Probabilistic Inference from Arbitrary Uncertainty using Mixtures of Factorized Generalized Gaussians

This paper presents a general and efficient framework for probabilistic inference and learning from arbitrary uncertain information. It exploits the calculation properties of finite mixture models, conjugate families and factorization. Both the joint probability density of the variables and the likelihood function of the (objective or subjective) observation are approximated by a special mixture model, in such a way that any desired conditional distribution can be directly obtained without numerical integration. We have developed an extended version of the expectation maximization (EM) algorithm to estimate the parameters of mixture models from uncertain training examples (indirect observations). As a consequence, any piece of exact or uncertain information about both input and output values is consistently handled in the inference and learning stages. This ability, extremely useful in certain situations, is not found in most alternative methods. The proposed framework is formally justified from standard probabilistic principles and illustrative examples are provided in the fields of nonparametric pattern classification, nonlinear regression and pattern completion. Finally, experiments on a real application and comparative results over standard databases provide empirical evidence of the utility of the method in a wide range of applications

Rare or poorly known scorpions from Colombia. III. On the taxonomy and distribution of \u3cem\u3eRhopalurus laticauda\u3c/em\u3e Thorell, 1876 (Scorpiones: Buthidae), with description of a new species of the genus

A new species of the genus Rhopalurus Thorell, 1876 is herein described from northeastern Colombia; the new species is closely related to (and has been previously confused with) Rhopalurus laticauda Thorell, 1876. Also, some comments on the taxonomy and distribution of the latter taxon are included

A new species of \u3cem\u3eAnanteris\u3c/em\u3e Thorell, 1891 (Scorpiones: Buthidae) from the Caribbean region of Colombia

A new species of Ananteris Thorell, 1891 is herein described from a single locality in the Caribbean lowlands of Colombia, in northern Bolívar Department. This new taxon shares several unusual features with Ananteris tolimana Teruel et García, 2007, from the Andean region of Tolima Department. With this addition, the number of Colombian species of Ananteris is raised to seven

Rare or poorly known scorpions from Colombia. IV. Additions, synonymies and new records (Scorpiones: Buthidae, Scorpionidae)

The results of the study of new samples of scorpions from Colombia are presented. Tityus erikae Lourenço, 1999 is demonstrated to be a junior synonym of Tityus tayrona Lourenço, 1991, and the adult female of Tarsoporosus macuira Teruel et Roncallo, 2007 is described for the first time. Also, new locality records and supplementary information on morphological variability (including some diagnosis updates) are given for Centruroides margaritatus (Gervais, 1841), Rhopalurus caribensis Teruel et Roncallo, 2008, Tityus tayrona, and Tarsoporosus macuira

A new species of \u3cem\u3eTarsoporosus\u3c/em\u3e Francke, 1978 (Scorpiones: Scorpionidae: Diplocentrinae) from northeastern Colombia

A new species of Tarsoporosus Francke, 1978 is described herein from two localities in La Guajira Peninsula, northeastern Colombia. This new taxon inhabits arid environments, and is morphologically close to Tarsoporosus flavus González-Sponga, 1984, from northwestern Venezuela. With this addition, the total number of described species in Tarsoporosus is raised to five, with at least two occurring in Colombia

Shear flow dynamics in the Beris-Edwards model of nematic liquid crystals

We consider the Beris-Edwards model describing nematic liquid crystal dynamics and restrict to a shear flow and spatially homogeneous situation. We analyze the dynamics focusing on the effect of the flow. We show that in the co-rotational case one has gradient dynamics, up to a periodic eigenframe rotation, while in the non-co-rotational case we identify the short and long time regime of the dynamics. We express these in terms of the physical variables and compare with the predictions of other models of liquid crystal dynamics

