Real-to-Virtual Domain Unification for End-to-End Autonomous Driving

In the spectrum of vision-based autonomous driving, vanilla end-to-end models are not interpretable and suboptimal in performance, while mediated perception models require additional intermediate representations such as segmentation masks or detection bounding boxes, whose annotation can be prohibitively expensive as we move to a larger scale. More critically, all prior works fail to deal with the notorious domain shift if we were to merge data collected from different sources, which greatly hinders the model generalization ability. In this work, we address the above limitations by taking advantage of virtual data collected from driving simulators, and present DU-drive, an unsupervised real-to-virtual domain unification framework for end-to-end autonomous driving. It first transforms real driving data to its less complex counterpart in the virtual domain and then predicts vehicle control commands from the generated virtual image. Our framework has three unique advantages: 1) it maps driving data collected from a variety of source distributions into a unified domain, effectively eliminating domain shift; 2) the learned virtual representation is simpler than the input real image and closer in form to the "minimum sufficient statistic" for the prediction task, which relieves the burden of the compression phase while optimizing the information bottleneck tradeoff and leads to superior prediction performance; 3) it takes advantage of annotated virtual data which is unlimited and free to obtain. Extensive experiments on two public driving datasets and two driving simulators demonstrate the performance superiority and interpretive capability of DU-drive

Summarizing First-Person Videos from Third Persons' Points of Views

Video highlight or summarization is among interesting topics in computer vision, which benefits a variety of applications like viewing, searching, or storage. However, most existing studies rely on training data of third-person videos, which cannot easily generalize to highlight the first-person ones. With the goal of deriving an effective model to summarize first-person videos, we propose a novel deep neural network architecture for describing and discriminating vital spatiotemporal information across videos with different points of view. Our proposed model is realized in a semi-supervised setting, in which fully annotated third-person videos, unlabeled first-person videos, and a small number of annotated first-person ones are presented during training. In our experiments, qualitative and quantitative evaluations on both benchmarks and our collected first-person video datasets are presented.Comment: 16+10 pages, ECCV 201

Representation learning for cross-modality classification

Differences in scanning parameters or modalities can complicate image analysis based on supervised classification. This paper presents two representation learning approaches, based on autoencoders, that address this problem by learning representations that are similar across domains. Both approaches use, next to the data representation objective, a similarity objective to minimise the difference between representations of corresponding patches from each domain. We evaluated the methods in transfer learning experiments on multi-modal brain MRI data and on synthetic data. After transforming training and test data from different modalities to the common representations learned by our methods, we trained classifiers for each of pair of modalities. We found that adding the similarity term to the standard objective can produce representations that are more similar and can give a higher accuracy in these cross-modality classification experiments

MOON: A Mixed Objective Optimization Network for the Recognition of Facial Attributes

Attribute recognition, particularly facial, extracts many labels for each image. While some multi-task vision problems can be decomposed into separate tasks and stages, e.g., training independent models for each task, for a growing set of problems joint optimization across all tasks has been shown to improve performance. We show that for deep convolutional neural network (DCNN) facial attribute extraction, multi-task optimization is better. Unfortunately, it can be difficult to apply joint optimization to DCNNs when training data is imbalanced, and re-balancing multi-label data directly is structurally infeasible, since adding/removing data to balance one label will change the sampling of the other labels. This paper addresses the multi-label imbalance problem by introducing a novel mixed objective optimization network (MOON) with a loss function that mixes multiple task objectives with domain adaptive re-weighting of propagated loss. Experiments demonstrate that not only does MOON advance the state of the art in facial attribute recognition, but it also outperforms independently trained DCNNs using the same data. When using facial attributes for the LFW face recognition task, we show that our balanced (domain adapted) network outperforms the unbalanced trained network.Comment: Post-print of manuscript accepted to the European Conference on Computer Vision (ECCV) 2016 http://link.springer.com/chapter/10.1007%2F978-3-319-46454-1_

Acridine Orange Fluroscence Study of Lung - Histopathology in Autopsy Cases of Burns

