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48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Guo, Huijie Shi, Lei Beihang University Beijing Advanced Innovation Center for Big Data and Brain Computing China

ISBN: (纸本)9781728163277

Unsupervised learning has received more attention due to the superior performance of contrastive learning methods. Most contrastive methods use data augmentation techniques to construct positive and negative pairs. The augmented view of the same sample is regarded as a positive sample, while the rest are negative samples. This negative sampling strategy has strong randomness and ignores samples that are semantically similar to anchors, namely sampling bias. This problem has been addressed by weighting the similarity of negative samples. In this paper, we propose a novel ultimate negative sampling for contrastive learning. Unlike random sampling, we set a more extreme negative sample selection mechanism based on the ideal representation of the sample. Furthermore, we constrain the consistency between samples across the space. Experiment results demonstrate the proposed method's superiority on multiple benchmark datasets. © 2023 IEEE.

关键词： Learning systems

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Stability of connected and automated vehicles platoon considering communications failures

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Chinese Physics B 2023年第7期32卷 598-609页

作者：刘润坤于海洋任毅龙崔志勇 School of Transportation Science and Engineering Beijing Key Laboratory for Cooperative Vehicle Infrastructure Systems and Safety ControlBeihang UniversityBeijing 100191China Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang UniversityBeijing 100191China

As a form of a future traffic system,a connected and automated vehicle(CAV)platoon is a typical nonlinear physical *** can communicate with each other and exchange ***,communication failures can change the platoon system *** characterize this change,a dynamic topology-based car-following model and its generalized form are proposed in this ***,a stability analysis method is ***,taking the dynamic cooperative intelligent driver model(DC-IDM)for example,a series of numerical simulations is conducted to analyze the platoon stability in different communication topology *** results show that the communication failures reduce the stability,but information from vehicles that are farther ahead and the use of a larger desired time headway can improve ***,the critical ratio of communication failures required to ensure stability for different driving parameters is studied in this work.

关键词： connected and automated vehicle car-following model communication failures string stability

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Distributed Truss Computation in Dynamic Graphs

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Tsinghua Science and Technology 2023年第5期28卷 873-887页

作者： Ziwei Mo Qi Luo Dongxiao Yu Hao Sheng Jiguo Yu Xiuzhen Cheng School of Computer Science and Technology Shandong UniversityQingdao 266200China State Key Laboratory of Software Development Environment School of Computer Science and Engineering and the Beijing Advanced Innovation Center for Big Data and Brain ComputingBeihang UniversityBeijing 100191China Big Data Institute Qilu University of Technology(Shandong Academy of Sciences)Jinan 250353China

Large-scale graphs usually exhibit global sparsity with local cohesiveness,and mining the representative cohesive subgraphs is a fundamental problem in graph *** k-truss is one of the most commonly studied cohesive subgraphs,in which each edge is formed in at least k 2 triangles.A critical issue in mining a k-truss lies in the computation of the trussness of each edge,which is the maximum value of k that an edge can be in a *** works mostly focus on truss computation in static graphs by sequential ***,the graphs are constantly changing dynamically in the real *** study distributed truss computation in dynamic graphs in this *** particular,we compute the trussness of edges based on the local nature of the k-truss in a synchronized node-centric distributed *** decomposing the trussness of edges by relying only on local topological information is possible with the proposed distributed decomposition ***,the distributed maintenance algorithm only needs to update a small amount of dynamic information to complete the *** experiments have been conducted to show the scalability and efficiency of the proposed algorithm.

关键词： distributed algorithm dynamic graph graph mining cohesive subgraph k-truss

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RankFIRST: Visual Analysis for Factor Investment By Ranking Stock Timeseries

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IEEE Transactions on Visualization and Computer Graphics 2022年 PP卷 1-10页

作者： Guo, Huijie Liu, Meijun Yang, Bowen Sun, Ye Qu, Huamin Shi, Lei SKLSDE and Beijing Advanced Innovation Center for Big Data and Brain Computing School of Computer Science and Engineering Beihang University China Hong Kong University of Science and Technology Hong Kong

