检索结果-内蒙古大学图书馆

Congress on Evolutionary Computation

作者： Jeisson Prieto Elizabeth León Max H. Garzon Computing Systems and Industrial Engineering Department Universidad Nacional de Colombia Bogotá Colombia Department of Computer Science and the Bioinformatics Program University of Memphis Memphis USA

DNA has emerged as a new computational resource for data encoding and processing. The fundamental problem of DNA Codeword Design (CWD) calls for finding effective ways to encode and process data in DNA. The problem has shown to be of interest in other areas as well, including computational memories, self-assembly and phylogenetic analysis, among others. In prior work, a framework to analyze this problem has been developed and simple versions of CWD have been shown to be NP-complete using any single reasonable metric that approximates the Gibbs energy, thus practically making it very difficult to find a general procedure for finding optimal efficient encodings. We present a Self-adaptive Evolutionary Algorithm for CWD (SaEA-CWD) as an extension of the Hybrid Adaptive Evolutionary algorithm (HAEA). SaEA-CWD is a parameter adaptation technique that automatically adapts the rates of its genetic operator applications to exploit structural properties of the search space to improve the speed and quality of the solutions. An implementation and preliminary results are evaluated in spaces where searches are already prohibitive to ordinary methods (such as 8- and 10-mers) due to the combinatorial explosion of the solution DNA space. Applications to other problems are suggested, such as a general technique for dimensionality reduction based on SaEA-CWD.

关键词： DNA Evolutionary computation Encoding Extraterrestrial measurements Self-assembly

来源：评论

学校读者我要写书评

暂无评论

TOI-2458 b: A mini-Neptune consistent with in situ hot Jupiter formation

arXiv

引用

arXiv 2024年

作者： Šubjak, Jan Gandolfi, Davide Goffo, Elisa Rapetti, David Jankowski, Dawid Mizuki, Toshiyuki Dai, Fei Serrano, Luisa M. Wilson, Thomas G. Goździewski, Krzysztof Nowak, Grzegorz Jenkins, Jon M. Twicken, Joseph D. Winn, Joshua N. Bieryla, Allyson Ciardi, David R. Cochran, William D. Collins, Karen A. Deeg, Hans J. Garcia, Rafael A. Guenther, Eike W. Hatzes, Artie P. Kabath, Petr Korth, Judith Latham, David W. Livingston, John H. Lund, Michael B. Mathur, Savita Narita, Norio Orell-Miquel, Jaume Palle, Enric Persson, Carina M. Redfield, Seth Schwarz, Richard P. Watanabe, David Ziegler, Carl Astronomical Institute Czech Academy of Sciences Fričova 298 Ondřejov251 65 Czech Republic Center for Astrophysics | Harvard & Smithsonian 60 Garden Street CambridgeMA02138 United States Dipartimento di Fisica Universita degli Studi di Torino via Pietro Giuria 1 TorinoI-10125 Italy Thuringer Landessternwarte Tautenburg Sternwarte 5 Tautenburg07778 Germany NASA Ames Research Center Moffett FieldCA94035 United States Research Institute for Advanced Computer Science Universities Space Research Association WashingtonDC20024 United States Institute of Astronomy Faculty of Physics Astronomy and Informatics Nicolaus Copernicus University Grudziadzka 5 Toruń87-100 Poland Astrobiology Center of NINS 2-21-1 Osawa Mitaka Tokyo181-8588 Japan National Astronomical Observatory of Japan 2-21-2 Osawa Mitaka Tokyo181-8588 Japan Institute for Astronomy University of Hawai'i 2680 Woodlawn Drive HonoluluHI96822 United States Department of Physics University of Warwick Gibbet Hill Road CoventryCV4 7AL United Kingdom SETI Institute Mountain ViewCA94043 United States Department of Astrophysical Sciences Princeton University PrincetonNJ08544 United States NASA Exoplanet Science Institute-Caltech/IPAC PasadenaCA91125 United States McDonald Observatory The University of Texas AustinTX United States Center for Planetary Systems Habitability The University of Texas AustinTX United States C. Via Lactea S/N Tenerife La LagunaE-38205 Spain Dept. de Astrofisica Tenerife La LagunaE-38206 Spain Universite Paris-Saclay Universite Paris Cite CEA CNRS AIM Gif-sur-Yvette91191 France Lund Observatory Division of Astrophysics Department of Physics Lund University Box 118 Lund22100 Sweden Astronomical Science Program Graduate University for Advanced Studies SOKENDAI 2-21-1 Osawa Mitaka Tokyo181-8588 Japan Komaba Institute for Science The University of Tokyo 3-8-1 Komaba Meguro Tokyo153-8902 Japan Chalmers University of Technology Departmen

