检索结果-内蒙古大学图书馆

arXiv 2018年

作者： Chin, Alex Eckles, Dean Ugander, Johan Department of Statistics Stanford University Sloan School of Management Massachusetts Institute of Technology Institute for Data Systems & Society Massachusetts Institute of Technology Management Science & Engineering Department Stanford University Institute for Computational & Mathematical Engineering Stanford University

When trying to maximize the adoption of a behavior in a population connected by a social network, it is common to strategize about where in the network to seed the behavior, often with an element of randomness. Selecting seeds uniformly at random is a basic but compelling strategy in that it distributes seeds broadly throughout the network. A more sophisticated stochastic strategy, one-hop targeting, is to select random network neighbors of random individuals;this exploits a version of the friendship paradox, whereby the friend of a random individual is expected to have more friends than a random individual, with the hope that seeding a behavior at more connected individuals leads to more adoption. Many seeding strategies have been proposed, but empirical evaluations have demanded large field experiments designed specifically for this purpose and have yielded relatively imprecise comparisons of strategies. Here we show how stochastic seeding strategies can be evaluated more efficiently in such experiments, how they can be evaluated "off-policy" using existing data arising from experiments designed for other purposes, and how to design more efficient experiments. In particular, we consider contrasts between stochastic seeding strategies and analyze nonparametric estimators adapted from policy evaluation and importance sampling. We use simulations on real networks to show that the proposed estimators and designs can dramatically increase precision while yielding valid inference. We then apply our proposed estimators to two field experiments, one that assigned households to an intensive marketing intervention and one that assigned students to an anti-bullying intervention. Copyright © 2018, The Authors. All rights reserved.

