检索结果-内蒙古大学图书馆

arXiv 2019年

作者： Jiang, Shu Zhang, Zhuosheng Zhao, Hai Li, Jiangtong Yang, Yang Lu, Bao-Liang Xia, Ning Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai200240 China Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Shanghai Jiao Tong University Shanghai200240 China College of Zhiyuan Shanghai Jiao Tong University Shanghai200240 China Chemical.AI Shanghai200240 China

Chemical reaction practicality is the core task among all symbol intelligence based chemical information processing, for example, it provides indispensable clue for further automatic synthesis route inference. Considering that chemical reactions have been represented in a language form, we propose a new solution to generally judge the practicality of organic reaction without considering complex quantum physical modeling or chemistry knowledge. While tackling the practicality judgment as a machine learning task from positive and negative (chemical reaction) samples, all existing studies have to carefully handle the serious insufficiency issue on the negative samples. We propose an auto-construction method to well solve the extensively existed long-term difficulty. Experimental results show our model can effectively predict the practicality of chemical reactions, which achieves a high accuracy of 99.76% on real large-scale chemical lab reaction practicality judgment. Copyright © 2019, The Authors. All rights reserved.

关键词： Chemical reactions

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OWL-QL: A Language for Deductive Query Answering on the Semantic Web

SSRN

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SSRN 2018年

作者： Fikes, Richard Hayes, Patrick Horrocks, Ian Artificial Intelligence Laboratory Computer Science Department Stanford University StanfordCA United States Institute for Human and Computer Cognition University of West Florida PensacolaFL United States School of Computer Science

This paper discusses the issues involved in designing a query language for the Semantic Web and presents the OWL Query Language (OWL-QL) as a candidate standard language and protocol for query-answering dialogues among Semantic Web computational agents using knowledge represented in the W3C's Ontology Web Language (OWL). OWL-QL is a formal language and precisely specifies the semantic relationships among a query, a query answer, and the knowledge base(s) used to produce the answer. Unlike standard database and Web query languages, OWL-QL supports query-answering dialogues in which the answering agent may use automated reasoning methods to derive answers to queries, as well as dialogues in which the knowledge to be used in answering a query may be in multiple knowledge bases on the Semantic Web, and/or where those knowledge bases are not specified by the querying agent. In this setting, the set of answers to a query may be of unpredictable size and may require an unpredictable amount of time to compute. © 2018, The Authors. All rights reserved.

关键词： Query languages

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Author Correction: APOE4 impairs myelination via cholesterol dysregulation in oligodendrocytes

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Nature 2024年第8043期636卷 E9页

作者： Joel W Blanchard Leyla Anne Akay Jose Davila-Velderrain Djuna von Maydell Hansruedi Mathys Shawn M Davidson Audrey Effenberger Chih-Yu Chen Kristal Maner-Smith Ihab Hajjar Eric A Ortlund Michael Bula Emre Agbas Ayesha Ng Xueqiao Jiang Martin Kahn Cristina Blanco-Duque Nicolas Lavoie Liwang Liu Ricardo Reyes Yuan-Ta Lin Tak Ko Lea R'Bibo William T Ralvenius David A Bennett Hugh P Cam Manolis Kellis Li-Huei Tsai Picower Institute for Learning and Memory Massachusetts Institute of Technology Cambridge MA USA. Department of Brain and Cognitive Sciences Massachusetts Institute of Technology Cambridge MA USA. Nash Family Department of Neuroscience Black Family Stem Cell Institute Ronald M. Loeb Center for Alzheimer's Disease Friedman Brain Institute Icahn School of Medicine at Mt Sinai New York NY USA. MIT Computer Science and Artificial Intelligence Laboratory Cambridge MA USA. Human Technopole Milan Italy. Department of Neurobiology University of Pittsburgh Pittsburgh PA USA. Lewis-Sigler Institute for Integrative Genomics Princeton University Princeton NJ USA. Department of Medicine Emory Integrated Metabolomics and Lipidomics Core Emory University School of Medicine Atlanta GA USA. Department of Neurology Emory University School of Medicine Atlanta GA USA. Department of Biochemistry Emory University School of Medicine Atlanta GA USA. Rush Alzheimer's Disease Center Rush University Medical Center Chicago IL USA. MIT Computer Science and Artificial Intelligence Laboratory Cambridge MA USA. manoli@mit.edu. Broad Institute of Harvard and MIT Cambridge MA USA. manoli@mit.edu. Picower Institute for Learning and Memory Massachusetts Institute of Technology Cambridge MA USA. lhtsai@mit.edu. Department of Brain and Cognitive Sciences Massachusetts Institute of Technology Cambridge MA USA. lhtsai@mit.edu. Broad Institute of Harvard and MIT Cambridge MA USA. lhtsai@mit.edu.

