Deep Multi-task Multi-label CNN for Effective Facial Attribute Classification

Facial Attribute Classification (FAC) has attracted increasing attention in computer vision and pattern recognition. However, state-of-the-art FAC methods perform face detection/alignment and FAC independently. The inherent dependencies between these tasks are not fully exploited. In addition, most methods predict all facial attributes using the same CNN network architecture, which ignores the different learning complexities of facial attributes. To address the above problems, we propose a novel deep multi-task multi-label CNN, termed DMM-CNN, for effective FAC. Specifically, DMM-CNN jointly optimizes two closely-related tasks (i.e., facial landmark detection and FAC) to improve the performance of FAC by taking advantage of multi-task learning. To deal with the diverse learning complexities of facial attributes, we divide the attributes into two groups: objective attributes and subjective attributes. Two different network architectures are respectively designed to extract features for two groups of attributes, and a novel dynamic weighting scheme is proposed to automatically assign the loss weight to each facial attribute during training. Furthermore, an adaptive thresholding strategy is developed to effectively alleviate the problem of class imbalance for multi-label learning. Experimental results on the challenging CelebA and LFWA datasets show the superiority of the proposed DMM-CNN method compared with several state-of-the-art FAC methods

When Sparse Neural Network Meets Label Noise Learning: A Multistage Learning Framework

Recent methods in network pruning have indicated that a dense neural network involves a sparse subnetwork (called a winning ticket), which can achieve similar test accuracy to its dense counterpart with much fewer network parameters. Generally, these methods search for the winning tickets on well-labeled data. Unfortunately, in many real-world applications, the training data are unavoidably contaminated with noisy labels, thereby leading to performance deterioration of these methods. To address the above-mentioned problem, we propose a novel two-stream sample selection network (TS 3 -Net), which consists of a sparse subnetwork and a dense subnetwork, to effectively identify the winning ticket with noisy labels. The training of TS 3 -Net contains an iterative procedure that switches between training both subnetworks and pruning the smallest magnitude weights of the sparse subnetwork. In particular, we develop a multistage learning framework including a warm-up stage, a semisupervised alternate learning stage, and a label refinement stage, to progressively train the two subnetworks. In this way, the classification capability of the sparse subnetwork can be gradually improved at a high sparsity level. Extensive experimental results on both synthetic and real-world noisy datasets (including MNIST, CIFAR-10, CIFAR-100, ANIMAL-10N, Clothing1M, and WebVision) demonstrate that our proposed method achieves state-of-the-art performance with very small memory consumption for label noise learning. Code is available at https://github.com/Runqing-forMost/TS3-Net/tree/master

Drop Loss for Person Attribute Recognition With Imbalanced Noisy-Labeled Samples

Person attribute recognition (PAR) aims to simultaneously predict multiple attributes of a person. Existing deep learning-based PAR methods have achieved impressive performance. Unfortunately, these methods usually ignore the fact that different attributes have an imbalance in the number of noisy-labeled samples in the PAR training datasets, thus leading to suboptimal performance. To address the above problem of imbalanced noisy-labeled samples, we propose a novel and effective loss called drop loss for PAR. In the drop loss, the attributes are treated differently in an easy-to-hard way. In particular, the noisy-labeled candidates, which are identified according to their gradient norms, are dropped with a higher drop rate for the harder attribute. Such a manner adaptively alleviates the adverse effect of imbalanced noisy-labeled samples on model learning. To illustrate the effectiveness of the proposed loss, we train a simple ResNet-50 model based on the drop loss and term it DropNet. Experimental results on two representative PAR tasks (including facial attribute recognition and pedestrian attribute recognition) demonstrate that the proposed DropNet achieves comparable or better performance in terms of both balanced accuracy and classification accuracy over several state-of-the-art PAR methods

Weighted Measurement Fusion White Noise Deconvolution Filter with Correlated Noise for Multisensor Stochastic Systems

