检索结果-内蒙古大学图书馆

Author Correction: BigNeuron: a resource to benchmark and predict performance of algorithms for automated tracing of neurons in light microscopy datasets

引用

Nature methods 2024年第10期21卷 1959页

作者： Linus Manubens-Gil Zhi Zhou Hanbo Chen Arvind Ramanathan Xiaoxiao Liu Yufeng Liu Alessandro Bria Todd Gillette Zongcai Ruan Jian Yang Miroslav Radojević Ting Zhao Li Cheng Lei Qu Siqi Liu Kristofer E Bouchard Lin Gu Weidong Cai Shuiwang Ji Badrinath Roysam Ching-Wei Wang Hongchuan Yu Amos Sironi Daniel Maxim Iascone Jie Zhou Erhan Bas Eduardo Conde-Sousa Paulo Aguiar Xiang Li Yujie Li Sumit Nanda Yuan Wang Leila Muresan Pascal Fua Bing Ye Hai-Yan He Jochen F Staiger Manuel Peter Daniel N Cox Michel Simonneau Marcel Oberlaender Gregory Jefferis Kei Ito Paloma Gonzalez-Bellido Jinhyun Kim Edwin Rubel Hollis T Cline Hongkui Zeng Aljoscha Nern Ann-Shyn Chiang Jianhua Yao Jane Roskams Rick Livesey Janine Stevens Tianming Liu Chinh Dang Yike Guo Ning Zhong Georgia Tourassi Sean Hill Michael Hawrylycz Christof Koch Erik Meijering Giorgio A Ascoli Hanchuan Peng Institute for Brain and Intelligence Southeast University Nanjing China. Microsoft Corporation Redmond WA USA. Tencent AI Lab Bellevue WA USA. Computing Environment and Life Sciences Directorate Argonne National Laboratory Lemont IL USA. Kaya Medical Seattle WA USA. University of Cassino and Southern Lazio Cassino Italy. Center for Neural Informatics Structures and Plasticity Krasnow Institute for Advanced Study George Mason University Fairfax VA USA. Faculty of Information Technology Beijing University of Technology Beijing China. Beijing International Collaboration Base on Brain Informatics and Wisdom Services Beijing China. Nuctech Netherlands Rotterdam the Netherlands. Janelia Research Campus Howard Hughes Medical Institute Ashburn VA USA. Department of Electrical and Computer Engineering University of Alberta Edmonton Alberta Canada. Ministry of Education Key Laboratory of Intelligent Computation and Signal Processing Anhui University Hefei China. Paige AI New York NY USA. Scientific Data Division and Biological Systems and Engineering Division Lawrence Berkeley National Lab Berkeley CA USA. Helen Wills Neuroscience Institute and Redwood Center for Theoretical Neuroscience UC Berkeley Berkeley CA USA. RIKEN AIP Tokyo Japan. Research Center for Advanced Science and Technology (RCAST) The University of Tokyo Tokyo Japan. School of Computer Science University of Sydney Sydney New South Wales Australia. Texas A&M University College Station TX USA. Cullen College of Engineering University of Houston Houston TX USA. Graduate Institute of Biomedical Engineering National Taiwan University of Science and Technology Taipei Taiwan. National Centre for Computer Animation Bournemouth University Poole UK. PROPHESEE Paris France. Department of Neuroscience Columbia University New York NY USA. Mortimer B. Zuckerman Mind Brain Behavior Institute Columbia University New York NY USA. Department of Computer Science Northern Illinois Universit

来源：评论

学校读者我要写书评

暂无评论

Recognizing opinion sources based on a new categorization of opinion types 25

Recognizing opinion sources based on a new categorization of...

引用

25th International Joint Conference on Artificial Intelligence, IJCAI 2016

作者： Lingjia, Deng Wiebe, Janyce Intelligent Systems Program University of Pittsburgh United States Intelligent Systems Program Department of Computer Science University of Pittsburgh United States

Recognizing sources of opinions is an important task in sentiment analysis. Different from previous works which categorize an opinion according to whether the source is the writer or the source is a noun phrase, we propose a new categorization of opinions according to the role that the source plays. The source of a participant opinion is a participant in the event that triggers the opinion. On the contrary, the source of a non-participant opinion is not a participant. Based on this new categorization, we classify an opinion using phrase-level embeddings. A transductive learning method is used for the classifier since there is no existing annotated corpora of this new categorization. A joint prediction model of Probabilistic Soft Logic then recognizes the sources of the two types of opinions in a single model. The experiments have shown that our model improves recognizing sources of opinions over baselines and several state-of-the-art works.

