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arXiv 2021年

作者： Yin, Hoover H.F. Yang, Shenghao Zhou, Qiaoqiao Yung, Lily M.L. Ng, Ka Hei The Institute of Network Coding The Chinese University of Hong Kong Hong Kong Hong Kong The School of Science and Engineering The Chinese University of Hong Kong Shenzhen Shenzhen China Shenzhen Key Laboratory of IoT Intelligent Systems and Wireless Network Technology Shenzhen Research Institute of Big Data Shenzhen China The Department of Information Engineering The Chinese University of Hong Kong Hong Kong Hong Kong The Department of Computer Science and Engineering The Chinese University of Hong Kong Hong Kong Hong Kong The Department of Physics The Chinese University of Hong Kong Hong Kong Hong Kong

Multi-hop networks become popular network topologies in various emerging Internet of things applications. Batched network coding (BNC) is a solution to reliable communications in such networks with packet loss. By grouping packets into small batches and restricting recoding to the packets belonging to the same batch, BNC has a much smaller computational and storage requirements at the intermediate nodes compared with a direct application of random linear network coding. In this paper, we propose a practical recoding scheme called blockwise adaptive recoding (BAR) which learns the latest channel knowledge from short observations so that BAR can adapt to the fluctuation of channel conditions. We focus on investigating practical concerns such as the design of efficient BAR algorithms. We also design and investigate feedback schemes for BAR under imperfect feedback systems. Our numerical evaluations show that BAR has significant throughput gain for small batch size compared with the existing baseline recoding scheme. More importantly, this gain is insensitive to inaccurate channel knowledge. This encouraging result suggests that BAR is suitable to be realized in practice as the exact channel model and its parameters could be unknown and subject to change from time to time. Copyright © 2021, The Authors. All rights reserved.

关键词： Network coding

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Gene set correlation enrichment analysis for interpreting and annotating gene expression profiles

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Nucleic Acids Research 2024年第3期52卷 E17页

作者： Chang, Lan-Yun Lee, Meng-Zhan Wu, Yujia Lee, Wen-Kai Ma, Chia-Liang Chang, Jun-Mao Chen, Ciao-Wen Huang, Tzu-Chun Lee, Chia-Hwa Lee, Jih-Chin Tseng, Yu-Yao Lin, Chun-Yu Institute of Bioinformatics and Systems Biology National Yang Ming Chiao Tung University Hsinchu 300 Taiwan School of Medical Laboratory Science and Biotechnology College of Medical Science and Technology Taipei Medical University New Taipei City 235 Taiwan Center for Intelligent Drug Systems and Smart Bio-devices (IDS2B) National Yang Ming Chiao Tung University Hsinchu 300 Taiwan TMU Research Center of Cancer Translational Medicine Taipei Medical University Taipei 110 Taiwan Ph.D. Program in Medical Biotechnology College of Medical Science and Technology Taipei Medical University New Taipei City 235 Taiwan Department of Otolaryngology-Head and Neck Surgery Tri-Service General Hospital National Defense Medical Center Taipei 110 Taiwan Department of Food Science Nutrition and Nutraceutical Biotechnology Shih Chien University Taipei 104 Taiwan Department of Biological Science and Technology National Yang Ming Chiao Tung University Hsinchu 300 Taiwan Cancer and Immunology Research Center National Yang Ming Chiao Tung University Taipei 112 Taiwan Institute of Data Science and Engineering National Yang Ming Chiao Tung University Hsinchu 300 Taiwan School of Dentistry Kaohsiung Medical University Kaohsiung 807 Taiwan

