DNA, statistics and the law : a cross-disciplinary approach to forensic inference

The use of results of DNA analyses in the legal process is a highly ambivalent topic. On the one hand, scientists have never been in a better position to analyse biological matter of various natures, even in limited quantities and degraded conditions. On the other hand, the increasing amounts of scientific data that can be gen-erated through modern analytical processes do not necessarily imply that evaluative questions that arise in the legal context are given more satisfactory answers. A fundamental question that has accompanied DNA analyses since the early days of their use in the legal process thus remains: how do we handle the challenges presented to us by the use of contemporary scientific and tech-nological developments in the field of law? Under the general theme “DNA, statistics and the law, ” the collection of articles in this Frontiers Research Topic pursues the goal of investigating this question from an interdisciplinary perspective, and with a

Towards a Bayesian evaluation of features in questioned handwritten signatures

In this work, we propose the construction of a evaluative framework for supporting experts in questioned signature examinations. Through the use of Bayesian networks, we envision to quantify the probative value of well defined measurements performed on questioned signatures, in a way that is both formalised and part of a coherent approach to evaluation. At the current stage, our project is explorative, focusing on the broad range of aspects that relate to comparative signature examinations. The goal is to identify writing features which are both highly discriminant, and easy for forensic examiners to detect. We also seek for a balance between case-specific features and characteristics which can be measured in the vast majority of signatures. Care is also taken at preserving the interpretability at every step of the reasoning process. This paves the way for future work, which will aim at merging the different contributions to a single probabilistic measure of strength of evidence using Bayesian networks

Prediction in forensic science: a critical examination of common understandings

In this commentary, we argue that the term 'prediction' is overly used when in fact, referring to foundational writings of de Finetti, the correspondent term should be inference. In particular, we intend (i) to summarize and clarify relevant subject matter on prediction from established statistical theory, and (ii) point out the logic of this understanding with respect practical uses of the term prediction. Written from an interdisciplinary perspective, associating statistics and forensic science as an example, this discussion also connects to related fields such as medical diagnosis and other areas of application where reasoning based on scientific results is practiced in societal relevant contexts. This includes forensic psychology that uses prediction as part of its vocabulary when dealing with matters that arise in the course of legal proceedings

La naturaleza decisoria de las conclusiones de los expertos en ciencia forense (The decisionalization of individualization)

En la ciencia forense y ramas de la ciencia adyacentes, tanto investigadores del ámbito académico como quienes las practican continúan divergiendo en la percepción y comprensión del término “individualización”, es decir, la defensa de la tesis de que es posible reducir un conjunto de potenciales donantes de un vestigio forense a una única fuente. En concreto, se ha puesto de manifiesto que recientes cambios que entienden la práctica de la individualización como una decisión no son más que un mero cambio de etiqueta [1], dejando los cambios fundamentales en el orden del pensar y del entender aún pendientes. Es más, asociaciones profesionales y expertos huyen de adherirse a la noción de decisión tal y como la define la teoría formal de la decisión en la que la individualización puede contextualizarse, principalmente por las dificultades para tratar sobre las medidas de deseabilidad o no de las consecuencias de las decisiones (por ejemplo, utilizando las funciones de utilidad). Apoyándose en investigaciones existentes en esta área, este artículo presenta y discute sobre conceptos fundamentales de utilidades y costes, con particular referencia a su aplicación a la individualización forense. El artículo subraya que una adecuada comprensión de las herramientas de la decisión no solo reduce el número de asignaciones individuales que la aplicación de la teoría de la decisión requiere, sino que también muestra cómo esas asignaciones pueden relacionarse significativamente con las propiedades constituyentes del problema de la decisión en el mundo real al que se aplica la teoría. Se argumenta que la “decisionalización” de la individualización requiere esa percepción fundamental para iniciar cambios en las comprensiones subyacentes de esos campos, no meramente en el ámbito de sus etiquetas

Magnon softening in a ferromagnetic monolayer: a first-principles spin dynamics study

We study the Fe/W(110) monolayer system through a combination of first principles calculations and atomistic spin dynamics simulations. We focus on the dispersion of the spin waves parallel to the [001] direction. Our results compare favorably with the experimental data of Prokop et al. [Phys. Rev. Lett. 102, 177206], and correctly capture a drastic softening of the magnon spectrum, with respect to bulk bcc Fe. The suggested shortcoming of the itinerant electron model, in particular that given by density functional theory, is refuted. We also demonstrate that finite temperature effects are significant, and that atomistic spin dynamics simulations represent a powerful tool with which to include these.Comment: v1: 11 pages, 3 figures. v2: double column, 5 pages, 3 figures, typos corrected, references adde

Bayes Factors for Forensic Decision Analyses with R

Bayes Factors for Forensic Decision Analyses with R provides a self-contained introduction to computational Bayesian statistics using R. With its primary focus on Bayes factors supported by data sets, this book features an operational perspective, practical relevance, and applicability—keeping theoretical and philosophical justifications limited. It offers a balanced approach to three naturally interrelated topics: – Probabilistic Inference: Relies on the core concept of Bayesian inferential statistics, to help practicing forensic scientists in the logical and balanced evaluation of the weight of evidence. – Decision Making: Features how Bayes factors are interpreted in practical applications to help address questions of decision analysis involving the use of forensic science in the law. – Operational Relevance: Combines inference and decision, backed up with practical examples and complete sample code in R, including sensitivity analyses and discussion on how to interpret results in context. Over the past decades, probabilistic methods have established a firm position as a reference approach for the management of uncertainty in virtually all areas of science, including forensic science, with Bayes' theorem providing the fundamental logical tenet for assessing how new information—scientific evidence—ought to be weighed. Central to this approach is the Bayes factor, which clarifies the evidential meaning of new information, by providing a measure of the change in the odds in favor of a proposition of interest, when going from the prior to the posterior distribution. Bayes factors should guide the scientist's thinking about the value of scientific evidence and form the basis of logical and balanced reporting practices, thus representing essential foundations for rational decision making under uncertainty. This book would be relevant to students, practitioners, and applied statisticians interested in inference and decision analyses in the critical field of forensic science. It could be used to support practical courses on Bayesian statistics and decision theory at both undergraduate and graduate levels, and will be of equal interest to forensic scientists and practitioners of Bayesian statistics for driving their evaluations and the use of R for their purposes

Flat type thick film inductive sensors

Two thick film flat-type inductive sensors are described and tested for distance and profile measurement. The first one is a single-layer spiral while the second one is a multi-layer structure consisting of ten spirals one over the other. The paper describes their geometric configurations together with their simulated magnetic fields and it reports the results from the characterization test i.e. the series-equivalent circuit parameters, the sensitivity and the cross-sensitivity to temperature. An experimental analysis of the sensitivity suggests that optimized values are obtained by an appropriate choice of the working frequency. The sensors are shielded against e.m. noise coming from the nonsensitive area. Moreover, two sensors have been tested in the laboratory using the single layer as a distance sensor and the multi-layer as a transducer for the measurement of a metallic object profile. The results of the tests show a maximum sensitivity of 14mV/µm and a resolution of 0.6 µm for the single layer, while the multi layer one reconstructs the profile with an axial resolution of a few microns and a lateral resolution better than 200 mm