关键词： Sentiment analysis

来源：评论

学校读者我要写书评

暂无评论

Parse tree fragmentation of ungrammatical sentences 25

Parse tree fragmentation of ungrammatical sentences

引用

25th International Joint Conference on Artificial Intelligence, IJCAI 2016

作者： Hashemi, Homa B. Hwa, Rebecca Intelligent Systems Program Computer Science Department University of Pittsburgh United States

Ungrammatical sentences present challenges for statistical parsers because the well-formed trees they produce may not be appropriate for these sentences. We introduce a framework for reviewing the parses of ungrammatical sentences and extracting the coherent parts whose syntactic analyses make sense. We call this task parse tree fragmentation. In this paper, we propose a training methodology for fragmenting parse trees without using a task-specific annotated corpus. We also propose some fragmentation strategies and compare their performance on an extrinsic task - fluency judgments in two domains: English-as-a-Second Language (ESL) and machine translation (MT). Experimental results show that the proposed fragmentation strategies are competitive with existing methods for making fluency judgments;they also suggest that the overall framework is a promising way to handle syntactically unusual sentences.

关键词： Syntactics

来源：评论

学校读者我要写书评

暂无评论

An evaluation of parser robustness for ungrammatical sentences

An evaluation of parser robustness for ungrammatical sentenc...

引用

2016 Conference on Empirical Methods in Natural Language Processing, EMNLP 2016

作者： Hashemi, Homa B. Hwa, Rebecca Intelligent Systems Program University of Pittsburgh United States Computer Science Department University of Pittsburgh United States

ISBN: (纸本)9781945626258

For many NLP applications that require a parser, the sentences of interest may not be well-formed. If the parser can overlook problems such as grammar mistakes and produce a parse tree that closely resembles the correct analysis for the intended sentence, we say that the parser is robust. This paper compares the performances of eight state-of-the-art dependency parsers on two domains of ungrammatical sentences: learner English and machine translation outputs. We have developed an evaluation metric and conducted a suite of experiments. Our analyses may help practitioners to choose an appropriate parser for their tasks, and help developers to improve parser robustness against ungrammatical sentences. © 2016 Association for Computational Linguistics

关键词： Natural language processing systems

来源：评论

学校读者我要写书评

暂无评论

Molybdenum Disulfide: Scalable Fabrication of Molybdenum Disulfide Nanostructures and their Assembly (Adv. Mater. 43/2020)

引用

Advanced Materials 2020年第43期32卷

作者： Yun Huang Kang Yu Huaizhi Li Kai Xu Zexi Liang Debora Walker Paulo Ferreira Peer Fischer Donglei (Emma) Fan Materials Science and Engineering Program Texas Materials Institute The University of Texas at Austin Austin TX 78712 USA The Department of Advanced Electron Microscopy Imaging and Spectroscopy International Iberian Nanotechnology Laboratory Braga 4715‐330 Portugal Electrical and Computer Engineering University of Illinois at Urbana‐Champaign Urbana IL 61801 USA School of Engineering and Applied Sciences Harvard University Cambridge MA 02138 USA Mechanical Engineering Department and IDMEC Instituto Superior Técnico University of Lisbon Av. Rovisco Pais Lisboa 1049‐001 Portugal Max‐Planck‐Institute for Intelligent Systems Heisenbergstr. 3 Stuttgart 70569 Germany Institute of Physical Chemistry University of Stuttgart Pfaffenwaldring 55 Stuttgart 70569 Germany Walker Department of Mechanical Engineering The University of Texas at Austin Austin TX 78712 USA

