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Neuroprosthetics: Durable and Fatigue-Resistant Soft Peripheral Neuroprosthetics for In Vivo Bidirectional Signaling (Adv. Mater. 20/2021)

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Advanced Materials 2021年第20期33卷

作者： Hyunseon Seo Sang Ihn Han Kang-Il Song Duhwan Seong Kyungwoo Lee Sun Hong Kim Taesung Park Ja Hoon Koo Mikyung Shin Hyoung Won Baac Ok Kyu Park Soong Ju Oh Hyung-Seop Han Hojeong Jeon Yu-Chan Kim Dae-Hyeong Kim Taeghwan Hyeon Donghee Son School of Medicine Sungkyunkwan University Suwon 16419 Republic of Korea Center for Biomaterials Biomedical Research Institute Korea Institute of Science and Technology Seoul 02792 Republic of Korea Center for Nanoparticle Research Institute of Basic Science (IBS) Seoul 08826 Republic of Korea School of Chemical and Biological Engineering Seoul National University Seoul 08826 Republic of Korea Medical Device Development Center Daegu-Gyeongbuk Medical Innovation Foundation Daegu 41061 Republic of Korea Department of Electrical and Computer Engineering Sungkyunkwan University Suwon 16419 Republic of Korea Department of Electrical and Computer Engineering Inter-University Semiconductor Research Center Seoul National University Seoul 08826 Republic of Korea Department of Materials Science and Engineering Korea University Seoul 02841 Republic of Korea Interdisciplinary Program for Bioengineering Seoul National University Seoul 08826 Republic of Korea Department of Biomedical Engineering SKKU Institute for Convergence Sungkyunkwan University Suwon 16419 Republic of Korea Center for Neuroscience Imaging Research Institute for Basic Science (IBS) Suwon 16419 Republic of Korea Department of Superintelligence Engineering Sungkyunkwan University Suwon 16419 Republic of Korea

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

arXiv

引用

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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Probing the universality of topological defect formation in a quantum annealer: Kibble-Zurek mechanism and beyond

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Physical Review Research 2020年第3期2卷 033369-033369页

作者： Yuki Bando Yuki Susa Hiroki Oshiyama Naokazu Shibata Masayuki Ohzeki Fernando Javier Gómez-Ruiz Daniel A. Lidar Sei Suzuki Adolfo del Campo Hidetoshi Nishimori Institute of Innovative Research Tokyo Institute of Technology Nagatsuta-cho Midori-ku Yokohama 226-8503 Japan Institute of Innovative Research Tokyo Institute of Technology Oh-okayama Meguro-ku Tokyo 152-8550 Japan Department of Physics Tohoku University Sendai 980-8578 Japan Graduate School of Information Sciences Tohoku University Sendai 980-8579 Japan Sigma-i Co. Ltd. Konan Minato-ku Tokyo 108-0075 Japan Donostia International Physics Center E-20018 San Sebastián Spain Departments of Electrical and Computer Engineering Chemistry and Physics University of Southern California Los Angeles California 90089 USA Center for Quantum Information Science & Technology University of Southern California Los Angeles California 90089 USA Department of Liberal Arts Saitama Medical University Moroyama Saitama 350-0495 Japan IKERBASQUE Basque Foundation for Science E-48013 Bilbao Spain Department of Physics University of Massachusetts Boston 100 Morrissey Boulevard Boston Massachusetts 02125 USA RIKEN Interdisciplinary Theoretical and Mathematical Sciences Program (iTHEMS) Wako Saitama 351-0198 Japan

The number of topological defects created in a system driven through a quantum phase transition exhibits a power-law scaling with the driving time. This universal scaling law is the key prediction of the Kibble-Zurek mechanism (KZM), and testing it using a hardware-based quantum simulator is a coveted goal of quantum information science. Here we provide such a test using quantum annealing. Specifically, we report on extensive experimental tests of topological defect formation via the one-dimensional transverse-field Ising model on two different D-Wave quantum annealing devices. We find that the quantum simulator results can indeed be explained by the KZM for open-system quantum dynamics with phase-flip errors, with certain quantitative deviations from the theory likely caused by factors such as random control errors and transient effects. In addition, we probe physics beyond the KZM by identifying signatures of universality in the distribution and cumulants of the number of kinks and their decay, and again find agreement with the quantum simulator results. This implies that the theoretical predictions of the generalized KZM theory, which assumes isolation from the environment, applies beyond its original scope to an open system. We support this result by extensive numerical computations. To check whether an alternative, classical interpretation of these results is possible, we used the spin-vector Monte Carlo model, a candidate classical description of the D-Wave device. We find that the degree of agreement with the experimental data from the D-Wave annealing devices is better for the KZM, a quantum theory, than for the classical spin-vector Monte Carlo model, thus favoring a quantum description of the device. Our work provides an experimental test of quantum critical dynamics in an open quantum system, and paves the way to new directions in quantum simulation experiments.