In the era of quantitative investment, factor-based investing models are widely adopted in the construction of stock portfolios. These models explain the performance of individual stocks by a set of financial factors, e.g., market beta and company size. In industry, open investment platforms allow the online building of factor-based models, yet set a high bar on the engineering expertise of end-users. State-of-the-art visualization systems integrate the whole factor investing pipeline, but do not directly address domain users' core requests on ranking factors and stocks for portfolio construction. The current model lacks explainability, which downgrades its credibility with stock investors. To fill the gap in modeling, ranking, and visualizing stock time series for factor investment, we designed and implemented a visual analytics system, namely RankFIRST. The system offers built-in support for an established factor collection and a cross-sectional regression model viable for human interpretation. A hierarchical slope graph design is introduced according to the desired characteristics of good factors for stock investment. A novel firework chart is also invented extending the well-known candlestick chart for stock time series. We evaluated the system on the full-scale Chinese stock market data in the recent 30 years. Case studies and controlled user evaluation demonstrate the superiority of our system on factor investing, in comparison to both passive investing on stock indices and existing stock market visual analytics tools. IEEE

关键词： Investments

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Analysis on n-level quantum systems by means of a coordinate transformation 63

Analysis on n-level quantum systems by means of a coordinate...

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63rd IEEE Conference on Decision and Control, CDC 2024

作者： Xu, Huilong Miao, Zibo Gao, Qing Harbin Institute of Technology School of Mechanical Engineering and Automation Shenzhen518055 China Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University School of Automation Science and Electrical Engineering Beijing100191 China

ISBN: (纸本)9798350316339

The structural decomposition of two-level quantum systems has recently been established, related to various applications in quantum information science. As an extension to previous work on two-level quantum systems, this paper aims to investigate the structural properties of n-level quantum systems. We develop algebraic conditions under which the attainment of quantum back-action evading (BAE) measurements, the generation of decoherence-free (DF) subspaces and quantum non-demolition (QND) variables, as well as the existence of steadystate solutions for the Lindblad equations, can be realized. In particular, a specific coordinate transformation is explicitly constructed to facilitate the achievement of above-mentioned objectives, accompanied by an illustrative numerical example. © 2024 IEEE.

关键词： Quantum optics

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Ultimate Negative Sampling for Contrastive Learning

Ultimate Negative Sampling for Contrastive Learning

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Huijie Guo Lei Shi Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University

关键词： Benchmark testing Signal processing Acoustics Speech processing Unsupervised learning

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POINE2: Improving Poincaré Embeddings for Hierarchy-Aware Complex Query Reasoning over Knowledge Graphs 26

POINE2: Improving Poincaré Embeddings for Hierarchy-Aware C...

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26th European Conference on Artificial Intelligence, ECAI 2023

作者： Liu, Junnan Mao, Qianren Li, Jianxin Fu, Xingcheng Wang, Zheng School of Computer Science and Engineering Beihang University Beijing China Beijing Advanced Innovation Center for Big Data and Brain Computing Beijing China Zhongguancun Laboratory Beijing China The School of Computing University of Leeds United Kingdom

ISBN: (纸本)9781643684369

Reasoning complex logical queries on incomplete and massive knowledge graphs (KGs) remains a significant challenge. The prevailing method for this problem is query embedding, which embeds KG units (i.e., entities and relations) and complex queries into low-dimensional space. Recent developments in the field show that embedding queries as geometric shapes is a viable means for modeling entity set and logical relationships between them. Despite being promising, current geometric-based methods face challenges in capturing hierarchical structures of complex queries, which leaves considerable room for improvement. This paper presents POINE2, a geometric-based query embedding framework based on hyperbolic geometry to handle complex queries on knowledge graphs. POINE2 maps entities and queries as geometric shapes on a Cartesian product space of Poincaré ball spaces. To capture the hierarchical structures of complex queries, we use the Poincaré radius to represent the different levels of the hierarchy, and we use the aperture of the shape to indicate semantic differences at the same level of the hierarchy. Additionally, POINE2 offers a flexible and expressive definition of logical operations. Experimental results show that POINE2 outperforms existing salient geometric-based embedding methods and significantly improves these methods on evaluation datasets. © 2023 The Authors.

关键词： Knowledge graph

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Analysis of Factors Influencing Driver Lane-Changing Intentions Based on a Naturalistic Trajectory data Set on Highways 24

Analysis of Factors Influencing Driver Lane-Changing Intenti...

