The purpose of the present paper is to give an elementary method for the computation of the cohomology groups Hq（X,Ω^p X（L））, （0 ≤q ≤ n） of an n-dimensional non-primary Hopf manifold X with arbitrary fundamen...The purpose of the present paper is to give an elementary method for the computation of the cohomology groups Hq（X,Ω^p X（L））, （0 ≤q ≤ n） of an n-dimensional non-primary Hopf manifold X with arbitrary fundamental group. We use the method of Zhou to generalize the results for primary Hopf manifolds and non-primary Hopf manifold with an Abelian fundamental group.展开更多
Time-series databases(TSDBs)are essential for managing large-scale time-series data in fields like finance,IoT,and agriculture.However,traditional query optimization methods,such as dynamic programming,struggle with h...Time-series databases(TSDBs)are essential for managing large-scale time-series data in fields like finance,IoT,and agriculture.However,traditional query optimization methods,such as dynamic programming,struggle with high computational complexity and inaccurate cost estimates.This paper proposes a novel query optimization module for TSDBs using reinforcement learning(RL),specifically Deep Q-Networks(DQN)and Double Deep Q-Networks(DDQN).These algorithms dynamically learn optimal join orders based on query workloads and connection costs.Experiments show that RL-based methods achieve better optimization performance and stability compared to traditional heuristics,especially under complex cost models.This work highlights the potential of RL in improving query optimization for TSDBs.展开更多
基金supported by 973 Project Foundation of China and Outstanding Youth science Grant of NSFC(Grant No.19825105)
文摘The purpose of the present paper is to give an elementary method for the computation of the cohomology groups Hq（X,Ω^p X（L））, （0 ≤q ≤ n） of an n-dimensional non-primary Hopf manifold X with arbitrary fundamental group. We use the method of Zhou to generalize the results for primary Hopf manifolds and non-primary Hopf manifold with an Abelian fundamental group.
基金supported by Sichuan Science and Technology Program(2024YFHZ0161).
文摘Time-series databases(TSDBs)are essential for managing large-scale time-series data in fields like finance,IoT,and agriculture.However,traditional query optimization methods,such as dynamic programming,struggle with high computational complexity and inaccurate cost estimates.This paper proposes a novel query optimization module for TSDBs using reinforcement learning(RL),specifically Deep Q-Networks(DQN)and Double Deep Q-Networks(DDQN).These algorithms dynamically learn optimal join orders based on query workloads and connection costs.Experiments show that RL-based methods achieve better optimization performance and stability compared to traditional heuristics,especially under complex cost models.This work highlights the potential of RL in improving query optimization for TSDBs.
