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Small P values may not yield robust findings:an example using REST-meta-PD

science Bulletin 2021年第21期66卷 2148-2152,M0003页

作者： Xi-Ze Jia Na Zhao Hao-Ming Dong Jia-Wei Sun Marek Barton Roxana Burciu Nicolas Carrière Antonio Cerasa Bo-Yu Chen Jun Chen Stephen Coombes Luc Defebvre Christine Delmaire Kathy Dujardin Fabrizio Esposito Guo-Guang Fan Federica Di Nardo Yi-Xuan Feng Brett W.Fling Saurabh Garg Moran Gilat Martin Gorges Shu-Leong Ho Fay BHorak Xiao Hu Xiao-Fei Hu Biao Huang Pei-Yu Huang Ze-Juan Jia Christina Jones Jan Kassubek Lenka Krajcovicova Ajay Kurani Jing Li Qing Li Ai-Ping Liu Bo Liu Hu Liu Wei-Guo Liu Renaud Lopes Yu-Ting Lou Wei Luo Tara Madhyastha Ni-Ni Mao Grainne McAlonan Martin J.McKeown Shirley Pang Andrea Quattrone Irena Rektorova Alessia Sarica Hui-Fang Shang James M.Shine Priyank Shukla Tomas Slavicek Xiao-Peng Song Gioacchino Tedeschi Alessandro Tessitore David Vaillancourt Jian Wang Jue Wang Z.Jane Wang Lu-Qing Wei Xia Wu Xiao-Jun Xu Lei Yan Jing Yang Wan-Qun Yang Nai-Lin Yao De-Long Zhang Jiu-Quan Zhang Min-Ming Zhang Yan-Ling Zhang Cai-Hong Zhou Chao-Gan Yan Xi-Nian Zuo Mark Hallett Tao Wu Yu-Feng Zang Center for Cognition and Brain Disorders the Affiliated HospitalHangzhou Normal UniversityHangzhou 310015China Zhejiang Key Laboratory for Research in Assessment of Cognitive Impairments Hangzhou 311121China National Basic Science Data Center Beijing 100190China State Key Laboratory of Cognitive Neuroscience and Learning&McGovern Institute for Brain Research Beijing Normal UniversityBeijing 100875China School of Information and Electronics Technology Jiamusi UniversityJiamusi 154007China Neuroscience Program Central European Institute of TechnologyCEITECMasaryk UniversityBrno 62500Czech Republic Department of Applied Physiology and Kinesiology University of FloridaGainesville FL 32611USA Univ.Lille InsermCHU LilleU1172-LilNCog-Lille Neuroscience&CognitionLille F-59000France The Institute for Biomedical Research and Innovation National Research CouncilMangone CS 87050Italy Department of Radiology The First Affiliated Hospital of China Medical UniversityShenyang 110001China Department of Radiology Guangdong Provincial Hospital of Chinese MedicineGuangzhou 510120China Department of Medicine Surgery and DentistryScuola Medica SalernitanaUniversity of SalernoFiscianoSA 132-84084Italy Department of Advanced Medical and Surgery Sciences University of Campania“Luigi Vanvitelli”Caserta 81100Italy Eye Center of the 2nd Affiliated Hospital Medical College of Zhejiang UniversityHangzhou 310020China Zhejiang Provincial Key Lab of Ophthalmology Hangzhou 310020China Department of Health and Exercise Science Colorado State UniversityFort Collins CO 80523USA Pacific Parkinson’s Research Centre University of British ColumbiaVancouver BC V6E 2M6Canada Department of Medicine(Neurology)University of British Columbia Vancouver BC V6T 1B7Canada Brain and Mind Center The University of SydneySydney NSW 2006Australia Department of Neurology Ulm UniversityUlm 89081Germany Division of Neurology Department of MedicineQueen Mary HospitalUniversity of Hong KongHong Kong 999077China Department of

Thousands of resting state functional magnetic resonance imaging(RS-f MRI)articles have been published on brain *** precise localization of abnormal brain activity,a voxel-level comparison is *** of the large number of voxels in the brain,multiple comparison correction(MCC)must be performed to reduce false positive rates,and a smaller P value(usually including either liberal or stringent MCC)is widely recommended[1].

