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23rd International Conference on Medical Image Computing and computer-Assisted Intervention, MICCAI 2020

作者： Hang, Wenlong Feng, Wei Liang, Shuang Yu, Lequan Wang, Qiong Choi, Kup-Sze Qin, Jing School of Computer Science and Technology Nanjing Tech University Nanjing China State Key Laboratory for Novel Software Technology Nanjing University Nanjing China Smart Health Big Data Analysis and Location Services Engineering Lab of Jiangsu Province Nanjing University of Posts and Telecommunications Nanjing China Department of Radiation Oncology Stanford University StanfordCA94305 United States CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institutes of Advanced Technology Shenzhen China School of Nursing Hong Kong Polytechnic University Hong Kong

ISBN: (纸本)9783030597092

Emerging self-ensembling methods have achieved promising semi-supervised segmentation performances on medical images through forcing consistent predictions of unannotated data under different perturbations. However, the consistency only penalizes on independent pixel-level predictions, making structure-level information of predictions not exploited in the learning procedure. In view of this, we propose a novel structure-aware entropy regularized mean teacher model to address the above limitation. Specifically, we firstly introduce the entropy minimization principle to the student network, thereby adjusting itself to produce high-confident predictions of unannotated images. Based on this, we design a local structural consistency loss to encourage the consistency of inter-voxel similarities within the same local region of predictions from teacher and student networks. To further capture local structural dependencies, we enforce the global structural consistency by matching the weighted self-information maps between two networks. In this way, our model can minimize the prediction uncertainty of unannotated images, and more importantly that it can capture local and global structural information and their complementarity. We evaluate the proposed method on a publicly available 3D left atrium MR image dataset. Experimental results demonstrate that our method achieves outstanding segmentation performances than the state-of-the-art approaches in scenes with limited annotated images. © Springer Nature Switzerland AG 2020.

关键词： Forecasting

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Federated Learning with LoRA Optimized DeiT and Multiscale Patch Embedding for Secure Eye Disease Recognition

arXiv

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arXiv 2025年

作者： Borno, Md. Naimur Asif Shovon, Md. Sakib Hossain Sikder, M.D. Hanif Rimi, Iffat Firozy Alahmadi, Tahani Jaser Moni, Mohammad Ali Mechatronics Engineering Rajshahi University of Engineering & Technology Rajshahi6204 Bangladesh Kim Jaechul Graduate School of Artificial Intelligence Korea Advanced Institute of Science & Technology Daehak-ro Daejeon34141 Korea Republic of Department of Computer Science University of South Asia-Bangladesh Dhaka1216 Bangladesh Department of Computer Science and Engineering Daffodil International University Dhaka Bangladesh Department of Information Systems College of Computer and Information Sciences Princess Nourah bint Abdulrahman University P.O. Box Riyadh84428 Saudi Arabia Faculty of Health Medicine and Behavioural Sciences The University of Queensland 308 Queen St Brisbane CityQLD4000 Australia

Recent progress in image-based medical disease detection encounters challenges such as limited annotated data sets, inadequate spatial feature analysis, data security issues, and inefficient training frameworks. This study introduces a data-efficient image transformer (DeIT)-based approach that overcomes these challenges by utilizing multiscale patch embedding for better feature extraction and stratified weighted random sampling to address class imbalance. The model also incorporates a LoRA-enhanced transformer encoder, a distillation framework, and federated learning for decentralized training, improving both efficiency and data security. Consequently, it achieves state-of-the-art performance, with the highest AUC, F1 score, precision, minimal loss, and Top-5 accuracy. Additionally, Grad-CAM++ visualizations improve interpretability by highlighting critical pathological regions, enhancing the model’s clinical relevance. These results highlight the potential of this approach to advance AI-powered medical imaging and disease detection. Copyright © 2025, The Authors. All rights reserved.

