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Author Correction: Opportunities and challenges of single-cell and spatially resolved genomics methods for neuroscience discovery

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Nature neuroscience 2025年第1期28卷 216页

作者： Boyan Bonev Gonçalo Castelo-Branco Fei Chen Simone Codeluppi M Ryan Corces Jean Fan Myriam Heiman Kenneth Harris Fumitaka Inoue Manolis Kellis Ariel Levine Mo Lotfollahi Chongyuan Luo Kristen R Maynard Mor Nitzan Vijay Ramani Rahul Satijia Lucas Schirmer Yin Shen Na Sun Gilad S Green Fabian Theis Xiao Wang Joshua D Welch Ozgun Gokce Genevieve Konopka Shane Liddelow Evan Macosko Omer Ali Bayraktar Naomi Habib Tomasz J Nowakowski Helmholtz Pioneer Campus Helmholtz Zentrum München Neuherberg Germany. Physiological Genomics Biomedical Center Ludwig-Maximilians-Universität München Munich Germany. Laboratory of Molecular Neurobiology Department of Medical Biochemistry and Biophysics Karolinska Institutet Stockholm Sweden. The Broad Institute of MIT and Harvard Cambridge MA USA. Moleculent Stockholm Sweden. Gladstone Institute of Neurological Disease San Francisco CA USA. Gladstone Institute of Data Science and Biotechnology San Francisco CA USA. Department of Neurology University of California San Francisco San Francisco CA USA. Department of Biomedical Engineering Johns Hopkins University Baltimore MD USA. Department of Brain and Cognitive Sciences MIT Cambridge MA USA. The Picower Institute for Learning and Memory MIT Cambridge MA USA. UCL Queen Square Institute of Neurology University College London London UK. Institute for the Advanced Study of Human Biology (WPI-ASHBi) Kyoto University Kyoto Japan. Computer Science and Artificial Intelligence Lab Massachusetts Institute of Technology Cambridge MA USA. Spinal Circuits and Plasticity Unit National Institute of Neurological Disorders and Stroke Bethesda MD USA. Institute of Computational Biology Helmholtz Center Munich - German Research Center for Environmental Health Neuherberg Germany. Wellcome Sanger Institute Wellcome Genome Campus Cambridge UK. Department of Human Genetics University of California Los Angeles Los Angeles CA USA. Lieber Institute for Brain Development Baltimore MD USA. Department of Psychiatry Johns Hopkins School of Medicine Baltimore MD USA. Department of Neuroscience Johns Hopkins School of Medicine Baltimore MD USA. School of Computer Science and Engineering The Hebrew University of Jerusalem Jerusalem Israel. Racah Institute of Physics The Hebrew University of Jerusalem Jerusalem Israel. Faculty of Medicine The Hebrew University of Jerusalem Jerusalem Israel. Department of Biochemis

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Dense sampling of bird diversity increases power of comparative genomics (vol 587, pg 252, 2020)

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NATURE 2021年第7856期592卷 E24-E24页