关键词：帕金森病 precise correction

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Flow smoothing and denoising: Graph signal processing in the edge-space

arXiv

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arXiv 2018年

作者： Schaub, Michael T. Segarra, Santiago Institute for Data Systems and Society Massachusetts Institute of Technology CambridgeMA United States Department of Engineering Science University of Oxford Oxford United Kingdom

This paper focuses on devising graph signal processing tools for the treatment of data defined on the edges of a graph. We first show that conventional tools from graph signal processing may not be suitable for the analysis of such signals. More specifically, we discuss how the underlying notion of a 'smooth signal' inherited from (the typically considered variants of) the graph Laplacian are not suitable when dealing with edge signals that encode a notion of flow. To overcome this limitation we introduce a class of filters based on the Edge-Laplacian, a special case of the Hodge-Laplacian for simplicial complexes of order one. We demonstrate how this Edge-Laplacian leads to low-pass filters that enforce (approximate) flow-conservation in the processed signals. Moreover, we show how these new filters can be combined with more classical Laplacianbased processing methods on the line-graph. Finally, we illustrate the developed tools by denoising synthetic traffic flows on the London street network. Copyright © 2018, The Authors. All rights reserved.

关键词： Laplace transforms

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The Past, Present, and Future of the Brain Imaging data Structure (BIDS)

arXiv

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arXiv 2023年

作者： Poldrack, Russell A. Markiewicz, Christopher J. Appelhoff, Stefan Ashar, Yoni K. Auer, Tibor Baillet, Sylvain Bansal, Shashank Beltrachini, Leandro Bénar, Christian G. Bertazzoli, Giacomo Bhogawar, Suyash Blair, Ross W. Bortoletto, Marta Boudreau, Mathieu Brooks, Teon L. Calhoun, Vince D. Castelli, Filippo Maria Clement, Patricia Cohen, Alexander L. Cohen-Adad, Julien D'Ambrosio, Sasha de Hollander, Gilles de la Iglesia-Vayá, María de la Vega, Alejandro Delorme, Arnaud Devinsky, Orrin Draschkow, Dejan Duff, Eugene Paul DuPre, Elizabeth Earl, Eric Esteban, Oscar Feingold, Franklin W. Flandin, Guillaume Galassi, Anthony Gallitto, Giuseppe Ganz, Melanie Gau, Rémi Gholam, James Ghosh, Satrajit S. Giacomel, Alessio Gillman, Ashley G. Gleeson, Padraig Gramfort, Alexandre Guay, Samuel Guidali, Giacomo Halchenko, Yaroslav O. Handwerker, Daniel A. Hardcastle, Nell Herholz, Peer Hermes, Dora Honey, Christopher J. Innis, Robert B. Ioanas, Horea-Ioan Jahn, Andrew Karakuzu, Agah Keator, David B. Kiar, Gregory Kincses, Balint Laird, Angela R. Lau, Jonathan C. Lazari, Alberto Legarreta, Jon Haitz Li, Adam Li, Xiangrui Love, Bradley C. Lu, Hanzhang Maumet, Camille Mazzamuto, Giacomo Meisler, Steven L. Mikkelsen, Mark Mutsaerts, Henk Nichols, Thomas E. Nikolaidis, Aki Nilsonne, Gustav Niso, Guiomar Norgaard, Martin Okell, Thomas W. Oostenveld, Robert Ort, Eduard Park, Patrick J. Pawlik, Mateusz Pernet, Cyril R. Pestilli, Franco Petr, Jan Phillips, Christophe Poline, Jean-Baptiste Pollonini, Luca Raamana, Pradeep Reddy Ritter, Petra Rizzo, Gaia Robbins, Kay A. Rockhill, Alexander P. Rogers, Christine Rokem, Ariel Rorden, Chris Routier, Alexandre Saborit-Torres, Jose Manuel Salo, Taylor Schirner, Michael Smith, Robert E. Spisak, Tamas Sprenger, Julia Swann, Nicole C. Szinte, Martin Takerkart, Sylvain Thirion, Bertrand Thomas, Adam G. Torabian, Sajjad Varoquaux, Gael Voytek, Bradley Welzel, Julius Wilson, Martin Yarkoni, Tal Gorgolewski, Krzysztof J. Department of Psychology Stanford University StanfordCA United States Max Planck Institute for Human Development Berlin Germany University of Colorado Anschutz Medical Campus AuroraCO United States School of Psychology University of Surrey Guildford United Kingdom McConnell Brain Imaging Centre Montréal Neurological Institute McGill University Montréal Canada Department of Bioengineering University of California San Diego La Jolla CA United States School of Physics Astronomy Cardiff University Wales United Kingdom Institut de Neurosciences des Systèmes Marseille France Neurophysiology Lab IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli Brescia Italy Center for Mind/Brain Sciences - CIMeC University of Trento TN Rovereto Italy Brigham and Women’s Hospital BostonMA United States Massachusetts General Hospital BostonMA United States Harvard Medical School BostonMA United States Rackspace Technology San AntonioTX United States NeuroPoly Lab Polytechnique Montréal MontréalQC Canada Georgia State Georgia Tech Emory AtlantaGA United States University of Florence Sesto Fiorentino Italy Bioretics srl Cesena Italy Department of Medical Imaging Ghent University Hospital Ghent Belgium Department of Diagnostic Sciences Ghent University Ghent Belgium Department of Neurology Boston Children's Hospital BostonMA United States Dipartimento di Scienze della Salute dell'Università degli Studi di Milano Milan Italy Department of Clinical and Experimental Epilepsy University College London United Kingdom Zurich Center for Neuroeconomics Department of Economics University of Zurich Zurich Switzerland UMIB-FISABIO Valencia Spain The University of Texas at Austin AustinTX United States SCCN University of California San Diego La Jolla CA United States Department of Neurology NYU Langone Medical Center New YorkNY United States Department of Experimental Psychology University of Oxford Oxford United Kingdom UK Dementia Research Ins