关键词： Image coding

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Effects of long term isolation on the emotion change of ‘‘Lunar Palace 365” crewmembers

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Science Bulletin 2019年第13期64卷 881-884页

作者： Zikai Hao Yinzhen Zhu Siyuan Feng Chen Meng Dawei Hu Hui Liu Hong Liu Beijing Advanced Innovation Centre for Biomedical Engineering Beihang UniversityBeijing 102402China Institute of Environmental Biology and Life Support Technology School of Biological Science and Medical EngineeringBeihang UniversityBeijing 100083China State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and EngineeringBeihang UniversityBeijing 100083China International Joint Research Center of Aerospace Biotechnology & Medical Engineering. Beihang University Beijing 100083China

The psychological health risk is one of the most serious and difficult risks to mitigate during manned deep space exploration[1].In a space capsule or station,the environment is isolated,and communication with the outside is limited to telephone or delayed *** monotonous working schedule also aggravates loneliness and homesickness,which could trigger negative moods,such as depression and ***,an astronaut’s unstable emotional state could be a threat to the safety and performance of a space mission.

关键词： Effects schedule trigger most is be

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Photo-assisted technologies for environmental remediation

Nature Reviews Clean Technology

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Nature Reviews Clean technology 2025年第3期1卷 1-15页

作者： Bo Weng Jiangquan Lv Ning Han Bao-Lian Su Maarten Roeffaers Johan Hofkens Yongfa Zhu Shaobin Wang Wonyong Choi State Key Laboratory of Advanced Environmental Technology Institute of Urban Environment Chinese Academy of Sciences Xiamen China University of Chinese Academy of Sciences Beijing China College of Electronics and Information Science Fujian Jiangxia University Fuzhou China Department of Electrical and Computer Engineering University of Toronto Toronto Canada Laboratory of Living Materials at the State Key Laboratory of Advanced Technology for Materials Synthesis and Processing Wuhan University of Technology Wuhan China cMACS Department of Microbial and Molecular Systems KU Leuven Leuven Belgium Department of Chemistry KU Leuven Leuven Belgium Department of Chemistry Tsinghua University Beijing China School of Chemical Engineering University of Adelaide Adelaide South Australia Australia Department of Energy Engineering Korea Institute of Energy Technology (KENTECH) Naju South Korea

Industrial processes can lead to air and water pollution, particularly from organic contaminants such as toluene and antibiotics, posing threats to human health. Photo-assisted chemical oxidation technologies leverage light energy to mineralize these contaminants. In this Review, we discuss the mechanisms and efficiencies of photo-assisted advanced oxidation processes for wastewater treatment and photothermal technologies for air purification. The integration of solar energy enhances degradation efficiency and reduces energy consumption, enabling more efficient remediation methods. We evaluate the technological aspects of photo-assisted technologies, such as photo-Fenton, photo-persulfate activation, photo-ozonation and photoelectrochemical oxidation, emphasizing their potential for practical applications. Finally, we discuss the challenges in scaling up photo-assisted technologies for specific environmental remediation needs. Photo-assisted technologies have demonstrated effectiveness in environmental remediation, although large-scale applications remain constrained by high costs. Future potential applications of photo-assisted technologies will require that technology selection be tailored to specific pollution scenarios and engineering processes optimized to minimize costs.

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Corrigendum to “The Spatial neural network model with disruptive technology for property appraisal in real estate industry” ✰

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Technological Forecasting and Social Change 2022年 177卷

作者： Regina Fang-Ying Lin Chiye Ou Kuo-Kun Tseng Deng Bowen K.L. Yung W.H. Ip Business School Shenzhen Technology University School of Economics and Management Harbin Institute of Technology (Shenzhen) Shenzhen China School of Computer Science and Technology Harbin Institute of Technology (Shenzhen) Shenzhen China Department of Advanced Computing Kings College London United Kingdom Department of Industrial and Systems Engineering The Hong Kong Polytechnic University Hong Kong China

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Supervised Multi-topology Network Cross-diffusion for Population-driven Brain Network Atlas Estimation

arXiv

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arXiv 2020年

作者： Mhiri, Islem Mahjoub, Mohamed Ali Rekik, Islem BASIRA Lab Faculty of Computer and Informatics Istanbul Technical University Istanbul Turkey Université de Sousse Ecole Nationale d’Ingénieurs de Sousse LATIS- Laboratory of Advanced Technology and Intelligent Systems Sousse4023 Tunisia