关键词： Critical exponents Nonequilibrium statistical mechanics Open quantum systems Quantum computation Quantum master equation Quantum simulation Quantum statistical mechanics Quantum spin models

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

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Nature methods 2024年第10期21卷 1959页

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

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Author Correction: Battery-free, wireless soft sensors for continuous multi-site measurements of pressure and temperature from patients at risk for pressure injuries

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Nature communications 2021年第1期12卷 6827页

作者： Yong Suk Oh Jae-Hwan Kim Zhaoqian Xie Seokjoo Cho Hyeonseok Han Sung Woo Jeon Minsu Park Myeong Namkoong Raudel Avila Zhen Song Sung-Uk Lee Kabseok Ko Jungyup Lee Je-Sang Lee Weon Gi Min Byeong-Ju Lee Myungwoo Choi Ha Uk Chung Jongwon Kim Mengdi Han Jahyun Koo Yeon Sik Choi Sung Soo Kwak Sung Bong Kim Jeonghyun Kim Jungil Choi Chang-Mo Kang Jong Uk Kim Kyeongha Kwon Sang Min Won Janice Mihyun Baek Yujin Lee So Young Kim Wei Lu Abraham Vazquez-Guardado Hyoyoung Jeong Hanjun Ryu Geumbee Lee Kyuyoung Kim Seunghwan Kim Min Seong Kim Jungrak Choi Dong Yun Choi Quansan Yang Hangbo Zhao Wubin Bai Hokyung Jang Yongjoon Yu Jaeman Lim Xu Guo Bong Hoon Kim Seokwoo Jeon Charles Davies Anthony Banks Hyung Jin Sung Yonggang Huang Inkyu Park John A Rogers Center for Bio-Integrated Electronics Northwestern University Evanston IL USA. Department of Mechanical Engineering Korea Advanced Institute of Science and Technology Daejeon Republic of Korea. Department of Electrical and Computer Engineering University of Illinois at Urbana-Champaign Urbana IL USA. Department of Materials Science and Engineering University of Illinois at Urbana Champaign Urbana IL USA. Querrey Simpson Institute for Bioelectronics Northwestern University Evanston IL USA. State Key Laboratory of Structural Analysis for Industrial Equipment Department of Engineering Mechanics Dalian University of Technology Dalian People's Republic of China. Ningbo Institute of Dalian University of Technology Ningbo People's Republic of China. Department of Biomedical Engineering Texas A&M University College Station TX USA. Department of Mechanical Engineering Northwestern University Evanston IL USA. Advanced 3D Printing Technology Development Division Korea Atomic Energy Research Institute Daejeon Republic of Korea. Qualcomm Institute La Jolla CA USA. Sibel Health Inc Niles IL USA. Department of Rehabilitation Medicine Gimhae Hansol Rehabilitation & Convalescent Hospital Gimhae Republic of Korea. Department of Planning and Development Gimhae Hansol Rehabilitation & Convalescent Hospital Gimhae Republic of Korea. Department of Rehabilitation Medicine Pusan national university hospital Busan Republic of Korea. Department of Materials Science and Engineering KAIST Institute for The Nanocentury (KINC) Korea Advanced Institute of Science and Technology Daejeon Republic of Korea. Department of Materials Science and Engineering Northwestern University Evanston IL USA. Department of Mechanical Engineering Kyung Hee University Yongin Republic of Korea. Department of Biomedical Engineering College of Future Technology Peking University Beijing People's Republic of China. School of Biomedical Engineering Korea University Seoul Republic of Korea. Interdisci

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The evolutionary history of 2,658 cancers (vol 578, pg 122, 2020)

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NATURE 2023年第7948期614卷 E42-E42页

