Intraday pattern in bid-ask spreads and its power-law relaxation for Chinese A-share stocks

We use high-frequency data of 1364 Chinese A-share stocks traded on the Shanghai Stock Exchange and Shenzhen Stock Exchange to investigate the intraday patterns in the bid-ask spreads. The daily periodicity in the spread time series is confirmed by Lomb analysis and the intraday bid-ask spreads are found to exhibit $L$-shaped pattern with idiosyncratic fine structure. The intraday spread of individual stocks relaxes as a power law within the first hour of the continuous double auction from 9:30AM to 10:30AM with exponents $\beta_{\rm{SHSE}}=0.19\pm0.069$ for the Shanghai market and $\beta_{\rm{SZSE}}=0.18\pm0.067$ for the Shenzhen market. The power-law relaxation exponent $\beta$ of individual stocks is roughly normally distributed. There is evidence showing that the accumulation of information widening the spread is an endogenous process.Comment: 12 Elsart pages including 7 eps figure

Review on structural damage assessment via transmissibility with vibration based measurements

10.1088/1742-6596/842/1/012016Journal of Physics: Conference Series84211201

An effective ant-colony based routing algorithm for mobile ad-hoc network

An effective Ant-Colony based routing algorithm for mobile ad-hoc network is proposed in this paper. In this routing scheme, each path is marked by path grade, which is calculated from the combination of multiple constrained QoS parameters such as the time delay, packet loss rate and bandwidth, etc. packet routing is decided by the path grade and the queue buffer length of the node. The advantage of this scheme is that it can effectively improve the packet delivery ratio and reduce the end-to-end delay. The simulation results show that our proposed algorithm can improve the packet delivery ratio by 9%-22% and the end-to-end delay can be reduced by 14%-16% as compared with the conventional QAODV and ARA routing schemes

Efficient Cluster Selection for Personalized Federated Learning: A Multi-Armed Bandit Approach

Federated learning (FL) offers a decentralized training approach for machine learning models, prioritizing data privacy. However, the inherent heterogeneity in FL networks, arising from variations in data distribution, size, and device capabilities, poses challenges in user federation. Recognizing this, Personalized Federated Learning (PFL) emphasizes tailoring learning processes to individual data profiles. In this paper, we address the complexity of clustering users in PFL, especially in dynamic networks, by introducing a dynamic Upper Confidence Bound (dUCB) algorithm inspired by the multi-armed bandit (MAB) approach. The dUCB algorithm ensures that new users can effectively find the best cluster for their data distribution by balancing exploration and exploitation. The performance of our algorithm is evaluated in various cases, showing its effectiveness in handling dynamic federated learning scenarios