Behavioral Analytics for Myopic Agents.

Many multi-agent systems have a single coordinator providing incentives to a large number of agents. Two challenges faced by the coordinator are a finite budget from which to allocate incentives, and an initial lack of knowledge about the utility function of the agents. Here, we present a behavioral analytics approach for solving the coordinator's problem when the agents make decisions by maximizing utility functions that depend on prior system states, inputs, and other parameters that are initially unknown.

Balance control impairments in Fabry disease.

Background: Fabry disease (FD) is a rare inherited lysosomal storage disorder caused by the deficiency of the enzyme alpha-galactosidase A. This deficiency leads to an accumulation of glycosphingolipids leading to progressive and multisystemic disease, including renal, cardiac, and neurological damages. FD may also have neuro-otological and visual impairments, which can generate postural control alterations, inner ear, and vision being involved in this function.

[Diagnostic journey of type 1 Gaucher Disease patients: A survey including internists and hematologists].

Introduction: Gaucher disease (GD) is a rare genetic lysosomal storage disorder caused by a beta-glucocerebrosidase deficiency and responsible for a lysosomal storage disorder. GD is characterized by haematological, visceral and bone involvements. The aim of this study was to describe the diagnostic journey of type 1 GD patients as well as the role of the internist.

Behavioral Modeling in Weight Loss Interventions.

Designing systems with human agents is difficult because it often requires models that characterize agents' responses to changes in the system's states and inputs. An example of this scenario occurs when designing treatments for obesity. While weight loss interventions through increasing physical activity and modifying diet have found success in reducing individuals' weight, such programs are difficult to maintain over long periods of time due to lack of patient adherence.

Personalizing Mobile Fitness Apps using Reinforcement Learning.

Despite the vast number of mobile fitness applications (apps) and their potential advantages in promoting physical activity, many existing apps lack behavior-change features and are not able to maintain behavior change motivation. This paper describes a novel fitness app called CalFit, which implements important behavior-change features like dynamic goal setting and self-monitoring. CalFit uses a reinforcement learning algorithm to generate personalized daily step goals that are challenging but attainable.