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

Evaluating system architectures for driving range estimation and charge planning for electric vehicles

Due to sparse charging infrastructure and short driving ranges, drivers of battery electric vehicles (BEVs) can experience range anxiety, which is the fear of stranding with an empty battery. To help eliminate range anxiety and make BEVs more attractive for customers, accurate range estimation methods need to be developed. In recent years, many publications have suggested machine learning algorithms as a fitting method to achieve accurate range estimations. However, these algorithms use a large amount of data and have high computational requirements. A traditional placement of the software within a vehicle\u27s electronic control unit could lead to high latencies and thus detrimental to user experience. But since modern vehicles are connected to a backend, where software modules can be implemented, high latencies can be prevented with intelligent distribution of the algorithm parts. On the other hand, communication between vehicle and backend can be slow or expensive. In this article, an intelligent deployment of a range estimation software based on ML is analyzed. We model hardware and software to enable performance evaluation in early stages of the development process. Based on simulations, different system architectures and module placements are then analyzed in terms of latency, network usage, energy usage, and cost. We show that a distributed system with cloud‐based module placement reduces the end‐to‐end latency significantly, when compared with a traditional vehicle‐based placement. Furthermore, we show that network usage is significantly reduced. This intelligent system enables the application of complex, but accurate range estimation with low latencies, resulting in an improved user experience, which enhances the practicality and acceptance of BEVs

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

Effectiveness and longevity of fuel treatments in coniferous forests across California

Longevity of fuel treatment effectiveness to alter potential fire behavior is a critical question for managers preparing plans for fuel hazard reduction, prescribed burning, fire management, forest thinning, and other land management activities. Results from this study will help to reduce uncertainty associated with plan prioritization and maintenance activities. From 2001 to 2006, permanent plots were established in areas planned for hazardous fuel reduction treatments across 14 National Forests in California. Treatments included prescribed fire and mechanical methods (i.e., thinning of various sizes and intensities followed by a surface fuel treatment). After treatment, plots were re-measured at various intervals up to 10 years post-treatment. Very few empirically based studies exist with data beyond the first couple of years past treatment, and none span the breadth of California’s coniferous forests. With the data gathered, this research aimed to meet three main objectives: Objective 1) Determine the length of time that fuel treatments are effective at maintaining goals of reduced fire behavior, by a) measuring effects of treatments on canopy characteristics and surface fuel loads over time, and b) modeling potential fire behavior with custom fuel models. Objective 2) Quantify the uncertainty associated with the use of standard and custom fuel models. Objective 3) Assess prescribed fire effects on carbon stocks and validate modeled outputs. Results have shown initial reductions in surface fuels from prescribed fire treatments recover to pre-treatment levels by 10 yr post-treatment. Mechanical treatments continue to have variable effects on surface fuels. With the exception of mechanical treatments in red fir, both treatment types resulted in increased live understory vegetation by 8 yr post-treatment relative to pre-treatment. Mechanical treatment effects on stand structure remains fairly consistent through 8 yr post-treatment. Fire-induced delayed mortality contributes to slight decreases in canopy cover and canopy bulk density over time. For both treatment types, overall canopy base height decreases in later years due to in-growth of smaller trees, but it remains higher than pre-treatment. The changes in fuel loads and stand structure are reflected in fire behavior simulations via custom fuel modeling. Surface fire flame lengths were initially reduced as a result of prescribed fire, but by 10 yr post-treatment they exceeded the pre-treatment lengths. Though a low proportion of type of fire, initial reductions in potential crown fire returned to pre-treatment levels by 8 yr post-treatment; passive crown fire remained reduced relative to pre-treatment for the duration. Mechanical treatments showed variable and minimal effects on surface fire flame length over time; however the incidence of active crown fire was nearly halved from this treatment for the duration. The Fire and Fuels Extension to the Forest Vegetation Simulator (FFE-FVS) was used to model potential fire behavior for plots treated with prescribed fire to determine the differences in modeled fire behavior using standard and custom fuel models. In general predicted fire behavior from custom versus standard fuel models were similar with mean surface fire flame lengths slightly higher using standard fuel models for all time steps until the 8 yr post treatment. Similarly, custom fuel models predicted a higher instance of surface fire than standard fuel models with the exception of 8 yr post-treatment. To better understand the impact of prescribed fire on carbon stocks, we estimated aboveground and belowground (roots) carbon stocks using field measurements in FFE-FVS, and simulated wildfire emissions, before treatment and up to 8 yr post-prescribed fire. Prescribed fire treatments reduced total carbon by 13%, with the largest reduction in the forest floor (litter and duff) pool and the smallest the live tree pool. Combined carbon recovery and reduced wildfire emissions allowed the initial carbon source from wildfire and treatment to become a sink by 8 yr post-treatment relative to pre-treatment if both were to burn in a wildfire. In a comparison of field-derived versus FFE-FVS simulated carbon stocks, we found the total, tree, and belowground live carbon pools to be highly correlated. However, the variability within the other carbon pools compared was high (up to 212%)

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