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International Conference on Services Systems and Services Management, ICSSSM

作者： Yuzhou Chen Junji Wu Hui Bu School of Economics and Management Beihang University Beijing China Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing China

It has always been a challenging issue for people to understand the stock market and make reasonable predictions. For a very long period, investors are trying to manually extract useful features from the financial market which generates an enormous volume of data every single day. People create a lot of technical indicators like MACD, TR, MFI to describe momentum, volume and volatility signals of the financial time series. However, the limitation is evident due to the efficiency of manually feature engineering. With the rapidly growing volume of data, deep neural network shows excellent performance in many research areas like natural language processing, voice recognition, image identification. It provides a new view to dig out potentially useful information automatically. In this paper, we present a novel end-to-end training using an embedding method to automatically extract features and get a summary representation of the daily market. Moreover, we apply the Long Short-Term Memory (LSTM) with attention mechanism to predict daily return ratio of HS300 index. The features extracted by the embedding layer show greater predictive power than manually defined technical signals by 92.42% lower MSE. Moreover, the use of attention mechanism also provides an average enhance of 55.68% in MSE. Our study shows that deep neuron network structure has a strong potential for better understanding market complex behaviors.

关键词： Hidden Markov models Stock markets Predictive models Feature extraction Logic gates Machine learning data models

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Adversarial binary coding for efficient person re-identification

arXiv

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arXiv 2018年

作者： Liu, Zheng Qin, Jie Li, Annan Wang, Yunhong van Gool, Luc Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing China Computer Vision Laboratory ETH Zürich Switzerland

Person re-identification (ReID) aims at matching persons across different views/scenes. In addition to accuracy, the matching efficiency has received more and more attention because of demanding applications using large-scale data. Several binary coding based methods have been proposed for efficient ReID, which either learn projections to map high-dimensional features to compact binary codes, or directly adopt deep neural networks by simply inserting an additional fully-connected layer with tanh-like activations. However, the former approach requires time-consuming hand-crafted feature extraction and complicated (discrete) optimizations;the latter lacks the necessary discriminative information greatly due to the straightforward activation functions. In this paper, we propose a simple yet effective framework for efficient ReID inspired by the recent advances in adversarial learning. Specifically, instead of learning explicit projections or adding fully-connected mapping layers, the proposed Adversarial Binary Coding (ABC) framework guides the extraction of binary codes implicitly and effectively. The discriminability of the extracted codes is further enhanced by equipping the ABC with a deep triplet network for the ReID task. More importantly, the ABC and triplet network are simultaneously optimized in an end-to-end manner. Extensive experiments on three large-scale ReID benchmarks demonstrate the superiority of our approach over the state-of-the-art methods. Copyright © 2018, The Authors. All rights reserved.

关键词： Efficiency

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A Slicing Algorithm Based on Virtual Edge for 3D Printing

A Slicing Algorithm Based on Virtual Edge for 3D Printing

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2018 3rd International Conference on Electrical, Automation and Mechanical Engineering(EAME 2018)

作者： Yifei Hu Jiang Xin Guanying Huo Danlei Ye Zehong Lu Bolun Wang Zhiming Zheng LMIB and School of Mathematics and Systems Science Beihang University Big Data Brain Computing Center Beihang University School of Mathematical Science Peking University

Virtual edge method for point cloud slicing in 3D printing is a very common algorithm due to the advantage of low computational complexity. However, this approach is not robust to density variations. To overcome this shortcoming, in this paper, a concept of column neighborhood is introduced to estimate whether the virtual edge model should be used in certain situations. Concretely, based on the technic of generating a set of multiple contours, we present an improved algorithm for high resolution point cloud slicing. To deal with multiple contours, a classify method based on convex hull is proposed, which reduces considerable calculations and separates each module from the others. At the last step, a method based on minimum angle is proposed to generate a single contour for each class. This algorithm is verified to be efficient by testing on kinds of typical benchmarks.

关键词： virtual edge convex hull classify

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Master stability functions for complete, intralayer, and interlayer synchronization in multiplex networks of coupled Rössler oscillators

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Physical Review E 2019年第1期99卷 012304-012304页

作者： Longkun Tang Xiaoqun Wu Jinhu Lü Jun-an Lu Raissa M. D'Souza Fujian Province University Key Laboratory of Computation Science School of Mathematical Science Huaqiao University Quanzhou 362021 China School of Mathematics and Statistics Wuhan University Wuhan 430072 China Department of Computer Science University of California Davis California 95616 USA School of Automation Science and Electrical Engineering State Key Laboratory of Software Development Environment and Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing 100191 China

Synchronization phenomena are of broad interest across disciplines and increasingly of interest in a multiplex network setting. For the multiplex network of coupled Rössler oscillators, here we show how the master stability function, a celebrated framework for analyzing synchronization on a single network, can be extended to certain classes of multiplex networks with different intralayer and interlayer coupling functions. We derive three master stability equations that determine, respectively, the necessary regions of complete synchronization, intralayer synchronization, and interlayer synchronization. We calculate these three regions explicitly for the case of a two-layer network of Rössler oscillators and show that the overlap of the regions determines the type of synchronization achieved. In particular, if the interlayer or intralayer coupling function is such that the interlayer or intralayer synchronization region is empty, complete synchronization cannot be achieved regardless of the coupling strength. Furthermore, for any network structure, the occurrence of intralayer and interlayer synchronization depends mainly on the coupling functions of nodes within a layer and across layers, respectively. Our mathematical analysis requires that the intralayer and interlayer supra-Laplacians commute. But, we show this is only a sufficient, and not necessary, condition and that the results can be applied more generally.

