Learning on the Edge: Investigating Boundary Filters in CNNs

Convolutional neural networks (CNNs) handle the case where filters extend beyond the image boundary using several heuristics, such as zero, repeat or mean padding. These schemes are applied in an ad-hoc fashion and, being weakly related to the image content and oblivious of the target task, result in low output quality at the boundary. In this paper, we propose a simple and effective improvement that learns the boundary handling itself. At training-time, the network is provided with a separate set of explicit boundary filters. At testing-time, we use these filters which have learned to extrapolate features at the boundary in an optimal way for the specific task. Our extensive evaluation, over a wide range of architectural changes (variations of layers, feature channels, or both), shows how the explicit filters result in improved boundary handling. Furthermore, we investigate the efficacy of variations of such boundary filters with respect to convergence speed and accuracy. Finally, we demonstrate an improvement of 5–20% across the board of typical CNN applications (colorization, de-Bayering, optical flow, disparity estimation, and super-resolution). Supplementary material and code can be downloaded from the project page: http://geometry.cs.ucl.ac.uk/projects/2019/investigating-edge/

Plausible Shading Decomposition For Layered Photo Retouching

Photographers routinely compose multiple manipulated photos of the same scene (layers) into a single image, which is better than any individual photo could be alone. Similarly, 3D artists set up rendering systems to produce layered images to contain only individual aspects of the light transport, which are composed into the final result in post-production. Regrettably, both approaches either take considerable time to capture, or remain limited to synthetic scenes. In this paper, we suggest a system to allow decomposing a single image into a plausible shading decomposition (PSD) that approximates effects such as shadow, diffuse illumination, albedo, and specular shading. This decomposition can then be manipulated in any off-the-shelf image manipulation software and recomposited back. We perform such a decomposition by learning a convolutional neural network trained using synthetic data. We demonstrate the effectiveness of our decomposition on synthetic (i.e., rendered) and real data (i.e., photographs), and use them for common photo manipulation, which are nearly impossible to perform otherwise from single images

Evaluating the Factor Structure of the Emotion Dysregulation Scale-Short (EDS-s): A Preliminary Study

Emotion dysregulation (ED) can be considered a psychopathological transdiagnostic dimension, the presence of which should be reliably screened in clinical settings. The aim of the current study was to validate the Italian version of the Emotion Dysregulation Scale-short (EDS-s), a brief self-report tool assessing emotion dysregulation, in a non-clinical sample of 1087 adults (768 women and 319 men). We also assessed its convergent validity with scales measuring binge eating and general psychopathology. Structural equation modeling suggested the fit of a one-factor model refined with correlations between the errors of three pairs of items (χ2 = 255.56, df = 51, p < 0.001, RMSEA = 0.08, CFI = 0.94, TLI = 0.93, SRMR = 0.04). The EDS-s demonstrated satisfactory internal consistency (ordinal alpha = 0.94). Moreover, EDS-s scores partly explained the variance of both binge eating (0.35, p < 0.001) and general psychopathology (0.60, p < 0.001). In conclusion, the EDS-s can be considered to be a reliable and valid measure of ED

Gender Felt Pressure, Affective Domains, and Mental Health Outcomes among Transgender and Gender Diverse (TGD) Children and Adolescents: A Systematic Review with Developmental and Clinical Implications

Although capable of mobilizing significant resilience factors to face stigma and discrimination, transgender and gender diverse (TGD) children and adolescents tend to suffer from more adverse mental health outcomes compared to their cisgender counterparts. The minority stressors that this population faces are mainly due to the gender-based pressure to conform to their assigned gender. This systematic review was aimed at assessing the potential mental health issues that affect the TGD population. The literature search was conducted in three databases; namely, Scopus, PubMed, and Web of Science, based on the PRISMA guidelines. The 33 articles included in the systematic review pointed out how TGD children and adolescents experience high levels of anxiety and depression, as well as other emotional and behavioral problems, such as eating disorders and substance use. Resilience factors have been also pointed out, which aid this population in facing these negative mental health outcomes. The literature review highlighted that, on the one hand, TGD individuals appear to exhibit high levels of resilience; nonetheless, health disparities exist for TGD individuals compared with the general population, which are mainly attributable to the societal gender pressure to conform to their assigned gender. Considerations for research and clinical practice are provided

Quality of Life and Personality Traits in Patients with Malignant Pleural Mesothelioma and Their First-Degree Caregivers.

Asbestos exposure causes significant pleural diseases, including malignant pleural mesothelioma (MPM). Taking into account the impact of MPM on emotional functioning and wellbeing, this study aimed to evaluate the quality of life and personality traits in patients with MPM and their first-degree caregivers through the World Health Organization Quality of Life–BREF (WHOQOL-BREF) and the Minnesota Multiphasic Personality Inventory-2 Restructured Form (MMPI-2-RF). The sample was composed of 27 MPM patients, 55 first-degree relatives enrolled in Casale Monferrato and Monfalcone (Italy), and 40 healthy controls (HC). Patients and relatives reported poorer physical health than the HC. Patients had a higher overall sense of physical debilitation and poorer health than relatives and the HC, more numerous complaints of memory problems and difficulties in concentrating, and a greater belief that goals cannot be reached or problems solved, while often claiming that they were more indecisive and inefficacious than the HC. First-degree relatives reported lower opinions of others, a greater belief that goals cannot be reached or problems solved, support for the notion that they are indecisive and inefficacious, and were more likely to suffer from fear that significantly inhibited normal activities than were HC. In multinomial regression analyses, partial models indicated that sex, physical comorbidities, and the True Response Inconsistency (TRIN-r), Malaise (MLS), and Behavior-Restricting Fears (BRF) dimensions of the MMPI-2-RF had significant effects on group differences. In conclusion, health care providers should assess the ongoing adjustment and emotional wellbeing of people with MPM and their relatives, and provide support to reduce emotional distress

