Applied deep learning in intelligent transportation systems and embedding exploration

Deep learning techniques have achieved tremendous success in many real applications in recent years and show their great potential in many areas including transportation. Even though transportation becomes increasingly indispensable in people’s daily life, its related problems, such as traffic congestion and energy waste, have not been completely solved, yet some problems have become even more critical. This dissertation focuses on solving the following fundamental problems: (1) passenger demand prediction, (2) transportation mode detection, (3) traffic light control, in the transportation field using deep learning. The dissertation also extends the application of deep learning to an embedding system for visualization and data retrieval. The first part of this dissertation is about a Spatio-TEmporal Fuzzy neural Network (STEF-Net) which accurately predicts passenger demand by incorporating the complex interaction of all known important factors, such as temporal, spatial and external information. Specifically, a convolutional long short-term memory network is employed to simultaneously capture spatio-temporal feature interaction, and a fuzzy neural network to model external factors. A novel feature fusion method with convolution and an attention layer is proposed to keep the temporal relation and discriminative spatio-temporal feature interaction. Experiments on a large-scale real-world dataset show the proposed model outperforms the state-of-the-art approaches. The second part is a light-weight and energy-efficient system which detects transportation modes using only accelerometer sensors in smartphones. Understanding people’s transportation modes is beneficial to many civilian applications, such as urban transportation planning. The system collects accelerometer data in an efficient way and leverages a convolutional neural network to determine transportation modes. Different architectures and classification methods are tested with the proposed convolutional neural network to optimize the system design. Performance evaluation shows that the proposed approach achieves better accuracy than existing work in detecting people’s transportation modes. The third component of this dissertation is a deep reinforcement learning model, based on Q learning, to control the traffic light. Existing inefficient traffic light control causes numerous problems, such as long delay and waste of energy. In the proposed model, the complex traffic scenario is quantified as states by collecting data and dividing the whole intersection into grids. The timing changes of a traffic light are the actions, which are modeled as a high-dimension Markov decision process. The reward is the cumulative waiting time difference between two cycles. To solve the model, a convolutional neural network is employed to map states to rewards, which is further optimized by several components, such as dueling network, target network, double Q-learning network, and prioritized experience replay. The simulation results in Simulation of Urban MObility (SUMO) show the efficiency of the proposed model in controlling traffic lights. The last part of this dissertation studies the hierarchical structure in an embedding system. Traditional embedding approaches associate a real-valued embedding vector with each symbol or data point, which generates storage-inefficient representation and fails to effectively encode the internal semantic structure of data. A regularized autoencoder framework is proposed to learn compact Hierarchical K-way D-dimensional (HKD) discrete embedding of data points, aiming at capturing semantic structures of data. Experimental results on synthetic and real-world datasets show that the proposed HKD embedding can effectively reveal the semantic structure of data via visualization and greatly reduce the search space of nearest neighbor retrieval while preserving high accuracy

A Deep Spatio-Temporal Fuzzy Neural Network for Passenger Demand Prediction

In spite of its importance, passenger demand prediction is a highly challenging problem, because the demand is simultaneously influenced by the complex interactions among many spatial and temporal factors and other external factors such as weather. To address this problem, we propose a Spatio-TEmporal Fuzzy neural Network (STEF-Net) to accurately predict passenger demands incorporating the complex interactions of all known important factors. We design an end-to-end learning framework with different neural networks modeling different factors. Specifically, we propose to capture spatio-temporal feature interactions via a convolutional long short-term memory network and model external factors via a fuzzy neural network that handles data uncertainty significantly better than deterministic methods. To keep the temporal relations when fusing two networks and emphasize discriminative spatio-temporal feature interactions, we employ a novel feature fusion method with a convolution operation and an attention layer. As far as we know, our work is the first to fuse a deep recurrent neural network and a fuzzy neural network to model complex spatial-temporal feature interactions with additional uncertain input features for predictive learning. Experiments on a large-scale real-world dataset show that our model achieves more than 10% improvement over the state-of-the-art approaches.Comment: https://epubs.siam.org/doi/abs/10.1137/1.9781611975673.1

Perspective of monochromatic gamma-ray line detection with the High Energy cosmic-Radiation Detection (HERD) facility onboard China's Space Station