Pathway analysis, including nontopology-based (non-TB) and topology-based (TB) methods, is widely used to interpret the biological phenomena underlying differences in expression data between two phenotypes. By considering dependencies and interactions between genes, TB methods usually perform better than non-TB methods in identifying pathways that include closely relevant or directly causative genes for a given phenotype. However, most TB methods may be limited by incomplete pathway data used as the reference network or by difficulties in selecting appropriate reference networks for different research topics. Here, we propose a gene set correlation enrichment analysis method, Gscore, based on an expression dataset-derived coexpression network to examine whether a differentially expressed gene (DEG) list (or each of its DEGs) is associated with a known gene set. Gscore is better able to identify target pathways in 89 human disease expression datasets than eight other state-of-the-art methods and offers insight into how disease-wide and pathway-wide associations reflect clinical outcomes. When applied to RNA-seq data from COVID-19-related cells and patient samples, Gscore provided a means for studying how DEGs are implicated in COVID-19-related pathways. In summary, Gscore offers a powerful analytical approach for annotating individual DEGs, DEG lists, and genome-wide expression profiles based on existing biological knowledge. © 2024 Oxford University Press. All rights reserved.

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Characterizing environmental pollution with civil complaints and social media data: A case of the Greater Taipei Area

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Journal of Environmental Management 2023年 348卷 119310页

作者： Guo, Mengdi Lin, Yu Shyu, Rong-Juin Huang, Jianxiang School of Architecture Tianjin University No. 92 Weijin Road Nankai District Tianjin China School of Data Science City University of Hong Kong 16-201 16/F Lau Ming Wai Academic Building 83 Tat Chee Avenue Hong Kong SAR China Department of System Engineering and Naval Architecture National Taiwan Ocean University Keelung Taiwan Department of Urban Planning and Design Faculty of Architecture the University of Hong Kong Pok Fu Lam Rd Hong Kong SAR China The University of Hong Kong Shenzhen Institute of Research and Innovation 5/F Key Laboratory Platform Building Shenzhen Virtual University Park No.6 Yuexing 2nd Rd Nanshan Shenzhen China Urban Systems Institute The University of Hong Kong Pokfulam Road Hong Kong Special Administrative Region of China China

Environmental pollution is a major cause of nuisance and ill health among urban residents. Complaints are traditionally self-reported through phone-based systems. Social media provide novel channels to detect pollution-related incidents;however, their reliability has not been sufficiently evaluated. This study aimed to compare pollution incidents expressed on Twitter with those extracted from phone-based systems and to identify the built environment and socioeconomic attributes that can predict the likelihood of pollution incidents. A total of 639,746 tweets were retrieved from the Greater Taipei Area in 2017 and 110,716 self-reported pollution incidents were extracted from the Public Nuisance Petition system during the same period. The results suggest that complaints collected from phone-based systems and Twitter were found to have correlated with each other spatially, albeit they differ in temporal profiles and by the proportion of pollution categories. Catering businesses and the entertainment activities they attract appear to be the main sources of pollution complaints and can be precisely captured by geotagged tweets. This can serve as a strong predictor for pollution incidents, more than traditional indicators such as population density or industrial activities, as suggested by earlier studies. Social media analytics, with their ability to monitor and analyze online discussions in a timely manner, can be a valuable supplement to existing phone-based pollution monitoring procedures. The methodologies developed in this study have the potential to support the proactive management of urban environmental pollution, in which resources can be prioritized in key areas to further enhance the quality of urban services. © 2023 Elsevier Ltd

关键词： Machine learning

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Classification vs regression in overparameterized regimes: Does the loss function matter?

arXiv

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arXiv 2020年

作者： Muthukumar, Vidya Narang, Adhyyan Subramanian, Vignesh Belkin, Mikhail Hsu, Daniel Sahai, Anant Electrical and Computer Engineering and Industrial and Systems Engineering Georgia Institute of Technology AtlantaGA30332 United States Department of Electrical and Computer Engineering University of Washington SeattleWA98115 United States Department of Electrical Engineering and Computer Sciences University of California Berkeley BerkeleyCA94720 United States Halicioglu Data Science Institute UC San Diego San DiegoCA92093 United States Department of Computer Science Data Science Institute Columbia University New YorkNY10027 United States

We compare classification and regression tasks in an overparameterized linear model with Gaussian features. On the one hand, we show that with sufficient overparameterization all training points are support vectors: solutions obtained by least-squares minimum-norm interpolation, typically used for regression, are identical to those produced by the hard-margin support vector machine (SVM) that minimizes the hinge loss, typically used for training classifiers. On the other hand, we show that there exist regimes where these interpolating solutions generalize well when evaluated by the 0-1 test loss function, but do not generalize if evaluated by the square loss function, i.e. they approach the null risk. Our results demonstrate the very different roles and properties of loss functions used at the training phase (optimization) and the testing phase (generalization). Copyright © 2020, The Authors. All rights reserved.

