrid model).In contrast,the K-means exhibits a PRR ranging from 5%(hybrid model)to 35.29%(SF model)for the ten GWs ***,the network saturation is reduced to 10.185%and 9.503%,respectively,highlighting an improvement in the average efficiency of slotted ALOHA(91.1%)and pure ALOHA(90.7%).These enhancements increase the lifespan of EDs to 15,465.27 days.

关键词： DBSCAN Green transportation Hybrid model intelligent transportation K-means

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Improved Density Peaking Algorithm for Community Detection Based on Graph Representation Learning

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computer systems science & Engineering 2022年第12期43卷 997-1008页

作者： Jiaming Wang Xiaolan Xie Xiaochun Cheng Yuhan Wang Guangxi Key Laboratory of Embedded Technology and Intelligent System Guilin541006GuangxiChina School of Information Science and Engineering Guilin University of TechnologyGuilin541006GuangxiChina Department of Computer Science Middlesex UniversityLondon

There is a large amount of information in the network data that we canexploit. It is difficult for classical community detection algorithms to handle network data with sparse topology. Representation learning of network data is usually paired with clustering algorithms to solve the community detection ***, there is always an unpredictable distribution of class clusters outputby graph representation learning. Therefore, we propose an improved densitypeak clustering algorithm (ILDPC) for the community detection problem, whichimproves the local density mechanism in the original algorithm and can betteraccommodate class clusters of different shapes. And we study the communitydetection in network data. The algorithm is paired with the benchmark modelGraph sample and aggregate (GraphSAGE) to show the adaptability of ILDPCfor community detection. The plotted decision diagram shows that the ILDPCalgorithm is more discriminative in selecting density peak points compared tothe original algorithm. Finally, the performance of K-means and other clusteringalgorithms on this benchmark model is compared, and the algorithm is proved tobe more suitable for community detection in sparse networks with the benchmarkmodel on the evaluation criterion F1-score. The sensitivity of the parameters ofthe ILDPC algorithm to the low-dimensional vector set output by the benchmarkmodel GraphSAGE is also analyzed.

关键词： Representation learning data mining low-dimensional embedding community detection density peaking algorithm

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