Querying any point-of-interest (POI) in anywhere is a major part of location-based services (LBS) and has been applied to diverse wireless and/or mobile networks for realizing seamless services and mobile and ubiquitous computing. In particular, designing an efficient query processing scheme is admittedly challenging in mobile ad hoc networks (MANETs) because of the lack of centralized coordina...

Traditional machine learning sequence models, such as RNN and LSTM, can solve sequential data problems with the use of internal memory states. However, neuron units weights are shared at each time step to reduce computational costs, limiting their ability learn time-varying relationships between model inputs outputs. In this context, paper proposes two methods characterize dynamic in real-world...

We study time-varying semidefinite programs (TV-SDPs), which are whose data (and solutions) functions of time. Our focus is on the setting where vary polynomially with show that under a strict feasibility assumption, restricting solutions to also be polynomial time does not change optimal value TV-SDP. Moreover, by using Positivstellensatz (positive locus theorem) univariate matrices, we best s...

this paper presents dynamic portfolio model based on the merton's optimal investment-consumption model, which combines dynamic synthetic put option using risk-free and risky assets. this paper is extended version of methodological paper published by yuan yao (2012) cite{26}. because of the long history of the development of foreign financial market, with a variety of financial derivatives, the ...

This paper considers a distributed convex optimization problem with inequality constraints over time-varying unbalanced digraphs, where the cost function is a sum of local objectives, and each node of the graph only knows its local objective and inequality constraints. Although there is a vast literature on distributed optimization, most of them require the graph to be balanced, which is quite ...