来源：评论

学校读者我要写书评

暂无评论

The Brain Tumor Segmentation (BraTS) Challenge: Local Synthesis of Healthy Brain Tissue via Inpainting

arXiv

引用

arXiv 2023年

作者： Kofler, Florian Steinbauer, Felix Meissen, Felix Graf, Robert Ehrlich, Stefan K. Reinke, Annika Oswald, Eva Waldmannstetter, Diana Hoelzl, Florian Horvath, Izabela Turgut, Oezguen Shit, Suprosanna Bukas, Christina Yang, Kaiyuan Paetzold, Johannes C. de da Rosa, Ezequiel Mekki, Isra Vinayahalingam, Shankeeth Kassem, Hasan Zhang, Juexin Chen, Ke Weng, Ying Durrer, Alicia Cattin, Philippe C. Wolleb, Julia Sadique, M.S. Rahman, M.M. Farzana, W. Temtam, A. Iftekharuddin, K.M. Adewole, Maruf Anwar, Syed Muhammad Baid, Ujjwal Janas, Anastasia Kazerooni, Anahita Fathi LaBella, Dominic Bran Li, Hongwei Moawad, Ahmed W. Conte, Gian-Marco Farahani, Keyvan Eddy, James Sheller, Micah Pati, Sarthak Karagyris, Alexandros Aristizabal, Alejandro Bergquist, Timothy Chung, Verena Shinohara, Russell Takeshi Dako, Farouk Wiggins, Walter Reitman, Zachary Wang, Chunhao Liu, Xinyang Jiang, Zhifan Johanson, Elaine Meier, Zeke Familiar, Ariana Davatzikos, Christos Freymann, John Kirby, Justin Bilello, Michel Fathallah-Shaykh, Hassan M. Wiest, Roland Kirschke, Jan Colen, Rivka R. Kotrotsou, Aikaterini Lamontagne, Pamela Marcus, Daniel Milchenko, Mikhail Nazeri, Arash Weber, Marc-André Mahajan, Abhishek Mohan, Suyash Mongan, John Hess, Christopher Cha, Soonmee Villanueva-Meyer, Javier Colak, Errol Crivellaro, Priscila Jakab, Andras Fatade, Abiodun Omidiji, Olubukola Akinola Lagos, Rachel Olatunji, O.O. Khanna, Goldey Kirkpatrick, John Alonso-Basanta, Michelle Rashid, Arif Bornhorst, Miriam Nabavizadeh, Ali Lepore, Natasha Palmer, Joshua Porras, Antonio Albrecht, Jake Anazodo, Udunna Aboian, Mariam Calabrese, Evan Rudie, Jeffrey David Linguraru, Marius George Iglesias, Juan Eugenio Van Leemput, Koen Bakas, Spyridon Wiestler, Benedikt Ezhov, Ivan Piraud, Marie Menze, Bjoern H. Helmholtz AI Germany Technical University of Munich Germany Helmholtz Munich Germany Klinikum Rechts der Isar Munich Germany SETLabs Research GmbH Healthcare Technologies Munich Germany Heidelberg Division of Intelligent Medical Systems Germany Heidelberg HI Helmholtz Imaging Germany Institute of Clinical Neuroimmunology Faculty of Medicine Ludwig Maximilian University Munich Germany University of Zurich Switzerland Helmholtz Research Center Munich Germany Imperial College London United Kingdom Icometrix Leuven Belgium Department of Oral and Maxillofacial Surgery Radboudumc Netherlands Department of Artificial Intelligence Radboud University Netherlands MLCommons University of Nottingham Ningbo China Ningbo315100 China Center for medical Image Analysis and Navigation Department of Biomedical Engineering University of Basel Allschwil Switzerland Old Dominion University NorfolkVA23529 United States Lab Crestview Radiology Lagos Nigeria Children's National Hospital WashingtonDC United States George Washington University WashingtonDC United States University of Pennsylvania PhiladelphiaPA United States Department of Radiology Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Department of Pathology and Laboratory Medicine Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Yale University New HavenCT United States Children's Hospital of Philadelphia University of Pennsylvania PhiladelphiaPA United States Duke University Medical Center Department of Radiation Oncology United States Athinoula A Martinos Center for Biomedical Imaging Massachusetts General Hospital BostonMA United States Department of Neuroradiology Technical University of Munich Munich Germany Mercy Catholic Medical Center DarbyPA United States Department of Radiology Mayo Clinic RochesterMN United States Cancer Imaging Program National Cancer Institute National Institutes of Health BethesdaMD20814 U

