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Nature neuroscience 2023年第12期26卷 2250页

作者： Kyungchul Noh Woo-Hyun Cho Byung Hun Lee Dong Wook Kim Yoo Sung Kim Keebum Park Minkyu Hwang Ellane Barcelon Yoon Kyung Cho C Justin Lee Bo-Eun Yoon Se-Young Choi Hye Yoon Park Sang Beom Jun Sung Joong Lee Department of Physiology and Neuroscience Dental Research Institute Seoul National University School of Dentistry Seoul Republic of Korea. Department of Physics and Astronomy Seoul National University Seoul Republic of Korea. Department of Molecular Biology Dankook University Cheonan Republic of Korea. Department of Electronic and Electrical Engineering Ewha Womans University Seoul Republic of Korea. Center for Cognition and Sociality Institute for Basic Science (IBS) Daejeon Republic of Korea. Department of Electrical and Computer Engineering University of Minnesota Minneapolis MN USA. Graduate Program in Smart Factory Ewha Womans University Seoul Republic of Korea. Department of Brain & Cognitive Sciences Ewha Womans University Seoul Republic of Korea. Department of Physiology and Neuroscience Dental Research Institute Seoul National University School of Dentistry Seoul Republic of Korea. sjlee87@snu.ac.kr.

MPI-LiFE: Designing High-Performance Linear Fascicle Evaluation of brain Connectome with MPI 24

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MPI-LiFE: Designing High-Performance Linear Fascicle Evaluat...

24th IEEE International Conference on High Performance Computing, HiPC 2017

作者： Gugnani, Shashank Lu, Xiaoyi Pestilli, Franco Caiafa, Cesar Panda, Dhabaleswar K. Department of Computer Science and Engineering Ohio State University United States Psychological and Brain Sciences Engineering Computer Science Neuroscience and Cognitive Science Indiana University United States CONICET CCT La Plata Argentina

ISBN: (纸本)9781538622933

In this paper, we combine high-performance computing science with computational neuroscience methods to show how to speed-up cutting-edge methods for mapping and evaluation of the large-scale network of brain connections. More specifically, we use a recent factorization method of the Linear Fascicle Evaluation model (i.e., LiFE [1], [2]) that allows for statistical evaluation of brain connectomes. The method called ENCODE [3], [4] uses a Sparse Tucker Decomposition approach to represent the LiFE model. We show that we can implement the optimization step of the ENCODE method using MPI and OpenMP programming paradigms. Our approach involves the parallelization of the multiplication step of the ENCODE method. We model our design theoretically and demonstrate empirically that the design can be used to identify optimal configurations for the LiFE model optimization via ENCODE method on different hardware platforms. In addition, we co-design the MPI runtime with the LiFE model to achieve profound speed-ups. Extensive evaluation of our designs on multiple clusters corroborates our theoretical model. We show that on a single node on TACC Stampede2, we can achieve speed-ups of up to 8.7x as compared to the original approach. © 2017 IEEE.

关键词： Application programming interfaces (API)

REcovery from DEXmedetomidine-Induced Unresponsiveness (REDEX): A Study Protocol for a Single Center, Parallel Arm, Non-Randomized, Controlled Pilot Trial in Healthy Volunteers

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Nature and science of Sleep 2025年 17卷 1259-1269页

