Recovering 6D Object Pose and Predicting Next-Best-View in the Crowd

Object detection and 6D pose estimation in the crowd (scenes with multiple object instances, severe foreground occlusions and background distractors), has become an important problem in many rapidly evolving technological areas such as robotics and augmented reality. Single shot-based 6D pose estimators with manually designed features are still unable to tackle the above challenges, motivating the research towards unsupervised feature learning and next-best-view estimation. In this work, we present a complete framework for both single shot-based 6D object pose estimation and next-best-view prediction based on Hough Forests, the state of the art object pose estimator that performs classification and regression jointly. Rather than using manually designed features we a) propose an unsupervised feature learnt from depth-invariant patches using a Sparse Autoencoder and b) offer an extensive evaluation of various state of the art features. Furthermore, taking advantage of the clustering performed in the leaf nodes of Hough Forests, we learn to estimate the reduction of uncertainty in other views, formulating the problem of selecting the next-best-view. To further improve pose estimation, we propose an improved joint registration and hypotheses verification module as a final refinement step to reject false detections. We provide two additional challenging datasets inspired from realistic scenarios to extensively evaluate the state of the art and our framework. One is related to domestic environments and the other depicts a bin-picking scenario mostly found in industrial settings. We show that our framework significantly outperforms state of the art both on public and on our datasets.Comment: CVPR 2016 accepted paper, project page: http://www.iis.ee.ic.ac.uk/rkouskou/6D_NBV.htm

Restless legs syndrome is associated with major comorbidities in a population of Danish blood donors.

BACKGROUND: Restless Legs Syndrome (RLS) is characterized by uncomfortable nocturnal sensations in the legs making sedentary activities and sleep difficult, and is thus linked with psychosocial distress. Due to the symptomatology and neurobiology of RLS (disrupting brain iron and dopamine) it is likely that RLS associates with poorer health-related quality of life (HRQL) and depressive disorder. The objective of this study was to investigate the RLS-HRQL and the RLS-depressive disorder links in a generally healthy population that is not biased by medications. METHODS: Complete data, including the Cambridge-Hopkins RLS questionnaire, the 12-item short-form standardized health survey (SF-12), the Major Depression Inventory (MDI), body mass index, smoking status, alcohol consumption, and education were available for 24,707 participants enrolled in the Danish Blood Donor Study from May 1, 2015 to February 1, 2017. Information on quality of sleep was available for all RLS cases. T-tests and multivariable logistic regression models were applied to examine the associations of RLS and MDI scores, and the physical and mental component scores (PCS and MCS) of SF-12, respectively. Analyses were conducted separately for men and women. RESULTS: RLS associated with poorer MCS and poorer PCS. Moreover, Participants with RLS were more likely to classify with depressive disorder. Poor quality of sleep was associated with depressive disorder and poorer MCS among RLS cases, and with poorer PCS in female RLS cases. CONCLUSION: Thus, we demonstrated that RLS is associated with a significantly lower HRQL and a higher prevalence of depressive disorder among otherwise healthy individuals

A genome-wide meta-analysis yields 46 new loci associating with biomarkers of iron homeostasis

Bell et al. report 46 new loci associated with biomarkers of iron homeostasis, including ferritin levels, iron binding capacity, and iron saturation, in the Icelandic, Danish and UK populations. The associated loci point to new iron-regulating proteins and important genetic differences between men and women

A genome-wide meta-analysis yields 46 new loci associating with biomarkers of iron homeostasis

Abstract: Iron is essential for many biological functions and iron deficiency and overload have major health implications. We performed a meta-analysis of three genome-wide association studies from Iceland, the UK and Denmark of blood levels of ferritin (N = 246,139), total iron binding capacity (N = 135,430), iron (N = 163,511) and transferrin saturation (N = 131,471). We found 62 independent sequence variants associating with iron homeostasis parameters at 56 loci, including 46 novel loci. Variants at DUOX2, F5, SLC11A2 and TMPRSS6 associate with iron deficiency anemia, while variants at TF, HFE, TFR2 and TMPRSS6 associate with iron overload. A HBS1L-MYB intergenic region variant associates both with increased risk of iron overload and reduced risk of iron deficiency anemia. The DUOX2 missense variant is present in 14% of the population, associates with all iron homeostasis biomarkers, and increases the risk of iron deficiency anemia by 29%. The associations implicate proteins contributing to the main physiological processes involved in iron homeostasis: iron sensing and storage, inflammation, absorption of iron from the gut, iron recycling, erythropoiesis and bleeding/menstruation

A web framework for information aggregation and management of multilingual hate speech

Social media platforms have led to the creation of a vast amount of information produced by users and published publicly, facilitating participation in the public sphere, but also giving the opportunity for certain users to publish hateful content. This content mainly involves offensive/discriminative speech towards social groups or individuals (based on racial, religious, gender or other characteristics) and could possibly lead into subsequent hate actions/crimes due to persistent escalation. Content management and moderation in big data volumes can no longer be supported manually. In the current research, a web framework is presented and evaluated for the collection, analysis, and aggregation of multilingual textual content from various online sources. The framework is designed to address the needs of human users, journalists, academics, and the public to collect and analyze content from social media and the web in Spanish, Italian, Greek, and English, without prior training or a background in Computer Science. The backend functionality provides content collection and monitoring, semantic analysis including hate speech detection and sentiment analysis using machine learning models and rule-based algorithms, storing, querying, and retrieving such content along with the relevant metadata in a database. This functionality is assessed through a graphic user interface that is accessed using a web browser. An evaluation procedure was held through online questionnaires, including journalists and students, proving the feasibility of the use of the proposed framework by non-experts for the defined use-case scenarios