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Comparative cellular analysis of motor cortex in human, marmoset and mouse (vol 598, pg 111, 2021)

NATURE 2022年第7904期604卷 E8-E8页

作者： Bakken, Trygve E. Jorstad, Nikolas L. Hu, Qiwen Lake, Blue B. Tian, Wei Kalmbach, Brian E. Crow, Megan Hodge, Rebecca D. Krienen, Fenna M. Sorensen, Staci A. Eggermont, Jeroen Yao, Zizhen Aevermann, Brian D. Aldridge, Andrew I. Bartlett, Anna Bertagnolli, Darren Casper, Tamara Castanon, Rosa G. Crichton, Kirsten Daigle, Tanya L. Dalley, Rachel Dee, Nick Dembrow, Nikolai Diep, Dinh Ding, Song-Lin Dong, Weixiu Fang, Rongxin Fischer, Stephan Goldman, Melissa Goldy, Jeff Graybuck, Lucas T. Herb, Brian R. Hou, Xiaomeng Kancherla, Jayaram Kroll, Matthew Lathia, Kanan van Lew, Baldur Li, Yang Eric Liu, Christine S. Liu, Hanqing Lucero, Jacinta D. Mahurkar, Anup McMillen, Delissa Miller, Jeremy A. Moussa, Marmar Nery, Joseph R. Nicovich, Philip R. Niu, Sheng-Yong Orvis, Joshua Osteen, Julia K. Owen, Scott Palmer, Carter R. Pham, Thanh Plongthongkum, Nongluk Poirion, Olivier Reed, Nora M. Rimorin, Christine Rivkin, Angeline Romanow, William J. Sedeno-Cortes, Adriana E. Siletti, Kimberly Somasundaram, Saroja Sulc, Josef Tieu, Michael Torkelson, Amy Tung, Herman Wang, Xinxin Xie, Fangming Yanny, Anna Marie Zhang, Renee Ament, Seth A. Behrens, M. Margarita Bravo, Hector Corrada Chun, Jerold Dobin, Alexander Gillis, Jesse Hertzano, Ronna Hof, Patrick R. Hollt, Thomas Horwitz, Gregory D. Keene, C. Dirk Kharchenko, Peter V. Ko, Andrew L. Lelieveldt, Boudewijn P. Luo, Chongyuan Mukamel, Eran A. Pinto-Duarte, Antonio Preiss, Sebastian Regev, Aviv Ren, Bing Scheuermann, Richard H. Smith, Kimberly Spain, William J. White, Owen R. Koch, Christof Hawrylycz, Michael Tasic, Bosiljka Macosko, Evan Z. McCarroll, Steven A. Ting, Jonathan T. Zeng, Hongkui Zhang, Kun Feng, Guoping Ecker, Joseph R. Linnarsson, Sten Lein, Ed S. Allen Institute for Brain Science Seattle WA USA Department of Physiology and Biophysics University of Washington Seattle WA USA Department of Biomedical Informatics Harvard Medical School Boston MA USA Department of Bioengineering University of California San Diego La Jolla CA USA The Salk Institute for Biological Studies La Jolla CA USA Epilepsy Center of Excellence Department of Veterans Affairs Medical Center Seattle WA USA Stanley Institute for Cognitive Genomics Cold Spring Harbor Laboratory Cold Spring Harbor NY USA Department of Genetics Harvard Medical School Boston MA USA Broad Institute of MIT and Harvard Cambridge MA USA LKEB Department of Radiology Leiden University Medical Center Leiden The Netherlands Pattern Recognition and Bioinformatics group Delft University of Technology Delft The Netherlands J. Craig Venter Institute La Jolla CA USA Department of Pathology University of California San Diego CA USA Division of Vaccine Discovery La Jolla Institute for Immunology La Jolla CA USA Genomic Analysis Laboratory The Salk Institute for Biological Studies La Jolla CA USA Computer Science and Engineering Program University of California San Diego La Jolla CA USA Howard Hughes Medical Institute The Salk Institute for Biological Studies La Jolla CA USA Bioinformatics and Systems Biology Graduate Program University of California San Diego La Jolla CA USA Institute for Genomes Sciences University of Maryland School of Medicine Baltimore MD USA Center for Epigenomics Department of Cellular and Molecular Medicine University of California San Diego La Jolla CA USA Ludwig Institute for Cancer Research La Jolla CA USA Department of Computer Science University of Maryland College Park College Park MD USA Sanford Burnham Prebys Medical Discovery Institute La Jolla CA USA Biomedical Sciences Program School of Medicine University of California San Diego La Jolla CA USA University of Connecticut Storrs CT USA Department

