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Considerations and recommendations from the ISMRM Diffusion Study Group for preclinical diffusion MRI: Part 3 — Ex vivo imaging: data processing, comparisons with microscopy, and tractography

arXiv

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arXiv 2024年

作者： Schilling, Kurt G. Howard, Amy F.D. Grussu, Francesco Ianus, Andrada Hansen, Brian Barrett, Rachel L.C. Aggarwal, Manisha Michielse, Stijn Nasrallah, Fatima Syeda, Warda Wang, Nian Veraart, Jelle Roebroeck, Alard Bagdasarian, Andrew F. Eichner, Cornelius Sepehrband, Farshid Zimmermann, Jan Soustelle, Lucas Bowman, Christien Tendler, Benjamin C. Hertanu, Andreea Jeurissen, Ben Verhoye, Marleen Frydman, Lucio van de Looij, Yohan Hike, David Dunn, Jeff F. Miller, Karla Landman, Bennett A. Shemesh, Noam Anderson, Adam McKinnon, Emilie Farquharson, Shawna Dell' Acqua, Flavio Pierpaoli, Carlo Drobnjak, Ivana Leemans, Alexander Harkins, Kevin D. Descoteaux, Maxime Xu, Duan Huang, Hao Santin, Mathieu D. Grant, Samuel C. Obenaus, Andre Kim, Gene S. Wu, Dan Le Bihan, Denis Blackband, Stephen J. Ciobanu, Luisa Fieremans, Els Bai, Ruiliang Leergaard, Trygve B. Zhang, Jiangyang Dyrby, Tim B. Johnson, G. Allan Cohen-Adad, Julien Budde, Matthew D. Jelescu, Ileana O. Radiology and Radiological Sciences Vanderbilt University Medical Center NashvilleTN United States Vanderbilt University Institute of Imaging Science Vanderbilt University NashvilleTN United States Department of Bioengineering Imperial College London London United Kingdom FMRIB Centre Wellcome Centre for Integrative Neuroimaging Nuffield Department of Clinical Neurosciences University of Oxford Oxford United Kingdom Radiomics Group Vall d’Hebron Institute of Oncology Vall d’Hebron Barcelona Hospital Campus Barcelona Spain Queen Square MS Centre Queen Square Institute of Neurology Faculty of Brain Sciences University College London London United Kingdom Champalimaud Research Champalimaud Foundation Lisbon Portugal Center of Functionally Integrative Neuroscience Aarhus University Aarhus Denmark Department of Neuroimaging Institute of Psychiatry Psychology and Neuroscience King's College London London United Kingdom NatBrainLab Department of Forensics and Neurodevelopmental Sciences Institute of Psychiatry Psychology and Neuroscience King's College London London United Kingdom Russell H. Morgan Department of Radiology and Radiological Science Johns Hopkins University School of Medicine BaltimoreMD United States Maastricht University Medical Center Maastricht Netherlands The Queensland Brain Institute The University of Queensland Queensland Australia Melbourne Neuropsychiatry Centre The University of Melbourne ParkvilleVIC Australia Department of Radiology and Imaging Sciences Indiana University IN United States Stark Neurosciences Research Institute Indiana University School of Medicine IN United States Center for Biomedical Imaging NYU Grossman School of Medicine New YorkNY United States Faculty of psychology and Neuroscience Maastricht University Maastricht Netherlands Department of Chemical & Biomedical Engineering FAMU-FSU College of Engineering Florida State University TallahasseeFL United States Center for Interdisciplinary Magnetic Res

Preclinical diffusion MRI (dMRI) has proven value in methods development and validation, characterizing the biological basis of diffusion phenomena, and comparative anatomy. While dMRI enables in vivo non-invasive characterization of tissue, ex vivo dMRI is increasingly being used to probe tissue microstructure and brain connectivity. Ex vivo dMRI has several experimental advantages that facilitate high spatial resolution and high signal-to-noise ratio (SNR) images, cutting-edge diffusion contrasts, and direct comparison with histological data as a methodological validation. However, there are a number of considerations that must be made when performing ex vivo experiments. The steps from tissue preparation, image acquisition and processing, and interpretation of results are complex, with many decisions that not only differ dramatically from in vivo imaging of small animals, but ultimately affect what questions can be answered using the data. This work concludes a 3-part series of recommendations and considerations for preclinical dMRI. Herein, we describe best practices for dMRI of ex vivo tissue, with a focus on image pre-processing, data processing and model fitting, and tractography. In each section, we attempt to provide guidelines and recommendations, but also highlight areas for which no guidelines exist (and why), and where future work should lie. We end by providing guidelines on code sharing and data sharing, and point towards open-source software and databases specific to small animal and ex vivo imaging. © 2024, CC BY.