关键词： Support vector machines

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Towards 6G wireless communication networks:vision, enabling technologies, and new paradigm shifts

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science China(Information sciences) 2021年第1期64卷 5-78页

作者： Xiaohu YOU Cheng-Xiang WANG Jie HUANG Xiqi GAO Zaichen ZHANG Mao WANG Yongming HUANG Chuan ZHANG Yanxiang JIANG Jiaheng WANG Min ZHU Bin SHENG Dongming WANG Zhiwen PAN Pengcheng ZHU Yang YANG Zening LIU Ping ZHANG Xiaofeng TAO Shaoqian LI Zhi CHEN Xinying MA Chih-Lin I Shuangfeng HAN Ke LI Chengkang PAN Zhimin ZHENG Lajos HANZO Xuemin (Sherman) SHEN Yingjie Jay GUO Zhiguo DING Harald HAAS Wen TONG Peiying ZHU Ganghua YANG Jun WANG Erik G.LARSSON Hien Quoc NGO Wei HONG Haiming WANG Debin HOU Jixin CHEN Zhe CHEN Zhangcheng HAO Geoffrey Ye LI Rahim TAFAZOLLI Yue GAO H.Vincent POOR Gerhard P.FETTWEIS Ying-Chang LIANG National Mobile Communications Research Laboratory School of Information Science and EngineeringSoutheast University Purple Mountain Laboratories Shanghai Institute of Fog Computing Technology (SHIFT) ShanghaiTech University Research Center for Network Communication Peng Cheng Laboratory State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications National Engineering Laboratory for Mobile Network Technologies Beijing University of Posts and Telecommunications National Key Laboratory of Science and Technology on Communications University of Electronic Science and Technology of China (UESTC) China Mobile Research Institute School of Electronics and Computer Science University of Southampton Department of Electrical and Computer Engineering University of Waterloo Global Big Data Technologies Centre (GBDTC) University of Technology Sydney School of Electrical and Electronic Engineering The University of Manchester LiFi Research and Development Centre Institute for Digital CommunicationsSchool of EngineeringThe University of Edinburgh Huawei Technologies Canada Co. Ltd. Huawei Technologies Department of Electrical Engineering (ISY) Link?ping University Institute of Electronics Communications & Information Technology (ECIT)Queen's University Belfast State Key Laboratory of Millimeter Waves School of Information Science and EngineeringSoutheast University School of Electrical and Computer Engineering Georgia Institute of Technology 5G Innovation Centre University of Surrey Princeton University Vodafone Chair Mobile Communications Systems Technische Universit?t Dresden Center for Intelligent Networking and Communications (CINC) University of Electronic Science and Technology of China (UESTC)

The fifth generation(5G) wireless communication networks are being deployed worldwide from 2020 and more capabilities are in the process of being standardized, such as mass connectivity, ultra-reliability,and guaranteed low latency. However, 5G will not meet all requirements of the future in 2030 and beyond, and sixth generation(6G) wireless communication networks are expected to provide global coverage, enhanced spectral/energy/cost efficiency, better intelligence level and security, etc. To meet these requirements, 6G networks will rely on new enabling technologies, i.e., air interface and transmission technologies and novel network architecture, such as waveform design, multiple access, channel coding schemes, multi-antenna technologies, network slicing, cell-free architecture, and cloud/fog/edge computing. Our vision on 6G is that it will have four new paradigm shifts. First, to satisfy the requirement of global coverage, 6G will not be limited to terrestrial communication networks, which will need to be complemented with non-terrestrial networks such as satellite and unmanned aerial vehicle(UAV) communication networks, thus achieving a space-airground-sea integrated communication network. Second, all spectra will be fully explored to further increase data rates and connection density, including the sub-6GHz, millimeter wave(mmWave), terahertz(THz),and optical frequency bands. Third, facing the big datasets generated by the use of extremely heterogeneous networks, diverse communication scenarios, large numbers of antennas, wide bandwidths, and new service requirements, 6G networks will enable a new range of smart applications with the aid of artificial intelligence(AI) and big data technologies. Fourth, network security will have to be strengthened when developing 6G networks. This article provides a comprehensive survey of recent advances and future trends in these four aspects. Clearly, 6G with additional technical requirements beyond those of 5G will enable f