关键词： Stochastic systems

来源：评论

学校读者我要写书评

暂无评论

Highly ionized clumps shooting out from a quasar at relativistic speeds

arXiv

引用

arXiv 2025年

作者： Audard, Marc Awaki, Hisamitsu Ballhausen, Ralf Bamba, Aya Behar, Ehud Boissay-Malaquin, Rozenn Brenneman, Laura Brown, Gregory V. Corrales, Lia Costantini, Elisa Cumbee, Renata Trigo, María Díaz Done, Chris Dotani, Tadayasu Ebisawa, Ken Eckart, Megan Eckert, Dominique Enoto, Teruaki Eguchi, Satoshi Ezoe, Yuichiro Foster, Adam Fujimoto, Ryuichi Fujita, Yutaka Fukazawa, Yasushi Fukushima, Kotaro Furuzawa, Akihiro Gallo, Luigi García, Javier A. Gu, Liyi Guainazzi, Matteo Hagino, Kouichi Hamaguchi, Kenji Hatsukade, Isamu Hayashi, Katsuhiro Hayashi, Takayuki Hell, Natalie Hodges-Kluck, Edmund Hornschemeier, Ann Ichinohe, Yuto Ishida, Manabu Ishikawa, Kumi Ishisaki, Yoshitaka Kaastra, Jelle Kallman, Timothy Kara, Erin Katsuda, Satoru Kanemaru, Yoshiaki Kelley, Richard Kilbourne, Caroline Kitamoto, Shunji Kobayashi, Shogo Kohmura, Takayoshi Kubota, Aya Leutenegger, Maurice Loewenstein, Michael Maeda, Yoshitomo Markevitch, Maxim Matsumoto, Hironori Matsushita, Kyoko McCammon, Dan McNamara, Brian Mernier, François Miller, Eric D. Miller, Jon M. Mitsuishi, Ikuyuki Mizumoto, Misaki Mizuno, Tsunefumi Mori, Koji Mukai, Koji Murakami, Hiroshi Mushotzky, Richard Nakajima, Hiroshi Nakazawa, Kazuhiro Ness, Jan-Uwe Nobukawa, Kumiko Nobukawa, Masayoshi Noda, Hirofumi Odaka, Hirokazu Ogawa, Shoji Ogorzalek, Anna Okajima, Takashi Ota, Naomi Paltani, Stephane Petre, Robert Plucinsky, Paul Porter, Frederick Scott Pottschmidt, Katja Sato, Kosuke Sato, Toshiki Sawada, Makoto Seta, Hiromi Shidatsu, Megumi Simionescu, Aurora Smith, Randall Suzuki, Hiromasa Szymkowiak, Andrew Takahashi, Hiromitsu Takeo, Mai Tamagawa, Toru Tamura, Keisuke Tanaka, Takaaki Tanimoto, Atsushi Tashiro, Makoto Terada, Yukikatsu Terashima, Yuichi Tsuboi, Yohko Tsujimoto, Masahiro Tsunemi, Hiroshi Tsuru, Takeshi G. Uchida, Hiroyuki Uchida, Nagomi Uchida, Yuusuke Uchiyama, Hideki Ueda, Yoshihiro Uno, Shinichiro Vink, Jacco Watanabe, Shin Williams, Brian J. Yamada, Satoshi Yamada, Shinya Yamaguchi, Hiroya Yamaoka, Kazutaka Yamasaki, Noriko Yamauchi, Makoto Yamauchi, S Department of Astronomy University of Geneva VersoixCH-1290 Switzerland Department of Physics Ehime University Ehime790-8577 Japan Department of Astronomy University of Maryland College ParkMD20742 United States NASA / Goddard Space Flight Center GreenbeltMD20771 United States Center for Research and Exploration in Space Science and Technology NASA / GSFC CRESST II GreenbeltMD20771 United States Department of Physics University of Tokyo Tokyo113-0033 Japan Department of Physics Technion Technion City Haifa3200003 Israel BaltimoreMD21250 United States Center for Astrophysics — Harvard-Smithsonian MA02138 United States Lawrence Livermore National Laboratory CA94550 United States Department of Astronomy University of Michigan MI48109 United States SRON Netherlands Institute for Space Research Leiden Netherlands ESO Karl-Schwarzschild-Strasse 2 Garching bei München85748 Germany Centre for Extragalactic Astronomy Department of Physics University of Durham South Road DurhamDH1 3LE United Kingdom Kanagawa252-5210 Japan Department of Physics Kyoto University Kyoto606-8502 Japan Department of Economics Kumamoto Gakuen University Kumamoto862-8680 Japan Department of Physics Tokyo Metropolitan University Tokyo192-0397 Japan Department of Physics Hiroshima University Hiroshima739-8526 Japan Department of Physics Fujita Health University Aichi470-1192 Japan Department of Astronomy and Physics Saint Mary’s University NSB3H 3C3 Canada Cahill Center for Astronomy and Astrophysics California Institute of Technology PasadenaCA91125 United States Noordwijk2200 AG Netherlands Faculty of Engineering University of Miyazaki Miyazaki889-2192 Japan RIKEN Nishina Center Saitama351-0198 Japan Leiden Observatory University of Leiden P.O. Box 9513 LeidenNL-2300 RA Netherlands Kavli Institute for Astrophysics and Space Research Massachusetts Institute of Technology MA02139 United States Department of Physics Saitama University Sai

Evidence indicates that supermassive black holes exist at the centers of virtually all galaxies. Their mass correlates with the galactic bulge mass1, suggesting a co-evolution with their host galaxies2, most likely through powerful outflows/winds3. X-ray observations have revealed the presence of highly ionized winds outflowing at sub-relativistic speeds from the accretion disks around supermassive black holes4,5. However, the limited spectral resolution of current X-ray instruments has left the physical structure of the winds poorly understood, as well as their location, both of which are essential for estimating the wind kinetic power6,7. Here, the first XRISM observation of the luminous quasar, PDS 456, is reported. The high-resolution spectrometer Resolve onboard XRISM enabled the discovery of five discrete velocity components outflowing at 20–30% of the speed of light. This demonstrates that the wind structure is highly inhomogeneous, which is inferred to consist of up to a million clumps of size (0.2–1.2) × 1015 cm located (2–4) × 1016 cm away from the X-ray source. The mass outflow rate is estimated to be 60–170 solar masses per year. Subsequently, the wind kinetic power is more than 1047 erg s−1, exceeding the Eddington luminosity limit. This is more than enough to significantly impact the galaxy. Copyright © 2025, The Authors. All rights reserved.

