Zobrazeno 1 - 10
of 3 050
pro vyhledávání: '"Dongchen An"'
Autor:
Dongchen An, Guilherme Salgado Carrazoni, Ben-Hur Souto das Neves, Rudi D’Hooge, Steve Peigneur, Jan Tytgat
Publikováno v:
Biomedicines, Vol 12, Iss 2, p 454 (2024)
Cannabinoid receptors (CB1 and CB2) are promising targets for a better understanding of neurological diseases. Nevertheless, only a few ligands of CB have reached clinical application so far. Venoms are considered as interesting sources of novel biol
Externí odkaz:
https://doaj.org/article/b5313ba625424979a21860b9a7e63b66
Autor:
Yu, Youwei, Liu, Yanqing, Fu, Fengjie, He, Sihan, Zhu, Dongchen, Wang, Lei, Zhang, Xiaolin, Li, Jiamao
In this paper, we propose a fast extrinsic calibration method for fusing multiple inertial measurement units (MIMU) to improve visual-inertial odometry (VIO) localization accuracy. Currently, data fusion algorithms for MIMU highly depend on the numbe
Externí odkaz:
http://arxiv.org/abs/2409.16228
This paper demonstrates the existence of $\mathbb{Q}$-complements for algebraically integrable log-Fano foliations on klt ambient varieties. Additionally, we investigate properties of algebraically integrable Fano foliations such as a partial inversi
Externí odkaz:
http://arxiv.org/abs/2408.11738
Autor:
Pu, Yifan, Xia, Zhuofan, Guo, Jiayi, Han, Dongchen, Li, Qixiu, Li, Duo, Yuan, Yuhui, Li, Ji, Han, Yizeng, Song, Shiji, Huang, Gao, Li, Xiu
This paper identifies significant redundancy in the query-key interactions within self-attention mechanisms of diffusion transformer models, particularly during the early stages of denoising diffusion steps. In response to this observation, we presen
Externí odkaz:
http://arxiv.org/abs/2408.05710
Autor:
Li, Dongchen
The aim of this paper is twofold. First, motivated by the nearly-affine blender system found in [LT24], we introduce standard blenders and their variations, and prove their fundamental properties on the generation of $C^1$-robust tangencies. Next, as
Externí odkaz:
http://arxiv.org/abs/2406.12500
Autor:
Liu, Runze, Zhu, Dongchen, Zhang, Guanghui, Xu, Yue, Shi, Wenjun, Zhang, Xiaolin, Wang, Lei, Li, Jiamao
Unsupervised monocular depth estimation has received widespread attention because of its capability to train without ground truth. In real-world scenarios, the images may be blurry or noisy due to the influence of weather conditions and inherent limi
Externí odkaz:
http://arxiv.org/abs/2406.09782
Autor:
Han, Dongchen, Wang, Ziyi, Xia, Zhuofan, Han, Yizeng, Pu, Yifan, Ge, Chunjiang, Song, Jun, Song, Shiji, Zheng, Bo, Huang, Gao
Mamba is an effective state space model with linear computation complexity. It has recently shown impressive efficiency in dealing with high-resolution inputs across various vision tasks. In this paper, we reveal that the powerful Mamba model shares
Externí odkaz:
http://arxiv.org/abs/2405.16605
Autor:
Jiang, Dongchen, Fu, Chenxi
Isabelle2Cpp is a code generation framework that supports automatic generation of C++ code from Isabelle/HOL specifications. However, if some type information of Isabelle/HOL specification is missing, Isabelle2Cpp may not complete the code generation
Externí odkaz:
http://arxiv.org/abs/2404.18067
This paper aims to recover object materials from posed images captured under an unknown static lighting condition. Recent methods solve this task by optimizing material parameters through differentiable physically based rendering. However, due to the
Externí odkaz:
http://arxiv.org/abs/2404.11593
RNA design shows growing applications in synthetic biology and therapeutics, driven by the crucial role of RNA in various biological processes. A fundamental challenge is to find functional RNA sequences that satisfy given structural constraints, kno
Externí odkaz:
http://arxiv.org/abs/2404.11199