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arXiv 2023年

作者： Kazerooni, Anahita Fathi Khalili, Nastaran Liu, Xinyang Haldar, Debanjan Jiang, Zhifan Anwar, Syed Muhammed Albrecht, Jake Adewole, Maruf Anazodo, Udunna Anderson, Hannah Bagheri, Sina Baid, Ujjwal Bergquist, Timothy Borja, Austin J. Calabrese, Evan Chung, Verena Conte, Gian-Marco Dako, Farouk Eddy, James Ezhov, Ivan Familiar, Ariana Farahani, Keyvan Haldar, Shuvanjan Iglesias, Juan Eugenio Janas, Anastasia Johansen, Elaine Jones, Blaise V. Kofler, Florian LaBella, Dominic Lai, Hollie Anne Van Leemput, Koen Li, Hongwei Bran Maleki, Nazanin McAllister, Aaron S. Meier, Zeke Menze, Bjoern Moawad, Ahmed W. Nandolia, Khanak K. Pavaine, Julija Piraud, Marie Poussaint, Tina Prabhu, Sanjay P. Reitman, Zachary Rodriguez, Andres Rudie, Jeffrey D. Sanchez-Montano, Mariana Shaikh, Ibraheem Salman Shah, Lubdha M. Sheth, Nakul Shinohara, Russel Taki Tu, Wenxin Viswanathan, Karthik Wang, Chunhao Ware, Jeffrey B. Wiestler, Benedikt Wiggins, Walter Zapaishchykova, Anna Aboian, Mariam Bornhorst, Miriam de Blank, Peter Deutsch, Michelle Fouladi, Maryam Hoffman, Lindsey Kann, Benjamin Lazow, Margot Mikael, Leonie Nabavizadeh, Ali Packer, Roger Resnick, Adam Rood, Brian Vossough, Arastoo Bakas, Spyridon Linguraru, Marius George Children’s Hospital of Philadelphia PhiladelphiaPA United States Department of Neurosurgery University of Pennsylvania PhiladelphiaPA United States University of Pennsylvania PhiladelphiaPA United States Sheikh Zayed Institute for Pediatric Surgical Innovation Children’s National Hospital WashingtonDC United States Department of Neurosurgery Thomas Jefferson University Hospital PA United States Sage Bionetworks United States Lab Crestview Radiology Lagos Nigeria McGill University MontrealQC Canada 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 Department of Neurosurgery The University of Southern California CA United States Department of Radiology Duke University Medical Center United States Mayo Clinic MN United States Center for Global Health Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Department of Informatics Technical University Munich Germany TranslaTUM - Central Institute for Translational Cancer Research Technical University of Munich Germany Cancer Imaging Program National Cancer Institute National Institutes of Health BethesdaMD United States Biomedical Engineering Rutgers University New BrunswickNJ United States Athinoula A Martinos Center for Biomedical Imaging Massachusetts General Hospital BostonMA United States Yale University New HavenCT United States PrecisionFDA U.S. Food and Drug Administration Silver SpringMD United States Cincinnati Children’s Hospital Medical Center United States Helmholtz AI Helmholtz Munich Germany Department of Radiation Oncology Duke University Medical Center United States Department of Radiology Children’s Health Orange County CA United States Department of Applied Mathematics and Computer Science Technical University of Denmark Denmark Department of Radiol

Pediatric tumors of the central nervous system are the most common cause of cancer-related death in children. The five-year survival rate for high-grade gliomas in children is less than 20%. Due to their rarity, the diagnosis of these entities is often delayed, their treatment is mainly based on historic treatment concepts, and clinical trials require multi-institutional collaborations. The MICCAI Brain Tumor Segmentation (BraTS) Challenge is a landmark community benchmark event with a successful history of 12 years of resource creation for the segmentation and analysis of adult glioma. Here we present the CBTN-CONNECT-DIPGR-ASNR-MICCAI BraTS-PEDs 2023 challenge, which represents the first BraTS challenge focused on pediatric brain tumors with data acquired across multiple international consortia dedicated to pediatric neuro-oncology and clinical trials. The BraTS-PEDs 2023 challenge focuses on benchmarking the development of volumentric segmentation algorithms for pediatric brain glioma through standardized quantitative performance evaluation metrics utilized across the BraTS 2023 cluster of challenges. Models gaining knowledge from the BraTS-PEDs multi-parametric structural MRI (mpMRI) training data will be evaluated on separate validation and unseen test mpMRI dataof high-grade pediatric glioma. The CBTN-CONNECT-DIPGR-ASNR-MICCAI BraTS-PEDs 2023 challenge brings together clinicians and AI/imaging scientists to lead to faster development of automated segmentation techniques that could benefit clinical trials, and ultimately the care of children with brain tumors. © 2023, CC BY.

