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CEUR Workshop Proceedings 2022年 3091卷

作者： Kovalev, Igor Semenkin, Eugene Schwenker, Friedhelm Shaidurov, Vladimir Ganchev, Todor Škraba, Andrej Stanimirović, Predrag Kolehmainen, Mikko Kronberger, Gabriel Affenzeller, Michael Brester, Christina Ryzhikov, Ivan Kazakovtsev, Lev Sopov, Evgenii Semenkina, Maria Akhmedova, Shakhnaz Stanovov, Vladimir Krasnoyarsk Regional Science and Technology City Hall Department of Computer Science of Siberian Federal University Krasnoyarsk Russia Siberian Institute of Applied System Analysis named after A.N. Antamoshkin The Department ofsystem analysis and operations research of Reshetnev University Krasnoyarsk Russia Institute of Neural Information Processing Ulm University Ulm Germany The Institute of Computational Modeling SB RAS Krasnoyarsk Russia Technical University of Varna Varna Bulgaria University of Maribor UM Faculty of Organizational Sciences Cybernetics and Decision Support Systems Laboratory Slovenia Department of Computer Science Niš Serbia Department of Environmental and Biological Sciences Environmental Informatics University of Eastern Finland Kuopio Finland University of Applied Sciences Upper Austria Hagenberg Austria Department of Software Engineering School of Informatics Communications and Media University of Applied Sciences Upper Austria Hagenberg Austria University of Eastern Finland UEF · Department of Environmental Science Kuopio Finland The Department of Systems Analysis Reshetnev University of Science and Technology Krasnoyarsk Russia Siberian Institute of Applied System Analysis Krasnoyarsk Russia University of Applied Sciences Upper Austria and Softwarepark Hagenberg Austria Reshetnev Siberian States University of Science and Technology Siberian Institute of Applied System Analysis Krasnoyarsk Russia SibSAU · Institute of computer science and telecommunications Krasnoyarsk Russia

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Electronic nature of chiral charge order in the kagome superconductor CsV3Sb5

arXiv

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arXiv 2021年

作者： Wang, Zhiwei Jiang, Yu-Xiao Yin, Jia-Xin Li, Yongkai Wang, Guan-Yong Huang, Hai-Li Shao, Shen Liu, Jinjin Zhu, Peng Shumiya, Nana Hossain, Md Shafayat Liu, Hongxiong Shi, Youguo Duan, Junxi Li, Xiang Chang, Guoqing Dai, Pengcheng Ye, Zijin Xu, Gang Wang, Yanchao Zheng, Hao Jia, Jinfeng Hasan, M. Zahid Yao, Yugui School of Physics Beijing Institute of Technology Beijing100081 China Department of Physics Princeton University PrincetonNJ United States School of Physics and Astronomy Key Laboratory of Artificial Structures and Quantum Control Ministry of Education Shenyang National Laboratory for Materials Science Tsung-Dao Lee Institute Shanghai Jiao Tong University Shanghai200240 China State Key Laboratory of Superhard Materials College of Physics Jilin University Changchun130012 China International Center for Computational Method and Software College of Physics Jilin University Changchun130012 China Beijing National Laboratory for Condensed Matter Physics Institute of Physics Chinese Academy of Sciences Beijing100190 China University of Chinese Academy of Sciences Beijing100049 China Division of Physics and Applied Physics School of Physical and Mathematical Sciences Nanyang Technological University Singapore Singapore Department of Physics and Astronomy Rice University HoustonTX77005 United States Wuhan National High Magnetic Field Center Huazhong University of Science and Technology Hubei Wuhan430074 China Princeton Institute for the Science and Technology of Materials Princeton University PrincetonNJ08544 United States Materials Science Division Lawrence Berkeley National Laboratory BerkeleyCA94720 United States Quantum Science Center Oak RidgeTN37831 United States Beijing Key Lab of Nanophotonics and Ultrafine Optoelectronic Systems Beijing Institute of Technology Beijing100081 China