关键词： Collective dynamics Synchronization Synchronization transition

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Trajectory optimization for cooperative reentry of multiple hypersonic glide vehicle

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Hangkong Xuebao/Acta Aeronautica et Astronautica Sinica 2020年 41卷 723776页

作者： Jiang, Peng Guo, Dong Han, Liang Li, Qingdong Ren, Zhang College of Systems Engineering National University of Defense Technology Changsha410073 China China Academy of Launch Vehicle Technology Beijing100076 China School of Automation Science and Electrical Engineering Beihang University Beijing100083 China School of Sino-French Engineer Beihang University Beijing100083 China Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing100083 China

This paper presents a trajectory optimization method for the cooperative reentry of multiple hypersonic glide vehicles. First, the nominal longitudinal trajectory is planned to satisfy the path and terminal constraints. Second, the trajectory tracking law is used to track the nominal longitudinal trajectory. Meanwhile, a multi-layer bounded corridor for heading error considering the initial lateral state is proposed to control the lateral maneuver, so as to meet the requirement of arrival time and the terminal constraints. Then the trajectory planning with arrival time constraints for a single vehicle is implemented. On this basis, the arrival time distribution and the flight capability are analyzed, and the analysis and the calculation methods for the minimum and the maximum arrival time are given. For the multiple hypersonic glide vehicle cooperative reentry scenario, the cooperative flight time decision-making is completed. Finally, numerical results show that the trajectory optimization method achieves a good performance on the arrival time and the terminal constraints, which indicates that the proposed method can realize the cooperative reentry of multiple hypersonic glide vehicle. The results in dispersed cases indicate that the trajectory optimization method has good calculation accuracy and robustness. © 2020, Beihang University Aerospace Knowledge Press. All right reserved.

关键词： Reentry

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Model learning: A survey on foundation, tools and applications

arXiv

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arXiv 2018年

作者： Ali, Shahbaz Sun, Hailong Zhao, Yongwang School of Computer Science and Engineering Beihang University Beijing China Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing China

The quality and correct functioning of software components embedded in electronic systems are of utmost concern especially for safety and mission-critical systems. Model-based testing and formal verification techniques can be employed to enhance the reliability of software systems. Formal models form the basis and are prerequisite for the application of these techniques. An emerging and promising model learning technique can complement testing and verification techniques by providing learned models of black box systems fully automatically. This paper surveys one such state of the art technique called model learning which recently has attracted much attention of researchers especially from the domains of testing and verification. This survey paper reviews and provides comparison summaries highlighting the merits and shortcomings of learning techniques, algorithms, and tools which form the basis of model learning. This paper also surveys the successful applications of model learning technique in multidisciplinary fields making it promising for testing and verification of realistic systems. Copyright © 2018, The Authors. All rights reserved.

关键词： Learning algorithms

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Laser pulse induced efficient terahertz emission from Co/Al heterostructures

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Physical Review B 2020年第2期102卷 024435-024435页

作者： Hui Zhang Zheng Feng Jine Zhang He Bai Huaiwen Yang Jianwang Cai Weisheng Zhao Wei Tan Fengxia Hu Baogen Shen Jirong Sun Fert Beijing Institute School of Microelectronics Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijng 100191 People's Republic of China Microsystem and Terahertz Research Center China Academy of Engineering Physics Chengdu Sichuan 610200 People's Republic of China Institute of Electronic Engineering China Academy of Engineering Physics Mianyang Sichuan 621999 People's Republic of China Beijing National Laboratory for Condensed Matter Physics & Institute of Physics Chinese Academy of Sciences Beijing 100190 People's Republic of China School of Physical Sciences University of Chinese Academy of Sciences Beijing 100049 Peoples’ Republic of China Songshan Lake Materials Laboratory Dongguan Guangdong 523808 People's Republic of China Spintronics Institute University of Jinan Jinan Shandong 250022 People's Republic of China

Investigation on terahertz (THz) emission is important not only for fundamental research but also for practical application. A rapidly developing approach to generate THz emission is using a heterostructure composed of ferromagnetic metal (FM) and nonmagnetic metal (NM) which is pumped by femtosecond laser pulse. Previous works in this regard mainly focused on bilayer with heavy NM (such as Pt) with a large spin Hall angle. Here we present a comprehensive investigation on THz emission from Co/Al heterostructures stemming from the inverse spin Hall effect. It is surprising to find that although the spin Hall angle of Al is two orders of magnitude smaller than that of Pt, the measured THz signals are close to one-third of that of the typical Co/Pt heterostructures. To explore the underlying physics, theoretical models are employed to investigate the spin-related properties of Al. We obtain that the upper limit of the spin Hall angle of Al is 0.55% and the spin-diffusion length is 2.2 ± 0.2 nm; the diffusion length is comparable to that of Pt (2.3 ± 0.1 nm). This work shows that it is worth revisiting light metals for the spintronic THz emitter.

