Learning about Bayesian networks for forensic interpretation : An example based on the "the problem of multiple propositions"

Both, Bayesian networks and probabilistic evaluation are gaining more and more widespread use within many professional branches, including forensic science. Notwithstanding, they constitute subtle topics with definitional details that require careful study. While many sophisticated developments of probabilistic approaches to evaluation of forensic findings may readily be found in published literature, there remains a gap with respect to writings that focus on foundational aspects and on how these may be acquired by interested scientists new to these topics. This paper takes this as a starting point to report on the learning about Bayesian networks for likelihood ratio based, probabilistic inference procedures in a class of master students in forensic science. The presentation uses an example that relies on a casework scenario drawn from published literature, involving a questioned signature. A complicating aspect of that case study - proposed to students in a teaching scenario - is due to the need of considering multiple competing propositions, which is an outset that may not readily be approached within a likelihood ratio based framework without drawing attention to some additional technical details. Using generic Bayesian networks fragments from existing literature on the topic, course participants were able to track the probabilistic underpinnings of the proposed scenario correctly both in terms of likelihood ratios and of posterior probabilities. In addition, further study of the example by students allowed them to derive an alternative Bayesian network structure with a computational output that is equivalent to existing probabilistic solutions. This practical experience underlines the potential of Bayesian networks to support and clarify foundational principles of probabilistic procedures for forensic evaluation.

A Robust Multiple Feature Approach To Endpoint Detection In Car Environment Based On Advanced Classifiers

In this paper we propose an endpoint detection system based on the use of several features extracted from each speech frame, followed by a robust classifier (i.e Adaboost and Bagging of decision trees, and a multilayer perceptron) and a finite state automata (FSA). We present results for four different classifiers. The FSA module consisted of a 4-state decision logic that filtered false alarms and false positives. We compare the use of four different classifiers in this task. The look ahead of the method that we propose was of 7 frames, which are the number of frames that maximized the accuracy of the system. The system was tested with real signals recorded inside a car, with signal to noise ratio that ranged from 6 dB to 30dB. Finally we present experimental results demonstrating that the system yields robust endpoint detection.

Parameterization of written signatures based on EFD

In this work we propose a method to quantify written signatures from digitalized images based on the use of Elliptical Fourier Descriptors (EFD). As usually signatures are not represented as a closed contour, and being that a necessary condition in order to apply EFD, we have developed a method that represents the signatures by means of a set of closed contours. One of the advantages of this method is that it can reconstruct the original shape from all the coefficients, or an approximated shape from a reduced set of them finding the appropriate number of EFD coefficients required for preserving the important information in each application. EFD provides accurate frequency information, thus the use of EFD opens many possibilities. The method can be extended to represent other kind of shapes.

Alzheimer disease diagnosis based on automatic spontaneous speech analysis

Alzheimer’s disease (AD) is the most prevalent form of progressive degenerative dementia and it has a high socio-economic impact in Western countries, therefore is one of the most active research areas today. Its diagnosis is sometimes made by excluding other dementias, and definitive confirmation must be done trough a post-mortem study of the brain tissue of the patient. The purpose of this paper is to contribute to im-provement of early diagnosis of AD and its degree of severity, from an automatic analysis performed by non-invasive intelligent methods. The methods selected in this case are Automatic Spontaneous Speech Analysis (ASSA) and Emotional Temperature (ET), that have the great advantage of being non invasive, low cost and without any side effects.

EEG signal analysis via a cleaning procedure based on multivariate empirical mode decomposition

Artifacts are present in most of the electroencephalography (EEG) recordings, making it difficult to interpret or analyze the data. In this paper a cleaning procedure based on a multivariate extension of empirical mode decomposition is used to improve the quality of the data. This is achieved by applying the cleaning method to raw EEG data. Then, a synchrony measure is applied on the raw and the clean data in order to compare the improvement of the classification rate. Two classifiers are used, linear discriminant analysis and neural networks. For both cases, the classification rate is improved about 20%.