作者： David R Schreier Matteo Fecchio Christian S Guay Reid G Kovacs Mark Olchanyi Ariel L Mueller Timothy T Houle Brian L Edlow Emery N Brown Ken Solt 1 Department of Anesthesia Critical Care and Pain Medicine Massachusetts General Hospital and Department of Anaesthesia Harvard Medical School Boston MA USA2 Picower Institute for Learning and Memory Massachusetts Institute of Technology Cambridge MA USA3 Center for Neurotechnology and Neurorecovery Department of Neurology Massachusetts General Hospital and Harvard Medical School Boston MA USA4 Department of Neurology Inselspital Bern University Hospital and University of Bern Bern Switzerland 3 Center for Neurotechnology and Neurorecovery Department of Neurology Massachusetts General Hospital and Harvard Medical School Boston MA USA 1 Department of Anesthesia Critical Care and Pain Medicine Massachusetts General Hospital and Department of Anaesthesia Harvard Medical School Boston MA USA2 Picower Institute for Learning and Memory Massachusetts Institute of Technology Cambridge MA USA5 Department of Anesthesiology Perioperative and Pain Medicine The University of Utah Salt Lake City UT USA 1 Department of Anesthesia Critical Care and Pain Medicine Massachusetts General Hospital and Department of Anaesthesia Harvard Medical School Boston MA USA6 Institute for Medical Engineering and Science Massachusetts Institute of Technology Cambridge MA USA7 Department of Brain and Cognitive Sciences Massachusetts Institute of Technology Cambridge MA USA8 Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology Cambridge MA USA 2 Picower Institute for Learning and Memory Massachusetts Institute of Technology Cambridge MA USA3 Center for Neurotechnology and Neurorecovery Department of Neurology Massachusetts General Hospital and Harvard Medical School Boston MA USA6 Institute for Medical Engineering and Science Massachusetts Institute of Technology Cambridge MA USA 1 Department of Anesthesia Critical Care and Pain Medicine Massachusetts General Hospital and Department of Anaesthesia Harvard Medical School Boston MA

PurposeDexmedetomidine (DEX) is a well-tolerated sedative drug that induces a sleep-like state. DEX sedation offers a model to study transitions between different states of consciousness (indicated by, eg, behavior, the electroencephalogram (EEG), or transcranial magnetic stimulation (TMS) evoked EEG responses). However, the effects of repeated DEX exposure on recovery are poorly understood and will be investigated in this pilot *** and MethodsWe aim to enroll 12 healthy volunteers (6 females, 6 males). Although we do not expect TMS-EEG to interfere with DEX sedation, due to the paucity of evidence this study uses a parallel arm design (TMS-EEG, non-TMS-EEG). Participants will be sedated twice, one week apart, and responsiveness monitored by a click-task to auditory beeps. A 64-channel EEG and additional physiological signals will be recorded. Cognition and vigilance tests will be performed before sedation (baseline), after return of responsiveness (ROR), and before discharge. TMS-EEG will be performed at baseline, during sedation, and during recovery. Using a smartwatch and questionnaires, we will assess sleep quality, sleepiness, and experiences during sedation and *** will report the difference of time to ROR between the first and second study visit, and explore potential differences across sex and study arms. We will evaluate state transitions by comparing responsiveness, traditional EEG signatures, TMS-evoked EEG responses (ie, perturbational complexity index), and report on cognition and vigilance test *** pilot trial will report on the effect of repeated DEX exposure on the recovery period, and the investigation of state transitions will advance our scientific understanding of altered states of consciousness. REDEX will provide valuable insights and data for designing future DEX sedation studies. Moreover, we will report on the potential of biological sex as a confounding factor and the feasibility of TMS-EE

关键词： consciousness sedation sleep electroencephalography transcranial magnetic stimulation target-controlled infusion

End-to-end speech separation with unfolded iterative phase reconstruction

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arXiv 2018年

作者： Wang, Zhong-Qiu Le Roux, Jonathan Wang, DeLiang Hershey, John R. United States Department of Computer Science and Engineering Ohio State University United States Center for Cognitive and Brain Sciences Ohio State University United States

