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Koopman Spectrum Nonlinear Regulators and Efficient Online Learning

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arXiv 2021年

作者： Ohnishi, Motoya Ishikawa, Isao Lowrey, Kendall Ikeda, Masahiro Kakade, Sham Kawahara, Yoshinobu Paul G. Allen School of Computer Science & Engineering University of Washington United States Ehime University Japan RIKEN Center for Advanced Intelligence Project Japan Et Cetera Robotics Keio University Japan Harvard University United States Graduate School of Information Science and Technology Osaka University Japan

Most modern reinforcement learning algorithms optimize a cumulative single-step cost along a trajectory. The optimized motions are often 'unnatural', representing, for example, behaviors with sudden accelerations that waste energy and lack predictability. In this work, we present a novel paradigm of controlling nonlinear systems via the minimization of the Koopman spectrum cost: a cost over the Koopman operator of the controlled dynamics. This induces a broader class of dynamical behaviors that evolve over stable manifolds such as nonlinear oscillators, closed loops, and smooth movements. We demonstrate that some dynamics characterizations that are not possible with a cumulative cost are feasible in this paradigm, which generalizes the classical eigenstructure and pole assignments to nonlinear decision making. Moreover, we present a sample efficient online learning algorithm for our problem that enjoys a sub-linear regret bound under some structural assumptions. Copyright © 2021, The Authors. All rights reserved.

关键词： Decision making

Dynamical causality under invisible confounders

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arXiv 2024年

作者： Yan, Jinling Zhang, Shao-Wu Zhang, Chihao Huang, Weitian Shi, Jifan Chen, Luonan MOE Key Laboratory of Information Fusion Technology School of Automation Northwestern Polytechnical University Xi’an710072 China Key Laboratory of Systems Biology Shanghai Institute of Biochemistry and Cell Biology Center for Excellence in Molecular Cell Science Chinese Academy of Sciences Shanghai200031 China NCMIS CEMS RCSDS Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing100190 China School of Mathematics Sciences University of Chinese Academy of Sciences Beijing100049 China School of Computer Science and Engineering South China University of Technology Guangdong Guangzhou510006 China Guangdong Institute of Intelligence Science and Technology Hengqin Guangdong Zhuhai519031 China Research Institute of Intelligent Complex Systems Fudan University Shanghai200433 China State Key Laboratory of Medical Neurobiology MOE Frontiers Center for Brain Science Institutes of Brain Science Fudan University Shanghai200032 China Shanghai Artificial Intelligence Laboratory Shanghai200232 China Key Laboratory of Systems Health Science of Zhejiang Province School of Life Science Hangzhou Institute for Advanced Study University of Chinese Academy of Sciences Chinese Academy of Sciences Hangzhou310024 China

Causality inference is prone to spurious causal interactions, due to the substantial confounders in a complex system. While many existing methods based on the statistical methods or dynamical methods attempt to address misidentification challenges, there remains a notable lack of effective methods to infer causality, in particular in the presence of invisible/unobservable confounders. As a result, accurately inferring causation with invisible confounders remains a largely unexplored and outstanding issue in data science and AI fields. In this work, we propose a method to overcome such challenges to infer dynamical causality under invisible confounders (CIC method) and further reconstruct the invisible confounders from time-series data by developing an orthogonal decomposition theorem in a delay embedding space. The core of our CIC method lies in its ability to decompose the observed variables not in their original space but in their delay embedding space into the common and private subspaces respectively, thereby quantifying causality between those variables both theoretically and computationally. This theoretical foundation ensures the causal detection for any high-dimensional system even with only two observed variables under many invisible confounders, which is actually a long-standing problem in the field. In addition to the invisible confounder problem, such a decomposition actually makes the intertwined variables separable in the embedding space, thus also solving the non-separability problem of causal inference. Extensive validation of the CIC method is carried out using various real datasets, and the experimental results demonstrates its effectiveness to reconstruct real biological networks even with unobserved confounders. © 2024, CC BY-NC-ND.

