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KRONECKER PRODUCT OF TENSORS AND HYPERGRAPHS: STRUCTURE AND DYNAMICS∗

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arXiv 2023年

作者： Pickard, Joshua Chen, Can Stansbury, Cooper Surana, Amit Bloch, Anthony Rajapakse, Indika Department of Computational Medicine & Bioinformatics University of Michigan Ann ArborMI48109 United States School of Data Science and Society Department of Mathematics University of North Carolina at Chapel Hill Chapel HillNC27599 United States Raytheon Technologies Research Center East HartfordCT06108 United States Department of Mathematics University of Michigan Ann ArborMI48109 United States Department of Computational Medicine & Bioinformatics Medical School Department of Mathematics University of Michigan Ann ArborMI48109 United States

Hypergraphs and tensors extend classic graph and matrix theory to account for multiway relationships, which are ubiquitous in engineering, biological, and social systems. While the Kronecker product is a potent tool for analyzing the coupling of systems in graph or matrix contexts, its utility in studying multiway interactions, such as those represented by tensors and hypergraphs, remains elusive. In this article, we present a comprehensive exploration of algebraic, structural, and spectral properties of the tensor Kronecker product. We express Tucker and tensor train decompositions and various tensor eigenvalues in terms of the tensor Kronecker product. Additionally, we utilize the tensor Kronecker product to form Kronecker hypergraphs, a tensor-based hypergraph product, and investigate the structure and stability of polynomial dynamics on Kronecker hypergraphs. Finally, we provide numerical examples to demonstrate the utility of the tensor Kronecker product in computing Z-eigenvalues, various tensor decompositions, and determining the stability of polynomial systems. © 2023, CC BY.

关键词： Tensors

Smart Dome 4.0: Low-Cost, Independent, Automated Energy System for Agricultural Purposes enabled by Machine Learning 2

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Smart Dome 4.0: Low-Cost, Independent, Automated Energy Syst...

2021 2nd International Symposium on Automation, Information and Computing, ISAIC 2021

作者： Budiman, Arief S Gunawan, Fergyanto Djuana, Endang Pardamean, Bens Romeli, Sugiarto Putri, Dianing Nn Aji, Daisman Pb Rahardjo, Kuat Stevanus Ilham Wibowo, M. Daffa, Nadiev Owen, Raymond Industrial Engineering Department BINUS Graduate Program - Master of Industrial Engineering Bina Nusantara University Jakarta Indonesia Department of Manufacturing and Mechanical Engineering and Technology Oregon Institute of Technology Klamath FallsOR97601 United States Electrical Engineering Department Faculty of Industrial Technology Universitas Trisakti Jakarta Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia Computer Science Department BINUS Graduate Program - Master of Computer Science Bina Nusantara University Jakarta Indonesia PT. Impack Pratama Industri Tbk Jakarta Indonesia Mechanical Engineering Department Faculty of Industrial Technology Universitas Trisakti Jakarta Indonesia Industrial Engineering Department Faculty of Engineering Bina Nusantara University Jakarta Indonesia

Many economically essential crops in Indonesia (such as coffee, tea, chocolate, or copra) require storage or drying under certain environmental conditions, especially temperature and humidity. The solar dryer dome, typically used for agricultural purposes in Indonesia, produces a sufficient amount of heat to increase the evaporation rate inside the dome and reduce the moisture content of the commodity. A hybrid solar dryer accompanied by a photovoltaic panel, fan, and ventilation system is generally suitable. The system can provide an optimum environment with minimum control. However, as the outdoor temperature and humidity change dramatically, such as at night time, more control is required. Based on Industry 4.0 technologies, we have developed a new kind of hybrid solar dryer that provides an optimum environment 24/7. The system, called Smart Dome 4.0, is an intelligent, low-cost, self-sufficient drying and storage system to support Indonesia Agriculture 4.0. The system has a local power generation unit to self-sustain the required energy and operate without connecting to the electricity grid. The system utilizes a machine-learning algorithm to predict the environmental condition and optimally uses self-generated electric power. The developed Smart Dome 4.0 technology is critical to producing a sustainable solar dome under drastic environmental dynamics. © Published under licence by IOP Publishing Ltd.