This paper proposes an end-to-end approach for single-channel speaker-independent multi-speaker speech separation, where time-frequency (T-F) masking, the short-time Fourier transform (STFT), and its inverse are represented as layers within a deep network. Previous approaches, rather than computing a loss on the reconstructed signal, used a surrogate loss based on the target STFT magnitudes. This ignores reconstruction error introduced by phase inconsistency. In our approach, the loss function is directly defined on the reconstructed signals, which are optimized for best separation. In addition, we train through unfolded iterations of a phase reconstruction algorithm, represented as a series of STFT and inverse STFT layers. While mask values are typically limited to lie between zero and one for approaches using the mixture phase for reconstruction, this limitation is less relevant if the estimated magnitudes are to be used together with phase reconstruction. We thus propose several novel activation functions for the output layer of the T-F masking, to allow mask values beyond one. On the publicly-available wsj0-2mix dataset, our approach achieves state-of-the-art 12.6 dB scale-invariant signal-to-distortion ratio (SI-SDR) and 13.1 dB SDR, revealing new possibilities for deep learning based phase reconstruction and representing a fundamental progress towards solving the notoriously-hard cocktail party problem. Copyright © 2018, The Authors. All rights reserved.

关键词： Iterative methods

Slip Detection with Combined Tactile and Visual Information

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Slip Detection with Combined Tactile and Visual Information

IEEE International Conference on Robotics and Automation (ICRA)

作者： Jianhua Li Siyuan Dong Edward Adelson Computer Science and Artificial Intelligence Laboratory (CSAIL) MIT Cambridge MA USA Department of Electrical Engineering & Computer Science and CSAIL MIT Cambridge MA USA Faculty of Department of Brain and Cognitive Sciences and CSAIL MIT Cambridge MA USA

Slip detection plays a vital role in robotic manipulation and it has long been a challenging problem in the robotic community. In this paper, we propose a new method based on deep neural network (DNN) to detect slip. The training data is acquired by a GelSight tactile sensor and a camera mounted on a gripper when we use a robot arm to grasp and lift 94 daily objects with different grasping forces and grasping positions. The DNN is trained to classify whether a slip occurred or not. To evaluate the performance of the DNN, we test 10 unseen objects in 152 grasps. A detection accuracy as high as 88.03 % is achieved. It is anticipated that the accuracy can be further improved with a larger dataset. This method is beneficial for robots to make stable grasps, which can be widely applied to automatic force control, grasping strategy selection and fine manipulation.

关键词： Grippers Grasping Cameras Tactile sensors Force

brain network construction and classification toolbox (brainnetclass)

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arXiv 2019年

作者： Zhou, Zhen Chen, Xiaobo Zhang, Yu Qiao, Lishan Yu, Renping Pan, Gang Zhang, Han Shen, Dinggang College of Computer Science and Technology Zhejiang University Hangzhou310027 China Department of Radiology and BRIC University of North Carolina at Chapel Hill Chapel HillNC27599 United States Automotive Engineering Research Institute Jiangsu University Zhenjiang212013 China Department of Psychiatry and Behavior Sciences Stanford University StanfordCA94305 United States School of Mathematics Science Liaocheng University Liaocheng252000 China School of Electric Engineering Zhengzhou University Zhengzhou450001 China Department of Brain and Cognitive Engineering Korea University Seoul02841 Korea Republic of

brain functional network has become an increasingly used approach in understanding brain functions and diseases. Many network construction methods have been developed, whereas the majority of the studies still used static pairwise Pearson’s correlation-based functional connectivity. The goal of this work is to introduce a toolbox namely "brain Network Construction and Classification" (brainNetClass) to the field to promote more advanced brain network construction methods. It comprises various brain network construction methods, including some state-of-the-art methods that were recently developed to capture more complex interactions among brain regions along with connectome feature extraction, reduction, parameter optimization towards network-based individualized classification. brainNetClass is a MATLAB-based, open-source, cross-platform toolbox with graphical user-friendly interfaces for cognitive and clinical neuroscientists to perform rigorous computer-aided diagnosis with interpretable result presentations even though they do not possess neuroimage computing and machine learning knowledge. We demonstrate the implementations of this toolbox on real datasets. brainNetClass (v1.0) can be downloaded from https://***/zzstefan/brainNetClass. Copyright © 2019, The Authors. All rights reserved.

