Service Development Programme: Maximising Life Opportunities for Teenagers. Teenagers' Views and Experiences of Sex and Relationships Education, Sexual Health Services and Family Support Services in Kent - Survey findings for Year 2

This brief report provides findings from data collected in year 2 of a survey of teenagers' views and experiences of sex and relationships education and sexual health services in Kent. The data in year 2 was collected in Autumn 2005, a year after the data collected in year 1. The purpose of this report is to highlight the results in year 2 which differ from the year 1 survey data. It is to be used in conjunction with the report in year 1 entitled "Service Development Programme: Maximising Life Opportunies for Teenagers: Teenagers' Views and Experiences of Sex and Relationships Educatioon, Sexual Health Services and Family Suupport Services in Kent: Survey Findings July 2005". The final report on the survey will consist of findings from further analysis of the data from year 1 and year 2 merged together, available at the end of 2006

Expected exponential loss for gaze-based video and volume ground truth annotation

Many recent machine learning approaches used in medical imaging are highly reliant on large amounts of image and ground truth data. In the context of object segmentation, pixel-wise annotations are extremely expensive to collect, especially in video and 3D volumes. To reduce this annotation burden, we propose a novel framework to allow annotators to simply observe the object to segment and record where they have looked at with a \$200 eye gaze tracker. Our method then estimates pixel-wise probabilities for the presence of the object throughout the sequence from which we train a classifier in semi-supervised setting using a novel Expected Exponential loss function. We show that our framework provides superior performances on a wide range of medical image settings compared to existing strategies and that our method can be combined with current crowd-sourcing paradigms as well.Comment: 9 pages, 5 figues, MICCAI 2017 - LABELS Worksho

Signatures of partition functions and their complexity reduction through the KP II equation

A statistical amoeba arises from a real-valued partition function when the positivity condition for pre-exponential terms is relaxed, and families of signatures are taken into account. This notion lets us explore special types of constraints when we focus on those signatures that preserve particular properties. Specifically, we look at sums of determinantal type, and main attention is paid to a distinguished class of soliton solutions of the Kadomtsev-Petviashvili (KP) II equation. A characterization of the signatures preserving the determinantal form, as well as the signatures compatible with the KP II equation, is provided: both of them are reduced to choices of signs for columns and rows of a coefficient matrix, and they satisfy the whole KP hierarchy. Interpretations in term of information-theoretic properties, geometric characteristics, and the relation with tropical limits are discussed.Comment: 42 pages, 11 figures. Section 7.1 has been added, the organization of the paper has been change

Fast Primal-Dual Gradient Method for Strongly Convex Minimization Problems with Linear Constraints

In this paper we consider a class of optimization problems with a strongly convex objective function and the feasible set given by an intersection of a simple convex set with a set given by a number of linear equality and inequality constraints. A number of optimization problems in applications can be stated in this form, examples being the entropy-linear programming, the ridge regression, the elastic net, the regularized optimal transport, etc. We extend the Fast Gradient Method applied to the dual problem in order to make it primal-dual so that it allows not only to solve the dual problem, but also to construct nearly optimal and nearly feasible solution of the primal problem. We also prove a theorem about the convergence rate for the proposed algorithm in terms of the objective function and the linear constraints infeasibility.Comment: Submitted for DOOR 201

Large-scale Nonlinear Variable Selection via Kernel Random Features

We propose a new method for input variable selection in nonlinear regression. The method is embedded into a kernel regression machine that can model general nonlinear functions, not being a priori limited to additive models. This is the first kernel-based variable selection method applicable to large datasets. It sidesteps the typical poor scaling properties of kernel methods by mapping the inputs into a relatively low-dimensional space of random features. The algorithm discovers the variables relevant for the regression task together with learning the prediction model through learning the appropriate nonlinear random feature maps. We demonstrate the outstanding performance of our method on a set of large-scale synthetic and real datasets.Comment: Final version for proceedings of ECML/PKDD 201

Fast stable direct fitting and smoothness selection for Generalized Additive Models

