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Structural analysis based on unsupervised learning: Search for a characteristic low-dimensional space by local structures in atomistic simulations

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Physical Review B 2022年第7期105卷 075107-075107页

作者： Ryo Tamura Momo Matsuda Jianbo Lin Yasunori Futamura Tetsuya Sakurai Tsuyoshi Miyazaki International Center for Materials Nanoarchitectonics National Institute for Materials Science Tsukuba 305-0044 Japan Research and Services Division of Materials Data and Integrated System National Institute for Materials Science Tsukuba 305-0044 Japan Graduate School of Frontier Sciences The University of Tokyo Chiba 277-8568 Japan Department of Computer Science University of Tsukuba Tsukuba 305-8573 Japan Center for Artificial Intelligence University of Tsukuba Tsukuba 305-8573 Japan Master's/Doctoral Program in Life Science Innovation University of Tsukuba Tsukuba 305-8577 Japan

Owing to the advances in computational techniques and the increase in computational power, atomistic simulations of materials can simulate large systems with higher accuracy. Complex phenomena can be observed in such state-of-the-art atomistic simulations. However, it has become increasingly difficult to understand what is actually happening and mechanisms, for example, in molecular dynamics (MD) simulations. We propose an unsupervised machine learning method to analyze the local structure around a target atom. The proposed method, which uses the two-step locality preserving projections (TS-LPP), can find a low-dimensional space wherein the distributions of data points for each atom or groups of atoms can be properly captured. We demonstrate that the method is effective for analyzing the MD simulations of crystalline, liquid, and amorphous states and the melt-quench process from the perspective of local structures. The proposed method is demonstrated on a silicon single-component system, a silicon-germanium binary system, and a copper single-component system.

关键词： Amorphous materials Machine learning Molecular dynamics

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Limited angle acousto-electrical tomography

arXiv

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arXiv 2017年

作者： Hubmer, Simon Knudsen, Kim Li, Changyou Sherina, Ekaterina Johannes Kepler University Linz Doctoral Program Computational Mathematics Altenbergerstraße 69 LinzA-4040 Austria Technical University of Denmark Department of Applied Mathematics and Computer Science Asmussens Allé Kongens Lyngby2800 Denmark Technical University of Denmark Department of Applied Mathematics and Computer Science Asmussens Allé Kongens Lyngby2800 Denmark School of Electronics and Information Northwestern Polytechnical University Xian China Technical University of Denmark Department of Applied Mathematics and Computer Science Asmussens Allé Kongens Lyngby2800 Denmark

This paper considers the reconstruction problem in Acousto-Electrical Tomography, i.e., the problem of estimating a spatially varying conductivity in a bounded domain from measurements of the internal power densities resulting from different prescribed boundary conditions. Particular emphasis is placed on the limited angle scenario, in which the boundary conditions are supported only on a part of the boundary. The reconstruction problem is formulated as an optimization problem in a Hilbert space setting and solved using Landweber iteration. The resulting algorithm is implemented numerically in two spatial dimensions and tested on simulated data. The results quantify the intuition that features close to the measurement boundary are stably reconstructed and features further away are less well reconstructed. Finally, the ill-posedness of the limited angle problem is quantified numerically using the singular value decomposition of the corresponding linearized problem.65J22, 35R30, 65M32 Copyright © 2017, The Authors. All rights reserved.

关键词： Boundary conditions

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New quantum circuit architecture for classifying big data

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IOP Conference Series: Materials science and Engineering 2021年第1期1088卷

作者： Solikhun Syahril Efendi Muhammad Zarlis Poltak Sihombing Doctoral Program Faculty of Computer Science and Information Technology Universitas Sumatera Utara Faculty of Computer Science and Information Technology Universitas Sumatera Utara

This research is motivated by the incomplete use of quantum in existing learning algorithms, so that the proposed learning algorithm is not optimal. Research (Fahri & Neven, 2018) shows that the proposed method of architectural form still uses classical architecture but inputs, weights and targets already use a quantum approach. Based on the results of previous studies, it shows that quantum computing is better than classical computation. Many researchers use quantum computing in the proposed learning algorithm. The model proposed is a quantum circuit architecture with the quantum perceptron method consisting of a quantum bit gate that uses a quantum computational approach as the architecture of the quantum perceptron learning algorithm. Then the authors conduct training and testing of the proposed quantum circuit architecture to test the quantum circuit model that the author proposes. The result of this research is a quantum circuit model with the quantum perceptron method which can be used to solve the learning optimization problem by using a quantum circuit architecture with 5 measurement measurements to show error training and testing = 0, with 9 measurements showing an error training of 1.13%, error testing 2.06%.

