Zobrazeno 1 - 10
of 33 452
pro vyhledávání: '"Wang, Ting"'
Despite the impressive advancements made in recent low-light image enhancement techniques, the scarcity of paired data has emerged as a significant obstacle to further advancements. This work proposes a mean-teacher-based semi-supervised low-light en
Externí odkaz:
http://arxiv.org/abs/2409.16604
When Unmanned Aerial Vehicles (UAVs) perform high-precision communication tasks, such as searching for users and providing emergency coverage, positioning errors between base stations and users make it challenging to deploy trajectory planning algori
Externí odkaz:
http://arxiv.org/abs/2409.15798
Autor:
Zhang, Dingqi, Loquercio, Antonio, Tang, Jerry, Wang, Ting-Hao, Malik, Jitendra, Mueller, Mark W.
This paper introduces a learning-based low-level controller for quadcopters, which adaptively controls quadcopters with significant variations in mass, size, and actuator capabilities. Our approach leverages a combination of imitation learning and re
Externí odkaz:
http://arxiv.org/abs/2409.12949
Metro operation management relies on accurate predictions of passenger flow in the future. This study begins by integrating cross-city (including source and target city) knowledge and developing a short-term passenger flow prediction framework (METcr
Externí odkaz:
http://arxiv.org/abs/2409.01515
The path-specific effect (PSE) is of primary interest in mediation analysis when multiple intermediate variables between treatment and outcome are observed, as it can isolate the specific effect through each mediator, thus mitigating potential bias a
Externí odkaz:
http://arxiv.org/abs/2409.01248
Physics-informed deep learning (PIDL)-based models have recently garnered remarkable success in traffic state estimation (TSE). However, the prior knowledge used to guide regularization training in current mainstream architectures is based on determi
Externí odkaz:
http://arxiv.org/abs/2409.00644
In this paper, we study user association and wireless bandwidth allocation for a hierarchical federated learning system that consists of mobile users, edge servers, and a cloud server. To minimize the length of a global round in hierarchical federate
Externí odkaz:
http://arxiv.org/abs/2408.09076
CAD-Mesher: A Convenient, Accurate, Dense Mesh-based Mapping Module in SLAM for Dynamic Environments
Most LiDAR odometry and SLAM systems construct maps in point clouds, which are discrete and sparse when zoomed in, making them not directly suitable for navigation. Mesh maps represent a dense and continuous map format with low memory consumption, wh
Externí odkaz:
http://arxiv.org/abs/2408.05981
We propose the convex-roof extension of quantum conditional mutual information ("co(QCMI)") as a diagnostic of long-range entanglement in a mixed state. We focus primarily on topological states subjected to local decoherence, and employ the Levin-Wen
Externí odkaz:
http://arxiv.org/abs/2407.20500
Autor:
Zeng, Yu, Patel, Vishal M., Wang, Haochen, Huang, Xun, Wang, Ting-Chun, Liu, Ming-Yu, Balaji, Yogesh
Personalized text-to-image generation models enable users to create images that depict their individual possessions in diverse scenes, finding applications in various domains. To achieve the personalization capability, existing methods rely on finetu
Externí odkaz:
http://arxiv.org/abs/2407.06187