The Brain Imaging data Structure (BIDS) is a community-driven standard for the organization of data and metadata from a growing range of neuroscience modalities. This paper is meant as a history of how the standard has developed and grown over time. We outline the principles behind the project, the mechanisms by which it has been extended, and some of the challenges being addressed as it evolves. We also discuss the lessons learned through the project, with the aim of enabling researchers in other domains to learn from the success of BIDS. © 2023, CC BY.

关键词： data structures

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A Two-Week IXPE Monitoring Campaign on Mrk 421

arXiv

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arXiv 2024年

作者： Maksym, W. Peter Liodakis, Ioannis Lynne, M. Saade Kim, Dawoon E. Middei, Riccardo Gesu, Laura Di Kiehlmann, Sebastian Matzeu, Gabriele Agudo, Iván Marscher, Alan P. Ehlert, Steven R. Jorstad, Svetlana G. Kaaret, Philip Marshall, Herman L. Pacciani, Luigi Perri, Matteo Puccetti, Simonetta Kouch, Pouya M. Lindfors, Elina Aceituno, Francisco José Bonnoli, Giacomo Casanova, Víctor Escudero, Juan Agís-González, Beatriz Husillos, César Morcuende, Daniel Otero-Santos, Jorge Sota, Alfredo Piirola, Vilppu Imazawa, Ryo Sasada, Mahito Fukazawa, Yasushi Kawabata, Koji S. Uemura, Makoto Mizuno, Tsunefumi Nakaoka, Tatsuya Akitaya, Hiroshi McCall, Callum Jermak, Helen E. Steele, Iain A. Borman, George A. Grishina, Tatiana S. Hagen-Thorn, Vladimir A. Kopatskaya, Evgenia N. Larionova, Elena G. Morozova, Daria A. Savchenko, Sergey S. Shishkina, Ekaterina V. Troitskiy, Ivan S. Troitskaya, Yulia V. Vasilyev, Andrey A. Zhovtan, Alexey V. Myserlis, Ioannis Gurwell, Mark Keating, Garrett Rao, Ramprasad Pauley, Colt Angelakis, Emmanouil Kraus, Alexander Berdyugin, Andrei V. Kagitani, Masato Kravtsov, Vadim Poutanen, Juri Sakanoi, Takeshi Kang, Sincheol Lee, Sang-Sung Kim, Sang-Hyun Cheong, Whee Yeon Jeong, Hyeon-Woo Song, Chanwoo Blinov, Dmitry Shablovinskaya, Elena Antonelli, Lucio Angelo Bachetti, Matteo Baldini, Luca Baumgartner, Wayne H. Bellazzini, Ronaldo Bianchi, Stefano Bongiorno, Stephen D. Bonino, Raffaella Brez, Alessandro Bucciantini, Niccoló Capitanio, Fiamma Castellano, Simone Cavazzuti, Elisabetta Chen, Chien-Ting Ciprini, Stefano Costa, Enrico De Rosa, Alessandra Del Monte, Ettore Di Lalla, Niccoló Di Marco, Alessandro Donnarumma, Immacolata Doroshenko, Victor Dovčiak, Michal Enoto, Teruaki Evangelista, Yuri Fabiani, Sergio Ferrazzoli, Riccardo Garcia, Javier A. Gunji, Shuichi Hayashida, Kiyoshi Heyl, Jeremy Iwakiri, Wataru Karas, Vladimir Kislat, Fabian Kitaguchi, Takao Kolodziejczak, Jeffery J. Krawczynski, Henric Monaca, Fabio La Latronico, Luca Maldera, Simone Manfreda, Alberto Marin, Frédéric Marinucci, Andrea Mass NASA Marshall Space Flight Center HuntsvilleAL35812 United States Institute of Astrophysics Foundation for Research and Technology-Hellas HeraklionGR-70013 Greece Science & Technology Institute Universities Space Research Association 320 Sparkman Drive HuntsvilleAL35805 United States INAF Istituto di Astrofisica e Planetologia Spaziali Via del Fosso del Cavaliere 100 Roma00133 Italy Dipartimento di Fisica Universitá degli Studi di Roma "La Sapienza" Piazzale Aldo Moro 5 Roma00185 Italy Dipartimento di Fisica Universitá degli Studi di Roma "Tor Vergata" Via della Ricerca Scientifica 1 Roma00133 Italy ASI - Agenzia Spaziale Italiana Via del Politecnico snc Roma00133 Italy Department of Physics University of Crete HeraklionGR-70013 Greece Science Operations Department Villanueva de la Cañada Madrid28692 Spain Instituto de Astrofísica de Andalucía IAA-CSIC Glorieta de la Astronomía s/n Granada18008 Spain Institute for Astrophysical Research Boston University 725 Commonwealth Avenue BostonMA02215 United States Saint Petersburg State University 7/9 Universitetskaya nab. St. Petersburg199034 Russia MIT Kavli Institute for Astrophysics and Space Research Massachusetts Institute of Technology 77 Massachusetts Avenue CambridgeMA02139 United States Space Science Data Center Agenzia Spaziale Italiana Via del Politecnico snc Roma00133 Italy Monte Porzio Catone00078 Italy Department of Physics and Astronomy University of Turku 20014 Finland Finnish Centre for Astronomy with ESO University of Turku 20014 Finland Aalto University Metsähovi Radio Observatory Metsähovintie 114 KylmäläFI-02540 Finland Merate23807 Italy CSIC Calle Ríos Rosas 23 Madrid28003 Spain Department of Physics Graduate School of Advanced Science and Engineering Hiroshima University Kagamiyama 1-3-1 Higashi-Hiroshima Hiroshima739-8526 Japan Department of Physics Tokyo Institute of Technology 2-12-1 Ookayama Meguro-ku Tokyo152-8551 Japan Hiroshima Astrophysic

