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Network constraints on the mixing patterns of binary node metadata

Physical Review E 2020年第6期102卷 062310-062310页

作者： Matteo Cinelli Leto Peel Antonio Iovanella Jean-Charles Delvenne Ca' Foscari University of Venice Department of Environmental Sciences Informatics and Statistics 30172 Mestre (VE) Italy Applico Lab CNR-ISC 00185 Rome Italy Institute of Data Science Faculty of Science and Engineering Maastricht University Maastricht The Netherlands Department of Data Analytics and Digitalisation School of Business and Economics Maastricht University Maastricht The Netherlands University of Rome “Tor Vergata” Via del Politecnico 1 Rome Italy ICTEAM Université catholique de Louvain Louvain-la-Neuve Belgium CORE Université catholique de Louvain Louvain-la-Neuve Belgium

We consider the network constraints on the bounds of the assortativity coefficient, which aims to quantify the tendency of nodes with the same attribute values to be connected. The assortativity coefficient can be considered as the Pearson's correlation coefficient of node metadata values across network edges and lies in the interval [−1,1]. However, properties of the network, such as degree distribution and the distribution of node metadata values, place constraints upon the attainable values of the assortativity coefficient. This is important as a particular value of assortativity may say as much about the network topology as about how the metadata are distributed over the network—a fact often overlooked in literature where the interpretation tends to focus simply on the propensity of similar nodes to link to each other, without any regard on the constraints posed by the topology. In this paper we quantify the effect that the topology has on the assortativity coefficient in the case of binary node metadata. Specifically, we look at the effect that the degree distribution, or the full topology, and the proportion of each metadata value has on the extremal values of the assortativity coefficient. We provide the means for obtaining bounds on the extremal values of assortativity for different settings and demonstrate that under certain conditions the maximum and minimum values of assortativity are severely limited, which may present issues in interpretation when these bounds are not considered.

关键词： Clustering Network structure Topological tools

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Over Parameterized Two-level Neural Networks Can Learn Near Optimal Feature Representations

arXiv

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arXiv 2019年

作者： Fang, Cong Dong, Hanze Zhang, Tong Shenzhen Research Institute of Big Data Department of Mathematics HKUST Department of Computer Science and Mathematics HKUST

Recently, over-parameterized neural networks have been extensively analyzed in the literature (Zhang et al., 2016;Mei et al., 2018;Du et al., 2019a). However, the previous studies cannot satisfactorily explain why fully trained neural networks are successful in practice. In this paper, we present a new theoretical framework for analyzing over-parameterized neural networks which we call neural feature repopulation. Our analysis can satisfactorily explain the empirical success of two level neural networks that are trained by standard learning algorithms. Our key theoretical result is that in the limit of infinite number of hidden neurons, over-parameterized two-level neural networks trained via the standard (noisy) gradient descent learns a well-defined feature distribution (population), and the limiting feature distribution is nearly optimal for the underlying learning task under certain conditions. Empirical studies confirm that predictions of our theory are consistent with the results observed in real practice. Copyright © 2019, The Authors. All rights reserved.

关键词： Parameterization

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A Learning Based Human Interaction Modeling Using Mobile Sensing

A Learning Based Human Interaction Modeling Using Mobile Sen...

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IEEE International Conference on big data and Cloud Computing (BdCloud)

作者： Tarun Kulshrestha Divya Saxena Rajdeep Niyogi Department of CSE IIT Roorkee Roorkee India The Hong Kong PolyU University Research Facility in Big Data Analytics Kowloon Hong Kong

ISBN: (数字)9781728143286

ISBN: (纸本)9781728143293

Online social networks are emerging as a convenient platform where users build social relations with other individuals having similar interests, family/work background, etc. However, existing human interaction modeling is based on social graphs which are not more precise for friend suggestions in real-life. In this paper, we leverage the basic feats of deep learning for developing human interaction system, named MyCompanion, based on the user's lifestyle/activity information collected using the mobile crowd sensing. We collect a user's local knowledge, such as local information, ambient, and activity type, activity location and activity time. Then, the collected information is further aggregated and transferred to the deep learning enabled cloud server for user's daily schedule/activities analysis. We propose a schedule matching algorithm which finds the similarity among individuals' activities w.r.t. their activity type, activity time and activity location to recommend the most suitable friend(s) to the users. We develop a real-time testbed to perform a spatio-temporal analysis of the collected data from the users' smartphones. We also perform several experiments for evaluating the system performance. Our proof-of-concept prototype shows the usability of the proposed system.