HERD is the High Energy cosmic-Radiation Detection instrument proposed to operate onboard China's space station in the 2020s. It is designed to detect energetic cosmic ray nuclei, leptons and photons with a high energy resolution ($\sim1\%$ for electrons and photons and $20\%$ for nuclei) and a large geometry factor ($>3\,{ m^2\,sr}$ for electrons and diffuse photons and $>2\,{ m^2\,sr}$ for nuclei). In this work we discuss the capability of HERD to detect monochromatic $\gamma$-ray lines, based on simulations of the detector performance. It is shown that HERD will be one of the most sensitive instruments for monochromatic $\gamma$-ray searches at energies between $\sim10$ to a few hundred GeV. Above hundreds of GeV, Cherenkov telescopes will be more sensitive due to their large effective area. As a specific example, we show that a good portion of the parameter space of a supersymmetric dark matter model can be probed with HERD.Comment: 9 pages, 7 figures, matches version published in Astropart.Phy

Dynamic and Static Combination Analysis Method of Slope Stability Analysis during Earthquake

The results of laboratory model tests for simulating the slope failure due to vibration, including unreinforced slope and the slope reinforced by using geotextile, show that the slope failure occurs when a cumulative plastic displacement exceeds a certain critical value. To overcome the defects of conventional stability analysis, which evaluates the slope characteristics only by its strength parameters, a numerical procedure considering the stiffness and deformation of materials and geosynthetics is proposed to evaluate the seismic slope stability. In the proposed procedure, the failure of slope is defined when the cumulative plastic displacement calculated by a dynamic response analysis using actual seismic wave exceeds the critical value of displacement estimated by a static stability analysis considering seismic coefficient. The proposed procedure is applied to the laboratory model tests and an actual failure of slope in earthquake. The case study shows the possibility that the proposed procedure gives the realistic evaluation of seismic slope stability

Structure-Feature based Graph Self-adaptive Pooling

Various methods to deal with graph data have been proposed in recent years. However, most of these methods focus on graph feature aggregation rather than graph pooling. Besides, the existing top-k selection graph pooling methods have a few problems. First, to construct the pooled graph topology, current top-k selection methods evaluate the importance of the node from a single perspective only, which is simplistic and unobjective. Second, the feature information of unselected nodes is directly lost during the pooling process, which inevitably leads to a massive loss of graph feature information. To solve these problems mentioned above, we propose a novel graph self-adaptive pooling method with the following objectives: (1) to construct a reasonable pooled graph topology, structure and feature information of the graph are considered simultaneously, which provide additional veracity and objectivity in node selection; and (2) to make the pooled nodes contain sufficiently effective graph information, node feature information is aggregated before discarding the unimportant nodes; thus, the selected nodes contain information from neighbor nodes, which can enhance the use of features of the unselected nodes. Experimental results on four different datasets demonstrate that our method is effective in graph classification and outperforms state-of-the-art graph pooling methods.Comment: 7 pages, 4 figures, The Web Conference 202

Identification and Characterization of microRNAs from Peanut (Arachis hypogaea L.) by High-Throughput Sequencing

BACKGROUND: MicroRNAs (miRNAs) are noncoding RNAs of approximately 21 nt that regulate gene expression in plants post-transcriptionally by endonucleolytic cleavage or translational inhibition. miRNAs play essential roles in numerous developmental and physiological processes and many of them are conserved across species. Extensive studies of miRNAs have been done in a few model plants; however, less is known about the diversity of these regulatory RNAs in peanut (Arachis hypogaea L.), one of the most important oilseed crops cultivated worldwide. RESULTS: A library of small RNA from peanut was constructed for deep sequencing. In addition to 126 known miRNAs from 33 families, 25 novel peanut miRNAs were identified. The miRNA* sequences of four novel miRNAs were discovered, providing additional evidence for the existence of miRNAs. Twenty of the novel miRNAs were considered to be species-specific because no homolog has been found for other plant species. qRT-PCR was used to analyze the expression of seven miRNAs in different tissues and in seed at different developmental stages and some showed tissue- and/or growth stage-specific expression. Furthermore, potential targets of these putative miRNAs were predicted on the basis of the sequence homology search. CONCLUSIONS: We have identified large numbers of miRNAs and their related target genes through deep sequencing of a small RNA library. This study of the identification and characterization of miRNAs in peanut can initiate further study on peanut miRNA regulation mechanisms, and help toward a greater understanding of the important roles of miRNAs in peanut