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AI-assisted MAC for reconfigurable intelligent surface-aided wireless networks: Challenges and opportunities

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arXiv 2021年

作者： Cao, Xuelin Yang, Bo Huang, Chongwen Yuen, Chau Renzo, Marco Di Han, Zhu Niyato, Dusit Poor, H. Vincent Hanzo, Lajos Engineering Product Development Pillar Singapore University of Technology and Design Singapore Singapore Zhejiang Provincial Key Lab of information processing Communication and networking Zhejiang University China Université Paris-Saclay CNRS CentraleSupélec Laboratoire des Signaux et Systèmes 3 Rue Joliot-Curie Gif-sur-Yvette91192 France Department of Electrical and Computer Engineering University of Houston United States School of Computer Science and Engineering Nanyang Technological University Singapore Singapore Department of Electrical Engineering Princeton University United States School of Electronics and Computer Science University of Southampton United Kingdom

Recently, significant research attention has been devoted to the study of reconfigurable intelligent surfaces (RISs), which are capable of reconfiguring the wireless propagation environment by exploiting the unique properties of metamaterials-based integrated large arrays of inexpensive antennas. Existing research demonstrates that RISs significantly improve the physical layer performance, including the wireless coverage, achievable data rate and energy efficiency. However, the medium access control (MAC) of multiple users accessing an RIS-enabled channel is still in its infancy, while many open issues remain to be addressed. In this article, we present four typical RIS-aided multi-user scenarios with special emphasis on the MAC schemes. We then propose and elaborate upon centralized, distributed and hybrid artificial-intelligence (AI)-assisted MAC architectures in RIS-aided multi-user communications systems. Finally, we discuss some challenges, perspectives and potential applications of RISs as they are related to MAC design. Copyright © 2021, The Authors. All rights reserved.

关键词： Medium access control

Sleepposenet: Multi-View multi-task learning for sleep postural transition recognition using UWB

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arXiv 2020年

作者： Piriyajitakonkij, Maytus Warin, Patchanon Lakhan, Payongkit Leelaarporn, Pitsharponrn Pianpanit, Theerasarn Niparnan, Nattee Mukhopadhyay, Subhas Chandra Wilaiprasitporn, Theerawit Bio-inspired Robotics and Neural Engineering Lab School of Information Science and Technology Vidyasirimedhi Institute of Science & Technology Rayong Thailand Nakhon Nayok Thailand Computer Engineering Department Chulalongkorn University Bangkok Thailand School of Engineering Macquarie University SydneyNSW2109 Australia

Recognizing the movements during sleep is crucial for monitoring of patients with sleep disorders. However, the utilization of the deep learning approaches ultra-wideband (UWB)-based indoor applications for the classification of human sleeping postures has not been explored widely. This study investigates the performance of the off-the-shelf single antenna UWB in a novel application of sleep postural transition (SPT) recognition. The proposed Multi-View Multi-Task Learning, entitled SleepPoseNet or SPN, with time series data augmentation aims to classify four standard SPTs. SPN exhibits an ability to capture both time and frequency features, including the movement and direction of sleeping positions. Three cost functions for multi-views, multi-tasks, and classification are proposed to optimize SPN. The data recorded from 26 volunteers displayed that SPN with mean accuracy of 79.7±0.5% significantly outperformed the mean accuracy of 73.5 ± 0.7% obtained from deep convolution neural network (DCNN) in a recent state-of-the-art work on human activity recognition using UWB. Apart from UWB system, SPN with the data augmentation can ultimately be adopted to learn and classify time series data in various applications. Copyright © 2020, The Authors. All rights reserved.

关键词： Convolution

Versatile Multiple Choice Learning and Its Application to Vision Computing

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Versatile Multiple Choice Learning and Its Application to Vi...