X-ray polarization is a unique new probe of the particle acceleration in astrophysical jets made possible through the Imaging X-ray Polarimetry Explorer. Here we report on the first dense X-ray polarization monitoring campaign on the blazar Mrk 421. Our observations were accompanied by an even denser radio and optical polarization campaign. We find significant short-timescale variability in both X-ray polarization degree and angle, including a ∼ 90◦ angle rotation about the jet axis. We attribute this to random variations of the magnetic field, consistent with the presence of turbulence but also unlikely to be explained by turbulence alone. At the same time, the degree of lower-energy polarization is significantly lower and shows no more than mild variability. Our campaign provides further evidence for a scenario in which energy-stratified shock-acceleration of relativistic electrons, combined with a turbulent magnetic field, is responsible for optical to X-ray synchrotron emission in blazar jets. Copyright © 2024, The Authors. All rights reserved.

关键词： Light polarization

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Hierarchical deep convolutional neural networks for multi-category diagnosis of gastrointestinal disorders on histopathological images

arXiv

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arXiv 2020年

作者： Sali, Rasoul Adewole, Sodiq Ehsan, Lubaina Denson, Lee A. Kelly, Paul Amadi, Beatrice C. Holtz, Lori Ali, Syed Asad Moore, Sean R. Syed, Sana Brown, Donald E. Department of Systems and Information Engineering University of Virginia CharlottesvilleVA United States Department of Pediatrics School of Medicine University of Virginia CharlottesvilleVA United States School of Data Science University of Virginia CharlottesvilleVA United States Division of Gastroenterology Hepatology and Nutrition Cincinnati Children's Hospital Medical Center CincinnatiOH United States Blizard Institute Barts and The London School of Medicine Queen Mary University of London London United Kingdom Tropical Gastroenterology and Nutrition group University of Zambia School of Medicine Lusaka Zambia Department of Pediatrics School of Medicine Washington University Saint LouisMO United States Department of Pediatrics and Child Health Aga Khan University Karachi Pakistan