Estimating a representative and discriminative brain network atlas (BNA) is a nascent research field with untapped potentials in mapping a population of brain networks in health and disease. Although limited, existing BNA estimation methods have several limitations. First, they primarily rely on a similarity network diffusion and fusion technique, which only considers node degree as a topological measure in the cross-network diffusion process, thereby overlooking rich topological measures of the brain network (e.g., centrality). Second, both diffusion and fusion techniques are implemented in fully unsupervised manner, which might decrease the discriminative power of the estimated BNAs. To fill these gaps, we propose a supervised multi-topology network cross-diffusion (SM-netFusion) framework for estimating a BNA satisfying: (i) well-representativeness (captures shared traits across subjects), (ii) well-centeredness (optimally close to all subjects), and (iii) high discriminativeness (can easily and efficiently identify discriminative brain connections that distinguish between two populations). For a specific class, given the cluster labels of the training data, we learn a weighted combination of the topological diffusion kernels derived from degree, closeness and eigenvector centrality measures in a supervised manner. Specifically, we learn the cross-diffusion process by normalizing the training brain networks using the learned diffusion kernels. This normalization well captures shared networks between individuals at different topological scales, improving the representativeness and centeredness of the estimated multi-topology BNA. Our SM-netFusion produces the most centered and representative template in comparison with its variants and state-of-the-art methods and further boosted the classification of autistic subjects by 5 to 15%. SM-netFusion presents the first work for supervised network cross-diffusion based on graph topological measures, which can be further

关键词： Topology

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Directly wireless communication of human minds via non-invasive brain-computer-metasurface platform

arXiv

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arXiv 2022年

作者： Ma, Qian Gao, Wei Xiao, Qiang Ding, Lingsong Gao, Tianyi Zhou, Yajun Gao, Xinxin Yan, Tao Liu, Che Gu, Ze Kong, Xianghong Abbasi, Qammer H. Li, Lianlin Qiu, Cheng-Wei Li, Yuanqing Cui, Tie Jun Institute of Electromagnetic Space Southeast University Nanjing210096 China School of Automation Science and Engineering South China University of Technology Guangzhou510641 China State Key Laboratory of Millimeter Wave Southeast University Nanjing210096 China Center of Intelligent Metamaterials Pazhou Laboratory Guangzhou510330 China Research Center for Brain-Computer Interface Pazhou Lab Guangzhou510330 China Department of Electrical and Computer Engineering National University of Singapore Singapore Singapore University of Glasgow James Watt School of Engineering GlasgowG12 8QQ United Kingdom State Key Laboratory of Advanced Optical Communication Systems and Networks Department of Electronics Peking University Beijing100871 China

Brain-computer interfaces (BCIs), invasive or non-invasive, have projected unparalleled vision and promise for assisting patients in need to better their interaction with the surroundings. Inspired by the BCI-based rehabilitation technologies for nerve-system impairments and amputation, we propose an electromagnetic brain-computer-metasurface (EBCM) paradigm, regulated by human's cognition by brain signals directly and non-invasively. We experimentally show that our EBCM platform can translate human's mind from evoked potentials of P300-based electroencephalography to digital coding information in the electromagnetic domain non-invasively, which can be further processed and transported by an information metasurface in automated and wireless fashions. Directly wireless communications of the human minds are performed between two EBCM operators with accurate text transmissions. Moreover, several other proof-of-concept mind-control schemes are presented using the same EBCM platform, exhibiting flexibly-customized capabilities of information processing and synthesis like visual-beam scanning, wave modulations, and pattern encoding. © 2022, CC BY.