关键词： Data Sharing

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Applications of Game Theory in Vehicular Networks: A Survey

arXiv

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arXiv 2021年

作者： Sun, Zemin Liu, Yanheng Wang, Jian Li, Guofa Anil, Carie Li, Keqiang Guo, Xinyu Sun, Geng Tian, Daxin Cao, Dongpu College of Computer Science and Technology Jilin University Changchun130012 China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun130012 China Institute of Human Factors and Ergonomics College of Mechatronics and Control Engineering Shenzhen University Guangdong Shenzhen518060 China School of Computer Science and Engineering VIT-AP Andhra Pradesh Amaravati522237 India College of Engineering Nanjing Agricultural University Nanjing210031 China State Key Laboratory of Automotive Safety and Energy Department of Automotive Engineering Tsinghua University Beijing100084 China Beijing Advanced Innovation Center for Big Data and Brain Computing Beijing Key Laboratory for Cooperative Vehicle Infrastructure Systems and Safety Control School of Transportation Science and Engineering Beihang University Beijing100191 China Waterloo Cognitive Autonomous Driving Lab University of Waterloo N2L 3G1 Canada

In the Internet of things (IoT) era, vehicles and other intelligent components in an intelligent transportation system (ITS) are connected, forming vehicular networks (VNs) that provide efficient and safe traffic and ubiquitous access to various applications. However, as the number of nodes in an ITS increases, it is challenging to satisfy a varied and large number of service requests with different quality of service (QoS) and security requirements in highly dynamic VNs. Intelligent nodes in VNs can compete or cooperate for limited network resources to achieve the objective for either an individual or a group. Game theory (GT), a theoretical framework designed for strategic interactions among rational decision makers sharing scarce resources, can be used to model and analyze individual or group behaviors of communicating entities in VNs. This paper primarily surveys the recent developments of GT in solving various challenges of VNs. This survey starts with an introduction to the background of VNs. A review of GT models studied in the VNs is then introduced, including the basic concepts, classifications, and applicable vehicular issues. After discussing the requirements of VNs and the motivation of using GT, a comprehensive literature review on GT applications in dealing with the challenges of current VNs is provided. Furthermore, recent contributions of GT to VNs that are integrated with diverse emerging 5G technologies are surveyed. Finally, the lessons learned are given, and several key research challenges and possible solutions of applying GT in VNs are outlined. Copyright © 2021, The Authors. All rights reserved.

关键词： Quality of service

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Control flow in active inference systems

arXiv

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arXiv 2023年

作者： Fields, Chris Fabrocini, Filippo Friston, Karl Glazebrook, James F. Hazan, Hananel Levin, Michael Marcianò, Antonino Allen Discovery Center Tufts University MedfordMA02155 United States College of Design and Innovation Tongji University 281 Fuxin Rd Shanghai200092 China Institute for Computing Applications "Mario Picone" Italy National Research Council Via dei Taurini 19 Rome00185 Italy Wellcome Centre for Human Neuroimaging University College London LondonWC1N 3AR United Kingdom VERSES Research Lab Los AngelesCA90016 United States Department of Mathematics and Computer Science Eastern Illinois University CharlestonIL61920 United States Adjunct Faculty Department of Mathematics University of Illinois at Urbana-Champaign UrbanaIL61801 United States Wyss Institute for Biologically Inspired Engineering Harvard University BostonMA02115 United States Center for Field Theory and Particle Physics Department of Physics Fudan University Shanghai China Laboratori Nazionali di Frascati INFN Frascati Rome Italy INFN sezione Roma "Tor Vergata" RomeI-00133 Italy

Living systems face both environmental complexity and limited access to free-energy resources. Survival under these conditions requires a control system that can activate, or deploy, available perception and action resources in a context specific way. We show here that when systems are described as executing active inference driven by the free-energy principle (and hence can be considered Bayesian prediction-error minimizers), their control flow systems can always be represented as tensor networks (TNs). We show how TNs as control systems can be implmented within the general framework of quantum topological neural networks, and discuss the implications of these results for modeling biological systems at multiple scales. © 2023, CC BY.

