A novel polar-based human face recognition computational model

Motivated by a recently proposed biologically inspired face recognition approach, we investigated the relation between human behavior and a computational model based on Fourier-Bessel (FB) spatial patterns. We measured human recognition performance of FB filtered face images using an 8-alternative forced-choice method. Test stimuli were generated by converting the images from the spatial to the FB domain, filtering the resulting coefficients with a band-pass filter, and finally taking the inverse FB transformation of the filtered coefficients. The performance of the computational models was tested using a simulation of the psychophysical experiment. In the FB model, face images were first filtered by simulated V1- type neurons and later analyzed globally for their content of FB components. In general, there was a higher human contrast sensitivity to radially than to angularly filtered images, but both functions peaked at the 11.3-16 frequency interval. The FB-based model presented similar behavior with regard to peak position and relative sensitivity, but had a wider frequency band width and a narrower response range. The response pattern of two alternative models, based on local FB analysis and on raw luminance, strongly diverged from the human behavior patterns. These results suggest that human performance can be constrained by the type of information conveyed by polar patterns, and consequently that humans might use FB-like spatial patterns in face processing.

Oceanografia e Química: unindo conhecimentos em prol dos oceanos e da sociedade

The marine environment is certainly one of the most complex systems to study, not only because of the challenges posed by the nature of the waters, but especially due to the interactions of physical, chemical and biological processes that control the cycles of the elements. Together with analytical chemists, oceanographers have been making a great effort in the advancement of knowledge of the distribution patterns of trace elements and processes that determine their biogeochemical cycles and influences on the climate of the planet. The international academic community is now in prime position to perform the first study on a global scale for observation of trace elements and their isotopes in the marine environment (GEOTRACES) and to evaluate the effects of major global changes associated with the influences of megacities distributed around the globe. This action can only be performed due to the development of highly sensitive detection methods and the use of clean sampling and handling techniques, together with a joint international program working toward the clear objective of expanding the frontiers of the biogeochemistry of the oceans and related topics, including climate change issues and ocean acidification associated with alterations in the carbon cycle. It is expected that the oceanographic data produced this coming decade will allow a better understanding of biogeochemical cycles, and especially the assessment of changes in trace elements and contaminants in the oceans due to anthropogenic influences, as well as its effects on ecosystems and climate. Computational models are to be constructed to simulate the conditions and processes of the modern oceans and to allow predictions. The environmental changes arising from human activity since the 18th century (also called the Anthropocene) have made the Earth System even more complex. Anthropogenic activities have altered both terrestrial and marine ecosystems, and the legacy of these impacts in the oceans include: a) pollution of the marine environment by solid waste, including plastics; b) pollution by chemical and medical (including those for veterinary use) substances such as hormones, antibiotics, legal and illegal drugs, leading to possible endocrine disruption of marine organisms; and c) ocean acidification, the collateral effect of anthropogenic emissions of CO2 into the atmosphere, irreversible in the human life time scale. Unfortunately, the anthropogenic alteration of the hydrosphere due to inputs of plastics, metal, hydrocarbons, contaminants of emerging concern and even with formerly "exotic" trace elements, such us rare earth elements is likely to accelerate in the near future. These emerging contaminants would likely soon present difficulties for studies in pristine environments. All this knowledge brings with it a great responsibility: helping to envisage viable adaptation and mitigation solutions to the problems identified. The greatest challenge faced by Brazil is currently to create a framework project to develop education, science and technology applied to oceanography and related areas. This framework would strengthen the present working groups and enhance capacity building, allowing a broader Brazilian participation in joint international actions and scientific programs. Recently, the establishment of the National Institutes of Science and Technology (INCTs) for marine science, and the creation of the National Institute of Oceanographic and Hydrological Research represent an exemplary start. However, the participation of the Brazilian academic community in the latest assaults on the frontier of chemical oceanography is extremely limited, largely due to: i. absence of physical infrastructure for the preparation and processing of field samples at ultra-trace level; ii. limited access to oceanographic cruises, due to the small number of Brazilian vessels and/or absence of "clean" laboratories on board; iii. restricted international cooperation; iv. limited analytical capacity of Brazilian institutions for the analysis of trace elements in seawater; v. high cost of ultrapure reagents associated with processing a large number of samples, and vi. lack of qualified technical staff. Advances in knowledge, analytic capabilities and the increasing availability of analytical resources available today offer favorable conditions for chemical oceanography to grow. The Brazilian academic community is maturing and willing to play a role in strengthening the marine science research programs by connecting them with educational and technological initiatives in order to preserve the oceans and to promote the development of society.

