We present a robust distributed algorithm for approximate probabilistic inference in dynamical systems, such as sensor networks and teams of mobile robots. Using assumed density filtering, the network nodes maintain a tractable representation of the belief state in a distributed fashion. At each time step, the nodes coordinate to condition this distribution on the observations made throughout t...

Most existing algorithms used for constructing virtual backbones are based on the ideal deterministic network model (DNM) in which any pair of nodes is either fully connected or completely disconnected. Different from DNM, the probabilistic network model (PNM), which presumes that there is a probability to connect and communicate between any pair of nodes, is more suitable to the practice in ma...

Probabilistic Neural Networks (PNN) typically learn more quickly than many neural network models and have had success on a variety of applications. However, in their basic form, they tend to have a large number of hidden nodes. One common solution to this problem is to keep only a randomly-selected subset of the original training data in building the network. This paper presents an algorithm ca...

Aggregation of system-wide information in large-scale distributed systems, such as p2p systems and Grids, can be unfairly influenced by nodes that are selfish, colluding with each other, or are offline most of the time. We present AVCOL, which uses probabilistic and gossip-style techniques to provide availability-aware aggregation. Concretely, AVCOL is the first aggregation system that: (1) imp...

We present a robust distributed algorithm for approximate probabilistic inference in dynamical systems, such as sensor networks and teams of mobile robots. Using assumed density filtering, the network nodes maintain a tractable representation of the belief state in a distributed fashion. At each time step, the nodes coordinate to condition this distribution on the observations made throughout t...

Probabilistic graphical models such as Bayesian networks are widely used to model stochastic systems perform various types of analysis probabilistic prediction, risk analysis, and system health monitoring, which can become computationally expensive in large-scale systems. While demonstrations true quantum supremacy remain rare, computing applications managing exploit the advantages amplitude am...

A reliability analysis of suspension bridges against buffeting failure due to gustiness of wind velocity is carried out using the concept of PRA (probabilistic risk analysis) procedure. For this purpose, the bending stresses at the critical nodes of the bridge deck are obtained for buffeting forces using a spectral analysis technique and a finite element approach. For the purpose of reliability...

introduction: lymphoscintigraphy is imaging of the lymphatic system and has been integrated into the sentinel node mapping procedures. lymphoscintigraphy usually encompasses early or delayed static images. however, immediate dynamic imaging of the lymphatic basins and tumors has also been used as an adjunct lymphoscintigraphy imaging. the aim of this study was to assess the role of early dynami...

In blockchain, ensuring integrity of data when updating distributed ledgers is a challenging and very fundamental process. Most blockchain networks use Merkle tree to verify the authenticity received from other peers on network. However, creating for each block in network composing branch every transaction verification request are time-consuming process requiring heavy computations. Moreover, s...

This article studies the Generalized Steiner Problem with Node-Connectivity Constraints and Hostile Reliability introduces a metaheuristic resolution approach based on Greedy Randomized Adaptive Search Procedure Variable Neighborhood Descent. Under hostile model, nodes links are subject to probabilistic failures. The research focuses studying relationship between optimization reliability evalua...