Facing the music or burying our heads in the sand?: Adaptive emotion regulation in mid- and late-life

Psychological defense theories postulate that keeping threatening information out of awareness brings short-term reduction of anxiety at the cost of longer-term dysfunction. By contrast, Socioemotional Selectivity Theory suggests that preference for positively-valenced information is a manifestation of adaptive emotion regulation in later life. Using six decades of longitudinal data on 61 men, we examined links between emotion regulation indices informed by these distinct conceptualizations: defense patterns in earlier adulthood and selective memory for positively-valenced images in late life. Men who used more avoidant defenses in midlife recognized fewer emotionally-valenced and neutral images in a memory test 35-40 years later. Late-life satisfaction was positively linked with mid-life engaging defenses but negatively linked at the trend level with concurrent positivity bias

Grid Loss: Detecting Occluded Faces

Detection of partially occluded objects is a challenging computer vision problem. Standard Convolutional Neural Network (CNN) detectors fail if parts of the detection window are occluded, since not every sub-part of the window is discriminative on its own. To address this issue, we propose a novel loss layer for CNNs, named grid loss, which minimizes the error rate on sub-blocks of a convolution layer independently rather than over the whole feature map. This results in parts being more discriminative on their own, enabling the detector to recover if the detection window is partially occluded. By mapping our loss layer back to a regular fully connected layer, no additional computational cost is incurred at runtime compared to standard CNNs. We demonstrate our method for face detection on several public face detection benchmarks and show that our method outperforms regular CNNs, is suitable for realtime applications and achieves state-of-the-art performance.Comment: accepted to ECCV 201

Social support as protective factor of the effects of part- time work on psychological health: a moderated mediation model

Purpose:The purpose of this study is to examine the protective effect of social support on psychological health and how it differs by gender in the context of part-time employment. Design/methodology/approach: The sample consisted of 22,786 employees from four service sector organisations. Structural equation modelling was used to test a moderated mediation model assessing the relationship between employment status (part-time vs full-time) and psychological health mediated by social support (from management and colleagues) and moderated by gender. Findings: Social support from management and colleagues was associated with fewer symptoms of stress, anxiety and depression. Notably, management support had a stronger association than that of colleagues’ support on each of the three health-related variables. Social support was also found to be a mediator of part-time working on health such that lower social support led to increased health symptoms. Moreover, we found moderating gender effects between social support and psychological health such that colleague support had a stronger effect on reduced depression and stress among men than women whilst management support had a stronger effect on reduced anxiety for women. Finally, significant moderated mediating paths were found, but further research is needed to identify other potential moderators of the mediating effects. Originality/value: The findings suggest complex relationships between part-time employment, social support, psychological health and gender not examined in previous studies. It highlights the value of diverse sources of support and the necessity of addressing specific gender’s needs for enhancing psychological health of part-time employees

A theoretical and numerical study of a phase field higher-order active contour model of directed networks.

We address the problem of quasi-automatic extraction of directed networks, which have characteristic geometric features, from images. To include the necessary prior knowledge about these geometric features, we use a phase field higher-order active contour model of directed networks. The model has a large number of unphysical parameters (weights of energy terms), and can favour different geometric structures for different parameter values. To overcome this problem, we perform a stability analysis of a long, straight bar in order to find parameter ranges that favour networks. The resulting constraints necessary to produce stable networks eliminate some parameters, replace others by physical parameters such as network branch width, and place lower and upper bounds on the values of the rest. We validate the theoretical analysis via numerical experiments, and then apply the model to the problem of hydrographic network extraction from multi-spectral VHR satellite images

Multi-view Face Detection Using Deep Convolutional Neural Networks

In this paper we consider the problem of multi-view face detection. While there has been significant research on this problem, current state-of-the-art approaches for this task require annotation of facial landmarks, e.g. TSM [25], or annotation of face poses [28, 22]. They also require training dozens of models to fully capture faces in all orientations, e.g. 22 models in HeadHunter method [22]. In this paper we propose Deep Dense Face Detector (DDFD), a method that does not require pose/landmark annotation and is able to detect faces in a wide range of orientations using a single model based on deep convolutional neural networks. The proposed method has minimal complexity; unlike other recent deep learning object detection methods [9], it does not require additional components such as segmentation, bounding-box regression, or SVM classifiers. Furthermore, we analyzed scores of the proposed face detector for faces in different orientations and found that 1) the proposed method is able to detect faces from different angles and can handle occlusion to some extent, 2) there seems to be a correlation between dis- tribution of positive examples in the training set and scores of the proposed face detector. The latter suggests that the proposed methods performance can be further improved by using better sampling strategies and more sophisticated data augmentation techniques. Evaluations on popular face detection benchmark datasets show that our single-model face detector algorithm has similar or better performance compared to the previous methods, which are more complex and require annotations of either different poses or facial landmarks.Comment: in International Conference on Multimedia Retrieval 2015 (ICMR

New Treatment Approach in Indian Visceral Leishmaniasis: Single-Dose Liposomal Amphotericin B Followed by Short-Course Oral Miltefosine

