检索结果-内蒙古大学图书馆

Spatio-temporal Parking behaviour forecasting and analysis before and during COVID-19

学校读者我要写书评

暂无评论

arXiv 2021年

作者： Gong, Shuhui Mo, Xiaopeng Cao, Rui Liu, Yu Tu, Wei Bai, Ruibin School of Information Engineering China University of Geosciences Beijing Beijing China School of Computer Science University of Nottingham Ningbo China Ningbo China Dept. of LSGI & SCRI The Hong Kong Polytechnic University Hong Kong Institute of Remote Sensing and Geographical Information Systems Peking University Beijing China GD Key Lab. of Urban Informatics Dept. of Urban Informatics Shenzhen University Shenzhen China

Parking demand forecasting and behaviour analysis have received increasing attention in recent years because of their critical role in mitigating traffic congestion and understanding travel behaviours. However, previous studies usually only consider temporal dependence but ignore the spatial correlations among parking lots for parking prediction. This is mainly due to the lack of direct physical connections or observable interactions between them. Thus, how to quantify the spatial correlation remains a significant challenge. To bridge the gap, in this study, we propose a spatial-aware parking prediction framework, which includes two steps, i.e. spatial connection graph construction and spatio-temporal forecasting. A case study in Ningbo, China is conducted using parking data of over one million records before and during COVID-19. The results show that the approach is superior on parking occupancy forecasting than baseline methods, especially for the cases with high temporal irregularity such as during COVID-19. Our work has revealed the impact of the pandemic on parking behaviour and also accentuated the importance of modelling spatial dependence in parking behaviour forecasting, which can benefit future studies on epidemiology and human travel behaviours. © 2021, CC BY-NC-ND.

关键词： Forecasting

Neural architecture search under black-box objectives with deep reinforcement learning and increasingly-sparse rewards 2

学校读者我要写书评

暂无评论

Neural architecture search under black-box objectives with d...

2nd International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020

作者： Alioscha-Perez, Mitchel Berenguer, Abel Diaz Pei, Ercheng Oveneke, Meshia Cedric Sahli, Hichem Vrije Universiteit Brussel VUB-NPU Joint AVSP Lab Electronics and Informatics Dept. Brussels1050 Belgium Northwestern Polytechnical University School of Computer Science VUB-NPU Joint AVSP Lab Xi'an710072 China Interuniv. MicroElectronics Centre Heverlee Belgium

ISBN: (纸本)9781728149851

In this paper, we address the problem of neural architecture search (NAS) in a context where the optimality policy is driven by a black-box Oracle \mathcal{O} with unknown form and derivatives. In this scenario, \mathcal{O}(A-{C}) typically provides readings from a set of sensors on how a neural network architecture A-{C} fares in a target hardware, including its: power consumption, working temperature, \mathbf{cpu}/\mathbf{gpu} usage, central bus occupancy, and more. Current differentiable NAS approaches fail in this problem context due to lack of access to derivatives, whereas traditional reinforcement learning NAS approaches remain too expensive computationally. As solution, we propose a reinforcement learning NAS strategy based on policy gradient with increasingly sparse rewards. We rely on the fact [1] that one does not need to fully train the weights of two neural networks to compare them. Our solution starts by comparing architecture candidates with almost fixed weights and no training, and progressively shifts toward comparisons under full weights training. Experimental results confirmed both the accuracy and training efficiency of our solution, as well as its compliance with soft/hard constraints imposed on the sensors feedback. Our strategy allows finding near-optimal architectures significantly faster, in approximately 1/3 of the time it would take otherwise. © 2020 IEEE.