Deep convolutional neural networks (CNNs) have been successful for a wide range of computer vision tasks including image classification. A specific area of application lies in digital pathology for pattern recognition in tissue-based diagnosis of gastrointestinal (GI) diseases. This domain can utilize CNNs to translate histopathological images into precise diagnostics. This is challenging since these complex biopsies are heterogeneous and require multiple levels of assessment. This is mainly due to structural similarities in different parts of the GI tract and shared features among different gut diseases. Addressing this problem with a flat model which assumes all classes (parts of the gut and their diseases) are equally difficult to distinguish leads to an inadequate assessment of each class. Since hierarchical model restricts classification error to each sub-class, it leads to a more informative model compared to a flat model. In this paper we propose to apply hierarchical classification of biopsy images from different parts of the GI tract and the receptive diseases within each. We embedded a class hierarchy into the plain VGGNet to take advantage of the hierarchical structure of its layers. The proposed model was evaluated using an independent set of image patches from 373 whole slide images. The results indicate that hierarchical model can achieve better results compared to the flat model for multi-category diagnosis of GI disorders using histopathological images. Copyright © 2020, The Authors. All rights reserved.

关键词： Deep neural networks

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Optical computing and logic operation with GST-enabled silicon photonics

Optical computing and logic operation with GST-enabled silic...

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Asia Communications and Photonics Conference

作者： Hanyu Zhang Linjie Zhou Liangjun Lu Jianping Chen B. M. A. Rahman State Key Laboratory of Advanced Optical Communication Systems and Networks Shanghai Key Lab of Navigation and Location Services Shanghai Institute for Advanced Communication and Data Science Department of Electronic Engineering Shanghai Jiao Tong University Shanghai 200240 China Department of Electrical and Electronic Engineering City University of London London EC 1V 0HB U.K

We report a Si-Ge_2Sb_2Te_5 hybrid photonic device for in-memory multiplication and logic operation. The phase change of Ge_2Sb_2Te_5 can be induced by electrical pulses, optical pulses, or both, extending its applica... 详细信息

ISBN: (纸本)9781943580705

关键词： Germanium Integrated optics Multimode interference Refractive index Silicon photonics Transmittance

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The Brain Tumor Segmentation (BraTS-METS) Challenge 2023: Brain Metastasis Segmentation on Pre-treatment MRI

arXiv

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arXiv 2023年

作者： Moawad, Ahmed W. Janas, Anastasia Baid, Ujjwal Ramakrishnan, Divya Saluja, Rachit Ashraf, Nader Jekel, Leon Amiruddin, Raisa Adewole, Maruf Albrecht, Jake Anazodo, Udunna Aneja, Sanjay Anwar, Syed Muhammad Bergquist, Timothy Calabrese, Evan Chiang, Veronica Chung, Verena Conte, Gian Marco Marco Dako, Farouk Eddy, James Ezhov, Ivan Familiar, Ariana Farahani, Keyvan Iglesias, Juan Eugenio Jiang, Zhifan Johanson, Elaine Kazerooni, Anahita Fathi Kofler, Florian Krantchev, Kiril LaBella, Dominic Van Leemput, Koen Li, Hongwei Bran Linguraru, Marius George Link, Katherine E. Liu, Xinyang Maleki, Nazanin Meier, Zeke Menze, Bjoern H. Moy, Harrison Osenberg, Klara Piraud, Marie Reitman, Zachary Shinohara, Russel Takeshi Tahon, Nourel hoda Nada, Ayman Velichko, Yuri S. Wang, Chunhao Wiestler, Benedikt Wiggins, Walter Shafique, Umber Willms, Klara Avesta, Arman Bousabarah, Khaled Chakrabarty, Satrajit Gennaro, Nicolo Holler, Wolfgang Kaur, Manpreet LaMontagne, Pamela Lin, MingDe Lost, Jan Marcus, Daniel S. Maresca, Ryan Merkaj, Sarah Nada, Ayaman Pedersen, Gabriel Cassinelli von Reppert, Marc Sotiras, Aristeidis Teytelboym, Oleg Tillmans, Niklas Westerhoff, Malte Youssef, Ayda Godfrey, Devon Floyd, Scott Rauschecker, Andreas Villanueva-Meyer, Javier Pflüger, Irada Cho, Jaeyoung Bendszus, Martin Brugnara, Gianluca Cramer, Justin Perez-Carillo, Gloria Guzman J. Johnson, Derek R. Kam, Anthony Kwan, Benjamin Yin Ming Lai, Lillian Lall, Neil U. Memon, Fatima Patro, Satya Narayana Petrovic, Bojan So, Tiffany Y. Thompson, Gerard Wu, Lei Schrickel, E. Brooke Bansal, Anu Barkhof, Frederik Besada, Cristina Chu, Sammy Druzgal, Jason Dusoi, Alexandru Farage, Luciano Feltrin, Fabricio Fong, Amy Fung, Steve H. Gray, R. Ian Ikuta, Ichiro Iv, Michael Postma, Alida A. Mahajan, Amit Joyner, David Krumpelman, Chase Letourneau-Guillon, Laurent Lincoln, Christie M. Maros, Mate E. Miller, Elka Morón, Fanny Nimchinsky, Esther A. Ozsarlak, Ozkan Patel, Uresh Rohatgi, Saurabh Saha, Atin Sayah, Anousheh Schwartz, Eric D. Shih, Robert Shiroishi, Mark Trinity health Mid Atlantic Hosiptals DarbyPA United States Department of Radiology and Biomedical Imaging Yale School of Medicine New HavenCT United States Division of Computational Pathology Department of Pathology and Laboratory Medicine School of Medicine Indiana University IndianapolisIN United States Department of Electical and Computer Engineering Cornell University Cornell Tech New YorkNY United States Department of Radiology Weill Cornell Medicine New YorkNY United States ImagineQuant Children’s Hospital of Philadelphia PhiladelphiaPA United States College of Medicine Alfaisal University Riyadh Saudi Arabia DKFZ Division of Translational Neurooncology The WTZ German Cancer Consortium DKTK Partner Site University Hospital Essen Essen Germany Medical Artificial Intelligence Lab Crestview Radiology Lagos Nigeria Sage Bionetworks SeattleWA United States Montreal Neurological Institute McGill University Montreal Canada lab Crestview Radiology Lagos Nigeria Department of Therapeutic Radiology Yale School of Medicine New HavenCT United States Sheikh Zayed Institute for Pediatric Surgical Innovation Children’s National Hospital WashingtonDC United States Department of Radiology Mayo Clinic RochesterMN United States Department of Radiology Duke University Medical Center DurhamNC United States Department of Neurosurgery Yale School of Medicine New HavenCT United States Center for Global Health Perelman School of Medicine University of Pennsylvania PA United States Department of Informatics Technical University Munich Germany Children’s Hospital of Philadelphia University of Pennsylvania PhiladelphiaPA United States Cancer Imaging Program National Cancer Institute National Institutes of Health BethesdaMD United States Athinoula A. Martinos Center for Biomedical Imaging Massachusetts General Hospital BostonMA United States Children’s National Hospital WashingtonDC United States PrecisionFDA U.S. Food and Drug Administration S