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A Novel Blockchain-Based Strategy for Energy Conservation in Cognitive Wireless Sensor Networks

A Novel Blockchain-Based Strategy for Energy Conservation in...

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Blockchain Technology and Information Security (ICBCTIS), International Conference on

作者： Khemraj Sharma Rohit Goyal Sukesh Kumar Bhagat Sheenu Agarwal Girish Singh Bisht Mohemmed Hussien Department of Business Administration Doctoral Program in Management Sciences Nanhua University Dalin Taiwan School of Engineering and Computing Dev Bhoomi Uttarakhand University Dehradun India Department of Computer Science & Engineering Uttaranchal University Dehradun India Department of Electronics and Communication Engineering Uttarakhand Technical University Dehradun India Department of Computer Science and Engineering Tula's Institute Dehradun India Dehradun India The Islamic university Najaf Iraq

ISBN: (数字)9798331528324

ISBN: (纸本)9798331528331

Internet of Things (IoT) industrial applications have increased in internet-connected devices. As a result, accessibility, scalability, connectivity, and adaptability have become major challenges. It is possible to create connections between loT devices over the wireless medium, but efficiently utilizing scarce spectrum is the biggest concern. The paper proposes a novel method for selecting sensing nodes with the goal of reducing energy usage and increasing sensing efficiency. In order to ensure that node trust values conform to each other without any ambiguity, cryptography encrypts all information that relates to a node in a central node (CN). Nodes participating in CSS are selected based on their performance. Based on a comparison of various algorithms using varying numbers of nodes, the proposed algorithm achieves a 10% sensing efficiency.

关键词： Wireless communication Wireless sensor networks Adaptation models Machine learning algorithms Heuristic algorithms Scalability Sensors Blockchains Internet of Things Reliability

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FetalNet: Low-light fetal echocardiography enhancement and dense convolutional network classifier for improving heart defect prediction

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Informatics in Medicine Unlocked 2022年 35卷

作者： Sutarno, Sutarno Nurmaini, Siti Partan, Radiyati Umi Sapitri, Ade Iriani Tutuko, Bambang Naufal Rachmatullah, Muhammad Darmawahyuni, Annisa Firdaus, Firdaus Bernolian, Nuswil Sulistiyo, Deny Intelligent System Research Group Faculty of Computer Science Universitas Sriwijaya Indonesia Doctoral Program Faculty of Engineering Universitas Sriwijaya Indonesia Faculty of Medicine Universitas Sriwijaya Indonesia Division of Fetomaternal Department of Obstetrics and Gynaecology Mohammad Hoesin General Hospital Indonesia

Background: Fetal heart defect (FHD) examination by ultrasound (US) is challenging because it involves low light, contrast, and brightness. Inadequate US images of fetal echocardiography play an important role in the failure to detect FHDs manually. The automatic interpretation of fetal echocardiography was proposed in a previous study. However, the low quality of US images reduces the prediction rate of computer-assisted diagnosis results. Methods: To increase the FHD prediction rate, we propose low-light fetal echocardiography enhancement stacking with a dense convolutional network classifier named “FetalNet.” Our proposed FetalNet model was developed using 460 US images to produce an image enhancement model. The results showed that all raw US images could be improved with satisfactory performance in terms of increasing the peak signal-to-noise ratio of 30.85 dB, a structural similarity index of 0.96, and a mean squared error of 18.16. Furthermore, all reconstructed images were used as inputs in a convolutional neural network to generate the best classifier for predicting FHD. Results: The proposed FetalNet model increased the FHD prediction rate by approximately 25% in terms of accuracy, sensitivity, and specificity and produced 100% predictive negative using unseen data. Conclusions: The proposed deep learning model has the potential to identify FHD accurately and shows potential for practical use in identifying congenital heart diseases in the future. © 2022 The Author(s)