作者： Feng, Shaohong Stiller, Josefin Deng, Yuan Armstrong, Joel Fang, Qi Reeve, Andrew Hart Xie, Duo Chen, Guangji Guo, Chunxue Faircloth, Brant C. Petersen, Bent Wang, Zongji Zhou, Qi Diekhans, Mark Chen, Wanjun Andreu-Sanchez, Sergio Margaryan, Ashot Howard, Jason Travis Parent, Carole Pacheco, George Sinding, Mikkel-Holger S. Puetz, Lara Cavill, Emily Ribeiro, Angela M. Eckhart, Leopold Fjeldsa, Jon Hosner, Peter A. Brumfield, Robb T. Christidis, Les Bertelsen, Mads F. Sicheritz-Ponten, Thomas Tietze, Dieter Thomas Robertson, Bruce C. Song, Gang Borgia, Gerald Claramunt, Santiago Lovette, Irby J. Cowen, Saul J. Njoroge, Peter Dumbacher, John Philip Ryder, Oliver A. Fuchs, Jerome Bunce, Michael Burt, David W. Cracraft, Joel Meng, Guanliang Hackett, Shannon J. Ryan, Peter G. Jonsson, Knud Andreas Jamieson, Ian G. da Fonseca, Rute R. Braun, Edward L. Houde, Peter Mirarab, Siavash Suh, Alexander Hansson, Bengt Ponnikas, Suvi Sigeman, Hanna Stervander, Martin Frandsen, Paul B. van der Zwan, Henriette van der Sluis, Rencia Visser, Carina Balakrishnan, Christopher N. Clark, Andrew G. Fitzpatrick, John W. Bowman, Reed Chen, Nancy Cloutier, Alison Sackton, Timothy B. Edwards, Scott V. Foote, Dustin J. Shakya, Subir B. Sheldon, Frederick H. Vignal, Alain Soares, Andre E. R. Shapiro, Beth Gonzalez-Solis, Jacob Ferrer-Obiol, Joan Rozas, Julio Riutort, Marta Tigano, Anna Friesen, Vicki Dalen, Love Urrutia, Araxi O. Szekely, Tamas Liu, Yang Campana, Michael G. Corvelo, Andre Fleischer, Robert C. Rutherford, Kim M. Gemmell, Neil J. Dussex, Nicolas Mouritsen, Henrik Thiele, Nadine Delmore, Kira Liedvogel, Miriam Franke, Andre Hoeppner, Marc P. Krone, Oliver Fudickar, Adam M. Mila, Borja Ketterson, Ellen D. Fidler, Andrew Eric Friis, Guillermo Parody-Merino, Angela M. Battley, Phil F. Cox, Murray P. Lima, Nicholas Costa Barroso Prosdocimi, Francisco Parchman, Thomas Lee Schlinger, Barney A. Loiselle, Bette A. Blake, John G. Lim, Haw Chuan Day, Lainy B. Fuxjager, Matthew J. Baldwin, Maude W. Braun, Michael J. Wirthlin, Morgan Dikow, China National GeneBank BGI-Shenzhen Shenzhen China State Key Laboratory of Genetic Resources and Evolution Kunming Institute of Zoology Chinese Academy of Sciences Kunming China BGI-Shenzhen Shenzhen China Villum Centre for Biodiversity Genomics Section for Ecology and Evolution Department of Biology University of Copenhagen Copenhagen Denmark BGI Education Center University of Chinese Academy of Sciences Shenzhen China MOE Laboratory of Biosystems Homeostasis and Protection Life Sciences Institute Zhejiang University Hangzhou China Department of Neuroscience and Developmental Biology University of Vienna Vienna Austria Center for Excellence in Animal Evolution and Genetics Chinese Academy of Sciences Kunming China James D. Watson Institute of Genome Sciences Hangzhou China UC Santa Cruz Genomics Institute UC Santa Cruz Santa Cruz CA USA Natural History Museum of Denmark University of Copenhagen Copenhagen Denmark Center for Macroecology Evolution and Climate GLOBE Institute University of Copenhagen Copenhagen Denmark Department of Biological Sciences Louisiana State University Baton Rouge LA USA Museum of Natural Science Louisiana State University Baton Rouge LA USA Centre of Excellence for Omics-Driven Computational Biodiscovery (COMBio) Faculty of Applied Sciences AIMST University Kedah Malaysia Section for Evolutionary Genomics The GLOBE Institute Faculty of Health and Medical Sciences University of Copenhagen Copenhagen Denmark Institute of Molecular Biology National Academy of Sciences Yerevan Armenia University Museum Norwegian University of Science and Technology Trondheim Norway Center for Reproductive Medicine The 2nd Affiliated Hospital School of Medicine Zhejiang University Hangzhou China Novogene Durham NC USA Duke University Medical Center Durham NC USA The Rockefeller University New York NY USA Howard Hughes Medical Institute Chevy Chase MD USA Department of Dermatology Medical University of Vienna Vienna Austria

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Speech Emotion Recognition Based on SVM and GMM-HMM Hybrid System

Speech Emotion Recognition Based on SVM and GMM-HMM Hybrid S...