关键词： Spin-orbit coupling Spintronics Ultrafast magnetic effects Metals Terahertz techniques

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An Adaptive Control Method for Arterial Signal Coordination Based on Deep Reinforcement Learning*

An Adaptive Control Method for Arterial Signal Coordination ...

引用

International Conference on Intelligent Transportation

作者： Peng Chen Zemao Zhu Guangquan Lu Beijing Key Laboratory for Cooperative Vehicle Infrastructure Systems and Safety Control School of Transportation Science and Engineering Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Hai Dian District Beijing Beijing Key Laboratory for Cooperative Vehicle Infrastructure Systems and Safety Control School of Transportation Science and Engineering Beihang University Hai Dian District Beijing

ISBN: (纸本)9781538670255

Traffic signal control is a complex problem and it is difficult to determine an optimal strategy to control multi-directional traffic at multiple intersections. Recent years have witnessed numerous successes of deep learning neural networks in the fields of artificial intelligence. Motivated by the dominant performance of neural networks, this study attempts to develop a novel adaptive signal control approach by fusing deep learning (DL) and reinforcement learning (RL), i.e., deep reinforcement learning (DRL), for arterial signal coordination. DRL can considerably improve the ability to deal with large amounts of data processing, systematic perception and expression, which is key to coordinated control of arterial intersections. The proposed algorithm is implemented by utilizing real-time traffic detection data and aims to optimize the hybrid global and local reward functions. The experimental results obtained by traffic simulation software SUMO demonstrate the advantage of the proposed approach, as well as its efficiency and effectiveness compared with fixed-time and actual signal control methods.

关键词： Aerospace electronics Machine learning Adaptive control Traffic control Roads Optimization

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SVM-based deep stacking networks

arXiv

引用

arXiv 2019年

作者： Wang, Jingyuan Feng, Kai Wu, Junjie MOE Engineering Research Center of Advanced Computer Application Technology School of Computer Science Engineering Beihang University Beijing100191 China Beijing Key Laboratory of Emergency Support Simulation Technologies for City Operations School of Economics and Management Beihang University Beijing100191 China Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing100191 China

The deep network model, with the majority built on neural networks, has been proved to be a powerful framework to represent complex data for high performance machine learning. In recent years, more and more studies turn to nonneural network approaches to build diverse deep structures, and the Deep Stacking Network (DSN) model is one of such approaches that uses stacked easy-to-learn blocks to build a parameter-training-parallelizable deep network. In this paper, we propose a novel SVM-based Deep Stacking Network (SVM-DSN), which uses the DSN architecture to organize linear SVM classifiers for deep learning. A BP-like layer tuning scheme is also proposed to ensure holistic and local optimizations of stacked SVMs simultaneously. Some good math properties of SVM, such as the convex optimization, is introduced into the DSN framework by our model. From a global view, SVM-DSN can iteratively extract data representations layer by layer as a deep neural network but with parallelizability, and from a local view, each stacked SVM can converge to its optimal solution and obtain the support vectors, which compared with neural networks could lead to interesting improvements in anti-saturation and interpretability. Experimental results on both image and text data sets demonstrate the excellent performances of SVM-DSN compared with some competitive benchmark models. Copyright © 2019, The Authors. All rights reserved.

关键词： Convex optimization

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Automatic Facial Attractiveness Prediction by Deep Multi-Task Learning

Automatic Facial Attractiveness Prediction by Deep Multi-Tas...

引用

International Conference on Pattern Recognition

作者： Lian Gao Weixin Li Zehua Huang Di Huang Yunhong Wang IRIP Lab Beihang University Beijing China Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing China TuSimple San Diego USA

Facial Attractiveness Prediction (FAP) is a useful yet challenging problem in the domain of computer vision. In this paper, we propose a deep learning based approach. Different from the existing deep methods, the proposed one models both the texture and shape clues within a multi-task learning framework consisting of attractiveness score prediction and fiducial landmark localization, thus highlighting both of their roles in assessing attractiveness of faces. Considering that the training data are not extensive, a lightweight CNN is designed to jointly learn the facial representation, landmark location, and facial attractiveness score. The proposed method is evaluated on the SCUT-FBP database, and a prediction correlation 0.92, is delivered, which shows the effectiveness of our method. Furthermore, two additional experiments in terms of comparison between facial images before and after make-up or beautification are conducted. The results also prove the advantage of the proposed method.

关键词： Task analysis Shape Feature extraction Training Convolution Predictive models Facial features

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