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arXiv 2021年

作者： Chen, Chen Qin, Chen Ouyang, Cheng Li, Zeju Wang, Shuo Qiu, Huaqi Chen, Liang Tarroni, Giacomo Bai, Wenjia Rueckert, Daniel Department of Computing Imperial College London United Kingdom Institute for Digital Communications School of Engineering University of Edinburgh United Kingdom Digital Medicine Research Centre School of Basic Medical Sciences Fudan University China Shanghai Key Laboratory of MICCAI Shanghai China CitAI Research Centre Department of Computer Science City University of London United Kingdom Department of Brain Sciences Imperial College London United Kingdom Data Science Institute Imperial College London United Kingdom Klinikum Rechts der Isar Technical University of Munich Germany

The success of neural networks on medical image segmentation tasks typically relies on large labeled datasets for model training. However, acquiring and manually labeling a large medical image set is resource-intensive, expensive, and sometimes impractical due to data sharing and privacy issues. To address this challenge, we propose AdvChain, a generic adversarial data augmentation framework, aiming at improving both the diversity and effectiveness of training data for medical image segmentation tasks. AdvChain augments data with dynamic data augmentation, generating randomly chained photo-metric and geometric transformations to resemble realistic yet challenging imaging variations to expand training data. By jointly optimizing the data augmentation model and a segmentation network during training, challenging examples are generated to enhance network generalizability for the downstream task. The proposed adversarial data augmentation does not rely on generative networks and can be used as a plug-in module in general segmentation networks. It is computationally efficient and applicable for both low-shot supervised and semi-supervised learning. We analyze and evaluate the method on two MR image segmentation tasks: cardiac segmentation and prostate segmentation with limited labeled data. Results show that the proposed approach can alleviate the need for labeled data while improving model generalization ability, indicating its practical value in medical imaging applications. Copyright © 2021, The Authors. All rights reserved.

关键词： Image segmentation

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A Multi-Scale Two-Branch Fusion Network for Simultaneous Segmentation in Electronic Laryngoscope Images

SSRN

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SSRN 2023年

作者： Chen, Hao Hao, Mayang Wang, Chenwu Wang, Pei School of Computer Science and Technology Xi’an University of Posts and Telecommunications 563 South Changan Road Yanta District Shaanxi Xi’an710061 China Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing Xi’an University of Posts and Telecommunications 563 South Changan Road Yanta District Shaanxi Xi’an710061 China School of Modern Post Xi’an University of Posts and Telecommunications 563 South Changan Road Yanta District Shaanxi Xi’an710061 China School of Computer Science Northwestern Polytechnical University 127 West Youyi Road Beilin District Shaanxi Xi’an710072 China

Three issues reduced the performance of networks for handling the organs and lesions’ simultaneous segmentation in electronic laryngoscopy images. Firstly, the moving endoscope will cause noticeable variations of the shape and angle in lesions and organs. Secondly, the lesions, mainly the polyps, and the major organs differ considerably in size. Moreover, the boundaries between the lesions or organs and their backgrounds are usually indistinguishable since their color and texture are very close to the mucosal tissues. To improve the simultaneous segmentation accuracy, we propose a multi-scale two-branch fusion network (MsFusionNet), which adopted an asymmetric two-branch structure to fuse the fine-grained feature maps extracted by the convolution neural network with the global context feature maps extracted by the Vision Transformer at different scales. In addition, a Multi-scale Dark Part Feature Enhancement module (MsDFE) was designed to enhance the non-salient details of organs before the feature fusion in the two-branch network. To evaluate the universality and effectiveness of the proposed method, we used a mixed dataset collected from three institutions, including 2425 electronic laryngoscope images with major organs in the pharynx and larynx. The results show the proposed method performs better than nine existing segmentation networks in dealing with the experiment dataset, which has good potential for clinical practice. © 2023, The Authors. All rights reserved.

关键词： Image enhancement

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Symptom Dimensions and Cognitive Impairments in Individuals at Clinical High Risk for Psychosis

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Biological Psychiatry: Cognitive Neuroscience and Neuroimaging 2024年

作者： Zhang, TianHong Xu, LiHua Wei, YanYan Cui, HuiRu Tang, XiaoChen Hu, YeGang Liu, HaiChun Wang, ZiXuan Chen, Tao Yi, ZhengHui Li, ChunBo Wang, JiJun Shanghai Mental Health Center Shanghai Jiaotong University School of Medicine Shanghai Engineering Research Center of Intelligent Psychological Evaluation and Intervention Shanghai Key Laboratory of Psychotic Disorders Shanghai China Department of Automation Shanghai Jiao Tong University Shanghai China Department of Psychology Shanghai Xinlianxin Psychological Counseling Center Shanghai China Big Data Research Laboratory University of Waterloo Waterloo ON Canada Labor and Worklife Program Harvard University Cambridge Massachusetts United States Center for Excellence in Brain Science and Intelligence Technology Chinese Academy of Sciences Shanghai China Institute of Psychology and Behavioral Science Shanghai Jiao Tong University Shanghai China