关键词： Time series

Multibit quantum digital signature with continuous variables using basis encoding over insecure channels

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Physical Review A 2021年第1期103卷 012410-012410页

作者： Wei Zhao Ronghua Shi Jinjing Shi Peng Huang Ying Guo Duan Huang School of Computer Science and Engineering Central South University Changsha 410083 China State Key Laboratory of Advanced Optical Communication Systems and Networks Shanghai Key Laboratory on Navigation and Location-Based Service and Center of Quantum Information Sensing and Processing(QSIP) Shanghai Jiao Tong University Shanghai 200240 China School of Automation Central South University Changsha 410083 China

A quantum digital signature (QDS) is utilized to authenticate classical messages and offers information theoretical security guaranteed by the laws of quantum mechanics. Focusing on signing a one-bit message, previous continuous-variable quantum digital signature (CV-QDS) protocols that shared secret keys are generated by quantum key distribution, and it is only mentioned to suitably iterate the one-bit protocol for a multibit message. Although the CV-QDS protocol is unconditionally secure while sending single-bit messages, iterating the one-bit protocol in a naive way will allow some forgery attacks. In this paper, for signing one-bit and multibit messages, a CV-QDS protocol is proposed, on the condition that nonrepudiation and no forging are guaranteed. For one-bit and multibit schemes, we compare the difference in the distribution stage and messaging stage, analyzing the security as well as summarizing some formulas for calculation. We hope the protocol with continuous variables could further improve the performance of QDS.

关键词： Quantum cryptography

Capsule Vision 2024 Challenge: Multi-Class Abnormality Classification for Video Capsule Endoscopy

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arXiv 2024年

作者： Handa, Palak Mahbod, Amirreza Schwarzhans, Florian Woitek, Ramona Goel, Nidhi Dhir, Manas Chhabra, Deepti Jha, Shreshtha Sharma, Pallavi Thakur, Vijay Chawla, Simarpreet Singh Gunjan, Deepak Kakarla, Jagadeesh Raman, Balasubramanian Research Center for Medical Image Analysis and Artificial Intelligence Department of Medicine Danube Private University Krems Austria Department of Electronics and Communication Engineering Indira Gandhi Delhi Technical University for Women Delhi India Department of Artificial Intelligence and Data Sciences Indira Gandhi Delhi Technical University for Women Delhi India Department of Artificial Intelligence and Machine Learning University School of Automation and Robotics Guru Gobind Singh Indraprastha University Delhi India Department of Electronics and Communication Engineering Delhi Technological University Delhi India Columbia University New YorkNY United States Department of Gastroenterology and HNU All India Institute of Medical Sciences Delhi India Chennai Kancheepuram India Department of Computer Science and Engineering Indian Institute of Technology Roorkee India

We present the Capsule Vision 2024 Challenge: Multi-Class Abnormality Classification for Video Capsule Endoscopy. It was virtually organized by the Research center for Medical Image Analysis and Artificial Intelligence (MIAAI), Department of Medicine, Danube Private University, Krems, Austria in collaboration with the 9th International Conference on computer Vision & Image Processing (CVIP 2024) being organized by the Indian Institute of Information Technology, Design and Manufacturing (IIITDM) Kancheepuram, Chennai, India. This document provides an overview of the challenge, including the registration process, rules, submission format, description of the datasets used, qualified team rankings, all team descriptions, and the benchmarking results reported by the organizers. © 2024, CC BY.

关键词： Endoscopy

Potential of multimodal large language models for data mining of medical images and free-text reports