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Deep learning of preconditioners for conjugate gradient solvers in urban water related problems

arXiv

引用

arXiv 2019年

作者： Sappl, Johannes Seiler, Laurent Harders, Matthias Rauch, Wolfgang Unit of Environmental Engineering Faculty of Engineering Sciences Universität Innsbruck Technikerstraße 13 Innsbruck6020 Austria Interactive Graphics and Simulation Group Department of Computer Science Universität Innsbruck Technikerstraße 21 A Innsbruck6020 Austria

Solving systems of linear equations is a problem occuring frequently in water engineering applications. Usually the size of the problem is too large to be solved via direct factorization. One can resort to iterative approaches, in particular the conjugate gradients method if the matrix is symmetric positive definite. Preconditioners further enhance the rate of convergence but hitherto only handcrafted ones requiring expert knowledge have been used. We propose an innovative approach employing Machine Learning, in particular a Convolutional Neural Network, to unassistedly design preconditioning matrices specifically for the problem at hand. Based on an in-depth case study in fluid simulation we are able to show that our learned preconditioner is able to improve the convergence rate even beyond well established methods like incomplete Cholesky factorization or Algebraic MultiGrid. Copyright © 2019, The Authors. All rights reserved.

关键词： Conjugate gradient method

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Sobel Heuristic Kernel for Aerial Semantic Segmentation

Sobel Heuristic Kernel for Aerial Semantic Segmentation

引用

IEEE International Conference on Image Processing

作者： Tao Hu Yao Wang Yisong Chen Peng Lu Heng Wang Guoping Wang Graphics and Interaction Laboratory Peking University Department of Computer Sciences Peking University School of Computer Science Beijing University of Posts and Telecommunications

Misclassification in semantic segmentation mostly occurs in the pixels around the semantic contour. In this work, we address the task of aerial image segmentation by borrowing the kernel prior from classical edge detecting operator. We propose a module called Sobel Heuristic Kernel(SHK). Our work makes several main contributions and experimentally shows good performance. To the best of our knowledge, we are the first to combine traditional edge detection method and deep learning method in semantic segmentation. Our SHK module reaches state of the art in the Inria Aerial Image Labeling dataset.

关键词： Kernel Semantics Image segmentation Image edge detection Neural networks Detectors Convolution

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A Configurable RISC-V for NoC-Based MPSoCs: A Framework for Hardware Emulation

A Configurable RISC-V for NoC-Based MPSoCs: A Framework for ...

引用

International Workshop on Network on Chip Architectures, NoCArc

作者： Mostafa Khamis Sameh El-Ashry Ahmed Shalaby Mohamed AbdElsalam M. Watheq El-Kharashi Department of Computer and Systems Engineering Ain Shams University Cairo Egypt Mentor Graphics Cairo Egypt Department of Computer Science Benha University Egypt

Manycore architectures with thousands of processing tiles create interesting challenges to maintain high processing speeds with satisfiable energy dissipation. One of these architectures, Networks-on-Chips (NoC) proved to be a suitable candidate to achieve that target goal. In this paper, we introduce a highly scalable, flexible hardware RISC-V MPSoC-based NoC hardware emulation and testbench acceleration co-modeling framework through which NoCs, built upon various types of network schemes can be validated and evaluated. In our proposed platform, traffic is injected or self-generated through the open source RISC-V Instruction Set Architecture (ISA) as a processing tile through an implemented Core Network Interface (CNI), which acts as an NI-plugin. Hardware emulation environment has been developed to be auto-generated to evaluate variant parametric NoC routers in different terms of reconfigurability for measuring their throughput and average latency. A scalable and portable Universal Verification Methodology (UVM) environment is proposed to support the hardware acceleration using the Testbench-Xpress (TBX) technology for network congestion-aware and bus routing failures detection. Experimental results have been applied on several case studies to demonstrate and asses the validity of the proposed framework of evaluating configurable and different NoC architectures obtain results indicate speedup performance gain in the order of 40X over software simulators.

