Dealing with Integer-valued Variables in Bayesian Optimization with Gaussian Processes

Bayesian optimization (BO) methods are useful for optimizing functions that are expensive to evaluate, lack an analytical expression and whose evaluations can be contaminated by noise. These methods rely on a probabilistic model of the objective function, typically a Gaussian process (GP), upon which an acquisition function is built. This function guides the optimization process and measures the expected utility of performing an evaluation of the objective at a new point. GPs assume continous input variables. When this is not the case, such as when some of the input variables take integer values, one has to introduce extra approximations. A common approach is to round the suggested variable value to the closest integer before doing the evaluation of the objective. We show that this can lead to problems in the optimization process and describe a more principled approach to account for input variables that are integer-valued. We illustrate in both synthetic and a real experiments the utility of our approach, which significantly improves the results of standard BO methods on problems involving integer-valued variables.Comment: 7 page

Examining Different Reasons Why People Accept Or Reject Scientific Claims

The current project was designed to examine how cognitive style, cultural worldview, and conspiracy ideation correspond to various levels of agreement with scientific claims. Additionally, the kinds of justifications people provide for their position on scientific issues and the kinds of possible refutations of their scientific beliefs people are able to generate were qualitatively coded and analyzed. Participants were presented with a short survey asking about their level of agreement with scientific claims about biological evolution, anthropogenic climate change, pediatric vaccines, and genetically modified foods. Participants were asked two open-ended questions about each topic, one prompting participants to justify their level-of-agreement rating and the other prompting participants to generate possible refutations to their belief. Participants also filled in measures of cognitive style, cultural worldview, and conspiracy ideation. I predicted that analytical thinking style would be associated with overall higher levels of agreement with scientific claims, intuitive thinking and conspiracy ideation would be associated with overall lower levels of agreement with scientific claims, and agreement with scientific claims would be a function of cultural worldview. Results showed that greater agreement with all four scientific claims is related to a greater predisposition to analytical thinking and stronger self-reported political liberalism. I further hypothesized that the frequency of distinct categories of justifications and refutations would be predicted by level of agreement with scientific claims. Broadly, justifications were coded as non-justifications, subjective, evidential, or deferential, and refutations were broadly coded as denials, subjective, evidential, or deferential. Results of chi-squared analysis revealed topic-specific patterns in participants’ reasoning, suggesting that people do not reason about scientific topics consistently. Different scientific claims appear, instead, to be accepted or rejected for different reasons. For example, evidence may be cited for one socio-scientific issue, but subjective experience or reasoning may be used to justify others. Regression analyses indicated further the nuanced relationship between explicit reasoning provided by participants and their agreement with scientific claims. Higher agreement with all scientific claims was related to a greater frequency of explicitly referencing evidence in some form, but other categories of belief justification and belief refutation showed topic-specific relationships. Generally, findings from this research provide a crucial next step for better understanding why individuals reject established science, as well as for developing more effective means of improving scientific literacy

Perivascular adipose tissue as a relevant fat depot for cardiovascular risk in obesity

Obesity is associated with increased risk of premature death, morbidity, and mortality from several cardiovascular diseases (CVDs), including stroke, coronary heart disease (CHD), myocardial infarction, and congestive heart failure. However, this is not a straightforward relationship. Although several studies have substantiated that obesity confers an independent and additive risk of all-cause and cardiovascular death, there is significant variability in these associations, with some lean individuals developing diseases and others remaining healthy despite severe obesity, the so-called metabolically healthy obese. Part of this variability has been attributed to the heterogeneity in both the distribution of body fat and the intrinsic properties of adipose tissue depots, including developmental origin, adipogenic and proliferative capacity, glucose and lipid metabolism, hormonal control, thermogenic ability, and vascularization. In obesity, these depot-specific differences translate into specific fat distribution patterns, which are closely associated with differential cardiometabolic risks. The adventitial fat layer, also known as perivascular adipose tissue (PVAT), is of major importance. Similar to the visceral adipose tissue, PVAT has a pathophysiological role in CVDs. PVAT influences vascular homeostasis by releasing numerous vasoactive factors, cytokines, and adipokines, which can readily target the underlying smooth muscle cell layers, regulating the vascular tone, distribution of blood flow, as well as angiogenesis, inflammatory processes, and redox status. In this review, we summarize the current knowledge and discuss the role of PVAT within the scope of adipose tissue as a major contributing factor to obesity-associated cardiovascular risk. Relevant clinical studies documenting the relationship between PVAT dysfunction and CVD with a focus on potential mechanisms by which PVAT contributes to obesity-related CVDs are pointed out

