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Two drifting paths of Sargassum bloom in the Yellow Sea and East China Sea during 2019-2020

Acta Oceanologica Sinica 2022年第6期41卷 78-87页

作者： Chao Yuan Jie Xiao Xuelei Zhang Mingzhu Fu Zongling Wang The MNR Key Laboratory of Marine Eco-Environmental Science and Technology First Institute of OceanographyMinistry of Natural ResourcesQingdao 266061China Laboratory of Marine Ecology and Environmental Science Pilot National Laboratory for Marine Science and Technology(Qingdao)Qingdao 266237China National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology Xi'an 710129China

The macroalgal blooms of floating brown algae Sargassum horneri are increasing in the Yellow Sea and East China Sea during the past few ***,the annual pattern of Sargassum bloom is not well *** study the developing pattern and explore the impacts from hydro-meteorologic environment,high resolution satellite imageries were used to monitor the distribution,coverage and drifting of the pelagic Sargassum rafts in the Yellow Sea and East China Sea from September 2019 to August *** blooms were detected from October 2019 to June 2020 and presented two successive drifting paths that both initiated from around 37°*** first path spanned smaller spatial scale and shorter period,starting with a bloom of 3 km^(2) distribution area near the eastern tip of Shandong Peninsula in late October 2019 and drifted southwards,hit the Pyropia aquaculture area in early January 2020,then vanished in the northwest of East China Sea(ca.32°N)around end of *** second path began with a large distribution area of 23000 km^(2) east of 123°E in late January 2020,firstly moved southwards in the central Yellow Sea and northern East China Sea(north of 29°N)till late April,then turned northwards with monsoon wind and vanished from late June to *** mean sea surface temperature of 8℃ to 20℃ in the Sargassum bloom areas corresponded to in situ observed temperature range for vegetative growth and floating of *** was no observed floating Sargassum blooms during July through September in the Yellow Sea and East China *** results indicate that floating *** is unable to complete life cycle in the Yellow Sea and East China Sea,and provide insights to the future management of Sargassum *** studies are needed to validate the pattern and source of annual Sargassum bloom in the Yellow Sea and East China Sea.

关键词： Sargassum horneri macroalgal bloom high resolution remote sensing wind directions sea surface temperature Yellow Sea and East China Sea

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Quantum-Inspired Distributed Memetic Algorithm

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Complex System Modeling and Simulation 2022年第4期2卷 334-353页

作者： Guanghui Zhang Wenjing Ma keyi Xing Lining Xing Kesheng Wang the School of Information Science and Technology and the Hebei Key Laboratory of Agricultural Big Data Hebei Agricultural UniversityBaoding 071001China the School of Information Science and Technology Hebei Agricultural UniversityBaoding 071001China. the State Key Laboratory for Manufacturing System Engineering and the Systems Engineering Institute Xi’an Jiaotong UniversityXi’an 710049China the School of Electronic Xidian UniversityXi’an 710071China the Department of Production and Quality Engineering Norwegian University of Science and TechnologyTrondheim 7491Norway

This paper proposed a novel distributed memetic evolutionary model,where four modules distributed exploration,intensified exploitation,knowledge transfer,and evolutionary restart are coevolved to maximize their strengths and achieve superior global *** exploration evolves three independent populations by heterogenous *** exploitation evolves an external elite archive in parallel with exploration to balance global and local *** transfer is based on a point-ring communication topology to share successful experiences among distinct search *** restart adopts an adaptive perturbation strategy to control search diversity *** computation is a newly emerging technique,which has powerful computing power and parallelized ***,this paper further fuses quantum mechanisms into the proposed evolutionary model to build a new evolutionary algorithm,referred to as quantum-inspired distributed memetic algorithm(QDMA).In QDMA,individuals are represented by the quantum characteristics and evolved by the quantum-inspired evolutionary optimizers in the quantum *** QDMA integrates the superiorities of distributed,memetic,and quantum *** experiments are carried out to evaluate the superior performance of *** results demonstrate the effectiveness of special designs and show that QDMA has greater superiority compared to the compared state-of-the-art algorithms based on Wilcoxon’s rank-sum *** superiority is attributed not only to good cooperative coevolution of distributed memetic evolutionary model,but also to superior designs of each special component.