Background: The major cause of death in the burn patients includes multiple organ failure and septicemia but, sometimes the exact cause of death in many fatally burn patients is difficult to detect. Aim: The aim was to study various histopathological changes in lung in the post-mortem cases of burns, by using routine Haematoxylin and Eosin stain (H&E stain). Periodic and Schiff ’s Stain (PAS) stain to study the role of acridine orange fluorescence study, to explore the forensic utility of this study and to find out the relationship between duration of survival and histopathological changes observed. Material & Methods: Total 32 cases of death due to burns were autopsied at mortuary from october 2010 to september 2012, department of Forensic Medicine and Toxicology in our hospital. These were forwarded to Department of Pathology for histopathological examination. Result: In the present study, maximum number of burns cases in 21-30 years of age group & female predominance. Grossly, 19 cases (59.38%) showed congestion while microscopy showed diffuse alveolar damage (34.38%). The sections stained by acridine orange and observed under fluorescent microscope were negative in 28 cases (87.50%) and lightly positive in 04 cases (12.50%). Conclusion: Routine microscopy does help us in getting specific lesions in lung due to burns. But PAS and Acridine orange fluorescence do not add anything further in our knowledge of pathology due to burns. However, none of these add any new tool to resolve any forensic issues of burns. Therefore, microscopy (including PAS and fluorescent), if done would be redundant

Prediction of sarcomere mutations in subclinical hypertrophic cardiomyopathy.

BACKGROUND: Sarcomere protein mutations in hypertrophic cardiomyopathy induce subtle cardiac structural changes before the development of left ventricular hypertrophy (LVH). We have proposed that myocardial crypts are part of this phenotype and independently associated with the presence of sarcomere gene mutations. We tested this hypothesis in genetic hypertrophic cardiomyopathy pre-LVH (genotype positive, LVH negative [G+LVH-]). METHODS AND RESULTS: A multicenter case-control study investigated crypts and 22 other cardiovascular magnetic resonance parameters in subclinical hypertrophic cardiomyopathy to determine their strength of association with sarcomere gene mutation carriage. The G+LVH- sample (n=73) was 29 ± 13 years old and 51% were men. Crypts were related to the presence of sarcomere mutations (for ≥1 crypt, β=2.5; 95% confidence interval [CI], 0.5-4.4; P=0.014 and for ≥2 crypts, β=3.0; 95% CI, 0.8-7.9; P=0.004). In combination with 3 other parameters: anterior mitral valve leaflet elongation (β=2.1; 95% CI, 1.7-3.1; P<0.001), abnormal LV apical trabeculae (β=1.6; 95% CI, 0.8-2.5; P<0.001), and smaller LV end-systolic volumes (β=1.4; 95% CI, 0.5-2.3; P=0.001), multiple crypts indicated the presence of sarcomere gene mutations with 80% accuracy and an area under the curve of 0.85 (95% CI, 0.8-0.9). In this G+LVH- population, cardiac myosin-binding protein C mutation carriers had twice the prevalence of crypts when compared with the other combined mutations (47 versus 23%; odds ratio, 2.9; 95% CI, 1.1-7.9; P=0.045). CONCLUSIONS: The subclinical hypertrophic cardiomyopathy phenotype measured by cardiovascular magnetic resonance in a multicenter environment and consisting of crypts (particularly multiple), anterior mitral valve leaflet elongation, abnormal trabeculae, and smaller LV systolic cavity is indicative of the presence of sarcomere gene mutations and highlights the need for further study

Graph Adaptive Knowledge Transfer for Unsupervised Domain Adaptation

Unsupervised domain adaptation has caught appealing attentions as it facilitates the unlabeled target learning by borrowing existing well-established source domain knowledge. Recent practice on domain adaptation manages to extract effective features by incorporating the pseudo labels for the target domain to better solve cross-domain distribution divergences. However, existing approaches separate target label optimization and domain-invariant feature learning as different steps. To address that issue, we develop a novel Graph Adaptive Knowledge Transfer (GAKT) model to jointly optimize target labels and domain-free features in a unified framework. Specifically, semi-supervised knowledge adaptation and label propagation on target data are coupled to benefit each other, and hence the marginal and conditional disparities across different domains will be better alleviated. Experimental evaluation on two cross-domain visual datasets demonstrates the effectiveness of our designed approach on facilitating the unlabeled target task learning, compared to the state-of-the-art domain adaptation approaches