关键词： Social networking (online) Smart phones Real-time systems Recurrent neural networks Deep learning Sensors Schedules

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Crafting the future: Enhancing gas separation with nanoarchitectonics of bimetallic Ni–Co-ZIF-67 in polysulfone-based mixed matrix membranes

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Case Studies in Chemical and Environmental Engineering 2024年 9卷

作者： Sulistiono, Dety Oktavia Ali, Badrut Tamam Ibnu Widyanto, Alvin Rahmad Ediati, Ratna Widiastuti, Nurul Lestari, Witri Wahyu Akhlus, Syafsir Iqbal, Rendy Muhamad Senda, Semuel Pati Fansuri, Hamzah Department of Chemistry Faculty of Science and Data Analytics Institut Teknologi Sepuluh Nopember Sukolilo Surabaya 60111 Indonesia Automotive Engineering Engineering Department Politeknik Negeri Jember Jember 68101 Indonesia Center for Process and Manufacturing Industry Technology National Research and Innovation Agency Technology Energy Cluster PUSPIPTEK 625 Building South Tangerang Indonesia Department of Applied Chemistry Shibaura Institute of Technology 3-7-5 Toyosu Koto-ku Tokyo 135-8548 Japan Department of Chemistry Faculty of Mathematics and Natural Sciences Universitas Sebelas Maret Jl. Ir. Sutami No. 36A Kentingan Jebres Surakarta 57126 Indonesia Department of Chemistry Faculty of Mathematics and Natural Sciences Universitas Palangka Raya Kampus UPR Tunjung Nyaho Palangkaraya 73111 Indonesia Advanced Membrane Technology Research Center (AMTEC) Faculty of Chemical and Energy Engineering Universiti Teknologi Malaysia Johor Skudai 81310 Malaysia

In response to global clean energy demands, this study develops mixed matrix membranes (MMMs) for gas separation in renewable energy applications. MMMs are produced through phase inversion by employing polysulfone and bimetallic-ZIF (Ni–Co-ZIF-67). Bimetallic-ZIF, formed in-situ by Co and Ni metals in water solvent with triethylamine, enhances gas selectivity. MMMs with 4 wt% bimetallic-ZIF, Co: Ni ratio of 9:1 (4Ni10Z67), exhibit remarkable selectivity: CO₂/CH₄ (21.19), H₂/N₂ (1.47), H₂/CH₄ (44.82), and N₂/CH₄ (30.40). Strong interactions between bimetallic-ZIF and CO₂, facilitated by molecular sieving and Ni–Co sites, contribute to these results. © 2024 The Authors

关键词： Bimetallic-ZIF CO2 capture Gas emissions Metal substitution Renewable energy

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Federated multi-view learning for private medical data integration and analysis

arXiv

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arXiv 2021年

作者： Che, Sicong Peng, Hao Sun, Lichao Chen, Yong He, Lifang College of Information and Electrical Engineering China Agricultural University Beijing100083 China Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University No. 37 Xue Yuan Road Haidian District Beijing100191 China Department of Computer Science and Engineering Lehigh University BethlehemPA United States Department of Biostatistics Epidemiology and Informatics University of Pennsylvania PhiladelphiaPA United States

Along with the rapid expansion of information technology and digitalization of health data, there is an increasing concern on maintaining data privacy while garnering the benefits in medical field. Two critical challenges are identified: Firstly, medical data is naturally distributed across multiple local sites, making it difficult to collectively train machine learning models without data leakage. Secondly, in medical applications, data are often collected from different sources and views, resulting in heterogeneity and complexity that requires reconciliation. This paper aims to provide a generic Federated Multi-View Learning (FedMV) framework for multi-view data leakage prevention, which is based on different types of local data availability and enables to accommodate two types of problems: Vertical Federated Multi-View Learning (V-FedMV) and Horizontal Federated Multi-View Learning (H-FedMV). We experimented with real-world keyboard data collected from BiAffect study. The results demonstrated that the proposed FedMV approach can make full use of multi-view data in a privacy-preserving way, and both V-FedMV and H-FedMV methods perform better than their single-view and pairwise counterparts. Besides, the proposed model can be easily adapted to deal with multi-view sequential data in a federated environment, which has been modeled and experimentally studied. To the best of our knowledge, this framework is the first to consider both vertical and horizontal diversification in the multi-view setting as well as their sequential federated learning. Copyright © 2021, The Authors. All rights reserved.

关键词： data integration

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A meta confirmatory factor analysis of the underdeveloped areas in the Java Island

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AIP Conference Proceedings 2021年第1期2360卷

作者： Bambang Widjanarko Otok Agus Suharsono Purhadi Rahmawati Erma Standsyah Harun Al Azies Department of Statistics Faculty of Science and Data Analytics Institut Teknologi Sepuluh Nopember Jalan Raya ITS Kampus ITS Sukolilo 60111 Surabaya-Indonesia

Underdeveloped areas are regencies whose territories and communities are less developed than other regions at the national level. The regions of Java island are faster-developing areas than other areas outside of Java, but there are still areas in Java that are classified as underdeveloped areas. The backwardness of the area is measured based on six main criteria, namely economy, human resources, infrastructure, regional financial capacity, accessibility, and regional characteristics. This research can be carried out by compiling a model on the influencing factors of underdeveloped areas with the meta-analysis CFA TSSEM approach. The results of this study show that the analysis of the structural model can be accepted to explain the underdevelopment of areas in Java based on the result the Goodness of Fit Indicates, it is the RMSEA < 0.008 so it can be confirmed that the indicators used in infrastructure variables, HR variables, and regional characteristic variables are suitable to be used to measure the underdeveloped areas in Java.