关键词： distributed evolutionary algorithm memetic algorithm quantum-inspired evolutionary algorithm quantum distributed memetic algorithm

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Amplitude-Invariant Phase Shifter Based on Carrier-Injection Mechanism for Optical Phased Array

Amplitude-Invariant Phase Shifter Based on Carrier-Injection...

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CLEO: Applications and Technology in CLEO 2024, CLEO: A and T 2024 - Part of Conference on Lasers and Electro-Optics

作者： Yuan, Qiqi Xu, Weihan Ran, Shihuan Yang, Yunhong Lu, Liangjun Chen, Jianping Zhou, Linjie State Key Laboratory of Advanced Optical Communication Systems and Networks Shanghai Key Lab of Navigation and Location Services Shanghai Institute for Advanced Communication and Data Science Department of Electronic Engineering Shanghai Jiao Tong University Shanghai200240 China SJTU-Pinghu Institute of Intelligent Optoelectronics Pinghu314200 China

We present an amplitude-invariant phase shifter based on a PIN diode embedded in a Mach-Zehnder Interferometer for optical phased arrays. The dynamic power contrast is lower than 0.42 dB over a p phase shift. © O... 详细信息

关键词： Phase shifters

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Maximum Entropy Inverse Reinforcement Learning based Trajectory Planning for Autonomous Driving 3

Maximum Entropy Inverse Reinforcement Learning based Traject...

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3rd International Conference on High Performance Computing and Communication engineering, HPCCE 2023

作者： Zhang, Puhao Xie, Shumin Lu, Xiaoya Zhong, Zuodong Li, Qing School of Automation and Electrical Engineering Beijing University of Science and Technology 30 Xueyuan Road Haidian District Beijing100083 China School of Mechanics and Civil Engineering and Architecture Northwestern Polytechnical University 127 Youyi West Road Xi'an710072 China Key Laboratory of Knowledge Automation for Industrial Processes Ministry of Education 30 Xueyuan Road Haidian District Beijing100083 China

ISBN: (数字)9781510674653

ISBN: (纸本)9781510674646

With the rapid advancement of autonomous driving technology, effective trajectory planning has become crucial for ensuring road safety and driving efficiency. Traditional trajectory planning methods often rely on preset rules and models, making them ill-suited for the complex and dynamic traffic environment. To address this, a Maximum Entropy Inverse Reinforcement Learning (MaxEnt IRL) based trajectory planning method is proposed in this paper, aiming at learning from expert driving behaviors to infer an efficient reward function, which in turn guides decision-making and path planning for autonomous vehicles. This study begins by analyzing expert driving data to extract key state and action features. Then, the MaxEnt IRL algorithm is applied to learn the reward function underlying these features, reflecting the decision-making logic of expert drivers. The learned reward function is subsequently used to guide the trajectory planning of the autonomous driving system, generating safe and efficient driving paths. A series of experiments conducted in a simulated environment demonstrate that the MaxEnt IRL-based method proposed in this paper exhibits higher adaptability and efficiency in handling complex traffic scenarios compared to traditional trajectory planning methods. © 2024 SPIE.

关键词： Decision making

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Beam Scanning for Integrated Sensing and Communication in IRS-aided mmWave Systems 24

Beam Scanning for Integrated Sensing and Communication in IR...

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24th IEEE International Workshop on Signal Processing Advances in Wireless Communications, SPAWC 2023

作者： Li, Renwang Shao, Xiaodan Sun, Shu Tao, Meixia Zhang, Rui Shanghai Jiao Tong University Department of Electronic Engineering Shanghai China Shanghai Jiao Tong University Shanghai Key Laboratory of Digital Media Processing and Transmission Shanghai China The Chinese University of HongKong School of Science and Engineering Shenzhen Research Institute of Big Data Shenzhen China National University of Singapore Department of Electrical and Computer Engineering Singapore