We report on the discovery and spectroscopic confirmation of TOI-2458 b, a transiting mini-Neptune around an F-type star leaving the mainsequence with a mass of M*= 1.05 ± 0.03Mo, a radius of R*= 1.31 ± 0.03 Ro, an effective temperature of Teff = 6005 ± 50 K, and a metallicity of -0.10 ± 0.05 dex. By combining TESS photometry with high-resolution spectra acquired with the HARPS spectrograph, we found that the transiting planet has an orbital period of ∼3.74 days, a mass of Mp = 13.31±0.99M⊕ and a radius of Rp = 2.83±0.20 R⊕. The host star TOI-2458 shows a short activity cycle of ∼54 days revealed in the HARPS S-index and Hα times series. We took the opportunity to investigate other F stars showing activity cycle periods comparable to that of TOI-2458 and found that they have shorter rotation periods than would be expected based on the gyrochronology predictions. In addition, we determined TOI-2458's stellar inclination angle to be i*= 10.6+13.3 -10.6 degrees. We discuss that both phenomena (fast stellar rotation and planet orbit inclination) could be explained by in situ formation of a hot Jupiter interior to TOI-2458 b. It is plausible that this hot Jupiter was recently engulfed by the star. Analysis of HARPS spectra has identified the presence of another planet with a period of P = 16.55 ± 0.06 days and a minimum mass of Mp sin i = 10.22 ± 1.90M⊕. Using dynamical stability analysis, we constrained the mass of this planet to the range Mc (10, 25)M⊕. © 2024, CC BY.

关键词： Stars

来源：评论

学校读者我要写书评

暂无评论

Computationally designed proteins mimic antibody immune evasion in viral evolution

引用

Immunity 2025年

作者： Youssef, Noor Gurev, Sarah Ghantous, Fadi Brock, Kelly P. Jaimes, Javier A. Thadani, Nicole N. Dauphin, Ann Sherman, Amy C. Yurkovetskiy, Leonid Soto, Daria Estanboulieh, Ralph Kotzen, Ben Notin, Pascal Kollasch, Aaron W. Cohen, Alexander A. Dross, Sandra E. Erasmus, Jesse Fuller, Deborah H. Bjorkman, Pamela J. Lemieux, Jacob E. Luban, Jeremy Seaman, Michael S. Marks, Debora S. Department of Systems Biology Harvard Medical School Boston 02115 MA United States Broad Institute of Harvard and MIT Cambridge 02139 MA United States Department of Electrical Engineering and Computer Science MIT Cambridge 02139 MA United States Center for Virology and Vaccine Research Beth Israel Deaconess Medical Center Boston 02215 MA United States Program in Molecular Medicine University of Massachusetts Chan Medical School Worcester 01655 MA United States Division of Infectious Diseases Department of Medicine Brigham and Women's Hospital Boston 02115 MA United States Massachusetts General Hospital Boston 02114 MA United States Division of Biology and Biological Engineering California Institute of Technology Pasadena 91125 CA United States Department of Microbiology University of Washington Seattle 98195 WA United States National Primate Research Center Seattle 98109 WA United States HDT Bio Seattle 98109 WA United States Massachusetts Consortium on Pathogen Readiness Boston 02115 MA United States Ragon Institute of MGH MIT and Harvard Cambridge 02139 MA United States

