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pro vyhledávání: '"Zhang Xiaojian"'
Hurricane Ian is the deadliest and costliest hurricane in Florida's history, with 2.5 million people ordered to evacuate. As we witness increasingly severe hurricanes in the context of climate change, mobile device location data offers an unprecedent
Externí odkaz:
http://arxiv.org/abs/2407.15249
Accurately assessing building damage is critical for disaster response and recovery. However, many existing models for detecting building damage have poor prediction accuracy due to their limited capabilities of identifying detailed, comprehensive st
Externí odkaz:
http://arxiv.org/abs/2404.07399
Publikováno v:
PLoS ONE, Vol 15, Iss 5, p e0232272 (2020)
Non-small cell lung cancer (NSCLC) remains a leading cause of cancer death globally. More accurate and reliable diagnostic methods/biomarkers are urgently needed. Joint application of metabolomics and transcriptomics technologies possesses the high e
Externí odkaz:
https://doaj.org/article/642227fb33d14724ad8be1b06f436455
Autor:
Wang, Chenguang, Liu, Yepeng, Zhang, Xiaojian, Li, Xuechun, Paramygin, Vladimir, Subgranon, Arthriya, Sheng, Peter, Zhao, Xilei, Xu, Susu
Timely and accurate assessment of hurricane-induced building damage is crucial for effective post-hurricane response and recovery efforts. Recently, remote sensing technologies provide large-scale optical or Interferometric Synthetic Aperture Radar (
Externí odkaz:
http://arxiv.org/abs/2310.01565
Autor:
Shen, Changqing, Mao, Sihao, Xu, Bingzhou, Wang, Ziwei, Zhang, Xiaojian, Yan, Sijie, Ding, Han
The generation of smoother and shorter spiral complete coverage paths in multi-connected domains is a crucial research topic in path planning for robotic cavity machining and other related fields. Traditional methods for spiral path planning in multi
Externí odkaz:
http://arxiv.org/abs/2309.10655
Accurate shared micromobility demand predictions are essential for transportation planning and management. Although deep learning models provide powerful tools to deal with demand prediction problems, studies on forecasting highly-accurate spatiotemp
Externí odkaz:
http://arxiv.org/abs/2306.13897
Real-time forecasting of travel demand during wildfire evacuations is crucial for emergency managers and transportation planners to make timely and better-informed decisions. However, few studies focus on accurate travel demand forecasting in large-s
Externí odkaz:
http://arxiv.org/abs/2304.06233
Autor:
Zhang, Xiaojian
In this paper, we study block Jacobi operators on $\mathbb{Z}$ with quasi-periodic meromorphic potential. We prove the non-perturbative Anderson localization for such operators in the large coupling regime.
Externí odkaz:
http://arxiv.org/abs/2303.01072
Artificial Intelligence (AI) and machine learning have been increasingly adopted for travel demand forecasting. The AI-based travel demand forecasting models, though generate accurate predictions, may produce prediction biases and raise fairness issu
Externí odkaz:
http://arxiv.org/abs/2303.01692
The growing significance of ridesourcing services in recent years suggests a need to examine the key determinants of ridesourcing demand. However, little is known regarding the nonlinear effects and spatial heterogeneity of ridesourcing demand determ
Externí odkaz:
http://arxiv.org/abs/2209.07980