关键词： Free energy

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Attribute-Driven Feature Disentangling and Temporal Aggregation for Video Person Re-Identification

Attribute-Driven Feature Disentangling and Temporal Aggregat...

引用

IEEE/CVF Conference on computer Vision and Pattern Recognition

作者： Yiru Zhao Xu Shen Zhongming Jin Hongtao Lu Xian-sheng Hua Key Lab of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Department of Computer Science and Engineering MoE Key Lab of Artificial Intelligence AI Institute Shanghai Jiao Tong University Alibaba Damo Academy Alibaba Group

ISBN: (纸本)9781728132945

Video-based person re-identification plays an important role in surveillance video analysis, expanding image-based methods by learning features of multiple frames. Most existing methods fuse features by temporal average-pooling, without exploring the different frame weights caused by various viewpoints, poses, and occlusions. In this paper, we propose an attribute-driven method for feature disentangling and frame re-weighting. The features of single frames are disentangled into groups of sub-features, each corresponds to specific semantic attributes. The sub-features are re-weighted by the confidence of attribute recognition and then aggregated at the temporal dimension as the final representation. By means of this strategy, the most informative regions of each frame are enhanced and contributes to a more discriminative sequence representation. Extensive ablation studies verify the effectiveness of feature disentangling as well as temporal re-weighting. The experimental results on the iLIDS-VID, PRID-2011 and MARS datasets demonstrate that our proposed method outperforms existing state-of-the-art approaches.

关键词： Frame FRAMES Persons Videotapes Occlusion confidence Single frame

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INCREASE APPARENT PUBLIC SPEAKING FLUENCY BY SPEECH AUGMENTATION

arXiv

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arXiv 2018年

作者： Das, Sagnik Gandhi, Nisha Naik, Tejas Shilkrot, Roy Human Interaction Lab Stony Brook University Department of Computer Science Stony BrookNY United States

Fluent and confident speech is desirable to every speaker. But professional speech delivering requires a great deal of experience and practice. In this paper, we propose a speech stream manipulation system which can help non-professional speakers to produce fluent, professional-like speech content, in turn contributing towards better listener engagement and comprehension. We propose to achieve this task by manipulating the disfluencies in human speech, like the sounds uh and um, the filler words and awkward long silences. Given any unrehearsed speech we segment and silence the filled pauses and doctor the duration of imposed silence as well as other long pauses (disfluent) by a predictive model learned using professional speech dataset. Finally, we output a audio stream in which speaker sounds more fluent, confident and practiced compared to the original speech he/she recorded. According to our quantitative evaluation, we significantly increase the fluency of speech by reducing rate of pauses and fillers. Copyright © 2018, The Authors. All rights reserved.

关键词： Fillers

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An incremental turn-taking model for task-oriented dialog systems

arXiv

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arXiv 2019年

作者： Coman, Andrei C. Yoshino, Koichiro Murase, Yukitoshi Nakamura, Satoshi Riccardi, Giuseppe Signals and Interactive Systems Lab Department of Information Engineering and Computer Science University of Trento Italy Augmented Human Communication Laboratory Graduate School of Information Science Nara Institute of Science and Technology Japan

In a human-machine dialog scenario, deciding the appropriate time for the machine to take the turn is an open research problem. In contrast, humans engaged in conversations are able to timely decide when to interrupt the speaker for competitive or non-competitive reasons. In state-of-the-art turn-by-turn dialog systems the decision on the next dialog action is taken at the end of the utterance. In this paper, we propose a token-by-token prediction of the dialog state from incremental transcriptions of the user utterance. To identify the point of maximal understanding in an ongoing utterance, we a) implement an incremental Dialog State Tracker which is updated on a token basis (iDST) b) re-label the Dialog State Tracking Challenge 2 (DSTC2) dataset and c) adapt it to the incremental turn-taking experimental scenario. The re-labeling consists of assigning a binary value to each token in the user utterance that allows to identify the appropriate point for taking the turn. Finally, we implement an incremental Turn Taking Decider (iTTD) that is trained on these new labels for the turn-taking decision. We show that the proposed model can achieve a better performance compared to a deterministic handcrafted turn-taking algorithm. Copyright © 2019, The Authors. All rights reserved.