Variational implicit processes

We introduce the implicit processes (IPs), a stochastic process that places implicitly defined multivariate distributions over any finite collections of random variables. IPs are therefore highly flexible implicit priors over functions, with examples including data simulators, Bayesian neural networks and non-linear transformations of stochastic processes. A novel and efficient approximate inference algorithm for IPs, namely the variational implicit processes (VIPs), is derived using generalised wake-sleep updates. This method returns simple update equations and allows scalable hyper-parameter learning with stochastic optimization. Experiments show that VIPs return better uncertainty estimates and lower errors over existing inference methods for challenging models such as Bayesian neural networks, and Gaussian processes

Interacción entre dronedarona y simvastatina identificada durante el seguimiento farmacoterapéutico en una farmacia comunitaria

A raíz del programa de atención al paciente polimedicado (ADCOM) realizado por Médicos, Enfermeros y Farmacéuticos comunitarias de Castilla y León se ha incrementado la posibilidad de revisar tratamientos farmacológicos; en este contexto se están encontrando problemas relacionados con la medicación (PRM) y resultados negativos relacionados con la medicación (RNM) relevantes y se ha incrementado la colaboración del farmacéutico con el resto de profesionales sanitarios que intervienen en el tratamiento del paciente. La dronedarona es un medicamento eficaz para el mantenimiento del ritmo sinusal después de una cardioversión efectiva en pacientes adultos y clínicamente estables con fibrilación auricular paroxística o persistente. Presenta interacciones con inhibidores potentes del CYP3A4, eritromicina, antagonistas del calcio, rifampicina ó inhibidores de la MAO1,2, sin embargo no existen registrados en bibliografía casos clínicos de interacciones entre dronedarona y simvastatina aunque sí entre amiodarona y simvastatina3 (http://www.ncbi.nlm.nih.gov/pubmed/ consultado el 10 de junio de 2014 keyterms: stains, simvastatin, dronedarone, and interaction)

Interacción entre dronedarona y simvastatina identificada durante el seguimiento farmacoterapéutico en una farmacia comunitaria

A raíz del programa de atención al paciente polimedicado (ADCOM) realizado por Médicos, Enfermeros y Farmacéuticos comunitarias de Castilla y León se ha incrementado la posibilidad de revisar tratamientos farmacológicos; en este contexto se están encontrando problemas relacionados con la medicación (PRM) y resultados negativos relacionados con la medicación (RNM) relevantes y se ha incrementado la colaboración del farmacéutico con el resto de profesionales sanitarios que intervienen en el tratamiento del paciente. La dronedarona es un medicamento eficaz para el mantenimiento del ritmo sinusal después de una cardioversión efectiva en pacientes adultos y clínicamente estables con fibrilación auricular paroxística o persistente. Presenta interacciones con inhibidores potentes del CYP3A4, eritromicina, antagonistas del calcio, rifampicina ó inhibidores de la MAO1,2, sin embargo no existen registrados en bibliografía casos clínicos de interacciones entre dronedarona y simvastatina aunque sí entre amiodarona y simvastatina3 (http://www.ncbi.nlm.nih.gov/pubmed/ consultado el 10 de junio de 2014 keyterms: stains, simvastatin, dronedarone, and interaction)

Cluster Structures with Machine Learning Support in Neutron Star M-R relations

Neutron stars (NS) are compact objects with strong gravitational fields, and a matter composition subject to extreme physical conditions. The properties of strongly interacting matter at ultra-high densities and temperatures impose a big challenge to our understanding and modelling tools. Some difficulties are critical, since one cannot reproduce such conditions in our laboratories or assess them purely from astronomical observations. The information we have about neutron star interiors are often extracted indirectly, e.g., from the star mass-radius relation. The mass and radius are global quantities and still have a significant uncertainty, which leads to great variability in studying the micro-physics of the neutron star interior. This leaves open many questions in nuclear astrophysics and the suitable equation of state (EoS) of NS. Recently, new observations appear to constrain the mass-radius and consequently has helped to close some open questions. In this work, utilizing modern machine learning techniques, we analyze the NS mass-radius (M-R) relationship for a set of EoS containing a variety of physical models. Our objective is to determine patterns through the M-R data analysis and develop tools to understand the EoS of neutron stars in forthcoming works.Comment: Contribution to the XLIV Brazilian Workshop on Nuclear Physics, Brazi