关键词： computer aided diagnosis

Towards universal dialogue state tracking

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arXiv 2018年

作者： Ren, Liliang Xie, Kaige Chen, Lu Yu, Kai Key Lab. of Shanghai Education Commission for Intelligent Interaction and Cognitive Eng. SpeechLab Department of Computer Science and Engineering Brain Science and Technology Research Center Shanghai Jiao Tong University Shanghai China

Dialogue state tracking is the core part of a spoken dialogue system. It estimates the beliefs of possible user's goals at every dialogue turn. However, for most current approaches, it's difficult to scale to large dialogue domains. They have one or more of following limitations: (a) Some models don't work in the situation where slot values in ontology changes dynamically;(b) The number of model parameters is proportional to the number of slots;(c) Some models extract features based on hand-crafted lexicons. To tackle these challenges, we propose StateNet, a universal dialogue state tracker. It is independent of the number of values, shares parameters across all slots, and uses pre-trained word vectors instead of explicit semantic dictionaries. Our experiments on two datasets show that our approach not only overcomes the limitations, but also significantly outperforms the performance of state-of-the-art approaches. Copyright © 2018, The Authors. All rights reserved.

关键词： Semantics

Policy Adaptation for Deep Reinforcement Learning-Based Dialogue Management

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Policy Adaptation for Deep Reinforcement Learning-Based Dial...

IEEE International Conference on Acoustics, Speech and Signal Processing

作者： Lu Chen Cheng Chang Zhi Chen Bowen Tan Milica Gasic Kai Yu Key Lab. of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering SpeechLab Department of Computer Science and Engineering Brain Science and Technology Research Center Shanghai Jiao Tong University Shanghai China Engineering Department University of Cambridge Cambridge UK

ISBN: (纸本)9781538646595

Policy optimization is the core part of statistical dialogue management. Deep reinforcement learning has been successfully used for dialogue policy optimization for a static pre-defined domain. However, when the domain changes dynamically, e.g. a new previously unseen concept (or slot) which can be then used as a database search constraint is added, or the policy for one domain is transferred to another domain, the dialogue state space and action sets both will change. Therefore, the model structures for different domains have to be different. This makes dialogue policy adaptation/transfer challenging. Here a multi -agent dialogue policy (MADP) is proposed to tackle these problems. MADP consists of some slot-dependent agents (S-Agents) and a slot-independent agent (G-Agent). S-Agents have shared parameters in addition to private parameters for each one. During policy transfer, the shared parameters in S-Agents and the parameters in G-Agent can be directly transferred to the agents in extended/new domain. Simulation experiments showed that MADP can significantly speed up the policy learning and facilitate policy adaptation.

关键词： dialogue policy deep reinforcement learning adaptation multi-agent Policies Domain Agents learning (artificial intelligence) state space Reinforcement

Soft Realization: a Bio-inspired Implementation Paradigm

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arXiv 2018年

作者： Mahdiani, H.R. Bojnordi, M.N. Fakhraie, S.M. Computer Science and Engineering Department Shahid Beheshti University Tehran Iran Institute for Cognitive and Brain Science Shahid Beheshti University Tehran Iran School of Computing University of Utah Salt Lake City United States Electrical and Computer Engineering Department University of Tehran Tehran Iran