关键词： Recurrent neural networks

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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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Structured dialogue policy with graph neural networks 27

Structured dialogue policy with graph neural networks

引用

27th International Conference on Computational Linguistics, COLING 2018

作者： Chen, Lu Tan, Bowen Long, Sishan 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

ISBN: (纸本)9781948087506

Recently, deep reinforcement learning (DRL) has been used for dialogue policy optimization. However, many DRL-based policies are not sample-efficient. Most recent advances focus on improving DRL optimization algorithms to address this issue. Here, we take an alternative route of designing neural network structure that is better suited for DRL-based dialogue management. The proposed structured deep reinforcement learning is based on graph neural networks (GNN), which consists of some sub-networks, each one for a node on a directed graph. The graph is defined according to the domain ontology and each node can be considered as a sub-agent. During decision making, these sub-agents have internal message exchange between neighbors on the graph. We also propose an approach to jointly optimize the graph structure as well as the parameters of GNN. Experiments show that structured DRL significantly outperforms previous state-of-the-art approaches in almost all of the 18 tasks of the PyDial benchmark. © 2018 COLING 2018 - 27th International Conference on Computational Linguistics, Proceedings. All rights reserved.

关键词： Reinforcement learning

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How End Users Express Conditionals in Programming by Demonstration for Mobile Apps

How End Users Express Conditionals in Programming by Demonst...

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IEEE Symposium on Visual Languages and human Centric Computing (VL/HCC)

作者： Marissa Radensky Toby Jia-Jun Li Brad A. Myers Computer Science Department Amherst College Amherst MA Human-Computer Interaction Institute Carnegie Mellon University Pittsburgh PA

Though conditionals are an integral component of programming, providing an easy means of creating conditionals remains a challenge for programming-by-demonstration (PBD) systems for task automation. We hypothesize that a promising method for implementing conditionals in such systems is to incorporate the use of verbal instructions. Verbal instructions supplied concurrently with demonstrations have been shown to improve the generalizability of PBD. However, the challenge of supporting conditional creation using this multi-modal approach has not been addressed. In this extended abstract, we present our study on understanding how end users describe conditionals in natural language for mobile app tasks. We conducted a formative study of 56 participants asking them to verbally describe conditionals in different settings for 9 sample tasks and to invent conditional tasks. Participant responses were analyzed using open coding and revealed that, in the context of mobile apps, end users often omit desired else statements when explaining conditionals, sometimes use ambiguous concepts in expressing conditionals, and often desire to implement complex conditionals. Based on these findings, we discuss the implications for designing a multimodal PBD interface to support the creation of conditionals.

关键词： Task analysis Programming Natural languages Automation Encoding Public transportation Visualization

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Predicting risk of dyslexia with an online gamified test

arXiv

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arXiv 2019年

作者： Rello, Luz Baeza-Yates, Ricardo Ali, Abdullah Bigham, Jeffrey P. Serra, Miquel Department of Information Systems and Technology IE University Madrid Spain KCCIS Northeastern University at Silicon Valley San JoseCA United States School of Computer Science University of Washington SeattleWA United States Human-Computer Interaction Institute Carnegie Mellon University PittsburghPA United States Department of Cognition and Development University of Barcelona Barcelona Spain

Dyslexia is a specific learning disorder related to school failure. Detection is both crucial and challenging, especially in languages with transparent orthographies, such as Spanish. To make detecting dyslexia easier, we designed an online gamified test and associated predictive machine learning model. In a study with more than 4,300 participants, our model correctly detected nearly 80% of participants with dyslexia. Our work shows that dyslexia can be screened using a computational approach. An online screening tool based on our methods has already been used by more than 200,000 people. Copyright © 2019, The Authors. All rights reserved.

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