Weakly Supervised Learning by a Confusion Matrix of Contexts

© 2019, Springer Nature Switzerland AG. Context consideration can help provide more background and related information for weakly supervised learning. The inclusion of less documented historical and environmental context in researching diabetes amongst Pima Indians uncovered reasons which were more likely to explain why some Pima Indians had much higher rates of diabetes than Caucasians, primarily due to historical, environmental and social causes rather than their specific genetic patterns or ethnicity as suggested by many medical studies. If historical and environmental factors are considered as external contexts when not included as part of a dataset for research, some forms of internal contexts may also exist inside the dataset without being declared. This paper discusses a context construction model that transforms a confusion matrix into a matrix of categorical, incremental and correlational context to emulate a kind of internal context to search for more informative patterns in order to improve weakly supervised learning from limited labeled samples for unlabeled data. When the negative and positive labeled samples and misclassification errors are compared to “happy families” and “unhappy families”, the contexts constructed by this model in the classification experiments reflected the Anna Karenina principle well - “Happy families are all alike; every unhappy family is unhappy in its own way”, an encouraging sign to further explore contexts associated with harmonizing patterns and divisive causes for knowledge discovery in a world of uncertainty

Future Logistics: What to Expect, How to Adapt

As a result of global societal and economic as well as technological developments logistics and supply chains face unprecedented challenges. Climate change, the need for more sustainable products and processes, major political changes, the advance of “Industry 4.0” and cyber-physical system are some of the challenges that require radical solutions, but also present major opportunities. The authors argue that logistics has to reinvent itself, not only to address these chal-lenges but also to cope with mass individualization on the one hand while exploit-ing broad-fielded business applications of artificial intelligence on the other hand. An essential challenge will be to find a compromise between these two develop-ments – in line and in combination with the known triple-bottom line for sustaina-bility – that will define supply chains and logistics concepts of the future

Study protocol: developing a decision system for inclusive housing: applying a systematic, mixed-method quasi-experimental design

Background Identifying the housing preferences of people with complex disabilities is a much needed, but under-developed area of practice and scholarship. Despite the recognition that housing is a social determinant of health and quality of life, there is an absence of empirical methodologies that can practically and systematically involve consumers in this complex service delivery and housing design market. A rigorous process for making effective and consistent development decisions is needed to ensure resources are used effectively and the needs of consumers with complex disability are properly met. Methods/Design This 3-year project aims to identify how the public and private housing market in Australia can better respond to the needs of people with complex disabilities whilst simultaneously achieving key corporate objectives. First, using the Customer Relationship Management framework, qualitative (Nominal Group Technique) and quantitative (Discrete Choice Experiment) methods will be used to quantify the housing preferences of consumers and their carers. A systematic mixed-method, quasi-experimental design will then be used to quantify the development priorities of other key stakeholders (e.g., architects, developers, Government housing services etc.) in relation to inclusive housing for people with complex disabilities. Stakeholders randomly assigned to Group 1 (experimental group) will participate in a series of focus groups employing Analytical Hierarchical Process (AHP) methodology. Stakeholders randomly assigned to Group 2 (control group) will participate in focus groups employing existing decision making processes to inclusive housing development (e.g., Risk, Opportunity, Cost, Benefit considerations). Using comparative stakeholder analysis, this research design will enable the AHP methodology (a proposed tool to guide inclusive housing development decisions) to be tested. Discussion It is anticipated that the findings of this study will enable stakeholders to incorporate consumer housing preferences into commercial decisions. Housing designers and developers will benefit from the creation of a parsimonious set of consumer-led housing preferences by which to make informed investments in future housing and contribute to future housing policy. The research design has not been applied in the Australian research context or elsewhere, and will provide a much needed blueprint for market investment to develop viable, consumer directed inclusive housing options for people with complex disability

Community Management of Endemic Scabies in Remote Aboriginal Communities of Northern Australia: Low Treatment Uptake and High Ongoing Acquisition