关键词： Tumors

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BrainNet: A Multi-Person Brain-to-Brain Interface for Direct Collaboration Between Brains

arXiv

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arXiv 2018年

作者： Jiang, Linxing Stocco, Andrea Losey, Darby M. Abernethy, Justin A. Prat, Chantel S. Rao, Rajesh P.N. University of Washington Paul G. Allen School of Computer Science & Engineering SeattleWA98195 United States University of Washington Department of Psychology SeattleWA98195 United States University of Washington Institute for Learning and Brain Sciences SeattleWA98195 United States Carnegie Mellon University Joint Program in Neural Computation & Machine Learning PittsburghPA15213 United States Carnegie Mellon University Center for the Neural Basis of Cognition PittsburghPA15213 United States University of Washington Institute for Neuroengineering SeattleWA98195 United States University of Washington Center for Neurotechnology SeattleWA98195 United States

We present BrainNet which, to our knowledge, is the first multi-person non-invasive direct brain-to-brain interface for collaborative problem solving. The interface combines electroencephalography (EEG) to record brain signals and transcranial magnetic stimulation (TMS) to deliver information noninvasively to the brain. The interface allows three human subjects to collaborate and solve a task using direct brain-to-brain communication. Two of the three subjects are designated as "Senders" whose brain signals are decoded using real-time EEG data analysis. The decoding process extracts each Sender’s decision about whether to rotate a block in a Tetris-like game before it is dropped to fill a line. The Senders’ decisions are transmitted via the Internet to the brain of a third subject, the "Receiver," who cannot see the game screen. The Senders’ decisions are delivered to the Receiver’s brain via magnetic stimulation of the occipital cortex. The Receiver integrates the information received from the two Senders and uses an EEG interface to make a decision about either turning the block or keeping it in the same orientation. A second round of the game provides an additional chance for the Senders to evaluate the Receiver’s decision and send feedback to the Receiver’s brain, and for the Receiver to rectify a possible incorrect decision made in the first round. We evaluated the performance of BrainNet in terms of (1) Group-level performance during the game, (2) True/False positive rates of subjects’ decisions, and (3) Mutual information between subjects. Five groups, each with three human subjects, successfully used BrainNet to perform the collaborative task, with an average accuracy of 81.25%. Furthermore, by varying the information reliability of the Senders by artificially injecting noise into one Sender’s signal, we investigated how the Receiver learns to integrate noisy signals in order to make a correct decision. We found that like conventional social networks, BrainN

关键词： Electrophysiology

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Fixing the problems of deep neural networks will require better training data and learning algorithms

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BEHAVIORAL AND BRAIN SCIENCES 2023年 46卷 e400页

作者： Bowers, Jeffrey S. Malhotra, Gaurav Dujmovic, Marin Montero, Milton Llera Tsvetkov, Christian Biscione, Valerio Puebla, Guillermo Adolfi, Federico Hummel, John E. Heaton, Rachel F. Evans, Benjamin D. Mitchell, Jeffrey Blything, Ryan Anderson, Barton L. Storrs, Katherine R. Fleming, Roland W. Bever, Thomas G. Chomsky, Noam Fong, Sandiway Piattelli-Palmarini, Massimo Chandran, Keerthi S. Paul, Amrita Mukherjee Paul, Avijit Ghosh, Kuntal de Vries, Jelmer Philip Flachot, Alban Morimoto, Takuma Gegenfurtner, Karl R. DiCarlo, James J. Yamins, Daniel L. K. Ferguson, Michael E. Fedorenko, Evelina Bethge, Matthias Bonnen, Tyler Schrimpf, Martin German, Joseph Scott Jacobs, Robert A. Golan, Tal Taylor, JohnMark Schutt, Heiko Peters, Benjamin Sommers, Rowan P. Seeliger, Katja Doerig, Adrien Linton, Paul Konkle, Talia van Gerven, Marcel Kording, Konrad Richards, Blake Kietzmann, Tim C. Lindsay, Grace W. Kriegeskorte, Nikolaus Gur, Moshe Hermann, Katherine Nayebi, Aran van Steenkiste, Sjoerd Jones, Matt Houghton, Conor Kazanina, Nina Sukumaran, Priyanka Kellman, Philip J. Baker, Nicholas Garrigan, Patrick Phillips, Austin Lu, Hongjing Koculak, Marcin Wierzchon, Michal Li, Aedan Y. Mur, Marieke Lin, Hause Linsley, Drew Serre, Thomas Liu, Jianghao Bartolomeo, Paolo Love, Bradley C. Mok, Robert M. Moldoveanu, Mihnea Op de Beeck, Hans Bracci, Stefania Rothkopf, Constantin Bremmer, Frank Fiehler, Katja Dobs, Katharina Triesch, Jochen Slagter, Heleen A. Spratling, Michael W. Srivastava, Nisheeth Sifar, Anjali Srinivasan, Narayanan Summerfield, Christopher Thompson, Jessica A. F. Tarr, Michael J. Veit, Walter Browning, Heather Wichmann, Felix A. Kornblith, Simon Geirhos, Robert Xu, Yaoda Vaziri-Pashkam, Maryam Yovel, Galit Abudarham, Naphtali Univ Bristol Sch Psychol Sci Bristol Avon England Max Planck Gesell Ernst Strungmann Inst ESI Neurosci Cooperat Frankfurt Germany Univ Illinois Dept Psychol Champaign IL USA Univ Sussex Sch Engn & Informat Dept Informat Brighton E Sussex England Aston Univ Sch Psychol Birmingham W Midlands England Univ Sydney Sch Psychol Sydney NSW Australia Univ Auckland Dept Psychol Auckland New Zealand Justus Liebig Univ Giessen Dept Psychol Giessen Germany Univ Marburg & Giessen Ctr Mind Brain & Behav Giessen Germany Univ Arizona Dept Linguist Tucson AZ USA Indian Stat Inst Ctr Soft Comp Res Kolkata India Indian Stat Inst Machine Intelligence Unit Kolkata India IIIT Allahabad Applied Sci Prayagraj Uttarakhand India Tufts Univ Biomed Engn Medford MA USA Justus Liebig Univ Dept Psychol Giessen Germany York Univ Dept Psychol Toronto ON Canada Univ Oxford Dept Expt Psychol Oxford England MIT Quest Intelligence Dept Brain & Cognit Sci Cambridge MA USA MIT McGovern Inst Brain Res Cambridge MA USA Stanford Univ Wu Tsai Neurosci Inst Stanford CA USA Univ Tubingen Tubingen AI Ctr Tubingen Germany Ecole Polytech Fed Lausanne Lausanne Switzerland Univ Calif San Diego Dept Cognit Sci San Diego CA USA Univ Rochester Dept Brain & Cognit Sci Rochester NY USA Ben Gurion Univ Negev Dept Cognit & Brain Sci Beer Sheva Israel Columbia Univ Zuckerman Mind Brain Behav Inst New York NY USA NYU Ctr Neural Sci New York NY USA Univ Glasgow Sch Psychol & Neurosci Glasgow Lanark Scotland Max Planck Inst Psycholinguist Dept Neurobiol Language Nijmegen Netherlands Max Planck Inst Human Cognit & Brain Sci Leipzig Germany Univ Osnabruck Inst Cognit Sci Osnabruck Germany Columbia Univ Ctr Sci & Soc Presidential Scholars Soc & Neurosci New York NY USA Columbia Univ Italian Acad Adv Studies Amer New York NY USA Harvard Univ Dept Psychol & Ctr Brain Sci Cambridge MA USA Donders Inst Brain Cognit & Behav Nijmegen Netherlands Univ Penn Dept Bioengn & N