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第十四届全国人机语音通讯学术会议

作者： Kaiyu Shi Xuan Liu Yanmin Qian 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

Speech emotion recognition is one of the latest challenges in speech processing,we implemented a hybrid system for speech emotion *** methods were proposed,compared and *** first,we utilized GMM-HMM model fed with MFCC feature to exploit the dynamics of emotional *** a balanced SVM classifier was applied on the static LLD *** order to take advantage of generative model and discriminate model,these two methods were combined by taking the probability representation of the GMM-HMM model as the additional features for the SVM *** hybrid system makes full use of both MFCC feature and LLD feature,the performance of the hybrid system also proves the effectiveness of our proposed system.

关键词： speech emotion recognition SVM GMMHMM hybrid system

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Author Correction: Pan-cancer analysis of whole genomes identifies driver rearrangements promoted by LINE-1 retrotransposition

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Nature genetics 2023年第6期55卷 1080页

作者： Bernardo Rodriguez-Martin Eva G Alvarez Adrian Baez-Ortega Jorge Zamora Fran Supek Jonas Demeulemeester Martin Santamarina Young Seok Ju Javier Temes Daniel Garcia-Souto Harald Detering Yilong Li Jorge Rodriguez-Castro Ana Dueso-Barroso Alicia L Bruzos Stefan C Dentro Miguel G Blanco Gianmarco Contino Daniel Ardeljan Marta Tojo Nicola D Roberts Sonia Zumalave Paul A Edwards Joachim Weischenfeldt Montserrat Puiggròs Zechen Chong Ken Chen Eunjung Alice Lee Jeremiah A Wala Keiran M Raine Adam Butler Sebastian M Waszak Fabio C P Navarro Steven E Schumacher Jean Monlong Francesco Maura Niccolo Bolli Guillaume Bourque Mark Gerstein Peter J Park David C Wedge Rameen Beroukhim David Torrents Jan O Korbel Iñigo Martincorena Rebecca C Fitzgerald Peter Van Loo Haig H Kazazian Kathleen H Burns Peter J Campbell Jose M C Tubio Department of Zoology Genetics and Physical Anthropology Universidade de Santiago de Compostela Santiago de Compostela Spain. Biomedical Research Centre (CINBIO) University of Vigo Vigo Spain. Centre for Research in Molecular Medicine and Chronic Diseases (CIMUS) Universidade de Santiago de Compostela Santiago de Compostela Spain. Transmissible Cancer Group Department of Veterinary Medicine University of Cambridge Cambridge UK. The Biomedical Research Centre (CINBIO) Universidade de Vigo Vigo Spain. Wellcome Sanger Institute Wellcome Genome Campus Cambridge UK. Genome Data Science Institute for Research in Biomedicine (IRB Barcelona) The Barcelona Institute of Science and Technology (BIST) Barcelona Spain. Institucio Catalana de Recerca i Estudis Avançats (ICREA) Barcelona Spain. The Francis Crick Institute London UK. Department of Human Genetics University of Leuven Leuven Belgium. Genomes and Disease Centre for Research in Molecular Medicine and Chronic Diseases (CIMUS) Universidade de Santiago de Compostela Santiago de Compostela Spain. Graduate School of Medical Science and Engineering Korea Advanced Institute of Science and Technology Daejeon South Korea. Cancer Ageing and Somatic Mutation Programme Wellcome Sanger Institute Cambridge UK. Department of Biochemistry Genetics and Immunology University of Vigo Vigo Spain. Galicia Sur Health Research Institute Vigo Spain. Faculty of Science and Technology University of Vic-Central University of Catalonia (UVic-UCC) Vic Spain. Barcelona Supercomputing Center (BSC) Barcelona Spain. Experimental Cancer Genetics Wellcome Sanger Institute Cambridge UK. Oxford Big Data Institute University of Oxford Oxford UK. DNA Repair and Genome Integrity Centre for Research in Molecular Medicine and Chronic Diseases (CIMUS) Universidade de Santiago de Compostela Santiago de Compostela Spain. Department of Biochemistry and Molecular Biology Universidade de Santiago de Compostela Santiago de Compostela Spain. Medical R

