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

A polymer physics view on universal and sequence-specific aspects of chromosome folding

International audienc

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

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

Effect of prevention measures on incidence of human listeriosis, France, 1987-1997.

To assess the impact of preventive measures by the food industry, we analyzed food monitoring data as well as trends in the incidence of listeriosis estimated through three independent sources: the National Reference Center of Listeriosis; a laboratory-based active surveillance network; and two consecutive nationwide surveys of public hospital laboratories. From 1987 to 1997, the incidence of listeriosis decreased by an estimated 68%. A substantial reduction in the proportion of Listeria monocytogenes-contaminated products was observed at the retail level. The temporal relationship between prevention measures by the food industry, reduction in L. monocytogenes-contaminated foodstuffs, and reduction in listeriosis incidence suggests a causal relationship and indicates that a substantial part of the reduction in illness is related to prevention efforts

Quantitative test of the barrier nucleosome model for statistical positioning of nucleosomes up- and downstream of transcription start sites

The positions of nucleosomes in eukaryotic genomes determine which parts of the DNA sequence are readily accessible for regulatory proteins and which are not. Genome-wide maps of nucleosome positions have revealed a salient pattern around transcription start sites, involving a nucleosome-free region (NFR) flanked by a pronounced periodic pattern in the average nucleosome density. While the periodic pattern clearly reflects well-positioned nucleosomes, the positioning mechanism is less clear. A recent experimental study by Mavrich et al. argued that the pattern observed in S. cerevisiae is qualitatively consistent with a `barrier nucleosome model', in which the oscillatory pattern is created by the statistical positioning mechanism of Kornberg and Stryer. On the other hand, there is clear evidence for intrinsic sequence preferences of nucleosomes, and it is unclear to what extent these sequence preferences affect the observed pattern. To test the barrier nucleosome model, we quantitatively analyze yeast nucleosome positioning data both up- and downstream from NFRs. Our analysis is based on the Tonks model of statistical physics which quantifies the interplay between the excluded-volume interaction of nucleosomes and their positional entropy. We find that although the typical patterns on the two sides of the NFR are different, they are both quantitatively described by the same physical model, with the same parameters, but different boundary conditions. The inferred boundary conditions suggest that the first nucleosome downstream from the NFR (the +1 nucleosome) is typically directly positioned while the first nucleosome upstream is statistically positioned via a nucleosome-repelling DNA region. These boundary conditions, which can be locally encoded into the genome sequence, significantly shape the statistical distribution of nucleosomes over a range of up to ~1000 bp to each side.Comment: includes supporting materia

Genomes of multicellular organisms have evolved to attract nucleosomes to promotor regions

Theoretical Physic