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SSRN 2022年

作者： Li, Ying Sun, Hang Fang, Wensi Ma, Qin Han, Siyu Wang-Sattler, Rui Du, Wei Yu, Qiong Key Laboratory of Symbolic Computation and Knowledge Engineering College of Computer Science and Technology Jilin University Qianjin Street Jilin Changchun130012 China Department of Biomedical Informatics Ohio State University Lane Ave ColumbusOH43210 United States TUM School of Medicine Technical University of Munich Ismaninger Straße 22 Bavaria Munich81675 Germany Helmholtz Center Munich German Research Center for Environmental Health Ingolstadter Landstraße 1 Bavaria Neuherberg85764 Germany Department of Epidemiology and Biostatistics School of public health Jilin University Qianjin Street Jilin Changchun130012 China

For many classification tasks, only experimentally validated positive samples are available, and experimentally validated negative samples are not recorded. The lack of negative samples poses a great challenge for using supervised machine learning. To address this problem, we propose a novel deep reinforcement learning based model to screen reliable negative samples from unlabeled samples, named SURE. SURE has two modules: sample selector and sample inspector. The sample selector screens reliable negative samples from unlabeled samples by two reinforcement strategies. The sample inspector classifies samples and provides rewards to the sample selector. In this paper, we focus on one popular issue in the field of bioinformatics: the ncRNA-protein interaction (NPI) prediction task, which lacks reliable negative samples. Thirty datasets for NPI prediction are used to test the screening effect of SURE. The Experimental results show that our model has a robust negative sample screening capability and is superior to all outstanding sample screening methods used in the NPI prediction task. In addition, we refine 5 NPI datasets containing reliable negative samples screened by SURE, and a webserver (***/sure) is available offering the NPI prediction refined by SURE. © 2022, The Authors. All rights reserved.

关键词： Reinforcement learning

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Incorporating Surprisingly Popular Algorithm and Euclidean Distance-based Adaptive Topology into PSO

arXiv

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arXiv 2021年

作者： Wu, Xuan Han, Jizong Wang, Di Gao, Pengyue Cui, Quanlong Chen, Liang Liang, Yanchun Huang, Han Lee, Heow Pueh Miao, Chunyan Zhou, You Wu, Chunguo Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education College of Computer Science and Technology Jilin University Changchun130012 China Joint NTU-UBC Research Centre of Excellence in Active Living for the Elderly Nanyang Technological University 639798 Singapore State Key Laboratory of Superhard Materials International Center for Computational Method and Software College of Physics Jilin University Changchun130012 China Commercial Quality and Efficiency Department Baidu Inc Beijing100085 China Department of Computer Science Shantou University Shantou515063 China School of Computer Science Zhuhai College of Science and Technology Zhuhai519041 China School of Software Engineering The South China University of Technology Guangzhou510641 China Department of Mechanical Engineering National University of Singapore 117575 Singapore School of Computer Science and Engineering Nanyang Technological University 639798 Singapore College of Software Jilin University Changchun130012 China

While many Particle Swarm Optimization (PSO) algorithms only use fitness to assess the performance of particles, in this work, we adopt Surprisingly Popular Algorithm (SPA) as a complementary metric in addition to fitness. Consequently, particles that are not widely known also have the opportunity to be selected as the learning exemplars. In addition, we propose a Euclidean distance-based adaptive topology to cooperate with SPA, where each particle only connects to k number of particles with the shortest Euclidean distance during each iteration. We also introduce the adaptive topology into heterogeneous populations to better solve large-scale problems. Specifically, the exploration sub-population better preserves the diversity of the population while the exploitation sub-population achieves fast convergence. Therefore, large-scale problems can be solved in a collaborative manner to elevate the overall performance. To evaluate the performance of our method, we conduct extensive experiments on various optimization problems, including three benchmark suites and two real-world optimization problems. The results demonstrate that our Euclidean distance-based adaptive topology outperforms the other widely adopted topologies and further suggest that our method performs significantly better than state-of-the-art PSO variants on small, medium, and large-scale problems. Copyright © 2021, The Authors. All rights reserved.