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SMALL-FOOTPRINT CONVOLUTIONAL NEURAL NETWORK FOR SPOOFING DETECTION

SMALL-FOOTPRINT CONVOLUTIONAL NEURAL NETWORK FOR SPOOFING DE...

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International Joint Conference on Neural Networks

作者： Heinrich Dinkel Yanmin Qian Kai Yu 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

ISBN: (纸本)9781509061839

Albeit recent progress in speaker verification engendered powerful models, malicious attacks in the form of spoofed speech, are generally not coped with. In previous attempts, deep neural networks were used to extract high dimensional features which were later classified using an independent classifier. Even though the results of this approach are promising, this architecture's disadvantage is it's complexity of optimizing both, neural network and back-end classifier. In this paper we present a simplified neural network approach to address this problem based on the convolutional neural network architecture. Our model concatenates the output of all abstract convolutional representations within the network into a single high-dimensional vector. By preserving all the information within the network, the networks generalization capabilities are greatly enhanced, resulting in an favorable error rate of 5.4% on the S10 condition. Scores are frame wise obtained by directly extracting the posteriors from the output neurons and further reduced to an utterance score by the use of variance reduction. We show that by using variance posterior score reduction, large performance gains can be achieved. This model outperforms standard feature extracting neural network approaches, in addition on being more versatile, robust and faster to train. Our best model achieves an error rate of 0.7% on the ASVspoof corpus, utilizing common PLP features. It significantly outperforms conventional feature extraction neural networks, while only having 100k parameters.

关键词： Neural network Error analysis Feature extraction speaker recognition Classifiers Information Network neural net architecture

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Fusion Model for Speech Emotion Recognition with Low Level Descriptor Features

Fusion Model for Speech Emotion Recognition with Low Level D...

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第十四届全国人机语音通讯学术会议

作者： Cheng Chang Huifeng Zhang Zhangxuan Gu Yanmin Qian 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

Speech emotion recognition is one of the most challenging speech processing tasks,with many applications in the field of human-Machine interaction(HMI).Traditional works have been using Gaussian Mixture Models(GMM) for *** recent researches,Deep Neural Networks(DNN) have shown strong ability in feature learning and modeling in many *** this paper,we present to utilize the DNN to learn extra features from Low Level Descriptor(LLD) features for other classifiers,and propose a decision-level fusion model for speech emotion *** carry out experiments and evaluations on a public German corpus called FAU-AIBO with five *** our experimental results using LLD features demonstrate that our proposed approach improves the recognition performance on unweighted results and outperforms the baseline using GMM significantly.

关键词： speech emotion recognition deep neural network low level descriptor fusion

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GLEU-guided Multi-Resolution Network for Short Text Conversation

GLEU-guided Multi-Resolution Network for Short Text Conversa...

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第十四届全国人机语音通讯学术会议

作者： Xuan Liu Kai Yu 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

With the recent development of sequence-to-sequence framework,generation approach for short text conversation becomes *** sequence-to-sequence method for short text conversation often suffers from dull response ***-resolution generation approach has been introduced to address this problem by dividing the generation process into two steps:keywords-sequence generation and response ***,this method still tends to generate short and common *** this work,a new multi-resolution generation framework is *** 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 *** show that the proposed approach can generate longer and more diverse ***,it achieves better score in human evaluation.

