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GLEU-guided multi-resolution network for short text conversation 1

14th National Conference on Man-Machine Speech Communication, NCMMSC 2017

作者： Liu, Xuan Yu, Kai Key Laboratory 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

ISBN: (数字)9789811081118

ISBN: (纸本)9789811081101

With the recent development of sequence-to-sequence framework, generation approach for short text conversation becomes attractive. Traditional sequence-to-sequence method for short text conversation often suffers from dull response problem. Multi-resolution generation approach has been introduced to address this problem by dividing the generation process into two steps: keywords-sequence generation and response generation. However, this method still tends to generate short and dull keywords-sequence. In this work, a new multi-resolution generation framework is proposed. Instead of using the word-level maximum likelihood criterion, we optimize the sequence-level GLEU score of the entire generated keywords-sequence using a policy gradient approach in reinforcement learning. Experiments show that the proposed approach can generate longer and more diverse keywords-sequence. Meanwhile, it achieves better scores in the human evaluation. © 2018, Springer Nature Singapore Pte Ltd.

关键词： Reinforcement learning

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PENet-a scalable deep-learning model for automated diagnosis of pulmonary embolism using volumetric CT imaging (vol 66, pg 121, 2020)

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NPJ DIGITAL MEDICINE 2020年第1期3卷 61页

作者： Huang, Shih-Cheng Kothari, Tanay Banerjee, Imon Chute, Chris Ball, Robyn L. Borus, Norah Huang, Andrew Patel, Bhavik N. Rajpurkar, Pranav Irvin, Jeremy Dunnmon, Jared Bledsoe, Joseph Shpanskaya, Katie Dhaliwal, Abhay Zamanian, Roham Ng, Andrew Y. Lungren, Matthew P. 1Department of Biomedical Data Science Stanford University Stanford CA USA. 3Department of Computer Science Stanford University Stanford CA USA. 2Center for Artificial Intelligence in Medicine & Imaging Stanford University Stanford CA USA. 5Department of Radiology Stanford University Stanford CA USA. 6Department of Emergency Medicine Intermountain Medical Center Salt Lake Valley UT USA. 7Michigan State University College of Human Medicine East Lansing MI USA. 8Department of Pulmonary Critical Care Medicine Stanford University Stanford CA USA.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

关键词： Cardiovascular diseases Radiography

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Multi-Phase Locking Value: A Generalized Method for Determining Instantaneous Multi-frequency Phase Coupling

arXiv

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arXiv 2021年

作者： Vasudeva, Bhavya Tian, Runfeng Wu, Dee H. James, Shirley A. Refai, Hazem H. He, Fei Yang, Yuan Indian Statistical Institute West Bengal Kolkata700108 India Stephenson School of Biomedical Engineering The University of Oklahoma TulsaOK74135 United States Department of Radiological Sciences The University of Oklahoma Health Sciences Center Oklahoma CityOK73104 United States Department of Rehabilitation Sciences College of Allied Health The University of Oklahoma Health Sciences Center Oklahoma CityOK73117 United States Department of Electrical and Computer Engineering The University of Oklahoma TulsaOK74135 United States Centre for Computational Science and Mathematical Modelling Coventry University CoventryCV1 2JH United Kingdom Department of Physical Therapy and Human Movement Sciences Feinberg School of Medicine Northwestern University ChicagoIL60611 United States