ISBN: (纸本)9781665496261

This paper investigates an intelligent reflecting surface (IRS) aided millimeter-wave integrated sensing and communication (ISAC) system. Specifically, based on the passive beam scanning in the downlink, the IRS finds the optimal beam for reflecting the signals from the base station to a communication user. Meanwhile, the IRS estimates the angle of a nearby target based on its echo signal received by the sensing elements mounted on the IRS (i.e., semi-passive IRS). We propose an ISAC protocol for achieving the above objective via simultaneous (beam) training and sensing (STAS). Then, we derive the achievable rate of the communication user and the Cramer-Rao bound (CRB) of the angle estimation for the sensing target in closed-form. The achievable rate and CRB exhibit different performance against the duration of beam scanning. Specifically, the average achievable rate initially rises and subsequently declines, while the CRB monotonically decreases. Consequently, the duration of beam scanning should be carefully selected to balance communication and sensing performance. Simulation results have verified our analytical findings and shown that, thanks to the efficient use of downlink beam scanning signal for simultaneous communication and target sensing, the STAS protocol outperforms the benchmark protocol with orthogonal beam training and sensing. © 2023 IEEE.

关键词： Millimeter waves

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Ppt4j: Patch Presence Test for Java Binaries 24

Ppt4j: Patch Presence Test for Java Binaries

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44th ACM/IEEE International Conference on Software engineering, ICSE 2024

作者： Pan, Zhiyuan Hu, Xing Xia, Xin Zhan, Xian Lo, David Yang, Xiaohu Zhejiang University The State Key Laboratory of Blockchain and Data Security Hangzhou China Huawei Software Engineering Application Technology Lab Hangzhou China The Hong Kong Polytechnic University Hong Kong School of Computing and Information Systems Singapore Management University Singapore

ISBN: (纸本)9798400702174

The number of vulnerabilities reported in open source software has increased substantially in recent years. Security patches provide the necessary measures to protect software from attacks and vulnerabilities. In practice, it is difficult to identify whether patches have been integrated into software, especially if we only have binary files. Therefore, the ability to test whether a patch is applied to the target binary, a.k.a. patch presence test, is crucial for practitioners. However, it is challenging to obtain accurate semantic information from patches, which could lead to incorrect results. In this paper, we propose a new patch presence test framework named Ppt4j (Patch Presence Test forJava Binaries). Ppt4j is designed for open-source Java libraries. It takes Java binaries (i.e. bytecode files) as input, extracts semantic information from patches, and uses feature-based techniques to identify patch lines in the binaries. To evaluate the effectiveness of our proposed approach Ppt4j, we construct a dataset with binaries that include 110 vulnerabilities. The results show that Ppt4j achieves an F1 score of 98.5% with reasonable efficiency, improving the baseline by 14.2%. Furthermore, we conduct an in-the-wild evaluation of Ppt4j on JetBrains IntelliJ IDEA. The results suggest that a third-party library included in the software is not patched for two CVEs, and we have reported this potential security problem to the vendor. © 2024 ACM.

关键词： Open source software

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Cross-modal image text retrieval based on Cross-optimization and Joint strategy

Cross-modal image text retrieval based on Cross-optimization...

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2024 International Conference on Image, Signal Processing, and Pattern Recognition, ISPP 2024

作者： Zheng, Wei Han, Na Hu, Yan Kang, Peipei Zhang, Yue Yang, Aqing Zhang, Lei School of Computer Science Guangdong Polytechnic Normal University China Guangdong Provincial Key Laboratory of Intellectual Property and Big Data China School of Computer Science and Technology Guangdong University of Technology China School of Information Engineering East University of Heilongjiang China

ISBN: (纸本)9781510680425

In the fields of computer vision and natural language processing, cross-modal retrieval is of great importance that cannot be ignored. In existing multi-granularity alignment methods, significant progress has been made by globally aligning images and sentences or locally aligning regions and words. However, most of these methods rely on attention mechanisms, which may be affected by noise and cannot balance the differences between modalities, resulting in suboptimal image-text alignment. In addition, training with attention mechanisms consumes a lot of computational resources, and the retrieval process is time-consuming. To address these challenges, we develop a novel multigranularity image-text alignment model, which we call DFVLM. First, we independently train the intra-modal encoder and cross-modal encoder to reduce interference between different modal encoders and better learn intra-modal and inter-modal information. Then, we propose a joint strategy to combine the intra-modal and inter-modal information to better capture the information between image and text. In addition, we introduce a hard negative pair-based method to train the performance of the intra-modal multi-granularity encoder without using attention mechanisms. Unlike existing methods, We treat image and text retrieval as a two-way process and delve into the intrinsic connections between the two matrices. We propose a cross-validation method to optimize the retrieval result based on common image-to-text alignment scenarios in daily life, i.e., during the image-to-text retrieval process, a text-to-image reverse verification is performed on the retrieved corresponding text. Through extensive qualitative experiments and analysis, our approach performs well on both Flickr30K and MSCOCO datasets and also significantly reduces the time required for testing. © 2024 SPIE.

