What interventions facilitate client progress through the assimilation model? A task analysis of interventions in the psychodynamic treatment of depression.

A variation of task analysis was used to build an empirical model of how therapists may facilitate client assimilation process, described in the Assimilation of Problematic Experiences Scale. A rational model was specified and considered in light of an analysis of therapist in-session performances (N = 117) drawn from six inpatient therapies for depression. The therapist interventions were measured by the Comprehensive Psychotherapeutic Interventions Rating Scale. Consistent with the rational model, confronting interventions were particularly useful in helping clients elaborate insight. However, rather than there being a small number of progress-related interventions at lower levels of assimilation, therapists' use of interventions was broader than hypothesized and drew from a wide range of therapeutic approaches. Concerning the higher levels of assimilation, there was insufficient data to allow an analysis of the therapist's progress-related interventions.

Vulnerability as a heuristic for interdisciplinary research: Assessing the thematic and methodological structure of empirical life-course studies

Changes in human lives are studied in psychology, sociology, and adjacent fields as outcomes of developmental processes, institutional regulations and policies, culturally and normatively structured life courses, or empirical accounts. However, such studies have used a wide range of complementary, but often divergent, concepts. This review has two aims. First, we report on the structure that has emerged from scientific life course research by focusing on abstracts from longitudinal and life course studies beginning with the year 2000. Second, we provide a sense of the disciplinary diversity of the field and assess the value of the concept of 'vulnerability' as a heuristic tool for studying human lives. Applying correspondence analysis to 10,632 scientific abstracts, we find a disciplinary divide between psychology and sociology, and observe indications of both similarities of-and differences between-studies, driven at least partly by the data and methods employed. We also find that vulnerability takes a central position in this scientific field, which leads us to suggest several reasons to see value in pursuing theory development for longitudinal and life course studies in this direction.

An efficient total variation algorithm for super-resolution in fetal brain MRI with adaptive regularization.

Although fetal anatomy can be adequately viewed in new multi-slice MR images, many critical limitations remain for quantitative data analysis. To this end, several research groups have recently developed advanced image processing methods, often denoted by super-resolution (SR) techniques, to reconstruct from a set of clinical low-resolution (LR) images, a high-resolution (HR) motion-free volume. It is usually modeled as an inverse problem where the regularization term plays a central role in the reconstruction quality. Literature has been quite attracted by Total Variation energies because of their ability in edge preserving but only standard explicit steepest gradient techniques have been applied for optimization. In a preliminary work, it has been shown that novel fast convex optimization techniques could be successfully applied to design an efficient Total Variation optimization algorithm for the super-resolution problem. In this work, two major contributions are presented. Firstly, we will briefly review the Bayesian and Variational dual formulations of current state-of-the-art methods dedicated to fetal MRI reconstruction. Secondly, we present an extensive quantitative evaluation of our SR algorithm previously introduced on both simulated fetal and real clinical data (with both normal and pathological subjects). Specifically, we study the robustness of regularization terms in front of residual registration errors and we also present a novel strategy for automatically select the weight of the regularization as regards the data fidelity term. Our results show that our TV implementation is highly robust in front of motion artifacts and that it offers the best trade-off between speed and accuracy for fetal MRI recovery as in comparison with state-of-the art methods.

Can the administration be trusted? An analysis of the concept of trust, applied to the public sector

In the first part of this paper, we present the various academic debates and, where applicable, questions that remain open in the literature, particularly regarding the nature of trust, the distinction between trust and trustworthiness, its role in specific relationships and its relationship to control. We then propose a way of demarcating and operationalizing the concepts of trust and trustworthiness. In the second part, on the basis of the conceptual clarifications we present, we put forward a number of "anchor points" regarding how trust is apprehended in the public sector with regard to the various relations hips that can be studied. Schematically, we distinguish between two types of relations hips in the conceptual approach to trust: on one hand, the trust that citizens, or third parties, place in the State or in various public sector authorities or entities, and on the other hand, trust within the State or the public sector, between its various authorities, entities, and actors. While studies have traditionally focused on citizens' trust in their institutions, the findings, limitations and problems observed in public - sector coordination following the reforms associated with New Public Management have also elicited growing interest in the study of trust in the relationships between the various actors within the public sector. Both the theoretical debates we present and our propositions have been extracted and adapted from an empirical comparative study of coordination between various Swiss public - service organizations and their politico - administrative authority. Using the analysis model developed for this specific relationship, between various actors within the public service, and in the light of theoretical elements on which development of this model was based, we propose some avenues for further study - questions that remain open - regarding the consideration and understanding of citizens' trust in the public sector.

Adjusting for the environment in DEA: a comparison of alternative models based on empirical data

Due to the existence of free software and pedagogical guides, the use of Data Envelopment Analysis (DEA) has been further democratized in recent years. Nowadays, it is quite usual for practitioners and decision makers with no or little knowledge in operational research to run their own efficiency analysis. Within DEA, several alternative models allow for an environmental adjustment. Four alternative models, each user-friendly and easily accessible to practitioners and decision makers, are performed using empirical data of 90 primary schools in the State of Geneva, Switzerland. Results show that the majority of alternative models deliver divergent results. From a political and a managerial standpoint, these diverging results could lead to potentially ineffective decisions. As no consensus emerges on the best model to use, practitioners and decision makers may be tempted to select the model that is right for them, in other words, the model that best reflects their own preferences. Further studies should investigate how an appropriate multi-criteria decision analysis method could help decision makers to select the right model.

Self-deception as affective coping. An empirical perspective on philosophical issues.

In the philosophical literature, self-deception is mainly approached through the analysis of paradoxes. Yet, it is agreed that self-deception is motivated by protection from distress. In this paper, we argue, with the help of findings from cognitive neuroscience and psychology, that self-deception is a type of affective coping. First, we criticize the main solutions to the paradoxes of self-deception. We then present a new approach to self-deception. Self-deception, we argue, involves three appraisals of the distressing evidence: (a) appraisal of the strength of evidence as uncertain, (b) low coping potential and (c) negative anticipation along the lines of Damasio's somatic marker hypothesis. At the same time, desire impacts the treatment of flattering evidence via dopamine. Our main proposal is that self-deception involves emotional mechanisms provoking a preference for immediate reward despite possible long-term negative repercussions. In the last part, we use this emotional model to revisit the philosophical paradoxes.

Change points detection in crime-related time series: an on-line fuzzy approach based on a shape space representation

The extension of traditional data mining methods to time series has been effectively applied to a wide range of domains such as finance, econometrics, biology, security, and medicine. Many existing mining methods deal with the task of change points detection, but very few provide a flexible approach. Querying specific change points with linguistic variables is particularly useful in crime analysis, where intuitive, understandable, and appropriate detection of changes can significantly improve the allocation of resources for timely and concise operations. In this paper, we propose an on-line method for detecting and querying change points in crime-related time series with the use of a meaningful representation and a fuzzy inference system. Change points detection is based on a shape space representation, and linguistic terms describing geometric properties of the change points are used to express queries, offering the advantage of intuitiveness and flexibility. An empirical evaluation is first conducted on a crime data set to confirm the validity of the proposed method and then on a financial data set to test its general applicability. A comparison to a similar change-point detection algorithm and a sensitivity analysis are also conducted. Results show that the method is able to accurately detect change points at very low computational costs. More broadly, the detection of specific change points within time series of virtually any domain is made more intuitive and more understandable, even for experts not related to data mining.