Background: Understanding the intricate relationships between symptom dimensions, clusters, and cognitive impairments is crucial for early detection and intervention in individuals at clinical high risk for psychosis. This study delves into this complex interplay in a clinical high risk sample with the aim of predicting the conversion to psychosis. Methods: A comprehensive cognitive assessment was performed in 744 clinical high risk individuals. The study included a 3-year follow-up period to allow assessment of conversion to psychosis. Symptom profiles were determined using the Structured Interview for Prodromal Syndromes. By applying factor analysis, symptom dimensions were categorized as dominant negative symptoms (NS), positive symptoms-stressful, and positive symptoms-odd. The factor scores were used to define 3 dominant symptom groups. Latent class analysis (LCA) and the factor mixture model (FMM) were employed to identify discrete clusters based on symptom patterns. The 3-class solution was chosen for the LCA and FMM analysis. Results: Individuals in the dominant NS group exhibited significantly higher conversion rates to psychosis than those in the other groups. Specific cognitive variables, including performance on the Brief Visuospatial Memory Test–Revised (odds ratio = 0.702, p = .001) and Neuropsychological Assessment Battery Mazes Test (odds ratio = 0.776, p = .024), significantly predicted conversion to psychosis. Notably, cognitive impairments associated with NS and positive symptoms-stressful groups affected different cognitive domains. LCA and FMM cluster 1, which was characterized by severe NS and positive symptoms-odd, exhibited more impairments in cognitive domains than other clusters. No significant difference in the conversion rate was observed among the LCA and FMM clusters. Conclusions: These findings highlight the importance of NS in the development of psychosis and suggest specific cognitive domains that are affected by symptom dimensions.

关键词： Negative symptoms Neurocognition Subtype Transition Ultra high risk

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Optical IRS for Visible Light Communication: Modeling, Design, and Open Issues

arXiv

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arXiv 2024年

作者： Sun, Shiyuan Yang, Fang Mei, Weidong Song, Jian Han, Zhu Zhang, Rui The Department of Electronic Engineering Tsinghua University Beijing100084 China The National Key Laboratory of Wireless Communications University of Electronic Science and Technology of China Chengdu611731 China The Shenzhen International Graduate School Tsinghua University Shenzhen518055 China The Department of Electrical and Computer Engineering The University of Houston HoustonTX77004 United States The Department of Computer Science and Engineering Kyung Hee University Seoul446-701 Korea Republic of School of Science and Engineering Shenzhen Research Institute of Big Data The Chinese University of Hong Kong Guangdong Shenzhen518172 China The Department of Electrical and Computer Engineering National University of Singapore Singapore117583 Singapore

Optical intelligent reflecting surface (OIRS) offers a new and effective approach to resolving the line-of-sight blockage issue in visible light communication (VLC) by enabling redirection of light to bypass obstacles, thereby dramatically enhancing indoor VLC coverage and reliability. This article provides a comprehensive overview of OIRS for VLC, including channel modeling, design techniques, and open issues. First, we present the characteristics of OIRS-reflected channels and introduce two practical models, namely, optics model and association model, which are then compared in terms of applicable conditions, configuration methods, and channel parameters. Next, under the more practically appealing association model, we discuss the main design techniques for OIRS-aided VLC systems, including beam alignment, channel estimation, and OIRS reflection optimization. Finally, open issues are identified to stimulate future research in this area. Copyright © 2024, The Authors. All rights reserved.

关键词： Visible light communication

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CloudBrain-ReconAI: A Cloud computing Platform for MRI Reconstruction and Radiologists’ Image Quality Evaluation

arXiv

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arXiv 2022年

作者： Zhou, Yirong Qian, Chen Li, Jiayu Wang, Zi Hu, Yu Qu, Biao Zhu, Liuhong Zhou, Jianjun Kang, Taishan Lin, Jianzhong Hong, Qing Dong, Jiyang Guo, Di Qu, Xiaobo The Department of Electronic Science Biomedical Intelligent Cloud R&D Center Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance National Institute for Data Science in Health and Medicine Xiamen University Xiamen361102 China The Department of Instrumental and Electrical Engineering Xiamen University Xiamen361005 China Fudan University Xiamen361015 China The Department of Radiology Zhongshan Hospital Xiamen University School of Medicine Xiamen University Xiamen361004 China The China Mobile Group Xiamen361000 China The School of Computer and Information Engineering Xiamen University of Technology Xiamen361024 China