Kagome superconductors with TC up to 7K have been discovered over 40 years. Recently, unconventional chiral charge order has been reported in kagome superconductor KV3Sb5, with an ordering temperature of one order of magnitude higher than the TC. However, the chirality of the charge order has not been reported in the cousin kagome superconductor CsV3Sb5, and the electronic nature of the chirality remains elusive. In this letter, we report the observation of electronic chiral charge order in CsV3Sb5 via scanning tunneling microscopy (STM). We observe a 2×2 charge modulation and a 1×4 superlattice in both topographic data and tunneling spectroscopy. 2×2 charge modulation is highly anticipated as a charge order by fundamental kagome lattice models at van Hove filling, and is shown to exhibit intrinsic chirality. We find that the 1×4 superlattices forms various small domain walls, and can be a surface effect as supported by our first-principles calculations. Crucially, we find that the amplitude of the energy gap opened by the charge order exhibits real space modulations, and features 2×2 wave vectors with chirality, highlighting the electronic nature of the chiral charge order. STM study at 0.4K reveals a superconducting energy gap with a gap size 2Δ=0.85meV, which estimates a moderate superconductivity coupling strength with 2Δ/kBTC=3.9. When further applying a c-axis magnetic field, vortex core bound states are observed within this gap, indicative of clean-limit superconductivity. © 2021, CC BY.

关键词： Chirality

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A survey on dragonfly algorithm and its applications in engineering

arXiv

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arXiv 2020年

作者： Rahman, Chnoor M. Rashid, Tarik A. Alsadoon, Abeer Bacanin, Nebojsa Fattah, Polla Mirjalili, Seyedali Applied Computer Department College of Health and Applied Sciences Charmo University Sulaimany Iraq Science and Engineering Department University of Kurdistan Hewler Erbil Iraq School of Computing and Mathematics Charles Sturt University Sydney Australia School of Computing Engineering and Mathematics Western Sydney University Sydney City Campus Australia Information Technology Department Sydney Australia Kent Institute Australia Information Technology Department Sydney Australia Singidunum University Belgrade Serbia Software and Informatics Engineering Salahaddin University-Erbil Erbil Iraq Centre for Artificial Intelligence Research and Optimisation Torrens University Australia Yonsei Frontier Lab Yonsei University Seoul Korea Republic of

The dragonfly algorithm was developed in 2016. It is one of the algorithms used by researchers to optimize an extensive series of uses and applications in various areas. At times, it offers superior performance compared to the most well-known optimization techniques. However, this algorithm faces several difficulties when it is utilized to enhance complex optimization problems. This work addressed the robustness of the method to solve real-world optimization issues, and its deficiency to improve complex optimization problems. This review paper shows a comprehensive investigation of the dragonfly algorithm in the engineering area. First, an overview of the algorithm is discussed. Besides, we also examined the modifications of the algorithm. The merged forms of this algorithm with different techniques and the modifications that have been done to make the algorithm perform better are addressed. Additionally, a survey on applications in the engineering area that used the dragonfly algorithm is offered. The utilized engineering applications are the applications in the field of mechanical engineering problems, electrical engineering problems, optimal parameters, economic load dispatch, and loss reduction. The algorithm is tested and evaluated against particle swarm optimization algorithm and firefly algorithm. To evaluate the ability of the dragonfly algorithm and other participated algorithms a set of traditional benchmarks (TF1-TF23) were utilized. Moreover, to examine the ability of the algorithm to optimize large-scale optimization problems CEC-C2019 benchmarks were utilized. A comparison is made between the algorithm and other metaheuristic techniques to show its ability to enhance various problems. The outcomes of the algorithm from the works that utilized the dragonfly algorithm previously and the outcomes of the benchmark test functions proved that in comparison with participated algorithms (GWO, PSO, and GA), the dragonfly algorithm owns an excellent performance,

关键词： Multiobjective optimization

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Electrical Control of Magnetic Phase Transition in a Type-I Multiferroic Double-Metal Trihalide Monolayer

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Physical Review Letters 2020年第6期124卷 067602-067602页