关键词： Small-world networks

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Novel Smart N95 Filtering Facepiece Respirator with Real-time Adaptive Fit Functionality and Wireless Humidity Monitoring for Enhanced Wearable Comfort

arXiv

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arXiv 2023年

作者： Kwon, Kangkyu Lee, Yoon Jae Jung, Yeongju Soltis, Ira Choi, Chanyeong Na, Yewon Romero, Lissette Kim, Myung Chul Rodeheaver, Nathan Kim, Hodam Lloyd, Michael S. Zhuang, Ziqing King, William Xu, Susan Ko, Seung-Hwan Lee, Jinwoo Yeo, Woon-Hong School of Electrical and Computer Engineering College of Engineering Georgia Institute of Technology AtlantaGA30332 United States Center for Human-Centric Interfaces and Nanoengineering Institute for Electronics and Nanotechnology Georgia Institute of Technology AtlantaGA30332 United States Applied Nano and Thermal Science Lab Department of Mechanical Engineering Seoul National University 1 Gwanak-ro Gwanak-gu Seoul08826 Korea Republic of George W. Woodruff School of Mechanical Engineering Georgia Institute of Technology AtlantaGA30332 United States School of Industrial Design Georgia Institute of Technology AtlantaGA30332 United States Division of Cardiology Emory University School of Medicine AtlantaGA30322 United States National Institute for Occupational Safety and Health National Personal Protective Technology Laboratory PittsburghPA15236 United States Seoul National University Gwanak-ro Gwanak-gu Seoul08826 Korea Republic of Department of Mechanical Robotics and Energy Engineering Dongguk University 30 Pildongro 1-gil Jung-gu Seoul04620 Korea Republic of Wallace H. Coulter Department of Biomedical Engineering Parker H. Petit Institute for Bioengineering and Biosciences Neural Engineering Center Institute for Materials Institute for Robotics and Intelligent Machines Georgia Institute of Technology AtlantaGA30332 United States

The widespread emergence of the COVID-19 pandemic has transformed our lifestyle, and facial respirators have become an essential part of daily life. Nevertheless, the current respirators possess several limitations such as poor respirator fit because they are incapable of covering diverse human facial sizes and shapes, potentially diminishing the effect of wearing respirators. In addition, the current facial respirators do not inform the user of the air quality within the smart facepiece respirator in case of continuous long-term use. Here, we demonstrate the novel smart N-95 filtering facepiece respirator that incorporates the humidity sensor and pressure sensory feedback-enabled self-fit adjusting functionality for the effective performance of the facial respirator to prevent the transmission of airborne pathogens. The laser-induced graphene (LIG) constitutes the humidity sensor, and the pressure sensor array based on the dielectric elastomeric sponge monitors the respirator contact on the user’s face, providing the sensory information for a closed-loop feedback mechanism. As a result of the self-fit adjusting mode along with elastomeric lining, the fit factor is increased by 3.20 and 5 times at average and maximum respectively. We expect that the experimental proof-of-concept of this work will offer viable solutions to the current commercial respirators to address the *** Codes 92C55 Copyright © 2023, The Authors. All rights reserved.

关键词： Graphene

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A generalized unscented transformation for probability distributions

arXiv

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arXiv 2021年

作者： Ebeigbe, Donald Berry, Tyrus Norton, Michael M. Whalen, Andrew J. Simon, Dan Sauer, Timothy Schiff, Steven J. Center for Neural Engineering Department of Engineering Science and Mechanics Pennsylvania State University University ParkPA United States Department of Mathematical Sciences George Mason University FairfaxVA United States Department of Neurosurgery Massachusetts General Hospital Harvard Medical School BostonMA United States Department of Electrical Engineering and Computer Science Cleveland State University ClevelandOH United States Center for Infectious Disease Dynamics Department of Neurosurgery Pennsylvania State University University ParkPA United States

The unscented transform uses a weighted set of samples called sigma points to propagate the means and covariances of nonlinear transformations of random variables. However, unscented transforms developed using either the Gaussian assumption or a minimum set of sigma points typically fall short when the random variable is not Gaussian distributed and the nonlinearities are substantial. In this paper, we develop the generalized unscented transform (GenUT), which uses 2n + 1 sigma points to accurately capture up to the diagonal components of the skewness and kurtosis tensors of most probability distributions. Constraints can be analytically enforced on the sigma points while guaranteeing at least second-order accuracy. The GenUT uses the same number of sigma points as the original unscented transform while also being applicable to non-Gaussian distributions, including the assimilation of observations in the modeling of infectious diseases such as coronavirus (SARS-CoV-2) causing COVID-19. © 2021, CC BY-NC-ND.