Efficient collaboration between engineers and radiologists is important for image reconstruction algorithm development and image quality evaluation in magnetic resonance imaging (MRI). Here, we develop CloudBrain-ReconAI, an online cloud computing platform, for algorithm deployment, fast and blind reader study. This platform supports online image reconstruction using state-of-the-art artificial intelligence and compressed sensing algorithms with applications for fast imaging (Cartesian and non-Cartesian sampling) and high-resolution diffusion imaging. Through visiting the website, radiologists can easily score and mark images. Then, automatic statistical analysis will be provided. CloudBrain-ReconAI is now open accessed at https://***/*** and will be continually improved to serve the MRI research community. Copyright © 2022, The Authors. All rights reserved.

关键词： Cloud platforms

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Channel Estimation for Optical intelligent Reflecting Surface-Assisted VLC System: A Joint Space-Time Sampling Approach

arXiv

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arXiv 2024年

Optical intelligent reflecting surface (OIRS) has attracted increasing attention due to its capability of overcoming signal blockages in visible light communication (VLC), an emerging technology for the next-generation advanced transceivers. However, current works on OIRS predominantly assume known channel state information (CSI), which are essential to practical OIRS configuration. To bridge such a gap, this paper proposes a new and customized channel estimation protocol for OIRSs under the alignment-based channel model. Specifically, we first unveil OIRS spatial and temporal coherence characteristics and derive the coherence distance and the coherence time in closed-form. Next, to achieve fast beam alignment over different coherence time, we propose to dynamically tune the rotational angles of the OIRS reflecting elements following a geometric optics-based non-uniform codebook. Given the above beam alignment, we propose an efficient joint space-time sampling-based algorithm to estimate the OIRS channel. In particular, we divide the OIRS into multiple subarrays based on the coherence distance and sequentially estimate their associated CSI, followed by a spacetime interpolation to retrieve full CSI for other non-aligned transceiver antennas. Numerical results validate our theoretical analyses and demonstrate the efficacy of our proposed OIRS channel estimation scheme as compared to other benchmark schemes. Copyright © 2024, The Authors. All rights reserved.

关键词： Channel estimation

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Deceiving LLM through Compositional Instruction with Hidden Attacks

引用

ACM Transactions on Autonomous and Adaptive Systems 1000年

作者： Shuyu Jiang Xingshu Chen Rui Tang School of Cyber Science and Engineering Sichuan University China School of Cyber Science and Engineering Sichuan University Key Laboratory of Data Protection and Intelligent Management (Sichuan University) Ministry of Education Cyber Science Research Institute Sichuan University China

Recently, large language models (LLMs) have demonstrated promising applications in the autonomous driving (AD) domain, including language-based interactions and decision-making. Ensuring they safely handle harmful inputs is crucial before formal deployment. However, research reveals emerging hand-crafted jailbreak attacks, which pack harmful prompts into harmless instructions, can bypass LLMs’ security mechanisms and elicit harmful responses. To deeply understand such jailbreaks, this paper introduces a Compositional Instruction Attack (CIA) framework to generalize them, and develop two CIA jailbreaking methods to automatically generate tailored jailbreak prompts for each harmful prompt. Then, this paper builds the first CIA question-answering (CIAQA) dataset with 2.7K multiple-choice questions of 900 successful jailbreaks, for assessing LLMs’ ability to identify underlying harmful intents, harmfulness, and task priority in CIA jailbreaks. Combined with experimental analysis on CIAQA and other datasets, this paper concludes three possible reasons for the failure of LLM defenses against CIAs. Finally, we propose an intent-based defense paradigm (IBD), enabling LLMs to defend against CIA by leveraging its capability to identify intents. Experimental results show CIA can achieve attack success rates (ASR) of 95%+ and 85%+ in AD and common harmful scenarios for three well-known LLMs (GPT-4, GPT-3.5, and Llama2-70b-chat), and IBD reduces ASR by 74%+.