Interventions to increase the consumption of water among children

The aim of this study was to conduct a systematic review and meta-analysis on the effectiveness of interventions to increase children's water consumption. A systematic literature search was conducted in seven electronic databases. Studies published in English before 18 February 2019 that evaluated any type of intervention that measured change in water consumption among children aged 2 to 12 years by applying any type of design were included. Of the 47 interventions included in the systematic review, 24 reported a statistically significant increase in water consumption. Twenty-four interventions (17 randomized controlled trials and seven studies with other controlled designs) were included in the meta-analysis. On average, children in intervention groups consumed 29 mL/d (confidence interval [CI] = 13–46 mL/d) more water than did children in control groups. This effect was larger in eight interventions focused specifically on diet (MD = 73 mL/d, CI = 20–126 mL/d) than in 16 interventions focused also on other lifestyle factors (MD = 15 mL/d, CI = 1–29 mL/d). Significant subgroup differences were also found by study setting and socioecological level targeted but not by children's age group, intervention strategy, or study design. In conclusion, there is evidence that, on average, lifestyle interventions can lead to small increases in children's daily water consumption. More research is needed to further understand th

Frequent and Persistent PLCG1 Mutations in Sezary Cells Directly Enhance PLC gamma 1 Activity and Stimulate NF kappa B, AP-1, and NFAT Signaling

Phospholipase C Gamma 1 (PLCG1) is frequently mutated in primary cutaneous T-cell lymphoma (CTCL). This study functionally interrogated nine PLCG1 mutations (p.R48W, p.S312L, p.D342N, p.S345F, p.S520F, p.R1158H, p.E1163K, p.D1165H, and the in-frame indel p.VYEEDM1161V) identified in Sézary Syndrome, the leukemic variant of CTCL. The mutations were demonstrated in diagnostic samples and persisted in multiple tumor compartments over time, except in patients who achieved a complete clinical remission. In basal conditions, the majority of the mutations confer PLCγ1 gain-of-function activity through increased inositol phosphate production and the downstream activation of NFκB, AP-1, and NFAT transcriptional activity. Phosphorylation of the p.Y783 residue is essential for the proximal activity of wild-type PLCγ1, but we provide evidence that activating mutations do not require p.Y783 phosphorylation to stimulate downstream NFκB, NFAT, and AP-1 transcriptional activity. Finally, the gain-of-function effects associated with the p.VYEEDM1161V indel suggest that the C2 domain may have a role in regulating PLCγ1 activity. These data provide compelling evidence to support the development of therapeutic strategies targeting mutant PLCγ1

Semi-supervised Adversarial Learning to Generate Photorealistic Face Images of New Identities from 3D Morphable Model

We propose a novel end-to-end semi-supervised adversarial framework to generate photorealistic face images of new identities with wide ranges of expressions, poses, and illuminations conditioned by a 3D morphable model. Previous adversarial style-transfer methods either supervise their networks with large volume of paired data or use unpaired data with a highly under-constrained two-way generative framework in an unsupervised fashion. We introduce pairwise adversarial supervision to constrain two-way domain adaptation by a small number of paired real and synthetic images for training along with the large volume of unpaired data. Extensive qualitative and quantitative experiments are performed to validate our idea. Generated face images of new identities contain pose, lighting and expression diversity and qualitative results show that they are highly constraint by the synthetic input image while adding photorealism and retaining identity information. We combine face images generated by the proposed method with the real data set to train face recognition algorithms. We evaluated the model on two challenging data sets: LFW and IJB-A. We observe that the generated images from our framework consistently improves over the performance of deep face recognition network trained with Oxford VGG Face dataset and achieves comparable results to the state-of-the-art