The translation of AI-generated brain metastases (BM) segmentation into clinical practice relies heavily on diverse, high-quality annotated medical imaging datasets. The BraTS-METS 2023 challenge has gained momentum for testing and benchmarking algorithms using rigorously annotated internationally compiled real-world datasets. This study presents the results of the segmentation challenge and characterizes the challenging cases that impacted the performance of the winning algorithms. Untreated brain metastases on standard anatomic MRI sequences (T1, T2, FLAIR, T1PG) from eight contributed international datasets were annotated in stepwise method: published UNET algorithms, student, neuroradiologist, final approver neuroradiologist. Segmentations were ranked based on lesion-wise Dice and Hausdorff distance (HD95) scores. False positives (FP) and false negatives (FN) were rigorously penalized, receiving a score of 0 for Dice and a fixed penalty of 374 for HD95. The mean scores for the teams were calculated. Eight datasets comprising 1303 studies were annotated, with 402 studies (3076 lesions) released on Synapse as publicly available datasets to challenge competitors. Additionally, 31 studies (139 lesions) were held out for validation, and 59 studies (218 lesions) were used for testing. Segmentation accuracy was measured as rank across subjects, with the winning team achieving a LesionWise mean score of 7.9. The Dice score for the winning team was 0.65 ± 0.25. Common errors among the leading teams included false negatives for small lesions and misregistration of masks in space. The Dice scores and lesion detection rates of all algorithms diminished with decreasing tumor size, particularly for tumors smaller than 100 mm3. In conclusion, algorithms for BM segmentation require further refinement to balance high sensitivity in lesion detection with the minimization of false positives and negatives. The BraTS-METS 2023 challenge successfully curated well-annotated, diverse d

关键词： Image segmentation

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Identification and Prioritization of urban issues from Smart City data

Identification and Prioritization of urban issues from Smart...