Background: Many physical, biological and neural systems behave as coupled oscillators, with characteristic phase coupling across different frequencies. Methods such as n : m phase locking value (where two coupling frequencies are linked as: mf1 = nf2) and bi-phase locking value have previously been proposed to quantify phase coupling between two resonant frequencies (e.g. f, 2f/3) and across three frequencies (e.g. f1, f2, f1 + f2), respectively. However, the existing phase coupling metrics have their limitations and limited applications. They cannot be used to detect or quantify phase coupling across multiple frequencies (e.g. f1, f2, f3, f4, f1 + f2 + f3 − f4), or coupling that involves non-integer multiples of the frequencies (e.g. f1, f2, 2f1/3 + f2/3). New methods: To address the gap, this paper proposes a generalized approach, named multi-phase locking value (M-PLV), for the quantification of various types of instantaneous multi-frequency phase coupling. Different from most instantaneous phase coupling metrics that measure the simultaneous phase coupling, the proposed M-PLV method also allows the detection of delayed phase coupling and the associated time lag between coupled oscillators. Results: The M-PLV has been tested on cases where synthetic coupled signals are generated using white Gaussian signals, and a system comprised of multiple coupled Rössler oscillators, as well as a human subject dataset. Results indicate that the M-PLV can provide a reliable estimation of the time window and frequency combination where the phase coupling is significant, as well as a precise determination of time lag in the case of delayed coupling. This method has the potential to become a powerful new tool for exploring phase coupling in complex nonlinear dynamic systems. Copyright © 2021, The Authors. All rights reserved.

关键词： Natural frequencies

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

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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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Publisher Correction: Detection of mosaic and population-level structural variants with Sniffles2

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Nature biotechnology 2024年第10期42卷 1616页

作者： Moritz Smolka Luis F Paulin Christopher M Grochowski Dominic W Horner Medhat Mahmoud Sairam Behera Ester Kalef-Ezra Mira Gandhi Karl Hong Davut Pehlivan Sonja W Scholz Claudia M B Carvalho Christos Proukakis Fritz J Sedlazeck Human Genome Sequencing Center Baylor College of Medicine Houston TX USA. Department of Molecular and Human Genetics Baylor College of Medicine Houston TX USA. Department of Clinical and Movement Neurosciences Royal Free Campus Queen Square Institute of Neurology University College London London UK. Aligning Science Across Parkinson's (ASAP) Collaborative Research Network Chevy Chase MD USA. Pacific Northwest Research Institute (PNRI) Seattle WA USA. Bionano Genomics San Diego CA USA. Division of Neurology and Developmental Neuroscience Department of Pediatrics Baylor College of Medicine Houston TX USA. Neurodegenerative Diseases Research Unit National Institute of Neurological Disorders and Stroke Bethesda MD USA. Department of Neurology Johns Hopkins University Medical Center Baltimore MD USA. Human Genome Sequencing Center Baylor College of Medicine Houston TX USA. Fritz.Sedlazeck@bcm.edu. Department of Molecular and Human Genetics Baylor College of Medicine Houston TX USA. Fritz.Sedlazeck@bcm.edu. Aligning Science Across Parkinson's (ASAP) Collaborative Research Network Chevy Chase MD USA. Fritz.Sedlazeck@bcm.edu. Department of Computer Science Rice University Houston TX USA. Fritz.Sedlazeck@bcm.edu.

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Patella-femoral joint loading during the modified Star Excursion Balance Test: Preliminary results of an extensive simulation study

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Gait & Posture 2022年 97卷 S5-S6页

作者： Horsak, B. Simonlehner, M. Dumphart, B. Kainz, H. Killen, B. Jonkers, I. St. Pölten University of Applied Sciences Center for Digital Health and Social Innovation St. Pölten Austria St. Pölten University of Applied Sciences Institute of Health Sciences St. Pölten Austria University of Vienna- Centre for Sport Science and University Sports Department of Biomechanics- Kinesiology and Computer Science in Sport Vienna Austria KU Leuven Department of Movement Science- Human Movement Biomechanics Research Group Leuven Belgium

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Non-invasive Assessment of Hepatic Venous Pressure Gradient (HVPG) Based on MR Flow Imaging and Computational Fluid Dynamics

arXiv

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arXiv 2021年

作者： Wang, Kexin Wang, Shuo Xiong, Minghua Wang, Chengyan Wang, He Department of Physics Fudan University Shanghai China Digital Medical Research Center School of Basic Medical Sciences Fudan University Shanghai China Shanghai Key Laboratory of Medical Image Computing and Computer Assisted Intervention Shanghai China Shanghai Zhiyu Software Information Co. Ltd China Human Phenome Institute Fudan University Shanghai China Institute of Science and Technology for Brain-inspired Intelligence Fudan University Shanghai China Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence Fudan University Ministry of Education China