关键词： Diseases

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Hydrodynamic simulations of white dwarf-white dwarf mergers and the origin of R Coronae Borealis stars

arXiv

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arXiv 2024年

作者： Shiber, Sagiv De Marco, Orsola Motl, Patrick M. Munson, Bradley Marcello, Dominic C. Frank, Juhan Diehl, Patrick Clayton, Geoffrey C. Skinner, Bennett N. Kaiser, Hartmut Daiß, Gregor Pflüger, Dirk Staff, Jan E. Department of Physics and Astronomy Louisiana State University Baton RougeLA70803 United States School of Mathematical and Physical Sciences Macquarie University SydneyNSW2109 Australia Astronomy Astrophysics and Astrophotonics Research Centre Macquarie University SydneyNSW2109 Australia The School of Sciences Indiana University Kokomo KokomoIN46902 United States LSU Center for Computation & Technology Louisiana State University Baton RougeLA70803 United States Los Alamos National Laboratory Los AlamosNM87545 United States NSF-supported REU Student at LSU United States IPVS University of Stuttgart Stuttgart Stuttgart70174 Germany Division of Computer Science and Engineering Louisiana State University Baton RougeLA70803 United States Department of Space Earth and Environment Chalmers University of Technology GothenburgSE-41296 Sweden

We study the properties of double white dwarf (DWD) mergers by performing hydrodynamic simulations using the new and improved adaptive mesh refinement code Octo-Tiger. We follow the orbital evolution of DWD systems of mass ratio q = 0.7 for tens of orbits until and after the merger to investigate them as a possible origin for R Coronae Borealis (RCB) type stars. We reproduce previous results, finding that during the merger, the helium WD donor star is tidally disrupted within 20-80 minutes since the beginning of the simulation onto the accretor carbon-oxygen WD, creating a high temperature shell around the accretor. We investigate the possible helium burning in this shell and the merged object’s general structure. Specifically, we are interested in the amount of oxygen-16 dredged-up from the accretor to the hot shell and the amount of oxygen-18 produced. This is critical as the discovery of very low oxygen-16 to oxygen-18 ratios in RCB stars pointed out the merger scenario as a favorable explanation for their origin. A small amount of hydrogen in the donor may help keep the oxygen-16 to oxygen-18 ratios within observational bounds, even if moderate dredge-up from the accretor occurs. In addition, we perform a resolution study to reconcile the difference found in the amount of oxygen-16 dredge-up between smoothed-particle hydrodynamics and grid-based simulations. © 2024, CC BY.

关键词： White dwarfs

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Modeling the performance and bandwidth of single-atom adiabatic quantum memories

APL Quantum

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APL Quantum 2024年第2期1卷

作者： Takla Nateeboon Chanaprom Cholsuk Tobias Vogl Sujin Suwanna Optical and Quantum Physics Laboratory Department of Physics Faculty of Science Mahidol University Bangkok 10400 Thailand Department of Computer Engineering TUM School of Computation Information and Technology Technical University of Munich 80333 Munich Germany Abbe Center of Photonics Institute of Applied Physics Friedrich Schiller University Jena 07745 Jena Germany

Quantum memories are essential for quantum repeaters, which will form the backbone of the future quantum internet. Such memory can capture a signal state for a controllable amount of time, after which this state can be retrieved. In this work, we theoretically investigated how atomic material and engineering parameters affect the performance and bandwidth of a quantum memory. We have applied a theoretical model for quantum memory operation based on the Lindblad master equation and adiabatic quantum state manipulation. The materials’ properties and their uncertainty are evaluated to determine the performance of Raman-type quantum memories by showcasing two defects in two-dimensional hexagonal boron nitride. We have derived a scheme to calculate the signal bandwidth based on the material parameters as well as the maximum efficiency that can be realized. The bandwidth depends on four factors: the signal photon frequency, the dipole transition moments in the electronic structure, the cavity volume, and the strength of the external control electric field. As our scheme is general and independent of materials, it can be applied to many other quantum materials with a suitable three-level structure. We, therefore, provided a promising route for designing and selecting materials for quantum memories. Our work is, therefore, an important step toward the realization of a large-scale quantum network.

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Human Cognition and Hyper Intelligence

Human Cognition and Hyper Intelligence

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IEEE International Symposium on Dependable, Autonomic and Secure Computing (DASC)

作者： D. Frank Hsu Mohammad Shehata Shao-Min Hung Shinsuke Shimojo Ryota Kanai Christina Schweikert Michael W. David Vincenzo Piuri Jianhua Ma Fordham University New York NY U.S.A. Division of Biology & Biological Engineering/Computation & Neural Systems California Institute of Technology Pasadena U.S.A. Araya Inc. Tokyo Japan Division of Computer Science Mathematics and Science St. John's University Queens New York U.S.A. School of Science & Technology Intelligence National Intelligence University Bethesda MD U.S.A. Università degli Studi di Milano Milano Italy Faculty of Computer and Information Sciences Hosei University Tokyo Japan

ISBN: (纸本)9781665421751

Human cognition is a complex neural process ranging from sensor-motor tasks to decision making. Hyper intelligence strives to accomplish complex tasks through creation or combination of diverse intelligent systems used by humans or machines based on a variety of methodologies, algorithms, and models in statistics, computation, and mathematics. In this paper, we give perspectives on various aspects of human cognition and hyper intelligence, as well as their relations, interactions, and causal effects. In particular, we focus on a diverse set of topics: cognitive process, functions of consciousness, model fusion, supply chain development, and convergence of technologies. Opportunities and challenges of this emerging field are also discussed. In addition, several issues for further investigation are suggested.