关键词： Reinforcement learning

Neurite Exchange Imaging (NEXI): A minimal model of diffusion in gray matter with inter-compartment water exchange

学校读者我要写书评

暂无评论

arXiv 2021年

作者： Jelescu, Ileana O. de Skowronski, Alexandre Geffroy, Françoise Palombo, Marco Novikov, Dmitry S. CIBM Center for Biomedical Imaging Animal Imaging and Technology Ecole Polytechnique Fédérale de Lausanne Lausanne Switzerland Lausanne Switzerland Lausanne Switzerland NeuroSpin CEA Saclay France School of Psychology Cardiff University Cardiff United Kingdom School of Computer Science and Informatics Cardiff University Cardiff United Kingdom Centre for Medical Image Computing Dept. of Computer Science University College London London United Kingdom Center for Biomedical Imaging Department of Radiology New York University School of Medicine New YorkNY United States

Biophysical models of diffusion in white matter have been center-stage over the past two decades and are essentially based on what is now commonly referred to as the "Standard Model" (SM) of non-exchanging anisotropic compartments with Gaussian diffusion. In this work, we focus on diffusion MRI in gray matter, which requires rethinking basic microstructure modeling blocks. In particular, at least three contributions beyond the SM need to be considered for gray matter: water exchange across the cell membrane - between neurites and the extracellular space;non-Gaussian diffusion along neuronal and glial processes - resulting from structural disorder;and signal contribution from soma. For the first contribution, we propose Neurite Exchange Imaging (NEXI) as an extension of the SM of diffusion, which builds on the anisotropic Kärger model of two exchanging compartments. Using datasets acquired at multiple diffusion weightings (b) and diffusion times (t) in the rat brain in vivo, we investigate the suitability of NEXI to describe the diffusion signal in the gray matter, compared to the other two possible contributions. Our results for the investigated diffusion time window (~10-45 ms) show minimal diffusivity time-dependence and more pronounced kurtosis decay with time, which is well fit by the exchange model. Moreover, we observe lower signal for longer diffusion times at high b. In light of these observations, we identify exchange as the mechanism that best explains these signal signatures in both low-b and high-b regime, and thereby propose NEXI as the minimal model for gray matter microstructure mapping. We finally highlight multi-b multi-t acquisitions protocols as being best suited to estimate NEXI model parameters reliably. Using this approach, we estimate the inter-compartment water exchange time to be 15 - 60 ms in the rat cortex and hippocampus in vivo, which is of the same order or shorter than the diffusion time in typical diffusion MRI acquisitions. This sugg

关键词： Magnetic resonance imaging

Hardware Acceleration of Monte-Carlo Sampling for Energy Efficient Robust Robot Manipulation

学校读者我要写书评

暂无评论

Hardware Acceleration of Monte-Carlo Sampling for Energy Eff...

International Conference on Field Programmable Logic and Applications

作者： Yanqi Liu Giuseppe Calderoni Ruth Iris Bahar Dept. of Computer Science Brown University Providence RI USA Dept. of Automation and Informatics Politecnico di Torino Torino Italy Brown University Providence RI USA

ISBN: (数字)9781728199023

ISBN: (纸本)9781728199030

Algorithms based on Monte-Carlo sampling have been widely adapted in robotics and other areas of engineering due to their performance robustness. However, these sampling-based approaches have high computational requirements, making them unsuitable for real-time applications with tight energy constraints. In this paper, we investigate 6 degree-of-freedom (6DoF) pose estimation for robot manipulation using this method, which uses rendering combined with sequential Monte-Carlo sampling. While potentially very accurate, the significant computational complexity of the algorithm makes it less attractive for mobile robots, where runtime and energy consumption are tightly constrained. To address these challenges, we develop a novel hardware implementation of Monte-Carlo sampling on an FPGA with lower computational complexity and memory usage, while achieving high parallelism and modularization. Our results show 12X-21X improvements in energy efficiency over low-power and high-end GPU implementations, respectively. Moreover, we achieve real time performance without compromising accuracy.

关键词：

CADA: Multi-scale collaborative adversarial domain adaptation for unsupervised optic disc and cup segmentation

学校读者我要写书评

暂无评论

arXiv 2021年

作者： Liu, Peng Tran, Charlie T. Kong, Bin Fang, Ruogu J. Crayton Pruitt Family Dept. of Biomedical Engineering University of Florida GainesvilleFL32611 United States Dept. of Electrical and Computer Engineering University of Florida GainesvilleFL32611 United States Center for Cognitive Aging and Memory University of Florida GainesvilleFL32611 United States Department of Computer Science Unc Charlotte CharlotteNC28223 United States