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Meta-Radiology

Meta-Radiology 2024年第4期2卷

作者： Zhang, Yutong Pan, Yi Zhong, Tianyang Dong, Peixin Xie, Kangni Liu, Yuxiao Jiang, Hanqi Wu, Zihao Liu, Zhengliang Zhao, Wei Zhang, Wei Zhao, Shijie Zhang, Tuo Jiang, Xi Shen, Dinggang Liu, Tianming Zhang, Xin Institute of Medical Research Northwestern Polytechnical University Xi'an 710072 China School of Computing The University of Georgia Athens 30602 United States School of Life Science and Technology University of Electronic Science and Technology of China Chengdu 611731 China School of Automation Northwestern Polytechnical University Xi'an 710072 China School of Electronics and Information Northwestern Polytechnical University Xi'an 710072 China School of Biomedical Engineering ShanghaiTech University Shanghai 201210 China Department of Radiology The Second Xiangya Hospital Central South University Changsha 410011 China Clinical Research Center for Medical Imaging in Hunan Province Changsha 410011 China Institute of Biomedical and Health Engineering Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen 518055 China School of Computer and Cyber Sciences Augusta University Augusta 30912 United States Shanghai United Imaging Intelligence Co. Ltd. Shanghai 200230 China Shanghai Clinical Research and Trial Center Shanghai 201210 China

Medical images and radiology reports are essential for physicians to diagnose medical conditions. However, the vast diversity and cross-source heterogeneity inherent in these data have posed significant challenges to the generalizability of current data-mining methods for clinical decision-making. Recently, multimodal large language models (MLLMs), especially Gemini-Vision-series (Gemini) and GPT-4-series (GPT-4) models, have revolutionized numerous domains, significantly impacting the medical field. In this study, we conducted a detailed evaluation of the performance of the Gemini series models (including Gemini-1.0-Pro-Vision, Gemini-1.5-Pro, and Gemini-1.5-Flash) and GPT series models (including GPT-4o, GPT-4-Turbo, and GPT-3.5-Turbo) across 14 medical datasets, covering 5 medical imaging categories (dermatology, radiology, dentistry, ophthalmology, and endoscopy) and 3 radiology report datasets. The investigated tasks encompass disease classification, lesion segmentation, anatomical localization, disease diagnosis, report generation, and lesion detection. Moreover, we also validated the performance of the Claude-3-Opus, Yi-Large, Yi-Large-Turbo, and LLaMA 3 models to gain a comprehensive understanding of the MLLM models in the medical field. Our experimental results demonstrated that Gemini-series models excelled in report generation and lesion detection but faces challenges in disease classification and anatomical localization. Conversely, GPT-series models exhibited proficiency in lesion segmentation and anatomical localization but encountered difficulties in disease diagnosis and lesion detection. Additionally, both the Gemini series and GPT series contain models that have demonstrated commendable generation efficiency. While both models hold promise in reducing physician workload, alleviating pressure on limited healthcare resources, and fostering collaboration between clinical practitioners and artificial intelligence technologies, substantial enhancements

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Drug-food Interactions in the Era of Molecular Big Data, Machine Intelligence, and Personalized Health

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Recent Advances in Food, Nutrition and Agriculture 2022年第1期13卷 27-50页

作者： Roy, Romy Marakkar, Shamsudheen Vayalil, Munawar Peringadi Shahanaz, Alisha Anil, Athira Panicker Kunnathpeedikayil, Shameer Rawal, Ishaan Shetty, Kavya Shameer, Zahrah Sathees, Saraswathi Prasannakumar, Adarsh Pooradan Mathew, Oommen Kaleeckal Subramanian, Laksh-Minarayanan Shameer, Khader Yadav, Kamlesh K. Molecular Robotics Kerala Cochin India Sanaria Inc Rockville MD United States Mar Athanasious College for Advanced Studies Tiruvalla India People Care Health LLP Thrissur Kerala Thiruvalla India Urbana Middle School Urbana 21754 MD United States University of Washington Seattle WA United States Indian Institute of Information Technology (IIIT) Kerala Kottayam India Department of Computer Science Courant Institute of Mathematical Sciences New York University New York NY United States Northwell Health New York NY United States Faculty of Medicine Imperial College London London United Kingdom School of Engineering Medicine Texas A&M University Houston 77030 TX United States Department of Translational Medical Sciences Center for Genomic and Precision Medicine Texas A&M University Houston 77030 TX United States