关键词： Natural language processing systems

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Amplitude-Invariant Phase Shifter Based on Carrier-Injection Mechanism for Optical Phased Array

Amplitude-Invariant Phase Shifter Based on Carrier-Injection...

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2024 Conference on Lasers and Electro-Optics, CLEO 2024

作者： Yuan, Qiqi Xu, Weihan Ran, Shihuan Yang, Yunhong Lu, Liangjun Chen, Jianping Zhou, Linjie State Key Laboratory of Advanced Optical Communication Systems and Networks Shanghai Key Lab Navigation and Location Services Shanghai Institute for Advanced Communication and Data Science Department of Electronic Engineering Shanghai Jiao Tong University Shanghai200240 China SJTU-Pinghu Institute of Intelligent Optoelectronics Pinghu314200 China

ISBN: (纸本)9781957171395

关键词： Mach-Zehnder interferometers

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Switchable Online knowledge Distillation

arXiv

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arXiv 2022年

作者： Qian, Biao Wang, Yang Yin, Hongzhi Hong, Richang Wang, Meng Key Laboratory of Knowledge Engineering with Big Data Ministry of Education School of Computer Science and Information Engineering Hefei University of Technology China The University of Queensland

Online knowledge Distillation (OKD) improves the involved models by reciprocally exploiting the difference between teacher and student. Several crucial bottlenecks over the gap between them — e.g., Why and when does a large gap harm the performance, especially for student? How to quantify the gap between teacher and student? — have received limited formal study. In this paper, we propose Switchable Online knowledge Distillation (SwitOKD), to answer these questions. Instead of focusing on the accuracy gap at test phase by the existing arts, the core idea of SwitOKD is to adaptively calibrate the gap at training phase, namely distillation gap, via a switching strategy between two modes — expert mode (pause the teacher while keep the student learning) and learning mode (restart the teacher). To possess an appropriate distillation gap, we further devise an adaptive switching threshold, which provides a formal criterion as to when to switch to learning mode or expert mode, and thus improves the student’s performance. Meanwhile, the teacher benefits from our adaptive switching threshold and keeps basically on a par with other online arts. We further extend SwitOKD to multiple networks with two basis topologies. Finally, extensive experiments and analysis validate the merits of SwitOKD for classification over the state-of-the-arts. Our code is available at https://***/hfutqian/SwitOKD. Copyright © 2022, The Authors. All rights reserved.

关键词： Students

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TREC: transient redundancy elimination-based convolution 22

TREC: transient redundancy elimination-based convolution

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Proceedings of the 36th International Conference on Neural Information Processing Systems

作者： Jiawei Guan Feng Zhang Jiesong Liu Hsin-Hsuan Sung Ruofan Wu Xiaoyong Du Xipeng Shen Key Laboratory of Data Engineering and Knowledge Engineering (MOE) and School of Information Renmin University of China Computer Science Department North Carolina State University

ISBN: (纸本)9781713871088

The intensive computations in convolutional neural networks (CNNs) pose challenges for resource-constrained devices; eliminating redundant computations from convolution is essential. This paper gives a principled method to detect and avoid transient redundancy, a type of redundancy existing in input data or activation maps and hence changing across inferences. By introducing a new form of convolution (TREC), this new method makes transient redundancy detection and avoidance an inherent part of the CNN architecture, and the determination of the best configurations for redundancy elimination part of CNN backward propagation. We provide a rigorous proof of the robustness and convergence of TREC-equipped CNNs. TREC removes over 96% computations and achieves 3.51× average speedups on microcontrollers with minimal (about 0.7%) accuracy loss.

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