IEEE/CVF Conference on computer Vision and Pattern Recognition

作者： Kai Tian Yi Xu Shuigeng Zhou Jihong Guan Shanghai Key Lab of Intelligent Information Processing and School of Computer Science Fudan University Department of Computer Science & Technology Tongji University

ISBN: (纸本)9781728132945

Most existing ensemble methods aim to train the underlying embedded models independently and simply aggregate their final outputs via averaging or weighted voting. As many prediction tasks contain uncertainty, most of these ensemble methods just reduce variance of the predictions without considering the collaborations among the ensembles. Different from these ensemble methods, multiple choice learning (MCL) methods exploit the cooperation among all the embedded models to generate multiple diverse hypotheses. In this paper, a new MCL method, called vMCL (the abbreviation of versatile Multiple Choice Learning), is developed to extend the application scenarios of MCL methods by ensembling deep neural networks. Our vMCL method keeps the advantage of existing MCL methods while overcoming their major drawback, thus achieves better performance. The novelty of our vMCL lies in three aspects: (1) a choice network is designed to learn the confidence level of each specialist which can provide the best prediction base on multiple hypotheses;(2) a hinge loss is introduced to alleviate the overconfidence issue in MCL settings;(3) Easy to be implemented and can be trained in an end-to-end manner, which is a very attractive feature for many real-world applications. Experiments on image classification and image segmentation task show that vMCL outperforms the existing state-of-the-art MCL methods.

关键词： Choice learning MAXIMUM CONTAMINANT LEVELS end-to-end Image classification image segmentation Specialists averaging

Does my rebuttal matter? Insights from a major NLP conference

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arXiv 2019年

作者： Gao, Yang Eger, Steffen Kuznetsov, Ilia Gurevych, Iryna Miyao, Yusuke Ubiquitous Knowledge Processing Lab Department of Computer Science Technische Universität Darmstadt Department of Computer Science Graduate School of Information Science and Technology University of Tokyo

Peer review is a core element of the scientific process, particularly in conference-centered fields such as ML and NLP. However, only few studies have evaluated its properties empirically. Aiming to fill this gap, we present a corpus that contains over 4k reviews and 1.2k author responses from ACL-2018. We quantitatively and qualitatively assess the corpus. This includes a pilot study on paper weaknesses given by reviewers and on quality of author responses. We then focus on the role of the rebuttal phase, and propose a novel task to predict after-rebuttal (i.e., final) scores from initial reviews and author responses. Although author responses do have a marginal (and statistically significant) influence on the final scores, especially for borderline papers, our results suggest that a reviewer's final score is largely determined by her initial score and the distance to the other reviewers' initial scores. In this context, we discuss the conformity bias inherent to peer reviewing, a bias that has largely been overlooked in previous research. We hope our analyses will help better assess the usefulness of the rebuttal phase in NLP conferences. Copyright © 2019, The Authors. All rights reserved.

关键词： Natural language processing systems

Domain-invariant similarity activation map contrastive learning for retrieval-based long-term visual localization

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arXiv 2020年

作者： Hu, Hanjiang Wang, Hesheng Liu, Zhe Chen, Weidong Autonomous Robot Lab Department of Automation Key Laboratory of System Control and Information Processing Ministry of Education Shanghai Jiao Tong University Shanghai200240 China The State Key Laboratory of Robotics and System HIT The Department of Computer Science and Technology University of Cambridge United Kingdom

Visual localization is a crucial component in the application of mobile robot and autonomous driving. Image retrieval is an efficient and effective technique in image-based localization methods. Due to the drastic variability of environmental conditions, e.g. illumination, seasonal and weather changes, retrieval-based visual localization is severely affected and becomes a challenging problem. In this work, a general architecture is first formulated probabilistically to extract domain-invariant feature through multi-domain image translation. And then a novel gradient-weighted similarity activation mapping loss (Grad-SAM) is incorporated for finer localization with high accuracy. We also propose a new adaptive triplet loss to boost the contrastive learning of the embedding in a self-supervised manner. The final coarse-to-fine image retrieval pipeline is implemented as the sequential combination of models without and with Grad-SAM loss. Extensive experiments have been conducted to validate the effectiveness of the proposed approach on the CMU-Seasons dataset. The strong generalization ability of our approach is verified on RobotCar dataset using models pre-trained on urban part of CMU-Seasons dataset. Our performance is on par with or even outperforms the state-of-the-art image-based localization baselines in medium or high precision, especially under the challenging environments with illumination variance, vegetation and night-time images. The code and pretrained models are available on https://***/HanjiangHu/DISAM. Copyright © 2020, The Authors. All rights reserved.