关键词： Hyper-intelligent systems Cognitive processes computational modeling Supply chains Decision making Big Data Mathematical models

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Publisher’s Note: “Modeling the performance and bandwidth of single-atom adiabatic quantum memories” [APL Quantum 1, 026107 (2024)]

APL Quantum

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APL Quantum 2024年第2期1卷

This article was originally published online on 29 April 2024 with an affiliation identifier missing for the second author and affiliations 2 and 3 out of order

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Social and geographical disparities in Twitter use during Hurricane Harvey

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International Journal of Digital Earth 2019年第11期12卷 1300-1318页

作者： Lei Zou Nina S.N.Lam Shayan Shams Heng Cai Michelle A.Meyer Seungwon Yang Kisung Lee Seung-Jong Park Margaret A.Reams Department of Geography College StationTXUSA Department of Environmental Sciences Louisiana State UniversityBaton RougeLAUSA Division of Computer Science and Engineering Center for Computation and TechnologyLouisiana State UniversityBaton RougeLAUSA Department of Sociology Louisiana State UniversityBaton RougeLAUSA Department of Landscape Architecture and Urban Planning Texas A&M UniversityCollege StationTXUSA School of Library and Information Science Center for Computation and TechnologyLouisiana State UniversityBaton RougeLAUSA

Socialmedia such as Twitter is increasingly beingused as an effective platform to observe human behaviors in disastrous ***,uneven social media use among different groups of population in different regions could lead to biased consequences and affect disaster *** paper studies the Twitter use during 2017 Hurricane Harvey in 76 counties in Texas and *** seek to answer a fundamental question:did socialgeographical disparities of Twitter use exist during the three phases of emergency management(preparedness,response,recovery)?We employed a Twitter data mining framework to process the data and calculate two indexes:Ratio and *** analyses between the Ratio indexes and the social-geographical characteristics of the counties at the three phrases reveal significant social and geographical disparities in Twitter use during Hurricane *** with higher disasterrelated Twitter use in Harvey generally were communities having better social and geographical *** results of Twitter use patterns can be used to compare with future similar studies to see whether the Twitter use disparities have increased or *** research is also needed to examine the effects of Twitter use disparities on disaster resilience and to test whether Twitter use can predict community resilience.

关键词： Social media Twitter Hurricane Harvey emergency management digital divide social and geographical disparities

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Cerebrovascular Segmentation in MRA via Reverse Edge Attention Network 23rd

Cerebrovascular Segmentation in MRA via Reverse Edge Attenti...

引用

23rd International Conference on Medical Image Computing and computer-Assisted Intervention, MICCAI 2020

作者： Zhang, Hao Xia, Likun Song, Ran Yang, Jianlong Hao, Huaying Liu, Jiang Zhao, Yitian College of Information Engineering Capital Normal University Beijing China Cixi Institute of Biomedical Engineering Ningbo Institute of Industrial Technology Chinese Academy of Sciences Ningbo China School of Control Science and Engineering Shandong University Jinan China Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen China International Science and Technology Cooperation Base of Electronic System Reliability and Mathematical Interdisciplinary Capital Normal University Beijing China Laboratory of Neural Computing and Intelligent Perception Capital Normal University Beijing China Beijing Advanced Innovation Center for Imaging Theory and Technology Capital Normal University Beijing China

ISBN: (纸本)9783030597245

Automated extraction of cerebrovascular is of great importance in understanding the mechanism, diagnosis, and treatment of many cerebrovascular pathologies. However, segmentation of cerebrovascular networks from magnetic resonance angiography (MRA) imagery continues to be challenging because of relatively poor contrast and inhomogeneous backgrounds, and the anatomical variations, complex geometry and topology of the networks themselves. In this paper, we present a novel cerebrovascular segmentation framework that consists of image enhancement and segmentation phases. We aim to remove redundant features, while retaining edge information in shallow features when combining these with deep features. We first employ a Retinex model, which is able to model noise explicitly to aid removal of imaging noise, as well as reducing redundancy within an image and emphasizing the vessel regions, thereby simplifying the subsequent segmentation problem. Subsequently, a reverse edge attention module is employed to discover edge information by paying particular attention to the regions that are not salient in high-level semantic features. The experimental results show that the proposed framework enables the reverse edge attention network to deliver a reliable cerebrovascular segmentation. © 2020, Springer Nature Switzerland AG.

关键词： Image enhancement

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