Recurrent waves of viral infection necessitate vaccines and therapeutics that remain effective against emerging viruses. Our ability to evaluate interventions is currently limited to assessments against past or circulating variants, which likely differ in their immune escape potential compared with future variants. To address this, we developed EVE-Vax, a computational method for designing antigens that foreshadow immune escape observed in future viral variants. We designed 83 SARS-CoV-2 spike proteins that transduced ACE2-positive cells and displayed neutralization resistance comparable to variants that emerged up to 12 months later in the COVID-19 pandemic. Designed spikes foretold antibody escape from B.1-BA.4/5 bivalent booster sera seen in later variants. The designed constructs also highlighted the increased neutralization breadth elicited by nanoparticle-based, compared with mRNA-based, boosters in non-human primates. Our approach offers targeted panels of synthetic proteins that map the immune landscape for early vaccine and therapeutic evaluation against future viral strains. © 2025 The Authors

关键词： antibody escape generative protein models immune landscape machine learning protein design vaccine evaluation viral evolution

来源：评论

学校读者我要写书评

暂无评论

AIRR community curation and standardised representation for immunoglobulin and T cell receptor germline sets

引用

Immunoinformatics (Amsterdam, Netherlands) 2023年 10卷 100025页

作者： William D Lees Scott Christley Ayelet Peres Justin T Kos Brian Corrie Duncan Ralph Felix Breden Lindsay G Cowell Gur Yaari Martin Corcoran Gunilla B Karlsson Hedestam Mats Ohlin Andrew M Collins Corey T Watson Christian E Busse Institute of Structural and Molecular Biology Birkbeck College London England. Human-Centered Computing and Information Science Institute for Systems and Computer Engineering Technology and Science Porto Portugal. Peter O'Donnell Jr. School of Public Health UT Southwestern Medical Center Dallas TX USA. Bioengineering Program Faculty of Engineering Bar-Ilan University Ramat Gan Israel. Department of Biochemistry and Molecular Genetics School of Medicine University of Louisville KY USA. Department of Biological Sciences Simon Fraser University Burnaby BC Canada. Fred Hutchinson Cancer Research Center Seattle WA USA. Peter O'Donnell Jr. School of Public Health Department of Immunology School of Biomedical Sciences UT Southwestern Medical Center Dallas TX USA. Department of Microbiology Tumor and Cell Biology Karolinska Institutet Stockholm Swede. Department of Immunotechnology and SciLifeLab Lund University Lund Sweden. School of Biotechnology and Biomolecular Sciences University of New South Wales Sydney NSW Australia. Division of B Cell Immunology German Cancer Research Center Heidelberg Germany.

Analysis of an individual's immunoglobulin or T cell receptor gene repertoire can provide important insights into immune function. High-quality analysis of adaptive immune receptor repertoire sequencing data depends upon accurate and relatively complete germline sets, but current sets are known to be incomplete. Established processes for the review and systematic naming of receptor germline genes and alleles require specific evidence and data types, but the discovery landscape is rapidly changing. To exploit the potential of emerging data, and to provide the field with improved state-of-the-art germline sets, an intermediate approach is needed that will allow the rapid publication of consolidated sets derived from these emerging sources. These sets must use a consistent naming scheme and allow refinement and consolidation into genes as new information emerges. Name changes should be minimised, but, where changes occur, the naming history of a sequence must be traceable. Here we outline the current issues and opportunities for the curation of germline IG/TR genes and present a forward-looking data model for building out more robust germline sets that can dovetail with current established processes. We describe interoperability standards for germline sets, and an approach to transparency based on principles of findability, accessibility, interoperability, and reusability.