关键词： X ray detectors

来源：评论

学校读者我要写书评

暂无评论

AGlobal benchmark of algorithms for segmentinglate gadolinium-enhanced cardiac magnetic resonance imaging

arXiv

引用

arXiv 2020年

作者： Xiong, Zhaohan Xia, Qing Hu, Zhiqiang Huang, Ning Bian, Cheng Zheng, Yefeng Vesal, Sulaiman Ravikumar, Nishant Maier, Andreas Yang, Xin Heng, Pheng-Ann Ni, Dong Li, Caizi Tong, Qianqian Si, Weixin Puybareau, Elodie Khoudli, Younes Géraud, Thierry Chen, Chen Bai, Wenjia Rueckert, Daniel Xu, Lingchao Zhuang, Xiahai Luo, Xinzhe Jia, Shuman Sermesant, Maxime Liu, Yashu Wang, Kuanquan Borra, Davide Masci, Alessandro Corsi, Cristiana De Vente, Coen Veta, Mitko Karim, Rashed Preetha, Chandrakanth Jayachandran Engelhardt, Sandy Qiao, Menyun Wang, Yuanyuan Tao, Qian Nuñez-Garcia, Marta Camara, Oscar Savioli, Nicolo Lamata, Pablo Zhao, Jichao Auckland Bioengineering Institute University of Auckland Auckland New Zealand State Key Lab of Virtual Reality Technology and Systems Beihang University Beijing China School of Electronics Engineering and Computer Science Peking University Beijing China SenseTime Inc Shenzhen China Tencent Jarvis Laboratory Shenzhen China Pattern Recognition Lab Friedrich-Alexander-Universität Erlangen-Nürnberg Erlangen Germany Department of Computer Science and Engineering Chinese University of Hong Kong Hong Kong National-Regional Key Technology Engineering Laboratory for Medical Ultrasound School of Biomedical Engineering Shenzhen University Shenzhen China School of Computer Science Wuhan University Wuhan China Shenzhen Key Laboratory of Virtual Reality and Human Interaction Technology Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen China Epita Research and Development Laboratory Paris France Department of Computing Imperial College London London United Kingdom School of Naval Architecture Shanghai Jiao Tong University Ocean & Civil Engineering Shanghai China School of Data Science Fudan University Shanghai China Epione Research Group Universite Cote d'Azur Inria Sophia Antipolis France Harbin Institute of Technology School of Computer Science and Technology Harbin China Department of Electric University of Bologna Electronic and Information Engineering Cesena Italy Department of Biomedical Engineering Eindhoven University of Technology Eindhoven Netherlands School of Biomedical Engineering and Imaging Sciences Kings College London London United Kingdom Faculty of Electrical Engineering and Information Technology University of Magdeburg Magdeburg Germany Heidelberg University Hospital Germany Biomedical Engineering Center Fudan University Shanghai China Department of Radiology Leiden University Medical Center Leiden Netherlands Department of Information and Communication Technologies Universitat Pompeu Fabra Barcelona Spain D

Segmentation of cardiac images, particularly late gadolinium-enhanced magnetic resonance imaging (LGE-MRI)widely used for visualizing diseased cardiacstructures, is a crucial first step for clinical diagnosis and treatment. However, direct segmentation ofLGE-MRIsischallenging due to *** most clinical studies have relied on manual and labor-intensive approaches, automatic methods are of high interest, particularly optimized machine learning *** address this, we organized the "2018 Left Atrium Segmentation Challenge" using 154 3D LGE-MRIs, currently the world's largest cardiac LGE-MRI dataset, and associated labels of the left atriumsegmented by three medical experts,ultimately attractingthe participation of27 international teams. In this paper, extensive analysis of the submitted algorithms using technical and biological metrics was performed by undergoing subgroup analysis and conducting hyper-parameter analysis,offeringan overall picture ofthe major design choices of convolutional neural networks(CNNs) and practical considerations for achieving state-of-the-art left atrium segmentation. Results show the top methodachieved a dice score of 93.2%and a mean surface to a surface distance of 0.7mm, significantly outperforming prior state-of-the-art. Particularly, our analysisdemonstrated that double, sequentially usedCNNs, in which a first CNN is used for automatic region-of-interestlocalization and a subsequent CNN is used for refinedregional segmentation, achieved far superiorresults than traditional methods and pipelinescontaining *** findings from this study can potentially be extended to other imaging datasets and modalities, having an impact on the wider medical imaging *** large-scale benchmarking study makes a significant step towards much-improved segmentation methods for cardiac LGE-MRIs, and will serveas animportant benchmark for evaluating and comparing the future works in the field. Copyright © 2020, Th

