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Human-multimodal deep learning collaboration in ‘precise’ diagnosis of lupus erythematosus subtypes and similar skin diseases

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Journal of the European Academy of Dermatology and Venereology 2024年第12期38卷 2268-2279页

作者： Li, Qianwen Yang, Zhi Chen, Kaili Zhao, Ming Long, Hai Deng, Yueming Hu, Haoran Jia, Chen Wu, Meiyu Zhao, Zhidan Zhu, Huan Zhou, Suqing Zhao, Mingming Cao, Pengpeng Zhou, Shengnan Song, Yang Tang, Guishao Liu, Juan Jiang, Jiao Liao, Wei Zhou, Wenhui Yang, Bingyi Xiong, Feng Zhang, Suhan Gao, Xiaofei Jiang, Yiqun Zhang, Wei Zhang, Bo He, Yan-ling Ran, Liwei Zhang, Chunlei Wu, Wenting Suolang, Quzong Luo, Hanhuan Kang, Xiaojing Wu, Caoying Jin, Hongzhong Chen, Lei Guo, Qing Gui, Guangji Li, Shanshan Si, Henan Guo, Shuping Liu, Hong-Ye Liu, Xiguang Ma, Guo-Zhang Deng, Danqi Yuan, Limei Lu, Jianyun Zeng, Jinrong Jiang, Xian Lyu, Xiaoyan Chen, Liuqing Hu, Bin Tao, Juan Liu, Yuhao Wang, Gang Zhu, Guannan Yao, Zhirong Xu, Qianyue Yang, Bin Wang, Yu Ding, Yan Yang, Xianxu Kai, Hu Wu, Haijing Lu, Qianjin Department of Dermatology Hunan Key Laboratory of Medical Epigenomics The Second Xiangya Hospital of Central South University Changsha China Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education Xiangtan University Xiangtan China Department of Dermatology Hunan Children's Hospital Changsha China Institute of Dermatology Chinese Academy of Medical Sciences and Peking Union Medical College Nanjing China Department of Dermatology Beijing Chao-Yang Hospital Capital Medical University Beijing China Department of Dermatology Peking University Third Hospital Beijing China Department of Dermatology People's Hospital of Tibet Autonomous Region Lhasa China Department of Dermatology People's Hospital of Xinjiang Uygur Autonomous Region Urumqi China Department of Dermatology State Key Laboratory of Complex Severe and Rare Diseases Peking Union Medical College Hospital Chinese Academy of Medical Science and Peking Union Medical College Beijing China Department of Dermatology Sun Yat-Sen Memorial Hospital Sun Yat-Sen University Guangzhou China Department of Dermatology The First Bethune Hospital of Jilin University Changchun China Department of Dermatology The First Hospital of Shanxi Medical University Taiyuan China Department of Dermatology The Hei Long Jiang Provincial Hospital Harbin China Department of Dermatology The Second Affiliated Hospital of Kunming Medical University Kunming China Department of Dermatology The Third Xiangya Hospital Central South University Changsha China Department of Dermatology West China Hospital Sichuan University Chengdu China Department of Dermatology Wuhan No. 1 Hospital Wuhan China Department of Dermatology Wuhan Union Hospital of China Wuhan China Department of Dermatology Xijing Hospital Xi'an China Department of Dermatology Xinhua Hospital Affiliated to Shanghai Jiao Tong University School of Medicine Shanghai China Dermatology Hospital of Southern Medical University Guangzhou Chin

Background: Lupus erythematosus (LE) is a spectrum of autoimmune diseases. Due to the complexity of cutaneous LE (CLE), clinical skin image-based artificial intelligence is still experiencing difficulties in distinguishing subtypes of LE. Objectives: We aim to develop a multimodal deep learning system (MMDLS) for human-AI collaboration in diagnosis of LE subtypes. Methods: This is a multi-centre study based on 25 institutions across China to assist in diagnosis of LE subtypes, other eight similar skin diseases and healthy subjects. In total, 446 cases with 800 clinical skin images, 3786 multicolor-immunohistochemistry (multi-IHC) images and clinical data were collected, and EfficientNet-B3 and ResNet-18 were utilized in this study. Results: In the multi-classification task, the overall performance of MMDLS on 13 skin conditions is much higher than single or dual modals (Sen = 0.8288, Spe = 0.9852, Pre = 0.8518, AUC = 0.9844). Further, the MMDLS-based diagnostic-support help improves the accuracy of dermatologists from 66.88% ± 6.94% to 81.25% ± 4.23% (p = 0.0004). Conclusions: These results highlight the benefit of human-MMDLS collaborated framework in telemedicine by assisting dermatologists and rheumatologists in the differential diagnosis of LE subtypes and similar skin diseases. © 2024 European Academy of Dermatology and Venereology.