Selection via simulated individual BLUP based on family genotypic effects in sugarcane

The objective of this work was to propose a new selection strategy for the initial stages of sugarcane improvement, based on the methodology 'simulated individual BLUP (BLUPIS)', which promotes a dynamic allocation of individuals selected in each full-sib family, using BLUP as a base for both the genotypic effects of the referred families and plot effects. The method proposed applies to single full-sib families or those obtained from unbalanced or balanced diallel crosses, half-sib families and self-pollinated families. BLUPIS indicates the number of individuals to be selected within each family, the total number of clones to be advanced, and the number of families to contribute with selected individuals. Correlation between BLUPIS and true BLUP was 0.96, by method validation. Additionally, BLUPIS allows the identification of which replication contains the best individuals of each family.

Diets based on soybean protein for Mediterranean fruit fly

The objective of this work was to develop suitable and economic diets for mass rearing Mediterranean fruit fly, Ceratitis capitata (Diptera: Tephritidae). Diets containing sugar beet bagase, wheat bran, brewer yeast, and others with wheat bran and palletized soybean protein from Brazil were tested. Diets based on soybean protein have shown promising results regarding pupal recovery, pupal weight and adult emergence. Soybean bagase in the form of pellets with 60% of protein can be a very important substitute for other expensive sources of protein.

Cerebral aneurysm exclusion by CT angiography based on subarachnoid hemorrhage pattern: a retrospective study.

BACKGROUND: To identify patients with spontaneous subarachnoid hemorrhage for whom CT angiography alone can exclude ruptured aneurysms. METHODS: An observational retrospective review was carried out of all consecutive patients with non-traumatic subarachnoid hemorrhage who underwent both CT angiography and catheter angiography to exclude an aneurysm. CT angiography negative cases (no aneurysm) were classified according to their CT hemorrhage pattern as "aneurismal", "perimesencephalic" or as "no-hemorrhage." RESULTS: Two hundred and forty-one patients were included. A CT angiography aneurysm detection sensitivity and specificity of 96.4% and 96.0% were observed. All 35 cases of perimesencephalic or no-hemorrhage out of 78 CT angiography negatives also had negative angiography findings. CONCLUSIONS: CT angiography is self-reliant to exclude ruptured aneurysms when either a perimesencephalic hemorrhage or no-hemorrhage pattern is identified on the CT within a week of symptom onset.

A non-linear Bayesian approach for upscaling local-scale hydraulic conductivity measurements based on local- and regional-scale geophysical data

Significant progress has been made with regard to the quantitative integration of geophysical and hydrological data at the local scale. However, extending the corresponding approaches to the regional scale represents a major, and as-of-yet largely unresolved, challenge. To address this problem, we have developed an upscaling procedure based on a Bayesian sequential simulation approach. This method is then applied to the stochastic integration of low-resolution, regional-scale electrical resistivity tomography (ERT) data in combination with high-resolution, local-scale downhole measurements of the hydraulic and electrical conductivities. Finally, the overall viability of this upscaling approach is tested and verified by performing and comparing flow and transport simulation through the original and the upscaled hydraulic conductivity fields. Our results indicate that the proposed procedure does indeed allow for obtaining remarkably faithful estimates of the regional-scale hydraulic conductivity structure and correspondingly reliable predictions of the transport characteristics over relatively long distances.

Strength Development of Concrete Based on Maturity and Pulse Velocity, MLR-95-03, 1995

The effect of curing temperature, in the range of 4.4 to 22.8 degrees C (40 to 73 degrees F), on strength development was studied based on the maturity and pulse velocity measurements in this report. The strength-maturity relationships for various mixes using a Type I cement and using a Type IP cement, respectively, were experimentally developed. The similar curves for early age strength development of both the patching concrete, using a Type I cement with the addition of calcium chloride, and the fast track concrete, using a Type III cement and fly ash, have also been proposed. For the temperature ranges studied, the strength development of concrete can be determined using a pulse velocity measurement, but only for early ages up to 24 hours. These obtained relationships can be used to determine when a pavement can be opened to traffic. The amount of fly ash substitution, up to 30%, did not have a significant influence on the strength-maturity relationship.