Existing computationally efficient methods for penalized likelihood GAM fitting employ iterative smoothness selection on working linear models (or working mixed models). Such schemes fail to converge for a non-negligible proportion of models, with failure being particularly frequent in the presence of concurvity. If smoothness selection is performed by optimizing `whole model' criteria these problems disappear, but until now attempts to do this have employed finite difference based optimization schemes which are computationally inefficient, and can suffer from false convergence. This paper develops the first computationally efficient method for direct GAM smoothness selection. It is highly stable, but by careful structuring achieves a computational efficiency that leads, in simulations, to lower mean computation times than the schemes based on working-model smoothness selection. The method also offers a reliable way of fitting generalized additive mixed models

Implicitly Constrained Semi-Supervised Least Squares Classification

We introduce a novel semi-supervised version of the least squares classifier. This implicitly constrained least squares (ICLS) classifier minimizes the squared loss on the labeled data among the set of parameters implied by all possible labelings of the unlabeled data. Unlike other discriminative semi-supervised methods, our approach does not introduce explicit additional assumptions into the objective function, but leverages implicit assumptions already present in the choice of the supervised least squares classifier. We show this approach can be formulated as a quadratic programming problem and its solution can be found using a simple gradient descent procedure. We prove that, in a certain way, our method never leads to performance worse than the supervised classifier. Experimental results corroborate this theoretical result in the multidimensional case on benchmark datasets, also in terms of the error rate.Comment: 12 pages, 2 figures, 1 table. The Fourteenth International Symposium on Intelligent Data Analysis (2015), Saint-Etienne, Franc

Strong "quantum" chaos in the global ballooning mode spectrum of three-dimensional plasmas

The spectrum of ideal magnetohydrodynamic (MHD) pressure-driven (ballooning) modes in strongly nonaxisymmetric toroidal systems is difficult to analyze numerically owing to the singular nature of ideal MHD caused by lack of an inherent scale length. In this paper, ideal MHD is regularized by using a $k$-space cutoff, making the ray tracing for the WKB ballooning formalism a chaotic Hamiltonian billiard problem. The minimum width of the toroidal Fourier spectrum needed for resolving toroidally localized ballooning modes with a global eigenvalue code is estimated from the Weyl formula. This phase-space-volume estimation method is applied to two stellarator cases.Comment: 4 pages typeset, including 2 figures. Paper accepted for publication in Phys. Rev. Letter

Vestiges of the proto-Caribbean seaway: origin of the San Souci Volcanic Group, Trinidad

Outcrops of volcanic–hypabyssal rocks in Trinidad document the opening of the proto-Caribbean seaway during Jurassic–Cretaceous break-up of the Americas. The San Souci Group on the northern coast of Trinidad comprises the San Souci Volcanic Formation (SSVF) and passive margin sediments of the ~ 130–125 Ma Toco Formation. The Group was trapped at the leading edge of the Pacific-derived Caribbean Plate during the Cretaceous–Palaeogene, colliding with the para-autochthonous margin of Trinidad during the Oligocene–Miocene. In-situ U–Pb ion probe dating of micro-zircons from a mafic volcanic breccia reveal the SSVF crystallised at 135.0 ± 7.3 Ma. The age of the SSVF is within error of the age of the Toco Formation. Assuming a conformable contact, geodynamic models indicate a likely origin for the SSVF on the passive margin close to the northern tip of South America. Immobile element and Nd–Hf radiogenic isotope signatures of the mafic rocks indicate the SSVF was formed by ≪10% partial melting of a heterogeneous spinel peridotite source with no subduction or continental lithospheric mantle component. Felsic breccias within the SSVF are more enriched in incompatible elements, with isotope signatures that are less radiogenic than the mafic rocks of the SSVF. The felsic rocks may be derived from re-melting of mafic crust. Although geochemical comparisons are drawn here with proto-Caribbean igneous outcrops in Venezuela and elsewhere in the Caribbean more work is needed to elucidate the development of the proto-Caribbean seaway and its rifted margins. In particular, ion probe dating of micro-zircons may yield valuable insights into magmatism and metamorphism in the Caribbean, and in altered basaltic terranes more generally