'Can policies enhance fertility in Europe?' and questions beyond

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Surgical opportunities for the treatment of the tubal tonsil hyperplasia – pilot study

Извършихме пилотно проучване с цел да апробираме хирургична техника по отношение на хиперплазията на тубарната тонзила. При 11 пациенти (6 деца и 5 възрастни) след огледа и дефинирането на патологията в епифаринкса, ексцесивно хиперплазиралата лимфоидна тъкан в областта на торус тубариус се отстрани посредством апарат Coblаtor II (Arthrocare®). На контролните прегледи на 6 месеца след операцията 7 от оперираните показаха нормални аудиологични показатели, 3-ма имаха подобрение и 1 получи рецидив. Ние считаме, че евапорацията на хиперплазиралата тубарна тонзила посредством Coblator е един обещаващ хирургичен метод при тубарна дисфункция.---------------------------------------------------The pilot study we conducted to was to investigate a surgical technique for the hyperplasia of the tubal tonsil. In 11 patients (6 children and 5 adults) after we examined and defined the pathology in the epipharynx, we removed the excessive lymphoid tissue in torus tubarius by Coblator II (Arthrocare®) medical device. The control examinations (6 months after surgery) revealed that, 7 of the patients showed normal audiological parameters, 3 had improvement of the ear status and 1 showed reoccurrence. We believe that the evaporation of the hypertrophied tubal tonsil using Coblator is a promising surgical method in patients with tubal dysfunction

Online Structured Laplace Approximations For Overcoming Catastrophic Forgetting

We introduce the Kronecker factored online Laplace approximation for overcoming catastrophic forgetting in neural networks. The method is grounded in a Bayesian online learning framework, where we recursively approximate the posterior after every task with a Gaussian, leading to a quadratic penalty on changes to the weights. The Laplace approximation requires calculating the Hessian around a mode, which is typically intractable for modern architectures. In order to make our method scalable, we leverage recent block-diagonal Kronecker factored approximations to the curvature. Our algorithm achieves over 90% test accuracy across a sequence of 50 instantiations of the permuted MNIST dataset, substantially outperforming related methods for overcoming catastrophic forgetting.Comment: 13 pages, 6 figure

Practical Gauss-Newton Optimisation for Deep Learning

We present an efficient block-diagonal ap- proximation to the Gauss-Newton matrix for feedforward neural networks. Our result- ing algorithm is competitive against state- of-the-art first order optimisation methods, with sometimes significant improvement in optimisation performance. Unlike first-order methods, for which hyperparameter tuning of the optimisation parameters is often a labo- rious process, our approach can provide good performance even when used with default set- tings. A side result of our work is that for piecewise linear transfer functions, the net- work objective function can have no differ- entiable local maxima, which may partially explain why such transfer functions facilitate effective optimisation.Comment: ICML 201

Monte Carlo Estimation of the Density of the Sum of Dependent Random Variables

We study an unbiased estimator for the density of a sum of random variables that are simulated from a computer model. A numerical study on examples with copula dependence is conducted where the proposed estimator performs favourably in terms of variance compared to other unbiased estimators. We provide applications and extensions to the estimation of marginal densities in Bayesian statistics and to the estimation of the density of sums of random variables under Gaussian copula dependence