关键词： Electrophysiology

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全球对流解析模式在台风路径预测中的显著进展（英文）

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Science Bulletin 2025年第9期 1424-1428页

作者：顾俊赵纯李顾东泽冯家望许明月杜秋燕夏子涵李煜斌陈光华郝晓宇陈俊仕安虹 Deep Space Exploration Laboratory/School of Earth and Space Sciences/CMA-USTC Laboratory of Fengyun Remote Sensing/State Key Laboratory of Fire Science/Institute of Advanced Interdisciplinary Research on High-Performance Computing Systems and Software University of Science and Technology of China Laoshan Laboratory CAS Center for Excellence in Comparative Planetology University of Science and Technology of China School of Atmospheric Physics Nanjing University of Information Science and Technology Key Laboratory of Cloud-Precipitation Physics and Severe Storms Institute of Atmospheric Physics Chinese Academy of Sciences School of Computer Science and Technology University of Science and Technology of China

Typhoons pose significant threats to coastal and inland regions,with their impacts exacerbated by climate change and population growth [1]. Recent studies have shown increased frequency of powerful storms, slower translation speeds, northward shifts in the Western North Pacific (WNP) basin, and a near tripling of global exposure to typhoons since 1970 [1–4]. These changes present new challenges for forecasters and researchers, particularly in predicting impacts on inland and high-latitude populations lacking prior exposure or resilience to typhoon effects.

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UAV Communication Channel Measurement, Modeling, and Application

引用

Journal of Communications and Information Networks 2019年第4期4卷 32-43页

作者： Xiang Cheng Yiran Li Lin Bai State Key Laboratory of Advanced Optical Communication Systems and Networks Department of ElectronicsSchool of Electronics Engineering and Computer SciencePeking UniversityBeijing 100871China Shenzhen Research Institute of Big Data Shenzhen 518000China School of Cyber Science and Technology Beihang UniversityBeijing 100191China

As unmanned aerial vehicles(UAV)deploy-ment is shifting from military to civilian sectors,and in particular as a means of facilitating more flexible and versatile wireless communications,the UAV channels that were previously not well understood are attracting increasing research and *** the key component facilitating the UAV-aided communications,UAV channel characteristics and modeling are of critical importance to the designed UAV system to achieve satisfactory *** this article,we will provide a comprehensive overview and the future perspective of envisioned UAV communication system benefits,the existing and needed UAV channel measurements and modeling approaches,together with the new viewpoint of UAV channel applications in different communication and networking *** existing work and future directions are extensively covered.

关键词： UAV channel measurements modeling application future challenges

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A perspective on meta-boundaries

arXiv

引用

arXiv 2022年

作者： Zhang, Xinyan Chen, Jialin Chen, Ruoxi Wang, Chan Cai, Tong Chen, Hongsheng Lin, Xiao Interdisciplinary Center for Quantum Information State Key Laboratory of Modern Optical Instrumentation ZJU-Hangzhou Global Scientific and Technological Innovation Center Zhejiang University Hangzhou310027 China International Joint Innovation Center Key Lab. of Advanced Micro/Nano Electronic Devices & Smart Systems of Zhejiang The Electromagnetics Academy Zhejiang University Zhejiang University Haining314400 China Department of Electrical and Computer Engineering Technion-Israel Institute of Technology Haifa32000 Israel Air and Missile Defense College Air Force Engineering University Xi’an710051 China

The judicious design of electromagnetic boundary provides a crucial route to control light-matter interactions, and it is thus fundamental to basic science and practical applications. General design approaches rely on the manipulation of bulk properties of superstrate or substrate and on the modification of boundary geometries. Due to the recent advent of metasurfaces and low-dimensional materials, the boundary can be flexibly featured with a surface conductivity, which can be rather complex but provide an extra degree of freedom to regulate the propagation of light. In this perspective, we denote the boundary with a non-zero surface conductivity as the meta-boundary. The meta-boundaries are categorized into four types, namely isotropic, anisotropic, biisotropic and bianisotropic meta-boundaries, according to the electromagnetic boundary conditions. Accordingly, the latest development for these four kinds of meta-boundaries are reviewed. Finally, an outlook on the research tendency of meta-boundaries is provided, particularly on the manipulation of light-matter interactions by simultaneously exploiting meta-boundaries and metamaterials. © 2022, CC BY.

关键词： Degrees of freedom (mechanics)

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