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Deterministic realization of collective measurements via photonic quantum walks

arXiv

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arXiv 2017年

作者： Hou, Zhibo Tang, Jun-Feng Shang, Jiangwei Zhu, Huangjun Li, Jian Yuan, Yuan Wu, Kang-Da Xiang, Guo-Yong Li, Chuan-Feng Guo, Guang-Can Key Laboratory of Quantum Information University of Science and Technology of China CAS Hefei230026 China Synergetic Innovation Center of Quantum Information and Quantum Physics University of Science and Technology of China Hefei230026 China Naturwissenschaftlich-Technische Fakultät Universität Siegen Siegen57068 Germany Beijing Key Laboratory of Nanophotonics Ultrafine Optoelectronic Systems School of Physics Beijing Institute of Technology Beijing100081 China Institute for Theoretical Physics University of Cologne Cologne50937 Germany Department of Physics Center for Field Theory and Particle Physics Fudan University Shanghai200433 China Institute for Nanoelectronic Devices and Quantum Computing Fudan University Shanghai200433 China State Key Laboratory of Surface Physics Fudan University Shanghai200433 China Collaborative Innovation Center of Advanced Microstructures Nanjing210093 China Institute of Signal Processing Transmission Nanjing University of Posts and Telecommunications Nanjing210003 China Key Lab of Broadband Wireless Communication and Sensor Network Technology Nanjing University of Posts and Telecommunications Ministry of Education Nanjing210003 China

Collective measurements on identically prepared quantum systems can extract more information than local measurements, thereby enhancing information-processing efficiency. Although this nonclassical phenomenon has been known for two decades, it has remained a challenging task to demonstrate the advantage of collective measurements in experiments. Here we introduce a general recipe for performing deterministic collective measurements on two identically prepared qubits based on quantum walks. Using photonic quantum walks, we realize experimentally an optimized collective measurement with fidelity 0.9946 without post selection. As an application, we achieve the highest tomographic efficiency in qubit state tomography to date. Our work offers an effective recipe for beating the precision limit of local measurements in quantum state tomography and metrology. In addition, our study opens an avenue for harvesting the power of collective measurements in quantum information processing and for exploring the intriguing physics behind this power. Copyright © 2017, The Authors. All rights reserved.

关键词： Quantum optics

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ON THE OPPORTUNITIES OF GREEN computing: A SURVEY

arXiv

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arXiv 2023年

作者： Zhou, You Lin, Xiujing Zhang, Xiang Wang, Maolin Jiang, Gangwei Lu, Huakang Wu, Yupeng Zhang, Kai Yang, Zhe Wang, Kehang Sui, Yongduo Jia, Fengwei Tang, Zuoli Zhao, Yao Zhang, Hongxuan Yang, Tiannuo Chen, Weibo Mao, Yunong Li, Yi Bao, De Li, Yu Liao, Hongrui Liu, Ting Liu, Jingwen Guo, Jinchi Zhao, Xiangyu Wei, Ying Qian, Hong Liu, Qi Wang, Xiang Chan, Wai Kin Li, Chenliang Li, Yusen Yang, Shiyu Yan, Jining Mou, Chao Han, Shuai Jin, Wuxia Zhang, Guannan Zeng, Xiaodong Tsinghua University China Shanghai Jiaotong University China University of Science and Technology of China China Key Laboratory of Aerospace Information Security and Trusted Computing Ministry of Education School of Cyber Science and Engineering Wuhan University China Huazhong University of Science and Technology China Shanghai Institute of AI for Education School of Computer Science and Technology East China Normal University China Xi’an Jiaotong University China College of Computer Science Nankai University China School of Computer Science China University of Geosciences China School of Information Science and Technology Beijing Forestry University China Engineering Research Center for Forestry-Oriented Intelligent Information Processing National Forestry and Grassland Administration China Guangzhou University China Nanyang Technological University Singapore City University of Hong Kong Hong Kong Wuhan University China State Key Laboratory for Novel Software Technology Nanjing University China Ant Group China

