Zobrazeno 1 - 10
of 9 092
pro vyhledávání: '"Cao, Jian"'
Autor:
Cao, Jian, Zhang, Jin-Peng, Shi, Jia-Hao, Qin, Zhi-Ying, Zhang, Wen-Chao, Zheng, Hua, Lei, An-Ke, She, Zhi-Lei, Zhou, Dai-Mei, Yan, Yu-Liang, Sa, Ben-Hao
Inspired by the BESIII newest observation of X(2370) glueball-like particle production in $e^+e^-$ collisions, we search its production in proton-proton (pp) collisions at $\sqrt{s}=$ 13 TeV with a parton and hadron cascade model PACIAE. In this mode
Externí odkaz:
http://arxiv.org/abs/2408.04130
Autor:
Sun, Ke, Cao, Jian, Wang, Qi, Tian, Linrui, Zhang, Xindi, Zhuo, Lian, Zhang, Bang, Bo, Liefeng, Zhou, Wenbo, Zhang, Weiming, Gao, Daiheng
Virtual Try-On (VTON) has become a transformative technology, empowering users to experiment with fashion without ever having to physically try on clothing. However, existing methods often struggle with generating high-fidelity and detail-consistent
Externí odkaz:
http://arxiv.org/abs/2407.16224
Co-locating latency-critical services (LCSs) and best-effort jobs (BEJs) constitute the principal approach for enhancing resource utilization in production. Nevertheless, the co-location practice hurts the performance of LCSs due to resource competit
Externí odkaz:
http://arxiv.org/abs/2407.12248
Autor:
Cao, Jian, Katzfuss, Matthias
We propose a linear-complexity method for sampling from truncated multivariate normal (TMVN) distributions with high fidelity by applying nearest-neighbor approximations to a product-of-conditionals decomposition of the TMVN density. To make the sequ
Externí odkaz:
http://arxiv.org/abs/2406.17307
Detecting anomalies in business processes is crucial for ensuring operational success. While many existing methods rely on statistical frequency to detect anomalies, it's important to note that infrequent behavior doesn't necessarily imply undesirabi
Externí odkaz:
http://arxiv.org/abs/2406.15781
An effective auto-scaling framework is essential for microservices to ensure performance stability and resource efficiency under dynamic workloads. As revealed by many prior studies, the key to efficient auto-scaling lies in accurately learning perfo
Externí odkaz:
http://arxiv.org/abs/2406.15769
Autor:
Zhang, Xiran, Abdulah, Sameh, Cao, Jian, Ltaief, Hatem, Sun, Ying, Genton, Marc G., Keyes, David E.
Addressing the statistical challenge of computing the multivariate normal (MVN) probability in high dimensions holds significant potential for enhancing various applications. One common way to compute high-dimensional MVN probabilities is the Separat
Externí odkaz:
http://arxiv.org/abs/2405.14892
Autor:
Park, Chanwook, Saha, Sourav, Guo, Jiachen, Xie, Xiaoyu, Mojumder, Satyajit, Bessa, Miguel A., Qian, Dong, Chen, Wei, Wagner, Gregory J., Cao, Jian, Liu, Wing Kam
The evolution of artificial intelligence (AI) and neural network theories has revolutionized the way software is programmed, shifting from a hard-coded series of codes to a vast neural network. However, this transition in engineering software has fac
Externí odkaz:
http://arxiv.org/abs/2404.10296
With its elastic power and a pay-as-you-go cost model, the deployment of deep learning inference services (DLISs) on serverless platforms is emerging as a prevalent trend. However, the varying resource requirements of different layers in DL models hi
Externí odkaz:
http://arxiv.org/abs/2404.02445
Heterogeneous Federated Learning (HtFL) enables collaborative learning on multiple clients with different model architectures while preserving privacy. Despite recent research progress, knowledge sharing in HtFL is still difficult due to data and mod
Externí odkaz:
http://arxiv.org/abs/2403.15760