Detection and Tracking of Traffic Signs Using a Recursive Bayesian Decision Framework

In this paper we propose a new method for the automatic detection and tracking of road traffic signs using an on-board single camera. This method aims to increase the reliability of the detections such that it can boost the performance of any traffic sign recognition scheme. The proposed approach exploits a combination of different features, such as color, appearance, and tracking information. This information is introduced into a recursive Bayesian decision framework, in which prior probabilities are dynamically adapted to tracking results. This decision scheme obtains a number of candidate regions in the image, according to their HS (Hue-Saturation). Finally, a Kalman filter with an adaptive noise tuning provides the required time and spatial coherence to the estimates. Results have shown that the proposed method achieves high detection rates in challenging scenarios, including illumination changes, rapid motion and significant perspective distortion

Multicollinearity and financial constraint in investment decisions: a Bayesian generalized ridge regression

This paper addresses the investment decisions considering the presence of financial constraints of 373 large Brazilian firms from 1997 to 2004, using panel data. A Bayesian econometric model was used considering ridge regression for multicollinearity problems among the variables in the model. Prior distributions are assumed for the parameters, classifying the model into random or fixed effects. We used a Bayesian approach to estimate the parameters, considering normal and Student t distributions for the error and assumed that the initial values for the lagged dependent variable are not fixed, but generated by a random process. The recursive predictive density criterion was used for model comparisons. Twenty models were tested and the results indicated that multicollinearity does influence the value of the estimated parameters. Controlling for capital intensity, financial constraints are found to be more important for capital-intensive firms, probably due to their lower profitability indexes, higher fixed costs and higher degree of property diversification.

Bayesian decision procedures for dose-escalation based on evidence of undesirable events and therapeutic benefit.

In this paper, Bayesian decision procedures are developed for dose-escalation studies based on bivariate observations of undesirable events and signs of therapeutic benefit. The methods generalize earlier approaches taking into account only the undesirable outcomes. Logistic regression models are used to model the two responses, which are both assumed to take a binary form. A prior distribution for the unknown model parameters is suggested and an optional safety constraint can be included. Gain functions to be maximized are formulated in terms of accurate estimation of the limits of a therapeutic window or optimal treatment of the next cohort of subjects, although the approach could be applied to achieve any of a wide variety of objectives. The designs introduced are illustrated through simulation and retrospective implementation to a completed dose-escalation study. Copyright © 2006 John Wiley & Sons, Ltd.

Bayesian decision procedures for binary and continuous bivariate dose-escalation studies

In this paper, Bayesian decision procedures are developed for dose-escalation studies based on binary measures of undesirable events and continuous measures of therapeutic benefit. The methods generalize earlier approaches where undesirable events and therapeutic benefit are both binary. A logistic regression model is used to model the binary responses, while a linear regression model is used to model the continuous responses. Prior distributions for the unknown model parameters are suggested. A gain function is discussed and an optional safety constraint is included. Copyright (C) 2006 John Wiley & Sons, Ltd.

Multicollinearity and financial constraint in investment decisions: a bayesian generalized ridge regression

This paper addresses the investment decisions considering the presence of financial constraints of 373 large Brazilian firms from 1997 to 2004, using panel data. A Bayesian econometric model was used considering ridge regression for multicollinearity problems among the variables in the model. Prior distributions are assumed for the parameters, classifying the model into random or fixed effects. We used a Bayesian approach to estimate the parameters, considering normal and Student t distributions for the error and assumed that the initial values for the lagged dependent variable are not fixed, but generated by a random process. The recursive predictive density criterion was used for model comparisons. Twenty models were tested and the results indicated that multicollinearity does influence the value of the estimated parameters. Controlling for capital intensity, financial constraints are found to be more important for capital-intensive firms, probably due to their lower profitability indexes, higher fixed costs and higher degree of property diversification.

