Bayesian nonparametric extraction of hidden contexts from pervasive honest signals

Hidden patterns and contexts play an important part in intelligent pervasive systems. Most of the existing works have focused on simple forms of contexts derived directly from raw signals. High-level constructs and patterns have been largely neglected or remained under-explored in pervasive computing, mainly due to the growing complexity over time and the lack of efficient principal methods to extract them. Traditional parametric modeling approaches from machine learning find it difficult to discover new, unseen patterns and contexts arising from continuous growth of data streams due to its practice of training-then-prediction paradigm. In this work, we propose to apply Bayesian nonparametric models as a systematic and rigorous paradigm to continuously learn hidden patterns and contexts from raw social signals to provide basic building blocks for context-aware applications. Bayesian nonparametric models allow the model complexity to grow with data, fitting naturally to several problems encountered in pervasive computing. Under this framework, we use nonparametric prior distributions to model the data generative process, which helps towards learning the number of latent patterns automatically, adapting to changes in data and discovering never-seen-before patterns, contexts and activities. The proposed methods are agnostic to data types, however our work shall demonstrate to two types of signals: accelerometer activity data and Bluetooth proximal data. © 2014 IEEE.

Approximation of centroid end-points and switch points for replacing type reduction algorithms

Despite several years of research, type reduction (TR) operation in interval type-2 fuzzy logic system (IT2FLS) cannot perform as fast as a type-1 defuzzifier. In particular, widely used Karnik-Mendel (KM) TR algorithm is computationally much more demanding than alternative TR approaches. In this work, a data driven framework is proposed to quickly, yet accurately, estimate the output of the KM TR algorithm using simple regression models. Comprehensive simulation performed in this study shows that the centroid end-points of KM algorithm can be approximated with a mean absolute percentage error as low as 0.4%. Also, switch point prediction accuracy can be as high as 100%. In conjunction with the fact that simple regression model can be trained with data generated using exhaustive defuzzification method, this work shows the potential of proposed method to provide highly accurate, yet extremely fast, TR approximation method. Speed of the proposed method should theoretically outperform all available TR methods while keeping the uncertainty information intact in the process.

Previsão da estrutura a termo brasileira utilizando de fatores macroeconômicos

Este artigo estuda a previsão da estrutura a termo da taxa de juros brasileira utilizando de fatores comuns extraídos de uma vasta base de séries macroeconômicas. Os períodos para estimação e previsão compreendem o intervalo de Janeiro de 2000 a Maio de 2012. Foram empregas 171 séries mensais para a construção da base. Primeiramente foi implementado o modelo proposto por Moench (2008), no qual a dinâmica da taxa de juros de curto prazo é modelada através de um FAVAR e a estrutura a termo é derivada utilizando-se de restrições implicadas por não arbitragem. A escolha pela adoção deste modelo se deve aos resultados obtidos no estudo original, nos quais tal modelagem apresentou melhor desempenho preditivo para horizontes intermediários e longos quando comparado com benchmarks usuais. Contudo, tais resultados também apresentaram uma deterioração progressiva à medida que as maturidades aumentam, evidenciando uma possível inadequação do modelo para as partes intermediária e longa da curva. A implementação deste modelo para a estrutura a termo brasileira levou a resultados muito similares ao do estudo original. Visando contornar a deterioração mencionada, foi proposta uma modelagem alternativa na qual a dinâmica de cada taxa é modelada conjuntamente com os fatores macroeconômicos, eliminando-se as restrições implicadas por não arbitragem. Tal modelagem proporcionou resultados de previsão amplamente superiores e através dela foi possível confirmar a inadequação descrita. Por fim, também foi realizada a inserção dos fatores macro na dinâmica dos fatores beta do modelo de Diebold e Li (2006), levando a um grande ganho de capacidade preditiva, principalmente para horizontes maiores de previsão.

Estudo do genoma do vírus causador da mionecrose infecciosa em camarões e desenvolvimento de métodos para detecção de diferentes variantes

