Psychoanalytic and psychodynamic therapies for depression. The evidence base.

David Taylor, a consultant psychotherapist at the Tavistock & Portman NHS Foundation Trust (120 Belsize Lane, London NW3 5BA, UK. Email: [email protected]), is the clinical lead of the Tavistock Adult Depression Study (a randomised controlled trial of 60 sessions of weekly psychoanalytic psychotherapy v. treatment as usual for patients with chronic, refractory depression). He is a training and supervising psychoanalyst at the Institute of Psychoanalysis. This article argues that the current approach to guideline development for the treatment of depression is not supported by the evidence: clearly depression is not a disease for which treatment efficacy is best determined by short-term randomised controlled trials. As a result, important findings have been marginalised. Different principles of evidence-gathering are described. When a wider range of the available evidence is critically considered the case for dynamic approaches to the treatment of depression can be seen to be stronger than is often thought. Broadly, the benefits of short-term psychodynamic therapies are equivalent in size to the effects of antidepressants and cognitive–behavioural therapy (CBT). The benefits of CBT may occur more quickly, but those of short-term psychodynamic therapies may continue to increase after treatment. There may be a ceiling on the effects of short-term treatments of whatever type. Longer-term psychodynamic treatments may improve associated social, work and personal dysfunctions as well as reductions in depressive symptoms

Geodesic gaussian processes for the parametric reconstruction of a free-form surface

Reconstructing a free-form surface from 3-dimensional (3D) noisy measurements is a central problem in inspection, statistical quality control, and reverse engineering. We present a new method for the statistical reconstruction of a free-form surface patch based on 3D point cloud data. The surface is represented parametrically, with each of the three Cartesian coordinates (x, y, z) a function of surface coordinates (u, v), a model form compatible with computer-aided-design (CAD) models. This model form also avoids having to choose one Euclidean coordinate (say, z) as a “response” function of the other two coordinate “locations” (say, x and y), as commonly used in previous Euclidean kriging models of manufacturing data. The (u, v) surface coordinates are computed using parameterization algorithms from the manifold learning and computer graphics literature. These are then used as locations in a spatial Gaussian process model that considers correlations between two points on the surface a function of their geodesic distance on the surface, rather than a function of their Euclidean distances over the xy plane. We show how the proposed geodesic Gaussian process (GGP) approach better reconstructs the true surface, filtering the measurement noise, than when using a standard Euclidean kriging model of the “heights”, that is, z(x, y). The methodology is applied to simulated surface data and to a real dataset obtained with a noncontact laser scanner. Supplementary materials are available online

Fitting ARMA Time Series Models without Identification: A Proximal Approach

Fitting autoregressive moving average (ARMA) time series models requires model identification before parameter estimation. Model identification involves determining the order of the autoregressive and moving average components which is generally performed by inspection of the autocorrelation and partial autocorrelation functions or other offline methods. In this work, we regularize the parameter estimation optimization problem with a nonsmooth hierarchical sparsity-inducing penalty based on two path graphs that allows performing model identification and parameter estimation simultaneously. A proximal block coordinate descent algorithm is then proposed to solve the underlying optimization problem efficiently. The resulting model satisfies the required stationarity and invertibility conditions for ARMA models. Numerical studies supporting the performance of the proposed method and comparing it with other schemes are presented

Riemannian Stochastic Gradient Method for Nested Composition Optimization

This work considers optimization of composition of functions in a nested form over Riemannian manifolds where each function contains an expectation. This type of problems is gaining popularity in applications such as policy evaluation in reinforcement learning or model customization in meta-learning. The standard Riemannian stochastic gradient methods for non-compositional optimization cannot be directly applied as stochastic approximation of inner functions create bias in the gradients of the outer functions. For two-level composition optimization, we present a Riemannian Stochastic Composition Gradient Descent (R-SCGD) method that finds an approximate stationary point, with expected squared Riemannian gradient smaller than $\epsilon$, in $O(\epsilon^{-2})$ calls to the stochastic gradient oracle of the outer function and stochastic function and gradient oracles of the inner function. Furthermore, we generalize the R-SCGD algorithms for problems with multi-level nested compositional structures, with the same complexity of $O(\epsilon^{-2})$ for the first-order stochastic oracle. Finally, the performance of the R-SCGD method is numerically evaluated over a policy evaluation problem in reinforcement learning

