DenseReg: fully convolutional dense shape regression in-the-wild

In this paper we propose to learn a mapping from image pixels into a dense template grid through a fully convolutional network. We formulate this task as a regression problem and train our network by leveraging upon manually annotated facial landmarks “in-the-wild”. We use such landmarks to establish a dense correspondence field between a three-dimensional object template and the input image, which then serves as the ground-truth for training our regression system. We show that we can combine ideas from semantic segmentation with regression networks, yielding a highly-accurate ‘quantized regression’ architecture. Our system, called DenseReg, allows us to estimate dense image-to-template correspondences in a fully convolutional manner. As such our network can provide useful correspondence information as a stand-alone system, while when used as an initialization for Statistical Deformable Models we obtain landmark localization results that largely outperform the current state-of-the-art on the challenging 300W benchmark. We thoroughly evaluate our method on a host of facial analysis tasks, and demonstrate its use for other correspondence estimation tasks, such as the human body and the human ear. DenseReg code is made available at http://alpguler.com/DenseReg.html along with supplementary materials

Impact of a stress management program on weight loss, mental health and lifestyle in adults with obesity: a randomized controlled trial

Aim: To evaluate the impact of a stress management program on weight loss, depression, anxiety and stress as well as on the adoption of healthy lifestyle in adults with obesity. Methods: Adults with obesity who sought help for weight loss at a medical obesity clinic were consecutively enrolled in the study and were randomly assigned to the intervention or control group. All participants received standard instructions for a healthy lifestyle. The intervention group attended an 8-week stress management program that comprised diaphragmatic breathing, progressive muscle relaxation, guided visualization and instructions about healthy nutrition/dietary habits. Anthropometric parameters were assessed and several questionnaires were completed by all participants, at the beginning and at the end of the study. Results: A total of 45 adults (mean age±SD 45.7±10.55 years) with obesity were enrolled in the study; 22 in the intervention group and 23 in the control group. Participants in the two groups were matched for age and BMI. Participants in the intervention group achieved a significantly larger reduction in BMI compared to the control group (ΔBMI -3.1 vs. -1.74 kg/m2 respectively, P<0.001). In addition, they displayed ameliorated depression and anxiety scores and a reduction in the health locus of control based on chance

Electrochemical oxidation of butyl paraben on boron doped diamond in environmental matrices and comparison with sulfate radical-AOP

The electrochemical oxidation (EO) of butyl paraben (BP) over boron-doped diamond (BDD) anode was studied in this work. Emphasis was put on degradation performance in various actual water matrices, including secondary treated wastewater (WW), bottled water (BW), surface water (SW), ultrapure water (UW), and ultrapure water spiked with humic acid (HA). Experiments were performed utilizing 0.1 M Na2SO4 as the electrolyte. Interestingly, matrix complexity was found to favor BP degradation, i.e. in the order WW ~ BW > SW > UW, thus implying some kind of synergy between the water matrix constituents, the reactive oxygen species (ROS) and the anode surface. The occurrence of chloride in water matrices favors reaction presumably due to the formation of chlorine-based oxidative species, and this can partially offset the need to work at increased current densities in the case of chlorine-free electrolytes. No pH effect in the range 3–8 on degradation was recorded. EO oxidation was also compared with a sulfate radical process using carbon black as activator of sodium persulfate. The matrix effect was, in this case, detrimental (i.e. UW > BW > WW), pinpointing the different behavior of different processes in similar environments

Face Normals "in-the- wild" using Fully Convolutional Networks

In this work we pursue a data-driven approach to the problem of estimating surface normals from a single intensity image, focusing in particular on human faces. We introduce new methods to exploit the currently available facial databases for dataset construction and tailor a deep convolutional neural network to the task of estimating facial surface normals in-the-wild. We train a fully convolutional network that can accurately recover facial normals from images including a challenging variety of expressions and facial poses. We compare against state-of-the-art face Shape-from-Shading and 3D reconstruction techniques and show that the proposed network can recover substantially more accurate and realistic normals. Furthermore, in contrast to other existing face-specific surface recovery methods, we do not require the solving of an explicit alignment step due to the fully convolutional nature of our network

Impact of unhealthy lifestyle on cardiorespiratory fitness and heart rate recovery of medical science students

Background: Medical science students represent valuable labour resources for better future medicine and medical technology. However, little attention was given to the health and well-being of these early career medical science professionals. The aim of this study is to investigate the impact of lifestyle components on cardiorespiratory fitness and heart rate recovery measured after moderate exercise in this population. Methods: Volunteers without documented medical condition were recruited randomly and continuously from the first-year medical science students during 2011-2014 at the University of Surrey, UK. Demographics and lifestyle components (the levels of smoking, alcohol intake, exercise, weekend outdoor activity and screen-time, daily sleep period, and self-assessment of fitness) were gathered through pre-exercise questionnaire. Cardiorespiratory fitness (VO2max) and heart rate recovery were determined using Åstrand–Rhyming submaximal cycle ergometry test. Data were analysed using SPSS version 25. Results: Among 614 volunteers, 124 had completed both lifestyle questionnaire and the fitness test and were included for this study. Within 124 participants (20.6±4 years), 46.8% were male and 53.2% were female, 11.3% were overweight and 8.9% were underweight, 8.9% were current smokers and 33.1% consumed alcohol beyond the UK recommendation. There were 34.7% of participants admitted to have <3 h/week of moderate physical activity assessed according to UK Government National Physical Activity Guidelines and physically not fit (feeling tiredness). Fitness test showed that VO2max distribution was inversely associated with heart rate recovery at 3 min and both values were significantly correlated with the levels of exercise, self-assessed fitness and BMI. Participants who had <3h/week exercise, or felt not fit or were overweight had significantly lower VO2max and heart rate recovery than their peers. Conclusion: One in three new medical science students were physically inactive along with compromised cardiorespiratory fitness and heart rate recovery, which put them at risk of cardiometabolic diseases. Promoting healthy lifestyle at the beginning of career is crucial in keeping medical science professionals healthy

A Graph Theoretic Approach for Object Shape Representation in Compositional Hierarchies Using a Hybrid Generative-Descriptive Model

A graph theoretic approach is proposed for object shape representation in a hierarchical compositional architecture called Compositional Hierarchy of Parts (CHOP). In the proposed approach, vocabulary learning is performed using a hybrid generative-descriptive model. First, statistical relationships between parts are learned using a Minimum Conditional Entropy Clustering algorithm. Then, selection of descriptive parts is defined as a frequent subgraph discovery problem, and solved using a Minimum Description Length (MDL) principle. Finally, part compositions are constructed by compressing the internal data representation with discovered substructures. Shape representation and computational complexity properties of the proposed approach and algorithms are examined using six benchmark two-dimensional shape image datasets. Experiments show that CHOP can employ part shareability and indexing mechanisms for fast inference of part compositions using learned shape vocabularies. Additionally, CHOP provides better shape retrieval performance than the state-of-the-art shape retrieval methods.Comment: Paper : 17 pages. 13th European Conference on Computer Vision (ECCV 2014), Zurich, Switzerland, September 6-12, 2014, Proceedings, Part III, pp 566-581. Supplementary material can be downloaded from http://link.springer.com/content/esm/chp:10.1007/978-3-319-10578-9_37/file/MediaObjects/978-3-319-10578-9_37_MOESM1_ESM.pd