An Environmental and Behavioural Analysis of Arson in a Danish Sample

Abstract Background: Despite its significance as a costly and destructive criminal behaviour, there appears to be some consensus that we know relatively little about arson compared to many other areas of criminal behaviour. Moreover, most existing theory and research into arson has come from the USA, and has tended to concentrate on profiling the characteristics of offenders, rather than investigating, at least in any detail, features of the environment that may influence their behaviour. Aim: The main aim and overarching theme of the current thesis was, therefore, to describe and evaluate some of the main demographic and biographical characteristics, offence related behaviours, and environmental factors associated with arson, in a sample of arson offenders from a European country. Methods: Six empirical studies were conducted, each based on cases drawn from a sample of 746 cases committed by 540 offenders from Denmark between 2002 and 2010 in two police districts, one rural and one urban. Studies 1 and 2 examined a range of demographic and biographical characteristics of arson offenders (such as, gender and age); Studies 3 and 4 covered offence related behaviours (such as selection of targets, and travel distances), and employed regression analyses to look specifically at how these were predicted by other offence related and demographic and biographical variables. Study 5 then investigated the prediction of serial offending as an indicator of arson recidivism using the above demographic and biographical variables and offence related variables. Finally, Study 6 attempted to employ a new approach, via Google Earth, to examine the influence of a range of architectural and structural features of the environment on arson offending; these included 2 targets, presence of high rise buildings, territorial markers, population density and maintenance. Results and Discussion: Findings supported previous literature in identifying the typical arsonist as a young male offender; however, the results further suggested three possible divergent trends in the data corresponding to different categories of arson offender: 1) a more frequent opportunistic arsonist; 2) a less frequent, but more serious, often more persistent serial offender, and 3) a category of mainly female offenders who are less likely to be serial offenders but who may be reacting to dysfunctional home environments. Importantly, in relation to the latter finding, a bimodal peak in age emerged in the subgroup of female offenders, identifying a younger group of female offenders in their mid and late teen years and an older subgroup of female offenders in their late thirties and early forties. Another notable finding was that young male offenders who were not at school were particularly at risk for becoming serial offenders, suggesting that young males not attending school could be targeted in terms of prevention of persistent arson. Also, as predictors of arson, a number of environmental variables were significant and in line with predictions (for example, arson was more prevalant where there were vacant buildings, and very significantly, where the nearest police station was farthest away), but others were significant in a direction opposite to predictions (high building density was associated with lower rates of arson), and some potentially important predicted relationships failed to emerge as significant predictors (such as territorial markers). In addition to the above, two other major findings emerged. First, whilst it was possible be to predict crime scene behaviours from other crime scene behaviours with some degree of accuracy, and, similarly, demographic behaviours (like previous arson) from other demographic factors, predicting crime scene behaviours from demographic factors and vice versa proved to be considerably more difficult. In contrast, in terms of having 3 maximum impact on arson rates the environmental variables considered here did a relatively good job of predicting the presence of arson. A number of limitations and implications are also discussed. Conclusion Considering the results as a whole, notwithstanding some success in predicting arson from demographic and offence related variables, it is concluded that an extension of the kind of environmental approach explored in this thesis could potentially be used for developing environmental schemes for arson prevention that might be considerably easier to apply, and perhaps even more effective in reducing arson, than targeting ‘at risk’ groups of individuals

Fast Non-Rigid Radiance Fields from Monocularized Data

3D reconstruction and novel view synthesis of dynamic scenes from collectionsof single views recently gained increased attention. Existing work showsimpressive results for synthetic setups and forward-facing real-world data, butis severely limited in the training speed and angular range for generatingnovel views. This paper addresses these limitations and proposes a new methodfor full 360{\deg} novel view synthesis of non-rigidly deforming scenes. At thecore of our method are: 1) An efficient deformation module that decouples theprocessing of spatial and temporal information for acceleration at training andinference time; and 2) A static module representing the canonical scene as afast hash-encoded neural radiance field. We evaluate the proposed approach onthe established synthetic D-NeRF benchmark, that enables efficientreconstruction from a single monocular view per time-frame randomly sampledfrom a full hemisphere. We refer to this form of inputs as monocularized data.To prove its practicality for real-world scenarios, we recorded twelvechallenging sequences with human actors by sampling single frames from asynchronized multi-view rig. In both cases, our method is trained significantlyfaster than previous methods (minutes instead of days) while achieving highervisual accuracy for generated novel views. Our source code and data isavailable at our project pagehttps://graphics.tu-bs.de/publications/kappel2022fast.<br

A unified mechanism for intron and exon definition and back-splicing.

The molecular mechanisms of exon definition and back-splicing are fundamental unanswered questions in pre-mRNA splicing. Here we report cryo-electron microscopy structures of the yeast spliceosomal E complex assembled on introns, providing a view of the earliest event in the splicing cycle that commits pre-mRNAs to splicing. The E complex architecture suggests that the same spliceosome can assemble across an exon, and that it either remodels to span an intron for canonical linear splicing (typically on short exons) or catalyses back-splicing to generate circular RNA (on long exons). The model is supported by our experiments, which show that an E complex assembled on the middle exon of yeast EFM5 or HMRA1 can be chased into circular RNA when the exon is sufficiently long. This simple model unifies intron definition, exon definition, and back-splicing through the same spliceosome in all eukaryotes and should inspire experiments in many other systems to understand the mechanism and regulation of these processes

A Knowledge Distillation Framework For Enhancing Ear-EEG Based Sleep Staging With Scalp-EEG Data

Sleep plays a crucial role in the well-being of human lives. Traditional sleep studies using Polysomnography are associated with discomfort and often lower sleep quality caused by the acquisition setup. Previous works have focused on developing less obtrusive methods to conduct high-quality sleep studies, and ear-EEG is among popular alternatives. However, the performance of sleep staging based on ear-EEG is still inferior to scalp-EEG based sleep staging. In order to address the performance gap between scalp-EEG and ear-EEG based sleep staging, we propose a cross-modal knowledge distillation strategy, which is a domain adaptation approach. Our experiments and analysis validate the effectiveness of the proposed approach with existing architectures, where it enhances the accuracy of the ear-EEG based sleep staging by 3.46% and Cohen's kappa coefficient by a margin of 0.038.Comment: Code available at : https://github.com/Mithunjha/EarEEG_KnowledgeDistillatio