Adaptive online optimization algorithms, such as Adam, RMSprop, and AdaBound, have recently been tremendously popular they widely applied to address the issues in field of deep learning. Despite their prevalence prosperity, however, it is rare investigate distributed versions these adaptive algorithms. To fill gap, a subgradient learning algorithm over time-varying networks, called DAdaxBound, ...

In this paper, we develop a novel virtual-queue-based online algorithm for convex optimization (OCO) problems with long-term and time-varying constraints conduct performance analysis respect to the dynamic regret constraint violations. We design new update rule of dual variables way incorporating functions into variables. To best our knowledge, is first parameter-free simultaneously achieve sub...

In this paper, the speed tracking for permanent magnet synchronous motor (PMSM) in field oriented control (FOC) method is investigated using linear proportional-integral (PI) controller, sliding mode controller (SMC) and its advanced counterparts. The advanced SMCs considered in this paper are fuzzy SMC (FSMC) and sliding mode controller with time-varying switching gain (SMC+TG) which can effec...

Spreading processes represent a very efficient tool to investigate the structural properties of networks and the relative importance of their constituents, and have been widely used to this aim in static networks. Here we consider simple disease spreading processes on empirical time-varying networks of contacts between individuals, and compare the effect of several immunization strategies on th...

It has already been confirmed that the traffic in high-speed terrestrial network presents self-similarity, but there is little research on self-similarity of traffic in satellite network. Considering time-varying network topology and link status, this paper analyzes the aggregation and propagation of self-similar traffic between nodes in satellite network. Furthermore, a sort of special network...

We consider a wireless network consisting of multiple transmitters with multicast traffic destined for a set of receivers. We are interested in the problem of joint scheduling and rate control under two performance objectives; the objective of maximizing the total sum throughput of the network and of being proportionally fair with respect to the received rate at each receiver. We first consider...

Abstract Dynamically adapting the allocation of COVID-19 vaccines to evolving epidemiological situation could be key reduce burden. Here we developed a data-driven mechanistic model SARS-CoV-2 transmission explore optimal vaccine prioritization strategies in China. We found that time-varying vaccination program (i.e., allocating different target groups as epidemic evolves) can highly beneficial...