关键词： AIRR-seq Immune receptor Immune receptor germline Immune receptor repertoire Rep-seq

来源：评论

学校读者我要写书评

暂无评论

Input optics systems of the KAGRA detector during O3GK (vol 2023, 023F01, 2023)

引用

PROGRESS OF THEORETICAL AND EXPERIMENTAL PHYSICS 2023年第5期2023卷

作者： Akutsu, T. Ando, M. Arai, K. Arai, Y. Araki, S. Araya, A. Aritomi, N. Asada, H. Aso, Y. Bae, S. Bae, Y. Baiotti, L. Bajpai, R. Barton, M. A. Cannon, K. Cao, Z. Capocasa, E. Chan, M. Chen, C. Chen, K. Chen, Y. Chiang, C-I Chu, H. Chu, Y-K Eguchi, S. Enomoto, Y. Flaminio, R. Fujii, Y. Fujikawa, Y. Fukunaga, M. Fukushima, M. Furuhata, T. Gao, D. Ge, G-G Ha, S. Hagiwara, A. Haino, S. Han, W-B Hasegawa, K. Hattori, K. Hayakawa, H. Hayama, K. Himemoto, Y. Hiranuma, Y. Hirata, N. Hirose, E. Hong, Z. Hsieh, B-H Huang, G-Z Huang, H-Y Huang, P. Huang, Y-C Huang, Y-J Hui, D. C. Y. Ide, S. Ikenoue, B. Imam, S. Inayoshi, K. Inoue, Y. Ioka, K. Ito, K. Itoh, Y. Izumi, K. Jeon, C. Jin, H-B Jung, K. Jung, P. Kaihotsu, K. Kajita, T. Kakizaki, M. Kamiizumi, M. Kanbara, S. Kanda, N. Kang, G. Kataoka, Y. Kawaguchi, K. Kawai, N. Kawasaki, T. Kim, C. Kim, J. Kim, J. C. Kim, Ws Kim, Y-M Kimura, N. Kita, N. Kitazawa, H. Kojima, Y. Kokeyama, K. Komori, K. Kong, A. K. H. Kotake, K. Kozakai, C. Kozu, R. Kumar, R. Kume, J. Kuo, C. Kuo, H-S Kuromiya, Y. Kuroyanagi, S. Kusayanagi, K. Kwak, K. Lee, H. K. Lee, H. W. Lee, R. Leonardi, M. Li, K. L. Lin, L. C-C Lin, C-Y Lin, F-K Lin, F-L Lin, H. L. Liu, G. C. Luo, L-W Majorana, E. Marchio, M. Michimura, Y. Mio, N. Miyakawa, O. Miyamoto, A. Miyazaki, Y. Miyo, K. Miyoki, S. Mori, Y. Morisaki, S. Moriwaki, Y. Nagano, K. Nagano, S. Nakamura, K. Nakano, H. Nakano, M. Nakashima, R. Nakayama, Y. Narikawa, T. Naticchioni, L. Negishi, R. Quynh, L. Nguyen Ni, W-T Nishizawa, A. Nozaki, S. Obuchi, Y. Ogaki, W. Oh, J. J. Oh, S. H. Ohashi, M. Ohishi, N. Ohkawa, M. Ohta, H. Okutani, Y. Okutomi, K. Oohara, K. Ooi, C. Oshino, S. Otabe, S. Pan, K-C Pang, H. Parisi, A. Park, J. Arellano, F. E. Pena Pinto, I. Sago, N. Saito, S. Saito, Y. Sakai, K. Sakai, Y. Sakuno, Y. Sato, S. Sato, T. Sawada, T. Sekiguchi, T. Sekiguchi, Y. Shao, L. Shibagaki, S. Shimizu, R. Shimoda, T. Shimode, K. Shinkai, H. Shishido, T. Shoda, A. Somiya, K. Son, E. J. Sotani, H. Sugimoto, R. Suresh, J. Suzuki, T. Suzuki, T. Tagoshi, H. Takahashi, H Gravitational Wave Science Project National Astronomical Observatory of Japan 2-21-1 Osawa Mitaka City Tokyo 181-8588 Japan Advanced Technology Center National Astronomical Observatory of Japan 2-21-1 Osawa Mitaka City Tokyo 181-8588 Japan Department of Physics The University of Tokyo 7-3-1 Hongo Bunkyo-ku Tokyo 113-0033 Japan Research Center for the Early Universe The University of Tokyo 7-3-1 Hongo Bunkyo-ku Tokyo 113-0033 Japan Institute for Cosmic Ray Research KAGRA Observatory The University of Tokyo 5-1-5 Kashiwa-no-Ha Kashiwa City Chiba 277-8582 Japan Accelerator Laboratory High Energy Accelerator Research Organization (KEK) 1-1 Oho Tsukuba City Ibaraki 305-0801 Japan Earthquake Research Institute The University of Tokyo 1-1-1 Yayoi Bunkyo-ku Tokyo 113-0032 Japan Department of Mathematics and Physics Graduate School of Science and Technology Hirosaki University 3 Bunkyo-cho Hirosaki Aomori 036-8561 Japan Kamioka Branch National Astronomical Observatory of Japan 238 Higashi-Mozumi Kamioka-cho Hida City Gifu 506-1205 Japan The Graduate University for Advanced Studies (SOKENDAI) 2-21-1 Osawa Mitaka City Tokyo 181-8588 Japan Korea Institute of Science and Technology Information 245 Daehak-ro Yuseong-gu Daejeon 34141 Republic of Korea National Institute for Mathematical Sciences 70 Yuseong-daero 1689 Beon-gil Yuseong-gu Daejeon 34047 Republic of Korea International College Osaka University 1-1 Machikaneyama-cho Toyonaka City Osaka 560-0043 Japan School of High Energy Accelerator Science The Graduate University for Advanced Studies (SOKENDAI) 1-1 Oho Tsukuba City Ibaraki 305-0801 Japan Department of Astronomy Beijing Normal University Xinjiekouwai Street 19 Haidian District Beijing 100875 China Department of Applied Physics Fukuoka University 8-19-1 Nanakuma Jonan Fukuoka City Fukuoka 814-0180 Japan Department of Physics Tamkang University No. 151 Yingzhuan Road Danshui Dist. New Taipei City 25137 Taiwan Department of Physics