Bowers et al. argue that deep neural networks (DNNs) are poor models of biological vision because they often learn to rival human accuracy by relying on strategies that differ markedly from those of humans. We show that this problem is worsening as DNNs are becoming larger-scale and increasingly more accurate, and prescribe methods for building DNNs that can reliably model biological vision.

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Assessment of predicted enzymatic activity of α-N-acetylglucosaminidase variants of unknown significance for CAGI 2016

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HUMAN MUTATION 2019年第9期40卷 1519-1529页

作者： Clark, Wyatt T. Kasak, Laura Bakolitsa, Constantina Hu, Zhiqiang Andreoletti, Gaia Babbi, Giulia Bromberg, Yana Casadio, Rita Dunbrack, Roland Folkman, Lukas Ford, Colby T. Jones, David Katsonis, Panagiotis Kundu, Kunal Lichtarge, Olivier Martelli, Pier L. Mooney, Sean D. Nodzak, Conor Pal, Lipika R. Ivojac, Pred Rag Rad Savojardo, Castrense Shi, Xinghua Zhou, Yaoqi Uppal, Aneeta Xu, Qifang Yin, Yizhou Pejaver, Vikas Wang, Meng Wei, Liping Moult, John Yu, Guoying Karen Brenner, Steven E. LeBowitz, Jonathan H. BioMarin Pharmaceut Human Genet San Rafael CA USA Univ Calif Berkeley Dept Plant & Microbial Biol Berkeley CA 94720 USA Univ Tartu Dept Biomed Inst Biomed & Translat Med Tartu Estonia Univ Bologna Dept Pharm & Biotechnol Biocomp Grp Bologna Italy Rutgers State Univ Dept Biochem & Microbiol New Brunswick NJ USA Fox Chase Canc Ctr Inst Canc Res 7701 Burholme Ave Philadelphia PA 19111 USA ETH Dept Biosyst Sci & Engn Machine Learning & Computat Biol Lab Zurich Switzerland Univ North Carolina Charlotte Dept Bioinformat & Genom Charlotte NC USA UCL Bioinformat Grp Dept Comp Sci London England Baylor Coll Med Dept Mol & Human Genet Houston TX 77030 USA Univ Maryland Inst Biosci & Biotechnol Res Rockville MD USA Baylor Coll Med Dept Biochem & Mol Biol Houston TX 77030 USA Baylor Coll Med Dept Pharmacol Houston TX 77030 USA Baylor Coll Med Computat & Integrat Biomed Res Ctr Houston TX 77030 USA Buck Inst Res Aging Novato CA USA Indiana Univ Dept Comp Sci Bloomington IN 47405 USA Indiana Univ Sch Med Ctr Computat Biol & Bioinformat Indianapolis IN 46202 USA Indiana Univ Dept Informat Bloomington IN USA Peking Univ Ctr Bioinformat State Key Lab Prot & Plant Gene Res Sch Life Sci Beijing Peoples R China Univ Maryland Computat Biol Bioinformat & Genom Biol Sci Grad Program College Pk MD 20742 USA Univ Maryland Dept Cell Biol & Mol Genet College Pk MD 20742 USA

The NAGLU challenge of the fourth edition of the Critical Assessment of Genome Interpretation experiment (CAGI4) in 2016, invited participants to predict the impact of variants of unknown significance (VUS) on the enzymatic activity of the lysosomal hydrolase alpha-N-acetylglucosaminidase (NAGLU). Deficiencies in NAGLU activity lead to a rare, monogenic, recessive lysosomal storage disorder, Sanfilippo syndrome type B (MPS type IIIB). This challenge attracted 17 submissions from 10 groups. We observed that top models were able to predict the impact of missense mutations on enzymatic activity with Pearson's correlation coefficients of up to .61. We also observed that top methods were significantly more correlated with each other than they were with observed enzymatic activity values, which we believe speaks to the importance of sequence conservation across the different methods. Improved functional predictions on the VUS will help population-scale analysis of disease epidemiology and rare variant association analysis.