关键词： Magnetic resonance imaging

来源：评论

学校读者我要写书评

暂无评论

CHAOS Challenge - Combined (CT-MR) healthy abdominal organ segmentation

arXiv

引用

arXiv 2020年

作者： Kavur, A. Emre Gezer, N. Sinem Barış, Mustafa Conze, Pierre-Henri Groza, Vladimir Pham, Duc Duy Chatterjee, Soumick Ernst, Philipp Özkan, Savaş Baydar, Bora Lachinov, Dmitry Han, Shuo Pauli, Josef Isensee, Fabian Perkonigg, Matthias Sathish, Rachana Rajan, Ronnie Aslan, Sinem Sheet, Debdoot Dovletov, Gurbandurdy Speck, Oliver Nürnberger, Andreas Maier-Hein, Klaus H. Akar, Gözde Bozdagı Ünal, Gözde Dicle, Oguz Selver, M. Alper Graduate School of Natural and Applied Sciences Dokuz Eylul University Izmir Turkey Department of Radiology Faculty Of Medicine Dokuz Eylul University Izmir Turkey Dept. of Ophthalmology and Optometry Medical Uni. of Vienna Austria Intelligent Systems Faculty of Engineering University of Duisburg-Essen Germany Division of Medical Image Computing German Cancer Research Center Heidelberg Germany CIR Lab Dept of Biomedical Imaging and Image-guided Therapy Medical Uni. of Vienna Austria IMT Atlantique LaTIM UMR 1101 Brest France Department of Electrical and Electronics Engineering Middle East Technical University Ankara Turkey Department of Electrical Engineering Indian Institute of Technology Kharagpur India School of Medical Science and Technology Indian Institute of Technology Kharagpur India Ca’ Foscari University of Venice ECLT DAIS Venice Italy Ege University International Computer Institute Izmir Turkey Johns Hopkins University Baltimore United States Data and Knowledge Engineering Group Otto von Guericke University Magdeburg Germany Magnetic Resonance Otto von Guericke University Magdeburg Germany Median Technologies Valbonne France Department of Computer Engineering Istanbul Technical University Istanbul Turkey Department of Electrical and Electronics Engineering Dokuz Eylul University Izmir Turkey

—Segmentation of abdominal organs has been a comprehensive, yet unresolved, research field for many years. In the last decade, intensive developments in deep learning (DL) have introduced new state-of-the-art segmentation systems. Despite outperforming the overall accuracy of existing systems, the effects of DL model properties and parameters on the performance is hard to interpret. This makes comparative analysis a necessary tool to achieve explainable studies and systems. Moreover, the performance of DL for emerging learning approaches such as cross-modality and multi-modal tasks have been rarely discussed. In order to expand the knowledge in these topics, CHAOS – Combined (CT-MR) Healthy Abdominal Organ Segmentation challenge has been organized in the IEEE International Symposium on Biomedical Imaging (ISBI), 2019, in Venice, Italy. Despite a large number of the previous abdomen related challenges, the majority of which are focused on tumor/lesion detection and/or classification with a single modality, CHAOS providesboth abdominal CT and MR data from healthy subjects. Five different and complementary tasks have been designed to analyze the capabilities of the current approaches from multiple perspectives. The results are investigated thoroughly, compared with manual annotations and interactive methods. The outcomes are reported in detail to reflect the latest advancements in the field. CHAOS challenge and data will be available online to provide a continuous benchmark resource for segmentation. Copyright © 2020, The Authors. All rights reserved.

关键词： Medical imaging

来源：评论

学校读者我要写书评

暂无评论

The provision and utility of science and uncertainty to decision-makers: earth science case studies

引用

Environment systems and Decisions 2019年第3期39卷 307-348页

作者： Quigley, Mark C. Bennetts, Luke G. Durance, Patricia Kuhnert, Petra M. Lindsay, Mark D. Pembleton, Keith G. Roberts, Melanie E. White, Christopher J. School of Earth Sciences University of Melbourne Parkville 3010 VIC Australia School of Mathematical Sciences University of Adelaide Adelaide 5005 SA Australia GNS Science 1 Fairway Drive Lower Hutt 5010 New Zealand CSIRO Data61 GPO Box 1700 Canberra ACT Australia Centre for Exploration Targeting School of Earth Sciences University of Western Australia Crawley 6009 WA Australia School of Agricultural Computational and Environmental Sciences and Centre for Sustainable Agricultural Systems University of Southern Queensland Toowoomba 4350 QLD Australia School of Mathematics and Statistics University of Melbourne Parkville 3010 VIC Australia Australian Rivers Institute Griffith University Nathan 4111 QLD Australia School of Engineering University of Tasmania Hobart 7001 TAS Australia Department of Civil and Environmental Engineering University of Strathclyde Glasgow United Kingdom