KAGRA, the underground and cryogenic gravitational-wave detector, was operated for its solo observation from February 25 to March 10, 2020, and its first joint observation with the GEO 600 detector from April 7 to April 21, 2020 (O3GK). This study presents an overview of the input optics systems of the KAGRA detector, which consist of various optical systems, such as a laser source, its intensity and frequency stabilization systems, modulators, a Faraday isolator, mode-matching telescopes, and a high-power beam dump. These optics were successfully delivered to the KAGRA interferometer and operated stably during the observations. The laser frequency noise was observed to limit the detector sensitivity above a few kilohertz, whereas the laser intensity did not significantly limit the detector sensitivity.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Correction to: IonCRAM: a reference-based compression tool for ion torrent sequence files

引用

BMC bioinformatics 2020年第1期21卷 435页

作者： Moustafa Shokrof Mohamed Abouelhoda Faculty of Computer Science University of California at Davis Davis CA USA. King Faisal Specialist Hospital and Research Center Riyadh Saudi Arabia. mabouelhoda@***. Saudi Human Genome Program King Abdulaziz City for Science and Technology (KACST) Riyadh Saudi Arabia. mabouelhoda@***. Systems and Biomedical Engineering Department Faculty of Engineering Cairo University University Square Giza Egypt. mabouelhoda@***.

An amendment to this paper has been published and can be accessed via the original article.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Speech Emotion Recognition using Convolutional Neural Network with Audio Word-based Embedding

Speech Emotion Recognition using Convolutional Neural Networ...

引用

International Symposium on Chinese Spoken Language Processing

作者： Kun-Yi Huang Chung-Hsien Wu Qian-Bei Hong Ming-Hsiang Su Yuan-Rong Zeng Department of Computer Science and Information Engineering National Cheng Kung University Taiwan PhD Program for Multimedia Systems and Intelligent Computing National Cheng Kung University and Academia Sinica Taiwan