关键词： short text conversation sequence-to-sequence policy gradient

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NUSANTARA: A New Model of Knowledge Management in Government human Capital Management

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Procedia computer science 2017年 124卷 61-68页

作者： Elin Cahyaningsih Dana Indra Sensuse Aniati Murni Arymurthy Wahyu Catur Wibowo e-Government & e-Business Laboratory Faculty of Computer Science University of Indonesia Depok Indonesia Human Resource Department National Civil Service Agency Jakarta Indonesia

Knowledge management is one of organizational strategy to improve the organizational competitive value. Align with presidential regulation No. 81 Year 2010 about bureaucratic reform and PERMENPAN&RB No. 20 year 2010 about bureaucratic reform roadmap declare that one bureaucratic reform objective are human capital development. This program is important to increase the public service. Three government ministries based on the UU No. 5 Year 2014 about Government human Capital assigned to manage them. This research aims to develop the model of knowledge management for government human capital management. Research object implicate State Ministry for State Apparatus Reform (KEMENPAN & RB), National Civil Service Agencies (BKN) and National Institute of Public Administration (LAN). While the research stages are identify the strategic issue, develop the theoretical model, evaluate the theoretical model, identify the element model and knowledge and the last develop the model. Data analysis uses multiple methods in qualitative and quantitative technique. This model was developed using hybrid and synthesis method from the theoretical model of Indonesian knowledge management, knowledge management solution and foundation and the APO framework KMP. The model of government human capital knowledge management of Republic Indonesia (NUSANTARA) consist of eight component there are vision and mission, CSF, KM Mechanisms and Technologies, KMS, KM cycle, KM process, organizational core knowledge and outcome (government public services).

关键词： knowledge management knowledge management model government human capital management government human capital Knowledge management solution foundation

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Emotion Recognition Using Support Vector Machine and Deep Neural Network

Emotion Recognition Using Support Vector Machine and Deep Ne...

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第十四届全国人机语音通讯学术会议

作者： Chen Ruinian Zhou Ying Qian Yanmin 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

We propose a classification system which is a combination of three subsystem to solve the speech emotion recognition *** first subsystem utilizes support vector machines(SVMs) to classify the features *** second subsystem utilizes deep neural networks(DNNs) to classify the features *** the third subsystem,we utilize DNNs to extract high level features from raw data and show that they are effective for speech emotion *** first produce an emotion state probability distribution for each speech segment using DNNs,then we construct utterance-level features from segment-level probability *** utterance level features are fed into an SVM to identify utterance-level *** experimental results show that all the subsystems are outperform the HMM baseline system,and the combined system get the best result on F-score.

关键词： Emotion recognition Deep neural networks Support vector machine

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Learning Deep Representations with Diode Loss for Quantization-based Similarity Search

Learning Deep Representations with Diode Loss for Quantizati...

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International Joint Conference on Neural Networks

作者： Shicong Liu Hongtao Lu Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Department of Computer Science and Engineering Shanghai Jiao Tong University P.R. China

ISBN: (纸本)9781509061839

Recent advance of large scale similarity search involves using deeply learned representations to improve the search accuracy and apply vector quantization techniques to accelerate the search speed. However, simultaneous learning of deep representations and vector quantizers still remains ineffective. To this end, we propose to directly optimize the asymmetric distance between a query representation and the quantized database representations. A novel diode loss is proposed, it wraps a commonly used similarity loss function and then it allows effective end-to-end learning of both deep representations and vector quantizers with a siamese network. The proposed learning framework is compatible with various existing vector quantization approaches, and is compatible with commonly used loss functions for learning representations preserving similarities. Experimental results demonstrate that the proposed framework is effective, flexible and outperforms the state-of-the-art large scale similarity search methods.

关键词： image retrieval learning (artificial intelligence) optimisation vector quantisation

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