关键词： CAGI critical assessment enzymatic activity machine learning Sanfilippo syndrome variants of unknown significance alpha-N-acetylglucosaminidase NAGLU

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Analysis of the BraTS 2023 Intracranial Meningioma Segmentation Challenge

arXiv

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arXiv 2024年

作者： LaBella, Dominic Baid, Ujjwal Khanna, Omaditya McBurney-Lin, Shan McLean, Ryan Nedelec, Pierre Rashid, Arif Tahon, Nourel Hoda Altes, Talissa Bhalerao, Radhika Dhemesh, Yaseen Godfrey, Devon Hilal, Fathi Floyd, Scott Janas, Anastasia Kazerooni, Anahita Fathi Kirkpatrick, John Kent, Collin Kofler, Florian Leu, Kevin Maleki, Nazanin Menze, Bjoern Pajot, Maxence Reitman, Zachary J. Rudie, Jeffrey D. Saluja, Rachit Velichko, Yury Wang, Chunhao Warman, Pranav Adewole, Maruf Albrecht, Jake Anazodo, Udunna Anwar, Syed Muhammad Bergquist, Timothy Chen, Sully Francis Chung, Verena Chai, Rong Conte, Gian-Marco Dako, Farouk Eddy, James Ezhov, Ivan Khalili, Nastaran Iglesias, Juan Eugenio Jiang, Zhifan Johanson, Elaine Van Leemput, Koen Li, Hongwei Bran Linguraru, Marius George Liu, Xinyang Mahtabfar, Aria Meier, Zeke Moawad, Ahmed W. Mongan, John Piraud, Marie Shinohara, Russell Takeshi Wiggins, Walter F. Abayazeed, Aly H. Akinola, Rachel Jakab, András Bilello, Michel de Verdier, Maria Correia Crivellaro, Priscila Davatzikos, Christos Farahani, Keyvan Freymann, John Hess, Christopher Huang, Raymond Lohmann, Philipp Moassefi, Mana Pease, Matthew W. Vollmuth, Phillipp Sollmann, Nico Diffley, David Nandolia, Khanak K. Warren, Daniel I. Hussain, Ali Fehringer, Pascal Bronstein, Yulia Deptula, Lisa Stein, Evan G. Taherzadeh, Mahsa de Oliveira, Eduardo Portela Haughey, Aoife Kontzialis, Marinos Saba, Luca Turner, Benjamin Brüßeler, Melanie M.T. Ansari, Shehbaz Gkampenis, Athanasios Weiss, David Maximilian Mansour, Aya Shawali, Islam H. Yordanov, Nikolay Stein, Joel M. Hourani, Roula Moshebah, Mohammed Yahya Abouelatta, Ahmed Magdy Rizvi, Tanvir Willms, Klara Martin, Dann C. Okar, Abdullah D’Anna, Gennaro Taha, Ahmed Sharifi, Yasaman Faghani, Shahriar Kite, Dominic Pinho, Marco Haider, Muhammad Ammar Aristizabal, Alejandro Karargyris, Alexandros Kassem, Hasan Pati, Sarthak Sheller, Micah Alonso-Basanta, Michelle Villanueva-Meyer, Javier Rauschecker, Andreas M. Nada, Ayman Aboian, Mariam Flanders, Adam E. Wiestler, Benedikt Bakas, Duke University Medical Center Department of Radiation Oncology DurhamNC United States Division of Computational Pathology Department of Pathology and Laboratory Medicine Indiana University School of Medicine IndianapolisIN United States Center for Federated Learning in Medicine Indiana University IndianapolisIN United States Department of Neurosurgery Thomas Jefferson University PhiladelphiaPA United States University of California San FranciscoCA United States Yale University New HavenCT United States Department of Radiation Oncology Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States University of Missouri ColumbiaMO United States Children’s Hospital of Philadelphia University of Pennsylvania PhiladelphiaPA United States University of Pennsylvania PhiladelphiaPA United States Helmholtz AI Helmholtz Munich Germany Department of Informatics Technical University Munich Germany TranslaTUM - Central Institute for Translational Cancer Research Technical University of Munich Germany Department of Diagnostic and Interventional Neuroradiology School of Medicine Klinikum rechts der Isar Technical University of Munich Germany San FranciscoCA United States Biomedical Image Analysis and Machine Learning Department of Quantitative Biomedicine University of Zurich Switzerland Department of Neuroradiology Technical University of Munich Munich Germany University of San Diego CA United States Cornell University IthacaNY United States Department of Radiology Northwestern University EvanstonIL United States Duke University Medical Center School of Medicine DurhamNC United States Lab Crestview Radiology Lagos Nigeria Sage Bionetworks United States McGill University MontrealQC Canada Children’s National Hospital WashingtonDC United States George Washington University WashingtonDC United States Mayo Clinic RochesterMN United States Center for Global Health Perelman School of Medicine University of Pennsylvani