Computational modeling for irrigated agriculture planning. Part I: general description and linear programming

Linear programming models are effective tools to support initial or periodic planning of agricultural enterprises, requiring, however, technical coefficients that can be determined using computer simulation models. This paper, presented in two parts, deals with the development, application and tests of a methodology and of a computational modeling tool to support planning of irrigated agriculture activities. Part I aimed at the development and application, including sensitivity analysis, of a multiyear linear programming model to optimize the financial return and water use, at farm level for Jaíba irrigation scheme, Minas Gerais State, Brazil, using data on crop irrigation requirement and yield, obtained from previous simulation with MCID model. The linear programming model outputted a crop pattern to which a maximum total net present value of R$ 372,723.00 for the four years period, was obtained. Constraints on monthly water availability, labor, land and production were critical in the optimal solution. In relation to the water use optimization, it was verified that an expressive reductions on the irrigation requirements may be achieved by small reductions on the maximum total net present value.

Computational modeling for irrigated agriculture planning. Part II: risk analysis

Techniques of evaluation of risks coming from inherent uncertainties to the agricultural activity should accompany planning studies. The risk analysis should be carried out by risk simulation using techniques as the Monte Carlo method. This study was carried out to develop a computer program so-called P-RISCO for the application of risky simulations on linear programming models, to apply to a case study, as well to test the results comparatively to the @RISK program. In the risk analysis it was observed that the average of the output variable total net present value, U, was considerably lower than the maximum U value obtained from the linear programming model. It was also verified that the enterprise will be front to expressive risk of shortage of water in the month of April, what doesn't happen for the cropping pattern obtained by the minimization of the irrigation requirement in the months of April in the four years. The scenario analysis indicated that the sale price of the passion fruit crop exercises expressive influence on the financial performance of the enterprise. In the comparative analysis it was verified the equivalence of P-RISCO and @RISK programs in the execution of the risk simulation for the considered scenario.

Valuation models and Simon's bounded rationality

This paper aims at reconciling the evidence that sophisticated valuation models are increasingly used by companies in their investment appraisal with the literature of bounded rationality, according to which objective optimization is impracticable in the real world because it would demand an immense level of sophistication of the analytical and computational processes of human beings. We show how normative valuation models should rather be viewed as forms of reality representation, frameworks according to which the real world is perceived, fragmented for a better understanding, and recomposed, providing an orderly method for undertaking a task as complex as the investment decision.

ABOUT PSYCHOLOGICAL VARIABLES IN APPLICATION SCORING MODELS

The purpose of this study is to investigate the contribution of psychological variables and scales suggested by Economic Psychology in predicting individuals’ default. Therefore, a sample of 555 individuals completed a self-completion questionnaire, which was composed of psychological variables and scales. By adopting the methodology of the logistic regression, the following psychological and behavioral characteristics were found associated with the group of individuals in default: a) negative dimensions related to money (suffering, inequality and conflict); b) high scores on the self-efficacy scale, probably indicating a greater degree of optimism and over-confidence; c) buyers classified as compulsive; d) individuals who consider it necessary to give gifts to children and friends on special dates, even though many people consider this a luxury; e) problems of self-control identified by individuals who drink an average of more than four glasses of alcoholic beverage a day.

ANIMAL MODELS FOR THE STUDY OF LEISHMANIASIS IMMUNOLOGY

Leishmaniasis remains a major public health problem worldwide and is classified as Category I by the TDR/WHO, mainly due to the absence of control. Many experimental models like rodents, dogs and monkeys have been developed, each with specific features, in order to characterize the immune response to Leishmania species, but none reproduces the pathology observed in human disease. Conflicting data may arise in part because different parasite strains or species are being examined, different tissue targets (mice footpad, ear, or base of tail) are being infected, and different numbers (“low” 1×102 and “high” 1×106) of metacyclic promastigotes have been inoculated. Recently, new approaches have been proposed to provide more meaningful data regarding the host response and pathogenesis that parallels human disease. The use of sand fly saliva and low numbers of parasites in experimental infections has led to mimic natural transmission and find new molecules and immune mechanisms which should be considered when designing vaccines and control strategies. Moreover, the use of wild rodents as experimental models has been proposed as a good alternative for studying the host-pathogen relationships and for testing candidate vaccines. To date, using natural reservoirs to study Leishmania infection has been challenging because immunologic reagents for use in wild rodents are lacking. This review discusses the principal immunological findings against Leishmania infection in different animal models highlighting the importance of using experimental conditions similar to natural transmission and reservoir species as experimental models to study the immunopathology of the disease.

