The experiences of vicarious trauma and its related coping strategies among a group of South African psychologists : a phenomenological study

Magister Psychologiae - MPsychSignificant research efforts have focused on developing treatments for trauma survivors and evaluating their effectiveness. However, little attention has been given to understanding the impact of working with trauma survivors on psychologists. This research aimed to address this gap in the literature. In South Africa, there is a scarcity of published qualitative studies that focus on the experiences of VT among psychologists who work with survivors of trauma. Given the increasing prevalence of traumatic events in South Africa and increasing patient load, psychologists may be vulnerable to the development of VT. Beneficial treatments for trauma survivors largely depend on psychologists who can effectively handle their clients' intense traumatic material. If a psychologist is adversely affected by the work of trauma, the quality of treatment for trauma survivors will be compromised (Figley, 1999). Hence, it is critical that research continues to explore the effects of VT and ways to ameliorate them. Aim: to explore the experiences of VT among a group of psychologists from Cape Town, South Africa, who work with trauma survivors and the related coping strategies used by them. This research aimed to expand the local research on the phenomenon of VT. Findings of the study will help to facilitate a better understanding of vicarious impact of trauma work as well as the related coping techniques used by psychologists. Identification of protective factors and effective coping mechanisms of those professionals in this study was a distinct contribution to the South African literature base. This study has practical implications for training, supervision and clinical practice for psychologists in South Africa to enhance the efficiency of psychological service delivery. Exploring the challenges South African psychologists experience as a result of working with trauma survivors may help inform policy and develop effective programmes to address the effects of VT. As such, psychologists would be better equipped to care both for themselves and their clients, and to ensure ethical and professional practice

Principled control of approximate programs

In conventional computing, most programs are treated as implementations of mathematical functions for which there is an exact output that must computed from a given input. However, in many problem domains, it is sufficient to produce some approximation of this output. For example, when rendering a scene in graphics, it is acceptable to take computational short-cuts if human beings cannot tell the difference in the rendered scene. In other problem domains like machine learning, programs are often implementations of heuristic approaches to solving problems and therefore already compute approximate solutions to the original problem. This is the key insight for the new research area, approximate computing, which attempts to trade-off such approximations against the cost of computational resources such as program execution time, energy consumption, and memory usage. We believe that approximate computing is an important step towards a more fundamental and comprehensive goal that we call information-efficiency. Current applications compute more information (bits) than are needed to produce their outputs, and since producing and transporting bits of information inside a computer requires energy/computation time/memory usage, information-inefficient computing leads directly to resources inefficiency. Although there is now a fairly large literature on approximate computing, system researchers have focused mostly on what we can call the forward problem; that is, they have explored different ways in both hardware and software to introduce approximations in a program and have demonstrated that these approximations can enable significant execution speedups and energy savings with some quality degradation of the result. However, these efforts do not provide any guarantee on the amount of the quality degradation. Since the acceptable amount of degradation usually depends on the scenario in which the application is deployed, it is very important to be able to control the degree of approximation. In this dissertation, we refer to this problem as the inverse problem. Relatively little is known about how to solve the inverse problem in a disciplined way. This dissertation makes two contributions towards solving the inverse problem. First, we investigate a large set of approximate algorithms from a variety of domains in order to understand how approximation is used in real-world applications. From this investigation, we determine that many approximate programs are tunable approximate programs. Tunable approximate programs have one or more parameters called knobs that can be changed to vary the quality of the output of the approximate computation as well as the corresponding cost. For example, an iterative linear equation solver can vary the number of iterations to trade quality of the solution versus the execution time, a Monte Carlo path tracer can change the number of sampling light paths to trade the quality of the resulting image against execution time, etc. Tunable approximate programs provide many opportunities for trading accuracy versus cost. By carefully analyzing these algorithms, we have found a set of patterns for how approximation is applied in tunable programs. Our classification can be used to identify new approximation opportunities in programs. A second contribution of this dissertation is an approach to solving the inverse problem for tunable approximate programs. Concretely, the problem is to determine knob settings to minimize the cost while keeping the quality degradation within a given bound. There are four challenges: i) for real-world applications, the quality and cost are usually complex non-linear functions of the knobs and these functions are usually hard to express analytically; ii) the quality and the cost for an application vary greatly for different inputs; iii) when an acceptable quality degradation bound is presented, determining the knob setting has to be very efficient so that the extra overhead incurred by the identification will not exceed the cost saved by the approximation; and iv) the approach should be general so that it can be applied to many applications. To meet these requirements, we formulate the inverse problem as a constrained optimization problem and solve it using a machine learning based approach. We build a system which uses machine learning techniques to learn cost and quality models for the program by profiling the program with a set of representative inputs. Then, when a quality degradation bound is presented, the system searches these error and cost models to identify the knob settings which can achieve the best cost savings while simultaneously guaranteeing the quality degradation bound statistically. We evaluate the system with a set of real world applications, including a social network graph partitioner, an image search engine, a 2-D graph layout engine, a 3-D game physics engine, a SVM solver and a radar signal processing engine. The experiments showed great savings in execution time and energy savings for a variety of quality bounds.Computer Science

Correlations of BMI-1 expression and telomerase activity in ovarian cancer tissues