Artificial Intelligence (AI) has achieved significant advancements in technology and research with the development over several decades, and is widely used in many areas including computing vision, natural language processing, time-series analysis, speech synthesis, etc. During the age of deep learning, especially with the arise of Large Language Models, a large majority of researchers’ attention is paid on pursuing new state-of-the-art (SOTA) results, resulting in ever increasing of model size and computational complexity. The needs for high computing power brings higher carbon emission and undermines research fairness by preventing small or medium-sized research institutions and companies with limited funding in participating in research. To tackle the challenges of computing resources and environmental impact of AI, Green computing has become a hot research topic. In this survey, we give a systematic overview of the technologies used in Green computing. We propose the framework of Green computing and devide it into four key components: (1) Measures of Greenness, (2) Energy-Efficient AI, (3) Energy-Efficient computing Systems and (4) AI Use Cases for Sustainability. For each components, we discuss the research progress made and the commonly used techniques to optimize the AI efficiency. We conclude that this new research direction has the potential to address the conflicts between resource constraints and AI development. We encourage more researchers to put attention on this direction and make AI more environmental friendly. Copyright © 2023, The Authors. All rights reserved.

关键词： Carbon footprint

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基于子带瞬时能量谱的铝电解槽电压槽况敏感频域分段方法

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Engineering 2021年第9期 190-212页

作者：曾朝晖桂卫华陈晓方谢永芳张红亮孙玉波 School of Automation Central South University Key Laboratory of Intelligent Computing & Information Processing Ministry of EducationXiangtan University School of Metallurgy and Environment Central South University

槽电压是广泛使用且可在线测量的铝电解槽信号，多种电解槽槽况分析和控制用参数由槽电压计算得到。槽电压频域分段是设计获取这些参数的数字滤波器通带的依据。在对槽电压定性分析的基础上，本文提出子带瞬时能量谱（sub-band Instanta... 详细信息

槽电压是广泛使用且可在线测量的铝电解槽信号，多种电解槽槽况分析和控制用参数由槽电压计算得到。槽电压频域分段是设计获取这些参数的数字滤波器通带的依据。在对槽电压定性分析的基础上，本文提出子带瞬时能量谱（sub-band Instantaneous energy spectrum, SIEP），并用其对多种槽况下槽电压的频域特性进行定量表示，最终给出了槽电压槽况敏感频域分段方法。该频域分段方法将槽电压有效频段划分为低频信号区[0, 0.001] Hz和低频噪声区[0.001, 0.050] Hz；将低频噪声区再细分为[0.001, 0.010] Hz的铝液异常波动频段和[0.01, 0.05] Hz的次低频噪声频段。与基于经验模态分解的瞬时能量谱比较，SIEP能更精细地表示槽电压有效频段内任意频段的能量随时间变化规律。该槽电压频域分段方法对槽况更敏感，可更细致地获取在线槽况信息，为工业电解槽槽况监测和控制决策提供更可靠、准确的在线依据。

关键词：子带瞬时能量谱槽况敏感频段频域分段铝液异常波动铝电解

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Perovskite Solar Cells: Unique Seamlessly Bonded CNT@Graphene Hybrid Nanostructure Introduced in an Interlayer for Efficient and Stable Perovskite Solar Cells (Adv. Funct. Mater. 32/2018)

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Advanced Functional Materials 2018年第32期28卷

作者： Xuanhua Li Tengteng Tong Qinjiang Wu Shaohui Guo Qiang Song Jian Han Zhixiang Huang State Key Laboratory of Solidification Processing Research and Development Institute of Northwestern Polytechnical University In Shenzhen Center for Nano Energy Materials School of Materials Science and Engineering Northwestern Polytechnical University and Shaanxi Joint Lab of Graphene (NPU) Xi'an 710072 China Northwestern Polytechnical University‐Queen Mary University of London (NPU‐QMUL) Joint Research Institute of Advanced Materials and Structures (JRI‐AMAS) Xi'an 710072 China Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Anhui University No. 3 Feixi Road Hefei Anhui Province 230039 P. R. China

