Interactive and Audience Adaptive Digital Signage Using Real-Time Computer Vision

In this paper we present the development of an interactive, content‐aware and cost‐effective digital signage system. Using a monocular camera installed within the frame of a digital signage display, we employ real‐time computer vision algorithms to extract temporal, spatial and demographic features of the observers, which are further used for observer‐specific broadcasting of digital signage content. The number of observers is obtained by the Viola and Jones face detection algorithm, whilst facial images are registered using multi‐view Active Appearance Models. The distance of the observers from the system is estimated from the interpupillary distance of registered faces. Demographic features, including gender and age group, are determined using SVM classifiers to achieve individual observer‐specific selection and adaption of the digital signage broadcasting content. The developed system was evaluated at the laboratory study level and in a field study performed for audience measurement research. Comparison of our monocular localization module with the Kinect stereo‐system reveals a comparable level of accuracy. The facial characterization module is evaluated on the FERET database with 95% accuracy for gender classification and 92% for age group. Finally, the field study demonstrates the applicability of the developed system in real‐life environments

Modelling In-Store Consumer Behaviour Using Machine Learning and Digital Signage Audience Measurement Data

Audience adaptive digital signage is a new emerging tech- nology, where public broadcasting displays adapt their content to the audience demographic and temporal features. The collected audience measurement data can be used as a unique basis for statistical analysis of viewing patterns, interactive display applications and also for further research and observer modelling. Here, we use machine learning methods on real-world digital signage viewership data to predict consumer behav- iour in a retail environment, especially oriented towards the purchase decision process and the roles in purchasing situations. A case study is performed on data from a small retail shop where demographic and audience data of 1294 store customers were collected, manually verified and analysed. Among all customers, 246 store customers were involved in a buying process that resulted in an actual purchase. Comparison of different machine learning methods shows that by using support vector machines we can predict with 88.6 % classification accuracy whether a customer will actually make a purchase, which outperforms classification accuracy of a baseline (majority) classifier by 7.5%. A similar approach can also be used to predict the roles of an individual in the purchase decision process. We show that by extending the audience measurement dataset with additional heuristic features, the support vector machines classifier on average improves the classification accuracy of a baseline classifier by 15 %

Dynamic Anamorphosis as a Special, Computer-Generated User Interface

A classical or static anamorphic image requires a specific, usually a highly oblique view direction, from which the observer can see the anamorphosis in its correct form. This paper explains dynamic anamorphosis which adapts itself to the changing position of the observer so that wherever the observer moves, he sees the same undeformed image. This dynamic changing of the anamorphic deformation in concert with the movement of the observer requires from the system to track the 3D position of the observer’s eyes and the re-computation of the anamorphic deformation in real time. This is achieved using computer vision methods which consist of face detection and tracking the 3D position of the selected observer. An application of this system of dynamic anamorphosis in the context of an interactive art installation is described. We show that anamorphic deformation is also useful for improving eye contact in videoconferencing. Other possible applications involve novel user interfaces where the user can freely move and observe perspectively undeformed images

Dynamic Anamorphosis as a Special, Computer-Generated User Interface

A classical or static anamorphic image requires a specific, usually a highly oblique view direction, from which the observer can see the anamorphosis in its correct form. This paper explains dynamic anamorphosis which adapts itself to the changing position of the observer so that wherever the observer moves, he sees the same undeformed image. This dynamic changing of the anamorphic deformation in concert with the movement of the observer requires from the system to track the 3D position of the observer’s eyes and the re-computation of the anamorphic deformation in real time. This is achieved using computer vision methods which consist of face detection and tracking the 3D position of the selected observer. An application of this system of dynamic anamorphosis in the context of an interactive art installation is described. We show that anamorphic deformation is also useful for improving eye contact in videoconferencing. Other possible applications involve novel user interfaces where the user can freely move and observe perspectively undeformed images

Common and rare variant association analyses in amyotrophic lateral sclerosis identify 15 risk loci with distinct genetic architectures and neuron-specific biology

Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disease with a lifetime risk of one in 350 people and an unmet need for disease-modifying therapies. We conducted a cross-ancestry genome-wide association study (GWAS) including 29,612 patients with ALS and 122,656 controls, which identified 15 risk loci. When combined with 8,953 individuals with whole-genome sequencing (6,538 patients, 2,415 controls) and a large cortex-derived expression quantitative trait locus (eQTL) dataset (MetaBrain), analyses revealed locus-specific genetic architectures in which we prioritized genes either through rare variants, short tandem repeats or regulatory effects. ALS-associated risk loci were shared with multiple traits within the neurodegenerative spectrum but with distinct enrichment patterns across brain regions and cell types. Of the environmental and lifestyle risk factors obtained from the literature, Mendelian randomization analyses indicated a causal role for high cholesterol levels. The combination of all ALS-associated signals reveals a role for perturbations in vesicle-mediated transport and autophagy and provides evidence for cell-autonomous disease initiation in glutamatergic neurons. A cross-ancestry genome-wide association meta-analysis of amyotrophic lateral sclerosis (ALS) including 29,612 patients with ALS and 122,656 controls identifies 15 risk loci with distinct genetic architectures and neuron-specific biology

