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Distributed IoT Community Detection via Gromov-Wasserstein Metric

IEEE Internet of Things Journal 2024年第8期11卷 13281-13298页

作者： Chang, Shih Yu Chen, Yi Kao, Yi-Chih Chen, Hsiao-Hwa San Jose State University Department of Applied Data Science San JoseCA95192 United States Chengdu610103 China National Yang Ming Chiao Tung University Network and System Division Information Technology Service Center Hsinchu300 Taiwan National Cheng Kung University Department of Engineering Science Tainan70101 Taiwan

The Internet of Things (IoT) network is a complex system interconnected by different types of devices, e.g., sensors, smartphones, computers, etc. Community detection is a critical component to understand and manage complex IoT networks. Although several community detection algorithms were proposed, they in general suffer several issues, such as lack of optimal solutions and scalability, and difficulty to be applied to a dynamic IoT environment. In this work, we propose a framework that uses distributed community detection (DCD) algorithms based on Gromov-Wasserstein (GW) metric, namely, GW-DCD, to support scalable community detection and address the issues with the existing community detection algorithms. The proposed GW-DCD applies GW metric to detect communities of IoT devices embedded in a Euclidean space or in a graph space. GW-DCD is able to handle community detection problems in a dynamic IoT environment, utilizing translation/rotation invariance properties of the GW metric. In addition, DCD approach and parallel matrix computations can be integrated into GW-DCD to shorten the execution time of GW-DCD. Finally, a new metric, i.e., GW driven mutual information (GWMI), is derived to measure the performance of community detection by considering internal structure within each community. Numerical experiments for the proposed GW-DCD were conducted with simulated and real-world data sets. Compared to the existing community detection algorithms, the proposed GW-DCD can achieve a much better performance in terms of GWMI and the runtime. © 2014 IEEE.

关键词： Internet of things

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Tensor MIMO Perfect-Equalizer Design Using New Projection-Based Method and Approximation-Error Analysis

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IEEE systems Journal 2024年第1期18卷 668-679页

作者： Chang, Shih-Yu Wu, Hsiao-Chun Kao, Yi-Chih San Jose State University Department of Applied Data Science San JoseCA95192 United States Louisiana State University School of Electrical Engineering and Computer Science Baton RougeLA70803 United States Yuan Ze University Innovation Center for Ai Applications Chungli32003 Taiwan National Yang Ming Chiao Tung University Network and System Division Information Technology Service Center Hsinchu30010 Taiwan

As real environments are quite complex, multiple-input-multiple-output (MIMO) channels should be characterized by the multidimensional-multirelational models. Tensor linear algebra has been emerging as a powerful and promising tool for such MIMO channel modeling and estimation. However, to the best of our knowledge, there hardly exists any algebraic framework of MIMO channel equalization when the channel is modeled as an arbitrary-dimensional transfer tensor (a tensor whose entries are all rational z-functions). In this work, we first propose a new method based on the projection, namely, 'projection-based method,' to determine whether a given transfer tensor is a full-rank tensor or not and carry out the inversion of an invertible transfer tensor. Our idea is employing the projection subspace of the system transfer tensor to simplify the pertinent inversion process. Second, we design the finite-impulse-response (FIR) realization to approximate the inverse of an arbitrary transfer tensor such that all entries of the approximated inverse transfer tensor are finite-degree polynomials of z. Third, we also analyze the computational and memory complexities required by our proposed new projection-based method for checking the full-rank condition of a given transfer tensor and for undertaking the tensor inversion. Our developed new theories and analysis in this work can be applied to determine the FIR model-orders of an approximated inverse MIMO transfer tensor often encountered in signal processing, control, communications, etc. © 2007-2012 IEEE.

关键词： FIR filters

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Prediction of piRNA-mRNA interactions based on an interactive inference network

Prediction of piRNA-mRNA interactions based on an interactiv...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Liu, Yajun Li, Ru Li, Aimin Fei, Rong Guo, Xie Wu, Fang-Xiang Xi'an University of Technology Shaanxi Key Laboratory for Network Computing and Security Technology Xi'an China Xi'an University of Technology Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi'an China University of Saskatchewan Division of Biomedical Engineering Department of Computer Science Department of Mechanical Engineering Saskatoon Canada

ISBN: (纸本)9798350337488

As the largest class of small non-coding RNAs, piRNAs primarily present in the reproductive cells of mammals, which influence post-transcriptional processes of mRNAs in multiple ways. Effective methods for predicting piRNA and mRNA target relationships can help identify piRNA functions, investigate the possibility of piRNAs as biomarkers and therapeutic targets. In this study, we propose a computational approach for classifying the relationships of piRNA-mRNA pairs based on an interactive inference network (IIN). First, we gather piRNA-mRNA target data, collect sequence data by position alignment, and construct a benchmark dataset. Furthermore, a reliable negative set is constructed by positive-unlabeled learning. Finally, we view a piRNA and a mRNA sequence as a premise and hypothesis sentence, respectively, and IIN model is used to predict the relationship between them. The experiments demonstrate that our method effectively characterizes piRNA-mRNA interaction and could be beneficial for researchers to investigate piRNA functions. © 2023 IEEE.