Background. In Bihar, India, home to nearly one-half of the world's burden of visceral leishmaniasis, drug resistance has ended the usefulness of pentavalent antimony, which is the traditional first-line treatment. Although monotherapy with other agents is available, the use of 2 drugs with different modes of action might increase efficacy, shorten treatment duration, enhance compliance, and/or reduce the risk of parasite resistance. To test the feasibility of a new approach to combination therapy in visceral leishmaniasis (also known a kala-azar), we treated Indian patients with a single infusion of liposomal amphotericin B (L-AmB), followed 1 day later by short-course oral miltefosine. Methods. We used a randomized, noncomparative, group-sequential, triangular design and assigned 181 subjects to treatment with 5 mg/kg of L-AmB alone (group A; 45 subjects), 5 mg/kg of L-AmB followed by miltefosine for 10 days (group B; 46 subjects) or 14 days (group C; 45 subjects), or 3.75 mg/kg of L-AmB followed by miltefosine for 14 days (group D; 45 subjects). When it became apparent that all regimens were effective, 45 additional, nonrandomized patients were assigned to receive 5 mg/kg of L-AmB followed by miltefosine for 7 days (group E). Results. Each regimen was satisfactorily tolerated, and all 226 subjects showed initial apparent cure responses. Nine months after treatment, final cure rates were similar: group A, 91% (95% confidence interval [CI], 78%-97%]; group B, 98% (95% CI, 87%-100%); group C, 96% (95% CI, 84%-99%]; group D, 96% (95% CI, 84%-99%); and group E, 98% (95% CI, 87%-100%). Conclusions. These results suggest that treatment with single-dose L-AmB followed by 7-14 days of miltefosine is active against Indian kala-azar. This short-course, sequential regimen warrants additional testing in India and in those regions of endemicity where visceral leishmaniasis may be more difficult to treat. Trial registration. ClinicalTrials.gov identifier: NCT0037082

Robust iso-surface tracking for interactive character skinning

International audienceWe present a novel approach to interactive character skinning, which is robust to extreme character movements, handles skin contacts and produces the effect of skin elasticity (sliding). Our approach builds on the idea of implicit skinning in which the character is approximated by a 3D scalar field and mesh-vertices are appropriately re-projected. Instead of being bound by an initial skinning solution used to initialize the shape at each time step, we use the skin mesh to directly track iso-surfaces of the field over time. Technical problems are two-fold: firstly, all contact surfaces generated between skin parts should be captured as iso-surfaces of the implicit field; secondly, the tracking method should capture elastic skin effects when the joints bend, and as the character returns to its rest shape, so the skin must follow. Our solutions include: new composition operators enabling blending effects and local self-contact between implicit surfaces, as well as a tangential relaxation scheme derived from the as-rigid-as possible energy to solve the tracking problem

Spatiotemporal Atlas Estimation for Developmental Delay Detection in Longitudinal Datasets

PMID: 20426000International audienceWe propose a new methodology to analyze the anatomical variability of a set of longitudinal data (population scanned at several ages). This method accounts not only for the usual 3D anatomical variability (geometry of structures), but also for possible changes in the dynamics of evolution of the structures. It does not require that subjects are scanned the same number of times or at the same ages. First a regression model infers a continuous evolution of shapes from a set of observations of the same subject. Second, spatiotemporal registrations deform jointly (1) the geometry of the evolving structure via 3D deformations and (2) the dynamics of evolution via time change functions. Third, we infer from a population a prototype scenario of evolution and its 4D variability. Our method is used to analyze the morphological evolution of 2D profiles of hominids skulls and to analyze brain growth from amygdala of autistics, developmental delay and control children

Reconstructing ‘the Alcoholic’: Recovering from Alcohol Addiction and the Stigma this Entails

Public perception of alcohol addiction is frequently negative, whilst an important part of recovery is the construction of a positive sense of self. In order to explore how this might be achieved, we investigated how those who self-identify as in recovery from alcohol problems view themselves and their difficulties with alcohol and how they make sense of others’ responses to their addiction. Semi-structured interviews with six individuals who had been in recovery between 5 and 35 years and in contact with Alcoholics Anonymous were analysed using Interpretative Phenomenological Analysis. The participants were acutely aware of stigmatising images of ‘alcoholics’ and described having struggled with a considerable dilemma in accepting this identity themselves. However, to some extent they were able to resist stigma by conceiving of an ‘aware alcoholic self’ which was divorced from their previously unaware self and formed the basis for a new more knowing and valued identity

Quantitative monitoring of an activated sludge reactor using on-line UV-visible and near infrared spectroscopy

The performance of an activated sludge reactor can be significantly enhanced through use of continuous and real-time process-state monitoring, which avoids the need to sample for off-line analysis and to use chemicals. Despite the complexity associated with wastewater treatment systems, spectroscopic methods coupled with chemometric tools have been shown to be powerful tools for bioprocess monitoring and control. Once implemented and optimized, these methods are fast, nondestructive, user friendly, and most importantly, they can be implemented in situ, permitting rapid inference of the process state at any moment. In this work, UV-visible and NIR spectroscopy were used to monitor an activated sludge reactor using in situ immersion probes connected to the respective analyzers by optical fibers. During the monitoring period, disturbances to the biological system were induced to test the ability of each spectroscopic method to detect the changes in the system. Calibration models based on partial least squares (PLS) regression were developed for three key process parameters, namely chemical oxygen demand (COD), nitrate concentration (N-NO3−), and total suspended solids (TSS). For NIR, the best results were achieved for TSS, with a relative error of 14.1% and a correlation coefficient of 0.91. The UV-visible technique gave similar results for the three parameters: an error of ~25% and correlation coefficients of ~0.82 for COD and TSS and 0.87 for N-NO3−. The results obtained demonstrate that both techniques are suitable for consideration as alternative methods for monitoring and controlling wastewater treatment processes, presenting clear advantages when compared with the reference methods for wastewater treatment process qualification.Fundação para a Ciência e Tecnologia (FCT) - PPCDT/AMB/60141/2004, bolsa de doutoramento SFRH/BD/32614/200