Common and rare variant association analyses in amyotrophic lateral sclerosis identify 15 risk loci with distinct genetic architectures and neuron-specific biology

Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disease with a lifetime risk of one in 350 people and an unmet need for disease-modifying therapies. We conducted a cross-ancestry genome-wide association study (GWAS) including 29,612 patients with ALS and 122,656 controls, which identified 15 risk loci. When combined with 8,953 individuals with whole-genome sequencing (6,538 patients, 2,415 controls) and a large cortex-derived expression quantitative trait locus (eQTL) dataset (MetaBrain), analyses revealed locus-specific genetic architectures in which we prioritized genes either through rare variants, short tandem repeats or regulatory effects. ALS-associated risk loci were shared with multiple traits within the neurodegenerative spectrum but with distinct enrichment patterns across brain regions and cell types. Of the environmental and lifestyle risk factors obtained from the literature, Mendelian randomization analyses indicated a causal role for high cholesterol levels. The combination of all ALS-associated signals reveals a role for perturbations in vesicle-mediated transport and autophagy and provides evidence for cell-autonomous disease initiation in glutamatergic neurons. A cross-ancestry genome-wide association meta-analysis of amyotrophic lateral sclerosis (ALS) including 29,612 patients with ALS and 122,656 controls identifies 15 risk loci with distinct genetic architectures and neuron-specific biology

Common and rare variant association analyses in amyotrophic lateral sclerosis identify 15 risk loci with distinct genetic architectures and neuron-specific biology

A cross-ancestry genome-wide association meta-analysis of amyotrophic lateral sclerosis (ALS) including 29,612 patients with ALS and 122,656 controls identifies 15 risk loci with distinct genetic architectures and neuron-specific biology. Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disease with a lifetime risk of one in 350 people and an unmet need for disease-modifying therapies. We conducted a cross-ancestry genome-wide association study (GWAS) including 29,612 patients with ALS and 122,656 controls, which identified 15 risk loci. When combined with 8,953 individuals with whole-genome sequencing (6,538 patients, 2,415 controls) and a large cortex-derived expression quantitative trait locus (eQTL) dataset (MetaBrain), analyses revealed locus-specific genetic architectures in which we prioritized genes either through rare variants, short tandem repeats or regulatory effects. ALS-associated risk loci were shared with multiple traits within the neurodegenerative spectrum but with distinct enrichment patterns across brain regions and cell types. Of the environmental and lifestyle risk factors obtained from the literature, Mendelian randomization analyses indicated a causal role for high cholesterol levels. The combination of all ALS-associated signals reveals a role for perturbations in vesicle-mediated transport and autophagy and provides evidence for cell-autonomous disease initiation in glutamatergic neurons

Analysis of shared common genetic risk between amyotrophic lateral sclerosis and epilepsy

Because hyper-excitability has been shown to be a shared pathophysiological mechanism, we used the latest and largest genome-wide studies in amyotrophic lateral sclerosis (n = 36,052) and epilepsy (n = 38,349) to determine genetic overlap between these conditions. First, we showed no significant genetic correlation, also when binned on minor allele frequency. Second, we confirmed the absence of polygenic overlap using genomic risk score analysis. Finally, we did not identify pleiotropic variants in meta-analyses of the 2 diseases. Our findings indicate that amyotrophic lateral sclerosis and epilepsy do not share common genetic risk, showing that hyper-excitability in both disorders has distinct origins

Common and rare variant association analyses in amyotrophic lateral sclerosis identify 15 risk loci with distinct genetic architectures and neuron-specific biology

Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disease with a lifetime risk of one in 350 people and an unmet need for disease-modifying therapies. We conducted a cross-ancestry genome-wide association study (GWAS) including 29,612 patients with ALS and 122,656 controls, which identified 15 risk loci. When combined with 8,953 individuals with whole-genome sequencing (6,538 patients, 2,415 controls) and a large cortex-derived expression quantitative trait locus (eQTL) dataset (MetaBrain), analyses revealed locus-specific genetic architectures in which we prioritized genes either through rare variants, short tandem repeats or regulatory effects. ALS-associated risk loci were shared with multiple traits within the neurodegenerative spectrum but with distinct enrichment patterns across brain regions and cell types. Of the environmental and lifestyle risk factors obtained from the literature, Mendelian randomization analyses indicated a causal role for high cholesterol levels. The combination of all ALS-associated signals reveals a role for perturbations in vesicle-mediated transport and autophagy and provides evidence for cell-autonomous disease initiation in glutamatergic neurons.peer-reviewe

Georeferencing Works of Literature

A pilot web application for collaborative georeferencing of Slovenian works of literature was developed. Problems in georeferencing in general and georeferencing works of literature are discussed. The pilot system can be a model for establishing web sites for georeferencing literature for different languages. The application is based on Google Maps and implemented using PHP, JavaScript and MySQL. The intended users of the system are mainly lovers of literature, travelers and pupils