The economic reaction to non-pharmaceutical interventions during Covid-19

Policy makers have implemented a set of non-pharmaceutical interventions (NPIs) to contain the spread of Covid-19 and reduce the burden on health systems. These restrictive measures have had adverse effects on economic activity; however, these negative impacts differ with respect to each country. Based on daily data, this article studies governmental economic responses to the application of NPIs for 59 countries. Furthermore, we assess if these economic responses differ according to the economic and sectoral context of the countries. By applying a counting model to the economic support intensity, our results quantify the average reaction of governments in counterbalancing the imposition of NPIs. We further re-estimate the base model by dividing the countries according to their GDP per capita, the intensity of their service sectors, and the expenditure by tourists. Our results show how each NPI implied a different level of economic support and how the structural characteristics considered were relevant to the decision-making process

A Genetic Model of Impulsivity, Vulnerability to Drug Abuse and Schizophrenia-Relevant Symptoms With Translational Potential: The Roman High- vs. Low-Avoidance Rats

The bidirectional selective breeding of Roman high- (RHA) and low-avoidance (RLA) rats for respectively rapid vs. poor acquisition of active avoidant behavior has generated two lines/strains that differ markedly in terms of emotional reactivity, with RHA rats being less fearful than their RLA counterparts. Many other behavioral traits have been segregated along the selection procedure; thus, compared with their RLA counterparts, RHA rats behave as proactive copers in the face of aversive conditions, display a robust sensation/novelty seeking (SNS) profile, and show high impulsivity and an innate preference for natural and drug rewards. Impulsivity is a multifaceted behavioral trait and is generally defined as a tendency to express actions that are poorly conceived, premature, highly risky or inappropriate to the situation, that frequently lead to unpleasant consequences. High levels of impulsivity are associated with several neuropsychiatric conditions including attention-deficit hyperactivity disorder, obsessive/compulsive disorder, schizophrenia, and drug addiction. Herein, we review the behavioral and neurochemical differences between RHA and RLA rats and survey evidence that RHA rats represent a valid genetic model, with face, construct, and predictive validity, to investigate the neural underpinnings of behavioral disinhibition, novelty seeking, impulsivity, vulnerability to drug addiction as well as deficits in attentional processes, cognitive impairments and other schizophrenia-relevant traits

Improving Sustainability of Smart Cities through Visualization Techniques for Big Data from IoT Devices

Fostering sustainability is paramount for Smart Cities development. Lately, Smart Cities are benefiting from the rising of Big Data coming from IoT devices, leading to improvements on monitoring and prevention. However, monitoring and prevention processes require visualization techniques as a key component. Indeed, in order to prevent possible hazards (such as fires, leaks, etc.) and optimize their resources, Smart Cities require adequate visualizations that provide insights to decision makers. Nevertheless, visualization of Big Data has always been a challenging issue, especially when such data are originated in real-time. This problem becomes even bigger in Smart City environments since we have to deal with many different groups of users and multiple heterogeneous data sources. Without a proper visualization methodology, complex dashboards including data from different nature are difficult to understand. In order to tackle this issue, we propose a methodology based on visualization techniques for Big Data, aimed at improving the evidence-gathering process by assisting users in the decision making in the context of Smart Cities. Moreover, in order to assess the impact of our proposal, a case study based on service calls for a fire department is presented. In this sense, our findings will be applied to data coming from citizen calls. Thus, the results of this work will contribute to the optimization of resources, namely fire extinguishing battalions, helping to improve their effectiveness and, as a result, the sustainability of a Smart City, operating better with less resources. Finally, in order to evaluate the impact of our proposal, we have performed an experiment, with non-expert users in data visualization.This work has been co-funded by the ECLIPSE-UA (RTI2018-094283-B-C32) project funded by Spanish Ministry of Science, Innovation, and Universities and the DQIoT (INNO-20171060) project funded by the Spanish Center for Industrial Technological Development, approved with an EUREKA quality seal (E!11737DQIOT). Ana Lavalle holds an Industrial PhD Grant (I-PI 03-18) co-funded by the University of Alicante and the Lucentia Lab Spin-off Company