The drug-food interaction brings forth changes in the clinical effects of drugs. While favourable interactions bring positive clinical outcomes, unfavourable interactions may lead to toxicity. This article reviews the impact of food intake on drug-food interactions, the clinical effects of drugs, and the effect of drug-food in correlation with diet and precision medicine. Emerging areas in drug-food interactions are the food-genome interface (nutrigenomics) and nu-trigenetics. Understanding the molecular basis of food ingredients, including genomic sequencing and pharmacological implications of food molecules, helps to reduce the impact of drug-food interactions. Various strategies are being leveraged to alleviate drug-food interactions;measures including patient engagement, digital health, approaches involving machine intelligence, and big data are a few of them. Furthermore, delineating the molecular communications across diet-microbiome-drug-food-drug interactions in a pharmacomicrobiome framework may also play a vital role in personalized nutrition. Determining nutrient-gene interactions aids in making nutrition deeply personalized and helps mitigate unwanted drug-food interactions, chronic diseases, and adverse events from their onset. Translational bioinformatics approaches could play an essential role in the next generation of drug-food interaction research. In this landscape review, we discuss important tools, databases, and approaches along with key challenges and opportunities in drug-food interaction and its immediate impact on precision medicine. © 2022 Bentham science Publishers.

关键词： big data Drug-food interactions machine intelligence nutrigenomics pharmacomicrobiome precision medicine

Modeling and Detecting Communities in Node Attributed Networks

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arXiv 2021年

作者： Ren, Ren Shao, Jinliang Bishop, Adrian N. Zheng, Wei Xing School of Automation Engineering University of Electronic Science and Technology of China 611731 China The Research Center on Crowd Spectrum Intelligence Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen518054 China Australia Canberra Research Lab Australia The School of Computer Data and Mathematical Sciences Western Sydney University SydneyNSW2751 Australia

As a fundamental structure in real-world networks, in addition to graph topology, communities can also be reflected by abundant node attributes. In attributed community detection, probabilistic generative models (PGMs) have become the mainstream method due to their principled characterization and competitive performances. Here, we propose a novel PGM without imposing any distributional assumptions on attributes, which is superior to the existing PGMs that require attributes to be categorical or Gaussian distributed. Based on the block model of graph structure, our model incorporates the attribute by describing its effect on node popularity. To characterize the effect quantitatively, we analyze the community detectability for our model and then establish the requirements of the node popularity term. This leads to a new scheme for the crucial model selection problem in choosing and solving attributed community detection models. With the model determined, an efficient algorithm is developed to estimate the parameters and to infer the communities. The proposed method is validated from two aspects. First, the effectiveness of our algorithm is theoretically guaranteed by the detectability condition. Second, extensive experiments indicate that our method not only outperforms the competing approaches on the employed datasets, but also shows better applicability to networks with various node attributes. © 2021, CC BY.

关键词： Population dynamics

Boosting the Efficiency and Mechanical Stability of Organic Solar Cells Through a Polymer Acceptor by Reducing the Elastic Modulus

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advanced Energy Materials 2025年第0期

作者： Wang, Yan Yu, Han Zhao, Dan Liu, Wei Liu, Baoze Wu, Xin Gao, Danpeng Zhang, Dong Zhang, Shoufeng Sun, Xianglang Zhang, Chunlei Zhao, Chaoyue Fu, Yuchen Song, Wei Gong, Shaokuan Fu, Yuang Kwok, Chung Hang Ge, Ziyi Lu, Xinhui Chen, Xihan Xiao, Shuang Wong, Wai-Yeung Chai, Yu Yan, He Zhu, Zonglong Department of Chemistry City University of Hong Kong Kowloon999077 Hong Kong Department of Chemistry Guangdong-Hong Kong-Macao Joint Laboratory of Optoelectronic and Magnetic Functional Materials Energy Institute and Hong Kong Branch of Chinese National Engineering Research Center for Tissue Restoration & Reconstruction Hong Kong University of Science and Technology Clear Water Bay Kowloon999077 Hong Kong Department of Applied Biology and Chemical Technology and Research Institute for Smart Energy The Hong Kong Polytechnic University Hung Hom 999077 Hong Kong Hong Kong University of Science and Technology-Shenzhen Research Institute No. 9 Yuexing 1st RD Hi-tech Park Nanshan Guangdong Shenzhen518057 China School of Automation Engineering University of Electronic Science and Technology of China Sichuan Chengdu611731 China Department of Physics City University of Hong Kong Kowloon999077 Hong Kong Zhejiang Engineering Research Center for Energy Optoelectronic Materials and Devices Ningbo Institute of Materials Technology & Engineering Chinese Academy of Sciences Ningbo315201 China Shenzhen Key Laboratory of Intelligent Robotics and Flexible Manufacturing Systems SUSTech Energy Institute for Carbon Neutrality Department of Mechanical and Energy Engineering Southern University of Science and Technology Guangdong Shenzhen518055 China Department of Physics The Chinese University of Hong Kong Shatin999077 Hong Kong Center for Advanced Material Diagnostic Technology and College of Engineering Physics Shenzhen Technology University Guangdong Shenzhen518118 China Shenzhen Research Institute City University of Hong Kong Shenzhen518057 China