Clinically significant portal hypertension (CSPH) is a severe complication of chronic liver disease associated with cirrhosis, which is diagnosed by the measurement of hepatic venous pressure gradient (HVPG). However, HVPG measurement is invasive and therefore difficult to be widely applied in clinical routines. There is no currently available technique to measure HVPG noninvasively. Computational fluid dynamics (CFD) has been used for noninvasive measurement of vascular pressure gradient in the intracranial and coronary arteries. However, it has been scarcely employed in the hepatic vessel system due to the difficulties in reconstructing precise vascular anatomies and setting appropriate boundary conditions. Several computer tomography and ultrasound based studies have verified the effectiveness of virtual HVPG (vHVPG) by directly connecting the portal veins and hepatic veins before CFD simulations [16,12]. We apply the latest techniques of phase-contrast magnetic resonance imaging (PC-MRI) and DIXON to obtain the velocity and vessel anatomies at the same time. Besides, we improve the CFD pipeline in regards to the construction of vessel connections and reduction of calculation time. The proposed method shows high accuracy in the CSPH diagnosis in a study containing ten healthy volunteers and five patients. The MRI-based noninvasive HVPG measurement is promising in the clinical application of CSPH diagnosis. © 2021, CC BY.

关键词： Computational fluid dynamics

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Calculation of potential for setting up charging infrastructure for battery-powered electric vehicles – Focusing the calculation of potential according to the urban quarter level

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Transportation Research Procedia 2019年 41卷 436-439页

作者： Waldemar Brost Teresa Funke Ralf Philipsen Chair and Institute of Urban and Transportation Planning (ISB) RWTH Aachen University Mies-van-der-Rohe-Str. 1 52074 Aachen Germany Chair of Communication Science Human-Computer Interaction Center (HCIC) RWTH Aachen University Campus-Boulevard 57 52074 Aachen Germany

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High-quality voice conversion using spectrogram-based WaveNet Vocoder 19

High-quality voice conversion using spectrogram-based WaveNe...

引用

19th Annual Conference of the International Speech Communication, INTERSPEECH 2018

作者： Chen, Kuan Chen, Bo Lai, Jiahao Yu, Kai 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

Waveform generator is a key component in voice conversion. Recently, WaveNet waveform generator conditioned on the Mel-cepstrum (Mcep) has shown better quality over standard vocoder. In this paper, an enhanced WaveNet model based on spectrogram is proposed to further improve voice conversion performance. Here, Mel-frequency spectrogram is converted from source speaker to target speaker using an LSTM-RNN based frame-to-frame feature mapping. To evaluate the performance, the proposed approach is compared to an Mcep based LSTM-RNN voice conversion system. Both STRAIGHT vocoder and Mcep-based WaveNet vocoder are elected to produce the converted speech for Mcep conversion system. The fundamental frequency (F0) of the converted speech in different systems is analyzed. The naturalness, similarity and intelligibility are evaluated in subjective measures. Results show that the spectrogram based WaveNet waveform generator can achieve better voice conversion quality compared to traditional WaveNet approaches. The Mel-spectrogram based voice conversion can achieve significant improvement in speaker similarity and inherent F0 conversion. © 2018 International Speech Communication Association. All rights reserved.

关键词： Vocoders

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The state-of-the-art in Cardiac MRI Reconstruction: Results of the CMRxRecon Challenge in MICCAI 2023