作者： Meiling Xu Chengxi Huang Yinwei Li Siyu Liu Xin Zhong Puru Jena Erjun Kan Yanchao Wang School of Physics and Electronic Engineering Jiangsu Normal University Xuzhou 221116 China Department of Applied Physics and Institution of Energy and Microstructure Nanjing University of Science and Technology Nanjing Jiangsu 210094 China Physics Department Virginia Commonwealth University Richmond Virginia 23284 USA State Key Lab of Superhard Materials & International center of computational method and software College of Physics Jilin University Changchun 130012 China Key Laboratory of Functional Materials Physics and Chemistry of the Ministry of Education Jilin Normal University Changchun 130103 China

Controlling magnetism of two-dimensional multiferroics by an external electric field provides special opportunities for both fundamental research and future development of low-cost electronic nanodevices. Here, we report a general scheme for realizing a magnetic phase transition in 2D type-I multiferroic systems through the reversal of ferroelectric polarization. Based on first-principles calculations, we demonstrate that a single-phase 2D multiferroic, namely, ReWCl6 monolayer, exhibits two different low-symmetric (C2) phases with opposite in-plane electric polarization and different magnetic order. As a result, an antiferromagnetic-to-ferromagnetic phase transition can be realized by reversing the in-plane electric polarization through the application of an external electric field. These findings not only enrich the 2D multiferroic family, but also uncover a unique and general mechanism to control magnetism by electric field, thus stimulating experimental interest.

关键词： Ferroelectricity Magnetic phase transitions Magnetoelectric effect

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Implicit multi-feature learning for dynamic time series prediction of the impact of institutions

arXiv

引用

arXiv 2020年

作者： Bai, Xiaomei Zhang, Fuli Hou, Jie Xia, Feng Tolba, Amr Elashkar, Elsayed Key Laboratory for Ubiquitous Network and Service Software of Liaoning Province School of Software Dalian University of Technology Dalian116620 China Computing Center Anshan Normal University Anshan114007 China Library Anshan Normal University Anshan114007 China Computer Science Department Community College King Saud University Riyadh11437 Saudi Arabia Mathematics Department Faculty of Science Menoufia University Shebin El-Kom32511 Egypt Administrative Sciences Department Community College King Saud University Riyadh11437 Saudi Arabia Applied Statistics Department Faculty of Commerce Mansoura University Egypt

Predicting the impact of research institutions is an important tool for decision makers, such as resource allocation for funding bodies. Despite significant effort of adopting quantitative indicators to measure the impact of research institutions, little is known that how the impact of institutions evolves in time. Previous researches have focused on using the historical relevance scores of different institutions to predict potential future impact for these institutions. In this paper, we explore the factors that can drive the changes of the impact of institutions, finding that the impact of an institution, as measured by the number of the accepted papers of the institution, more is determined by the authors’ influence of the institution. Geographic location of institution feature and state GDP can drive the changes of the impact of institutions. Identifying these features allows us to formulate a predictive model that integrates the effects of individual ability, location of institution, and state GDP. The model unveils the underlying factors driving the future impact of institutions, which can be used to accurately predict the future impact of institutions. Copyright © 2020, The Authors. All rights reserved.

关键词： Forecasting

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A New Approach on Rule Mining Based on Granularity in Incomplete Information Systems

A New Approach on Rule Mining Based on Granularity in Incomp...

引用

IEEE International Conference on software Engineering and Service sciences (ICSESS)

作者： Wu Jie Liang Yan Ma Yuan School of Software University of Science and Technology Liaoning Anshan China School of Applied Technology University of Science and Technology Anshan China

ISBN: (纸本)9781538665664;9781538665657

The analysis of incomplete decision table is an important research topic in the field of intelligent information processing. This paper defines incomplete information system, incomplete decision table, granularity, tolerance granulation, deterministic operator, possible operator. Firstly, it extracts tolerance granulations of the object collections that are divided by decision attributes. Secondly, it gets the new object collections by calculating tolerance granulations via deterministic operators or possible operators. Thirdly, If the intersection of the information granulations from the different attribute values is an empty set or isn't a subset of the elements from the new object sets, then it chooses the information granules of the attribute values according to different conditions. The execution is out of the loop until it doesn't satisfy the cycle conditions. It outputs the deterministic or possible decision rules. And lastly, it continues to find the deterministic or possible decision rules from the rest of the new object collections. The paper presents a new method that the deterministic or possible decision rules are mined based on granularity in incomplete information systems. It gives the mining algorithms and the instance. The approach has the advantages of high efficiency, more rules, concise forms, good comprehensibility and generalization ability.

