Search Results for author: Qiang Gao

Found 11 papers, 2 papers with code

Spatial-Temporal Contrasting for Fine-Grained Urban Flow Inference

1 code implementation IEEE Transactions on Big Data 2023 Xovee Xu, Zhiyuan Wang, Qiang Gao, Ting Zhong, Bei Hui, Fan Zhou, Goce Trajcevski

Fine-grained urban flow inference (FUFI) problem aims to infer the fine-grained flow maps from coarse-grained ones, benefiting various smart-city applications by reducing electricity, maintenance, and operation costs.

Fine-Grained Urban Flow Inference Image Super-Resolution

Predicting Human Mobility via Self-supervised Disentanglement Learning

no code implementations17 Nov 2022 Qiang Gao, Jinyu Hong, Xovee Xu, Ping Kuang, Fan Zhou, Goce Trajcevski

However, most of the existing research concentrates on fusing different semantics underlying sequential trajectories for mobility pattern learning which, in turn, yields a narrow perspective on comprehending human intrinsic motions.

Disentanglement

High-Fidelity Simulation and Novel Data Analysis of the Bubble Creation and Sound Generation Processes in Breaking Waves

no code implementations6 Nov 2022 Qiang Gao, Grant B. Deane, Saswata Basak, Umberto Bitencourt, Lian Shen

In this work, we applied our bubble tracking algorithm to the breaking waves simulations and investigated the bubble trajectories, bubble creation mechanisms, and bubble acoustics based on our previous works.

Incorporating Interactive Facts for Stock Selection via Neural Recursive ODEs

no code implementations28 Oct 2022 Qiang Gao, Xinzhu Zhou, Kunpeng Zhang, Li Huang, Siyuan Liu, Fan Zhou

Stock selection attempts to rank a list of stocks for optimizing investment decision making, aiming at minimizing investment risks while maximizing profit returns.

Decision Making

Self-supervised Representation Learning for Trip Recommendation

no code implementations2 Sep 2021 Qiang Gao, Wei Wang, Kunpeng Zhang, Xin Yang, Congcong Miao

Although recent deep recursive models (e. g., RNN) are capable of alleviating these concerns, existing solutions hardly recognize the practical reality, such as the diversity of tourist demands, uncertainties in the trip generation, and the complex visiting preference.

Contrastive Learning point of interests +1

Particle-hole asymmetric superconducting coherence peaks in overdoped cuprates

no code implementations10 Mar 2021 Changwei Zou, Zhenqi Hao, Xiangyu Luo, Shusen Ye, Qiang Gao, Xintong Li, Miao Xu, Peng Cai, Chengtian Lin, Xingjiang Zhou, Dung-Hai Lee, Yayu Wang

To elucidate the superconductor to metal transition at the end of superconducting dome, the overdoped regime has stepped onto the center stage of cuprate research recently.

Superconductivity

Preparation of superconducting thin films of infinite-layer nickelate Nd$_{0.8}$Sr$_{0.2}$NiO$_{2}$

no code implementations20 Feb 2021 Qiang Gao, Yuchen Zhao, Xingjiang Zhou, Zhihai Zhu

The recent observation of superconductivity in infinite-layer nickelate Nd$_{0. 8}$Sr$_{0. 2}$NiO$_{2}$ has received considerable attention.

Superconductivity Strongly Correlated Electrons

Floquet-Bloch Oscillations and Intraband Zener Tunneling in an Oblique Spacetime Crystal

no code implementations1 Nov 2020 Qiang Gao, Qian Niu

We investigate an oblique spacetime crystal realized by a monoatomic crystal in which a mode of sound propagates.

Other Condensed Matter

Efficient Architecture Search for Continual Learning

no code implementations7 Jun 2020 Qiang Gao, Zhipeng Luo, Diego Klabjan

To reach these goals, we propose a novel approach named as Continual Learning with Efficient Architecture Search, or CLEAS in short.

Continual Learning Neural Architecture Search +1

Weibull Racing Survival Analysis with Competing Events, Left Truncation, and Time-varying Covariates

no code implementations2 Nov 2019 Quan Zhang, Qiang Gao, Mingfeng Lin, Mingyuan Zhou

Specifically, we study time to death of three types of lymphoma and show the potential of WDR in modeling nonlinear covariate effects and discovering new diseases.

Survival Analysis Methodology

Trajectory-User Linking via Variational AutoEncoder

1 code implementation International Joint Conference on Artificial Intelligence 2018 Fan Zhou, Qiang Gao, Goce Trajcevski, Kunpeng Zhang, Ting Zhong, Fengli Zhang

Trajectory-User Linking (TUL) is an essential task in Geo-tagged social media (GTSM) applications, enabling personalized Point of Interest (POI) recommendation and activity identification.

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