arXiv

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arXiv 2024年

作者： Lyu, Jun Qin, Chen Wang, Shuo Wang, Fanwen Li, Yan Wang, Zi Guo, Kunyuan Ouyang, Cheng Tänzer, Michael Liu, Meng Sun, Longyu Sun, Mengting Li, Qin Shi, Zhang Hua, Sha Li, Hao Chen, Zhensen Zhang, Zhenlin Xin, Bingyu Metaxas, Dimitris N. Yiasemis, George Teuwen, Jonas Zhang, Liping Chen, Weitian Pang, Yanwei Liu, Xiaohan Razumov, Artem Dylov, Dmitry V. Dou, Quan Yan, Kang Xue, Yuyang Du, Yuning Dietlmeier, Julia Garcia-Cabrera, Carles Hemidi, Ziad Al-Haj Vogt, Nora Xu, Ziqiang Zhang, Yajing Chu, Ying-Hua Chen, Weibo Bai, Wenjia Zhuang, Xiahai Qin, Jing Wu, Lianmin Yang, Guang Qu, Xiaobo Wang, He Wang, Chengyan Psychiatry Neuroimaging Laboratory Brigham and Women’s Hospital Harvard Medical School 399 Revolution Drive BostonMA02215 United States Department of Electrical and Electronic Engineering & I-X Imperial College London United Kingdom Digital Medical Research Center School of Basic Medical Sciences Fudan University Shanghai China Department of Bioengineering & I-X Imperial College London LondonW12 7SL United Kingdom Cardiovascular Magnetic Resonance Unit Royal Brompton Hospital Guy’s and St Thomas’ NHS Foundation Trust LondonSW3 6NP United Kingdom Department of Radiology Ruijin Hospital Shanghai Jiao Tong University School of Medicine Shanghai China Department of Electronic Science Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance National Institute for Data Science in Health and Medicine Institute of Artificial Intelligence Xiamen University Xiamen361102 China Department of Computing Department of Brain Sciences Imperial College London LondonSW7 2AZ United Kingdom Human Phenome Institute Fudan University 825 Zhangheng Road Pudong New District Shanghai201203 China Department of Radiology Zhongshan Hospital Fudan University Shanghai China Department of Cardiovascular Medicine Ruijin Hospital Lu Wan Branch Shanghai Jiao Tong University School of Medicine Shanghai China Institute of Science and Technology for Brain-Inspired Intelligence Fudan University Shanghai200433 China Department of Computer Science Rutgers University PiscatawayNJ08854 United States AI for Oncology Netherlands Cancer Institute Plesmanlaan 121 Amsterdam1066 CX Netherlands Department of Imaging and Interventional Radiology The Chinese University of Hong Kong Hong Kong TJK-BIIT Lab School of Electrical and Information Engineering Tianjin University Tianjin300072 China Skolkovo Institute Of Science And Technology Center for Artificial Intelligence Technology 30/1 Bolshoy blvd. Moscow121205 Russia Department of Biomedical Engineering University of Virginia

Cardiac magnetic resonance imaging (MRI) provides detailed and quantitative evaluation of the heart’s structure, function, and tissue characteristics with high-resolution spatial-temporal imaging. However, its slow imaging speed and motion artifacts are notable limitations. Undersampling reconstruction, especially data-driven algorithms, has emerged as a promising solution to accelerate scans and enhance imaging performance using highly under-sampled data. Nevertheless, the scarcity of publicly available cardiac k-space datasets and evaluation platform hinder the development of data-driven reconstruction algorithms. To address this issue, we organized the Cardiac MRI Reconstruction Challenge (CMRxRecon) in 2023, in collaboration with the 26th International Conference on Medical Image Computing and computer-Assisted Intervention (MICCAI). CMRxRecon presented an extensive k-space dataset comprising cine and mapping raw data, accompanied by detailed annotations of cardiac anatomical structures. With overwhelming participation, the challenge attracted more than 285 teams and over 600 participants. Among them, 22 teams successfully submitted Docker containers for the testing phase, with 7 teams submitted for both cine and mapping tasks. All teams use deep learning based approaches, indicating that deep learning has predominately become a promising solution for the problem. The first-place winner of both tasks utilizes the E2E-VarNet architecture as backbones. In contrast, U-Net is still the most popular backbone for both multi-coil and single-coil reconstructions. This paper provides a comprehensive overview of the challenge design, presents a summary of the submitted results, reviews the employed methods, and offers an in-depth discussion that aims to inspire future advancements in cardiac MRI reconstruction models. The summary emphasizes the effective strategies observed in Cardiac MRI reconstruction, including backbone architecture, loss function, pre-processing techn

关键词： Mapping

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