We describe the design and results from the BraTS 2023 Intracranial Meningioma Segmentation Challenge. The BraTS Meningioma Challenge differed from prior BraTS Glioma challenges in that it focused on meningiomas, which are typically benign extra-axial tumors with diverse radiologic and anatomical presentation and a propensity for multiplicity. Nine participating teams each developed deep-learning automated segmentation models using image data from the largest multi-institutional systematically expert annotated multilabel multi-sequence meningioma MRI dataset to date, which included 1000 training set cases, 141 validation set cases, and 283 hidden test set cases. Each case included T2, FLAIR, T1, and T1Gd brain MRI sequences with associated tumor compartment labels delineating enhancing tumor, non-enhancing tumor, and surrounding non-enhancing FLAIR hyperintensity. Participant automated segmentation models were evaluated and ranked based on a scoring system evaluating lesion-wise metrics including dice similarity coefficient (DSC) and 95% Hausdorff Distance. The top ranked team had a lesion-wise median dice similarity coefficient (DSC) of 0.976, 0.976, and 0.964 for enhancing tumor, tumor core, and whole tumor, respectively and a corresponding average DSC of 0.899, 0.904, and 0.871, respectively. These results serve as state-of-the-art benchmarks for future pre-operative meningioma automated segmentation algorithms. Additionally, we found that 1286 of 1424 cases (90.3%) had at least 1 compartment voxel abutting the edge of the skull-stripped image edge, which requires further investigation into optimal pre-processing face anonymization steps. © 2024, CC BY.

关键词： Deep learning

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The Brain Tumor Segmentation in Pediatrics (BraTS-PEDs) Challenge: Focus on Pediatrics (CBTN-CONNECT-DIPGR-ASNR-MICCAI BraTS-PEDs)

arXiv

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arXiv 2024年

作者： Kazerooni, Anahita Fathi Khalili, Nastaran Liu, Xinyang Gandhi, Deep Jiang, Zhifan Anwar, Syed Muhammed Albrecht, Jake Adewole, Maruf Anazodo, Udunna Anderson, Hannah Baid, Ujjwal Bergquist, Timothy Borja, Austin J. Calabrese, Evan Chung, Verena Conte, Gian-Marco Dako, Farouk Eddy, James Ezhov, Ivan Familiar, Ariana Farahani, Keyvan Franson, Andrea Gottipati, Anurag Haldar, Shuvanjan Iglesias, Juan Eugenio Janas, Anastasia Johansen, Elaine Jones, Blaise V. Khalili, Neda Kofler, Florian LaBella, Dominic Lai, Hollie Anne Van Leemput, Koen Li, Hongwei Bran Maleki, Nazanin McAllister, Aaron S. Meier, Zeke Menze, Bjoern Moawad, Ahmed W. Nandolia, Khanak K. Pavaine, Julija Piraud, Marie Poussaint, Tina Prabhu, Sanjay P. Reitman, Zachary Rudie, Jeffrey D. Sanchez-Montano, Mariana Shaikh, Ibraheem Salman Sheth, Nakul Tu, Wenxin Wang, Chunhao Ware, Jeffrey B. Wiestler, Benedikt Zapaishchykova, Anna Bornhorst, Miriam Deutsch, Michelle Fouladi, Maryam Lazow, Margot Mikael, Leonie Hummel, Trent Kann, Benjamin de Blank, Peter Hoffman, Lindsey Aboian, Mariam Nabavizadeh, Ali Packer, Roger Bakas, Spyridon Resnick, Adam Rood, Brian Vossough, Arastoo Linguraru, Marius George Children’s Hospital of Philadelphia PhiladelphiaPA United States Department of Neurosurgery University of Pennsylvania PhiladelphiaPA United States University of Pennsylvania PhiladelphiaPA United States Sheikh Zayed Institute for Pediatric Surgical Innovation Children’s National Hospital WashingtonDC United States Sage Bionetworks United States Lab Crestview Radiology Lagos Nigeria McGill University MontrealQC Canada Department of Radiology Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Division of Computational Pathology Department of Pathology and Laboratory Medicine Indiana University School of Medicine IndianapolisIN United States Department of Neurosurgery The University of Southern California CA United States Department of Radiology Duke University Medical Center DurhamNC United States Mayo Clinic MN United States Center for Global Health Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Department of Informatics Technical University Munich Germany TranslaTUM - Central Institute for Translational Cancer Research Technical University of Munich Germany Cancer Imaging Program National Cancer Institute National Institutes of Health BethesdaMD United States C. S. Mott Children’s Hospital University of Michigan MI United States Biomedical Engineering Rutgers University New BrunswickNJ United States Athinoula A Martinos Center for Biomedical Imaging Massachusetts General Hospital BostonMA United States Yale University New HavenCT United States PrecisionFDA U.S. Food and Drug Administration Silver SpringMD United States Cincinnati Children’s Hospital Medical Center OH United States Helmholtz AI Helmholtz Munich Germany Department of Radiation Oncology Duke University Medical Center DurhamNC United States Department of Radiology Children’s Health Orange County CA United States Department of Applied Mathematics and Computer Science Technical University of De

关键词： Deep learning

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A Data-Driven Approach for Inferring Student Proficiency from Game Activity Logs 16

A Data-Driven Approach for Inferring Student Proficiency fro...

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3rd Annual ACM Conference on learning at Scale (L at S)

作者： Falakmasir, Mohammad H. Gonzalez-Brenes, Jose P. Gordon, Geoffrey J. DiCerbo, Kristen E. Pearson Sch Res Hyderabad Telangana India Univ Pittsburgh Intelligent Syst Program Pittsburgh PA 15260 USA Carnegie Mellon Univ Machine Learning Pittsburgh PA 15213 USA

ISBN: (纸本)9781450337267

Student assessments are important because they allow collecting evidence about learning. However, time spent on evaluating students may be otherwise used for instructional activities. Computer-based learning platforms provide the opportunity for unobtrusively gathering students' digital learning footprints. This data can be used to track learning progress and make inference about student competencies. We present a novel data analysis pipeline, Student Proficiency Inferrer from Game data (SPRING), that allows modeling game playing behavior in educational games. Unlike prior work, SPRING is a fully data-driven method that does not require costly domain knowledge engineering. Moreover, it produces a simple interpretable model that not only fits the data but also predicts learning outcomes. We validate our framework using data collected from students playing 11 educational mini-games. Our results suggest that SPRING can predict math assessments accurately on withheld test data (Correlation=0.55, Spearman rho=0.51).