关键词： 6G vision network architecture air interface and transmission technologies space-air-ground-sea integrated network all spectra artificial intelligence network security

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UAV Communication Channel Measurement, Modeling, and Application

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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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Recent advances and applications of deep learning methods in materials science

arXiv

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arXiv 2021年

作者： Choudhary, Kamal DeCost, Brian Chen, Chi Jain, Anubhav Tavazza, Francesca Cohn, Ryan WooPark, Cheol Choudhary, Alok Agrawal, Ankit Billinge, Simon J.L. Holm, Elizabeth Ong, Shyue Ping Wolverton, Chris Materials Science and Engineering Division National Institute of Standards and Technology GaithersburgMD20899 United States Theiss Research San diegoCA92037 United States Material Measurement Science Division National Institute of Standards and Technology GaithersburgMD20899 United States Department of NanoEngineering University of California San Diego CA92093 United States Energy Technologies Area Lawrence Berkeley National Laboratory BerkeleyCA United States Department of Materials Science and Engineering Carnegie Mellon University PittsburghPA15213 United States Department of Materials Science and Engineering Northwestern University EvanstonIL60208 United States Department of Electrical and Computer Engineering Northwestern University EvanstonIL60208 United States Department of Applied Physics and Applied Mathematics and the Data Science Institute Fu Foundation School of Engineering and Applied Sciences Columbia University New YorkNY10027 United States

Deep learning (DL) is one of the fastest growing topics in materials data science, with rapidly emerging applications spanning atomistic, image-based, spectral, and textual data modalities. DL allows analysis of unstructured data and automated identification of features. Recent development of large materials databases has fueled the application of DL methods in atomistic prediction in particular. In contrast, advances in image and spectral data have largely leveraged synthetic data enabled by high quality forward models as well as by generative unsupervised DL methods. In this article, we present a high-level overview of deep-learning methods followed by a detailed discussion of recent developments of deep learning in atomistic simulation, materials imaging, spectral analysis, and natural language processing. For each modality we discuss applications involving both theoretical and experimental data, typical modeling approaches with their strengths and limitations, and relevant publicly available software and datasets. We conclude the review with a discussion of recent cross-cutting work related to uncertainty quantification in this field and a brief perspective on limitations, challenges, and potential growth areas for DL methods in materials science. The application of DL methods in materials science presents an exciting avenue for future materials discovery and design. © 2021, CC BY.

关键词： Application programs

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A microstrip-based sensor for glucose monitoring: towards non-invasive blood glucose detection

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Measurement 2025年 256卷

作者： Guodao Zhang Qiwen Zhang Chaochao Wang Xiaojun Ji Zhengqiu Weng Abdulilah Mohammad Mayet Xinjun Miao Institute of Intelligent Media Computing Hangzhou Dianzi University Hangzhou 310018 China Provincial Key Laboratory of Multimodal Perceiving and Intelligent Systems Jiaxing University Jiaxing 314000 China Shangyu Institute of Science and Engineering Co.Ltd. Hangzhou Dianzi University Shaoxing 312300 China Department of Cardiology Wenzhou Central Hospital Wenzhou 325000 China School of Data Science and Artificial Intelligence Wenzhou University of Technology Wenzhou 325000 China Electrical Engineering Department College of Engineering King Khalid University Abha Kingdom of Saudi Arabia Department of Emergency Wenzhou Central Hospital Wenzhou 325000 China

