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arXiv 2021年

作者： Schrom, Edward Kinzig, Ann Forrest, Stephanie Graham, Andrea L. Levin, Simon A. Bergstrom, Carl T. Castillo-Chavez, Carlos Collins, James P. De Boer, Rob J. Doupé, Adam Ensafi, Roya Feldman, Stuart Grenfell, Bryan T. Halderman, J. Alex Huijben, Silvie Maley, Carlo Moses, Melanie Perelson, Alan S. Perrings, Charles Plotkin, Joshua Rexford, Jennifer Tiwari, Mohit Department of Ecology and Evolutionary Biology Princeton University 106A Guyot Hall PrincetonNJ08544 United States School of Life Sciences Arizona State University TempeAZ85287 United States Julie Ann Wrigley Global Institute of Sustainability Arizona State University TempeAZ85287 United States Biodesign Center for Biocomputation Security and Society Arizona State University TempeAZ85287 United States School of Computing Informatics and Decision Sciences Engineering Arizona State University TempeAZ85287 United States Santa Fe Institute Santa FeNM87501 United States Department of Biology University of Washington SeattleWA98195 United States Arizona State University TempeAZ85287 United States Theoretical Biology and Bioinformatics Utrecht University Utrecht3584 CH Netherlands Center for Cybersecurity and Digital Forensics Global Security Initiative Arizona State University TempeAZ85287 United States Department of Electrical Engineering and Computer Science Computer Science and Engineering Division University of Michigan Ann ArborMI48109 United States Schmidt Futures New YorkNY10011 United States Princeton School of Public and International Affairs Princeton University PrincetonNJ08544 United States Center for Computer Security and Society University of Michigan Ann ArborMI48109 United States Center for Evolution and Medicine School of Life Sciences Arizona State University TempeAZ85287 United States Arizona Cancer Evolution Center Arizona State University TempeAZ85287 United States Department of Computer Science University of New Mexico AlbuquerqueNM87131 United States University of New Mexico Department of Computer Science AlbuquerqueNM87131 United States Department of Biology University of New Mexico AlbuquerqueNM87131 United States Theoretical Biology and Biophysics Group Los Alamos National Laboratory Los AlamosNM87545 United States Department of Biology University of Pennsylvania PhiladelphiaPA19104 Unite

We explore the commonalities between methods for assuring the security of computer systems (cybersecurity) and the mechanisms that have evolved through natural selection to protect vertebrates against pathogens, and how insights derived from studying the evolution of natural defenses can inform the design of more effective cybersecurity systems. More generally, security challenges are crucial for the maintenance of a wide range of complex adaptive systems, including financial systems, and again lessons learned from the study of the evolution of natural defenses can provide guidance for the protection of such *** Codes A.0. Copyright © 2021, The Authors. All rights reserved.

关键词： Cybersecurity

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metasnf: Meta Clustering with Similarity Network Fusion in R

arXiv

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arXiv 2024年

作者： Velayudhan, Prashanth S. Xu, Xiaoqiao Kallurkar, Prajkta Balbon, Ana Patricia Secara, Maria T. Taback, Adam Sabac, Denise Chan, Nicholas Ma, Shihao Wang, Bo Felsky, Daniel Ameis, Stephanie H. Cox, Brian Hawco, Colin Erdman, Lauren Wheeler, Anne L. The Hospital for Sick Children TorontoON Canada Department of Physiology The University of Toronto TorontoON Canada Centre for Computational Medicine Hospital for Sick Children TorontoON Canada Department of Obstetrics and Gynecology University of Toronto TorontoON Canada Campbell Family Mental Health Research Institute Centre for Addiction and Mental Health TorontoON Canada Institute of Medical Science University of Toronto TorontoON Canada The Edward S. Rogers Sr. Department of Electrical and Computer Engineering University of Toronto TorontoON Canada Krembil Centre for Neuroinformatics Centre for Addiction and Mental Health TorontoON Canada Division of Rheumatology Hospital for Sick Children TorontoON Canada Vector Institute TorontoON Canada Department of Computer Science University of Toronto TorontoON Canada Peter Munk Cardiac Centre University Health Network TorontoON Canada Department of Medical Biophysics University of Toronto TorontoON Canada Division of Biostatistics Dalla Lana School of Public Health University of Toronto TorontoON Canada Department of Psychiatry University of Toronto TorontoON Canada Department of Anthropology University of Toronto TorontoON Canada Department of Psychiatry Temerty Faculty of Medicine University of Toronto TorontoON Canada Department of Psychiatry The Hospital for Sick Children TorontoON Canada Division of Gastroenterology Department of Pediatrics Cincinnati Children’s Hospital Medical Center University of Cincinnati College of Medicine CincinnatiOH United States James M. Anderson Center for Health Systems Excellence Cincinnati Children’s Hospital Medical Center CincinnatiOH United States Department of Pediatrics University of Cincinnati College of Medicine CincinnatiOH United States