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24th COTA International Conference of Transportation Professionals: Resilient, Intelligent, Connected, and Lowcarbon Multimodal Transportation, CICTP 2024

作者： Wang, Guangchen Lu, Guangquan Wang, Jinghua Liu, Miaomiao School of Transportation Science and Engineering Beihang Univ. Beijing China Beijing Key Laboratory for Cooperative Vehicle Infrastructure Systems and Safety Control National Engineering Laboratory for Comprehensive Transportation Big Data Application Technology Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang Univ. Beijing China

ISBN: (数字)9780784485484

ISBN: (纸本)9780784485484

Accurately analyzing and predicting driver lane-changing intentions is of paramount importance, as it significantly enhances the safety of self-driving vehicles in their decision-making processes, holding great promise for the future of intelligent transportation systems. This study extracts fundamental lane-changing characteristic parameters from the Next Generation Simulation (NGSIM) trajectory data set and employs Structural Equation Modeling (SEM) to construct a model that elucidates the factors influencing drivers’ lane-changing intentions. Furthermore, it provides insight into how lane-changing intentions are influenced by lane-changing behavior and explores the intricate connection between intention and associated risks. The results emphasize the substantial negative impact of the longitudinal speed difference between the self-vehicle and the preceding vehicle on lane-changing intentions. Additionally, the consideration of lane-changing risk plays a pivotal role in drivers’ decisions to engage in lane-changing behavior. These findings have significant implications for a deeper comprehension of lane-changing intentions, guiding lane-changing decisions, and enhancing overall traffic safety. © ASCE.

关键词： Autonomous vehicles

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Asymmetric scattering behaviors of spin wave dependent on magnetic vortex chirality

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Chinese Physics B 2023年第10期32卷 635-640页

作者：张雪枫沈帝虎马晓萍宋成于海明朴红光 Hubei Engineering Research Center of Weak Magnetic-Field Detection China Three Gorges UniversityYichang 443002China Department of Physics College of ScienceYanbian UniversityYanji 133002China Key Laboratory of Advanced Materials(MOE) School of Materials Science and EngineeringTsinghua UniversityBeijing 100084China Fert Beijing Institute School of Integrated Circuit Science and EngineeringBeijing Advanced Innovation Center for Big Data and Brain ComputingBeihang UniversityBeijing 100191China

We investigate asymmetric spin wave scattering behaviors caused by vortex chirality in a cross-shaped ferromagnetic system by using the micromagnetic *** the system,four scattering behaviors are found:(i)asymmetric skew scattering,depending on the polarity of vortex core,(ii)back scattering(reflection),depending on the vortex core stiffness,(iii)side deflection scattering,depending on structural symmetry of the vortex circulation,and(iv)geometrical scattering,depending on waveguide *** first and second scattering behaviors are attributed to nonlinear topological magnon spin Hall effect related to magnon spin-transfer torque effect,which has value for magnonic exploration and application.

关键词： magnonics magnetic vortex spin wave magnetic chirality

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Learning from Noisy Crowd Labels with Logics 39

Learning from Noisy Crowd Labels with Logics

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39th IEEE International Conference on data Engineering, ICDE 2023

作者： Chen, Zhijun Sun, Hailong He, Haoqian Chen, Pengpeng Beihang University Sklsde Lab Beijing China Beihang University Beijing Advanced Innovation Center for Big Data and Brain Computing Beijing China China's Aviation System Engineering Research Institute Beijing China

ISBN: (纸本)9798350322279

This paper explores the integration of symbolic logic knowledge into deep neural networks for learning from noisy crowd labels. We introduce Logic-guided Learning from Noisy Crowd Labels (Logic-LNCL), an EM-alike iterative logic knowledge distillation framework that learns from both noisy labeled data and logic rules of interest. Unlike traditional EM methods, our framework contains a "pseudo-E-step"that distills from the logic rules a new type of learning target, which is then used in the "pseudo-M-step"for training the classifier. Extensive evaluations on two real-world datasets for text sentiment classification and named entity recognition demonstrate that the proposed framework improves the state-of-the-art and provides a new solution to learning from noisy crowd labels. © 2023 IEEE.

关键词： Crowdsourcing

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