Aim: To investigate the correlation between oncoprotein Bmi-1 and telomerase activity in ovarian cancer tissues. Methods: SP immunohistochemistry was adopted to detect the expression of Bmi-1 protein in tissues of 47 ovarian epithelial cancer cases. Modified telomeric repeat amplification protocol (TRAP, silver staining technique) was used to detect the telomerase activity. Results: While in 80.85% (38/47) of ovarian epithelial cancer cases Bmi-1 protein was overexpressed, 46.81% (22/47) had very strong expression level. Bmi-1 expression levels in ovarian carcinoma tissue differ depending on tissue grade (higher for G3 cancer cases — 93.10% than for grade G2 cases — 61.11%) and the stage of the disease (lower for phase II and phase III cases — 66.67% than for phase IV cases — 92.31%). In ovarian epithelial cancer tissues, 87.23% (41/47) demonstrated positive telomerase activity in contrast to zero activity in normal tissues. Majority (90.24%) of specimens with positive telomerase activity possessed high Bmi-1 expression levels. Spearman correlation analysis indicated that expression of Bmi-1 protein was positively correlated with the elevated telomerase activity. Conclusions: Bmi-1 protein is highly expressed in ovarian epithelial cancer tissues, and its expression level correlates with histological grade and clinical phase of the patients. Elevation of Bmi-1 expression is closely correlated to the increased telomerase activity.Цель: изучить корреляцию между экспрессией протеина Bmi-1 и активностью теломеразы при раке яичника. Методы: подобраны оптимальные условия для SP-иммуногистохимии для выявления экспрессии белка Bmi-1 при эпителиальном раке яичника (n = 47). Для определения активности теломеразы был использован усовершенствованый протокол амплификации теломерных повторов (TRAP, методика окрашивания серебром). Результаты: в 80,85% (38/47) случаев рака яичника была выявлена экспрессия белка Bmi-1, в 46,81% (22/47) случаев – на очень высоком уровне. Уровень экспрессии Bmi-1 зависел от степени дифференцировки опухоли (при G3 экспрессия Bmi-1 (93,10%) была выше, чем при G2 (61,11%)) и от стадии заболе- G3 экспрессия 3 экспрессия Bmi-1 (93,10%) была выше, чем при G2 (61,11%)) и от стадии заболе- Bmi-1 (93,10%) была выше, чем при G2 (61,11%)) и от стадии заболе- -1 (93,10%) была выше, чем при -1 (93,10%) была выше, чем при G2 (61,11%)) и от стадии заболе- G2 (61,11%)) и от стадии заболе- G2 (61,11%)) и от стадии заболе- 2 (61,11%)) и от стадии заболевания (уровень экпрессии ниже в стадиях II и III (66,67%), чем в стадии IV (92,31%)). В тканях эпителиального рака яичника в 87,23% (41/47) случаев выявлена положительная теломеразная активность, в отличие от нулевой активности в нормальных тканях. В большинстве исследованных случаев рака яичника (90,24%) при положительной активности теломеразы был отмечен высокий уровень экспрессии Bmi-1. Корреляционный анализ Спирмана показал, что экспрессия белка Bmi-1 положительно коррелирует с повышенной теломеразной активностью. Выводы: белок Bmi-1 экспрессирован на высоком уровне злокачествен- -1 экспрессирован на высоком уровне злокачествен- -1 экспрессирован на высоком уровне злокачественными клетками эпителиального рака яичника, и экспрессия этого белка коррелирует с гистологической градацией и клинической стадией рака. Увеличение экспрессии Bmi-1 коррелировало с повышенной теломеразной активностью

Exponential state estimation for competitive neural network via stochastic sampled-data control with packet losses

This paper investigates the exponential state estimation problem for competitive neural networks via stochastic sampled-data control with packet losses. Based on this strategy, a switched system model is used to describe packet dropouts for the error system. In addition, transmittal delays between neurons are also considered. Instead of the continuous measurement, the sampled measurement is used to estimate the neuron states, and a sampled-data estimator with probabilistic sampling in two sampling periods is proposed. Then the estimator is designed in terms of the solution to a set of linear matrix inequalities (LMIs), which can be solved by using available software. When the missing of control packet occurs, some sufficient conditions are obtained to guarantee that the exponentially stable of the error system by means of constructing an appropriate Lyapunov function and using the average dwell-time technique. Finally, a numerical example is given to show the effectiveness of the proposed method

Implicit Regularization in Over-Parameterized Support Vector Machine

In this paper, we design a regularization-free algorithm for high-dimensional support vector machines (SVMs) by integrating over-parameterization with Nesterov's smoothing method, and provide theoretical guarantees for the induced implicit regularization phenomenon. In particular, we construct an over-parameterized hinge loss function and estimate the true parameters by leveraging regularization-free gradient descent on this loss function. The utilization of Nesterov's method enhances the computational efficiency of our algorithm, especially in terms of determining the stopping criterion and reducing computational complexity. With appropriate choices of initialization, step size, and smoothness parameter, we demonstrate that unregularized gradient descent achieves a near-oracle statistical convergence rate. Additionally, we verify our theoretical findings through a variety of numerical experiments and compare the proposed method with explicit regularization. Our results illustrate the advantages of employing implicit regularization via gradient descent in conjunction with over-parameterization in sparse SVMs