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An Asynchronous Clustering and Mobile data Gathering Schema Based on Timer Mechanism in Wireless Sensor Networks

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Computers, Materials & Continua 2019年第3期58卷 711-725页

作者： Jin Wang Yu Gao Wei Liu Wenbing Wu Se-Jung Lim Hunan Provincial Key Laboratory of Intelligent Processing of Big Data on Transportation School of Computer&Communication EngineeringChangsha University of Science&TechnologyChangsha410114China School of Information Science and Engineering Fujian University of TechnologyFuzhou350000China College of Information Engineering Yangzhou UniversityYangzhou225000China Department of Computer Engineering Chonnam National UniversityYeosu596597Korea.

Recently,Wireless sensor networks(WSNs)have become very popular research topics which are applied to many *** provide pervasive computing services and techniques in various potential applications for the Internet of Things(IoT).An Asynchronous Clustering and Mobile data Gathering based on Timer Mechanism(ACMDGTM)algorithm is proposed which would mitigate the problem of“hot spots”among sensors to enhance the lifetime of *** clustering process takes sensors’location and residual energy into consideration to elect suitable cluster ***,one mobile sink node is employed to access cluster heads in accordance with the data overflow time and moving time from cluster heads to *** experimental results display that the presented method can avoid long distance communicate between sensor ***,this algorithm reduces energy consumption effectively and improves package delivery rate.

关键词： Internet of things wireless sensor networks clustering mobile data collection timer.

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Comparing digital histology slides with multiple staining based on decoloring and dyeing technique

Comparing digital histology slides with multiple staining ba...

引用

IEEE International Conference on Bioinformatics and Biomedicine

作者： Chunbao Wang Zhe Yang Kai Wang Pargorn Puttapirat Chen Li Guanjun Zhang Department of Pathology the First Affiliated Hospital of Xi'an Jiaotong University National Engineering Lab for Big Data Analytics Xi'an Jiao tong University

ISBN: (纸本)9781728118680

Information in histology slides are usually visualized by different staining techniques, each of them unveils specific chemical and biological substances within tissue samples. Correlations between different stains can be useful to predict how certain tissue slides may look like if they were stained by other staining techniques. This work investigates two stains including hematoxylin and eosin (H&E) and immunohistochemistry (IHC) in digital pathological slides. Four cases of surgical biopsies were used in this work. The specimens were subjected to two consecutive stains with a decoloring process based on ethanol and potassium permanganate in between. After each stain, slides were digitized and archived as results. Comparing the effects of the two staining pipelines, IHC slides after decoloring of H&E showed that the cell structure was clear, the positive IHC staining was accurate, the background of the slide was clean, there was no DAB residue, and tissue fragments were intact. However, the other pipeline where IHC was stained before H&E showed that the nuclear border was blurred. Eosin is lightly colored resulting in low contrast visualization of nucleoplasm, DAB is not completely decolored, and parts of tissue were fragmented. We conclude that, from the proposed staining and decoloring technique, tissue slides could be stained with IHC more effectively on decolored H&E slides than those stained with H&E after IHC. Utilizing digital section scanning technology, we can obtain pairs of tissue images stained differently while preserving the exact same tissue structure.

关键词： pathology color transform digital section decolorization H&E IHC staining staining helium Digital Block Slide Slide slump Degreening DECOLORIZATION IHCS pathology Eosine Yellowish Cellular Structures Stains

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RETRACTED ARTICLE: Continuous Activity-Aware Stress Detection Using Sensors

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Wireless Personal Communications 2021年第1期127卷 17-17页

作者： Karthick, T. Sangeetha, M. Ramprasath, M. Ananthajothi, K. Department of Data Science and Business Analytics School of Computing SRM Institue of Science and Technology Chennai India Department of Computer Science and Engineering Misrimal Navajee Munoth Jain Engineering College Chennai India

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Automatical Pulmonary Nodule Detection by Feature Contrast Learning 1

引用

15th International Conference on Intelligent Computing, ICIC 2019

作者： Chang, Jie Ye, Minquan Gu, Naijie Zhang, Xiaoci Lin, Chuanwen Ye, Hong School of Computer Science and Technology University of Science and Technology of China Hefei230000 China Research Center of Health Big Data Mining and Applications Wannan Medical College WuhuAnhui241002 China Department of Computer Science and Technology Hefei University Hefei230601 China

ISBN: (数字)9783030267636

ISBN: (纸本)9783030267629

With regard to pulmonary nodule detection, due to the similar texture and shape as particular tissues, it is difficult for Computer-Aided Detection (CAD) system in detecting pulmonary nodule with both high accuracy and sensitivity. To address this problem, we design a 3D automated pulmonary nodule detection where a auxiliary 3D generative adversarial network is embedded. This well-trained auxiliary component that fully learns volumetrically contextual information of nodule and non-nodule structure, is exploited for each input sample of detection model to generate a derivative which only preserve background context by removing all the nodules. By learning the feature contrast between each input and its derivative, our detection model achieves competitive performance to state-of-the-art approaches for the pulmonary nodule detection task. © 2019, Springer Nature Switzerland AG.

关键词： Textures

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