Recently, deep neural networks have demonstrated comparable and even better performance than board-certified ophthalmologists in well-annotated datasets. However, the diversity of retinal imaging devices poses a significant challenge: domain shift, which leads to performance degradation when applying the deep learning models trained on one domain to new testing domains. In this paper, we propose a multi-scale input along with multiple domain adaptors applied hierarchically in both feature and output spaces. The proposed training strategy and novel unsupervised domain adaptation framework, called Collaborative Adversarial Domain Adaptation (CADA), can effectively overcome the challenge. Multi-scale inputs can reduce the information loss due to the pooling layers used in the network for feature extraction, while our proposed CADA is an interactive paradigm that presents an exquisite collaborative adaptation through both adversarial learning and ensembling weights at different network layers. In particular, to produce a better prediction for the unlabeled target domain data, we simultaneously achieve domain invariance and model generalizability via adversarial learning at multi-scale outputs from different levels of network layers and maintaining an exponential moving average (EMA) of the historical weights during training. Without annotating any sample from the target domain, multiple adversarial losses in encoder and decoder layers guide the extraction of domain-invariant features to confuse the domain classifier. Meanwhile, the ensembling of weights via EMA reduces the uncertainty of adapting multiple discriminator learning. Comprehensive experimental results demonstrate that our CADA model incorporating multi-scale input training can overcome performance degradation and outperform state-of-the-art domain adaptation methods in segmenting retinal optic disc and cup from fundus images stemming from the REFUGE, Drishti-GS, and Rim-One-r3 datasets. The code will be avai

关键词： Network layers

Dynamic Resilient Graph Games for State-Dependent Jamming Attacks Analysis on Multi-Agent Systems

学校读者我要写书评

暂无评论

IFAC-PapersOnLine 2020年第2期53卷 3421-3426页

作者： Yurid Nugraha Ahmet Cetinkaya Tomohisa Hayakawa Hideaki Ishii Quanyan Zhu Dept. of Systems and Control Engineering Tokyo Institute of Technology Tokyo 152-8552 Japan Information Systems Architecture Science Research Division National Institute of Informatics Tokyo 101-8430 Japan Dept. of Computer Science Tokyo Insitute of Technology Yokohama 226-8502 Japan Dept. of Electrical and Computer Engineering New York University Brooklyn NY 11201 USA

A cybersecurity problem for a multi-agent consensus problem is investigated through a dynamic game formulation. Specifically, we consider a game repeatedly played between a jamming attacker and a defender. The attacker attempts to jam the links between a number of agents to delay their consensus. On the other hand, the defender tries to maintain the connection between agents by attempting to recover some of the jammed links with the goal of achieving faster consensus. In each game, the players decide which links to attack/recover and for how long to continue doing so based on a Lyapunov-like function representing the largest difference between the states of the agents. We analyze the subgame perfect equilibrium of the game and obtain an upper bound of the consensus time that is influenced by the strategies of the players. The results are illustrated with a numerical example.

关键词： Multi-agent systems Consensus problem Game theory Cybersecurity

Creating the English Grammar Tenses Pattern Using Regular Expression Method 7

学校读者我要写书评

暂无评论

Creating the English Grammar Tenses Pattern Using Regular Ex...

7th International Conference on Cyber and IT Service Management, CITSM 2019

作者： Bahaweres, Rizal Broer Prasetyo, Budhi Suseno, Hendra Bayu UIN Jakarta Informatics Department Indonesia Faculty of Mathematics and Natural Sciences IPB University Computer Science Dept. Bogor Indonesia

ISBN: (纸本)9781728129099

Speaking English, of course, cannot be separated from Grammar and Tenses. Grammar and Tenses are a vital part of English that must be mastered by English learners, especially for those who interact with English in both oral and written form. Regular Expressions are used to create patterns from Grammar and Tenses formulas in English, which then as the author basis reference to form a pattern of those formulas. The purpose of this research is to apply Regular Expressions to each Grammar and Tenses formula in English. The result of this research is in the form of testing regular expression patterns, which aims to know how far the quality of regex pattern we used. © 2019 IEEE.