metasnf is an R package that enables users to apply meta clustering, a method for efficiently searching a broad space of cluster solutions by clustering the solutions themselves, to clustering workflows based on similarity network fusion (SNF). SNF is a multi-modal data integration algorithm commonly used for biomedical subtype discovery. The package also contains functions to assist with cluster visualization, characterization, and validation. This package can help researchers identify SNF-derived cluster solutions that are guided by context-specific utility over context-agnostic measures of quality. Copyright © 2024, The Authors. All rights reserved.

关键词： Cluster analysis

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Design of an Infological Model for Prediction of Problems during Drilling Using Machine Learning and Systems Analysis

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IOP Conference Series: Earth and Environmental Science 2022年第4期988卷

作者： A L Zolkin V D Munister R V Faizullin N N Smoktal Ph.D. in Engineering Science Senior Lecturer of Computer and Information Sciences Department of the Povolzhskiy State University of Telecommunications and Informatics Department of Mathematical Methods and Computer-Aided Design Private educational institution 'Donetsk Academy of Transport' Department of Enterprise Economics State educational institution of higher professional education 'Donetsk National Technical University' Associate Professor of the Department of Information Technologies in Public Administration Institute of Management Technologies MIREA — Russian technological university Teacher at the Department of 'Renewable Energy Sources and Electrical Systems and Networks' Sevastopol State University

The article deals with the actual problem of identification of probabilistic processes as a result of the operation of drilling rigs in the oil industry. The world experience of finding ways to solve optimal forecasting tools using machine learning is summarized. Mnemonic rule for the implementation of classification and ranking systems in the detection of feedbacks as probable indicators of complications of ongoing technological processes is implemented through the description of the formal model of the drilling process in form of a hidden Markov model. The results of evaluation of the developed mathematical apparatus in the form of predictive analytics and a cut of basic complications in the drilling process are presented. An infological diagram of the developed architectural solution of the analysis project is proposed. The results of the control algorithms formalization are given in conclusion. These results allow to ensure the effective procees modes of equipment operation and make it possible to save electricity and water.

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Practical posterior error bounds from variational objectives

arXiv

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arXiv 2019年

作者： Huggins, Jonathan H. Kasprzak, Mikolaj Campbell, Trevor Broderick, Tamara Department of Biostatistics Harvard T. H. Chan School of Public Health Mathematics Research Unit University of Luxembourg Department of Statistics University of British Columbia Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology

Variational inference has become an increasingly attractive fast alternative to Markov chain Monte Carlo methods for approximate Bayesian inference. However, a major obstacle to the widespread use of variational methods is the lack of post-hoc accuracy measures that are both theoretically justified and computationally efficient. In this paper, we provide rigorous bounds on the error of posterior mean and uncertainty estimates that arise from full-distribution approximations, as in variational inference. Our bounds are widely applicable as they require only that the approximating and exact posteriors have polynomial moments. Our bounds are computationally efficient for variational inference in that they require only standard values from variational objectives, straightforward analytic calculations, and simple Monte Carlo estimates. We show that our analysis naturally leads to a new and improved workflow for variational inference. Finally, we demonstrate the utility of our proposed workflow and error bounds on a real-data example with a widely used multilevel hierarchical model. Copyright © 2019, The Authors. All rights reserved.