ISBN: (纸本)9781538656280;9781538656273

A complete emotional expression typically contains a complex temporal course in a natural conversation. Related research on utterance-level, segment-level and multi-level processing lacks understanding of the underlying relation of emotional speech. In this work, a convolutional neural network (CNN) with audio word-based embedding is proposed for emotion modeling. In this study, vector quantization is first applied to convert the low level features of each speech frame into audio words using k-means algorithm. Word2vec is adopted to convert an input speech utterance into the corresponding audio word vector sequence. Finally, the audio word vector sequences of the training emotional speech data with emotion annotation are used to construct the CNN- based emotion model. The NCKU-ES database, containing seven emotion categories: happiness, boredom, anger, anxiety, sadness, surprise and disgust, was collected and five-fold cross validation was used to evaluate the performance of the proposed CNN-based method for speech emotion recognition. Experimental results show that the proposed method achieved an emotion recognition accuracy of 82.34%, improving by 8.7% compared to the Long Short Term Memory (LSTM)- based method, which faced the challenging issue of long input sequence. Comparing with raw features, the audio word-based embedding achieved an improvement of 3.4% for speech emotion recognition.

关键词： Emotion recognition Feature extraction Speech recognition Acoustics Training Databases Neural networks

来源：评论

学校读者我要写书评

暂无评论

Retracted: Sylabus and Matrials Design for Vocational Student and Its Dissemination Using Website

Retracted: Sylabus and Matrials Design for Vocational Studen...

引用

2018 Workshop on Multidisciplinary and Its Applications: Applied Mathematics, computer science, Information systems, and Information Technology, WMA-Mathcomtech 2018

作者： Harahap, Astro Julida Ongko, Erianto Manurung, Hotler Kurnia, Dian Hartono, H. Abdullah, Dahlan Erliana, Cut Ita Djanggih, Hardianto Saddhono, Kundharu Rianita, Dian Setiawan, Muhammad Ikhsan Setiawan, Yuliyanto Budi Manurung, Rosida Tiurma Sriadhi, S. Sinurat, Mangasi Siregar, Aisyah Soni Hutauruk, Rapat Piter Sibarani, Nico Benjamain Department of Informatics Akademi Teknologi Industri Immanuel Medan Indonesia Department of Computer Systems Universitas Pancabudi Medan Indonesia Department of Computer Science STMIK IBBI Medan Indonesia Department of Informatics Universitas Malikussaleh Aceh Indonesia Department of Industrial Engineering Universitas Malikussaleh Aceh Indonesia Department of Law Universitas Tompotika Luwuk Banggai Indonesia Graduate Program Universitas Sebelas Maret Surakarta Indonesia Department of Administration University of Lancang Kuning Pekanbaru Indonesia Department of Civil Engineering Narotama University Surabaya Indonesia Study Program of Communication Studies Universitas Semarang Indonesia Graduate Program in Scientific Psychology Universitas Kristen Maranatha Indonesia Department of Electrical Engineering Universitas Negeri Medan Indonesia STIE Bina Karya Tebing Tinggi Indonesia

The learners' needs are an important factor in designing syllabus and materials design, this research deals with the syllabus and material design based on the professional's needs. It is expected that the syllabus and materials designes are communicatively applicable to the professional academy. Descriptive method is applied in this research. The sample of this research is 30 students of ATII Immanuel Academy Medan. They were selected by random sampling to get the data, the questioners were administered to students. the questioners consisted of 54 items and the semi structured interview consisted of 5 questions, the finding indicated that learners' needs were focused on reading and speaking skills. With reference appropriately and proportionally derived for students of the Professional Academy. Further on the basis of the syllabus, materials are designed in which the skills of using language become a priority. The results of this research will be disseminated using the website. © Published under licence by IOP Publishing Ltd.