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POLCOVID: a multicenter multiclass chest X-ray database (Poland, 2020-2021)

arXiv

引用

arXiv 2022年

作者： Suwalska, Aleksandra Tobiasz, Joanna Prazuch, Wojciech Socha, Marek Foszner, Pawel Piotrowski, Damian Gruszczynska, Katarzyna Sliwinska, Magdalena Walecki, Jerzy Popiela, Tadeusz Przybylski, Grzegorz Nowak, Mateusz Fiedor, Piotr Pawlowska, Malgorzata Flisiak, Robert Simon, Krzysztof Zapolska, Gabriela Gizycka, Barbara Szurowska, Edyta Marczyk, Michal Cieszanowski, Andrzej Polanska, Joanna Oronowicz-Jaskowiak, Agnieszka Golebiewski, Bogumil Sliwinska, Magdalena Rataj, Mateusz Chmielarz, Przemyslaw Tur, Adrianna Drabik, Grzegorz Kozub, Justyna Kozanecka, Anna Hildebrandt, Sebastian Krutul-Walenciej, Katarzyna Baron, Jan Jaroszewicz, Jerzy Walecki, Jerzy Wasilewski, Piotr Mazur, Samuel Klaude, Krzysztof Rataj, Katarzyna Golębiewski, Bogumil Pawlowska, Malgorzata Rabiko, Piotr Rajewski, Pawel Gizycka, Barbara Blewaska, Piotr Sznajder, Katarzyna Plesniak, Robert Department of Data Science and Engineering Silesian University of Technology Gliwice Poland Department of Computer Graphics Vision and Digital Systems Silesian University of Technology Gliwice Poland Department of Infectious Diseases and Hepatology Medical University of Silesia Katowice Poland Department of Radiology and Nuclear Medicine Medical University of Silesia Katowice Poland Department of Diagnostic Imaging Voivodship Specialist Hospital Wroclaw Poland Department of Diagnostic Radiology Central Clinical Hospital The Ministry of Internal Affairs and Administration Warsaw Poland Department of Radiology Jagiellonian University Medical College Krakow Poland Department of Lung Diseases Cancer and Tuberculosis Kujawsko-Pomorskie Pulmonology Center Bydgoszcz Poland Department of Radiology Silesian Hospital Cieszyn Poland Department of General and Transplantation Surgery Medical University of Warsaw Warsaw Poland Department of Infectious Diseases and Hepatology Collegium Medicum in Bydgoszcz Nicolaus Copernicus University Torun Poland Department of Infectious Diseases and Hepatology Medical University of Bialystok Bialystok Poland Department of Infectious Diseases and Hepatology Wroclaw Medical University Wroclaw Poland Department of Radiology Czerniakowski Hospital Warsaw Poland Department of Imaging Diagnostics MEGREZ Hospital Tychy Poland 2nd Department of Radiology Medical University of Gdansk Poland Yale Cancer Center Yale School of Medicine New HavenCT United States Department of Radiology I The Maria Sklodowska-Curie National Research Institute of Oncology Warsaw Poland The Maria Sklodowska-Curie National Research Institute of Oncology Warsaw Poland Voivodship Specialist Hospital Wroclaw Poland Prognostic Specialist Clinic Knurow Poland Collegium Medicum of the Jagiellonian University Krakow Poland Medical University of Silesia Katowice Poland Central Clinical Hospital of the Ministry of Interior in Warsaw Warsaw Poland Collegium Medicum

The outbreak of the SARS-CoV-2 pandemic has put healthcare systems worldwide to their limits, resulting in increased waiting time for diagnosis and required medical assistance. With chest radiographs (CXR) being one of the most common COVID-19 diagnosis methods, many artificial intelligence tools for image-based COVID-19 detection have been developed, often trained on a small number of images from COVID-19-positive patients. Thus, the need for high-quality and well-annotated CXR image databases increased. This paper introduces POLCOVID dataset, containing chest X-ray (CXR) images of patients with COVID-19 or other-type pneumonia, and healthy individuals gathered from 15 Polish hospitals. The original radiographs are accompanied by the preprocessed images limited to the lung area and the corresponding lung masks obtained with the segmentation model. Moreover, the manually created lung masks are provided for a part of POLCOVID dataset and the other four publicly available CXR image collections. POLCOVID dataset can help in pneumonia or COVID-19 diagnosis, while the set of matched images and lung masks may serve for the development of lung segmentation solutions. © 2022, CC BY.