This paper investigates how scientific information and expertise was provided to decision-makers for consideration in situations involving risk and uncertainty. Seven case studies from the earth sciences were used as a medium for this exposition: (1) the 2010–2011 Canterbury earthquake sequence in New Zealand, (2) agricultural farming-system development in North West Queensland, (3) operational flood models, (4) natural disaster risk assessment for Tasmania, (5) deep sea mining in New Zealand, (6) 3-D modelling of geological resource deposits, and (7) land-based pollutant loads to Australia’s Great Barrier Reef. Case studies are lead-authored by a diverse range of scientists, based either in universities, industry, or government science agencies, with diverse roles, experiences, and perspectives on the events discussed. The context and mechanisms by which scientific information was obtained, presented to decision-makers, and utilised in decision-making is presented. Sources of scientific uncertainties and how they were communicated to and considered in decision-making processes are discussed. Decisions enacted in each case study are considered in terms of whether they were scientifically informed, aligned with prevailing scientific evidence, considered scientific uncertainty, were informed by models, and were (or were not) precautionary in nature. The roles of other relevant inputs (e.g. political, socioeconomic considerations) in decision-making are also described. Here we demonstrate that scientific evidence may enter decision-making processes through diverse pathways, ranging from direct solicitations by decision-makers to independent requests from stakeholders following media coverage of relevant research. If immediately relevant scientific data cannot be provided with sufficient expediency to meet the demands of decision-makers, decision-makers may (i) seek expert scientific advice and judgement (to assist with decision-making under conditions of high epistemic

关键词： Decision-making Earth science Environmental science Natural disasters Policy Risk Uncertainty

来源：评论

学校读者我要写书评

暂无评论

Author Correction: Genomic footprints of activated telomere maintenance mechanisms in cancer (Dec, 10.1038/s41467-019-13824-9, 2022)

引用

NATURE COMMUNICATIONS 2022年第1期13卷 1-1页

作者： Sieverling, Lina Hong, Chen Koser, Sandra D. Ginsbach, Philip Kleinheinz, Kortine Hutter, Barbara Braun, Delia M. Cortes-Ciriano, Isidro Xi, Ruibin Kabbe, Rolf Park, Peter J. Eils, Roland Schlesner, Matthias Brors, Benedikt Rippe, Karsten Jones, David T. W. Feuerbach, Lars Division of Applied Bioinformatics German Cancer Research Center (DKFZ) 69120 Heidelberg Germany Faculty of Biosciences Heidelberg University 69120 Heidelberg Germany Division of Applied Bioinformatics German Cancer Research Center (DKFZ) Heidelberg Germany Faculty of Biosciences Heidelberg University Heidelberg Germany Division of Theoretical Bioinformatics German Cancer Research Center (DKFZ) 69120 Heidelberg Germany Department for Bioinformatics and Functional Genomics Institute for Pharmacy and Molecular Biotechnology (IPMB) and BioQuant 69120 Heidelberg Germany Division of Theoretical Bioinformatics German Cancer Research Center (DKFZ) Heidelberg Germany Institute of Pharmacy and Molecular Biotechnology and BioQuant Heidelberg University Heidelberg Germany German Cancer Consortium (DKTK) Heidelberg Germany National Center for Tumor Diseases (NCT) Heidelberg Heidelberg Germany Heidelberg Center for Personalized Oncology (DKFZ-HIPO) German Cancer Research Center (DKFZ) Heidelberg Germany German Cancer Consortium (DKTK) German Cancer Research Center (DKFZ) Heidelberg Germany Division of Chromatin Networks German Cancer Research Center (DKFZ) and BioQuant 69120 Heidelberg Germany Department of Biomedical Informatics Harvard Medical School Boston Massachusetts 02115 USA Department of Chemistry Centre for Molecular Science Informatics University of Cambridge Cambridge CB2 1EW UK Ludwig Center at Harvard Medical School Boston Massachusetts 02115 USA Department of Biomedical Informatics Harvard Medical School Boston MA USA Department of Chemistry Centre for Molecular Science Informatics University of Cambridge Cambridge UK Ludwig Center at Harvard Medical School Boston MA USA School of Mathematical Sciences and Center for Statistical Science Peking University Beijing 100871 China Heidelberg University Heidelberg Germany New BIH Digital Health Center Berlin Institute of Health (BIH) and Charité - Universitätsmedizin Berlin Berlin Germany Bioi

来源：评论

学校读者我要写书评

暂无评论

Coping with communication challenges after the Formosa Fun Coast Dust Explosion

Coping with communication challenges after the Formosa Fun C...