关键词： Diabetes Data mining Information processing Classification algorithms Knowledge acquisition Rough sets

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Privacy-Preserving Linear Regression for Brain-Computer Interface Applications

Privacy-Preserving Linear Regression for Brain-Computer Inte...

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2018 IEEE International Conference on Big Data, Big Data 2018

作者： Agarwal, Anisha Dowsley, Rafael McKinney, Nicholas D. Wu, Dongrui Lin, Chin-Teng Cock, Martine De Nascimento, Anderson School of Engineering and Technology University of Washington Tacoma United States Dept. of Computer Science Aarhus University Aarhus Denmark School of Automation Huazhong University of Science and Technology Wuhan China School of Software University of Technology Sydney Australia Dept. of Applied Math. Comp. Sc. and Statistics Ghent University Belgium

ISBN: (纸本)9781538650356

Many machine learning (ML) applications rely on large amounts of personal data for training and inference. Among the most intimate exploited data sources is electroencephalogram (EEG) data. The emergence of consumer-grade, low-cost brain-computer interfaces (BCIs) and corresponding software development kits1 is bringing the use of BCI within reach of application developers. The access that BCI applications have to neural signals rightly raises privacy concerns. Application developers can easily gain knowledge beyond the professed scope from unprotected EEG signals, including passwords, ATM PINs, and other personal data [1]. The challenge we address is how to engage in meaningful ML with EEG data while protecting the privacy of users. © 2018 IEEE.

关键词： Electroencephalography

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Protecting privacy of users in brain-computer interface applications

arXiv

引用

arXiv 2019年

作者： Agarwal, Anisha Dowsley, Rafael McKinney, Nicholas D. Wu, Dongrui Lin, Chin-Teng De Cock, Martine Nascimento, Anderson C.A. School of Engineering and Technology University of Washington TacomaWA98402 United States Department of Computer Science Bar-Ilan University Israel School of Engineering and Technology University of Washington TacomaWA98402 United States Key Laboratory of the Ministry of Education for Image Processing and Intelligent Control School of Automation Huazhong University of Science and Technology Wuhan China School of Software University of Technology Sydney Australia Department of Applied Mathematics Computer Science and Statistics Ghent University Ghent9000 Belgium

Machine learning (ML) is revolutionizing research and industry. Many ML applications rely on the use of large amounts of personal data for training and inference. Among the most intimate exploited data sources is electroencephalogram (EEG) data, a kind of data that is so rich with information that application developers can easily gain knowledge beyond the professed scope from unprotected EEG signals, including passwords, ATM PINs, and other intimate data. The challenge we address is how to engage in meaningful ML with EEG data while protecting the privacy of users. Hence, we propose cryptographic protocols based on Secure Multiparty Computation (SMC) to perform linear regression over EEG signals from many users in a fully privacy-preserving (PP) fashion, i.e. such that each individual's EEG signals are not revealed to anyone else. To illustrate the potential of our secure framework, we show how it allows estimating the drowsiness of drivers from their EEG signals as would be possible in the unencrypted case, and at a very reasonable computational cost. Our solution is the first application of commodity-based SMC to EEG data, as well as the largest documented experiment of secret sharing based SMC in general, namely with 15 players involved in all the computations. Copyright © 2019, The Authors. All rights reserved.

关键词： Machine learning

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Improving Quality of Life Based on Electronic Patient Reported Outcomes in Patients with CLL and MDS: The Mypal Study