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International Conference on Smart City and Emerging Technology (ICSCET)

作者： Vinay Kumar Mamata Jenamani Data Science and NLP Episource India Pvt. Ltd Mumbai Department of Industrial and Systems Engineering Indian Institute of Technology Kharagpur India

ISBN: (纸本)9781538611869

Government should be able to publish and interact better for offering e-government services effectively to their netizens. In this context publish refers to delivering information through web pages and interact refers to allowing citizens to provide feedback and suggestions. Developing a model for understanding and prioritizing urban governance issues from these user-generated contents is an important research issue. This paper proposes a four-step solution approach to prioritizing urban governance issues from user-generated contents using Aspect Based Sentiment Analysis (ABSA). First, a novel lexicon-based approach is used for selecting trigrams as aspect phrase and pruning the list using cross-domain stop words. Second, the opinionated sentences are extracted using the identified aspects. Third, polarity and subjectivity of the aspects and corresponding comments along with the bag-of-words for the aspects are considered as the features for learning the category of the urban issue. Fourth, the sentiment score of a specific category is used to prioritize the urban issues. The proposed methodology is applied to a real user-generated content extracted from a discussion forum on smart cities from an e-governance portal in India. Our research demonstrates that Aspect Based Sentiment Analysis (ABSA) with necessary modifications can be utilized to extract issues, suggestions, and ideas from crowd-sourced citizen-generated content (Social data). Sentiment analysis can be used to prioritize them as well. Analysis of the data for the candidate city shows that while the citizens are happy about educational facilities in the city they are highly concerned about health issues. A deeper investigation revealed the health issues are due to high degree of carbon emission, lack of solid waste management system and lack of sanitation among the others.

关键词： Portals Sentiment analysis Electronic government User-generated content Solids Urban areas

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CrowdWatcher: an open-source platform to catch the eye of the crowd

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Quality and User Experience 2019年第1期4卷 1-17页

作者： Lebreton, Pierre Hupont, Isabelle Hirth, Matthias Mäki, Toni Skodras, Evangelos Schubert, Anton Raake, Alexander Group of Networked Sensing and Control (NeSC) Zhejiang University Hangzhou China Institute of Intelligent Systems and Robotics Sorbonne University Paris France User-Centric Analysis of Multimedia Data Group Technische Universität Ilmenau Ilmenau Germany Department of Computer Science Aalto University Espoo Finland Department of Electrical and Computer Engineering University of Patras Patras Greece Audio Visual Technology Group Technische Universität Ilmenau Ilmenau Germany

This paper presents the open-source eye tracking platform CrowdWatcher. It enables researchers to measure gaze location and user engagement in a crowdsourcing context through traditional RGB webcams. The proposed platform particularly advances the field of Quality of Experience (QoE) research, as it allows the experimenter to collect remotely and with very limited effort novel information from crowds of participants, such as their commitment towards a task, attention and decision-making processes. Two different experiments are described that were conducted to demonstrate the platform’s potential. The first experiment addresses the measurement of participants’ behavior while performing a movie selection task. Results show that the platform provides complementary information to traditional self-reported data by taking gaze analysis into account. This is of particular relevance, since in a crowdsourcing context decision processes and attention are difficult to assess, and there is often limited control over the engagement of the test user with the task. A second experiment is conducted in the scenario of a multimedia QoE test. Prediction accuracy is compared to a professional infrared eye tracker. While CrowdWatcher performs less well than the professional eye tracker, it is still able to collect valuable gaze information in the far more challenging environment of crowdsourcing. As an outlook to further application domains, the usage of the platform to measure user engagement allows participants who do not pay attention to the task to be identified.

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XRISM Forecast for the Coma Cluster: Stormy, with a Steep Power Spectrum

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The Astrophysical Journal Letters 2025年第1期985卷 L20-L20页