引用

Resilience Week (RWS)

作者： Sheuwen Chuang Chia-Hsin Cheng Hsiao-Chun Chen Ching-An Lee David D Woods Graduate Institute of Data Science Taipei Medical University Taipei Republic of China Fire Department New Taipei City Republic of China Department of Integrated Systems Engineering Ohio USA

ISBN: (纸本)9781538669143

The Formosa Fun Coast Dust Explosion (FFCDE) occurred on 27 June 2015. It is the largest man-made disaster in Taiwan's history. The paper explores how participating actors dealt with the communication challenges to rescue 499 burn victims from the disaster scene and provide resuscitation and life support for mass burn casualties in hospitals following the FFCDE. data collection was via review of government reports and journal publications as well as in-depth individual interviews with 36 key participants in this event. Technological communication issues and human related communication issues were identified. The analysis reveals that the remodel of local incident command post at the disaster scene improved the difficulties caused by inadequate communications, and hospital staff' resilience to adapt was based on anticipation in the face of uncertainty and on coordination across roles and units to keep pace with the time varying demands.

关键词： Hospitals Iterative closest point algorithm Interviews Personnel Urban areas Explosions Fires

来源：评论

学校读者我要写书评

暂无评论

The provision and utility of earth science to decision-makers: synthesis and key findings

引用

Environment systems and Decisions 2019年第3期39卷 349-367页

作者： Quigley, Mark C. Bennetts, Luke G. Durance, Patricia Kuhnert, Petra M. Lindsay, Mark D. Pembleton, Keith G. Roberts, Melanie E. White, Christopher J. School of Earth Sciences University of Melbourne Parkville 3010 VIC Australia Department of Geological Sciences University of Canterbury Christchurch New Zealand School of Mathematical Sciences University of Adelaide Adelaide 5005 SA Australia GNS Science 1 Fairway Drive Lower Hutt 5010 New Zealand CSIRO Data61 GPO Box 1700 Canberra ACT Australia Centre for Exploration Targeting School of Earth Sciences University of Western Australia Crawley 6009 WA Australia School of Agricultural Computational and Environmental Sciences and Centre for Sustainable Agricultural Systems University of Southern Queensland Toowoomba 4350 QLD Australia Australian Rivers Institute Griffith University Nathan 4111 QLD Australia School of Mathematics and Statistics University of Melbourne Parkville 3010 VIC Australia School of Engineering University of Tasmania Hobart 7005 TAS Australia Department of Civil and Environmental Engineering University of Strathclyde Glasgow United Kingdom

This paper synthesizes important elements from case studies presented in its companion paper (Quigley et al. in Environ Syst Decis, 2019, https://***/10.1007/s10669-019-09728-0) to define mutual and distinct characteristics, and to develop a more holistic understanding of how earth science was used to support diverse examples of decision-making. We identify a suite of 28 different science actions used within the case studies that are classified as pertaining to (i) evidence acquisition and analysis, (ii) provision of science to target audience, or (iii) enhancing future science provision and utility. Sample action pathways provide empirically evidenced, albeit simplified, examples of how scientists may contribute to the progression of science through complex decision-making frameworks. Decision trees with multiple scientific and non-scientific inputs are presented based on empirical evidence and theory to provide scientists and decision-makers with simplified examples of complex multi-step decision-making processes under conditions of risk and uncertainty. Evidence for nonlinear engagement between decision-makers and science providers is presented, including non-traditional approaches such as provision of unsolicited science through the media and stakeholders. Examples of scientifically informed, precautionary decision-making with adaptive capacity, even where economically favourable decision alternatives exist, are provided. We undertake a self-elicitation exercise of case studies to derive values and uncertainties for % scientific agreement amongst utilized inputs and % uptake of potentially relevant and available science. We observe a tendency towards increased scientific uptake with increasing scientific agreement, but this is not ubiquitous;politically affected decisions and/or complex multi-decision scenarios under time pressure complicate this relationship. An increasing need for decision-making expediency that is not met by increased availability of rele

