Partitioned Sampling of Public Opinions Based on Their Social Dynamics

Public opinion polling is usually done by random sampling from the entire population, treating individual opinions as independent. In the real world, individuals' opinions are often correlated, e.g., among friends in a social network. In this paper, we explore the idea of partitioned sampling, which partitions individuals with high opinion similarities into groups and then samples every group separately to obtain an accurate estimate of the population opinion. We rigorously formulate the above idea as an optimization problem. We then show that the simple partitions which contain only one sample in each group are always better, and reduce finding the optimal simple partition to a well-studied Min-r-Partition problem. We adapt an approximation algorithm and a heuristic algorithm to solve the optimization problem. Moreover, to obtain opinion similarity efficiently, we adapt a well-known opinion evolution model to characterize social interactions, and provide an exact computation of opinion similarities based on the model. We use both synthetic and real-world datasets to demonstrate that the partitioned sampling method results in significant improvement in sampling quality and it is robust when some opinion similarities are inaccurate or even missing

Performance of multiple-input multiple-output wireless communications systems using distributed antennas

In this contribution we propose and investigate a multiple-input multiple-output (MIMO) wireless communications system, where multiple receive antennas are distributed in the area covered by a cellular cell and connected with the base-station (BS). We first analyze the total received power by the BS through the distributed antennas, when assuming that the mobile's signal is transmitted over lognormal shadowed Rayleigh fading channels. Then, the outage probability of the distributed antenna MIMO systems is investigated, when considering various antenna distribution patterns. Furthermore, space-time coding at the mobile transmitter is considered for enhancing the outage performance of the distributed antenna MIMO system. Our study and simulation results show that the outage performance of a distributed antenna MIMO system can be significantly improved, when either increasing the number of distributed receive antennas or increasing the number of mobile transmit antennas

Reveal flocking of birds flying in fog by machine learning

We study the first-order flocking transition of birds flying in low-visibility conditions by employing three different representative types of neural network (NN) based machine learning architectures that are trained via either an unsupervised learning approach called "learning by confusion" or a widely used supervised learning approach. We find that after the training via either the unsupervised learning approach or the supervised learning one, all of these three different representative types of NNs, namely, the fully-connected NN, the convolutional NN, and the residual NN, are able to successfully identify the first-order flocking transition point of this nonequilibrium many-body system. This indicates that NN based machine learning can be employed as a promising generic tool to investigate rich physics in scenarios associated to first-order phase transitions and nonequilibrium many-body systems.Comment: 7 pages, 3 figure