关键词： COVID-19

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Memorizing all for implicit discourse relation recognition

arXiv

引用

arXiv 2019年

作者： Bai, Hongxiao Zhao, Hai Zhao, Junhan Department of Computer Science and Engineering Shanghai Jiao Tong University Key Lab. of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Shanghai Jiao Tong University Shanghai China MoE Key Lab of Artificial Intelligence Ai Institute Shanghai Jiao Tong University Computer Graphics Technology Purdue University West LafayetteIN United States

Implicit discourse relation recognition is a challenging task due to the absence of the nec-essary informative clue from explicit connec-tives. The prediction of relations requires a deep understanding of the semantic meanings of sentence pairs. As implicit discourse relation recognizer has to carefully tackle the se-mantic similarity of the given sentence pairs and the severe data sparsity issue exists in the meantime, it is supposed to be beneficial from mastering the entire training data. Thus in this paper, we propose a novel memory mecha-nism to tackle the challenges for further performance improvement. The memory mecha-nism is adequately memorizing information by pairing representations and discourse relations of all training instances, which right fills the slot of the data-hungry issue in the current im-plicit discourse relation recognizer. Our experiments show that our full model with memo-rizing the entire training set reaches new state-of-the-art against strong baselines, which es-pecially for the first time exceeds the milestone of 60% accuracy in the 4-way task. Copyright © 2019, The Authors. All rights reserved.

关键词： Semantics

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TimeTubes: Visual Exploration of Observed Blazar Datasets

TimeTubes: Visual Exploration of Observed Blazar Datasets

引用

International Meeting on High-Dimensional Data-Driven science, HD3 2017

作者： Fujishiro, Issei Sawada, Naoko Nakayama, Masanori Wu, Hsiang-Yun Watanabe, Kazuho Takahashi, Shigeo Uemura, Makoto Center for Information and Computer Science Keio University 3-14-1 Hiyoshi Kohoku-ku Yokohama223-8522 Japan Institute of Computer Graphics and Algorithms TU Wien Favoritenstr. 9 / 5. Stock ViennaA-1040 Austria Department of Computer Science and Engineering Toyohashi University of Technology 1-1 Hibarigaoka Tempaku-cho Toyohashi441-8580 Japan School of Computer Science and Engineering University of Aizu Aizu Wakamatsu Fukushima965-8580 Japan Astrophysical Science Center Hiroshima University 1-3-1 Kagamiyama Higashi-Hiroshima739-8526 Japan

Blazars are attractive objects for astronomers to observe in order to burrow into the magnetic field in the relativistic jet. This paper presents TimeTubes as a novel visualization scheme that allows astronomers to interactively explore characteristic temporal variation patterns in observed blazar datasets. In the TimeTubes spatialization, the two Stokes parameters and their errors with a common timestamp are transformed into an ellipse. A series of such ellipses are aligned in parallel along the timeline to form a 3D volumetric tube. The resulting tube is then colorized by the observed intensities and colors of the blazar, and finally volume-rendered. A designated user interface is provided with visual exploration functions according to Shneiderman's Visual Information Seeking Mantra. In the latest version, an auxiliary mechanism, called visual data fusion, was incorporated to ameliorate data- and mapping-inherent uncertainties for more efficient and effective visual exploration. © Published under licence by IOP Publishing Ltd.

关键词： Data fusion

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Controller design and validation of dynamic response for full-scale helicopter flight simulator at CCSU

Controller design and validation of dynamic response for ful...