关键词： interactive inference network noncoding RNA piRNA-mRNA interactions positive-unlabeled learning

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Prediction of exosomal piRNAs based on deep learning for sequence embedding with attention mechanism

Prediction of exosomal piRNAs based on deep learning for seq...

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2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022

作者： Liu, Yajun Ding, Yulian Li, Aimin Fei, Rong Guo, Xie Wu, Fangxiang Xi'an University of Technology Shaanxi Key Laboratory for Network Computing and Security Technology Xi'an China University of Saskatchewan Division of Biomedical Engineering Saskatoon Canada Xi'an University of Technology Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi'an China University of Saskatchewan Division of Biomedical Engineering Department of Computer Science Department of Mechanical Engineering Saskatoon Canada

ISBN: (纸本)9781665468190

PIWI-interacting RNAs (piRNAs) are a type of small non-coding RNAs which bind with the PIWI proteins to exert biological effects in various regulatory mechanisms. A growing amount of evidence reveals that exosomal piRNAs are potential biomarkers for diagnosis and treatment of complex diseases. Effective methods for the prediction of exosomal piRNAs are the foundation of piRNA functional research. In this study, we propose an end-to-end deep network for identifying exosomal piRNAs based on features learned from natural language processing (NLP) models for sequence embedding with attention mechanism. First, a benchmark dataset is constructed by processing piRNA subcellular localization annotated data and sequence data. Moreover, bagging positive unlabeled learning is applied to get the reliable negative set. Finally, we treat a piRNA sequence as a sentence and its k-mer subsequence as a token. Sequence embedding models with self-attention mechanism is designed to extract features from exosome piRNA sequences, which are used for the prediction task. Compared with three competing methods, our model achieves the best performance and reveals the key factors of exosomal piRNA sequences by the attention mechanism. Our model characterizes exosomal piRNAs and could be beneficial for researchers to investigate exosomal piRNAs' functions. © 2022 IEEE.

关键词： Embeddings

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Development and Implementation of a Holistic Flexibility Market Architecture

Development and Implementation of a Holistic Flexibility Mar...

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2022 IEEE Power and Energy Society Innovative Smart Grid Technologies Conference, ISGT 2022

作者： Kraft, Oliver Pohl, Oliver Hager, Ulf Heussen, Kai Muller, Nils Afzal, Zeeshan Ekstedt, Mathias Farahmand, Hossein Ivanko, Dmytro Singh, Ankit Leksawat, Sasiphong Kubis, Andreas TU Dortmund University Institute of Energy Systems Energy Efficiency and Energy Economics Dortmund Germany Technical University of Denmark Department of Electrical Engineering Kongens Lyngby Denmark KTH Royal Institute of Technology Division of Network and System Engineering Stockholm Sweden Norwegian University of Science and Technology Department of Electric Power Engineering Trondheim Norway PSI Software AG Electrical Energy Division Research and Development Department Dortmund Germany

ISBN: (纸本)9781665437752

The demand for increasing flexibility use in power systems is stressed by the changing grid utilization. Making use of largely untapped flexibility potential is possible through novel flexibility markets. Different approaches for these markets are being developed and vary considering their handling of transaction schemes and relation of participating entities. This paper delivers the conceptual development of a holistic system architecture for the realization of an interregional flexibility market, which targets a market based congestion management in the transmission and distribution system through trading between system operators and flexibility providers. The framework combines a market mechanism with the required supplements like appropriate control algorithms for emergency situations, cyber-physical system monitoring and cyber-security assessment. The resulting methods are being implemented and verified in a remote-power-hardware-in-the-loop setup coupling a real world low voltage grid with a geographically distant real time simulation using state of the art control system applications with an integration of the aforementioned architecture components. © 2022 IEEE.