Like many impoverished areas around the world, Aboriginal communities in Australia experience an unacceptably high burden of scabies, skin infections, and secondary complications. Young children are most at risk. Our study investigated scabies in a remote setting with very high rates of skin disease, a high level of household overcrowding, and limited infrastructure for sanitation and preventive health measures. We assessed uptake of scabies treatment and scabies acquisition following provision of treatment by a community-based skin program. In a household where scabies was present, we found that treatment with topical permethrin cream of all close contacts can significantly reduce a susceptible individual's risk of infection. Our findings also demonstrate the challenges of achieving a high level of treatment participation, with limited permethrin use observed among household contacts. This suggests an urgent need for a more practical treatment option. International efforts to reduce childhood morbidity and mortality have demonstrated the efficacy of numerous child health interventions but have also highlighted the deficits in their delivery and implementation. Experiences like this, where the effectiveness of a coordinated local program delivering an efficacious intervention is hampered by poor treatment uptake and ongoing transmission, are an important and timely message for researchers, program managers, and policy-makers

Search for new phenomena in final states with an energetic jet and large missing transverse momentum in pp collisions at √ s = 8 TeV with the ATLAS detector

Results of a search for new phenomena in final states with an energetic jet and large missing transverse momentum are reported. The search uses 20.3 fb−1 of √ s = 8 TeV data collected in 2012 with the ATLAS detector at the LHC. Events are required to have at least one jet with pT > 120 GeV and no leptons. Nine signal regions are considered with increasing missing transverse momentum requirements between Emiss T > 150 GeV and Emiss T > 700 GeV. Good agreement is observed between the number of events in data and Standard Model expectations. The results are translated into exclusion limits on models with either large extra spatial dimensions, pair production of weakly interacting dark matter candidates, or production of very light gravitinos in a gauge-mediated supersymmetric model. In addition, limits on the production of an invisibly decaying Higgs-like boson leading to similar topologies in the final state are presente

Social Isolation-Induced Aggression Potentiates Anxiety and Depressive-Like Behavior in Male Mice Subjected to Unpredictable Chronic Mild Stress

Accumulating epidemiological evidence shows that life event stressors are major vulnerability factors for psychiatric diseases such as major depression. It is also well known that social isolation in male mice results in aggressive behavior. However, it is not known how social isolation-induced aggression affects anxiety and depressive-like behavior in isolated male mice subjected to unpredictable chronic mild stress (CMS), an animal model of depression.C57/B6 male mice were divided into 3 groups; non-stressed controls, in Group I; isolated mice subjected to the CMS protocol in Group II and aggression by physical contact in socially isolated mice subjected to the CMS protocol in Group III. In the sucrose intake test, ingestion of a 1% sucrose solution by mice in Groups II and III was significantly lower than in Group I. Furthermore, intake of this solution in Group III mice was significantly lower than in Group II mice. In the open field test, mice in Group III, showed reduced locomotor activity and reduced entry and retention time in the central zone, compared to Groups I and II mice. Moreover, the distances moved in 1 hour by Group III mice did not differ between night and morning. In the light/black box test, Groups II and III animals spent significantly less time in the light box compared to Group I animals. In the tail suspension test (TST) and forced swimming test (FST), the immobility times of Group II and Group III mice were significantly longer than in Group I mice. In addition, immobility times in the FST were significantly longer in Group III than in Group II mice.These findings show that social isolation-induced aggression could potentiate anxiety and depressive-like behaviors in isolated male mice subjected to CMS

Automatic Detection of Cyberbullying in Social Media Text

While social media offer great communication opportunities, they also increase the vulnerability of young people to threatening situations online. Recent studies report that cyberbullying constitutes a growing problem among youngsters. Successful prevention depends on the adequate detection of potentially harmful messages and the information overload on the Web requires intelligent systems to identify potential risks automatically. The focus of this paper is on automatic cyberbullying detection in social media text by modelling posts written by bullies, victims, and bystanders of online bullying. We describe the collection and fine-grained annotation of a training corpus for English and Dutch and perform a series of binary classification experiments to determine the feasibility of automatic cyberbullying detection. We make use of linear support vector machines exploiting a rich feature set and investigate which information sources contribute the most for this particular task. Experiments on a holdout test set reveal promising results for the detection of cyberbullying-related posts. After optimisation of the hyperparameters, the classifier yields an F1-score of 64% and 61% for English and Dutch respectively, and considerably outperforms baseline systems based on keywords and word unigrams.Comment: 21 pages, 9 tables, under revie