Researchers traditionally solve the computational problems through rigorous and deterministic algorithms called as Hard Computing. Except for some rare analog implementations, these precise algorithms have widely been realized using digital technology as an inherently reliable and accurate implementation platform, either in hardware or software forms. This rigid form of implementation which we refer as Hard Realization relies on strict algorithmic accuracy constraints dictated to digital design engineers. Regardless of the type of application, hard realization admits paying as much as necessary implementation costs to preserve computation precision and determinism throughout all the design and implementation steps. Despite all prior accomplishments in hard realization, this conventional paradigm has encountered serious challenges with today's emerging applications and implementation technologies. Unlike traditional hard computing, the emerging soft and bio-inspired algorithms do not rely on fully precise and deterministic computation, thereby not fully satisfied by old-fashion implementations. Moreover, the incoming nanotechnologies face increasing reliability issues that prevent them from being efficiently exploited in hard realization of applications. This article examines Soft Realization, a novel bio-inspired approach to design and implementation of an important category of applications noticing the internal brain structure. The proposed paradigm mitigates major weaknesses of hard realization by (1) alleviating incompatibilities with today's soft and bio-inspired algorithms such as artificial neural networks, fuzzy systems, and human sense signal processing applications, and (2) resolving the destructive inconsistency with unreliable nanotechnologies. Our experimental results on a set of well-known soft applications implemented using the proposed soft realization paradigm in both reliable and unreliable technologies indicate that significant energy, delay, and a

关键词： Nanotechnology

A lack of efficacy of continuous theta burst stimulation over the left dorsolateral prefrontal cortex in autism: A double blind randomized sham-controlled trial

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Autism research : official journal of the International Society for Autism Research 2023年第6期16卷 2023 May 23页

作者： Hsing-Chang Ni Yi-Lung Chen Yi-Ping Chao Chen-Te Wu Rao-Shayn Chen Tai-Li Chou Susan Shur-Fen Gau Hsiang-Yuan Lin Department of Psychiatry Chang Gung Memorial Hospital at Linkou Taoyuan Taiwan. Department of Healthcare Administration Asia University Taichung Taiwan. Department of Psychology Asia University Taichung Taiwan. Department of Computer Science and Information Engineering Chang Gung University Taoyuan Taiwan. Department of Otorhinolaryngology-Head and Neck Surgery Chang Gung Memorial Hospital at Linkou Taoyuan Taiwan. Department of Medical Imaging and Intervention Chang Gung Memorial Hospital at Linkou Taoyuan Taiwan. Department of Neurology Chang Gung Memorial Hospital at Linkou Taoyuan Taiwan. Department of Psychology National Taiwan University Taipei Taiwan. Graduate Institute of Brain and Mind Sciences College of Medicine National Taiwan University Taipei Taiwan. Neurobiology and Cognitive Science Center National Taiwan University Taipei Taiwan. Department of Psychiatry National Taiwan University Hospital and College of Medicine Taipei Taiwan. Azrieli Adult Neurodevelopmental Centre Campbell Family Mental Health Research Institute Centre for Addiction and Mental Health Toronto Ontario Canada. Department of Psychiatry Temerty Faculty of Medicine University of Toronto Toronto Ontario Canada.

Although previous open-label trials suggest the therapeutic potential of inhibitory repetitive transcranial magnetic stimulation (rTMS) over the dorsolateral prefrontal cortex (DLPFC) in autism spectrum disorder (ASD), methodological caveats exist. We conducted an 8-week randomized, double-blind sham-controlled trial to investigate the efficacy of inhibitory continuous theta burst stimulation (cTBS, a variant of rTMS) over the left DLPFC in individuals with ASD. Sixty children, adolescents and young adults (aged 8-30 years) with ASD without co-occurring intellectual disabilities were randomized to a 16-session 8-week cTBS versus sham stimulation course, with a follow-up 4 weeks after the trial. The Active group was not superior to the Sham group in any clinical or neuropsychological metrics at Week 8 or Week 12. Time effects of 8-week cTBS on symptoms and executive function were remarkable in both Active and Sham groups, with comparable response rates and effect sizes of changes in symptoms/cognition between groups. Our results from a sufficiently powered sample do not endorse the superior efficacy of cTBS over the left DLPFC to the shamed stimulation for children, adolescents and adults with ASD. These findings suggest that earlier positive open-label trial findings may be generalized by generalized/placebo effects. This highlights the urgent need for more rTMS/TBS studies with rigorous trial designs in ASD.

关键词： autism spectrum disorder dorsolateral prefrontal cortex inhibitory paradigm randomized sham-controlled trial repetitive magnetic stimulation theta burst stimulation