Shrimp farming is one of the activities that contribute most to the growth of global aquaculture. However, this business has undergone significant economic losses due to the onset of viral diseases such as Infectious Myonecrosis (IMN). The IMN is already widespread throughout Northeastern Brazil and affects other countries such as Indonesia, Thailand and China. The main symptom of disease is myonecrosis, which consists of necrosis of striated muscles of the abdomen and cephalothorax of shrimp. The IMN is caused by infectious myonecrosis virus (IMNV), a non-enveloped virus which has protrusions along its capsid. The viral genome consists of a single molecule of double-stranded RNA and has two Open Reading Frames (ORFs). The ORF1 encodes the major capsid protein (MCP) and a potential RNA binding protein (RBP). ORF2 encodes a probable RNA-dependent RNA polymerase (RdRp) and classifies IMNV in Totiviridae family. Thus, the objective of this research was study the IMNV complete genome and encoded proteins in order to develop a system differentiate virus isolates based on polymorphisms presence. The phylogenetic relationship among some totivirus was investigated and showed a new group to IMNV within Totiviridae family. Two new genomes were sequenced, analyzed and compared to two other genomes already deposited in GenBank. The new genomes were more similar to each other than those already described. Conserved and variable regions of the genome were identified through similarity graphs and alignments using the four IMNV sequences. This analyze allowed mapping of polymorphic sites and revealed that the most variable region of the genome is in the first half of ORF1, which coincides with the regions that possibly encode the viral protrusion, while the most stable regions of the genome were found in conserved domains of proteins that interact with RNA. Moreover, secondary structures were predicted for all proteins using various softwares and protein structural models were calculated using threading and ab initio modeling approaches. From these analyses was possible to observe that the IMNV proteins have motifs and shapes similar to proteins of other totiviruses and new possible protein functions have been proposed. The genome and proteins study was essential for development of a PCR-based detection system able to discriminate the four IMNV isolates based on the presence of polymorphic sites

Predicting body and carcass characteristics of 2 broiler chicken strains using support vector regression and neural network models

As a new modeling method, support vector regression (SVR) has been regarded as the state-of-the-art technique for regression and approximation. In this study, the SVR models had been introduced and developed to predict body and carcass-related characteristics of 2 strains of broiler chicken. To evaluate the prediction ability of SVR models, we compared their performance with that of neural network (NN) models. Evaluation of the prediction accuracy of models was based on the R-2, MS error, and bias. The variables of interest as model output were BW, empty BW, carcass, breast, drumstick, thigh, and wing weight in 2 strains of Ross and Cobb chickens based on intake dietary nutrients, including ME (kcal/bird per week), CP, TSAA, and Lys, all as grams per bird per week. A data set composed of 64 measurements taken from each strain were used for this analysis, where 44 data lines were used for model training, whereas the remaining 20 lines were used to test the created models. The results of this study revealed that it is possible to satisfactorily estimate the BW and carcass parts of the broiler chickens via their dietary nutrient intake. Through statistical criteria used to evaluate the performance of the SVR and NN models, the overall results demonstrate that the discussed models can be effective for accurate prediction of the body and carcass-related characteristics investigated here. However, the SVR method achieved better accuracy and generalization than the NN method. This indicates that the new data mining technique (SVR model) can be used as an alternative modeling tool for NN models. However, further reevaluation of this algorithm in the future is suggested.

Modelagem do escoamento transiente ao longo de trocadores de calor tubo capilar-linha de sucção

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

An assessment of the economic impact of climate change on the water sector in Grenada

Changing precipitation patterns and temperature relate directly to water resources and water security. This report presents the findings of an assessment of the water sector in Grenada with respect to the projected impact of climate change. Grenada‘s water resources comprise primarily surface water, with an estimated groundwater potential to satisfy about 10%-15% of the present potable requirement. On the smaller islands Carriacou and Petite Martinique, domestic water is derived exclusively from rainwater catchments. Rainfall seasonality is marked and the available surface water during the dry season declines dramatically. Changing land use patterns, increase in population, expansion in tourism and future implementation of proposed irrigation schemes are projected to increase future water requirements. Economic modeling approaches were implemented to estimate sectoral demand and supply between 2011 and 2050. Residential, tourism and domestic demand were analysed for the A2, B2 and BAU scenarios as illustrated. The results suggest that water supply will exceed forecasted water demand under B2 and BAU during all four decades. However under the A2 scenario, water demand will exceed water supply by the year 2025. It is important to note that the model has been constrained by the omission of several key parameters, and time series for climate indicators, data for which are unavailable. Some of these include time series for discharge data, rainfall-runoff data, groundwater recharge rates, and evapotranspiration. Further, the findings which seem to indicate adequacy of water are also masked by seasonality in a given year, variation from year to year, and spatial variation within the nation state. It is imperative that some emphasis be placed on data generation in order to better project for the management of Grenada‘s water security. This analysis indicates the need for additional water catchment, storage and distribution infrastructure, as well as institutional strengthening, in order to meet the future needs of the Grenadian population. Strategic priorities should be adopted to increase water production, increase efficiency, strengthen the institutional framework, and decrease wastage. Grenada has embarked on several initiatives that can be considered strategies toward adaptation to the variabilities associated with climate change. The Government should ensure that these programs be carried out to the optimal levels for reasons described above. The ―no-regrets approach‖ which intimates that measures will be beneficial with or without climate change should be adopted. A study on the Costs of Inaction for the Caribbean in the face of climate change listed Grenada among the countries which would experience significant impacts on GDP between now and 2100 without adaptation interventions. Investment in the water sector is germane to building Grenada‘s capacity to cope with the multivariate impact of changes in the parameters of climate.