Use of Poisson spatiotemporal regression models for the Brazilian Amazon Forest: malaria count data

INTRODUCTION: Malaria is a serious problem in the Brazilian Amazon region, and the detection of possible risk factors could be of great interest for public health authorities. The objective of this article was to investigate the association between environmental variables and the yearly registers of malaria in the Amazon region using Bayesian spatiotemporal methods. METHODS: We used Poisson spatiotemporal regression models to analyze the Brazilian Amazon forest malaria count for the period from 1999 to 2008. In this study, we included some covariates that could be important in the yearly prediction of malaria, such as deforestation rate. We obtained the inferences using a Bayesian approach and Markov Chain Monte Carlo (MCMC) methods to simulate samples for the joint posterior distribution of interest. The discrimination of different models was also discussed. RESULTS: The model proposed here suggests that deforestation rate, the number of inhabitants per km², and the human development index (HDI) are important in the prediction of malaria cases. CONCLUSIONS: It is possible to conclude that human development, population growth, deforestation, and their associated ecological alterations are conducive to increasing malaria risk. We conclude that the use of Poisson regression models that capture the spatial and temporal effects under the Bayesian paradigm is a good strategy for modeling malaria counts.

Modelling Amazonian forest eddy covariance data: a comparison of big leaf versus sun/shade models for the C-14 tower at Manaus I. Canopy photosynthesis

In this study, we concentrate on modelling gross primary productivity using two simple approaches to simulate canopy photosynthesis: "big leaf" and "sun/shade" models. Two approaches for calibration are used: scaling up of canopy photosynthetic parameters from the leaf to the canopy level and fitting canopy biochemistry to eddy covariance fluxes. Validation of the models is achieved by using eddy covariance data from the LBA site C14. Comparing the performance of both models we conclude that numerically (in terms of goodness of fit) and qualitatively, (in terms of residual response to different environmental variables) sun/shade does a better job. Compared to the sun/shade model, the big leaf model shows a lower goodness of fit and fails to respond to variations in the diffuse fraction, also having skewed responses to temperature and VPD. The separate treatment of sun and shade leaves in combination with the separation of the incoming light into direct beam and diffuse make sun/shade a strong modelling tool that catches more of the observed variability in canopy fluxes as measured by eddy covariance. In conclusion, the sun/shade approach is a relatively simple and effective tool for modelling photosynthetic carbon uptake that could be easily included in many terrestrial carbon models.

Development and Validation of Predictive Models of Cardiac Mortality and Transplantation in Resynchronization Therapy

AbstractBackground:30-40% of cardiac resynchronization therapy cases do not achieve favorable outcomes.Objective:This study aimed to develop predictive models for the combined endpoint of cardiac death and transplantation (Tx) at different stages of cardiac resynchronization therapy (CRT).Methods:Prospective observational study of 116 patients aged 64.8 ± 11.1 years, 68.1% of whom had functional class (FC) III and 31.9% had ambulatory class IV. Clinical, electrocardiographic and echocardiographic variables were assessed by using Cox regression and Kaplan-Meier curves.Results:The cardiac mortality/Tx rate was 16.3% during the follow-up period of 34.0 ± 17.9 months. Prior to implantation, right ventricular dysfunction (RVD), ejection fraction < 25% and use of high doses of diuretics (HDD) increased the risk of cardiac death and Tx by 3.9-, 4.8-, and 5.9-fold, respectively. In the first year after CRT, RVD, HDD and hospitalization due to congestive heart failure increased the risk of death at hazard ratios of 3.5, 5.3, and 12.5, respectively. In the second year after CRT, RVD and FC III/IV were significant risk factors of mortality in the multivariate Cox model. The accuracy rates of the models were 84.6% at preimplantation, 93% in the first year after CRT, and 90.5% in the second year after CRT. The models were validated by bootstrapping.Conclusion:We developed predictive models of cardiac death and Tx at different stages of CRT based on the analysis of simple and easily obtainable clinical and echocardiographic variables. The models showed good accuracy and adjustment, were validated internally, and are useful in the selection, monitoring and counseling of patients indicated for CRT.