关键词： Computational efficiency

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Overview of charging modes and connectors for the electric vehicles

Overview of charging modes and connectors for the electric v...

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International Youth Conference on Energetics (IYCE)

作者： Rebeka Raff Velimir Golub Denis Pelin Danijel Topić Public Institution County Development Agency of Osijek- Baranja County Osijek Croatia Vodovod Osijek d.o.o. Osijek Croatia Faculty of Electrical Engineering Computer Science and Information Technology University of Osijek Osijek Croatia

To reduce the CO 2 emissions in the atmosphere, several countries have already banned usage of the vehicles with internal combustion engines that uses diesel fuel. The greater alternative to internal combustion engines becomes more and more electric vehicles with which also follows the trend of development of batteries in terms of their quality and durability. Also, EV can be considered environmental-sustainable, but it depends on the energy mix if the electricity to power them is produced from RES. Due to the greater interest for this alternative mode of transport and in order to charge the batteries fast enough, it is necessary to develop charging station networks available to the all users of electric vehicles in every moment. This paper will present overview of different types of charging stations and connectors for the electric vehicle. The aim of the paper is to provide an overview of all advantages and disadvantages in respect to different technical parameters of charging stations and connectors. Conclusion regarding certain type of charging stations for implementation of desired solution will be presented. The paper will also consider the possibility of implementation of charging station in the public lighting system and provide the author's recommendation regarding the most acceptable option in previously mentioned system.

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Comparing Changes in Diet Quality Between Two Technology-Based Diet Tracking Devices

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Journal of Technology in Behavioral Science 2019年第1期4卷 25-32页

作者： Boutté, Alycia K. Turner-McGrievy, Gabrielle M. Wilcox, Sara Hutto, Brent Muth, Eric Hoover, Adam Department of Health Promotion Education and Behavior Arnold School of Public Health University of South Carolina 915 Greene Street Room 529 Columbia 29208 SC United States Department of Health Promotion Education and Behavior Arnold School of Public Health University of South Carolina 915 Greene Street Room 552 Columbia 29208 SC United States Department of Exercise Science Prevention Research Center Arnold School of Public Health University of South Carolina 921 Assembly Street Public Health Research Center 117 Columbia 29208 SC United States Statistician Prevention Research Center Arnold School of Public Health University of South Carolina 921 Assembly Street Public Health Research Center 129 Columbia 29208 SC United States Department of Psychology Clemson University 418 Brackett Hall Clemson 29634 SC United States Holcombe Department of Electrical and Computer Engineering Clemson University 433 Calhoun Dr. Clemson 29634 SC United States

This study aimed to examine changes in Healthy Eating Index-2010 (HEI-2010) scores, components, and energy intake between automated Bite Counter (Bite) and traditional diet tracking mobile app (App) groups. This was a secondary analysis of the DIET Mobile study, a 6-month weight loss intervention. Assessments were conducted at baseline, 3 and 6 months. Twenty-four-hour dietary recall data were collected. Overweight/obese adults (N = 81) were randomized to Bite or App groups. The intervention was delivered through behavioral podcasts. Participants were provided customized calorie/bite goals and used their device to track intake. We assessed changes in HEI-2010 scores from baseline to 6 months between groups. t tests, chi-square, and repeated measures ANOVA were performed. Models included time, group, and group×time interaction, controlling for no other covariates. There were no significant changes in HEI-2010 scores, components, or energy intake between groups at 3 or 6 months. This study found that both the Bite and App groups were able to reduce their energy intake and there was no difference in changes in diet quality between groups, which provides some support for using the less intensive, more automated method (Bite Counter) for long-term dietary self-monitoring. The study had a low sample size according to power calculations. Future interventions aimed at improving diet quality through mHealth technology should investigate the potential to develop a new app or modify an existing app that would allow for dietary self-monitoring that provides specific feedback on how users’ diets align with diet quality components in the HEI to improve overall diet quality. © 2018, Springer Nature Switzerland AG.