关键词： Image retrieval

Computational modeling of ketamine-induced changes in gamma-band oscillations: The contribution of parvalbumin and somatostatin interneurons

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PLoS computational biology 2025年第6期21卷 e1013118页

作者： Jessie Rademacher Tineke Grent-'t-Jong Davide Rivolta Andreas Sauer Bertram Scheller Guillermo Gonzalez-Burgos Christoph Metzner Peter J Uhlhaas Department of Child and Adolescent Psychiatry Charité Universitätsmedizin Berlin Germany. Department of Education Psychology and Communication University of Bari Aldo Moro Bari Italy. Max Planck Institute for Brain Research Frankfurt am Main Germany. Ernst Strüngmann Institute (ESI) for Neuroscience in Cooperation with Max Planck Society Frankfurt am Main Germany. SRH University Department of Applied Psychology Heidelberg Germany. Department of Anesthesiology and Intensive Care Medicine St. Josefs Hospital Wiesbaden Germany. Department of Psychiatry University of Pittsburgh Pittsburgh Pennsylvania United States of America. Neural Information Processing Group Institute of Software Engineering and Theoretical Computer Science Technische Universität Berlin Berlin Germany. School of Physics Engineering and Computer Science University of Hertfordshire Hatfield United Kingdom.

Ketamine, an NMDA receptor (NMDA-R) antagonist, produces psychotomimetic effects when administered in sub-anesthetic dosages. While previous research suggests that Ketamine alters the excitation/inhibition (E/I)-balance in cortical microcircuits, the precise neural mechanisms by which Ketamine produces these effects are not well understood. We analyzed resting-state MEG data from n = 12 participants who were administered Ketamine to assess changes in gamma-band (30-90 Hz) power and the slope of the aperiodic power spectrum compared to placebo. In addition, correlations of these effects with gene-expression of GABAergic interneurons and NMDA-Rs subunits were analyzed. Finally, we compared Ketamine-induced spectral changes to the effects of systematically changing NMDA-R levels on pyramidal cells, and parvalbumin-, somatostatin- and vasoactive intestinal peptide-expressing interneurons in a computational model of cortical layer-2/3 to identify crucial sites of Ketamine action. Ketamine resulted in a flatter aperiodic slope and increased gamma-band power across brain regions, with pronounced effects in prefrontal and central areas. These effects were correlated with the spatial distribution of parvalbumin and GluN2D gene expression. Computational modeling revealed that reduced NMDA-R activity in parvalbumin or somatostatin interneurons could reproduce increased gamma-band power by increasing pyramidal neuron firing rate, but did not account for changes in the aperiodic slope. The results suggest that parvalbumin and somatostatin interneurons may underlie increased gamma-band power following Ketamine administration in healthy volunteers, while changes in the aperiodic component could not be recreated. These findings have implications for current models of E/I-balance, as well as for understanding the mechanisms underlying the circuit effects of Ketamine.

关键词： Transcription

Identifying word translations in scientific literature based on labeled bilingual topic model and co-occurrence features 17th

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Identifying word translations in scientific literature based...