关键词： Educational Games Student Modeling Stealth Assessment

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DeePMD-kit v2: A software package for Deep Potential models

arXiv

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arXiv 2023年

作者： Zeng, Jinzhe Zhang, Duo Lu, Denghui Mo, Pinghui Li, Zeyu Chen, Yixiao Rynik, Marián Huang, Li'ang Li, Ziyao Shi, Shaochen Wang, Yingze Ye, Haotian Tuo, Ping Yang, Jiabin Ding, Ye Li, Yifan Tisi, Davide Zeng, Qiyu Bao, Han Xia, Yu Huang, Jiameng Muraoka, Koki Wang, Yibo Chang, Junhan Yuan, Fengbo Bore, Sigbjørn Løland Cai, Chun Lin, Yinnian Wang, Bo Xu, Jiayan Zhu, Jia-Xin Luo, Chenxing Zhang, Yuzhi Goodall, Rhys E.A. Liang, Wenshuo Singh, Anurag Kumar Yao, Sikai Zhang, Jingchao Wentzcovitch, Renata Han, Jiequn Liu, Jie Jia, Weile York, Darrin M. Weinan, E. Car, Roberto Zhang, Linfeng Wang, Han Laboratory for Biomolecular Simulation Research Institute for Quantitative Biomedicine Department of Chemistry and Chemical Biology Rutgers University PiscatawayNJ08854 United States AI for Science Institute Beijing100080 China DP Technology Beijing100080 China Academy for Advanced Interdisciplinary Studies Peking University Beijing100871 China HEDPS CAPT College of Engineering Peking University Beijing100871 China College of Electrical and Information Engineering Hunan University Changsha China Yuanpei College Peking University Beijing100871 China Program in Applied and Computational Mathematics Princeton University PrincetonNJ08540 United States Department of Experimental Physics Comenius University Mlynská Dolina F2 Bratislava842 48 Slovakia Center for Quantum Information Institute for Interdisciplinary Information Sciences Tsinghua University Beijing100084 China Center for Data Science Peking University Beijing100871 China ByteDance Research Zhonghang Plaza No. 43 North 3rd Ring West Road Haidian District Beijing China College of Chemistry and Molecular Engineering Peking University Beijing100871 China Baidu Inc. Beijing China Key Laboratory of Structural Biology of Zhejiang Province School of Life Sciences Westlake University Zhejiang Hangzhou China Westlake AI Therapeutics Lab Westlake Laboratory of Life Sciences and Biomedicine Zhejiang Hangzhou China Department of Chemistry Princeton University PrincetonNJ08544 United States SISSA Scuola Internazionale Superiore di Studi Avanzati Trieste34136 Italy Laboratory of Computational Science and Modeling Institute of Materials École Polytechnique Fédérale de Lausanne Lausanne1015 Switzerland Department of Physics National University of Defense Technology Hunan Changsha410073 China State Key Lab of Processors Institute of Computing Technology Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China School of Electronics Engineerin

DeePMD-kit is a powerful open-source software package that facilitates molecular dynamics simulations using machine learning potentials (MLP) known as Deep Potential (DP) models. This package, which was released in 2017, has been widely used in the fields of physics, chemistry, biology, and material science for studying atomistic systems. The current version of DeePMD-kit offers numerous advanced features such as DeepPot-SE, attention-based and hybrid descriptors, the ability to fit tensile properties, type embedding, model deviation, Deep Potential - Range Correction (DPRc), Deep Potential Long Range (DPLR), GPU support for customized operators, model compression, non-von Neumann molecular dynamics (NVNMD), and improved usability, including documentation, compiled binary packages, graphical user interfaces (GUI), and application programming interfaces (API). This article presents an overview of the current major version of the DeePMD-kit package, highlighting its features and technical details. Additionally, the article benchmarks the accuracy and efficiency of different models and discusses ongoing developments. © 2023, CC BY-NC-ND.

关键词： Molecular dynamics

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EMPRESS. XII. Statistics on the Dynamics and Gas Mass Fraction of Extremely Metal-poor Galaxies

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ASTROPHYSICAL JOURNAL 2024年第1期961卷 49-49页