关键词： Technology transfer

An overview of deep learning techniques for epileptic seizures detection and prediction based on neuroimaging modalities: Methods, challenges, and future works

学校读者我要写书评

暂无评论

arXiv 2021年

作者： Shoeibi, Afshin Moridian, Parisa Khodatars, Marjane Ghassemi, Navid Jafari, Mahboobeh Alizadehsani, Roohallah Kong, Yinan Gorriz, Juan Manuel Ramírez, Javier Khosravi, Abbas Nahavandi, Saeid Acharya, U. Rajendra Data Science and Computational Intelligence Institute University of Granada Spain Faculty of Engineering Science and Research Branch Islamic Azad University Tehran Iran Department of Medical Engineering Mashhad Branch Islamic Azad University Mashhad Iran Computer Engineering department Ferdowsi University of Mashhad Mashhad Iran Electrical and Computer Engineering Faculty Semnan University Semnan Iran Deakin University VIC3217 Australia School of Engineering Macquarie University Sydney2109 Australia Department of Signal Theory Networking and Communications Universidad de Granada Spain Ngee Ann Polytechnic Singapore599489 Singapore Dept. of Biomedical Informatics and Medical Engineering Asia University Taichung Taiwan Dept. of Biomedical Engineering School of Science and Technology Singapore University of Social Sciences Singapore

Epilepsy is a disorder of the brain denoted by frequent seizures. The symptoms of seizure include confusion, abnormal staring, and rapid, sudden, and uncontrollable hand movements. Epileptic seizure detection methods involve neurological exams, blood tests, neuropsychological tests, and neuroimaging modalities. Among these, neuroimaging modalities have received considerable attention from specialist physicians. One method to facilitate the accurate and fast diagnosis of epileptic seizures is to employ computer-aided diagnosis systems (CADS) based on deep learning (DL) and neuroimaging modalities. This paper has studied a comprehensive overview of DL methods employed for epileptic seizures detection and prediction using neuroimaging modalities. First, DL-based CADS for epileptic seizures detection and prediction using neuroimaging modalities are discussed. Also, descriptions of various datasets, preprocessing algorithms, and DL models which have been used for epileptic seizures detection and prediction have been included. Then, research on rehabilitation tools has been presented, which contains brain-computer interface (BCI), cloud computing, internet of things (IoT), hardware implementation of DL techniques on field-programmable gate array (FPGA), etc. In the discussion section, a comparison has been carried out between research on epileptic seizure detection and prediction. The challenges in epileptic seizures detection and prediction using neuroimaging modalities and DL models have been described. In addition, possible directions for future works in this field, specifically for solving challenges in datasets, DL, rehabilitation, and hardware models, have been proposed. The final section is dedicated to the conclusion which summarizes the significant findings of the paper. © 2021, CC BY-NC-ND.

关键词： Forecasting

Object classification using artificial neural network processing of data obtained by impulse GPR with 1 Tx + 4Rx antenna system

学校读者我要写书评

暂无评论

Object classification using artificial neural network proces...

2020 IEEE Ukrainian Microwave Week, UkrMW 2020

作者： Dumin, Oleksandr Plakhtii, Vadym Pochanin, Gennadiy Shyrokorad, Dmytro V. N. Karazin Kharkiv National University School of Radio Physics Biomedical Electronics and Computer Systems Kharkiv Ukraine O. Ya. Usikov Institute for Radiophysics and Electronics National Academy of Science of Ukraine Kharkiv Ukraine Zaporizhzhja National Technical University Dept. of System Analysis and Computer Mathematics Zaporizhzhja Ukraine

ISBN: (纸本)9781728173139

The system for detection of landmines and similar object is investigated. The processing of data obtained by impulse GPR with 1 Tx + 4Rx antenna system is proposed on the basis of artificial neural network approach. The unique antenna system collects important characteristics of subsurface object from reflected impulse electromagnetic wave. The filtration of received signals from interferences caused by experimental setup, its wave-like movement, and ground surface permits to prepare the data of the needed level of quality for further processing by artificial neural network. The network successfully classify a type of object and distance to them. The stability of the object recognition is checked for different level of additional noise. © 2020 IEEE.

关键词： Ground penetrating radar systems