Methods for Estimating Annual Exceedance-Probability Discharges for Streams in Iowa, Based on Data through Water Year 2010, TR-519, 2013

A statewide study was performed to develop regional regression equations for estimating selected annual exceedance- probability statistics for ungaged stream sites in Iowa. The study area comprises streamgages located within Iowa and 50 miles beyond the State’s borders. Annual exceedanceprobability estimates were computed for 518 streamgages by using the expected moments algorithm to fit a Pearson Type III distribution to the logarithms of annual peak discharges for each streamgage using annual peak-discharge data through 2010. The estimation of the selected statistics included a Bayesian weighted least-squares/generalized least-squares regression analysis to update regional skew coefficients for the 518 streamgages. Low-outlier and historic information were incorporated into the annual exceedance-probability analyses, and a generalized Grubbs-Beck test was used to detect multiple potentially influential low flows. Also, geographic information system software was used to measure 59 selected basin characteristics for each streamgage. Regional regression analysis, using generalized leastsquares regression, was used to develop a set of equations for each flood region in Iowa for estimating discharges for ungaged stream sites with 50-, 20-, 10-, 4-, 2-, 1-, 0.5-, and 0.2-percent annual exceedance probabilities, which are equivalent to annual flood-frequency recurrence intervals of 2, 5, 10, 25, 50, 100, 200, and 500 years, respectively. A total of 394 streamgages were included in the development of regional regression equations for three flood regions (regions 1, 2, and 3) that were defined for Iowa based on landform regions and soil regions. Average standard errors of prediction range from 31.8 to 45.2 percent for flood region 1, 19.4 to 46.8 percent for flood region 2, and 26.5 to 43.1 percent for flood region 3. The pseudo coefficients of determination for the generalized leastsquares equations range from 90.8 to 96.2 percent for flood region 1, 91.5 to 97.9 percent for flood region 2, and 92.4 to 96.0 percent for flood region 3. The regression equations are applicable only to stream sites in Iowa with flows not significantly affected by regulation, diversion, channelization, backwater, or urbanization and with basin characteristics within the range of those used to develop the equations. These regression equations will be implemented within the U.S. Geological Survey StreamStats Web-based geographic information system tool. StreamStats allows users to click on any ungaged site on a river and compute estimates of the eight selected statistics; in addition, 90-percent prediction intervals and the measured basin characteristics for the ungaged sites also are provided by the Web-based tool. StreamStats also allows users to click on any streamgage in Iowa and estimates computed for these eight selected statistics are provided for the streamgage.

A new heuristic method for solving spatially constrained forest planning problems based on mitigation of infeasibilities radiating outward from a forced choice

[Abstract]

Quantification of magmatic and hydrothermal processes in a peralkaline syenite-alkali granite complex based on textures, phase equilibria, and stable and radiogenic isotopes