Stochastic Composition Optimization of Functions without Lipschitz Continuous Gradient

In this paper, we study the stochastic optimization of two-level composition of functions without Lipschitz continuous gradient. The smoothness property is generalized by the notion of relative smoothness which provokes the Bregman gradient method. We propose three Stochastic Compositional Bregman Gradient algorithms for the three possible nonsmooth compositional scenarios and provide their sample complexities to achieve an $\epsilon$-approximate stationary point. For the smooth of relative smooth composition, the first algorithm requires $O(\epsilon^{-2})$ calls to the stochastic oracles of the inner function value and gradient as well as the outer function gradient. When both functions are relatively smooth, the second algorithm requires $O(\epsilon^{-3})$ calls to the inner function stochastic oracle and $O(\epsilon^{-2})$ calls to the inner and outer function stochastic gradient oracles. We further improve the second algorithm by variance reduction for the setting where just the inner function is smooth. The resulting algorithm requires $O(\epsilon^{-5/2})$ calls to the stochastic inner function value and $O(\epsilon^{-3/2})$ calls to the inner stochastic gradient and $O(\epsilon^{-2})$ calls to the outer function stochastic gradient. Finally, we numerically evaluate the performance of these algorithms over two examples

Stochastic Optimization Algorithms for Problems with Controllable Biased Oracles

Motivated by multiple emerging applications in machine learning, we consider an optimization problem in a general form where the gradient of the objective is available through a biased stochastic oracle. We assume the bias magnitude can be reduced by a bias-control parameter, however, a lower bias requires more computation/samples. For instance, for two applications on stochastic composition optimization and policy optimization for infinite-horizon Markov decision processes, we show that the bias follows a power law and exponential decay, respectively, as functions of their corresponding bias control parameters. For problems with such gradient oracles, the paper proposes stochastic algorithms that adjust the bias-control parameter throughout the iterations. We analyze the nonasymptotic performance of the proposed algorithms in the nonconvex regime and establish their sample or bias-control computation complexities to obtain a stationary point. Finally, we numerically evaluate the performance of the proposed algorithms over the two applications

Efficacy of Sodium Hypochlorite Activated With Laser in Intracanal Smear Layer Removal: An SEM Study

Introduction: The purpose of the present study was to evaluate the different concentrations of sodium hypochlorite activated with laser in removing of the smear layer in the apical, middle, and coronal segments of root canal walls by scanning electron microscopy analysis.Methods: Sixty single-rooted human mandibular teeth were decoronated to a standardized length. The samples were prepared by using Race rotary system to size 40, 0.04 taper and divided into 4 equal groups (n = 15). Group 1, irrigated with EDTA 17% and 5.25% NaOCl, groups 2, 3 and 4, 1%, 2.5%, and 5% NaOCl activated with Nd:YAG laser, respectively. Teeth were split longitudinally and subjected to scanning electron microscope (SEM). Data were analyzed by Kruskal-Wallis, Mann-Whitney tests. P value of <0.05 was considered statistically significant.Results: Five percent NaOCl LAI (laser-activated irrigation) showed best smear layer removal in test groups and the difference was statistically significant (P < 0.001). Control group (EDTA 17% and 5.25% NaOCl irrigation) showed significantly better outcomes in comparative with test groups (P < 0.001). In the apical third, compared to coronal and middle third, the canal walls were often contaminated by inorganic debris and smear layer.Conclusion: All different concentrations of sodium hypochlorite activated with laser have a positive effect on removing of smear layer. Sodium hypochlorite activated with laser removed smear layer more effectively at the coronal and middle third compared to the apical third