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Patterns of somatic structural variation in human cancer genomes (vol 578, pg 112, 2020)

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NATURE 2023年第7948期614卷 E38-E38页

作者： Li, Yilong Roberts, Nicola D. Wala, Jeremiah A. Shapira, Ofer Schumacher, Steven E. Kumar, Kiran Khurana, Ekta Waszak, Sebastian Korbel, Jan O. Haber, James E. Imielinski, Marcin Weischenfeldt, Joachim Beroukhim, Rameen Campbell, Peter J. Cancer Genome Project Wellcome Trust Sanger Institute Hinxton UK Totient Inc Cambridge MA USA Wellcome Sanger Institute Wellcome Genome Campus Hinxton UK Department of Haematology University of Cambridge Cambridge UK Cambridge University Hospitals NHS Foundation Trust Cambridge UK Korea Advanced Institute of Science and Technology Daejeon South Korea Department of Zoology Genetics and Physical Anthropology University of Santiago de Compostela Santiago de Compostela Spain Centre for Research in Molecular Medicine and Chronic Diseases (CIMUS) University of Santiago de Compostela Santiago de Compostela Spain The Biomedical Research Centre (CINBIO) University of Vigo Vigo Spain Centre for Research in Molecular Medicine and Chronic Diseases (CiMUS) Universidade de Santiago de Compostela Santiago de Compostela Spain Department of Zoology Genetics and Physical Anthropology (CiMUS) Universidade de Santiago de Compostela Santiago de Compostela Spain The Biomedical Research Centre (CINBIO) Universidade de Vigo Vigo Spain The Broad Institute of Harvard and MIT Cambridge MA USA Bioinformatics and Integrative Genomics Harvard University Cambridge MA USA Department of Cancer Biology Dana-Farber Cancer Institute Boston MA USA Broad Institute of MIT and Harvard Cambridge MA USA Department of Medical Oncology Dana-Farber Cancer Institute Boston MA USA Harvard Medical School Boston MA USA Massachusetts General Hospital Center for Cancer Research Charlestown MA USA Dana-Farber Cancer Institute Boston MA USA Department of Biostatistics and Computational Biology Dana-Farber Cancer Institute and Harvard Medical School Boston MA USA Weill Cornell Medical College New York NY USA Department of Physiology and Biophysics Weill Cornell Medicine New York NY USA Institute for Computational Biomedicine Weill Cornell Medicine New York NY USA Controlled Department and Institution New York NY USA Englander Institute for Precision Medicine Weill Cornell Medicine Ne

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Considerations and Recommendations from the ISMRM Diffusion Study Group for preclinical diffusion MRI: Part 1 — In vivo small-animal imaging

