A robust approach to model-based classification based on trimming and constraints

In a standard classification framework a set of trustworthy learning data are employed to build a decision rule, with the final aim of classifying unlabelled units belonging to the test set. Therefore, unreliable labelled observations, namely outliers and data with incorrect labels, can strongly undermine the classifier performance, especially if the training size is small. The present work introduces a robust modification to the Model-Based Classification framework, employing impartial trimming and constraints on the ratio between the maximum and the minimum eigenvalue of the group scatter matrices. The proposed method effectively handles noise presence in both response and exploratory variables, providing reliable classification even when dealing with contaminated datasets. A robust information criterion is proposed for model selection. Experiments on real and simulated data, artificially adulterated, are provided to underline the benefits of the proposed method

A Scoping Audit of the Use of Sport and Physical Activity as a Crime Prevention Tool Across Police Forces and Police and Crime Commissioners in Wales

In Wales, there is currently a lack of research into the use of sport or physical activity as a diversionary programme to help prevent young people becoming involved in criminal activity and the Criminal Justice System. Research has shown that using sport as a preventative measure for young people can help them deviate away from the criminal justice system and set them up for a positive transition into adulthood as sport offers a variety of benefits including life skills (Coalter et al., 2000). The promotion of physical activity and sport can help deliver against many of the objectives or pillars contained within Commissioners’ Police & Crime Plans, particularly in relation to youth crime, serious crime and for those who are most at risk of involvement in the Criminal Justice System. This study aims to identify and critically discuss the programmes currently being funded by every Police Force and/or Police and Crime Commissioner in Wales. The research adopts the RE-AIM framework (Glasgow, 2019) to explore available information on the Reach, Effectiveness, Adoption, Implementation and Maintenance of the programmes. Initial meetings were held with individuals working for the Police and Crime Commissioners’ offices to enable broad scoping of programmes offered in different areas of Wales. Subsequent informal interviews were held with the relevant programme leads and/or funders to obtain more detailed data regarding their programmes. There are four programmes throughout Wales with the majority located in Dyfed Powys, South Wales and Gwent Police Forces. The North Wales Police and Community Trust (PACT) indirectly funds one programme in North Wales, however, data are sparse regarding this programme. Preliminary analysis of the data gathered indicates that there is no homogeneity across areas in delivery and reporting mechanisms of the results. Youth involvement in the programmes varies across Wales, with direct referral requirements for South Wales and Gwent programmes and an open entry process elsewhere. Involvement of youth support networks (e.g., education of families) is included in some programmes but not all. Programmes report that they are successful, but indices of success are not all identified or clear and are not comparable across the programmes. In addition, financial investment towards sport programmes varies considerably across the Forces. Additional research is required to understand the long-term outcomes on crime rates for those young people involved in a programme over a sustained period of time. There was clear acknowledgement of the need for preventative programmes for young people and clarity on delivery details of the programmes, such as, number of sessions delivered by whom. However, there was insufficient evidence of long-term delivery and effects of programmes

Robust variable selection for model-based learning in presence of adulteration

The problem of identifying the most discriminating features when performing supervised learning has been extensively investigated. In particular, several methods for variable selection in model-based classification have been proposed. Surprisingly, the impact of outliers and wrongly labeled units on the determination of relevant predictors has received far less attention, with almost no dedicated methodologies available in the literature. In the present paper, we introduce two robust variable selection approaches: one that embeds a robust classifier within a greedy-forward selection procedure and the other based on the theory of maximum likelihood estimation and irrelevance. The former recasts the feature identification as a model selection problem, while the latter regards the relevant subset as a model parameter to be estimated. The benefits of the proposed methods, in contrast with non-robust solutions, are assessed via an experiment on synthetic data. An application to a high-dimensional classification problem of contaminated spectroscopic data concludes the paper

Robust classification of spectroscopic data in agri-food: First analysis on the stability of results

We investigate here the stability of the obtained results of a variable selection method recently introduced in the literature, and embedded into a modelbased classification framework. It is applied to chemometric data, with the purpose of selecting a few wavenumbers (of the order of tens) among the thousands measured ones, to build a (robust) decision rule for classification. The robust nature of the method safeguards it from potential label noise and outliers, which are particularly dangerous in the field of food-authenticity studies. As a by-product of the learning process, samples are grouped into similar classes, and anomalous samples are also singled out. Our first results show that there is some variability around a common pattern in the obtained selection

Modeling the effects of ecosystem changes on seagrass wrack valorization: Merging system dynamics with life cycle assessment

Seagrass meadows, while recognized as essential ecosystem service providers, are degrading worldwide. This has a profound impact on the environment but also on socioeconomic systems which hope to utilize beach-cast seagrass (wrack) as a bioresource. This study integrates system dynamics (SD) thinking with life cycle assessment (LCA) and life cycle costing (LCC) to understand how a degraded ecosystem feedbacks into the circular bioeconomy. An SD model was created to assess the impacts of seagrass meadow changes on wrack production and on ecosystem services accounting, considering an Italian case study of wrack deposited on a beach. Environmental and economic impacts of wrack valorization through anaerobic digestion (AD) were then determined through LCA and LCC. Finally, an extended LCC combined the results of the SD model, LCA, and LCC to demonstrate the cost of seagrass meadow degradation and the value of restoration. The results confirmed complexities in stakeholder perspective within the waste-to-resource framework. For the AD operator, meadow restoration would increase the profits from wrack valorization (23.10 €/ton), while for the municipality, meadow degradation would reduce the high costs associated with management (104.29–140.00 €/ton). When also considering the impacts on the environment and local community, valuation of ecosystem services and cost of restoration were influential. Meadow restoration with wrack valorization was the most favorable option if the natural capital of the seagrass meadows was valued appropriately (>0.065 €/m2) and direct costs of restoration could be kept relatively low (<1179 €/ha). Overall, the model resulted in a total net present cost of −3.161,462.40 € for the baseline scenario, −1,488,277.28 € for the scenario of wrack valorization, and −1,231,325.12 € for the scenario of wrack valorization and meadow restoration

Pbx1 Regulates Self-Renewal of Long-Term Hematopoietic Stem Cells by Maintaining Their Quiescence

SummarySelf-renewal is a defining characteristic of stem cells; however, the molecular pathways underlying its regulation are poorly understood. Here, we demonstrate that conditional inactivation of the Pbx1 proto-oncogene in the hematopoietic compartment results in a progressive loss of long-term hematopoietic stem cells (LT-HSCs) that is associated with concomitant reduction in their quiescence, leading to a defect in the maintenance of self-renewal as assessed by serial transplantation. Transcriptional profiling revealed that multiple stem cell maintenance factors are perturbed in Pbx1-deficient LT-HSCs, which prematurely express a large subset of genes, including cell-cycle regulators, normally expressed in non-self-renewing multipotent progenitors. A significant proportion of Pbx1-dependent genes is associated with the TGF-β pathway, which serves a major role in maintaining HSC quiescence. Prospectively isolated, Pbx1-deficient LT-HSCs display altered transcriptional responses to TGF-β stimulation in vitro, suggesting a possible mechanism through which Pbx1 maintenance of stem cell quiescence may in part be achieved