作者： Xu, Yi Ouchi, Masami Isobe, Yuki Nakajima, Kimihiko Ozaki, Shinobu Bouche, Nicolas F. Wise, John H. Emsellem, Eric Kusakabe, Haruka Hattori, Takashi Nagao, Tohru Chiaki, Gen Fukushima, Hajime Harikane, Yuichi Hayashi, Kohei Hirai, Yutaka Kim, Ji Hoon Maseda, Michael V. Nagamine, Kentaro Shibuya, Takatoshi Sugahara, Yuma Yajima, Hidenobu Aoyama, Shohei Fujimoto, Seiji Fukushima, Keita Hatano, Shun Inoue, Akio K. Ishigaki, Tsuyoshi Kawasaki, Masahiro Kojima, Takashi Komiyama, Yutaka Koyama, Shuhei Koyama, Yusei Lee, Chien-Hsiu Matsumoto, Akinori Mawatari, Ken Moriya, Takashi J. Motohara, Kentaro Murai, Kai Nishigaki, Moka Onodera, Masato Ono, Yoshiaki Rauch, Michael Saito, Tomoki Sasaki, Rin Suzuki, Akihiro Takeuchi, Tsutomu T. Umeda, Hiroya Umemura, Masayuki Watanabe, Kuria Yabe, Kiyoto Zhang, Yechi Univ Tokyo Inst Cosm Ray Res 5-1-5 Kashiwanoha Kashiwa Chiba 2778582 Japan Univ Tokyo Grad Sch Sci Dept Astron 7-3-1 Hongo Tokyo 1130033 Japan Natl Astron Observ Japan 2-21-1 Osawa Mitaka Tokyo 1818588 Japan Univ Tokyo Kavli Inst Phys & Math Universe WPI Kashiwa Chiba 2778583 Japan Univ Tokyo Grad Sch Sci Dept Phys 7-3-1 Hongo Tokyo 1130033 Japan Univ Lyon1 Univ Lyon ENS Lyon Ctr Rech Astrophys LyonCNRSUMR5574 F-69230 St Genis Laval France Georgia Inst Technol Sch Phys Ctr Relativist Astrophys Atlanta GA 30332 USA European Southern Observ Karl Schwarzschild Str 2 D-85748 Garching Germany Univ Geneva Observ Geneve 51 Ch Maillettes CH-1290 Versoix Switzerland Natl Inst Nat Sci Natl Astron Observ Japan Subaru Telescope 650 North Aohoku Pl Hilo HI 96720 USA Ehime Univ Res Ctr Space & Cosm Evolut Bunkyo Cho 2-5 Matsuyama Ehime 7908577 Japan Univ Tsukuba Ctr Computat Sci Ten Nodai 1-1-1 Tsukuba Ibaraki 3058577 Japan UCL Dept Phys & Astron Gower St London WC1E 6BT England Ichinoseki Coll Natl Inst Technol Hagisho Ichinoseki 0218511 Japan Tohoku Univ Astron Inst 6-3 AobaAoba Ku Sendai Miyagi 9808578 Japan Univ Notre Dame Dept Phys & Astron 225 Nieuwland Sci Hall Notre Dame IN 46556 USA Seoul Natl Univ Dept Phys & Astron Astron Program 1 Gwanak Ro Seoul 08826 South Korea Seoul Natl Univ SNU Astron Res Ctr 1 Gwanak Ro Seoul 08826 South Korea Univ Wisconsin Dept Astron 475 N Charter St Madison WI 53706 USA Osaka Univ Grad Sch Sci Dept Earth & Space Sci Theoret Astrophys 1-1 Machikaneyama Toyonaka Osaka 5600043 Japan Univ Nevada Dept Phys & Astron 4505 S Maryland Pkwy Las Vegas NV 89154 USA Kitami Inst Technol 165 Koen Cho Kitami Hokkaido 0908507 Japan Waseda Univ Fac Sci & Engn Waseda Res Inst Sci & Engn 3-4-1 OkuboShinjuku Tokyo 1698555 Japan Chiba Univ Inst Management & Informat Technol Inage Ward 1-33 Yayoi Cho Chiba 2638522 Japan Cosm Dawn Ctr DAWN Jagtvej 128 DK-2200 Copenhagen N Denmark Univ Copenhagen

We present the demography of the dynamics and gas mass fraction of 33 extremely metal-poor galaxies (EMPGs) with metallicities of 0.015-0.195 Z(circle dot) and low stellar masses of 10(4)-10(8)M(circle dot) in the local universe. We conduct deep optical integral field spectroscopy (IFS) for the low-mass EMPGs with the medium-high resolution (R = 7500) grism of the 8 m Subaru FOCAS IFU instrument by the EMPRESS 3D survey, and investigate the H alpha emission of the EMPGs. Exploiting the resolution high enough for the low-mass galaxies, we derive gas dynamics with the H alpha lines by the fitting of three-dimensional disk models. We obtain an average maximum rotation velocity (v(rot)) of 15 +/- 3 km s(-1) and an average intrinsic velocity dispersion (sigma(0)) of 27 +/- 10 km s(-1) for 15 spatially resolved EMPGs out of 33 EMPGs, and find that all 15 EMPGs have v(rot)/sigma(0) < 1 suggesting dispersion-dominated systems. There is a clear decreasing trend of v(rot)/sigma(0) with the decreasing stellar mass and metallicity. We derive the gas mass fraction (f(gas)) for all 33 EMPGs, and find no clear dependence on stellar mass and metallicity. These v(rot)/sigma(0) and f(gas) trends should be compared with young high-z galaxies observed by the forthcoming JWST IFS programs to understand the physical origins of the EMPGs in the local universe.