Organic solar cells (OSCs) are regarded as one of the most promising flexible power sources due to their lightweight and flexible properties, with the improvement of photovoltaic and mechanical performance. To improve the current density and power conversion efficiency (PCE), mPh4F-TS (TS) and PYSe2F-T (PA) are introduced into the binary host, PM6/mPh4F-TT (PM6/TT) as third components. It is demonstrated that the corresponding ternary devices, in both rigid and flexible devices, achieved superior efficiencies (19.6%/17.7% for PM6/TT+TS, and 19.2%/17.4% for PM6/TT+PA) outperform the binary counterparts (18.3%/16.4%). However, distinct differences in mechanical performance are observed between the polymer acceptor (PA) and small-molecular acceptor (TS). The PM6/TT+PA significantly improved the mechanical stability of flexible devices with a lower elastic modulus of 3.6 GPa, while the PM6/TT+TS resulted in the opposite effect with a higher elastic modulus of 5.5 GPa. Through in-depth investigation, a clear correlation between the elastic modulus, crack density, and mechanical stability of the active layer blends is successfully established, revealing the key role of reducing the elastic modulus in enhancing the mechanical stability of flexible OSCs. This study provides important guidance for the development of flexible photovoltaic devices with both high efficiency and mechanical robustness. © 2025 The Author(s). advanced Energy Materials published by Wiley-VCH GmbH.

关键词： Organic solar cells

Preface to the Special Issue on Flexible Energy Devices

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Journal of Semiconductors 2021年第10期42卷 1-2页

作者： Zhiyong Fan Yonghua Chen Yuanjing Lin Yunlong Zi Hyunhyub Ko Qianpeng Zhang Department of Electronic&Computer Engineering The Hong Kong University of Science and TechnologyHong Kong 999077China Institute of Advanced Materials Nanjing Tech UniversityNanjing 211816China School of Microelectronics Southern University of Science and TechnologyShenzhen 518055China Engineering Research Center of Integrated Circuits for Next-Generation Communications Ministry of EducationSouthern University of Science and TechnologyShenzhen 518055China Department of Mechanical and Automation Engineering The Chinese University of Hong KongHong Kong 999077China Energy and Chemical Engineering Ulsan National Institute of Science and TechnologyUlsan 44919Republic of Korea

Flexible energy devices are the building blocks for next-generation wearable *** energy devices are expected to have multiple functions,such as energy conversion from light to electricity and vice versa,energy generation from triboelectric,energy storage and so *** functions can be efficiently realized by solar cells,light-emitting diodes(LEDs),triboelectric nanogenerators(TENG),batteries and supercapacitors,*** flexible energy devices can be integrated into flexible,wearable,and/or portable platforms to enable wide application prospects in the fields of information,energy,medical care,national defense,***,flexible energy devices face more challenges when compared to their rigid counterparts,which requires more breakthroughs and research efforts on fabrication techniques,materials innovation,novel structure designs,and deep physical understandings.

关键词： breakthrough enable portable