关键词： Learning algorithms

Less is More: Reducing Overfitting in Deep Learning for EEG Classification

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Less is More: Reducing Overfitting in Deep Learning for EEG ...

Computers in Cardiology (CinC)

作者： Songchi Zhou Shijia Geng Jun Li Deyun Zhang Ziqian Xie Chuandong Cheng Shenda Hong Department of Bioinformatics and Biostatistics Shanghai Jiao Tong University Shanghai China HeartVoice Medical Technology Hefei China College of Electronic Science and Engineering Jilin University Changchun China University of Texas Health Science Center Houston TX US Department of Neurosurgery The First Affiliated Hospital of USTC Hefei China National Institute of Health Data Science Peking University Beijing China Institute of Medical Technology Health Science Center of Peking University Beijing China

Although most of the patients' recordings includes large scale long-term physiological time series, the patient-level quantity is relatively small, posing great challenges for machine learning methods. As part of the George B. Moody PhysioNet Challenge 2023, we aim to propose a series of Reducing Overfitting techniques in Deep learning for EEG (coined as RODE) in this scenario, for neurological recovery prognosis. RODE is a simple yet effective machine-learning method, which is mostly powered by generalizable deep-learning features. Specifically, we first pre-train a convolutional neural network on the segment level with a margin-based and mining-based loss, and extract deep features from it. Then we decrease the deep features' complexity using dimensionality reduction methods, which prove to be quite significant for reducing overfitting in deep learning. Finally, we combine the reduced features with static features in patient level, and put them into an ensemble model for classification. Our team, PKU_NIHDS, receives a Challenge score of 0.821 on the hidden validation set and 0.708 on the hidden test set.

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Community microgrid:an approach towards positive energy community in an urban area of Dhaka,Bangladesh

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Clean Energy 2023年第4期7卷 926-939页

作者： Mohammad Zeyad S.M.Masum Ahmed Sayeed Hasan Dewan Mahnaaz Mahmud Energy and Technology Research Division Advanced BioinformaticsComputational Biology and Data Science LaboratoryBangladesh(ABCD LaboratoryBangladesh)House:24Road:01Block:BHalishaharChattogram 4226Bangladesh School of Engineering and Physical Sciences(EPS) Heriot-Watt UniversityCampus The AvenueEdinburghScotland EH144ASUK Department of Energy Engineering University of the Basque Country(UPV/EHU)Ingeniero Torres Quevedo Plaza1BilbaoBiscay 48013Spain Department of Electrical Electronic and Control EngineeringSchool of Industrial EngineeringUniversity of ExtremaduraCampus UniversitarioAv.de Elvass/nBadajozBadajoz 06006Spain Centre for Research on Geography ResourcesEnvironmentEnergy and Networks(Green)Bocconi UniversityVia Roentgen 1MilanoLombardy 20136Italy Control and Robotics(CORO) Ecole Centrale De Nantes1 Rue de la No?44300 NantesFrance

The pollution and toxic greenhouse gases produced by fossil fuel combustion are troubling as global energy demand continues to *** mitigate the consequences of global warming,a transition to sustainable energy sources is *** manuscript presents a feasible community microgrid design in Hazaribagh,Dhaka based on meteorological data that leads to photovoltaic installation on the rooftop of a local community *** study shows a microgrid design of a system with the lowest cost of energy and a large renewable fraction,which is analysed using the HOMER Pro *** real-time data,analysis of the system cost,cost of energy,renewable fraction,unmet load,energy purchased and energy sold is discussed.A suitable case for electrification is also identified and presented for the selected *** proposed case yields a cost of energy of$0.0357/kWh,which is 52%less than the current tariff rate,with a 70%renewable *** study will provide people in this community with more green energy at a lower cost;in addition,this designed microgrid sells additional energy to the grid to avoid possible power *** potential for a positive energy community is also investigated in terms of energy consumption and renewable output of the planned microgrid.