关键词： Congenital heart disease Deep learning Low light image enhancement Ultrasound

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Cryo-EM structure of the plant 26S proteasome

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Plant Communications 2022年第3期3卷 116-126页

作者： Susanne Kandolf Irina Grishkovskaya Katarina Belacic Derek L.Bolhuis Sascha Amann Brent Foster Richard Imre Karl Mechtler Alexander Schleiffer Hemant D.Tagare Ellen D.Zhong Anton Meinhart Nicholas G.Brown David Haselbach Research Institute of Molecular Pathology(IMP) Vienna BioCenter(VBC)Campus-Vienna-BioCenter 11030 ViennaAustria Department of Radiology and Biomedical Imaging Yale UniversityNew HavenCT 06510USA Department of Pharmacology and Lineberger Comprehensive Cancer Center University of North Carolina School of MedicineChapel HillNC 27599USA Computer Science and Artificial Intelligence Laboratory Massachusetts Institute of TechnologyCambridgeMA 02139USA Vienna BioCenter PhD Program Doctoral School of the University at Vienna and Medical University of ViennaVienna BioCenter(VBC)ViennaAustria Institute of Physical Chemistry University of FreiburgAlbertstraße 21Freiburg 79104Germany CIBSS Centre for Integrative Biological Signalling Studies University of FreiburgFreiburgGermany

Targeted proteolysis is a hallmark of *** is especially important in long-lived cells that can be found in higher eukaryotes,like *** task is mainly fulfilled by the ubiquitin–proteasome ***,proteolysis by the 26S proteasome is vital to development,immunity,and cell *** the yeast and animal proteasomes are well characterized,there is only limited information on the plant *** determined the first plant 26S proteasome structure from Spinacia oleracea by single-particle electron cryogenic microscopy at an overall resolution of 3.3 A°.We found an almost identical overall architecture of the spinach proteasome compared with the known structures from mammals and ***,we noticed a structural difference in the proteolytic active b1 ***,we uncovered an unseen compression state by characterizing the proteasome’s conformational *** suspect that this new conformation of the 20S core protease,in correlation with a partial opening of the unoccupied gate,may contribute to peptide release after *** data provide a structural basis for the plant proteasome,which is crucial for further studies.

关键词： 26S proteasome spinach UPS cryo-EM conformational landscape

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Design and Implementation of Rivest Shamir Adleman's (RSA) Cryptography Algorithm in Text File Data Security

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Journal of Physics: Conference Series 2020年第1期1641卷

作者： Hengki Tamando Sihotang Syahril Efendi Elvyawati M Zamzami Herman Mawengkang Doctoral Program Faculty of Computer Science and Information Technology Universitas Sumatera Utara Sumatera Utara Indonesia

Security of data text file on a computer can be done by utilizing encryption and decryption techniques. One technique is encryption and decryption of data encryption system text file with cryptography. Cryptography is the science or art to randomize and secure the message to avoid the manipulation of data by performing encryption and decryption on the data of the text file. Cryptographic algorithm consists of several types of one example is the algorithm Rivest Shamir Adleman (RSA). The RSA algorithm is one of asymmetric cryptography technique ie riptografi modern technique that has two keys of the public key used for encryption and a private key techniques to make the process deskripsi. The purpose of this research is to design and implement RSA cryptography algorithm on text file data security. The results of this design is a dekstop based software. This research is expected to provide benefits to the users, individually as well for other organizations.

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Use of tangible interfaces to support a literacy system in children with intellectual disabilities

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Lecture Notes in computer science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 2014年 8867卷 108-115页

作者： Jadán-Guerrero, Janio López, Gustavo Guerrero, Luis A. Doctoral Program in Computer Science Universidad de Costa Rica San José Costa Rica Centro de Investigaciones en Tecnologías de la Información y Comunicación CITIC Universidad de Costa Rica San José Costa Rica Universidad Tecnológica Indoamérica Av. Machala y Sabanilla Quito Ecuador

ISBN: (纸本)9783319131016

People with intellectual disabilities should develop reading and writing skills since children. Otherwise, they will have additional difficulties in their daily life activities, as buying in a store, taking medicaments or getting a job. In this paper we present a prototype that uses tangible interfaces and augmented objects to support a literacy method for children with intellectual disabilities. The prototype was evaluated and the preliminary results showed that the system can not only assists teachers in the classroom, but also helps to better manage the interaction with children and non-traditional interfaces. © Springer International Publishing Switzerland 2014.