A myriad of algorithms for the automatic analysis of brain MR images is available to support clinicians in their decision-making. For brain tumor patients, the image acquisition time series typically starts with an already pathological scan. This poses problems, as many algorithms are designed to analyze healthy brains and provide no guarantee for images featuring lesions. Examples include but are not limited to algorithms for brain anatomy parcellation, tissue segmentation, and brain extraction. To solve this dilemma, we introduce the BraTS inpainting challenge. Here, the participants explore inpainting techniques to synthesize healthy brain scans from lesioned ones. The following manuscript contains the task formulation, dataset, and submission procedure. Later, it will be updated to summarize the findings of the challenge. The challenge is organized as part of the ASNR-BraTS MICCAI challenge. Copyright © 2023, The Authors. All rights reserved.

关键词： Magnetic resonance imaging

来源：评论

学校读者我要写书评

暂无评论

Making large Cox's proportional hazard models tractable in Bayesian networks 8

Making large Cox's proportional hazard models tractable in B...

引用

8th International Conference on Probabilistic Graphical Models, PGM 2016

作者： Kraisangka, Jidapa Druzdzel, Marek J. Decision System Laboratory School of Information Sciences Intelligent Systems Program University of Pittsburgh PittsburghPA15260 United States Faculty of Computer Science Bialystok University of Technology Wiejska 45A Bialystok15-351 Poland

Cox's proportional hazard (CPH) model is a statistical technique that captures the interaction between a set of risk factors and an effect variable. While the CPH model is popular in survival analysis, Bayesian networks offer an attractive alternative that is intuitive, general, theoretically sound, and avoids CPH model's restrictive assumptions. Existing CPH models are a great source of existing knowledge that can be reused in Bayesian networks. The main problem with applying Bayesian networks to survival analysis is their exponential growth in complexity as the number of risk factors increases. It is not uncommon to see complex CPH models with as many as 20 risk factors. Our paper focuses on making large survival analysis models derived from the CPH model tractable in Bayesian networks. We evaluate the effect of two complexity reduction techniques: (1) parent divorcing, and (2) removing less important risk factors based on the accuracy of the resulting models. 2016 Microtome Publishing. All rights reserved.