关键词： cost of energy microgrid positive energy community renewable fraction

Network security modeling using NetFlow data: Detecting botnet attacks in IP traffic

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arXiv 2021年

作者： Subramaniam, Ganesh Chen, Huan Varadhan, Ravi Archibald, Robert Data Science and AI Research At and T Chief Data Office Department of Biostatistics Johns Hopkins Bloomberg School of Public Health Division of Biostatistics and Bioinformatics Department of Oncology Johns Hopkins University

Cybersecurity, security monitoring of malicious events in IP traffic, is an important field largely unexplored by statisticians. Computer scientists have made significant contributions in this area using statistical anomaly detection and other supervised learning methods to detect specific malicious events. In this research, we investigate the detection of botnet command and control (C&C) hosts in massive IP traffic. We use the NetFlow data, the industry standard for monitoring of IP traffic for exploratory analysis and extracting new features. Using statistical as well as deep learning models, we develop a statistical intrusion detection system (SIDS) to predict traffic traces identified with malicious attacks. Employing interpretative machine learning techniques, botnet traffic signatures are derived. These models successfully detected botnet C&C hosts and compromised devices. The results were validated by matching predictions to existing blacklists of published malicious IP addresses. Copyright © 2021, The Authors. All rights reserved.

关键词： Network security

A Bayesian Precision Response-adaptive Phase II Clinical Trial Design for Radiotherapies with Competing Risk Survival Outcomes

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arXiv 2022年

作者： Park, Jina Hu, Wenjing Jin, Ick Hoon Liu, Hao Zang, Yong Department of Applied Statistics Yonsei University Korea Republic of Department of Statistics and Data Science Yonsei University Korea Republic of AT&T Company United States Department of Biostatistics and Epidemiology Cancer Institute of New Jersey Rutgers University United States Department of Biostatistics and Health Data Sciences Center of Computational Biology and Bioinformatics Indiana University United States

Many phase II clinical trials have used survival outcomes as the primary endpoints in recent decades. Suppose the radiotherapy is evaluated in a phase II trial using survival outcomes. In that case, the competing risk issue often arises because the time to disease progression can be censored by the time to normal tissue complications, and vice versa. Besides, much literature has examined that patients receiving the same radiotherapy dose may yield distinct responses due to their heterogeneous radiation susceptibility statuses. Therefore, the "one-dose-fit-all" strategy often fails, and it is more relevant to evaluate the subgroup-specific treatment effect with the subgroup defined by the radiation susceptibility status. In this paper, we propose a Bayesian precision phase II trial design evaluating the subgroup-specific treatment effects of radiotherapy. We use the cause-specific hazard approach to model the competing risk survival outcomes. We propose restricting the candidate radiation doses based on each patient’s radiation susceptibility status. Only the clinically feasible personalized dose will be considered, which enhances the benefit for the patients in the trial. In addition, we propose a stratified Bayesian adaptive randomization scheme such that more patients will be randomized to the dose reporting more favorable survival outcomes. Numerical studies have shown that the proposed design performed well and outperformed the conventional design ignoring the competing risk issue. Copyright © 2022, The Authors. All rights reserved.

关键词： Random processes

Leveraging Anatomical Constraints with Uncertainty for Pneumothorax Segmentation

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arXiv 2023年

作者： Yuan, Han Hong, Chuan Tran, Nguyen Tuan Anh Xu, Xinxing Liu, Nan Centre for Quantitative Medicine Duke-NUS Medical School Singapore Department of Biostatistics and Bioinformatics Duke University United States Department of Diagnostic Radiology Singapore General Hospital Singapore Institute of High Performance Computing Agency for Science Technology and Research Singapore Programme in Health Services and Systems Research Duke-NUS Medical School Singapore Institute of Data Science National University of Singapore Singapore