arXiv

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arXiv 2022年

作者： Jelescu, Ileana O. Grussu, Francesco Ianus, Andrada Hansen, Brian Barrett, Rachel L.C. Aggarwal, Manisha Michielse, Stijn Nasrallah, Fatima Syeda, Warda Wang, Nian Veraart, Jelle Roebroeck, Alard Bagdasarian, Andrew F. Eichner, Cornelius Sepehrband, Farshid Zimmermann, Jan Soustelle, Lucas Bowman, Christien Tendler, Benjamin C. Hertanu, Andreea Jeurissen, Ben Verhoye, Marleen Frydman, Lucio de Looij, Yohan van Hike, David Dunn, Jeff F. Miller, Karla Landman, Bennett A. Shemesh, Noam Anderson, Adam McKinnon, Emilie Farquharson, Shawna Acqua, Flavio Dell Pierpaoli, Carlo Drobnjak, Ivana Leemans, Alexander Harkins, Kevin D. Descoteaux, Maxime Xu, Duan Huang, Hao Santin, Mathieu D. Grant, Samuel C. Obenaus, Andre Kim, Gene S. Wu, Dan Le Bihan, Denis Blackband, Stephen J. Ciobanu, Luisa Fieremans, Els Bai, Ruiliang Leergaard, Trygve B. Zhang, Jiangyang Dyrby, Tim B. Johnson, G. Allan Cohen-Adad, Julien Budde, Matthew D. Schilling, Kurt G. Department of Radiology Lausanne University Hospital University of Lausanne Lausanne Switzerland CIBM Center for Biomedical Imaging Ecole Polytechnique Fédérale de Lausanne Lausanne Switzerland Radiomics Group Vall d’Hebron Institute of Oncology Vall d’Hebron Barcelona Hospital Campus Barcelona Spain Queen Square MS Centre Queen Square Institute of Neurology Faculty of Brain Sciences University College London London United Kingdom Champalimaud Research Champalimaud Foundation Lisbon Portugal Center of Functionally Integrative Neuroscience Aarhus University Aarhus Denmark Department of Neuroimaging Institute of Psychiatry Psychology and Neuroscience King's College London London United Kingdom NatBrainLab Department of Forensics and Neurodevelopmental Sciences Institute of Psychiatry Psychology and Neuroscience King's College London London United Kingdom Russell H. Morgan Department of Radiology and Radiological Science Johns Hopkins University School of Medicine BaltimoreMD United States Maastricht University Medical Center Maastricht Netherlands The Queensland Brain Institute The University of Queensland Queensland Australia Melbourne Neuropsychiatry Centre The University of Melbourne ParkvilleVIC Australia Department of Radiology and Imaging Sciences Indiana University IN United States Stark Neurosciences Research Institute Indiana University School of Medicine IN United States Center for Biomedical Imaging NYU Grossman School of Medicine New YorkNY United States Faculty of psychology and Neuroscience Maastricht University Maastricht Netherlands Department of Chemical & Biomedical Engineering FAMU-FSU College of Engineering Florida State University TallahasseeFL United States Center for Interdisciplinary Magnetic Resonance National HIgh Magnetic Field Laboratory TallahasseeFL United States Department of Neuropsychology Max Planck Institute for Human Cognitive and Brain Sciences Leipzig Germany USC Stevens Neuroimaging and Informatics Instit

Small-animal diffusion MRI (dMRI) has been used for methodological development and validation, characterizing the biological basis of diffusion phenomena, and comparative anatomy. The steps from animal setup and monitoring, to acquisition, analysis, and interpretation are complex, with many decisions that may ultimately affect what questions can be answered using the resultant data. This work aims to present selected recommendations and guidelines from the diffusion community, on best practices for preclinical dMRI of in vivo animals. We describe the general considerations and foundational knowledge that must be considered when designing experiments. We briefly describe differences in animal species and disease models and discuss why some may be more or less appropriate for different studies. We then give guidelines for in vivo acquisition protocols, including decisions on hardware, animal preparation, and imaging sequences, followed by advice for data processing including pre-processing, model-fitting, and tractography. Finally, we provide an online resource which lists publicly available preclinical dMRI datasets and software packages, to promote responsible and reproducible research. In each section, we attempt to provide guides and recommendations, but also highlight areas for which no guidelines exist (and why), and where future work should focus. While we mainly cover the central nervous system (on which most preclinical dMRI studies are focused), we also provide, where possible and applicable, recommendations for other organs of interest. An overarching goal herein is to enhance the rigor and reproducibility of small animal dMRI acquisitions and analyses, and thereby advance biomedical knowledge. © 2022, CC BY-NC-ND.

关键词： Invertebrates

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Robust Mask Estimation By Integrating Neural Network-Based and Clustering-Based Approaches for Adaptive Acoustic Beamforming

Robust Mask Estimation By Integrating Neural Network-Based a...