Modelagem de nicho ecológico de anuros da Mata Atlântica

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Análise Bayesiana de dados composicionais na presença de covariáveis

Pós-graduação em Matematica Aplicada e Computacional - FCT

Methodological Difficulties of Conducting Agroecological Studies from a Statistical Perspective

Statistical methods for analyzing agroecological data might not be able to help agroecologists to solve all of the current problems concerning crop and animal husbandry, but such methods could well help them assess, tackle, and resolve several agroecological issues in a more reliable and accurate manner. Therefore, our goal in this article is to discuss the importance of statistical tools for alternative agronomic approaches, because alternative approaches, such as organic farming, should not only be promoted by encouraging farmers to deploy agroecological techniques, but also by providing agroecologists with robust analyses based on rigorous statistical procedures.

A new probabilistic canopy dynamics model (SLCD) that is suitable for evergreen and deciduous forest ecosystems

There are strong uncertainties regarding LAI dynamics in forest ecosystems in response to climate change. While empirical growth & yield models (G&YMs) provide good estimations of tree growth at the stand level on a yearly to decennial scale, process-based models (PBMs) use LAI dynamics as a key variable for enabling the accurate prediction of tree growth over short time scales. Bridging the gap between PBMs and G&YMs could improve the prediction of forest growth and, therefore, carbon, water and nutrient fluxes by combining modeling approaches at the stand level.Our study aimed to estimate monthly changes of leaf area in response to climate variations from sparse measurements of foliage area and biomass. A leaf population probabilistic model (SLCD) was designed to simulate foliage renewal. The leaf population was distributed in monthly cohorts, and the total population size was limited depending on forest age and productivity. Foliage dynamics were driven by a foliation function and the probabilities ruling leaf aging or fall. Their formulation depends on the forest environment.The model was applied to three tree species growing under contrasting climates and soil types. In tropical Brazilian evergreen broadleaf eucalypt plantations, the phenology was described using 8 parameters. A multi-objective evolutionary algorithm method (MOEA) was used to fit the model parameters on litterfall and LAI data over an entire stand rotation. Field measurements from a second eucalypt stand were used to validate the model. Seasonal LAI changes were accurately rendered for both sites (R-2 = 0.898 adjustment, R-2 = 0.698 validation). Litterfall production was correctly simulated (R-2 = 0.562, R-2 = 0.4018 validation) and may be improved by using additional validation data in future work. In two French temperate deciduous forests (beech and oak), we adapted phenological sub-modules of the CASTANEA model to simulate canopy dynamics, and SLCD was validated using LAI measurements. The phenological patterns were simulated with good accuracy in the two cases studied. However, IA/max was not accurately simulated in the beech forest, and further improvement is required.Our probabilistic approach is expected to contribute to improving predictions of LAI dynamics. The model formalism is general and suitable to broadleaf forests for a large range of ecological conditions. (C) 2014 Elsevier B.V. All rights reserved.

Comparative effects of photodynamic therapy mediated by curcumin on standard and clinical isolate of Streptococcus mutans

Aim: The aim of this study was investigate the effect of photodynamic therapy (PDT) using curcumin (C) as a photosensitizing agent irradiated with an LED (L) in the blue wavelength as a light source on a standard and clinical isolate of Streptococcus mutans (S. mutans) in a planktonic suspension model. Materials and methods: Suspensions of both strains were divided into 4 groups as follows: absence of C and L (control group: C–L–), with C and without L (C group: C+L–), absence of C with L (L group: C–L+) and presence of C and L (PDT group: C+L+). Three different concentrations of curcumin (0.75 mg/ml, 1.5 mg/ml and 3 mg/ml) and three light fluences of studied light source (24, 48 and 72 J cm–2) were tested. Aliquots of each studied group was plated in BHI agar and submitted to colony forming units counting (CFU/ml) and the data transformed into logarithmical scale. Results: A high photoinactivation rate of more than 70% was verified to standard S. mutans strain submitted to PDT whereas the clinical isolate showed a lower sensitivity to all the associations of curcumin and LED. A slight bacterial reduction was verified to C+L– and C–L+, demonstrating no toxic effects to the isolated application of light and photosensitizer to both S. mutans strains tested. Conclusion: Photodynamic therapy using a combination of curcumin and blue LED presented a substantial antimicrobial effect on S. mutans standard strain in a planktonic suspension model with a less pronounced effect on its clinical isolate counterparts due to resistance to this alternative approach. Clinical significance: Alternative antimicrobial approaches, as photodynamic therapy, should be encouraged due to optimal results against cariogenic bacteria aiming to prevent or treat dental caries.

