Zobrazeno 1 - 10
of 3 908
pro vyhledávání: '"Shiyan, P."'
Large diffusion models have become mainstream generative models in both academic studies and industrial AIGC applications. Recently, a number of works further explored how to employ the power of large diffusion models as zero-shot classifiers. While
Externí odkaz:
http://arxiv.org/abs/2412.12594
Autor:
Zhong, Shiyan
In recent years, a new subclass of tidal disruption events (TDEs) was reported from the literature. The light curve of these TDEs show a re-brightening feature in the decline phase after the first peak, which then leads to a second flare. The re-brig
Externí odkaz:
http://arxiv.org/abs/2412.12549
Autor:
Wang, Hanru, Jiao, Yihan, Meng, Fanyu, Zhang, Xu, Dai, Dongzhe, Tu, Chengpeng, Zhao, Chengcheng, Xin, Lu, Huang, Sicheng, Lei, Hechang, Li, Shiyan
We present the ultralow-temperature thermal conductivity measurements on single crystals of transition-metal dichalcogenide material 4Hb-TaS$_{2}$, which has recently been proposed as a topological superconductor candidate. In zero field, a small res
Externí odkaz:
http://arxiv.org/abs/2412.08450
Vision-Language Models (VLMs) are crucial for applications requiring integrated understanding textual and visual information. However, existing VLMs struggle with long videos due to computational inefficiency, memory limitations, and difficulties in
Externí odkaz:
http://arxiv.org/abs/2411.15556
Many methods have been proposed for unsupervised time series anomaly detection. Despite some progress, research on predicting future anomalies is still relatively scarce. Predicting anomalies is particularly challenging due to the diverse reaction ti
Externí odkaz:
http://arxiv.org/abs/2410.15997
Artificial Neural Networks (ANNs) suffer from catastrophic forgetting, where the learning of new tasks causes the catastrophic forgetting of old tasks. Existing Machine Learning (ML) algorithms, including those using Stochastic Gradient Descent (SGD)
Externí odkaz:
http://arxiv.org/abs/2410.15318
While many diffusion models perform well when controlling for particular aspect among style, character, and interaction, they struggle with fine-grained control due to dataset limitations and intricate model architecture design. This paper introduces
Externí odkaz:
http://arxiv.org/abs/2410.01262
Autor:
Yang, Haobo, Zhang, Shiyan, Yang, Zhuoyi, Zhang, Xinyu, Wang, Li, Tang, Yifan, Guo, Jilong, Li, Jun
With the increasing complexity of the traffic environment, the significance of safety perception in intelligent driving is intensifying. Traditional methods in the field of intelligent driving perception rely on deep learning, which suffers from limi
Externí odkaz:
http://arxiv.org/abs/2409.00839
Autor:
Zhong, Shiyan, Xu, Xian, Chen, Xinlei, Guo, Helong, Fang, Yuan, Du, Guowang, Liu, Xiangkun, Liu, Xiaowei
We present the optical light curves of the tidal disruption event (TDE) AT 2023clx in the declining phase, observed with Mephisto. Combining our light curve with the ASAS-SN and ATLAS data in the rising phase, and fitting the composite multi-band lig
Externí odkaz:
http://arxiv.org/abs/2408.04448
One of the core risk management tasks is to identify hidden high-risky states that may lead to system breakdown, which can provide valuable early warning knowledge. However, due to high dimensionality and nonlinear interaction embedded in large-scale
Externí odkaz:
http://arxiv.org/abs/2407.20478