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Water Splitting: Optical and Electrical Enhancement of Hydrogen Evolution by MoS2@MoO3Core–Shell Nanowires with Designed Tunable Plasmon Resonance (Adv. Funct. Mater. 32/2018)

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Advanced Functional Materials 2018年第32期28卷

作者： Shaohui Guo Xuanhua Li Xingang Ren Lin Yang Jinmeng Zhu Bingqing Wei State Key Laboratory of Solidification Processing Center for Nano Energy Materials School of Materials Science and Engineering Northwestern Polytechnical University and Shaanxi Joint Lab of Graphene (NPU) Xi'an 710072 China Northwestern Polytechnical University‐Queen Mary University of London (NPU‐QMUL) Joint Research Institute of Advanced Materials and Structures (JRI‐AMAS) Xi'an 710072 China Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Anhui University No. 3 Feixi Road Hefei Anhui Province 230039 P. R. China Department of Mechanical Engineering University of Delaware Newark DE 19716 USA

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Experimental Combined Grouping Analysis Approach for Robust Battery pack design for Electric Vehicles with Higher Performance

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IOP Conference Series: Earth and Environmental Science 2019年第1期268卷

作者： Liu Yun Li Shui Liang Gao Zhun Fan C. Ruhatiya Chin-Tsan Wang Akhil Garg Intelligent Manufacturing Key Laboratory of Ministry of Education Shantou University Guangdong China State Key Lab of Digital Manufacturing Equipment & Technology School of Mechanical Science and Engineering Huazhong University of Science and Technology Wuhan China Guangdong Provincial Key Laboratory of Digital Signal and Image Processing College of Engineering Shantou University Shantou 515063 China School of Engineering Sciences Mahindra École Centrale Hyderabad Telangana 500043 India Department of Mechanical and Electro-Mechanical Engineering National I Lan University ILan Taiwan

Battery consistency is an important factor for battery pack performance. Excellent battery consistency can make battery packs more energy efficient and electric vehicles can have longer mileage and higher safety. Thus, in this study a comprehensive intelligent clustering methodology for the design of Li-ion battery pack on the basis of uniformity and equalization criteria of the cell was proposed. Firstly, multiple parameters (capacity, voltage, temperature and resistance) test of single cell performance was performed. Secondly, a clustering method combine with self-organizing map neural network (SOM) was proposed. Furthermore, a validation experiment (pack level) was carried out to verify the accuracy of proposed clustering algorithm. It can be concluded that the battery pack formed from SOM sorting results perform better than the battery pack having random cells combination as well as the pack originally purchased from the manufacturer.

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Click-iG: Simultaneous Enrichment and Profiling of Intact N-linked, O-GalNAc, and O-GlcNAcylated Glycopeptides

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Angewandte Chemie 2023年第36期135卷

作者： Dr. Jialin Liu Dr. Bo Cheng Dr. Xinqi Fan Xinyue Zhou Jiankun Wang Dr. Wen Zhou Hengyu Li Dr. Wenfeng Zeng Prof. Pengyuan Yang Prof. Xing Chen College of Chemistry and Molecular Engineering Beijing National Laboratory for Molecular Sciences Peking-Tsinghua Center for Life Sciences Synthetic and Functional Biomolecules Center and Key Laboratory of Bioorganic Chemistry and Molecular Engineering of Ministry of Education Peking University Beijing 100871 China Institute of Biomedical Sciences and Department of Chemistry Fudan University Shanghai 200433 China Key Lab of Intelligent Information Processing of Chinese Academy of Sciences (CAS) and Institute of Computing Technology CAS Beijing 100190 China Deseased

Proteins are ubiquitously modified with glycans of varied chemical structures through distinct glycosidic linkages, making the landscape of protein glycosylation challenging to map. Profiling of intact glycopeptides with mass spectrometry (MS) has recently emerged as a powerful tool for revealing matched information of the glycosylation sites and attached glycans (i.e., intact glycosites), but is largely limited to individual glycosylation types. Herein, we describe Click-iG, which integrates metabolic labeling of glycans with clickable unnatural sugars, an optimized MS method, and a tailored version of pGlyco3 software to enable simultaneous enrichment and profiling of three types of intact glycopeptides: N-linked, mucin-type O-linked, and O-GlcNAcylated glycopeptides. We demonstrate the utility of Click-iG by the identification of thousands of intact glycosites in cell lines and living mice. From the mouse lung, heart, and spleen, a total of 2053 intact N-glycosites, 262 intact O-GalNAc glycosites, and 1947 O-GlcNAcylation sites were identified. Click-iG-enabled comprehensive coverage of the protein glycosylation landscape lays the foundation for interrogating crosstalk between different glycosylation pathways.