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IEEE International Conference on Acoustics, Speech and Signal Processing

作者： Ying Zhou Yanmin Qian Department of Computer Science and Engineering Shanghai Jiao Tong University Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering SpeechLab China

ISBN: (纸本)9781538646595

Recently the mask-based beamforming approach received tremendous interest and is widely studied for multi-channel noise robust automatic speech recognition (ASR). Among the known mask estimation models, the neural network based mask estimation approach has received the most attention, resulting in a competitive performance. However this approach still suffers from training-testing mismatch between the simulated training and real test data. This paper proposes a new unsupervised scheme that can utilize the real data during NN-based mask estimator training. The clustering-based approach is applied on the real data first to generate the soft masks, which are then taken as the labels for NN-mask modeling. Moreover, acoustic adaptation technologies are borrowed from usual back-end acoustic modeling to the front-end NN-mask based beamformer, further reducing the training-testing acoustic mismatch. The proposed methods are evaluated on the CHIME-4 dataset. Experimental results show that the mismatch can be reduced significantly by the proposed strategies, leading to relative ～15.0% WER reduction compared to the conventional NN-mask beamforming for the real data under noisy conditions.

关键词： acoustic beamforming time-frequency mask deep neural network adaptation

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FAST ADAPTATION ON DEEP MIXTURE GENERATIVE NETWORK BASED ACOUSTIC MODELING

FAST ADAPTATION ON DEEP MIXTURE GENERATIVE NETWORK BASED ACO...

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IEEE International Conference on Acoustics, Speech and Signal Processing

作者： Wen Ding Tian Tan Yanmin Qian Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering SpeechLab Department of Computer Science and Engineering Shanghai Jiao Tong University China

ISBN: (纸本)9781538646595

Deep neural network (DNN) has achieved the state-of-the-art performance in automatic speech recognition (ASR). However, the meaning of parameters and neurons are hard to be interpreted in DNNs, which makes the regularizations and adaptation of DNNs difficult. In this work, we aim to do effective and efficient adaptation on a more interpretable model, deep mixture generative network (DMGN). Adapted means are first proposed to perform adaptation for DMGN. The speaker-dependent means are estimated in an unsupervised adaptation mode. Moreover, discriminative linear regression (DLR) is proposed to estimate more robust speaker-dependent means when lack of adaptation data. We evaluate our proposed methods on 50-hour subset of Switchboard. Experiments reveal that all proposed methods are better than speaker independent baseline, and a slight performance improvement is obtained compared with LHUC. In addition, we project the Gaussian mean of one senone and all inputs aligned to this senone to a 2D graph. The illustration shows that after applying DLR, the mean is indeed transferred from an average point to the speaker specific center, which demonstrates the better explanation of DMGN again.

关键词： Deep mixture generative network Speaker Adaptation Discriminative linear regression Acoustic Modeling speaker adaptation acoustic modeling speaker-dependent ILLUSTRATIONS Speech recognition Modeling performance boost

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Improving the design and usability of password creation systems

Improving the design and usability of password creation syst...

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2017 ACM SIGCHI Conference on human Factors in Computing Systems, CHI EA 2017

作者： Althubaiti, Saja Human Computer Interaction Research Group Department of Computer Science University of York York UKYO10 5GH United Kingdom

ISBN: (纸本)9781450346566

The usability of Password Creation Systems (PCSs) as interactive systems has been neglected in research compared to work on developing individual features for creating passwords such as strength meters. PCSs can include a strength meter, statement of password policy, suggestions for creating good passwords and feedback to users about errors. If users are struggling to understand how the PCS works due to usability issues, they will have less cognitive effort available to put into creating a strong password. Studying the usability of PCSs and ensuring they support users well when creating passwords is therefore an important issue. A series of studies is being conducted to understand the problems users encounter with PCSs, develop a model of PCSs, understand how PCSs should organize their supporting features, and to propose a set of heuristics for use in guiding the development of PCSs and in expert evaluations. Copyright © 2017 by the Association for Computing Machinery, Inc. (ACM).

关键词： Authentication

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Fast Oov Words Incorporation Using Structured Word Embeddings for Neural Network Language Model

Fast Oov Words Incorporation Using Structured Word Embedding...

