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In this paper we introduce a novel probabilistic topic model named rLDA-SNGRLD for motions or activities mining in complex scene. Based on the improvement of rLDA model, we developed SNGRLD algorithm which can inference in real-time with massive video stream data set and mine the video latent motion topics and motion regions online. Experiments prove that the application of this model for detecting...
This paper is concerned with the distributed averaging problem subject to a quantization constraint. Given a group of agents associated with scalar numbers, it is assumed that each pair of agents can communicate with a prescribed probability, and that the data being exchanged between them is quantized. In this part of the paper, it is proved that the stochastic gossip algorithm proposed in a recent...
We consider a distributed multi-agent network system where the goal is to minimize an objective function that can be written as the sum of component functions, each of which is known (with stochastic errors) to a specific network agent. We propose an asynchronous algorithm that is motivated by a random gossip scheme where each agent has a local Poisson clock. At each tick of its local clock, the agent...
The particle swarm optimization (PSO) was introduced as a population based stochastic search and optimization process for static environments; however, many real problems are dynamic, meaning that the environment and the characteristics of the global optimum can change over time. Thanks to its stochastic and population based nature, PSO can avoid being trapped in local optima and find the global optimum...
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