Zobrazeno 1 - 10
of 4 684
pro vyhledávání: '"ZHANG, Jiang"'
Predicting company growth is crucial for strategic adjustment, operational decision-making, risk assessment, and loan eligibility reviews. Traditional models for company growth often focus too much on theory, overlooking practical forecasting, or the
Externí odkaz:
http://arxiv.org/abs/2410.17587
Publikováno v:
Entropy 2024, 26, 618
After coarse-graining a complex system, the dynamics of its macro-state may exhibit more pronounced causal effects than those of its micro-state. This phenomenon, known as causal emergence, is quantified by the indicator of effective information. How
Externí odkaz:
http://arxiv.org/abs/2405.09207
Federated Learning (FL) with Secure Aggregation (SA) has gained significant attention as a privacy preserving framework for training machine learning models while preventing the server from learning information about users' data from their individual
Externí odkaz:
http://arxiv.org/abs/2405.04551
The theory of causal emergence (CE) with effective information (EI) posits that complex systems can exhibit CE, where macro-dynamics show stronger causal effects than micro-dynamics. A key challenge of this theory is its dependence on coarse-graining
Externí odkaz:
http://arxiv.org/abs/2402.15054
Demonstrating that logical qubits outperform their physical counterparts is a milestone for achieving reliable quantum computation. Here, we propose to protect logical qubits with a novel dynamical decoupling scheme that implements iSWAP gates on nea
Externí odkaz:
http://arxiv.org/abs/2402.05604
Autor:
Qiu, Jian-Jie, Zhang, Yong, Nakashima, Jun-ichi, Zhang, Jiang-Shui, Li, Fei, Lu, Deng-Rong, Tang, Xin-Di, Yu, Xiao-Ling, Jia, Lan-Wei
It has been more than 30 years since the enigmatic 21 {\mu}m emission feature was first discovered in protoplanetary nebulae (PPNs). Although dozens of different dust carrier candidates have been proposed, there is as yet no widely accepted one. We p
Externí odkaz:
http://arxiv.org/abs/2401.00387
Toxic content detection is crucial for online services to remove inappropriate content that violates community standards. To automate the detection process, prior works have proposed varieties of machine learning (ML) approaches to train Language Mod
Externí odkaz:
http://arxiv.org/abs/2312.08303
The rapid increase in the parameters of deep learning models has led to significant costs, challenging computational efficiency and model interpretability. In this paper, we introduce a novel and straightforward neural network pruning framework that
Externí odkaz:
http://arxiv.org/abs/2311.12526
Multiscale modeling of complex systems is crucial for understanding their intricacies. Data-driven multiscale modeling has emerged as a promising approach to tackle challenges associated with complex systems. On the other hand, self-similarity is pre
Externí odkaz:
http://arxiv.org/abs/2310.08282