Factorizing LambdaMART for cold start recommendations

Recommendation systems often rely on point-wise loss metrics such as the mean squared error. However, in real recommendation settings only few items are presented to a user. This observation has recently encouraged the use of rank-based metrics. LambdaMART is the state-of-the-art algorithm in learning to rank which relies on such a metric. Despite its success it does not have a principled regularization mechanism relying in empirical approaches to control model complexity leaving it thus prone to overfitting. Motivated by the fact that very often the users' and items' descriptions as well as the preference behavior can be well summarized by a small number of hidden factors, we propose a novel algorithm, LambdaMART Matrix Factorization (LambdaMART-MF), that learns a low rank latent representation of users and items using gradient boosted trees. The algorithm factorizes lambdaMART by defining relevance scores as the inner product of the learned representations of the users and items. The low rank is essentially a model complexity controller; on top of it we propose additional regularizers to constraint the learned latent representations that reflect the user and item manifolds as these are defined by their original feature based descriptors and the preference behavior. Finally we also propose to use a weighted variant of NDCG to reduce the penalty for similar items with large rating discrepancy. We experiment on two very different recommendation datasets, meta-mining and movies-users, and evaluate the performance of LambdaMART-MF, with and without regularization, in the cold start setting as well as in the simpler matrix completion setting. In both cases it outperforms in a significant manner current state of the art algorithms

Therapie bei Progression und Rezidiv des Ovarialkarzinoms

Secondary surgery after failure of primary treatment is a promising and reasonable option only for patients with a relapse-free interval of at least 6-12 months who should have ideally achieved a tumor-free status after primary therapy. As after primary surgery, size of residual tumor is the most significant predictor of survival after secondary surgery. Even in the case of multiple tumor sites, complete removal of the tumor can be achieved in nearly 30% of the patients. Treatment results are much better in specialized oncology centers with optimal interdisciplinary cooperation compared with smaller institutions. Chemotherapy can be used both for consolidation after successful secondary surgery and for palliation in patients with inoperable recurrent disease. Since paclitaxel has been integrated into first-line chemotherapy, there is no defined standard for second-line chemotherapy. Several cytotoxic agents have shown moderate activity in this setting, including treosulfan, epirubicin, and newer agents such as topotecan, gemcitabine, vinorelbine, and PEG(polyethylene glycol)-liposomal doxorubicin. Thus, the German Arbeitsgemeinschaft Gynakologische Onkologie (AGO) has initiated several randomized studies in patients with recurrent ovarian cancer in order to define new standards for second-line chemotherapy

Analyzing First-Person Stories Based on Socializing, Eating and Sedentary Patterns

First-person stories can be analyzed by means of egocentric pictures acquired throughout the whole active day with wearable cameras. This manuscript presents an egocentric dataset with more than 45,000 pictures from four people in different environments such as working or studying. All the images were manually labeled to identify three patterns of interest regarding people's lifestyle: socializing, eating and sedentary. Additionally, two different approaches are proposed to classify egocentric images into one of the 12 target categories defined to characterize these three patterns. The approaches are based on machine learning and deep learning techniques, including traditional classifiers and state-of-art convolutional neural networks. The experimental results obtained when applying these methods to the egocentric dataset demonstrated their adequacy for the problem at hand.Comment: Accepted at First International Workshop on Social Signal Processing and Beyond, 19th International Conference on Image Analysis and Processing (ICIAP), September 201

LEP1 vs. Future Colliders: Effective Operators And Extended Gauge Group

In an effective Lagrangian approach to physics beyond the Standard Model, it has been argued that imposing $SU(2) \times U(1)$ invariance severely restricts the discovery potential of future colliders. We exhibit a possible way out in an extended gauge group context.Comment: 14 pages , CERN-TH.6573/92 ULB.TH.04/92 (phyzzx, 3 eps-figs incl.

Promoting physical activity with a school-based dance mat exergaming intervention: qualitative findings from a natural experiment