In this study, a microstrip sensor with U-shaped resonators was designed and developed for non-invasive blood glucose measurement. The sensor comprises five U-shaped resonators forming a microstrip circuit, transmitting in the frequency ranges of 2–2.3 GHz and 3.9–4.2 GHz. Glucose solutions with concentrations from 50 mg/dL to 500 mg/dL were prepared and placed in a 3D-printed PLA container for testing. Samples were positioned non-contact on the sensor, and frequency response changes up to 6 GHz were recorded. The sensor exhibited high sensitivity, measuring 1.50 MHz per mg/dL. Due to environmental susceptibilities and the relatively low repeatability of microstrip sensors, each sample was tested five times, yielding a total of 50 measurements. These recorded responses were processed using a multilayer perceptron (MLP) neural network, which predicted glucose concentrations with a mean relative error (MRE%) of less than 2.48 %. This research highlights the development of a highly sensitive, non-invasive glucose sensor integrated with a neural network, offering precise and rapid blood glucose measurements. This system holds promise as a valuable tool for managing diabetes and other blood glucose-related conditions.

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A global overview of marine heatwaves in a changing climate (vol 5, 701, 2024)

COMMUNICATIONS EARTH & ENVIRONMENT

引用

COMMUNICATIONS EARTH & ENVIRONMENT 2024年第1期5卷 701页

作者： Capotondi, Antonietta Rodrigues, Regina R. Sen Gupta, Alex Benthuysen, Jessica A. Deser, Clara Froelicher, Thomas L. Lovenduski, Nicole S. Amaya, Dillon J. Le Grix, Natacha Xu, Tongtong Hermes, Juliet Holbrook, Neil J. Martinez-Villalobos, Cristian Masina, Simona Roxy, Mathew Koll Schaeffer, Amandine Schlegel, Robert W. Smith, Kathryn E. Wang, Chunzai University of Colorado Boulder Cooperative Institute for Research in Environmental Sciences Boulder USA (GRID:grid.266190.a) (ISNI:***) NOAA/Physical Sciences Laboratory Boulder USA (GRID:grid.511342.0) Federal University of Santa Catarina Florianópolis Brazil (GRID:grid.411237.2) (ISNI:0000 0001 2188 7235) University of New South Wales Climate Change Research Centre and Centre for Marine Science and Innovation Sydney Australia (GRID:grid.1005.4) (ISNI:0000 0004 4902 0432) University of New South Wales Australian Research Council Centre of Excellence for Climate Extremes Sydney Australia (GRID:grid.1005.4) (ISNI:0000 0004 4902 0432) Australian Institute of Marine Science Crawley Australia (GRID:grid.1046.3) (ISNI:0000 0001 0328 1619) National Center for Atmospheric Research Climate and Global Dynamics Laboratory Boulder USA (GRID:grid.57828.30) (ISNI:0000 0004 0637 9680) University of Bern Climate and Environmental Physics Physics Institute Bern Switzerland (GRID:grid.5734.5) (ISNI:0000 0001 0726 5157) University of Bern Oeschger Centre for Climate Change Research Bern Switzerland (GRID:grid.5734.5) (ISNI:0000 0001 0726 5157) University of Colorado Boulder Department of Atmospheric and Oceanic Sciences and Institute of Arctic and Alpine Research Boulder USA (GRID:grid.266190.a) (ISNI:***) NOAA/Physical Sciences Laboratory Boulder USA (GRID:grid.511342.0) South African Environmental Observation Network Cape Town South Africa (GRID:grid.507758.8) (ISNI:0000 0004 0499 441X) University of Tasmania Institute for Marine and Antarctic Studies Hobart Australia (GRID:grid.1009.8) (ISNI:0000 0004 1936 826X) University of Tasmania ARC Centre of Excellence for Climate Extremes Hobart Australia (GRID:grid.1009.8) (ISNI:0000 0004 1936 826X) Universidad Adolfo Ibáñez Faculty of Engineering and Science Santiago Chile (GRID:grid.440617.0) (ISNI:0000 0001 2162 5606) ANID Technology Center No. DO210001 Data Observatory Foundation Santiago Chile (GRID:grid.440617.0) CMCC Foundation—Euro-M