关键词： Large language model autonomous driving adversarial attack harmful prompt

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DeFeed: Secure Decentralized Cross-Contract data Feed in Web 3.0 for Connected Autonomous Vehicles

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ACM Transactions on Autonomous and Adaptive Systems 1000年

作者： Xingchen Sun Runhua Xu Wei Ni Li Duan Chao Li Beijing Key Laboratory of Security and Privacy in Intelligent Transportation Beijing Jiaotong University China School of Computer Science and Engineering Beihang University China School of Electrical and Data Engineering University of Technology Sydney Australia

Smart contracts have been a topic of interest in blockchain research and are a key enabling technology for Connected Autonomous Vehicles (CAVs) in the era of Web 3.0. These contracts enable trustless interactions without the need for intermediaries, as they operate based on predefined rules encoded on the blockchain. However, smart contacts face significant challenges in cross-contract communication and information sharing, making it difficult to establish seamless connectivity and collaboration among CAVs with Web 3.0. In this paper, we propose DeFeed, a novel secure protocol that incorporates various gas-saving functions for CAVs, originated from in-depth research into the interaction among smart contracts for decentralized cross-contract data feed in Web 3.0. DeFeed allows smart contracts to obtain information from other contracts efficiently in a single click, without complicated operations. We judiciously design and complete various functions with DeFeed, including a pool function and a cache function for gas optimization, a subscribe function for facilitating data access, and an update function for the future iteration of our protocol. Tailored for CAVs with Web 3.0 use cases, DeFeed enables efficient data feed between smart contracts underpinning decentralized applications and vehicle coordination. Implemented and tested on the Ethereum official test network, DeFeed demonstrates significant improvements in contract interaction efficiency, reducing computational complexity and gas costs. Our solution represents a critical step towards seamless, decentralized communication in Web 3.0 ecosystems.

关键词： Blockchain Ethereum Smart Contract data Feed Web3 CAVs

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Revisiting mid-long term ecosystem services projections: Integrating the interaction effects of future climate and land use changes

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Fundamental Research 2024年

作者： Yang, Qing Liu, Gengyuan Gonella, Francesco Wang, Qingrui Chen, Shiju Yang, Zhifeng Advanced Interdisciplinary Institute of Environment and Ecology Beijing Normal University Zhuhai 519087 China State Key Joint Laboratory of Environmental Simulation and Pollution Control School of Environment Beijing Normal University Beijing 100875 China Department of Molecular Sciences and Nanosystems Ca’ Foscari University of Venice Venezia Mestre 30170 Italy State Key Laboratory of Coal Combustion Huazhong University of Science and Technology Wuhan 430074 China Beijing Tengjing Big Data Research Institute Beijing 100020 China Key Laboratory for City Cluster Environmental Safety and Green Development of the Ministry of Education School of Ecology Environment and Resources Guangdong University of Technology Guangzhou 510006 China

Two of the most critical issues of sustainability are how future ecosystem services (ES) will change under natural and artificial stressors. To answer the above problems accurately with a quantitative manner and develop an intelligent and sustainable management of ecosystems, the projections of ES considering climate change under different Shared Socioeconomic Pathways (SSPs) are needed. However, current ES projection studies often lack quantitative methods to simulate ES dynamics, especially incapability to integrate the interactions between climate and land use changes on ES dynamics. To overcome these challenges, this study proposes a framework for ES simulation under climate and land use change stressors. The framework developed includes four modules: climatic attributes simulation, land use change simulation, scenario design, and ecosystem services simulation modules. Taking China as a case study, this study simulated the evolution of China's ES under the interactions between climate and land use change during the period of 2020–2100. The simulation results show that China's ES increases by 33%, 34%, 9%, and 60% under scenarios SSP1, SSP2, SSP3, and SSP5, respectively. Additionally, the overall growth rate of ES decreases by a gradually slowing rate from the mean at 1% during 2000–2020 to 0.5% during 2020–2100, respectively. Meanwhile, the ES in China shows significantly spatial variability based on simulation results. Shandong province yields the largest potential to be the key area with ES declining in the future. At ecosystem scale, increases in woodland ES are expected to dominate China's ES growth continuously. In the first four decades (2020–2060), development ways under SSP1 scenario are more helpful to promote increase in subtypes of ES, while in the last four decades (2060–2100), ES are expected to increase under SSP5 scenario. The key contributions of this study include tracking the causation and revealing the influencing mechanism of interactions of

关键词： Climate change Ecosystem conservation and management Ecosystem services Land use change Shared socioeconomic pathways (SSPs)

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Design of Convolutional Neural Network Based on FPGA

引用

Journal of Physics: Conference Series 2019年第6期1168卷

作者： Sheping Zhai Cheng Qiu Yuanyuan Yang Jing Li Yiming Cui School of Computer Science & Technology Xi'an University of Posts & Telecommunications Xi'an Shaanxi 710121 China Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing Xi'anShaanxi 710121 China

Recently, FPGAs have been widely used in the implementation of hardware accelerators for CNN, especially on mobile and embedded devices. This paper proposes an FPGA-based CNN accelerator. The highly reusable accelerator function is designed to construct the optimized convolutional neural network and memory optimization with a lower hardware resource consumption. The results show the advantage of performance and power when compared with Core i5 CPU and GTX 960 GPU.

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