Background: Physical activity is critical to improving health and well-being in children. Quantitative studies have found a decline in activity in the transition from primary to secondary education. Exergames (active video games) might increase physical activity in adolescents. In January 2011 exergame dance mat systems were introduced in to all secondary schools across two local authority districts in the UK. We performed a quasi-experimental evaluation of a natural experiment using a mixed methods design. The quantitative findings from this work have been previously published. The aim of this linked qualitative study was to explore the implementation of the dance mat scheme and offer insights into its uptake as a physical activity intervention. Methods: Embedded qualitative interviews at baseline and 12 month follow-up with purposively selected physical education teachers (n=20) and 25 focus groups with a convenience sample of pupils (n=120) from five intervention schools were conducted. Analysis was informed by sociology of translation approach. Results: At baseline, participants (both teachers and pupils) reported different expectations about the dance mats and how they could be employed. Variation in use was seen at follow-up. In some settings they were frequently used to engage hard to reach groups of pupils. Overall, the dance mats were not used routinely to increase physical activity. However there were other unanticipated benefits to pupils such as improved reaction time, co-ordination and mathematic skills. The use of dance mats was limited in routine physical education classes because of contextual issues (school/government policy) technological failures (batteries/updates) and because of expectations about how and where they could be used. Conclusions: Our linked quantitative study (previously published) suggested that the dance mats were not particularly effective in increasing physical activity, but the qualitative results (reported here) show that the dance mats were not used routinely enough to show a significant effect on physical activity of the intervention. This research demonstrates the benefit of using mixed methods to evaluate complex physical activity interventions. Those planning any intervention for promoting physical activity in schools need to understand the distinction between physical activity and physical education

Anomalous Neutrino Reactions at HERA

We study the sensitivity of HERA to new physics using the helicity suppressed reaction $e_R p \rightarrow \nu X$, where the final neutrino can be a standard model one or a heavy neutrino. The approach is model independent and is based on an effective lagrangian parametrization. It is shown that HERA will put significant bounds on the scale of new physics, though, in general, these are more modest than previously thought. If deviations from the standard model are observed in the above processes, future colliders such as the SSC and LHC will be able to directly probe the physics responsible for these discrepancies}Comment: 11 Pages + 2 figures is TOPDRAWER (included at the end or available by mail). Report UCRHEP-T113 (requires the macropackage PHYZZX). A line in the TeX file requesting an input file has been removed, it caused problem

Aharonov-Bohm Effect and Disclinations in an Elastic Medium

In this work we investigate quasiparticles in the background of defects in solids using the geometric theory of defects. We use the parallel transport matrix to study the Aharonov-Bohm effect in this background. For quasiparticles moving in this effective medium we demonstrate an effect similar to the gravitational Aharonov- Bohm effect. We analyze this effect in an elastic medium with one and $N$ defects.Comment: 6 pages, Revtex

Holonomy Transformation in the FRW Metric

In this work we investigate loop variables in Friedman-Robertson-Walker spacetime. We analyze the parallel transport of vectors and spinors in several paths in this spacetime in order to classify its global properties. The band holonomy invariance is analysed in this background.Comment: 8 page

Application of support vector machines on the basis of the first Hungarian bankruptcy model

In our study we rely on a data mining procedure known as support vector machine (SVM) on the database of the first Hungarian bankruptcy model. The models constructed are then contrasted with the results of earlier bankruptcy models with the use of classification accuracy and the area under the ROC curve. In using the SVM technique, in addition to conventional kernel functions, we also examine the possibilities of applying the ANOVA kernel function and take a detailed look at data preparation tasks recommended in using the SVM method (handling of outliers). The results of the models assembled suggest that a significant improvement of classification accuracy can be achieved on the database of the first Hungarian bankruptcy model when using the SVM method as opposed to neural networks

Situationally-sensitive knowledge translation and relational decision making in hyperacute stroke: a qualitive study

Stroke is a leading cause of disability. Early treatment of acute ischaemic stroke with rtPA reduces the risk of longer term dependency but carries an increased risk of causing immediate bleeding complications. To understand the challenges of knowledge translation and decision making about treatment with rtPA in hyperacute stroke and hence to inform development of appropriate decision support we interviewed patients, their family and health professionals. The emergency setting and the symptomatic effects of hyper-acute stroke shaped the form, content and manner of knowledge translation to support decision making. Decision making about rtPA in hyperacute stroke presented three conundrums for patients, family and clinicians. 1) How to allow time for reflection in a severely time-limited setting. 2) How to facilitate knowledge translation regarding important treatment risks and benefits when patient and family capacity is blunted by the effects and shock of stroke. 3) How to ensure patient and family views are taken into account when the situation produces reliance on the expertise of clinicians. Strategies adopted to meet these conundrums were fourfold: face to face communication; shaping decisions; incremental provision of information; and communication tailored to the individual patient. Relational forms of interaction were understood to engender trust and allay anxiety. Shaping decisions with patients was understood as an expression of confidence by clinicians that helped alleviate anxiety and offered hope and reassurance to patients and their family experiencing the shock of the stroke event. Neutral presentations of information and treatment options promoted uncertainty and contributed to anxiety. ‘Drip feeding’ information created moments for reflection: clinicians literally made time. Tailoring information to the particular patient and family situation allowed clinicians to account for social and emotional contexts. The principal responses to the challenges of decision making about rtPA in hyperacute stroke were relational decision support and situationally-sensitive knowledge translation