Patterson v. Commonwealth: An Illustration of the Legal Complexity of DNA Databases

In the old-fashioned drawing-room murder mysteries, the villain usually eliminated his target in some Victorian manner - asphyxiation in bed with a pillow, drowning in a clawed-foot tub, stabbing in the back with a letter opener - and then skulked away, convinced he had committed the perfect crime. In the plot of these mysteries, the foil to the crime always proved to be the experienced detection of a meticulous sleuth. AgathaChristie would use the skills of Hercule Poirot or Miss Marple within the plot of her novels to detect the one flaw in the murderer\u27s technique. Perhaps there was some painfully obvious motive held by one of the guests at the summer home. Perhaps the villain\u27s mother embroidered the pillow used to asphyxiate the victim, or the letter opener was engraved with a unique identifying mark. Regardless of the simplicity or complexity of the villain\u27s methods, he was always caught. In these novels, there was no such thing as the perfect crime

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

A Personalised Ranking Framework with Multiple Sampling Criteria for Venue Recommendation

Recommending a ranked list of interesting venues to users based on their preferences has become a key functionality in Location-Based Social Networks (LBSNs) such as Yelp and Gowalla. Bayesian Personalised Ranking (BPR) is a popular pairwise recommendation technique that is used to generate the ranked list of venues of interest to a user, by leveraging the user's implicit feedback such as their check-ins as instances of positive feedback, while randomly sampling other venues as negative instances. To alleviate the sparsity that affects the usefulness of recommendations by BPR for users with few check-ins, various approaches have been proposed in the literature to incorporate additional sources of information such as the social links between users, the textual content of comments, as well as the geographical location of the venues. However, such approaches can only readily leverage one source of additional information for negative sampling. Instead, we propose a novel Personalised Ranking Framework with Multiple sampling Criteria (PRFMC) that leverages both geographical influence and social correlation to enhance the effectiveness of BPR. In particular, we apply a multi-centre Gaussian model and a power-law distribution method, to capture geographical influence and social correlation when sampling negative venues, respectively. Finally, we conduct comprehensive experiments using three large-scale datasets from the Yelp, Gowalla and Brightkite LBSNs. The experimental results demonstrate the effectiveness of fusing both geographical influence and social correlation in our proposed PRFMC framework and its superiority in comparison to BPR-based and other similar ranking approaches. Indeed, our PRFMC approach attains a 37% improvement in MRR over a recently proposed approach that identifies negative venues only from social links

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

Conformal Field Theory Correlators from Classical Scalar Field Theory on AdSd+1AdS_{d+1}

We use the correspondence between scalar field theory on $AdS_{d+1}$ and a conformal field theory on $R^d$ to calculate the 3- and 4-point functions of the latter. The classical scalar field theory action is evaluated at tree level.Comment: 9 pages, LaTeX2e with amsmath, amsfonts packages, section 2 rewritten, references adde

The effect of dance mat exergaming systems on physical activity and health – related outcomes in secondary schools: results from a natural experiment

Background: Exergaming has been proposed as an innovative method for physical activity promotion. However, large effectiveness studies are rare. In January 2011, dance mat systems were introduced in secondary schools in two districts in England with the aim of promoting an innovative opportunity for physical activity. The aim of this natural experiment was to examine the effect of introducing the dance mat exergaming systems on physical activity and health-related outcomes in 11–13 year old students using a non-randomised controlled design and mixed methods. Methods: Participants were recruited from five schools in intervention districts (n = 280) and two schools in neighbouring control districts (n = 217). Data on physical activity (accelerometer), anthropometrics (weight, BMI and percentage of body fat), aerobic fitness (20-m multistage shuttle run test), health-related quality of life (Kidscreen questionnaire), self-efficacy (children’s physical activity self-efficacy survey), school attendance, focus groups with children and interviews with teachers were collected at baseline and approximately 12 months follow-up. Results: There was a negative intervention effect on total physical activity (-65.4 cpm CI: -12.6 to -4.7), and light and sedentary physical activity when represented as a percentage of wear time (Light: -2.3% CI: -4.5 to 0.2; Sedentary: 3.3% CI: 0.7 to 5.9). However, compliance with accelerometers at follow-up was poor. There was a significant positive intervention effect on weight (-1.7 kg, 95% CI: -2.9 to -0.4), BMI (-0.9 kg/m2, 95% CI: -1.3 to -0.4) and percentage of body fat (-2.2%, 95% CI: -4.2 to -0.2). There was also evidence of improvement in some health-related quality of life parameters: psychological well-being (2.5, 95% CI: 0.1 to 4.8) and autonomy and parent relation (4.2, 95% CI: 1.4 to 7.0). Conclusions: The implementation of a dance mat exergaming scheme was associated with improvement in anthropometric measurements and parameters of health-related quality of life. However, the mechanisms of these benefits are unclear as there was insufficient data from physical activity to draw robust conclusions. Qualitative findings suggest that there was declining support for the initiative over time, meaning that potential benefits may not have been achieved

Bounds on the electromagnetic interactions of excited spin-3/2 leptons

We discuss possible deviations from QED produced by a virtual excited spin-3/2 lepton in the reaction $e^+e^- \longrightarrow 2\gamma$. Data recorded by the OPAL Collaboration at a c.m. energy $\sqrt{s} = 183 GeV$ are used to establish bounds on the nonstandard-lepton mass and coupling strengths.Comment: Latex, 5 pages, 7 ps figures. To be published in Phys. Rev.

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