Background Pneumothorax is a medical emergency caused by abnormal accumulation of air in the pleural space – the potential space between the lungs and chest wall. On 2D chest radiographs, pneumothorax occurs within the thoracic cavity and outside of the mediastinum and we refer to this area as "lung+ space". While deep learning (DL) has increasingly been utilized to segment pneumothorax lesions in chest radiographs, many existing DL models employ an end-to-end approach. These models directly map chest radiographs to clinician-annotated lesion areas, often neglecting the vital domain knowledge that pneumothorax is inherently location-sensitive. Methods We propose a novel approach that incorporates the lung+ space as a constraint during DL model training for pneumothorax segmentation on 2D chest radiographs. To circumvent the need for additional annotations and to prevent potential label leakage on the target task, our method utilizes external datasets and an auxiliary task of lung segmentation. This approach generates a specific constraint of lung+ space for each chest radiograph. Furthermore, we have incorporated a discriminator to eliminate unreliable constraints caused by the domain shift between the auxiliary and target datasets. Results Our results demonstrated significant improvements, with average performance gains of 4.6%, 3.6%, and 3.3% regarding Intersection over Union (IoU), Dice Similarity Coefficient (DSC), and Hausdorff Distance (HD). These results were consistent across six baseline models built on three architectures (U-Net, LinkNet, or PSPNet) and two backbones (VGG-11 or MobileOne-S0). We further conducted an ablation study to evaluate the contribution of each component in the proposed method and undertook several robustness studies on hyperparameter selection to validate the stability of our method. Conclusion The integration of domain knowledge in DL models for medical applications has often been underemphasized. Our research underscores the signi

关键词： Domain Knowledge

Improvement of Jakarta's air quality during large scale social restriction 2

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Improvement of Jakarta's air quality during large scale soci...

2nd International Conference on Biospheric Harmony Advanced Research, ICOBAR 2020

作者： Rahutomo, R. Purwandari, K. Sigalingging, J.W.C. Pardamean, B. Information System Department School of Information Systems Bina Nusantara University Jakarta11480 Indonesia Computer Science Department BINUS Graduate Program Master of Computer Science Program Bina Nusantara University Jakarta11480 Indonesia Database Center Division of BMKG Meteorological Climatological and Geophysical Agency Jakarta10720 Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia

After Large-Scale Social Restriction (PSBB) established in Jakarta, a change of air quality was indicated by the citizens. Representatives of Indonesia's Agency for Meteorology, Climatology, and Geophysics (BMKG) supported as the fact that mobility of Jakarta's citizen and commuters were reduced. With utilizing Weather data Acquisition System managed by BMKG, this research compared fluctuations of Jakarta's air quality during two periods of time: Pre-PSBB and During-PSBB. This study proved that Jakarta's air quality has positively improved compared to the previous year, although in the lower range of unhealthy level. © Published under licence by IOP Publishing Ltd.

关键词： Air quality

CIGAN: A Python Package for Handling Class Imbalance using Generative Adversarial Networks

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arXiv 2022年

作者： Huang, Yuxiao Ma, Yan Data Science Columbian College of Arts & Sciences George Washington University WashingtonDC United States Department of Biostatistics and Bioinformatics Milken Institute School of Public Health George Washington University WashingtonDC United States

A key challenge in Machine Learning is class imbalance, where the sample size of some classes (majority classes) are much higher than that of the other classes (minority classes). If we were to train a classifier directly on imbalanced data, it is more likely for the classifier to predict a new sample as one of the majority classes. In the extreme case, the classifier could completely ignore the minority classes. This could have serious sociological implications in healthcare, as the minority classes are usually the disease classes (e.g., death or positive clinical test result). In this paper, we introduce a software that uses Generative Adversarial Networks to oversample the minority classes so as to improve downstream classification. To the best of our knowledge, this is the first tool that allows multi-class classification (where the target can have an arbitrary number of classes). The code of the tool is publicly available in our github repository (https://***/yuxiaohuang/research/tree/master/gwu/working/cigan/code). © 2022, CC BY.

关键词： Generative adversarial networks