关键词： Decision-making Earth science Natural disasters Policy

来源：评论

学校读者我要写书评

暂无评论

Mutual clustering on comparative texts via heterogeneous information networks

arXiv

引用

arXiv 2019年

作者： Cao, Jianping Wang, Senzhang Wen, Danyan Peng, Zhaohui Yu, Philip S. Wang, Fei-yue Research Center for Military Computational Experiments and Parallel Systems Technology National University of Defense Technology Changsha410073 China Ministry of Industry and Information Technology Nanjing211106 China School of Economics & Management Nanjing University of Science and Technology Nanjing210094 China School of Computer Science and Technology Shandong University Qingdao China Department of Computer Science University of Illinois at Chicago Chicago60607 United States Institute for Data Science Tsinghua University Beijing100084 China Beijing China Qingdao Academy of Intelligent Industries Qingdao266000 China Institute of Systems Engineering Macau University of Science and Technology Macau999078 China

Currently, many intelligence systems contain the texts from multi-sources, e.g., bulletin board system (BBS) posts, tweets and news. These texts can be "comparative" since they may be semantically correlated and thus provide us with different perspectives toward the same topics or events. To better organize the multi-sourced texts and obtain more comprehensive knowledge, we propose to study the novel problem of Mutual Clustering on Comparative Texts (MCCT), which aims to cluster the comparative texts simultaneously and collaboratively. The MCCT problem is difficult to address because 1) comparative texts usually present different data formats and structures and thus they are hard to organize, and 2) there lacks an effective method to connect the semantically correlated comparative texts to facilitate clustering them in an unified way. To this aim, in this paper we propose a Heterogeneous Information Network-based Text clustering framework HINT. HINT first models multi-sourced texts (e.g. news and tweets) as heterogeneous information networks by introducing the shared "anchor texts" to connect the comparative texts. Next, two similarity matrices based on HINT as well as a transition matrix for cross-text-source knowledge transfer are constructed. Comparative texts clustering are then conducted by utilizing the constructed matrices. Finally, a mutual clustering algorithm is also proposed to further unify the separate clustering results of the comparative texts by introducing a clustering consistency constraint. We conduct extensive experimental on three tweets-news datasets, and the results demonstrate the effectiveness and robustness of the proposed method in addressing the MCCT problem. Copyright © 2019, The Authors. All rights reserved.

关键词： Clustering algorithms

来源：评论

学校读者我要写书评

暂无评论

Understanding adversarial attacks on deep learning based medical image analysis systems

arXiv

引用

arXiv 2019年

作者： Ma, Xingjun Niu, Yuhao Gu, Lin Wang, Yisen Zhao, Yitian Bailey, James Lu, Feng State Key Laboratory of VR Technology and Systems School of CSE Beihang University Beijing China School of Computing and Information Systems The University of Melbourne ParkvilleVIC3010 Australia Beijing Advanced Innovation Center for Big Data-Based Precision Medicine Beihang University Beijing China National Institute of Informatics Tokyo101-8430 Japan Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai China Cixi Instuitue of Biomedical Engineering Ningbo Institute of Industrial Technology Chinese Academy of Sciences Ningbo China

Deep neural networks (DNNs) have become popular for medical image analysis tasks like cancer diagnosis and lesion detection. However, a recent study demonstrates that medical deep learning systems can be compromised by carefully-engineered adversarial examples/attacks with small imperceptible perturbations. This raises safety concerns about the deployment of these systems in clinical settings. In this paper, we provide a deeper understanding of adversarial examples in the context of medical images. We find that medical DNN models can be more vulnerable to adversarial attacks compared to models for natural images, according to two different viewpoints. Surprisingly, we also find that medical adversarial attacks can be easily detected, i.e., simple detectors can achieve over 98% detection AUC against state-of-the-art attacks, due to fundamental feature differences compared to normal examples. We believe these findings may be a useful basis to approach the design of more explainable and secure medical deep learning systems. Copyright © 2019, The Authors. All rights reserved.

关键词： Deep neural networks

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：