Crop, Native Vegetation, and Biofuels: Response of White- Tailed Deer to Changing Management Priorities

The expansion of the cellulosic biofuels industry throughout the United States has broad-scale implications for wildlife management on public and private lands. Knowledge is limited on the effects of reverting agriculture to native grass, and vice versa, on size of home range and habitat use of white-tailed deer (Odocoileus virginianus). We followed 68 radio-collared female deer from 1991 through 2004 that were residents of DeSoto National Wildlife Refuge (DNWR) in eastern Nebraska, USA. The refuge was undergoing conversion of vegetation out of row-crop agriculture and into native grass, forest, and emergent aquatic vegetation. Habitat in DNWR consisted of 30% crop in 1991 but removing crops to establish native grass and wetland habitat at DNWR resulted in a 44% reduction in crops by 2004. A decrease in the amount of crops on DNWR contributed to a decline in mean size of annual home range from 400 ha in 1991 to 200 ha in 2005 but percentage of crops in home ranges increased from 21% to 29%. Mean overlap for individuals was 77% between consecutive annual home ranges across 8 years, regardless of crop availability. Conversion of crop to native habitat will not likely result in home range abandonment but may impact disease transmission by increasing rates of contact between deer social groups that occupy adjacent areas. Future research on condition indices or changes in population parameters (e.g., recruitment) could be incorporated into the study design to assess impacts of habitat conversion for biofuel production.

Carbon Stocks and Isotopic Composition of the Organic Matter in Soils Covered by Native Vegetation and Pasture in Sorocaba, SP, Brazil

Land cover change constitutes one of main way of alteration of soil organic matter in both quantitative and qualitative terms. The goal of this study was to compare the carbon stock and the isotopic signature of the organic matter in the soil of areas with different land use,covered with forest and grass (pasture). The study area is located at Sorocaba, SP, Brazil. Using un-deformed soil samples, we measured the carbon content and bulk density. The isotopic signature of soil carbon was determined through the analysis of isotopic ratio (12)C/(13)C. The pasture soil stocks 48% less carbon than the soil covered by natural forest. The isotopic signature indicated that 42.2% of organic matter of the soil covered by pasture is originated from grasses. This characterizes a highly degradation of organic matter in the environment, both quantitatively and qualitatively. Hence, some guidelines of recuperation are described in order to restore the soil organic matter, structure and porosity.

Can native vegetation recover after slash pine cultivation in the Brazilian Savanna?

There is a widespread view that forest plantations with exotic species are green deserts, unable to sustain biodiversity. Few studies have demonstrated, however, that planted stands of exotic trees have a greater negative effect on the plant diversity of savanna vegetation. We compared the native woody flora under four stands of slash pine of about 45 years old with four stands where the previously existing native Cerrado vegetation was preserved and protected from disturbances for the same period, has changed into dense vegetation - the ""cerradao"", at Assis municipality, Sao Paulo State, Brazil. Aiming at understanding the potential ecological filters driving these communities, we assessed air and soil humidity, light availability and classified the native species on the basis of shade tolerance, dispersal syndrome and biomes in which they occur (Atlantic Forest or Cerrado). We recorded an average of 70 (+/- 13) species under pine stands and 54 (+/- 16) species in cerradao. Of the total of 136 species recorded, 78 occurred in both habitats, eight were exclusive to the ""cerradao"" (shade tolerant and also occurring in forest ecosystems) and 18 were recorded only under pine stands (82% heliophytic, exclusive to the Cerrado biome). Among the functional attributes and abiotic variables analyzed, only light availability explained the floristic differences found. Since richness was higher under pine, we refuted the hypothesis that exotic species constrain the establishment of the native species richness in the understory. On the other hand, the dark environment under the closed-canopy of the ""cerradao"" acts as a filter inhibiting the establishment of typical Cerrado species. Since pine stands, if managed in long cycle, maintain a reasonable pool of Cerrado endemic species in the understory pine plantations may be a good starting point for savanna restoration. (C) 2011 Elsevier B.V. All rights reserved.

Survey of the Hymenoptera parasitoids in Eucalyptus grandis and in a native vegetation area in Ipaba, State of Minas Gerais, Brazil

Foi feito um levantamento da fauna de Hymenoptera parasitóides em um transecto eucalipto/vegetação nativa/eucalipto, em Ipaba, Minas Gerais, no período de março de 1997 a março de 1998, com armadilhas Malaise. Foram coletados indivíduos de nove superfamílias (Ceraphronoidea, Chalcidoidea, Chrysidoidea, Cynipoidea, Evanioidea, Ichneumonoidea, Proctotrupoidea, Platygastroidea e Vespoidea), distribuídos em 26 famílias.