关键词： Dietary self-monitoring Healthy diet Mobile health Obesity

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Online Self-learning for Smart HVAC Control

Online Self-learning for Smart HVAC Control

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IEEE International Conference on Systems, Man and Cybernetics

作者： Tzu-Yin Chao Manh-Hung Nguyen Ching-Chun Huang Chien-Cheng Liang Chen-Wu Chung Dept. of Electrical Engineering National Chung Cheng University Chiayi Taiwan Dept. of Computer Science National Chiao Tung University Hsinchu Taiwan Public Utilities Service Department Taiwan Semiconductor Manufacturing Company Ltd.

In this paper, we introduce an online-learning method to model the property of an office building. Unlike conventional control methods where the building property is modeled via a simulator or through offline learning, our building model is adaptively updated according to the dynamic response of a real environment. Upon the building model for environment prediction, the proposed action agent can control the heating, ventilation, and air conditioning (HVAC) system in a smarter way by scheduling the temperature reference point. To online learn the model and improve the agent, two practical and seldom discussed issues are addressed. The first challenge is data bias where the collected initial training dataset can only partially reveal the statistical mapping between the control input and the environment response. Hence, the trained model may lack generalization. To overcome the data bias issue, a data augmentation method is proposed to embed physical logic in order to train a proper initial model. Next, an online learning process is introduced to update the model generality during the system operation phase. The second practical issue is the constraints on agent exploration for discovering unknown data samples. During the business hours, to comfort employees, a control agent is not allowed to explore the possible controlling space randomly. To balance data collection and control stability, we introduce a hybrid control strategy that considers both the human control rule and the agent action. A confidence score of the agent model is also automatically estimated to determine a suitable control strategy finally. Our experiments have realized in an office building. The results outperform conventional methods and show its superior in terms of control stability.

关键词： Buildings HVAC Adaptation models Training Temperature control Data models Predictive models

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Integrating multi-omics with neuroimaging and behavior: A preliminary model of dysfunction in football athletes

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Neuroimage: Reports 2021年第3期1卷 100032-100032页

作者： Bari, Sumra Vike, Nicole L. Stetsiv, Khrystyna Walter, Alexa Newman, Sharlene Kawata, Keisuke Bazarian, Jeffrey J. Papa, Linda Nauman, Eric A. Talavage, Thomas M. Slobounov, Semyon Breiter, Hans C. Warren Wright Adolescent Center Department of Psychiatry and Behavioral Sciences Feinberg School of Medicine Northwestern University Chicago IL United States Department of Kinesiology Pennsylvania State University University Park PA United States Department of Psychological and Brain Sciences Indiana University Bloomington IN United States Department of Kinesiology School of Public Health-Bloomington Indiana University Bloomington IN United States Program in Neuroscience College of Arts and Sciences Indiana University Bloomington IN United States Department of Emergency Medicine University of Rochester Rochester NY United States Department of Emergency Medicine Orlando Regional Medical Center Orlando FL United States Weldon School of Biomedical Engineering Purdue University West Lafayette IN United States School of Mechanical Engineering Purdue University West Lafayette IN United States Department of Basic Medical Sciences Purdue University West Lafayette IN United States School of Electrical and Computer Engineering Purdue University West Lafayette IN United States Department of Biomedical Engineering University of Cincinnati Cincinnati OH United States Laboratory of Neuroimaging and Genetics Department of Psychiatry Massachusetts General Hospital and Harvard School of Medicine Boston MA United States

