Bayesian Networks and Sex-related Homicides

We present a statistical investigation on the domain of sex-related homicides. As general sociological and psychological theory on this specific type of crime is incomplete or even lacking, a data-driven approach is implemented. In detail, graphical modelling is applied to learn the dependency structure and several structure learning algorithms are combined to yield a skeleton corresponding to distinct Bayesian Networks. This graph is subsequently analysed and presents a distinction between an offender and a situation driven crime.Bayesian Networks, structure learning, offender profiling

Antibody Fc Glycosylation Discriminates Between Latent and Active Tuberculosis

Background. Mycobacterium tuberculosis remains a global health problem and clinical management is complicated by difficulty in discriminating between latent infection and active disease. While M. tuberculosis-reactive antibody levels are heterogeneous, studies suggest that levels of IgG glycosylation differ between disease states. Here we extend this observation across antibody domains and M. tuberculosis specificities to define changes with the greatest resolving power. Methods. Capillary electrophoretic glycan analysis was performed on bulk non-antigen–specific IgG, bulk Fc domain, bulk Fab domain, and purified protein derivative (PPD)- and Ag85A-specific IgG from subjects with latent (n = 10) and active (n = 20) tuberculosis. PPD-specific isotype/subclass, PPD-specific antibody-dependent phagocytosis, cellular cytotoxicity, and natural killer cell activation were assessed. Discriminatory potentials of antibody features were evaluated individually and by multivariate analysis. Results. Parallel profiling of whole, Fc, and Fab domain-specific IgG glycosylation pointed to enhanced differential glycosylation on the Fc domain. Differential glycosylation was observed across antigen-specific antibody populations. Multivariate modeling highlighted Fc domain glycan species as the top discriminatory features, with combined PPD IgG titers and Fc domain glycans providing the highest classification accuracy. Conclusions. Differential glycosylation occurs preferentially on the Fc domain, providing significant discriminatory power between different states of M. tuberculosis infection and disease

Cell-type specific analysis of translating RNAs in developing flowers reveals new levels of control

Determining both the expression levels of mRNA and the regulation of its translation is important in understanding specialized cell functions. In this study, we describe both the expression profiles of cells within spatiotemporal domains of the Arabidopsis thaliana flower and the post-transcriptional regulation of these mRNAs, at nucleotide resolution. We express a tagged ribosomal protein under the promoters of three master regulators of flower development. By precipitating tagged polysomes, we isolated cell type specific mRNAs that are probably translating, and quantified those mRNAs through deep sequencing. Cell type comparisons identified known cell-specific transcripts and uncovered many new ones, from which we inferred cell type-specific hormone responses, promoter motifs and coexpressed cognate binding factor candidates, and splicing isoforms. By comparing translating mRNAs with steady-state overall transcripts, we found evidence for widespread post-transcriptional regulation at both the intron splicing and translational stages. Sequence analyses identified structural features associated with each step. Finally, we identified a new class of noncoding RNAs associated with polysomes. Findings from our profiling lead to new hypotheses in the understanding of flower development

Profile driven dataflow optimisation of mean shift visual tracking

Profile guided optimisation is a common technique used by compilers and runtime systems to shorten execution runtimes and to optimise locality aware scheduling and memory access on heterogeneous hardware platforms. Some profiling tools trace the execution of low level code, whilst others are designed for abstract models of computation to provide rich domain-specific context in profiling reports. We have implemented mean shift, a computer vision tracking algorithm, in the RVC-CAL dataflow language and use both dynamic runtime and static dataflow profiling mechanisms to identify and eliminate bottlenecks in our naive initial version. We use these profiling reports to tune the CPU scheduler reducing runtime by 88%, and to optimise our dataflow implementation that reduces runtime by a further 43% - an overall runtime reduction of 93%. We also assess the portability of our mean shift optimisations by trading off CPU runtime against resource utilisation on FPGAs. Applying all dataflow optimisations reduces FPGA design space significantly, requiring fewer slice LUTs and less block memory

The Borrowers: Researching the cognitive aspects of translation

The paper considers the interdisciplinary interaction of research on the cognitive aspects of translation. Examples of influence from linguistics, psychology, neuroscience, cognitive science, reading and writing research and language technology are given, with examples from specific sub-disciplines within each one. The breadth of borrowing by researchers in cognitive translatology is made apparent, but the minimal influence of cognitive translatology on the respective disciplines themselves is also highlighted. Suggestions for future developments are made, including ways in which the domain of cognitive translatology might exert greater influence on other disciplines

Alter ego, state of the art on user profiling: an overview of the most relevant organisational and behavioural aspects regarding User Profiling.

This report gives an overview of the most relevant organisational and\ud behavioural aspects regarding user profiling. It discusses not only the\ud most important aims of user profiling from both an organisation’s as\ud well as a user’s perspective, it will also discuss organisational motives\ud and barriers for user profiling and the most important conditions for\ud the success of user profiling. Finally recommendations are made and\ud suggestions for further research are given

On the distinction between metonymy and vertical polysemy in encyclopaedic semantics

In cognitive linguistics, metonymy is seen as a fundamental cognitive process where one conceptual entity affords access to another closely associated one. Cases of vertical polysemy have also often been treated as instances of metonymy (see e.g. Radden and Kövecses, 1999). In vertical polysemy a lexical form designates two or more senses that are in a relationship of categorial inclusion – e.g. dog ‘canine’, ‘male canine’. In this paper I present an account of cases of vertical polysemy from the point of view of domain-based encyclopaedic semantics as described in Langacker (1987). I claim that the domain configurations which underlie the broader and narrower meanings of vertical polysemes are very different from those involved in cases of metonymy. Croft (1993) argues that from a Langackerian viewpoint, metonymy involves a shift in the salience of two domains that form parts of a domain matrix against which a given concept is profiled. In cases of vertical polysemy, on the other hand, the relationship between the broader and narrower meanings may be effected in a number of different ways, none of which involve the kind of domain configurations found in metonymy. For example, the narrower ‘male canine’ sense of dog makes reference to an additional domain of SEX, a domain which is not an essential part of the domain structure of the broader ‘canine’ meaning

Profiling user activities with minimal traffic traces

Understanding user behavior is essential to personalize and enrich a user's online experience. While there are significant benefits to be accrued from the pursuit of personalized services based on a fine-grained behavioral analysis, care must be taken to address user privacy concerns. In this paper, we consider the use of web traces with truncated URLs - each URL is trimmed to only contain the web domain - for this purpose. While such truncation removes the fine-grained sensitive information, it also strips the data of many features that are crucial to the profiling of user activity. We show how to overcome the severe handicap of lack of crucial features for the purpose of filtering out the URLs representing a user activity from the noisy network traffic trace (including advertisement, spam, analytics, webscripts) with high accuracy. This activity profiling with truncated URLs enables the network operators to provide personalized services while mitigating privacy concerns by storing and sharing only truncated traffic traces. In order to offset the accuracy loss due to truncation, our statistical methodology leverages specialized features extracted from a group of consecutive URLs that represent a micro user action like web click, chat reply, etc., which we call bursts. These bursts, in turn, are detected by a novel algorithm which is based on our observed characteristics of the inter-arrival time of HTTP records. We present an extensive experimental evaluation on a real dataset of mobile web traces, consisting of more than 130 million records, representing the browsing activities of 10,000 users over a period of 30 days. Our results show that the proposed methodology achieves around 90% accuracy in segregating URLs representing user activities from non-representative URLs