17th China National Conference on Computational Linguistics, CCL 2018 and 6th International Symposium on Natural Language processing Based on Naturally Annotated Big Data, NLP-NABD 2018

作者： Tian, Mingjie Zhao, Yahui Cui, Rongyi Intelligent Information Processing Lab Department of Computer Science and Technology Yanbian University Yanji133002 China

ISBN: (纸本)9783030017156

Aiming at the increasingly rich multi language information resources and multi-label data in scientific literature, in order to mining the relevance and correlation in languages, this paper proposed the labeled bilingual topic model and co-occurrence feature based similarity metric which could be adopted to the word translation identifying task. First of all, it could assume that the keywords in the scientific literature are relevant to the abstract in the same article, then extracted the keywords and regard it as labels, labels with topics are assigned and the "latent" topic was instantiated. Secondly, the abstracts in article were trained by the labeled bilingual topic model and got the word representation on the topic distribution. Finally, the most similar word between both languages was matched with similarity metric proposed in this paper. The experiment result shows that the labeled bilingual topic model reaches better precision than "latent" topic model based bilingual model, and co-occurrence features enhance the attractiveness of the bilingual word pairs to improve the identifying effects. © Springer Nature Switzerland AG 2018.

关键词： Abstracting

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 2023年第6期20卷 824-835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

BigNeuron is an open community bench-testing platform with the goal of setting open standards for accurate and fast automatic neuron tracing. We gathered a diverse set of image volumes across several species that is representative of the data obtained in many neuroscience laboratories interested in neuron tracing. Here, we report generated gold standard manual annotations for a subset of the available imaging datasets and quantified tracing quality for 35 automatic tracing algorithms. The goal of generating such a hand-curated diverse dataset is to advance the development of tracing algorithms and enable generalizable benchmarking. Together with image quality features, we pooled the data in an interactive web application that enables users and developers to perform principal component analysis, t-distributed stochastic neighbor embedding, correlation and clustering, visualization of imaging and tracing data, and benchmarking of automatic tracing algorithms in user-defined data subsets. The image quality metrics explain most of the variance in the data, followed by neuromorphological features related to neuron size. We observed that diverse algorithms can provide complementary information to obtain accurate results and developed a method to iteratively combine methods and generate consensus reconstructions. The consensus trees obtained provide estimates of the neuron structure ground truth that typically outperform single algorithms in noisy datasets. However, specific algorithms may outperform the consensus tree strategy in specific imaging conditions. Finally, to aid users in predicting the most accurate automatic tracing results without manual annotations for comparison, we used support vector machine regression to predict reconstruction quality given an image volume and a set of automatic tracings.

关键词：

A Review of Generalized Zero-Shot Learning Methods

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arXiv 2020年

作者： Pourpanah, Farhad Abdar, Moloud Luo, Yuxuan Zhou, Xinlei Wang, Ran Lim, Chee Peng Wang, Xi-Zhao Jonathan Wu, Q.M. The Centre for Computer Vision and Deep Learning Department of Electrical and Computer Engineering University of Windsor WindsorONN9B 3P4 Canada Deakin University Australia The Department of Computer Science City University of Hong Kong Hong Kong The College of Mathematics and Statistics Shenzhen Key Lab. of Advanced Machine Learning and Applications Shenzhen University Shenzhen518060 China The College of Computer Science and Software Engineering Guangdong Key Lab. of Intelligent Information Processing Shenzhen University Shenzhen518060 China

Generalized zero-shot learning (GZSL) aims to train a model for classifying data samples under the condition that some output classes are unknown during supervised learning. To address this challenging task, GZSL leverages semantic information of the seen (source) and unseen (target) classes to bridge the gap between both seen and unseen classes. Since its introduction, many GZSL models have been formulated. In this review paper, we present a comprehensive review on GZSL. Firstly, we provide an overview of GZSL including the problems and challenges. Then, we introduce a hierarchical categorization for the GZSL methods and discuss the representative methods in each category. In addition, we discuss the available benchmark data sets and applications of GZSL, along with a discussion on the research gaps and directions for future investigations. Copyright © 2020, The Authors. All rights reserved.

关键词： Zero-shot learning