Contact sports affect measures at multiple scales such as transcriptomics, metabolomics, brain function, and behavior, but studies have not yet studied the statistical structure of how they are integrated. This preliminary study, examining collegiate American football players, integrated across-season changes (Δ) from transcriptomic and metabolomic variables (neuroinflammatory miRNAs and metabolites), neuroimaging (resting-state fMRI network fingerprint similarity), and virtual reality (VR)-based motor control. These findings were then assessed against head acceleration events (HAE). Using permutation-based moderation analysis (all pF^perm,pβ3^perm ≤0.05), we observed that (1) Δtridecenedioate, a mono-unsaturated fatty acid, interacted with ΔmiR-505 to predict default mode network (DMN) fingerprint similarity, meaning the interaction between two molecular biology measures predicted a neuroimaging measure. Further, (2) Δtridecenedioate and DMN fingerprint similarity interacted to predict motor control, indicating the interaction of a molecular and a neuroimaging measure predicted behavior. ΔmiR-505 was positively related to HAE and DMN fingerprint similarity was negatively related to HAE and reduced relative to non-athlete subjects. These multi-scale, moderating relationships between a potential ROS scavenger, neuroinflammatory miRNA, reduced brain connectivity, and diminished motor control argue that seemingly healthy athletes with frequent HAE may experience chronic neuroinflammation. This imaging-omics framework using permutation-based mediation/moderation analysis has general applicability for human-animal translational studies.

关键词： Football Head impacts miR-505 Motor control rs-fMRI Tridecenedioate

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Synchronization within synchronization: Transients and intermittency in ecological networks

arXiv

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arXiv 2020年

作者： Fan, Huawei Kong, Ling-Wei Wang, Xingang Hastings, Alan Lai, Ying-Cheng School of Physics and Information Technology Shaanxi Normal University Xi’an710062 China School of Electrical Computer and Energy Engineering Arizona State University TempeAZ85287 United States Department of Environmental Science and Policy University of California DavisCA95616 United States Santa Fe Institute 1399 Hyde Park Road Santa FeNM87501 United States Department of Physics Arizona State University TempeAZ85287 United States

Transients are fundamental to ecological systems with significant implications to management, conservation, and biological control. We uncover a type of transient synchronization behavior in spatial ecological networks whose local dynamics are of the chaotic, predator-prey type. In the parameter regime where there is phase synchronization among all the patches, complete synchronization (i.e., synchronization in both phase and amplitude) can arise in certain pairs of patches as determined by the network symmetry - henceforth the phenomenon of "synchronization within synchronization." Distinct patterns of complete synchronization coexist but, due to intrinsic instability or noise, each pattern is a transient and there is random, intermittent switching among the patterns in the course of time evolution. The probability distribution of the transient time is found to follow an algebraic scaling law with a divergent average transient lifetime. Based on symmetry considerations, we develop a stability analysis to understand these phenomena. The general principle of symmetry can also be exploited to explain previously discovered, counterintuitive synchronization behaviors in ecological networks. Copyright © 2020, The Authors. All rights reserved.

关键词： Synchronization

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Operating System Security Management and Ease of Implementation (Passwords, Firewalls and Antivirus) 15th

Operating System Security Management and Ease of Implementat...

引用

15th International Conference on Information Technology : New Generations, ITNG 2018

作者： Zare, Hossein Olsen, Peter Zare, Mohammad Jalal Azadi, Mojgan University of Maryland University College Upper MarlboroMD United States Department of Health Policy and Management The Johns Hopkins Center for Disparities Solution The Johns Hopkins Bloomberg School of Public Health BaltimoreMD United States Department of Computer Science and Electrical Engineering University of Maryland Baltimore County Catonsville BaltimoreMD United States Department of Computer Science and Engineering Azad University Yazd Iran The Johns Hopkins University BaltimoreMD United States

ISBN: (纸本)9783319770277

Recent widely-known hacking exploits have increased the focus on computer and network security. System users need systems to provide confidentiality, integrity, availability and authenticity for their data. Access control, firewalls, and antivirus software are three ways to provide system security. They address different aspects of computer security with complementary advantages and disadvantages. © 2018, Springer International Publishing AG, part of Springer Nature.

关键词： Personal computing

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