关键词： Dwarf galaxies (416) Galaxy dynamics (591) Galaxy kinematics (602) Unified Astronomy Thesaurus concepts: Galaxy evolution (594)

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Forecasting: theory and practice

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INTERNATIONAL JOURNAL OF FORECASTING 2022年第3期38卷 705-871页

作者： Petropoulos, Fotios Apiletti, Daniele Assimakopoulos, Vassilios Babai, Mohamed Zied Barrow, Devon K. Ben Taieb, Souhaib Bergmeir, Christoph Bessa, Ricardo J. Bijak, Jakub Boylan, John E. Browell, Jethro Carnevale, Claudio Castle, Jennifer L. Cirillo, Pasquale Clements, Michael P. Cordeiro, Clara Oliveira, Fernando Luiz Cyrino De Baets, Shari Dokumentov, Alexander Ellison, Joanne Fiszeder, Piotr Franses, Philip Hans Frazier, David T. Gilliland, Michael Gonul, M. Sinan Goodwin, Paul Grossi, Luigi Grushka-Cockayne, Yael Guidolin, Mariangela Guidolin, Massimo Gunter, Ulrich Guo, Xiaojia Guseo, Renato Harvey, Nigel Hendry, David F. Hollyman, Ross Januschowski, Tim Jeon, Jooyoung Jose, Victor Richmond R. Kang, Yanfei Koehler, Anne B. Kolassa, Stephan Kourentzes, Nikolaos Leva, Sonia Li, Feng Litsiou, Konstantia Makridakis, Spyros Martin, Gael M. Martinez, Andrew B. Meeran, Sheik Modis, Theodore Nikolopoulos, Konstantinos Onkal, Dilek Paccagnini, Alessia Panagiotelis, Anastasios Panapakidis, Ioannis Pavia, Jose M. Pedio, Manuela Pedregal, Diego J. Pinson, Pierre Ramos, Patricia Rapach, David E. Reade, J. James Rostami-Tabar, Bahman Rubaszek, Michal Sermpinis, Georgios Shang, Han Lin Spiliotis, Evangelos Syntetos, Aris A. Talagala, Priyanga Dilini Talagala, Thiyanga S. Tashman, Len Thomakos, Dimitrios Thorarinsdottir, Thordis Todini, Ezio Arenas, Juan Ramon Trapero Wang, Xiaoqian Winkler, Robert L. Yusupova, Alisa Ziel, Florian Univ Bath Sch Management Bath Avon England Politecn Torino Turin Italy Natl Tech Univ Athens Sch Elect & Comp Engn Forecasting & Strategy Unit Athens Greece Kedge Business Sch Talence France Univ Birmingham Birmingham Business Sch Dept Management Birmingham W Midlands England Univ Mons UMONS Big Data & Machine Learning Lab Mons Belgium Monash Univ Fac Informat Technol Melbourne Vic Australia INESC TEC Inst Syst & Comp Engn Technol & Sci Porto Portugal Univ Southampton Dept Social Stat & Demog Southampton Hants England Univ Lancaster Lancaster Univ Management Sch Ctr Mkt Analyt & Forecasting Lancaster England Univ Glasgow Sch Math & Stat Glasgow Lanark Scotland Univ Brescia Dept Mech & Ind Engn Brescia Italy Univ Oxford Magdalen Coll Oxford England Zurich Univ Appl Sci ZHAW Sch Management & Law Zurich Switzerland Univ Reading ICMA Ctr Henley Business Sch Reading Berks England Univ Algarve Fac Ciencias & Tecnol Faro Portugal Univ Lisbon Fac Ciencias CEAUL Lisbon Portugal Pontifical Catholic Univ Rio de Janeiro PUC Rio Rio De Janeiro Brazil Univ Ghent Fac Econ & Business Adm Dept Business Informat & Operat Management Ghent Belgium Lets Forecast Sydney NSW Australia Nicolaus Copernicus Univ Torun Fac Econ Sci & Management Torun Poland Erasmus Sch Econ Econometr Inst Rotterdam Netherlands Monash Univ Dept Econometr & Business Stat Melbourne Vic Australia SAS Cary NC USA Northumbria Univ Newcastle Business Sch Newcastle Upon Tyne Tyne & Wear England Univ Padua Dept Stat Sci Padua Italy Univ Virginia Darden Sch Business Charlottesville VA 22903 USA Bocconi Univ Finance Dept Milan Italy Baffi CAREFIN Ctr Milan Italy MODUL Univ Vienna Dept Tourism & Serv Management Vienna Austria Univ Maryland Robert H Smith Sch Business College Pk MD 20742 USA UCL Dept Expt Psychol London England Univ Oxford Nuffield Coll Oxford Martin Sch Oxford England Univ Oxford Inst New Econ Thinking Oxford Martin Sch Oxford England Amazon

Forecasting has always been at the forefront of decision making and planning. The uncertainty that surrounds the future is both exciting and challenging, with individuals and organisations seeking to minimise risks and maximise utilities. The large number of forecasting applications calls for a diverse set of forecasting methods to tackle real-life challenges. This article provides a non-systematic review of the theory and the practice of forecasting. We provide an overview of a wide range of theoretical, state-of-the-art models, methods, principles, and approaches to prepare, produce, organise, and evaluate forecasts. We then demonstrate how such theoretical concepts are applied in a variety of real-life contexts. We do not claim that this review is an exhaustive list of methods and applications. However, we wish that our encyclopedic presentation will offer a point of reference for the rich work that has been undertaken over the last decades, with some key insights for the future of forecasting theory and practice. Given its encyclopedic nature, the intended mode of reading is non-linear. We offer cross-references to allow the readers to navigate through the various topics. We complement the theoretical concepts and applications covered by large lists of free or open-source software implementations and publicly-available databases. (C) 2021 The Author( s). Published by Elsevier B.V. on behalf of International Institute of Forecasters.

关键词： Review Encyclopedia Methods Applications Principles Time series Prediction

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