Face Recognition from Sequential Sparse 3D Data via Deep Registration

Previous works have shown that face recognition with high accurate 3D data is more reliable and insensitive to pose and illumination variations. Recently, low-cost and portable 3D acquisition techniques like ToF(Time of Flight) and DoE based structured light systems enable us to access 3D data easily, e.g., via a mobile phone. However, such devices only provide sparse(limited speckles in structured light system) and noisy 3D data which can not support face recognition directly. In this paper, we aim at achieving high-performance face recognition for devices equipped with such modules which is very meaningful in practice as such devices will be very popular. We propose a framework to perform face recognition by fusing a sequence of low-quality 3D data. As 3D data are sparse and noisy which can not be well handled by conventional methods like the ICP algorithm, we design a PointNet-like Deep Registration Network(DRNet) which works with ordered 3D point coordinates while preserving the ability of mining local structures via convolution. Meanwhile we develop a novel loss function to optimize our DRNet based on the quaternion expression which obviously outperforms other widely used functions. For face recognition, we design a deep convolutional network which takes the fused 3D depth-map as input based on AMSoftmax model. Experiments show that our DRNet can achieve rotation error 0.95{\deg} and translation error 0.28mm for registration. The face recognition on fused data also achieves rank-1 accuracy 99.2% , FAR-0.001 97.5% on Bosphorus dataset which is comparable with state-of-the-art high-quality data based recognition performance.Comment: To be appeared in ICB201

End-to-End Photo-Sketch Generation via Fully Convolutional Representation Learning

Sketch-based face recognition is an interesting task in vision and multimedia research, yet it is quite challenging due to the great difference between face photos and sketches. In this paper, we propose a novel approach for photo-sketch generation, aiming to automatically transform face photos into detail-preserving personal sketches. Unlike the traditional models synthesizing sketches based on a dictionary of exemplars, we develop a fully convolutional network to learn the end-to-end photo-sketch mapping. Our approach takes whole face photos as inputs and directly generates the corresponding sketch images with efficient inference and learning, in which the architecture are stacked by only convolutional kernels of very small sizes. To well capture the person identity during the photo-sketch transformation, we define our optimization objective in the form of joint generative-discriminative minimization. In particular, a discriminative regularization term is incorporated into the photo-sketch generation, enhancing the discriminability of the generated person sketches against other individuals. Extensive experiments on several standard benchmarks suggest that our approach outperforms other state-of-the-art methods in both photo-sketch generation and face sketch verification.Comment: 8 pages, 6 figures. Proceeding in ACM International Conference on Multimedia Retrieval (ICMR), 201

SARS-associated Coronavirus Transmitted from Human to Pig

Severe acute respiratory syndrome–associatedcoronavirus (SARS-CoV) was isolated from a pig during a survey for possible routes of viral transmission after a SARS epidemic. Sequence and epidemiology analyses suggested that the pig was infected by a SARS-CoV of human origin

Investigation of Mechanochemically Treated Municipal Solid Waste Incineration Fly Ash as Replacement for Cement

Municipal solid waste incineration (MSWI) fly ash has been classified as hazardous waste in China because of the leachable toxic heavy metals and high concentrations of chlorides and polychlorinated dibenzo-p-dioxins and polychlorinated dibenzofurans (PCDD/Fs). Currently, the main treatment method is still landfilling after chemical treatment or cement solidification, and an effective approach to realize fly ash utilization is still lacking. In the present work, the fly ash was firstly water-washed to remove the soluble chlorine salts, which can improve the performance of the produced cement mortar in later work. Mechanochemical pre-treatment was adopted to destroy the PCDD/Fs and improve the heavy metals’ stabilization. The results show that 75% of PCDD/Fs can be degraded and that most of the heavy metals are stabilized. After the mechanochemical pre-treatment, the average particle size of the fly ash decreases to 2–5 μm, which is beneficial for promoting the activation energy and accelerating the hydration process in cement mortar production. The compressive and flexural strengths of the fly ash cement mortar improve to 6.2 MPa and 32.4 MPa, respectively, when 35% of the OPC is replaced by treated fly ash. The similarity in the 3-day and 28-day strength with or without the addition of the treated ash shows the light influence of the fly ash addition. Thus, the mechanochemical process can stabilize the heavy metals and activate the fly ash, allowing it to partly substitute ordinary Portland cement in building materials, such as cement raw materials and concrete

DARI: Distance Metric and Representation Integration for Person Verification

The past decade has witnessed the rapid development of feature representation learning and distance metric learning, whereas the two steps are often discussed separately. To explore their interaction, this work proposes an end-to-end learning framework called DARI, i.e. Distance metric And Representation Integration, and validates the effectiveness of DARI in the challenging task of person verification. Given the training images annotated with the labels, we first produce a large number of triplet units, and each one contains three images, i.e. one person and the matched/mismatch references. For each triplet unit, the distance disparity between the matched pair and the mismatched pair tends to be maximized. We solve this objective by building a deep architecture of convolutional neural networks. In particular, the Mahalanobis distance matrix is naturally factorized as one top fully-connected layer that is seamlessly integrated with other bottom layers representing the image feature. The image feature and the distance metric can be thus simultaneously optimized via the one-shot backward propagation. On several public datasets, DARI shows very promising performance on re-identifying individuals cross cameras against various challenges, and outperforms other state-of-the-art approaches

Galectin-1: A Traditionally Immunosuppressive Protein Displays Context-Dependent Capacities

Galectin–Carbohydrate interactions are indispensable to pathogen recognition and immune response. Galectin-1, a ubiquitously expressed 14-kDa protein with an evolutionarily conserved β-galactoside binding site, translates glycoconjugate recognition into function. That galectin-1 is demonstrated to induce T cell apoptosis has led to substantial attention to the immunosuppressive properties of this protein, such as inducing naive immune cells to suppressive phenotypes, promoting recruitment of immunosuppressing cells as well as impairing functions of cytotoxic leukocytes. However, only in recent years have studies shown that galectin-1 appears to perform a pro-inflammatory role in certain diseases. In this review, we describe the anti-inflammatory function of galectin-1 and its possible mechanisms and summarize the existing therapies and preclinical efficacy relating to these agents. In the meantime, we also discuss the potential causal factors by which galectin-1 promotes the progression of inflammation