Zobrazeno 1 - 10
of 3 559
pro vyhledávání: '"Liao, Hong"'
Explicit integrating factor Runge-Kutta methods are attractive and popular in developing high-order maximum bound principle preserving time-stepping schemes for Allen-Cahn type gradient flows. However, they always suffer from the non-preservation of
Externí odkaz:
http://arxiv.org/abs/2408.14984
Autor:
Wang, Chien-Yao, Liao, Hong-Yuan Mark
This is a comprehensive review of the YOLO series of systems. Different from previous literature surveys, this review article re-examines the characteristics of the YOLO series from the latest technical point of view. At the same time, we also analyz
Externí odkaz:
http://arxiv.org/abs/2408.09332
Autor:
Liao, Hong-lin, Wang, Xuping
We propose a unified theoretical framework to examine the energy dissipation properties at all stages of explicit exponential Runge-Kutta (EERK) methods for gradient flow problems. The main part of the novel framework is to construct the differential
Externí odkaz:
http://arxiv.org/abs/2404.14893
Today's deep learning methods focus on how to design the most appropriate objective functions so that the prediction results of the model can be closest to the ground truth. Meanwhile, an appropriate architecture that can facilitate acquisition of en
Externí odkaz:
http://arxiv.org/abs/2402.13616
We provide a new theoretical framework for the variable-step deferred correction (DC) methods based on the well-known BDF2 formula. By using the discrete orthogonal convolution kernels, some high-order BDF2-DC methods are proven to be stable on arbit
Externí odkaz:
http://arxiv.org/abs/2402.06129
Publikováno v:
Journal of Scientific Computing, 2024, 99:46
We build an asymptotically compatible energy of the variable-step L2-$1_{\sigma}$ scheme for the time-fractional Allen-Cahn model with the Caputo's fractional derivative of order $\alpha\in(0,1)$, under a weak step-ratio constraint $\tau_k/\tau_{k-1}
Externí odkaz:
http://arxiv.org/abs/2311.13216
Multi-task learning (MTL) aims to learn multiple tasks using a single model and jointly improve all of them assuming generalization and shared semantics. Reducing conflicts between tasks during joint learning is difficult and generally requires caref
Externí odkaz:
http://arxiv.org/abs/2309.16921
Publikováno v:
Jichu yixue yu linchuang, Vol 44, Iss 7, Pp 1029-1033 (2024)
Microglial inflammatory response is a pathological process frequently found in patients with depression and in animal models, which is believed to be closely related to depression. The potential mechanisms of inducing microglial inflammatory response
Externí odkaz:
https://doaj.org/article/6c8b985cf6e24db898c1df25f6222512
Publikováno v:
SIAM Journal on Numerical Analysis, 61(5), 2023, pp. 2157-2181
The discrete gradient structure and the positive definiteness of discrete fractional integrals or derivatives are fundamental to the numerical stability in long-time simulation of nonlinear integro-differential models. We build up a discrete gradient
Externí odkaz:
http://arxiv.org/abs/2301.12474
Autor:
Chen, Yu-Hsi, Wang, Chien-Yao, Yang, Cheng-Yun, Chang, Hung-Shuo, Lin, Youn-Long, Chuang, Yung-Yu, Liao, Hong-Yuan Mark
Publikováno v:
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2023, pp. 5139-5148
We propose a post-processor, called NeighborTrack, that leverages neighbor information of the tracking target to validate and improve single-object tracking (SOT) results. It requires no additional data or retraining. Instead, it uses the confidence
Externí odkaz:
http://arxiv.org/abs/2211.06663