UrbanFM: Inferring Fine-Grained Urban Flows

Urban flow monitoring systems play important roles in smart city efforts around the world. However, the ubiquitous deployment of monitoring devices, such as CCTVs, induces a long-lasting and enormous cost for maintenance and operation. This suggests the need for a technology that can reduce the number of deployed devices, while preventing the degeneration of data accuracy and granularity. In this paper, we aim to infer the real-time and fine-grained crowd flows throughout a city based on coarse-grained observations. This task is challenging due to two reasons: the spatial correlations between coarse- and fine-grained urban flows, and the complexities of external impacts. To tackle these issues, we develop a method entitled UrbanFM based on deep neural networks. Our model consists of two major parts: 1) an inference network to generate fine-grained flow distributions from coarse-grained inputs by using a feature extraction module and a novel distributional upsampling module; 2) a general fusion subnet to further boost the performance by considering the influences of different external factors. Extensive experiments on two real-world datasets, namely TaxiBJ and HappyValley, validate the effectiveness and efficiency of our method compared to seven baselines, demonstrating the state-of-the-art performance of our approach on the fine-grained urban flow inference problem

A Method of EV Detour-to-Recharge Behavior Modeling and Charging Station Deployment

Electric vehicles (EVs) are increasingly used in transportation. Worldwide use of EVs, for their limited battery capacity, calls for effective planning of EVs charging stations to enhance the efficiency of using EVs. This paper provides a methodology of describing EV detouring behavior for recharging, and based on this, we adopt the extra driving length caused by detouring and the length of uncompleted route as the indicators of evaluating an EV charging station deployment plan. In this way, we can simulate EV behavior based on travel data (demand). Then, a genetic algorithm (GA) based EV charging station sitting optimization method is developed to obtain an effective plan. A detailed case study based on a 100-node 203-branch transportation network within a 30 km * 30 km region is included to test the effectiveness of our method. Insights from our method may be applicable for charging station planning in various transportation networks

Deep Instance Segmentation with Automotive Radar Detection Points

Automotive radar provides reliable environmental perception in all-weather conditions with affordable cost, but it hardly supplies semantic and geometry information due to the sparsity of radar detection points. With the development of automotive radar technologies in recent years, instance segmentation becomes possible by using automotive radar. Its data contain contexts such as radar cross section and micro-Doppler effects, and sometimes can provide detection when the field of view is obscured. The outcome from instance segmentation could be potentially used as the input of trackers for tracking targets. The existing methods often utilize a clustering based classification framework, which fits the need of real-time processing but has limited performance due to minimum information provided by sparse radar detection points. In this paper, we propose an efficient method based on clustering of estimated semantic information to achieve instance segmentation for the sparse radar detection points. In addition, we show that the performance of the proposed approach can be further enhanced by incorporating the visual multi-layer perceptron. The effectiveness of the proposed method is verified by experimental results on the popular RadarScenes dataset, achieving 89.53% mCov and 86.97% mAP0.5, which is the best comparing to other approaches in the literature. More significantly, the proposed algorithm consumes memory around 1MB, and the inference time is less than 40ms. These two criteria ensure the practicality of the proposed method in real-world system

An On-demand Photonic Ising Machine with Simplified Hamiltonian Calculation by Phase-encoding and Intensity Detection

Photonic Ising machine is a new paradigm of optical computing, which is based on the characteristics of light wave propagation, parallel processing and low loss transmission. Thus, the process of solving the combinatorial optimization problems can be accelerated through photonic/optoelectronic devices. In this work, we have proposed and demonstrated the so-called Phase-Encoding and Intensity Detection Ising Annealer (PEIDIA) to solve arbitrary Ising problems on demand. The PEIDIA is based on the simulated annealing algorithm and requires only one step of optical linear transformation with simplified Hamiltonian calculation. With PEIDIA, the Ising spins are encoded on the phase term of the optical field and only intensity detection is required during the solving process. As a proof of principle, several 20 and 30-dimensional Ising problems have been solved with high ground state probability

New Perspectives on Sleep Regulation by Tea: Harmonizing Pathological Sleep and Energy Balance under Stress

Sleep, a conservative evolutionary behavior of organisms to adapt to changes in the external environment, is divided into natural sleep, in a healthy state, and sickness sleep, which occurs in stressful environments or during illness. Sickness sleep plays an important role in maintaining energy homeostasis under an injury and promoting physical recovery. Tea, a popular phytochemical-rich beverage, has multiple health benefits, including lowering stress and regulating energy metabolism and natural sleep. However, the role of tea in regulating sickness sleep has received little attention. The mechanism underlying tea regulation of sickness sleep and its association with the maintenance of energy homeostasis in injured organisms remains to be elucidated. This review examines the current research on the effect of tea on sleep regulation, focusing on the function of tea in modulating energy homeostasis through sickness sleep, energy metabolism, and damage repair in model organisms. The potential mechanisms underlying tea in regulating sickness sleep are further suggested. Based on the biohomology of sleep regulation, this review provides novel insights into the role of tea in sleep regulation and a new perspective on the potential role of tea in restoring homeostasis from diseases

Revisiting Convolutional Neural Networks for Urban Flow Analytics

2020 European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD 2020

Unsupervised Learning of Disentangled Location Embeddings

2020 International Joint Conference on Neural Networks (IJCNN 2020)United State