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IEEE International Conference on Acoustics, Speech and Signal Processing

作者： Ruinian Chen Kai Yu Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering SpeechLab Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai China

ISBN: (纸本)9781538646595

Recently, deep learning approaches have been widely used in language modeling and achieved great success. However, the out-of-vocabulary (OOV) words are often estimated in a rather crude way using only one special symbol, which ignores the linguistic information. In this paper we present an LSTM language model with structured word embeddings to tackle this problem. In our model, both input and output embeddings of LSTM language model are deployed with structured word embeddings. Utilizing syntactic-level and morphological-level parameters sharing, OOV words can be incorporated into the proposed model without retraining. The LSTM language model with structured word embeddings is instantiated for Chinese. Experiments show that the proposed model achieves PPL improvement on OOV words, and can be further integrated into automatic speech recognition systems for fast vocabulary updating.

关键词： OOV words language modeling syntactic embeddings morphological embeddings modelling languages speech recognition system Neural network Word linguistic data

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Genomic basis for RNA alterations in cancer (vol 578, pg 129, 2020)

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NATURE 2023年第7948期614卷 E37-E37页

作者： Calabrese, Claudia Davidson, Natalie R. Demircioglu, Deniz Fonseca, Nuno A. He, Yao Lehmann, Kjong-Van Liu, Fenglin Shiraishi, Yuichi Soulette, Cameron M. Urban, Lara Greger, Liliana Li, Siliang Liu, Dongbing Perry, Marc D. Xiang, Qian Zhang, Fan Zhang, Junjun Bailey, Peter Erkek, Serap Hoadley, Katherine A. Hou, Yong Huska, Matthew R. Kilpinen, Helena Korbel, Jan O. Marin, Maximillian G. Markowski, Julia Nandi, Tannistha Pan-Hammarstrom, Qiang Pedamallu, Chandra Sekhar Siebert, Reiner Stark, Stefan G. Su, Hong Tan, Patrick Waszak, Sebastian M. Yung, Christina Zhu, Shida Awadalla, Philip Creighton, Chad J. Meyerson, Matthew Ouellette, B. F. Francis Wu, Kui Yang, Huanming Brazma, Alvis Brooks, Angela N. Goke, Jonathan Ratsch, Gunnar Schwarz, Roland F. Stegle, Oliver Zhang, Zemin European Molecular Biology Laboratory European Bioinformatics Institute Hinxton UK European Molecular Biology Laboratory European Bioinformatics Institute (EMBL-EBI) Cambridge UK Genome Biology Unit European Molecular Biology Laboratory (EMBL) Heidelberg Germany CIBIO/InBIO - Research Center in Biodiversity and Genetic Resources Universidade do Porto Vairão Portugal Berlin Institute for Medical Systems Biology Max Delbruck Center for Molecular Medicine Berlin Germany German Cancer Consortium (DKTK) partner site Berlin Germany German Cancer Research Center (DKFZ) Heidelberg Germany Berlin Institute for Medical Systems Biology Max Delbrück Center for Molecular Medicine Berlin Germany German Cancer Consortium (DKTK) Partner site Berlin Berlin Germany European Molecular Biology Laboratory Genome Biology Unit Heidelberg Germany Division of Computational Genomics and Systems Genetics German Cancer Research Center (DKFZ) Heidelberg Germany ETH Zurich Zurich Switzerland Memorial Sloan Kettering Cancer Center New York NY USA Weill Cornell Medical College New York NY USA SIB Swiss Institute of Bioinformatics Lausanne Switzerland University Hospital Zurich Zurich Switzerland Computational Biology Center Memorial Sloan Kettering Cancer Center New York NY USA Department of Biology ETH Zurich Zürich Switzerland Department of Computer Science ETH Zurich Zurich Switzerland Korea University Seoul South Korea Computational and Systems Biology Program Memorial Sloan Kettering Cancer Center New York NY USA Department of Physiology and Biophysics Weill Cornell Medicine New York NY USA Institute for Computational Biomedicine Weill Cornell Medicine New York NY USA Controlled Department and Institution New York NY USA Englander Institute for Precision Medicine Weill Cornell Medicine New York NY USA National University of Singapore Singapore Singapore Genome Institute of Singapore Singapore Singapore Computational and Systems Biology Genome Institute of Singap

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