Narrow Results

Semidefinite integer programming model is an accurate tool for the structural design of networks. In this paper, we propose a semidefinite integer programming model with the constraints of spectral distributions and node degree distributions for the simulation of complex networks. Also, the feasible solutions and branch-and-bound solving algorithms of the model are designed. Based on eight metrics (e.g., spectral distribution, node degree distribution, clustering coefficients, etc.), the validity and practicability of the proposed method are illustrated.

A simple recurrent network with a perceptual simulation layer was trained on a corpus of affirmative and negated sentences. Linguistic negation can be encoded by the network via the inclusion (or absence) of features and categories associated with the senses, in one step, without the need for an explicit logical operation or for treating the negating word any differently than any other words. Visualizing negation as a trajectory in perceptual simulation space is explored in detail, and the implications for artificial intelligence, embodied computational models, and more practical implications of everyday use of negations are discussed.

Gene regulatory networks (GRNs) control the production of proteins in cells. It is well-known that this process is not deterministic. Numerous studies employed a non-deterministic transition structure to model these networks. However, it is not realistic to expect state-to-state transition probabilities to remain constant throughout an organism’s lifetime. In this work, we focus on modeling GRN state transition (edge) variability using an ever-changing set of propensities. We suspect that the source of this variation is due to internal noise at the molecular level and can be modeled by introducing additional stochasticity into GRN models. We employ a beta distribution, whose parameters are estimated to capture the pattern inherent in edge behavior with minimum error. Additionally, we develop a method for obtaining propensities from a pre-determined network.

Internet worms are one of the greatest threats to the Internet. Simulation is an efficient method to study the behavior of Internet worm propagation. All the current worm simulators can not provide the dynamic changing of topology and the dynamic routing mechanism, which badly compromises the fidelity of the Internet worm simulation. This paper presents a high-fidelity packet-level Internet worm simulator, called CCDRWS (Congestion Control and Dynamic Routing based Worm Simulator). CCDRWS includes accurate congestion control and dynamic routing models, thus greatly improves simulation fidelity. Experimental results show the correctness of CCDRWS, and also the effects of the congestion control and dynamic routing models on the behaviors of worm propagation.

With the development of wireless communications technology, ad-hoc network absorbs more and more concerns. In this paper, the author sets up a 3D simulation system for ad-hoc networks by introducing three-dimensional modeling techniques. The system has functions of three-dimensionally demonstrating the process of network organization and the routing process in multi-view, which makes it intuitive and convenient for researchers to analyze the result of the simulation and make better choices.