关键词： Cyber Physical system

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First-principles method justifying the Dieke diagram and beyond

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Physical Review Research 2023年第1期5卷 013111-013111页

作者： Katsuhiro Suzuki Takao Kotani Kazunori Sato Division of Materials and Manufacturing Science Graduate School of Engineering Osaka University Suita Osaka 565-0871 Japan Advanced Mechanical and Electronic System Research Center (AMES) Faculty of Engineering Tottori University Tottori 680-8552 Japan CSRN-Osaka Osaka University Toyonaka Osaka 560-8531 Japan Spintronics Research Network Division OTRI Osaka University Toyonaka Osaka 560-8531 Japan

We present a method to determine the model Hamiltonians to treat rare-earth multiplets in solids from the results of the quasiparticle self-consistent GW (QSGW) method. We apply the method to trivalent Eu compounds EuCl3, EuN, and Eu-doped GaN after examining free rare-earth ions. We solve the model Hamiltonian by the exact diagonalization. Our results justify applying the Dieke diagram to ions in solid, while its limitation is clarified. In particular, we show that the crystal fields cause sizable breaking of the Russell-Saunders coupling.

关键词： First-principles calculations Crystal-field theory Density functional theory Exact diagonalization GW method Photoluminescence Wannier function methods

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Prediction of piRNA-mRNA interactions based on an interactive inference network

Prediction of piRNA-mRNA interactions based on an interactiv...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Yajun Liu Ru Li Aimin Li Rong Fei Xie Guo Fang-Xiang Wu Shaanxi Key Laboratory for Network Computing and Security Technology Xi'an University of Technology Xi'an China Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi'an University of Technology Xi'an China Department of Computer Science Department of Mechanical Engineering Division of Biomedical Engineering University of Saskatchewan Saskatoon Canada

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Contribution of irrigation to the production of maize, wheat, and rice in the major global producing countries

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National Science Review 2024年第11期11卷 44-46页

作者： Zhipin Ai Julien Boulange Xin Zhao Fadong Li Rashid Mahmood Kiril Manevski Yonghui Yang Guirui Yu Key Laboratory of Ecosystem Network Observation and Modeling Institute of Geographic Sciences and Natural Resources ResearchChinese Academy of Sciences College of Resources and Environment University of Chinese Academy of Sciences United Graduate School of Agricultural Science Tokyo University of Agriculture and Technology Earth System Division National Institute for Environmental Studies Water Engineering and Management Asian Institute of Technology Sino-Danish College University of the Chinese Academy of Sciences Department of Agroecology Aarhus University Center for Agricultural Resources Research Institute of Genetics and Developmental Biology Chinese Academy of Sciences

Global megatrends in population growth and climate change, together with regional conflicts, are driving an increasing and unequal demand for food and consequently pose a serious threat to food security worldwide. Studies have shown that increasing cropland productivity, rather than expanding cropland area, may be a more viable strategy to promote food security [1]. From an agronomic perspective, irrigation can greatly enhance crop yield and productivity,

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A Comparison of Machine and Statistical Time Series Learning for Encrypted Traffic Prediction

A Comparison of Machine and Statistical Time Series Learning...

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2020 International Conference on Computing, networking and Communications, ICNC 2020

作者： He, Qing Koudouridis, Georgios P. Dan, Gyorgy KTH Royal Institute of Technology Division of Network and Systems Engineering Stockholm Sweden Huawei Technologies RAN System Lab Stockholm Research Center Stockholm Sweden

ISBN: (纸本)9781728149059

This paper studies the utilization of machine and statistical learning methods for predicting encrypted user traffic. To this end, a reference system model and performance metrics for traffic prediction have been defined for enabling on-line training. Based on a collection of representative traffic data sets including various video and web traffic, two different classes of predictors have been evaluated. Our results show that very good prediction performance can be achieved using long short-term memory (LSTM) recurrent neural networks at the price of a significant computational cost for training. © 2020 IEEE.

关键词： Long short-term memory

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Prediction of exosomal piRNAs based on deep learning for sequence embedding with attention mechanism

Prediction of exosomal piRNAs based on deep learning for seq...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Yajun Liu Yulian Ding Aimin Li Rong Fei Xie Guo Fangxiang Wu Shaanxi Key Laboratory for Network Computing and Security Technology Xi'an University of Technology Xi'an China Division of Biomedical Engineering University of Saskatchewan Saskatoon Canada Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi'an University of Technology Xi'an China Department of Computer Science Department of Mechanical Engineering Division of Biomedical Engineering University of Saskatchewan Saskatoon Canada

ISBN: (数字)9781665468190

ISBN: (纸本)9781665468206

关键词： Proteins Location awareness RNA Biological system modeling Predictive models Feature extraction Natural language processing

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