The lived experience of posttraumatic growth in gay men after an HIV diagnosis: An interpretative phenomenological analysis

As a result of advances in highly active antiretroviral therapy, human immunodeficiency virus (HIV) has been reconceptualised as a long-term chronic health condition instead of a death sentence. Nonetheless, receiving a positive diagnosis can still be an extremely traumatic experience. Whilst there are many people living with HIV who struggle with their diagnosis, some can also manage to find meaning from it and so experience positive change within their lives. This research seeks to explore the lived experience of eight HIV-positive gay men between the ages of 35 and 50 who have experienced posttraumatic growth (PTG) since their diagnosis. Interpretative phenomenological analysis (IPA) was used to analyse interview data. Four super-ordinate themes were identified: the first highlights the struggle as the men grapple with their diagnosis. The second theme explores how the men have developed more positive and meaningful relationships with themselves and other people, as well as embarking on a new relationship with their HIV. The third captures the men’s positive growth as they begin to find meaning, whilst creating a more positive mindset, and instigating experiences that would enable them to experience flow and positive emotions. The final super-ordinate theme captures the ways in which the men wrestle with their identities whilst living as HIV-positive gay men. I then discuss these in light of the literature and draw implications for counselling psychology

Group Importance Sampling for Particle Filtering and MCMC

Bayesian methods and their implementations by means of sophisticated Monte Carlo techniques have become very popular in signal processing over the last years. Importance Sampling (IS) is a well-known Monte Carlo technique that approximates integrals involving a posterior distribution by means of weighted samples. In this work, we study the assignation of a single weighted sample which compresses the information contained in a population of weighted samples. Part of the theory that we present as Group Importance Sampling (GIS) has been employed implicitly in different works in the literature. The provided analysis yields several theoretical and practical consequences. For instance, we discuss the application of GIS into the Sequential Importance Resampling framework and show that Independent Multiple Try Metropolis schemes can be interpreted as a standard Metropolis-Hastings algorithm, following the GIS approach. We also introduce two novel Markov Chain Monte Carlo (MCMC) techniques based on GIS. The first one, named Group Metropolis Sampling method, produces a Markov chain of sets of weighted samples. All these sets are then employed for obtaining a unique global estimator. The second one is the Distributed Particle Metropolis-Hastings technique, where different parallel particle filters are jointly used to drive an MCMC algorithm. Different resampled trajectories are compared and then tested with a proper acceptance probability. The novel schemes are tested in different numerical experiments such as learning the hyperparameters of Gaussian Processes, two localization problems in a wireless sensor network (with synthetic and real data) and the tracking of vegetation parameters given satellite observations, where they are compared with several benchmark Monte Carlo techniques. Three illustrative Matlab demos are also provided.Comment: To appear in Digital Signal Processing. Related Matlab demos are provided at https://github.com/lukafree/GIS.gi

Predictability of Equity Models

In this study, we verify the existence of predictability in the Brazilian equity market. Unlike other studies in the same sense, which evaluate original series for each stock, we evaluate synthetic series created on the basis of linear models of stocks. Following Burgess (1999), we use the “stepwise regression” model for the formation of models of each stock. We then use the variance ratio profile together with a Monte Carlo simulation for the selection of models with potential predictability. Unlike Burgess (1999), we carry out White’s Reality Check (2000) in order to verify the existence of positive returns for the period outside the sample. We use the strategies proposed by Sullivan, Timmermann & White (1999) and Hsu & Kuan (2005) amounting to 26,410 simulated strategies. Finally, using the bootstrap methodology, with 1,000 simulations, we find strong evidence of predictability in the models, including transaction costspredictability, variance ratio profile, Monte Carlo simulation, reality check, bootstrap, technical analysis

Testing the Hypothesis of Contagion using Multivariate Volatility Models

The aim of this paper is to test whether or not there was evidence of contagion across the various financial crises that assailed some countries in the 1990s. Data on sovereign debt bonds for Brazil, Mexico, Russia and Argentina were used to implement the test. The contagion hypothesis is tested using multivariate volatility models. If there is any evidence of structural break in volatility that can be linked to financial crises, the contagion hypothesis will be confirmed. Results suggest that there is evidence in favor of the contagion hypothesisContagion, Multivariate Volatility Models

Odd Triplet Pairing in clean Superconductor/Ferromagnet heterostructures

We study triplet pairing correlations in clean Ferromagnet (F)/Superconductor (S) nanojunctions, via fully self consistent solution of the Bogoliubov-de Gennes equations. We consider FSF trilayers, with S being an s-wave superconductor, and an arbitrary angle $\alpha$ between the magnetizations of the two F layers. We find that contrary to some previous expectations, triplet correlations, odd in time, are induced in both the S and F layers in the clean limit. We investigate their behavior as a function of time, position, and $\alpha$. The triplet amplitudes are largest at times on the order of the inverse ``Debye'' frequency, and at that time scale they are long ranged in both S and F. The zero temperature condensation energy is found to be lowest when the magnetizations are antiparallel.Comment: Four pages, including four figure

Testing the long-run implications of the expectation hypothesis using cointegration techniques with structural change

This paper investigates whether or not multivariate cointegrated process with structural change can describe the Brazilian term structure of interest rate data from 1995 to 2006. In this work the break point and the number of cointegrated vector are assumed to be known. The estimated model has four regimes. Only three of them are statistically different. The first starts at the beginning of the sample and goes until September of 1997. The second starts at October of 1997 until December of 1998. The third starts at January of 1999 and goes until the end of the sample. It is used monthly data. Models that allows for some similarities across the regimes are also estimated and tested. The models are estimated using the Generalized Reduced-Rank Regressions developed by Hansen (2003). All imposed restrictions can be tested using likelihood ratio test with standard asymptotic qui-squared distribution. The results of the paper show evidence in favor of the long run implications of the expectation hypothesis for Brazil.Term structure, cointegration, structural change