Decision-theoretic analysis of forensic sampling criteria using Bayesian decision networks

Sampling issues represent a topic of ongoing interest to the forensic science community essentially because of their crucial role in laboratory planning and working protocols. For this purpose, forensic literature described thorough (Bayesian) probabilistic sampling approaches. These are now widely implemented in practice. They allow, for instance, to obtain probability statements that parameters of interest (e.g., the proportion of a seizure of items that present particular features, such as an illegal substance) satisfy particular criteria (e.g., a threshold or an otherwise limiting value). Currently, there are many approaches that allow one to derive probability statements relating to a population proportion, but questions on how a forensic decision maker - typically a client of a forensic examination or a scientist acting on behalf of a client - ought actually to decide about a proportion or a sample size, remained largely unexplored to date. The research presented here intends to address methodology from decision theory that may help to cope usefully with the wide range of sampling issues typically encountered in forensic science applications. The procedures explored in this paper enable scientists to address a variety of concepts such as the (net) value of sample information, the (expected) value of sample information or the (expected) decision loss. All of these aspects directly relate to questions that are regularly encountered in casework. Besides probability theory and Bayesian inference, the proposed approach requires some additional elements from decision theory that may increase the efforts needed for practical implementation. In view of this challenge, the present paper will emphasise the merits of graphical modelling concepts, such as decision trees and Bayesian decision networks. These can support forensic scientists in applying the methodology in practice. How this may be achieved is illustrated with several examples. The graphical devices invoked here also serve the purpose of supporting the discussion of the similarities, differences and complementary aspects of existing Bayesian probabilistic sampling criteria and the decision-theoretic approach proposed throughout this paper.

Estimating Bayesian decision problems with heterogeneous priors

In many areas of economics there is a growing interest in how expertise andpreferences drive individual and group decision making under uncertainty. Increasingly, we wish to estimate such models to quantify which of these drive decisionmaking. In this paper we propose a new channel through which we can empirically identify expertise and preference parameters by using variation in decisionsover heterogeneous priors. Relative to existing estimation approaches, our \Prior-Based Identification" extends the possible environments which can be estimated,and also substantially improves the accuracy and precision of estimates in thoseenvironments which can be estimated using existing methods.

Toxic substances in blood: an analysis of current recommendations under a Bayesian (decision) approach

This article extends existing discussion in literature on probabilistic inference and decision making with respect to continuous hypotheses that are prevalent in forensic toxicology. As a main aim, this research investigates the properties of a widely followed approach for quantifying the level of toxic substances in blood samples, and to compare this procedure with a Bayesian probabilistic approach. As an example, attention is confined to the presence of toxic substances, such as THC, in blood from car drivers. In this context, the interpretation of results from laboratory analyses needs to take into account legal requirements for establishing the 'presence' of target substances in blood. In a first part, the performance of the proposed Bayesian model for the estimation of an unknown parameter (here, the amount of a toxic substance) is illustrated and compared with the currently used method. The model is then used in a second part to approach-in a rational way-the decision component of the problem, that is judicial questions of the kind 'Is the quantity of THC measured in the blood over the legal threshold of 1.5 μg/l?'. This is pointed out through a practical example.

Microbial communities in Cerrado soils under native vegetation subjected to prescribed fire and under pasture

The objective of this work was to evaluate the effects of fire regimes and vegetation cover on the structure and dynamics of soil microbial communities, through phospholipid fatty acid (PLFA) analysis. Comparisons were made between native areas with different woody covers ("cerrado stricto sensu" and "campo sujo"), under different fire regimes, and a 20-year-old active palisadegrass pasture in the Central Plateau of Brazil. Microbial biomass was higher in the native plots than in the pasture, and the highest monthly values were observed during the rainy season in the native plots. No significant differences were observed between fire regimes or between communities from the two native vegetation types. However, the principal component (PC) analysis separated the microbial communities by vegetation cover (native x pasture) and season (wet x dry), accounting for 45.8% (PC1 and PC3) and 25.6% (PC2 and PC3), respectively, of the total PLFA variability. Changes in land cover and seasonal rainfall in Cerrado ecosystems have significant effects on the total density of soil microorganisms and on the abundance of microbial groups, especially Gram-negative and Gram-positive bacteria.