关键词： Glycoproteomics Glycosylation Intact Glycosites Metabolic Labeling Unnatural Glycans

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From standard alpha-stable Lévy motions to horizontal visibility networks: dependence of multifractal and Laplacian spectrum

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Journal of Statistical Mechanics: Theory and Experiment 2018年第5期2018卷

作者： Hai-Long Zou Zu-Guo Yu Vo Anh Yuan-Lin Ma Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education and Hunan Key Laboratory for Computation and Simulation in Science and Engineering Xiangtan University Hunan 411105 People's Republic of China School of Electrical Engineering and Computer Science Queensland University of Technology GPO Box 2434 Brisbane Q4001 Australia Author to whom any correspondence should be addressed. School of Mathematical Sciences Queensland University of Technology GPO Box 2434 Brisbane Q4001 Australia

In recent years, researchers have proposed several methods to transform time series (such as those of fractional Brownian motion) into complex networks. In this paper, we construct horizontal visibility networks (HVNs) based on the -stable Lévy motion. We aim to study the relations of multifractal and Laplacian spectrum of transformed networks on the parameters and of the -stable Lévy motion. First, we employ the sandbox algorithm to compute the mass exponents and multifractal spectrum to investigate the multifractality of these HVNs. Then we perform least squares fits to find possible relations of the average fractal dimension , the average information dimension and the average correlation dimension against using several methods of model selection. We also investigate possible dependence relations of eigenvalues and energy on , calculated from the Laplacian and normalized Laplacian operators of the constructed HVNs. All of these constructions and estimates will help us to evaluate the validity and usefulness of the mappings between time series and networks, especially between time series of -stable Lévy motions and HVNs.

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The 5th International Conference on Biomedical Engineering and Biotechnology (ICBEB 2016) Abstracts

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BMC MEDICAL IMAGING 2016年第SUPPL 1期16卷 65-65页

作者： [Anonymous] Department of MRI Shandong Medical Imaging Research Institute Affiliated to Shandong University Jinan Shandong 250021 People’s Republic of China Department of Interventional Radiology Shandong Provincial Hospital Affiliated to Shandong University Jinan Shandong 250021 People’s Republic of China College of Information Science and Technology Engr. Research Center of Digitized Textile & Fashion Tech. for Ministry of Education Donghua University Shanghai 201620 China Intelligent multimedia information processing Lab College of Software Northeastern University Shenyang Liaoning Province 110004 China Institute of Biomedical and Health Engineering Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen 518055 China School of Computer Science and Technology Nanjing Normal University Nanjing China Department of Electrical Engineering The City College of New York CUNY New York USA Jiangsu Key Laboratory of 3D Printing Equipment and Manufacturing Nanjing China School of Electronic Science and Engineering Nanjing University Nanjing Jiangsu 210046 China College of Engineering Nanyang Technological University Singapore 639798 Singapore School of Electronic Information Shanghai Dianji University Shanghai China School of Natural Sciences and Mathematics Shepherd University Shepherdstown WV 25443 USA Davis College of Agriculture Natural Resources and Design West Virginia University Morgantown WV 26505 USA State Key Laboratory of Millimeter Waves Southeast University Nanjing 210096 China Center of Medical Physics and Technology Hefei Institutes of Physical Science Chinese Academy of Sciences Hefei China College of Agricultural and Life Sciences University of Florida Gainesville FL 32611 USA Courant Institute of Mathematical Sciences New York University New York NY 10012 USA Translational Imaging Division & MRI Unit Columbia University and New York State Psychiatric Institute New York NY 10032 USA Guangxi Key Laboratory of Manufacturing System & Adv

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