Learning on the Edge: Explicit Boundary Handling in CNNs

Convolutional neural networks (CNNs) handle the case where filters extend beyond the image boundary using several heuristics, such as zero, repeat or mean padding. These schemes are applied in an ad-hoc fashion and, being weakly related to the image content and oblivious of the target task, result in low output quality at the boundary. In this paper, we propose a simple and effective improvement that learns the boundary handling itself. At training-time, the network is provided with a separate set of explicit boundary filters. At testing-time, we use these filters which have learned to extrapolate features at the boundary in an optimal way for the specific task. Our extensive evaluation, over a wide range of architectural changes (variations of layers, feature channels, or both), shows how the explicit filters result in improved boundary handling. Consequently, we demonstrate an improvement of 5 % to 20 % across the board of typical CNN applications (colorization, de-Bayering, optical flow, and disparity estimation

Decomposing Single Images for Layered Photo Retouching

Photographers routinely compose multiple manipulated photos of the same scene into a single image, producing a fidelity difficult to achieve using any individual photo. Alternately, 3D artists set up rendering systems to produce layered images to isolate individual aspects of the light transport, which are composed into the final result in post-production. Regrettably, these approaches either take considerable time and effort to capture, or remain limited to synthetic scenes. In this paper, we suggest a method to decompose a single image into multiple layers that approximates effects such as shadow, diffuse illumination, albedo, and specular shading. To this end, we extend the idea of intrinsic images along two axes: first, by complementing shading and reflectance with specularity and occlusion, and second, by introducing directional dependence. We do so by training a convolutional neural network (CNN) with synthetic data. Such decompositions can then be manipulated in any off-the-shelf image manipulation software and composited back. We demonstrate the effectiveness of our decomposition on synthetic (i. e., rendered) and real data (i. e., photographs), and use them for photo manipulations, which are otherwise impossible to perform based on single images. We provide comparisons with state-of-the-art methods and also evaluate the quality of our decompositions via a user study measuring the effectiveness of the resultant photo retouching setup. Supplementary material and code are available for research use at geometry.cs.ucl.ac.uk/projects/2017/layered-retouching

Neurocognitive and Psychopathological Predictors of Weight Loss After Bariatric Surgery: A 4-Year Follow-Up Study

Twenty to thirty percent of patients experience weight regain at mid and long-term follow- up. Impaired cognitive functions are prevalent in people suffering from obesity and in those with binge eating disorder, thereby, affecting the weight-loss outcomes. The aim of our study was to investigate neurocognitive and psychopathological predictors of surgical efficacy in terms of percentage of excess weight loss (%EWL) at follow-up intervals of one year and 4-year. Psychosocial evaluation was completed in a sample of 78 bariatric surgery candidates and included psychometric instruments and a cognitive battery of neuropsychological tests. A schedule of 1-year and 4-year follow-ups was implemented. Wisconsin Sorting Card Test total correct responses, scores on the Raven’s Progressive Matrices Test, and age predicted %EWL at, both, early and long-term periods after surgery while the severity of pre-operative binge eating (BED) symptoms were associated with lower %EWL only four years after the operation. Due to the role of pre-operative BED in weight loss maintenance, the affected patients are at risk of suboptimal response requiring ongoing clinical monitoring, and psychological and pharmacological interventions when needed. As a result of our findings and in keeping with the latest guidelines we encourage neuropsychological assessment of bariatric surgery candidates. This data substantiated the rationale of providing rehabilitative interventions tailored to cognitive domains and time specific to the goal of supporting patients in their post-surgical course

High depression symptomatology and mental pain characterize suicidal psychiatric patients

Background: Symptoms of depression are transdiagnostic heterogenous features frequently assessed in psychiatric disorders, that impact the response to first-line treatment and are associated with higher suicide risk. This study assessed whether severe mental pain could characterize a specific phenotype of severely depressed high-risk psychiatric patients. We also aimed to analyze differences in treatments administered. Methods: 2,297 adult patients (1,404 females and 893 males; mean age = 43.25 years, SD = 15.15) treated in several Italian psychiatric departments. Patients were assessed for psychiatric diagnoses, mental pain, symptoms of depression, hopelessness, and suicide risk. Results: More than 23% of the patients reported high depression symptomatology and high mental pain (HI DEP/HI PAIN). Compared to patients with lower symptoms of depression, HI DEP/HI PAIN is more frequent among females admitted to an inpatient department and is associated with higher hopelessness and suicide risk. In addition, HI DEP/HI PAIN (compared to both patients with lower symptoms of depression and patients with higher symptoms of depression but lower mental pain) were more frequently diagnosed in patients with personality disorders and had different treatments. Conclusions: Patients reporting severe symptoms of depression and high mental pain presented a mixture of particular dangerousness (high trait hopelessness and the presence of suicide ideation with more frequency and less controllability and previous suicide behaviors). The presence of severe mental pain may act synergically in expressing a clinical phenotype that is likewise treated with a more complex therapeutic regime than that administered to those experiencing symptoms of depression without mental pain