关键词： Bayesian networks

来源：评论

学校读者我要写书评

暂无评论

Author Correction: π-HuB: the proteomic navigator of the human body

引用

Nature 2025年第8046期637卷 E22页

作者： Fuchu He Ruedi Aebersold Mark S Baker Xiuwu Bian Xiaochen Bo Daniel W Chan Cheng Chang Luonan Chen Xiangmei Chen Yu-Ju Chen Heping Cheng Ben C Collins Fernando Corrales Jürgen Cox Weinan E Jennifer E Van Eyk Jia Fan Pouya Faridi Daniel Figeys George Fu Gao Wen Gao Zu-Hua Gao Keisuke Goda Wilson Wen Bin Goh Dongfeng Gu Changjiang Guo Tiannan Guo Yuezhong He Albert J R Heck Henning Hermjakob Tony Hunter Narayanan Gopalakrishna Iyer Ying Jiang Connie R Jimenez Lokesh Joshi Neil L Kelleher Ming Li Yang Li Qingsong Lin Cui Hua Liu Fan Liu Guang-Hui Liu Yansheng Liu Zhihua Liu Teck Yew Low Ben Lu Matthias Mann Anming Meng Robert L Moritz Edouard Nice Guang Ning Gilbert S Omenn Christopher M Overall Giuseppe Palmisano Yaojin Peng Charles Pineau Terence Chuen Wai Poon Anthony W Purcell Jie Qiao Roger R Reddel Phillip J Robinson Paola Roncada Chris Sander Jiahao Sha Erwei Song Sanjeeva Srivastava Aihua Sun Siu Kwan Sze Chao Tang Liujun Tang Ruijun Tian Juan Antonio Vizcaíno Chanjuan Wang Chen Wang Xiaowen Wang Xinxing Wang Yan Wang Tobias Weiss Mathias Wilhelm Robert Winkler Bernd Wollscheid Limsoon Wong Linhai Xie Wei Xie Tao Xu Tianhao Xu Liying Yan Jing Yang Xiao Yang John Yates Tao Yun Qiwei Zhai Bing Zhang Hui Zhang Lihua Zhang Lingqiang Zhang Pingwen Zhang Yukui Zhang Yu Zi Zheng Qing Zhong Yunping Zhu State Key Laboratory of Medical Proteomics Beijing Proteome Research Center National Center for Protein Sciences (Beijing) Beijing Institute of Lifeomics Beijing China. hefc@***. International Academy of Phronesis Medicine (Guangdong) Guangdong China. hefc@***. Department of Biology Institute of Molecular Systems Biology ETH Zurich Zurich Switzerland. aebersold@imsb.biol.ethz.ch. Macquarie Medical School Macquarie University Sydney New South Wales Australia. Institute of Pathology and Southwest Cancer Center Southwest Hospital Third Military Medical University (Army Medical University) and Key Laboratory of Tumor Immunopathology Ministry of Education of China Chongqing China. Institute of Health Service and Transfusion Medicine Beijing China. Department of Pathology and The Sidney Kimmel Comprehensive Cancer Center Johns Hopkins University Baltimore MD USA. State Key Laboratory of Medical Proteomics Beijing Proteome Research Center National Center for Protein Sciences (Beijing) Beijing Institute of Lifeomics Beijing China. Key Laboratory of Systems Biology Center for Excellence in Molecular Cell Science Shanghai Institute of Biochemistry and Cell Biology Chinese Academy of Sciences Shanghai China. Department of Nephrology First Medical Center of Chinese PLA General Hospital Nephrology Institute of the Chinese People's Liberation Army State Key Laboratory of Kidney Diseases National Clinical Research Center for Kidney Diseases Beijing Key Laboratory of Kidney Disease Research Beijing China. Institute of Chemistry Academia Sinica Taipei China. National Biomedical Imaging Center State Key Laboratory of Membrane Biology Institute of Molecular Medicine Peking-Tsinghua Center for Life Sciences College of Future Technology Peking University Beijing China. School of Biological Sciences Queen's University of Belfast Belfast UK. Functional Proteomics Laboratory Centro Nacional de Biotecnología-CSIC Madrid Spain. Computational Systems Biochemistry Research Group Ma

来源：评论

学校读者我要写书评

暂无评论

Joint prediction for entity/event-level sentiment analysis using Probabilistic Soft Logic models

Joint prediction for entity/event-level sentiment analysis u...

引用

Conference on Empirical Methods in Natural Language Processing, EMNLP 2015

作者： Deng, Lingjia Wiebe, Janyce Intelligent Systems Program University of Pittsburgh United States Intelligent Systems Program Department of Computer Science University of Pittsburgh United States

ISBN: (纸本)9781941643327

In this work, we build an entity/event-level sentiment analysis system, which is able to recognize and infer both explicit and implicit sentiments toward entities and events in the text. We design Probabilistic Soft Logic models that integrate explicit sentiments, inference rules, and +/-effect event information (events that positively or negatively affect entities). The experiments show that the method is able to greatly improve over baseline accuracies in recognizing entity/event-level sentiments. © 2015 Association for Computational Linguistics.

关键词： Sentiment analysis

来源：评论

学校读者我要写书评

暂无评论

MPQA 3.0: An entity/event-level sentiment corpus

MPQA 3.0: An entity/event-level sentiment corpus

引用

Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL HLT 2015

作者： Deng, Lingjia Wiebe, Janyce Intelligent Systems Program Department of Computer Science University of Pittsburgh United States

ISBN: (纸本)9781941643495

This paper presents an annotation scheme for adding entity and event target annotations to the MPQA corpus, a rich span-annotated opinion corpus. The new corpus promises to be a valuable new resource for developing systems for entity/event-level sentiment analysis. Such systems, in turn, would be valuable in NLP applications such as Automatic Question Answering. We introduce the idea of entity and event targets (eTargets), describe the annotation scheme, and present the results of an agreement study. © 2015 Association for Computational Linguistics.

关键词： Sentiment analysis

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：