关键词： Medicaments

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Expert System Diagnosis Corn Pests And Diseases Using Certainty Factor Method

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Journal of Physics: Conference Series 2019年第1期1230卷

作者： Sulindawaty Muhammad Zarlis Zakarias Situmorang Hengki Tamando Sihotang Doctoral Program Faculty of Computer Science and InformationTechnology Universitas Sumatera Utara Indonesia Faculty of Computer Science and Information Technology Universitas Sumatera Utara Indonesia

The development of technology has been very significant, not only in the fields of information, industry, education, but in agriculture. Therefore technological sophistication is also utilized by corn farmers to obtain information about corn crops. Corn is included in plantation commodities in Indonesia. However, in corn cultivation there are various problems associated with the disease that is likely to attack the plant. Therefore, the authors designed the expert system of corn disease diagnosis using certainty method. This system will analyze based on symptoms - symptoms that are in the input by the user so that in obtaining a result of disease identification on corn in the name of the disease, the cause of the disease, the percentage value and how to handle it. This expert system is developed by using the visual basic application and Microsoft Access database with Certainty Factor method to measure the value of certainty of a hypothesis against a fact.

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Cross-system biological image quality enhancement based on the generative adversarial network as a foundation for establishing a multi-institute microscopy cooperative network

arXiv

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arXiv 2024年

作者： Panek, Dominik Rząca, Carina Szczypior, Maksymilian Sorysz, Joanna Misztal, Krzysztof Baster, Zbigniew Rajfur, Zenon Doctoral School of Exact and Natural Sciences Jagiellonian University ul. Lojasiewicza 11 Kraków30-348 Poland Department of Molecular and Interfacial Biophysics Faculty of Physics Astronomy and Applied Computer Science Jagiellonian University ul. Lojasiewicza 11 Kraków30‑348 Poland Undergraduate Program in Biophysics Faculty of Physics Astronomy and Applied Computer Science Jagiellonian University ul. Lojasiewicza 11 Kraków30‑348 Poland Undergraduate program in Biomedical Engineering Faculty of Electrical Engineering Automatics Computer Science and Biomedical Engineering AGH University of Science and Technology Al. A. Mickiewicza 30 Building B-1 Kraków30-059 Poland Division of Computational Mathematics Faculty of Mathematics and Computer Science Jagiellonian University 6 Lojasiewicza St. Kraków Poland

High-quality fluorescence imaging of biological systems is limited by processes like photobleaching and phototoxicity, and also in many cases, by limited access to the latest generations of microscopes. Moreover, low temporal resolution can lead to a motion blur effect in living systems. Our work presents a deep learning (DL) generative-adversarial approach to the problem of obtaining high-quality (HQ) images based on their low-quality (LQ) equivalents. We propose a generative-adversarial network (GAN) for contrast transfer between two different separate microscopy systems: a confocal microscope (producing HQ images) and a wide-field fluorescence microscope (producing LQ images). Our model proves that such transfer is possible, allowing us to receive HQ-generated images characterized by low mean squared error (MSE) values, high structural similarity index (SSIM), and high peak signal-to-noise ratio (PSNR) values. For our best model in the case of comparing HQ-generated images and HQ-ground truth images, the median values of the metrics are 6·10-4, 0.9413, and 31.87, for MSE, SSIM, and PSNR, respectively. In contrast, in the case of comparison between LQ and HQ ground truth median values of the metrics are equal to 0.0071, 0.8304, and 21.48 for MSE, SSIM, and PSNR respectively. Therefore, we observe a significant increase ranging from 14% to 49% for SSIM and PSNR respectively. These results, together with other single-system cross-modality studies, provide proof of concept for further implementation of a cross-system biological image quality enhancement. © 2024, CC BY-SA.

关键词： Fluorescence imaging

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