3D superimposition and understanding temporomandibular joint arthritis

Structured AbstractObjectivesTo investigate the 3D morphological variations in 169 temporomandibular ioint (TMJ) condyles, using novel imaging statistical modeling approaches.Setting and sample populationThe Department of Orthodontics and Pediatric Dentistry at the University of Michigan. Cone beam CT scans were acquired from 69 subjects with long-term TMJ osteoarthritis (OA, mean age 39.115.7years), 15 subjects at initial consult diagnosis of OA (mean age 44.914.8years), and seven healthy controls (mean age 4312.4years).Materials and methods3D surface models of the condyles were constructed, and homologous correspondent points on each model were established. The statistical framework included Direction-Projection-Permutation (DiProPerm) for testing statistical significance of the differences between healthy controls and the OA groups determined by clinical and radiographic diagnoses.ResultsCondylar morphology in OA and healthy subjects varied widely with categorization from mild to severe bone degeneration or overgrowth. DiProPerm statistics supported a significant difference between the healthy control group and the initial diagnosis of OA group (t=6.6, empirical p-value=0.006) and between healthy and long-term diagnosis of OA group (t=7.2, empirical p-value=0). Compared with healthy controls, the average condyle in OA subjects was significantly smaller in all dimensions, except its anterior surface, even in subjects with initial diagnosis of OA.ConclusionThis new statistical modeling of condylar morphology allows the development of more targeted classifications of this condition than previously possible.

Statistical analysis and simulation techniques in wall-bounded turbulence

The present work is devoted to the assessment of the energy fluxes physics in the space of scales and physical space of wall-turbulent flows. The generalized Kolmogorov equation will be applied to DNS data of a turbulent channel flow in order to describe the energy fluxes paths from production to dissipation in the augmented space of wall-turbulent flows. This multidimensional description will be shown to be crucial to understand the formation and sustainment of the turbulent fluctuations fed by the energy fluxes coming from the near-wall production region. An unexpected behavior of the energy fluxes comes out from this analysis consisting of spiral-like paths in the combined physical/scale space where the controversial reverse energy cascade plays a central role. The observed behavior conflicts with the classical notion of the Richardson/Kolmogorov energy cascade and may have strong repercussions on both theoretical and modeling approaches to wall-turbulence. To this aim a new relation stating the leading physical processes governing the energy transfer in wall-turbulence is suggested and shown able to capture most of the rich dynamics of the shear dominated region of the flow. Two dynamical processes are identified as driving mechanisms for the fluxes, one in the near wall region and a second one further away from the wall. The former, stronger one is related to the dynamics involved in the near-wall turbulence regeneration cycle. The second suggests an outer self-sustaining mechanism which is asymptotically expected to take place in the log-layer and could explain the debated mixed inner/outer scaling of the near-wall statistics. The same approach is applied for the first time to a filtered velocity field. A generalized Kolmogorov equation specialized for filtered velocity field is derived and discussed. The results will show what effects the subgrid scales have on the resolved motion in both physical and scale space, singling out the prominent role of the filter length compared to the cross-over scale between production dominated scales and inertial range, lc, and the reverse energy cascade region lb. The systematic characterization of the resolved and subgrid physics as function of the filter scale and of the wall-distance will be shown instrumental for a correct use of LES models in the simulation of wall turbulent flows. Taking inspiration from the new relation for the energy transfer in wall turbulence, a new class of LES models will be also proposed. Finally, the generalized Kolmogorov equation specialized for filtered velocity fields will be shown to be an helpful statistical tool for the assessment of LES models and for the development of new ones. As example, some classical purely dissipative eddy viscosity models are analyzed via an a priori procedure.

Environmental Assessment of the Agricultural Sector

One of the main problems recognized in sustainable development goals and sustainable agricultural objectives is Climate change. Farming contributes significantly to the overall Greenhouse gases (GHG) in the atmosphere, which is approximately 10-12 percent of total GHG emissions, but when taking in consideration also land-use change, including deforestation driven by agricultural expansion for food, fiber and fuel the number rises to approximately 30 percent (Smith et. al., 2007). There are two distinct methodological approaches for environmental impact assessment; Life Cycle Assessment (a bottom up approach) and Input-Output Analysis (a top down approach). The two methodologies differ significantly but there is not an immediate choice between them if the scope of the study is on a sectorial level. Instead, as an alternative, hybrid approaches which combine these two approaches have emerged. The aim of this study is to analyze in a greater detail the agricultural sectors contribution to Climate change caused by the consumption of food products. Hence, to identify the food products that have the greatest impact through their life cycle, identifying their hotspots and evaluating the mitigation possibilities for the same. At the same time evaluating methodological possibilities and models to be applied for this purpose both on a EU level and on a country level (Italy).