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Blood 2024年 144卷 2358-2358页

作者： Thomas Chatzikonstantinou Maria Vasilopoulou Dimitrios Kyrou Georgios Karakatsoulis Panagiotis Argyropaidas Sotiria Besikli-Dimou Panos Bonotis Maria Chatzimina Jana Didi Christos Demosthenous Tina Garani-Papadatos Stefan Hoffman Christine Kakalou Eleni Kazantzaki Stephan Kiefer Lefteris Koumakis Eva Hellstrom Lindberg Julie Ling Christos Maramis Marcel Meyerheim Lydia Scarfo Institute of Applied Biosciences Centre for Research and Technology Hellas Thessaloniki Greece Institute of Computer Science Foundation for Research and Technology-Hellas Heraklion Greece Center for Molecular Medicine Central European Institute of Technology Masaryk University Brno Czech Republic Hematology Department - BMT Unit G Papanicolaou Hospital Thessaloniki Greece Department of Public Health Policy School of Public Health University of West Attica Athens Greece Promotion Software GmbH Berlin Germany Department of Hematology University of Crete School of Medicine Heraklion Greece Fraunhofer Institute For Biomedical Engineering (IBMT) Sulzbach Germany Center for Hematology and Regenerative Medicine (HERM) Department of Medicine Huddinge Karolinska Institutet Stockholm Sweden European Association for Palliative Care Vilvoorde Belgium Department of Pediatric Oncology and Hematology Faculty of Medicine Saarland University Homburg Germany Strategic Research Program on CLL IRCCS Ospedale San Raffaele Milano Italy Università Vita-Salute San Raffaele Milan Italy

Introduction: Palliative care is a critical component of care for patients with chronic hematological malignancies. Leveraging eHealth approaches can potentially facilitate the delivery of palliative care, leading to improved quality of life (QoL). Methods: The MyPal study (NCT04370457) is a randomized controlled clinical trial assessing an eHealth intervention on QoL of patients. The study was conducted according to the Declaration of Helsinki. The ethical aspects of the study, given the vulnerability of the patients, were addressed with diligence and approvals were obtained from the appropriate Research Ethics Committees. Patients who were receiving or had previously received treatment for CLL or MDS were randomly assigned (1:1) to access the MyPal platform versus standard of care. The MyPal platform included a smartphone application used to report QoL status (ePRO questionnaires) and symptoms. The primary endpoint was QoL at 12 months, assessed by the European Organization for Research and Treatment of Cancer (EORTC) quality of life QLQ-C30 General Questionnaire and the Euroqol 5-dimension (Euroqol EQ-5D-3L). Secondary endpoints included physical and emotional functioning, measured by the Integrated Palliative Care Outcome Scale (IPOS), satisfaction with care, measured by the EORTC Patient Satisfaction with Cancer Care questionnaire PATSAT-C33 (PATSAT-C33), and overall survival (OS). Additionally, the Edmonton Symptom Assessment System (ESAS), the Brief Pain Inventory (BPI), and the Emotion Thermometers (ET) QoL questionnaires were assessed only in the intervention group. Results: A total of 171 patients (97 and 74 in the control and intervention arms, respectively) who answered multiple questionnaires were included in the analysis. The intervention group reported a significant decrease in pain (β2=-0.48 (-0.77, -0.19), p<0.001) compared to the control group (β1=0.3 (0.09, 0.5), p=0.01) as measured by the EORTC QLQ-C30. Communication and pain as measured by IPOS

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Scalable hadoop-based infrastructure for big data analytics 13th

Scalable hadoop-based infrastructure for big data analytics

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13th International Baltic Conference on Databases and Information Systems, DB and IS 2018

作者： Astrova, Irina Koschel, Arne Heine, Felix Kalja, Ahto Department of Software Science School of IT Tallinn University of Technology Akadeemia tee 21 Tallinn12618 Estonia Faculty IV Department of Computer Science Hannover University of Applied Sciences and Arts Ricklinger Stadtweg 120 Hannover30459 Germany

ISBN: (纸本)9783319975702

Cloud architectures are being used increasingly to support Big Data analytics by organizations that make ad hoc or routine use of the cloud in lieu of acquiring their own infrastructure. On the other hand, Hadoop has become the de-facto standard for storing and processing Big Data. It is hard to overstate how many advantages come with moving Hadoop into the cloud. The most important is scalability, meaning that the underlying infrastructure can be expanded or contracted according to the actual demand on resources. This paper presents a scalable Hadoop-based infrastructure for Big Data analytics, one that gets automatically adjusted if more computing power or storage capacity is needed. Adjustments are transparent to the users – the users seem to have nearly unlimited computation and storage resources. © Springer Nature Switzerland AG 2018.

关键词： Big data

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