作者： Gerstung, Moritz Jolly, Clemency Leshchiner, Ignaty Dentro, Stefan C. Gonzalez, Santiago Rosebrock, Daniel Mitchell, Thomas J. Rubanova, Yulia Anur, Pavana Yu, Kaixian Tarabichi, Maxime Deshwar, Amit Wintersinger, Jeff Kleinheinz, Kortine Vazquez-Garcia, Ignacio Haase, Kerstin Jerman, Lara Sengupta, Subhajit Macintyre, Geoff Malikic, Salem Donmez, Nilgun Livitz, Dimitri G. Cmero, Marek Demeulemeester, Jonas Schumacher, Steven Fan, Yu Yao, Xiaotong Lee, Juhee Schlesner, Matthias Boutros, Paul C. Bowtell, David D. Zhu, Hongtu Getz, Gad Imielinski, Marcin Beroukhim, Rameen Sahinalp, S. Cenk Ji, Yuan Peifer, Martin Markowetz, Florian Mustonen, Ville Yuan, Ke Wang, Wenyi Morris, Quaid D. Spellman, Paul T. Wedge, David C. Van Loo, Peter European Molecular Biology Laboratory European Bioinformatics Institute (EMBL-EBI) Cambridge UK European Molecular Biology Laboratory Genome Biology Unit Heidelberg Germany Wellcome Sanger Institute Cambridge UK Genome Biology Unit European Molecular Biology Laboratory (EMBL) Heidelberg Germany University of Ljubljana Ljubljana Slovenia The Francis Crick Institute London UK Big Data Institute University of Oxford Oxford UK Wellcome Sanger Institute Wellcome Genome Campus Hinxton UK Big Data Institute Li Ka Shing Centre University of Oxford Oxford UK University of Cambridge Cambridge UK Cambridge University Hospitals NHS Foundation Trust Cambridge UK Department of Applied Mathematics and Theoretical Physics Centre for Mathematical Sciences University of Cambridge Cambridge UK Department of Epidemiology and Biostatistics Memorial Sloan Kettering Cancer Center New York NY USA Department of Statistics Columbia University New York NY USA Department of Haematology University of Cambridge Cambridge UK University of Leuven Leuven Belgium Broad Institute of MIT and Harvard Cambridge MA USA Center for Cancer Research Massachusetts General Hospital Charlestown MA USA Department of Pathology Massachusetts General Hospital Boston MA USA Harvard Medical School Boston MA USA Center for Cancer Research Massachusetts General Hospital Boston MA USA Dana-Farber Cancer Institute Boston MA USA Department of Medical Oncology Dana-Farber Cancer Institute Boston MA USA Department of Cancer Biology Dana-Farber Cancer Institute Boston MA USA Oxford NIHR Biomedical Research Centre Oxford UK Oxford NIHR Biomedical Research Centre University of Oxford Oxford UK University of Toronto Toronto Ontario Canada Vector Institute Toronto Ontario Canada Vector Institute Toronto ON Canada Department of Computer Science University of Toronto Toronto ON Canada Ontario Institute for Cancer Research Toronto Ontario Canada University of California Los Angeles

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Abstracts

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JOURNAL OF SPINAL CORD MEDICINE 2021年第SUPPL 1期44卷 S278-S324页

作者： [Anonymous] Department of Medicine Division of Physical Medicine and Rehabilitation University of Toronto Toronto ON CA KITE - Toronto Rehabilitation Institute University Health Network Toronto ON CA Institute for Health Policy Management and Evaluation University of Toronto Toronto ON CA Rehabilitation Sciences Institute University of Toronto Toronto ON CA Institute of Medical Sciences University of Toronto Toronto ON CA. Rehabilitation Sciences Institute University of Toronto ON CA KITE Research Institute-Toronto Rehab-University Health Network Toronto ON CA Physical Therapy Department University of Toronto ON CA Praxis Spinal Cord Institute Vancouver BC Université de Montréal (École de réadaptation) Montreal QC CA Institut de réadaptation Gingras-Linsay-de-Montréal Montreal QC CA Division of Physical Medicine & Rehabilitation Department of Medicine Faculty of Medicine & Dentistry University of Alberta Edmonton AB CA University of Calgary Calgary AB CA Alberta Health Services Centre for Family Medicine Kitchener ON CA McMaster University Department of Family Medicine Hamilton ON CA Dept. of Physical Therapy University of Toronto Toronto ON CA KITE-University Health Network Toronto ON CA Praxis Spinal Cord Institute Vancouver BC CA Center for Interdisciplinary Research in Rehabilitation and Social Integration (CIRRIS) Quebec City QC CA Dept. of Rehabilitation Laval University Quebec City QC CA Institute of Health Policy Management and Evaluation University of Toronto Toronto ON CA School of Rehabilitation Science University of Saskatchewan Saskatoon SK CA Ontario Neurotrauma Foundation Toronto ON CA Canadian Spinal Research Organization Toronto ON CA CIUSSS du Centre-Sud-de-l ‘Île-de-Montréal (Installation Gingras-Lindsay) Montréal QC CA École de réadaptation Université de Montréal Montréal QC CA First Steps Wellness Centre Regina SK CA Stan Cassidy Centre for Rehabilitation Horizon Health Network Fredericton NB CA Departmen

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Resonant photonic structures in porous silicon for biosensing 9

Resonant photonic structures in porous silicon for biosensin...

