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_

Wavelet analysis of head-related transfer functions

The directional-dependent information in the head-related transfer function (HRTF) is important for the study of human sound localization system and the synthesis of virtual auditory signals. Its time-domain and frequency-domain characteristics have been widely studied by researchers. The purpose of this paper is to explore the ability of discrete wavelet transform to describe the time-scale characteristics of HRTFs. Both the time-domain characteristics and energy distribution of different frequency subbands were studied. Discrete wavelet analysis is found to be a new direction-dependence information showing the relation of the characteristics of the HRTFs to sound source directions.published_or_final_versio

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

Chemical Composition and Biological Properties of Essential Oils of Two Mint Species

Purpose: To analyze the composition of essential oils of two types of mint as well as compare the antimicrobial, antioxidant and anti-inflammatory activities of the two oils.Methods: Peppermint (M. piperita L.) and chocolate mint (M. piperita L.) oils were obtained by steam distillation in a Clevenger-type apparatus. The chemical composition of the essential oils was determined by gas chromatography-mass spectrometry (GC/MS). The minimal inhibitory concentration (MIC) of the essential oils were determined by broth dilution method. The antioxidant activities of the oils were determined by 2, 2-diphenyl-1-picrylhydrazyl (DPPH)DPPH radical scavenging assay, β-Carotene-linoleic acid assay, andnitric oxide (NO) radical scavenging assay.Results: The two essential oils contain high levels of alcohol (43.47-50.10%) and terpene (18.55-21.07%) with the major compound being menthol (28.19-30.35%). The antimicrobial activity (minimum inhibitory concentration, MIC) of peppermint oil against E. coli, S. aureus and P. aeruginosa (0.15, 0.08, 0.92 %v/v, respectively) was stronger than that of chocolate mint (0.23, 0.09, 1.22 %v/v, respectively). In the anti-oxidant test including DPPH and β-Carotenelinoleic acid assays, peppermint oil showed superior antioxidant properties to chocolate mint oil (4.45 - 19.86 μl/mL). However, with regard to scavenging NO radical activity, chocolate mint oil exhibited higher activity than peppermint (0.31 and 0.42 μl/mL, respectively). Chocolate mint oil also exhibited higher anti-inflammatory activity than peppermint oil (0.03 and 0.08 μl/mL, respectively).Conclusion: The results obtained should help to clarify the functional applications of these folk herbs and their essential oils for aromatherapeutic healing and other folkloric uses.Keywords: Peppermint, Chocolate mint, Anti-microbial, Anti-oxidant, Anti-inflammator

Knocking down 10-formyltetrahydrofolate dehydrogenase increased oxidative stress and impeded zebrafish embryogenesis by obstructing morphogenetic movement

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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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