With increasing amounts of interaction data collected by high-throughput techniques, understanding the structure and dynamics of biological networks becomes one of the central tasks in post-genomic molecular biology. Recent studies have shown that many biological networks contain a small set of “network motifs,” which are suggested to be the basic cellular information-processing units in these ...

In the past few decades, development of fluorescent technologies and microscopic techniques has greatly improved scientists' ability to observe real-time single-cell activities. this paper, we consider filtering problem associate with these advanced technologies, i.e., how estimate latent dynamic states an intracellular multiscale stochastic reaction network from time-course measurements report...

Deep neural networks are powerful parametric models that can be trained efficiently using the backpropagation algorithm. Stochastic neural networks combine the power of large parametric functions with that of graphical models, which makes it possible to learn very complex distributions. However, as backpropagation is not directly applicable to stochastic networks that include discrete sampling ...

The probable lack of some arcs and nodes in the stochastic networks is considered in this paper, and its effect is shown as the arrival probability from a given source node to a given sink node. A discrete time Markov chain with an absorbing state is established in a directed acyclic network. Then, the probability of transition from the initial state to the absorbing state is computed. It is as...

This paper proposes a stochastic model predictive control (MPC) framework for traffic signal coordination and control in urban traffic networks. One of the important features of the proposed stochastic MPC model is that uncertain traffic demands and stochastic disturbances are taken into account. Aiming to effectively model the uncertainties and avoid queue spillback in traffic networks, we dev...

Abstract: The stability problem for a class of stochastic neural networks with Markovian jump parameters and leakage delay is addressed in this study. The sufficient condition to ensure an exponentially stable stochastic neural networks system is presented and proven with Lyapunov functional theory, stochastic stability technique and linear matrix inequality method. The effect of leakage delay ...

One of the features of wireless sensor networks is that the nodes in this network have limited power sources. Therefore, assessment of energy consumption in these networks is very important. What has been common practice has been the use of traditional simulators to evaluate the energy consumption of the nodes in these networks. Simulators often have problems such as fluctuating output values i...

In this paper, stochastic bidirectional associative memory neural networks with constant or time-varying delays is considered. Based on a Lyapunov-Krasovskii functional and the stochastic stability analysis theory, we derive several sufficient conditions in order to guarantee the global asymptotically stable in the mean square. Our investigation shows that the stochastic bidirectional associati...

We address the problem of designing artificial agents capable of reproducing human behavior in a competitive game involving dynamic control. Given data consisting of multiple realizations of inputs generated by pairs of interacting players, we model each agent’s actions as governed by a time-varying latent goal state coupled to a control model. These goals, in turn, are described as stochastic ...

We study a tractable opinion dynamics model that generates long-run disagreements and persistent opinion fluctuations. Our model involves a inhomogeneous stochastic gossip process of continuous opinion dynamics in a society consisting of two types of agents: regular agents, who update their beliefs according to information that they receive from their social neighbors; and stubborn agents, who ...