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Frontiers in Biological Detection: From Nanosensors to Systems IX

作者： Zhao, Yiliang Rodriguez, Gilberto A. Graham, Yasmin M. Cao, Tengfei Gaur, Girija Weiss, Sharon M. Interdisciplinary Graduate Program in Materials Science Vanderbilt University NashvilleTN37235 United States Department of Electrical Engineering and Computer Science Vanderbilt University NashvilleTN37235 United States Department of Mechanical Engineering University of Maryland Baltimore County BaltimoreMD21250 United States

ISBN: (数字)9781510606043

ISBN: (纸本)9781510606036

The formation of resonant photonic structures in porous silicon leverages the benefit of high surface area for improved molecular capture that is characteristic of porous materials with the advantage of high detection sensitivity that is a feature of resonant optical devices. This review provides an overview of the biosensing capabilities of a variety of resonant porous silicon photonic structures including microcavities, Bloch surface waves, ring resonators, and annular Bragg resonators. Detection sensitivities > 1000 nm/RIU are achieved for small molecule detection. The challenge of detecting molecules that approach and exceed the pore diameter is also addressed. © 2017 SPIE.

关键词： Porous silicon

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Quantized memory-augmented neural networks

arXiv

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arXiv 2017年

作者： Park, Seongsik Kim, Seijoon Lee, Seil Bae, Ho Yoon, Sungroh Department of Electrical and Computer Engineering Seoul National University Seoul08826 Korea Republic of Interdisciplinary Program in Bioinformatics Seoul National University Seoul08826 Korea Republic of

Memory-augmented neural networks (MANNs) refer to a class of neural network models equipped with external memory (such as neural Turing machines and memory networks). These neural networks outperform conventional recurrent neural networks (RNNs) in terms of learning long-term dependency, allowing them to solve intriguing ai tasks that would otherwise be hard to address. This paper concerns the problem of quantizing MANNs. Quantization is known to be effective when we deploy deep models on embedded systems with limited resources. Furthermore, quantization can substantially reduce the energy consumption of the inference procedure. These benefits justify recent developments of quantized multilayer perceptrons, convolutional networks, and RNNs. However, no prior work has reported the successful quantization of MANNs. The in-depth analysis presented here reveals various challenges that do not appear in the quantization of the other networks. Without addressing them properly, quantized MANNs would normally suffer from excessive quantization error which leads to degraded performance. In this paper, we identify memory addressing (specifically, content-based addressing) as the main reason for the performance degradation and propose a robust quantization method for MANNs to address the challenge. In our experiments, we achieved a computation-energy gain of 22× with 8-bit fixed-point and binary quantization compared to the floating-point implementation. Measured on the bAbI dataset, the resulting model, named the quantized MANN (Q-MANN), improved the error rate by 46% and 30% with 8-bit fixed-point and binary quantization, respectively, compared to the MANN quantized using conventional techniques. Copyright © 2017, The Authors. All rights reserved.

关键词： Recurrent neural networks

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High-Throughput Screening: One-Step Generation of a Drug-Releasing Hydrogel Microarray-On-A-Chip for Large-Scale Sequential Drug Combination Screening (Adv. Sci. 3/2019)

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Advanced Science 2019年第3期6卷 1970014-1970014页

作者： Seo Woo Song Su Deok Kim Dong Yoon Oh Yongju Lee Amos Chungwon Lee Yunjin Jeong Hyung Jong Bae Daewon Lee Sumin Lee Jiyun Kim Sunghoon Kwon Department of Electrical and Computer Engineering Seoul National University Seoul 08826 South Korea Institutes of Entrepreneurial BioConvergence Seoul National University Seoul 08826 South Korea Department of Mechanical Science and Engineering University of Illinois at Urbana-Champaign Urbana IL 61801 USA Nano Systems Institute Seoul National University Seoul 08826 South Korea Interdisciplinary Program in Bioengineering Seoul National University Seoul 08826 South Korea School of Materials Science and Engineering Ulsan National Institute of Science and Technology Ulsan 44919 South Korea

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