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1.
PLoS One ; 16(8): e0255990, 2021.
Article in English | MEDLINE | ID: mdl-34383832

ABSTRACT

We propose a novel network formation game that explains the emergence of various hierarchical structures in groups where self-interested or utility-maximizing individuals decide to establish or severe relationships of authority or collaboration among themselves. We consider two settings: we first consider individuals who do not seek the other party's consent when establishing a relationship and then individuals who do. For both settings, we formally relate the emerged hierarchical structures with the novel inclusion of well-motivated hierarchy promoting terms in the individuals' utility functions. We first analyze the game via a static analysis and characterize all the hierarchical structures that can be formed as its solutions. We then consider the game played dynamically under stochastic interactions among individuals implementing better-response dynamics and analyze the nature of the converged networks.


Subject(s)
Cooperative Behavior , Dissent and Disputes , Game Theory , Group Processes , Hierarchy, Social , Social Networking , Humans , Motivation
2.
Annu Int Conf IEEE Eng Med Biol Soc ; 2016: 4117-4120, 2016 Aug.
Article in English | MEDLINE | ID: mdl-28269188

ABSTRACT

Pneumonia is a disease which causes high mortality in children under five years old, particularly in developing countries. This paper proposes a novel application of ultrasound video analysis for the detection of pneumonia. This application is based on the processing of small video chunks, in which an image processing algorithm analyzes each frame to get some overall video statistics. Then, based on these quantities, the likeness of presence of pneumonia in the video is determined. The algorithm exploits different geometrical properties of typical anatomical and pathological features that commonly appear in lung sonography and which are already clinically typified in the literature. Our technique has been tested on different transverse thoracic scanning protocols and probe's maneuvers, thus, under a variety of clinical and usage protocols. Then, it can be targeted towards screening applications. We present encouraging results (AUC measure between 0.7851 and 0.9177) based on the analysis of 346 videos with an average duration of eight seconds. The analyzed videos were taken from children who were between three and five years old. Finally, our algorithm can be used directly as a classifier, but we detail how its performance may be enhanced if used as a first stage of a larger pipeline of other complementary pneumonia detection processes.


Subject(s)
Algorithms , Image Processing, Computer-Assisted/methods , Pneumonia/diagnostic imaging , Ultrasonography/methods , Video Recording , Child, Preschool , Humans
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