The Puklen complex of the Mid-Proterozoic Gardar Province, South Greenland, consists of various silica-saturated to quartz-bearing syenites, which are intruded by a peralkaline granite. The primary mafic minerals in the syenites are augite +/- olivine + Fe-Ti oxide + amphibole. Ternary feldspar thermometry and phase equilibria among mafic silicates yield T = 950-750degreesC, a(SiO2) = 0.7-1 and an f(O2) of 1-3 log units below the fayalite-magnetite-quartz (FMQ) buffer at 1 kbar. In the granites, the primary mafic minerals are ilmenite and Li-bearing arfvedsonite, which crystallized at temperatures below 750degreesC and at f(O2) values around the FMQ buffer. In both rock types, a secondary post-magmatic assemblage overprints the primary magmatic phases. In syenites, primary Ca-bearing minerals are replaced by Na-rich minerals such as aegirine-augite and albite, resulting in the release of Ca. Accordingly, secondary minerals include ferro-actinolite, (calcite-siderite)(ss), titanite and andradite in equilibrium with the Na-rich minerals. Phase equilibria indicate that formation of these minerals took place over a long temperature interval from near-magmatic temperatures down to similar to300degreesC. In the course of this cooling, oxygen fugacity rose in most samples. For example, late-stage aegirine in granites formed at the expense of arfvedsonite at temperatures below 300degreesC and at an oxygen fugacity above the haematite-magnetite (HM) buffer. The calculated delta(18)O(melt) value for the syenites (+5.9 to +6.3parts per thousand) implies a mantle origin, whereas the inferred delta(18)O(melt) value of <+5.1parts per thousand for the granitic melts is significantly lower. Thus, the granites require an additional low-delta(18)O contaminant, which was not involved in the genesis of the syenites. Rb/Sr data for minerals of both rock types indicate open-system behaviour for Rb and Sr during post-magmatic metasomatism. Neodymium isotope compositions (epsilonNd(1170 Ma) = -3.8 to -6.4) of primary minerals in syenites are highly variable, and suggest that assimilation of crustal rocks occurred to variable extents. Homogeneous epsilon(Nd) values of -5.9 and -6.0 for magmatic amphibole in the granites lie within the range of the syenites. Because of the very similar neodymium isotopic compositions of magmatic and late- to post-magmatic minerals from the same syenite samples a principally closed-system behaviour during cooling is implied. In contrast, for the granites an externally derived fluid phase is required to explain the extremely low epsilon(Nd) values of about -10 and low delta(18)O between +2.0 and +0.5parts per thousand for late-stage aegirine, indicating an open system in the late-stage history. In this study we show that the combination of phase equilibria constraints with stable and radiogenic isotope data on mineral separates can provide much better constraints on magma evolution during emplacement and crystallization than conventional whole-rock studies.

Selective ablation of photovoltaic materials with UV laser sources for monolithic interconnection of devices based on a-Si:H

Lasers are essential tools for cell isolation and monolithic interconnection in thin-film-silicon photovoltaic technologies. Laser ablation of transparent conductive oxides (TCOs), amorphous silicon structures and back contact removal are standard processes in industry for monolithic device interconnection. However, material ablation with minimum debris and small heat affected zone is one of the main difficulty is to achieve, to reduce costs and to improve device efficiency. In this paper we present recent results in laser ablation of photovoltaic materials using excimer and UV wavelengths of diode-pumped solid-state (DPSS) laser sources. We discuss results concerning UV ablation of different TCO and thin-film silicon (a-Si:H and nc-Si:H), focussing our study on ablation threshold measurements and process-quality assessment using advanced optical microscopy techniques. In that way we show the advantages of using UV wavelengths for minimizing the characteristic material thermal affection of laser irradiation in the ns regime at higher wavelengths. Additionally we include preliminary results of selective ablation of film on film structures irradiating from the film side (direct writing configuration) including the problem of selective ablation of ZnO films on a-Si:H layers. In that way we demonstrate the potential use of UV wavelengths of fully commercial laser sources as an alternative to standard backscribing process in device fabrication.

Typing less common ovarian tumors: A training tool based on a pattern-based algorithm applied to a set of 20 virtual slides.

Context: Ovarian tumors (OT) typing is a competency expected from pathologists, with significant clinical implications. OT however come in numerous different types, some rather rare, with the consequence of few opportunities for practice in some departments. Aim: Our aim was to design a tool for pathologists to train in less common OT typing. Method and Results: Representative slides of 20 less common OT were scanned (Nano Zoomer Digital Hamamatsu®) and the diagnostic algorithm proposed by Young and Scully applied to each case (Young RH and Scully RE, Seminars in Diagnostic Pathology 2001, 18: 161-235) to include: recognition of morphological pattern(s); shortlisting of differential diagnosis; proposition of relevant immunohistochemical markers. The next steps of this project will be: evaluation of the tool in several post-graduate training centers in Europe and Québec; improvement of its design based on evaluation results; diffusion to a larger public. Discussion: In clinical medicine, solving many cases is recognized as of utmost importance for a novice to become an expert. This project relies on the virtual slides technology to provide pathologists with a learning tool aimed at increasing their skills in OT typing. After due evaluation, this model might be extended to other uncommon tumors.