Marine heatwaves have profoundly impacted marine ecosystems over large areas of the world oceans, calling for improved understanding of their dynamics and predictability. Here, we critically review the recent substantial advances in this active area of research, including the exploration of the three-dimensional structure and evolution of these extremes, their drivers, their connection with other extremes in the ocean and over land, future projections, and assessment of their predictability and current prediction skill. To make progress on predicting and projecting marine heatwaves and their impacts, a more complete mechanistic understanding of these extremes over the full ocean depth and at the relevant spatial and temporal scales is needed, together with models that can realistically capture the leading mechanisms at those scales. Sustained observing systems, as well as measuring platforms that can be rapidly deployed, are essential to achieve comprehensive event characterizations while also chronicling the evolving nature of these extremes and their impacts in our changing *** understanding of marine heatwave predictability and impacts requires analysis of these extremes at full ocean depth, using models and observations capturing their key drivers at the relevant scales, according to a broad literature review.

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Benchmarking Emergency department Triage Prediction Models with Machine Learning and Large Public Electronic Health Records

arXiv

引用

arXiv 2021年

作者： Xie, Feng Zhou, Jun Lee, Jin Wee Tan, Mingrui Li, Siqi Rajnthern, Logasan S.O. Chee, Marcel Lucas Chakraborty, Bibhas Wong, An-Kwok Ian Dagan, Alon Ong, Marcus Eng Hock Gao, Fei Liu, Nan Centre for Quantitative Medicine and Programme in Health Services and Systems Research Duke-NUS Medical School Singapore Singapore Singapore Singapore School of Electrical and Electronic Engineering Nanyang Technological University Singapore Singapore Faculty of Medicine Nursing and Health Sciences Monash University VIC Australia Department of Statistics and Data Science National University of Singapore Singapore Singapore Department of Biostatistics and Bioinformatics Duke University DurhamNC United States Division of Pulmonary Allergy and Critical Care Medicine Duke University DurhamNC United States Department of Emergency Medicine Beth Israel Deaconess Medical Center Harvard Medical School BostonMA United States MIT Critical Data Laboratory for Computational Physiology Institute for Medical Engineering and Science Massachusetts Institute of Technology CambridgeMA United States Department of Emergency Medicine Singapore General Hospital Singapore Singapore SingHealth AI Health Program Singapore Health Services Singapore Singapore Institute of Data Science National University of Singapore Singapore Singapore

The demand for emergency department (ED) services is increasing across the globe, particularly during the current COVID-19 pandemic. Clinical triage and risk assessment have become increasingly challenging due to the shortage of medical resources and the strain on hospital infrastructure caused by the pandemic. As a result of the widespread use of electronic health records (EHRs), we now have access to a vast amount of clinical data, which allows us to develop predictive models and decision support systems to address these challenges. To date, however, there are no widely accepted benchmark ED triage prediction models based on large-scale public EHR data. An open-source benchmarking platform would streamline research workflows by eliminating cumbersome data preprocessing, and facilitate comparisons among different studies and methodologies. In this paper, based on the Medical Information Mart for Intensive Care IV Emergency department (MIMIC-IV-ED) database, we developed a publicly available benchmark suite for ED triage predictive models and created a benchmark dataset that contains over 400,000 ED visits from 2011 to 2019. We introduced three ED-based outcomes (hospitalization, critical outcomes, and 72-hour ED reattendance) and implemented a variety of popular methodologies, ranging from machine learning methods to clinical scoring systems. We evaluated and compared the performance of these methods against benchmark tasks. Our codes are open-source, allowing anyone with MIMIC-IV-ED data access to perform the same steps in data processing, benchmark model building, and experiments. This study provides future researchers with insights, suggestions, and protocols for managing raw data and developing risk triaging tools for emergency care. © 2021, CC BY.

关键词： Learning systems

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