For the multisensor linear discrete time-invariant stochastic control systems with different measurement matrices and correlated noises, the centralized measurement fusion white noise estimators are presented by the linear minimum variance criterion under the condition that noise input matrix is full column rank. They have the expensive computing burden due to the high-dimension extended measurement matrix. To reduce the computing burden, the weighted measurement fusion white noise estimators are presented. It is proved that weighted measurement fusion white noise estimators have the same accuracy as the centralized measurement fusion white noise estimators, so it has global optimality. It can be applied to signal processing in oil seismic exploration. A simulation example for Bernoulli-Gaussian white noise deconvolution filter verifies the effectiveness

The Molecular Mechanism Of Alpha-Synuclein Dependent Regulation Of Protein Phosphatase 2A Activity

Background/Aims: Alpha-synuclein (α-Syn) is a neuronal protein that is highly implicated in Parkinson\u27s disease (PD), and protein phosphatase 2A (PP2A) is an important serine/threonine phosphatase that is associated with neurodegenerative diseases, such as PD. α-Syn can directly upregulate PP2A activity, but the underling mechanism remains unclear. Therefore, we investigated the molecular mechanism of α-Syn regulating PP2A activity. Methods: α-Syn and its truncations were expressed in E.coli, and purified by affinity chromatography. PP2A Cα and its mutants were expressed in recombinant baculovirus, and purified by affinity chromatography combined with gel filtration chromatography. The interaction between α-Syn and PP2A Cα was detected by GST pull-down assay. PP2A activity was investigated by the colorimetric assay. Results: The hydrophobic non-amyloid component (NAC) domain of α-Syn interacted with PP2A Cα and upregulated its activity. α-Syn aggregates reduced its ability to upregulate PP2A activity, since the hydrophobic domain of α-Syn was blocked during aggregation. Furthermore, in the hydrophobic center of PP2A Cα, the residue of I123 was responsible for PP2A to interact with α-Syn, and its hydrophilic mutation blocked its interaction with α-Syn as well as its activity upregulation by α-Syn. Conclusions: α-Syn bound to PP2A Cα by the hydrophobic interaction and upregulated its activity. Blocking the hydrophobic domain of α-Syn or hydrophilic mutation on the residue I123 in PP2A Cα all reduced PP2A activity upregulation by α-Syn. Overall, we explored the mechanism of α-Syn regulating PP2A activity, which might offer much insight into the basis underlying PD pathogenesis

catena-Poly[[[aqua­[3-(3-hy­droxy­phen­yl)prop-2-enoato]samarium(III)]-bis­[μ2-3-(3-hy­droxy­phen­yl)prop-2-enoato]] monohydrate]

The title SmIII compound, {[Sm(C9H7O3)3(H2O)]·H2O}n, was obtained under hydrothermal conditions. Its structure is isotypic with the analogous Eu complex. The latter was reported incorrectly in space group P1 by Yan et al. [J. Mol. Struct. (2008), 891, 298–304]. This was corrected by Marsh [Acta Cryst. B65, 782–783] to P-1. The SmIII ion is nine-coordinated by O atoms from one coordinating water molecule and the remaining ones from the 3-(3-hy­droxy­phen­yl)prop-2-enoatate anions (one bidentate, two bidentate and bridging, two monodentate bridging), leading to a distorted tricapped trigonal–prismatic coordination polyhedron surrounded by solvent water mol­ecules. In the crystal, extensive intermolecular O—H⋯O hydrogen-bonding inter­actions and π–π inter­actions [centroid–centroid separation = 3.9393 (1) Å] lead to the formation of a three-dimensional supra­molecular network

Exact quantum dissipative dynamics under external time-dependent fields driving

Exact and nonperturbative quantum master equation can be constructed via the calculus on path integral. It results in hierarchical equations of motion for the reduced density operator. Involved are also a set of well--defined auxiliary density operators that resolve not just system--bath coupling strength but also memory. In this work, we scale these auxiliary operators individually to achieve a uniform error tolerance, as set by the reduced density operator. An efficient propagator is then proposed to the hierarchical Liouville--space dynamics of quantum dissipation. Numerically exact studies are carried out on the dephasing effect on population transfer in the simple stimulated Raman adiabatic passage scheme. We also make assessments on several perturbative theories for their applicabilities in the present system of study