GPSP: Graph Partition and Space Projection based Approach for Heterogeneous Network Embedding

In this paper, we propose GPSP, a novel Graph Partition and Space Projection based approach, to learn the representation of a heterogeneous network that consists of multiple types of nodes and links. Concretely, we first partition the heterogeneous network into homogeneous and bipartite subnetworks. Then, the projective relations hidden in bipartite subnetworks are extracted by learning the projective embedding vectors. Finally, we concatenate the projective vectors from bipartite subnetworks with the ones learned from homogeneous subnetworks to form the final representation of the heterogeneous network. Extensive experiments are conducted on a real-life dataset. The results demonstrate that GPSP outperforms the state-of-the-art baselines in two key network mining tasks: node classification and clustering.Comment: WWW 2018 Poste

Quantum Interactions in Topological R166 Kagome Magnet

Kagome magnet has been found to be a fertile ground for the search of exotic quantum states in condensed matter. Arising from the unusual geometry, the quantum interactions in the kagome lattice give rise to various quantum states, including the Chern-gapped Dirac fermion, Weyl fermion, flat band and van Hove singularity. Here we review recent advances in the study of the R166 kagome magnet (RT6E6, R = rare earths; T = transition metals; and E = Sn, Ge, etc.) whose crystal structure highlights the transition-metal-based kagome lattice and rare-earth sublattice. Compared with other kagome magnets, the R166 family owns the particularly strong interplays between the d electrons on the kagome site and the localized f electrons on the rare-earth site. In the form of spin-orbital coupling, exchange interaction and many-body effect, the quantum interactions play an essential role in the Berry curvature field in both the reciprocal and real spaces of R166 family. We discuss the spectroscopic and transport visualization of the topological electrons hosted in the Mn kagome layer of RMn6Sn6 and the various topological effects due to the quantum interactions, including the Chern-gap opening, the exchange-biased effect, the topological Hall effect and the emergent inductance. We hope this work serves as a guide for future explorations of quantum magnets.Comment: Submitted versio

Study of pressure shock characteristics of pump-controlled hydraulic steering system

Owing to the complex working conditions, large load changes and inertia of variable pump, pressure shock seriously lowers the efficiency, stability and accuracy of pump-controlled hydraulic steering system. To study the pressure shock characteristics, a physical model of pump-controlled hydraulic steering system was deduced first, and the system dynamic characteristics were simulated by MATLAB/SIMULINK; then the AMESim model was also established to analyze the shock pressure further. By comparison with the simulation results in SIMULINK, the validity of AMESim model is verified. Based on AMESim model, the influence of the navigation speed, spring stiffness of feedback mechanism and rudder angular velocity to the shock characteristics were analyzed specially. According to the results, the design and control methods to reduce hydraulic shock are obtained, which provide a theoretical basis for improving the characteristics of pump-controlled rudder

Exact simulation of a truncated Lévy subordinator

A truncated Lévy subordinator is a Lévy subordinator in R+ with Lévy measure restricted from above by a certain level b. In this article, we study the path and distribution properties of this type of process in detail and set up an exact simulation framework based on a marked renewal process. In particular, we focus on a typical specification of truncated Lévy subordinator, namely the truncated stable process. We establish an exact simulation algorithm for the truncated stable process, which is very accurate and efficient. Compared to the existing algorithm suggested in Chi, our algorithm outperforms over all parameter settings. Using the distributional decomposition technique, we also develop an exact simulation algorithm for the truncated tempered stable process and other related processes. We illustrate an application of our algorithm as a valuation tool for stochastic hyperbolic discounting, and numerical analysis is provided to demonstrate the accuracy and effectiveness of our methods. We also show that variations of the result can also be used to sample two-sided truncated Lévy processes, two-sided Lévy processes via subordinating Brownian motions, and truncated Lévy-driven Ornstein-Uhlenbeck processes

Green Holographic MIMO Communications With A Few Transmit Radio Frequency Chains

Holographic multiple-input multiple-output (MIMO) communications are widely recognized as a promising candidate for the next-generation air interface. With holographic MIMO surface, the number of the spatial degrees-of-freedom (DoFs) considerably increases and also significantly varies as the user moves. To fully employ the large and varying number of spatial DoFs, the number of equipped RF chains has to be larger than or equal to the largest number of spatial DoFs. However, this causes much waste as radio frequency (RF) chains (especially the transmit RF chains) are costly and power-hungry. To avoid the heavy burden, this paper investigates green holographic MIMO communications with a few transmit RF chains under an electromagnetic-based communication model. We not only look at the fundamental capacity limits but also propose an effective transmission, namely non-uniform holographic pattern modulation (NUHPM), to achieve the capacity limit in the high signal-to-noise (SNR) regime. The analytical result sheds light on the green evaluation of MIMO communications, which can be realized by increasing the size of the antenna aperture without increasing the number of transmit RF chains. Numerical results are provided to verify our analysis and to show the great performance gain by employing the additional spatial DoFs as modulation resources.Comment: 10 figures; has been accepted by TGC

How machine learning informs ride-hailing services: A survey

In recent years, online ride-hailing services have emerged as an important component of urban transportation system, which not only provide significant ease for residents’ travel activities, but also shape new travel behavior and diversify urban mobility patterns. This study provides a thorough review of machine-learning-based methodologies for on-demand ride-hailing services. The importance of on-demand ride-hailing services in the spatio-temporal dynamics of urban traffic is first highlighted, with machine-learning-based macro-level ride-hailing research demonstrating its value in guiding the design, planning, operation, and control of urban intelligent transportation systems. Then, the research on travel behavior from the perspective of individual mobility patterns, including carpooling behavior and modal choice behavior, is summarized. In addition, existing studies on order matching and vehicle dispatching strategies, which are among the most important components of on-line ride-hailing systems, are collected and summarized. Finally, some of the critical challenges and opportunities in ride-hailing services are discussed