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_

Neural network model of binaural hearing based on spatial feature extraction of the head related transfer function

In spatial hearing, complex valued head-related transfer function (HRTF) can be represented as a real valued head-related impulse response (HRIR). Using Karhunen-Loeve expansion, the spatial features of the normalized HRIRs on measurement space can be extracted as spatial character functions. A neural network model based on Von-Mises function is used to approximate the discrete spatial character function of HRIR. As a result, a time-domain binaural model is established and it fits the measured HRIRs well.published_or_final_versio

Allergy in Hong Kong: an unmet need in service provision and training

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Structured Landmark Detection via Topology-Adapting Deep Graph Learning

Image landmark detection aims to automatically identify the locations of predefined fiducial points. Despite recent success in this field, higher-ordered structural modeling to capture implicit or explicit relationships among anatomical landmarks has not been adequately exploited. In this work, we present a new topology-adapting deep graph learning approach for accurate anatomical facial and medical (e.g., hand, pelvis) landmark detection. The proposed method constructs graph signals leveraging both local image features and global shape features. The adaptive graph topology naturally explores and lands on task-specific structures which are learned end-to-end with two Graph Convolutional Networks (GCNs). Extensive experiments are conducted on three public facial image datasets (WFLW, 300W, and COFW-68) as well as three real-world X-ray medical datasets (Cephalometric (public), Hand and Pelvis). Quantitative results comparing with the previous state-of-the-art approaches across all studied datasets indicating the superior performance in both robustness and accuracy. Qualitative visualizations of the learned graph topologies demonstrate a physically plausible connectivity laying behind the landmarks.Comment: Accepted to ECCV-20. Camera-ready with supplementary materia

Combining Contrast Invariant L1 Data Fidelities with Nonlinear Spectral Image Decomposition

This paper focuses on multi-scale approaches for variational methods and corresponding gradient flows. Recently, for convex regularization functionals such as total variation, new theory and algorithms for nonlinear eigenvalue problems via nonlinear spectral decompositions have been developed. Those methods open new directions for advanced image filtering. However, for an effective use in image segmentation and shape decomposition, a clear interpretation of the spectral response regarding size and intensity scales is needed but lacking in current approaches. In this context, $L^1$ data fidelities are particularly helpful due to their interesting multi-scale properties such as contrast invariance. Hence, the novelty of this work is the combination of $L^1$-based multi-scale methods with nonlinear spectral decompositions. We compare $L^1$ with $L^2$ scale-space methods in view of spectral image representation and decomposition. We show that the contrast invariant multi-scale behavior of $L^1-TV$ promotes sparsity in the spectral response providing more informative decompositions. We provide a numerical method and analyze synthetic and biomedical images at which decomposition leads to improved segmentation.Comment: 13 pages, 7 figures, conference SSVM 201

Elevated 5hmC levels characterize DNA of the cerebellum in Parkinson’s disease

5-methylcytosine and the oxidation product 5-hydroxymethylcytosine are two prominent epigenetic variants of the cytosine base in nuclear DNA of mammalian brains. We measured levels of 5-methylcytosine and 5-hydroxymethylcytosine by enzyme-linked immunosorbent assay in DNA from post-mortem cerebella of individuals with Parkinson’s disease and age-matched controls. 5-methylcytosine levels showed no significant differences between Parkinson’s disease and control DNA sample sets. In contrast, median 5-hydroxymethylcytosine levels were almost twice as high (p < 0.001) in both male and female Parkinson’s disease individuals compared with controls. The distinct epigenetic profile identified in cerebellar DNA of Parkinson’s disease patients raises the question whether elevated 5-hydroxymethylcytosine levels are a driver or a consequence of Parkinson’s disease

Hypoxia causes transgenerational impairments in reproduction of fish

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Structural basis for the RING catalyzed synthesis of K63 linked ubiquitin chains

This work was supported by grants from Cancer Research UK (C434/A13067), the Wellcome Trust (098391/Z/12/Z) and Biotechnology and Biological Sciences Research Council (BB/J016004/1).The RING E3 ligase catalysed formation of lysine 63 linked ubiquitin chains by the Ube2V2–Ubc13 E2 complex is required for many important biological processes. Here we report the structure of the RING domain dimer of rat RNF4 in complex with a human Ubc13~Ub conjugate and Ube2V2. The structure has captured Ube2V2 bound to the acceptor (priming) ubiquitin with Lys63 in a position that could lead to attack on the linkage between the donor (second) ubiquitin and Ubc13 that is held in the active “folded back” conformation by the RING domain of RNF4. The interfaces identified in the structure were verified by in vitro ubiquitination assays of site directed mutants. This represents the first view of the synthesis of Lys63 linked ubiquitin chains in which both substrate ubiquitin and ubiquitin-loaded E2 are juxtaposed to allow E3 ligase mediated catalysis.PostprintPeer reviewe