3,819 research outputs found
Statistical Agent Based Modelization of the Phenomenon of Drug Abuse
We introduce a statistical agent based model to describe the phenomenon of
drug abuse and its dynamical evolution at the individual and global level. The
agents are heterogeneous with respect to their intrinsic inclination to drugs,
to their budget attitude and social environment. The various levels of drug use
were inspired by the professional description of the phenomenon and this
permits a direct comparison with all available data. We show that certain
elements have a great importance to start the use of drugs, for example the
rare events in the personal experiences which permit to overcame the barrier of
drug use occasionally. The analysis of how the system reacts to perturbations
is very important to understand its key elements and it provides strategies for
effective policy making. The present model represents the first step of a
realistic description of this phenomenon and can be easily generalized in
various directions.Comment: 12 pages, 5 figure
The discriminative functional mixture model for a comparative analysis of bike sharing systems
Bike sharing systems (BSSs) have become a means of sustainable intermodal
transport and are now proposed in many cities worldwide. Most BSSs also provide
open access to their data, particularly to real-time status reports on their
bike stations. The analysis of the mass of data generated by such systems is of
particular interest to BSS providers to update system structures and policies.
This work was motivated by interest in analyzing and comparing several European
BSSs to identify common operating patterns in BSSs and to propose practical
solutions to avoid potential issues. Our approach relies on the identification
of common patterns between and within systems. To this end, a model-based
clustering method, called FunFEM, for time series (or more generally functional
data) is developed. It is based on a functional mixture model that allows the
clustering of the data in a discriminative functional subspace. This model
presents the advantage in this context to be parsimonious and to allow the
visualization of the clustered systems. Numerical experiments confirm the good
behavior of FunFEM, particularly compared to state-of-the-art methods. The
application of FunFEM to BSS data from JCDecaux and the Transport for London
Initiative allows us to identify 10 general patterns, including pathological
ones, and to propose practical improvement strategies based on the system
comparison. The visualization of the clustered data within the discriminative
subspace turns out to be particularly informative regarding the system
efficiency. The proposed methodology is implemented in a package for the R
software, named funFEM, which is available on the CRAN. The package also
provides a subset of the data analyzed in this work.Comment: Published at http://dx.doi.org/10.1214/15-AOAS861 in the Annals of
Applied Statistics (http://www.imstat.org/aoas/) by the Institute of
Mathematical Statistics (http://www.imstat.org
A Robot Model of OC-Spectrum Disorders : Design Framework, Implementation and First Experiments
© 2019 Massachusetts Institute of TechnologyComputational psychiatry is increasingly establishing itself as valuable discipline for understanding human mental disorders. However, robot models and their potential for investigating embodied and contextual aspects of mental health have been, to date, largely unexplored. In this paper, we present an initial robot model of obsessive-compulsive (OC) spectrum disorders based on an embodied motivation-based control architecture for decision making in autonomous robots. The OC family of conditions is chiefly characterized by obsessions (recurrent, invasive thoughts) and/or compulsions (an urge to carry out certain repetitive or ritualized behaviors). The design of our robot model follows and illustrates a general design framework that we have proposed to ground research in robot models of mental disorders, and to link it with existing methodologies in psychiatry, and notably in the design of animal models. To test and validate our model, we present and discuss initial experiments, results and quantitative and qualitative analysis regarding the compulsive and obsessive elements of OC-spectrum disorders. While this initial stage of development only models basic elements of such disorders, our results already shed light on aspects of the underlying theoretical model that are not obvious simply from consideration of the model.Peer reviewe
Combining deep and handcrafted image features for MRI brain scan classification
Progresses in the areas of artificial intelligence, machine learning, and medical imaging technologies have allowed the development of the medical image processing field with some astonishing results in the last two decades. These innovations enabled the clinicians to view the human body in high-resolution or three-dimensional cross-sectional slices, which resulted in an increase in the accuracy of the diagnosis and the examination of patients in a non-invasive manner. The fundamental step for MRI brain scans classifiers is their ability to extract meaningful features. As a result, many works have proposed different methods for features extraction to classify the abnormal growths in brain MRI scans. More recently, the application of deep learning algorithms to medical imaging lead to impressive performance enhancements in classifying and diagnosing complicated pathologies such as brain tumors. In this study, a deep learning feature extraction algorithm is proposed to extract the relevant features from MRI brain scans. In parallel, handcrafted features are extracted using the modified grey level co-occurrence matrix (MGLCM) method. Subsequently, the extracted relevant features are combined with handcrafted features to improve the classification process of MRI brain scans with SVM used as the classifier. The obtained results proved that the combination of the deep learning approach and the handcrafted features extracted by MGLCM improves the accuracy of classification of the SVM classifier up to 99.30%
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