Asymmetric Peer Influence in Smartphone Adoption in a Large Mobile Network

Understanding adoption patterns of smartphones is of vital importance to telecommunication managers in today’s highly dynamic mobile markets. In this paper, we leverage the network structure and specific position of each individual in the social network to account for and measure the potential heterogeneous role of peer influence in the adoption of the iPhone 3G. We introduce the idea of coreperiphery as a meso-level organizational principle to study the social network, which complements the use of centrality measures derived from either global network properties (macro-level) or from each individual\u27s local social neighbourhood (micro-level). Using millions of call detailed records from a mobile network operator in one country for a period of eleven months, we identify overlapping social communities as well as core and periphery individuals in the network. Our empirical analysis shows that core users exert more influence on periphery users than vice versa. Our findings provide important insights to help identify influential members in the social network, which is potentially useful to design optimal targeting strategies to improve current network-based marketing practices

2D Image head pose estimation via latent space regression under occlusion settings

Head orientation is a challenging Computer Vision problem that has been extensively researched having a wide variety of applications. However, current state-of-the-art systems still underperform in the presence of occlusions and are unreliable for many task applications in such scenarios. This work proposes a novel deep learning approach for the problem of head pose estimation under occlusions. The strategy is based on latent space regression as a fundamental key to better structure the problem for occluded scenarios. Our model surpasses several state-of-the-art methodologies for occluded HPE, and achieves similar accuracy for non-occluded scenarios. We demonstrate the usefulness of the proposed approach with: (i) two synthetically occluded versions of the BIWI and AFLW2000 datasets, (ii) real-life occlusions of the Pandora dataset, and (iii) a real-life application to human-robot interaction scenarios where face occlusions often occur. Specifically, the autonomous feeding from a robotic arm

Improving peripheral venous catheterization-related outcomes in oncology patients: an action research study in Portugal

info:eu-repo/semantics/publishedVersio

Parallel association of power semiconductors: an experimental evaluation with IGBTs and MOSFETs

This paper presents a study on the parallel association of power semiconductors. The main purpose of this paper is to demonstrate that the parallel association of lower rated power semiconductors can be more advantageous than the use of a single higher rated power semiconductor, both economically and in terms of dynamic performance, i.e., switching behavior and semiconductor temperature. In this context, two different power semiconductor technologies were tested: (1) Insulated gate bipolar transistors (IGBTs); and (2) Metal oxide semiconductor field effect transistors (MOSFETs). For each technology, the adopted methodology consisted of verifying the dynamic performance of a single higher rated power semiconductor, comparing it with the dynamic performance of a set of five parallel-connected lower rated power semiconductors, focusing on the current sharing between the devices. The obtained experimental results demonstrate that the parallel connection of lower rated power semiconductors can be advantageous over the use of a single higher rated power semiconductor above certain power levels, offering better switching characteristics and lower cost.INCT-EN -Instituto Nacional de Ciência e Tecnologia para Excitotoxicidade e Neuroproteção (SFRH/BD/134353/2017

Fast Object Detection By Regression in Robot Soccer

Abstract. Visual object detection in robot soccer is fundamental so the robots can act to accomplish their tasks. Current techniques rely on manually highly polished definitions of object models, that lead to accurate detection, but are quite often computationally inefficient. In this work, we contribute an efficient object detection through regression (ODR) method based on offline training. We build upon the observation that objects in robot soccer are of a well defined color and investigate an offline learning approach to model such objects. ODR consists of two main phases: (i) offline training, where the objects are automatically labeled offline by existing techniques, and (ii) online detection, where a given image is efficiently processed in real-time with the learned models. For each image, ODR determines whether the object is present and provides its position if so. We show comparing results with current techniques for precision and computational load. Keywords: Real-time Perception, Computer Vision

Convex solution of a permutation problem

AbstractIn this paper, we show that a problem of finding a permuted version of k vectors from RN such that they belong to a prescribed rank r subset, can be solved by convex optimization. We prove that under certain generic conditions, the wanted permutation matrix is unique in the convex set of doubly-stochastic matrices. In particular, this implies a solution of the classical correspondence problem of finding a permutation that transforms one collection of points in Rk into the another one. Solutions to these problems have a wide set of applications in Engineering and Computer Science