关键词： Information systems

来源：评论

学校读者我要写书评

暂无评论

Ten quick tips for deep learning in biology

arXiv

引用

arXiv 2021年

作者： Lee, Benjamin D. Gitter, Anthony Greene, Casey S. Raschka, Sebastian Maguire, Finlay Titus, Alexander J. Kessler, Michael D. Lee, Alexandra J. Chevrette, Marc G. Stewart, Paul Allen Britto-Borges, Thiago Cofer, Evan M. Yu, Kun-Hsing Carmona, Juan Jose Fertig, Elana J. Kalinin, Alexandr A. Signal, Beth Lengerich, Benjamin J. Triche, Timothy J. Boca, Simina M. In-Q-Tel Labs School of Engineering and Applied Sciences Harvard University Department of Genetics Harvard Medical School United States Department of Biostatistics and Medical Informatics University of Wisconsin-Madison MadisonWI United States Morgridge Institute for Research MadisonWI United States Department of Systems Pharmacology and Translational Therapeutics Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Department of Biochemistry and Molecular Genetics University of Colorado School of Medicine AuroraCO United States Center for Health AI University of Colorado School of Medicine AuroraCO United States Department of Statistics University of Wisconsin Madison United States Faculty of Computer Science Dalhousie University Canada University of New Hampshire Bioeconomy.XYZ United States Department of Oncology Johns Hopkins University United States Institute for Genome Sciences University of Maryland School of Medicine United States Genomics and Computational Biology Graduate Program University of Pennsylvania United States Department of Systems Pharmacology and Translational Therapeutics University of Pennsylvania United States Wisconsin Institute for Discovery Department of Plant Pathology University of Wisconsin-Madison United States Department of Biostatistics and Bioinformatics Moffitt Cancer Center TampaFL United States Section of Bioinformatics and Systems Cardiology Klaus Tschira Institute for Integrative Computational Cardiology University Hospital Heidelberg Germany University Hospital Heidelberg Germany Lewis-Sigler Institute for Integrative Genomics Princeton University PrincetonNJ United States Graduate Program in Quantitative and Computational Biology Princeton University PrincetonNJ United States Department of Biomedical Informatics Harvard Medical School United States Department of Pathology Brigham and Women's Hospital United States Philips Healthcare CambridgeMA United States Philips Research

Machine learning is a modern approach to problem-solving and task automation. In particular, machine learning is concerned with the development and applications of algorithms that can recognize patterns in data and use them for predictive modeling. Artificial neural networks are a particular class of machine learning algorithms and models that evolved into what is now described as deep learning. Given the computational advances made in the last decade, deep learning can now be applied to massive data sets and in innumerable contexts. Therefore, deep learning has become its own subfield of machine learning. In the context of biological research, it has been increasingly used to derive novel insights from high-dimensional biological data. To make the biological applications of deep learning more accessible to scientists who have some experience with machine learning, we solicited input from a community of researchers with varied biological and deep learning interests. These individuals collaboratively contributed to this manuscript's writing using the GitHub version control platform and the Manubot manuscript generation toolset. The goal was to articulate a practical, accessible, and concise set of guidelines and suggestions to follow when using deep learning. In the course of our discussions, several themes became clear: the importance of understanding and applying machine learning fundamentals as a baseline for utilizing deep learning, the necessity for extensive model comparisons with careful evaluation, and the need for critical thought in interpreting results generated by deep learning, among others. © 2021, CC BY.

关键词： Learning algorithms

来源：评论

学校读者我要写书评

暂无评论

Development and Validation of Instruments Adoption FinTech services in Indonesia (Perspective of Trust and Risk)

Development and Validation of Instruments Adoption FinTech s...

引用

International Conference on Sustainable Information engineering and Technology (SIET)

作者： Erick Fernando Surjandy Meyliana Derist Touriano Information Systems Department School of Information Systems Bina Nusantara University Jakarta Indonesia Information Systems Audit Program Information Systems Department School of Information Systems Bina Nusantara University Jakarta Indonesia Information Technology Department Faculty of computer science and engineering Adiwangsa Jambi University Jambi Indonesia

ISBN: (纸本)9781538674086;9781538674079

This study explores the influence of trust and risk in the use of FinTech in Indonesian society. This exploration is based on findings from previous studies that suggest that trust and risk are important aspects in considering the use of FinTech. The focus of this research is to develop and validate the research instruments that will be used next. The basic theory of model development uses the theory of Technology Acceptance Model (TAM) developed by Davis. The models and instruments developed will be through pilot studies involving 133 communities as respondents. This Study is quantitative research and data obtained were analyzed using smart pls v3.0. This analysis is conducted to ensure the level of reliability and validity of the instrument. The results of this analysis find 31 instruments that are stated reliable and validity. So it can be used for collecting data from a survey in accordance with the research.

关键词： Instruments Reliability Information systems Business Adaptation models Economics Transportation

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：