引用

AIAA/ASCE/AHS/ASC Structures, Structural Dynamics, and Materials Conference, 2018

作者： Broderick, David J. Wei, Fu-Shang John Hopwood, Jeremy School of Engineering Science and Technology Central Connecticut State University New BritainCT06050 United States Computer Electronics & Graphics Technology Department CCSU United States Engineering Department CCSU United States Mechanical Engineering CCSU United States

ISBN: (数字)9781624105326

ISBN: (纸本)9781624105326

A research team comprised of students and faculty from Central Connecticut State University's (CCSU) School of Engineering, science, and Technology has begun the design, production, and testing of a full-scale helicopter flight simulator with a moving platform. This paper will present the system identication, controller tuning and validation, and comparison of simulator dynamics against available helicopter dynamic models during typical flight maneuvers. The purposes for conducting this research include: i) to build a fully functional flight simulator that can be used as a test bed for students research projects;ii) to apply theoretical lessons of control of dynamic systems to a real, tangible electromechanical system;iii) to provide students with hands-on experience on how to design and y a helicopter. CCSU students have built and programed all of the mechanical and electrical systems. The basic design weight for the simulator is 300 lbs force applied to the pilot seat. The structures are designed to have enough strength to sustain a maximum of 3g and a minimum-1g flight conditions. There are three actuators in place that can be used to rotate the platform in the pitching, rolling, and yawing directions. All electrical systems were designed, fabricated, and assembled by CCSU students. The controller was designed and implemented by the students and faculty of CCSU. © 2018, American Institute of Aeronautics and Astronautics Inc, AIAA. All rights reserved.

关键词： Flight simulators

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A multilinear model for bidirectional craniofacial reconstruction

A multilinear model for bidirectional craniofacial reconstru...

引用

2018 Eurographics Workshop on Visual Computing for Biology and Medicine, VCBM 2018

作者： Achenbach, Jascha Brylka, Robert Gietzen, Thomas Zum Hebel, Katja Schömer, Elmar Schulze, Ralf Botsch, Mario Schwanecke, Ulrich Bielefeld University Computer Graphics and Geometry Processing Group Germany RheinMain University of Applied Sciences Computer Vision and Mixed Reality Group Wiesbaden Rüsselsheim Germany University Medical Center Johannes Gutenberg University Department of Prosthetic Dentistry Mainz Germany University Medical Center Johannes Gutenberg University Section of Oral Radiology Mainz Germany Johannes Gutenberg University Mainz Institute of Computer Science Germany

ISBN: (纸本)9783038680567

We present a bidirectional facial reconstruction method for estimating the skull given a scan of the skin surface and vice versa estimating the skin surface given the skull. Our approach is based on a multilinear model that describes the correlation between the skull and the facial soft tissue thickness (FSTT) on the one hand and the head/face surface geometry on the other hand. Training this model requires to densely sample the Cartesian product space of skull shape times FSTT variation, which cannot be obtained by measurements alone. We generate this data by enriching measured data—volumetric computed tomography scans and 3D surface scans of the head—by simulating statistically plausible FSTT variations. We demonstrate the versatility of our novel multilinear model by estimating faces from given skulls as well as skulls from given faces within just a couple of seconds. To foster further research in this direction, we will make our multilinear model publicly available. © 2018 The Author(s) Eurographics Proceedings © 2018 The Eurographics Association.

关键词： computerized tomography

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A Methodology to Determine the Sites of Variability in an LED Assembly

A Methodology to Determine the Sites of Variability in an LE...

引用

IEEE Symposium on Semiconductor Thermal Measurement and Management (SEMI-THERM)

作者： Robin Bornoff Thomas Mérelle Josephine Sari Alessandro Di Bucchianico Gabor Farkas Mentor Graphics Mechanical Analysis Division 81 Bridge Road Hampton Court Surrey UK Pi-Lighting Avenue Ritz 19 Sion Switzerland Department of Mathematics and Computer Science 513 Eindhoven University of Technology MB Eindhoven The Netherlands Mentor Graphics Mechanical Analysis Division Infopark D Gábor Dénes u. 2 Budapest Hungary

ISBN: (数字)9781735532509

ISBN: (纸本)9781728191294

Variations in the manufacturing assembly process together with any changes in materials sourcing can give rise to variation in the functional performance of an LED. These variations can lead to differences in thermal characteristics such as Zth (transient thermal impedance) and RthJC (thermal resistance between chip junction and case. This study aims to analyze the measured variations of a number of LED samples and explore a methodology that will provide insights into the root cause of those variations, In addition the statistical properties of the variations are applied in a Monte Carlo thermal modelling context. Such insights and modelling methods would be employed by a lighting engineer when selecting LEDs and when simulating their thermal dependent optical behavior.

关键词： LED Structure Function Variation Light emitting diodes SMIM10L2A gene Variation structure functions ROOT CAUSES functional performance assembly process thermal characteristics

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