作者： XRISM Collaboration Marc Audard Hisamitsu Awaki Ralf Ballhausen Aya Bamba Ehud Behar Rozenn Boissay-Malaquin Laura Brenneman Gregory V. Brown Lia Corrales Elisa Costantini Renata Cumbee Maria Diaz Trigo Chris Done Tadayasu Dotani Ken Ebisawa Megan E. Eckart Dominique Eckert Satoshi Eguchi Teruaki Enoto Yuichiro Ezoe Adam Foster Ryuichi Fujimoto Yutaka Fujita Yasushi Fukazawa Kotaro Fukushima Akihiro Furuzawa Luigi Gallo Javier A. García Liyi Gu Matteo Guainazzi Kouichi Hagino Kenji Hamaguchi Isamu Hatsukade Katsuhiro Hayashi Takayuki Hayashi Natalie Hell Edmund Hodges-Kluck Ann Hornschemeier Yuto Ichinohe Daiki Ishi Manabu Ishida Kumi Ishikawa Yoshitaka Ishisaki Jelle Kaastra Timothy Kallman Erin Kara Satoru Katsuda Yoshiaki Kanemaru Richard Kelley Caroline Kilbourne Shunji Kitamoto Shogo Kobayashi Takayoshi Kohmura Aya Kubota Maurice Leutenegger Michael Loewenstein Yoshitomo Maeda Maxim Markevitch Hironori Matsumoto Kyoko Matsushita Dan McCammon Brian McNamara François Mernier Eric D. Miller Jon M. Miller Ikuyuki Mitsuishi Misaki Mizumoto Tsunefumi Mizuno Koji Mori Koji Mukai Hiroshi Murakami Richard Mushotzky Hiroshi Nakajima Kazuhiro Nakazawa Jan-Uwe Ness Kumiko Nobukawa Masayoshi Nobukawa Hirofumi Noda Hirokazu Odaka Shoji Ogawa Anna Ogorzałek Takashi Okajima Naomi Ota Stéphane Paltani Robert Petre Paul Plucinsky Frederick S. Porter Katja Pottschmidt Kosuke Sato Toshiki Sato Makoto Sawada Hiromi Seta Megumi Shidatsu Aurora Simionescu Randall Smith Hiromasa Suzuki Andrew Szymkowiak Hiromitsu Takahashi Mai Takeo Toru Tamagawa Keisuke Tamura Takaaki Tanaka Atsushi Tanimoto Makoto Tashiro Yukikatsu Terada Yuichi Terashima Yohko Tsuboi Masahiro Tsujimoto Hiroshi Tsunemi Takeshi Tsuru Ayşegül Tümer Hiroyuki Uchida Nagomi Uchida Yuusuke Uchida Hideki Uchiyama Shutaro Ueda Yoshihiro Ueda Shinichiro Uno Jacco Vink Shin Watanabe Brian J. Williams Satoshi Yamada Shinya Yamada Hiroya Yamaguchi Kazutaka Yamaoka Noriko Yamasaki Makoto Yamauchi Shigeo Yamauchi Tahir Yaqoob Tomokage Yoneyama Tessei Yoshida Mihoko Yukita Irin Department of Astronomy University of Geneva Versoix CH-1290 Switzerland Department of Physics Ehime University Ehime 790-8577 Japan Department of Astronomy University of Maryland College Park MD 20742 USA NASA / Goddard Space Flight Center Greenbelt MD 20771 USA maxim.markevitch@nasa.gov Center for Research and Exploration in Space Science and Technology NASA / GSFC (CRESST II) Greenbelt MD 20771 USA Department of Physics University of Tokyo Tokyo 113-0033 Japan Department of Physics Technion Technion City Haifa 3200003 Israel Center for Space Sciences and Technology University of Maryland Baltimore County (UMBC) Baltimore MD 21250 USA Center for Astrophysics ∣ Harvard-Smithsonian Cambridge MA 02138 USA Lawrence Livermore National Laboratory Livermore CA 94550 USA Department of Astronomy University of Michigan Ann Arbor MI 48109 USA SRON Netherlands Institute for Space Research Leiden The Netherlands ESO Karl-Schwarzschild-Strasse 2 85748 Garching bei München Germany Centre for Extragalactic Astronomy Department of Physics University of Durham Durham DH1 3LE UK Institute of Space and Astronautical Science (ISAS) Japan Aerospace Exploration Agency (JAXA) Kanagawa 252-5210 Japan Department of Economics Kumamoto Gakuen University Kumamoto 862-8680 Japan Department of Physics Kyoto University Kyoto 606-8502 Japan Department of Physics Tokyo Metropolitan University Tokyo 192-0397 Japan Department of Physics Hiroshima University Hiroshima 739-8526 Japan Department of Physics Fujita Health University Aichi 470-1192 Japan Department of Astronomy and Physics Saint Mary’s University Nova Scotia B3H 3C3 Canada California Institute of Technology Pasadena CA 91125 USA European Space Agency (ESA) European Space Research and Technology Centre (ESTEC) 2200 AG Noordwijk The Netherlands Faculty of Engineering University of Miyazaki 1-1 Gakuen-Kibanadai-Nishi Miyazaki Miyazaki 889-2192 Japan RIKEN Nishina Center Saitama 351-0198 Japan Leiden Observa

The XRISM Resolve microcalorimeter array measured the velocities of hot intracluster gas at two positions in the Coma galaxy cluster: 3′×3′</ma

The XRISM Resolve microcalorimeter array measured the velocities of hot intracluster gas at two positions in the Coma galaxy cluster: ${3}^{{\prime} }\times {3}^{{\prime} }$ $data$ -mjx-texclass="ORD">3data-mjx-texclass="ORD">data-mjx-texclass="ORD">data-mjx-alternate="1">′×data-mjx-texclass="ORD">3data-mjx-texclass="ORD">data-mjx-texclass="ORD">data-mjx-alternate="1">′

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