Face Anti-Spoofing by Learning Polarization Cues in a Real-World Scenario

Face anti-spoofing is the key to preventing security breaches in biometric recognition applications. Existing software-based and hardware-based face liveness detection methods are effective in constrained environments or designated datasets only. Deep learning method using RGB and infrared images demands a large amount of training data for new attacks. In this paper, we present a face anti-spoofing method in a real-world scenario by automatic learning the physical characteristics in polarization images of a real face compared to a deceptive attack. A computational framework is developed to extract and classify the unique face features using convolutional neural networks and SVM together. Our real-time polarized face anti-spoofing (PAAS) detection method uses a on-chip integrated polarization imaging sensor with optimized processing algorithms. Extensive experiments demonstrate the advantages of the PAAS technique to counter diverse face spoofing attacks (print, replay, mask) in uncontrolled indoor and outdoor conditions by learning polarized face images of 33 people. A four-directional polarized face image dataset is released to inspire future applications within biometric anti-spoofing field.Comment: 14pages,8figure

Gunrock: GPU Graph Analytics

For large-scale graph analytics on the GPU, the irregularity of data access and control flow, and the complexity of programming GPUs, have presented two significant challenges to developing a programmable high-performance graph library. "Gunrock", our graph-processing system designed specifically for the GPU, uses a high-level, bulk-synchronous, data-centric abstraction focused on operations on a vertex or edge frontier. Gunrock achieves a balance between performance and expressiveness by coupling high performance GPU computing primitives and optimization strategies with a high-level programming model that allows programmers to quickly develop new graph primitives with small code size and minimal GPU programming knowledge. We characterize the performance of various optimization strategies and evaluate Gunrock's overall performance on different GPU architectures on a wide range of graph primitives that span from traversal-based algorithms and ranking algorithms, to triangle counting and bipartite-graph-based algorithms. The results show that on a single GPU, Gunrock has on average at least an order of magnitude speedup over Boost and PowerGraph, comparable performance to the fastest GPU hardwired primitives and CPU shared-memory graph libraries such as Ligra and Galois, and better performance than any other GPU high-level graph library.Comment: 52 pages, invited paper to ACM Transactions on Parallel Computing (TOPC), an extended version of PPoPP'16 paper "Gunrock: A High-Performance Graph Processing Library on the GPU

Improving Speech Emotion Recognition with Unsupervised Speaking Style Transfer

Humans can effortlessly modify various prosodic attributes, such as the placement of stress and the intensity of sentiment, to convey a specific emotion while maintaining consistent linguistic content. Motivated by this capability, we propose EmoAug, a novel style transfer model designed to enhance emotional expression and tackle the data scarcity issue in speech emotion recognition tasks. EmoAug consists of a semantic encoder and a paralinguistic encoder that represent verbal and non-verbal information respectively. Additionally, a decoder reconstructs speech signals by conditioning on the aforementioned two information flows in an unsupervised fashion. Once training is completed, EmoAug enriches expressions of emotional speech with different prosodic attributes, such as stress, rhythm and intensity, by feeding different styles into the paralinguistic encoder. EmoAug enables us to generate similar numbers of samples for each class to tackle the data imbalance issue as well. Experimental results on the IEMOCAP dataset demonstrate that EmoAug can successfully transfer different speaking styles while retaining the speaker identity and semantic content. Furthermore, we train a SER model with data augmented by EmoAug and show that the augmented model not only surpasses the state-of-the-art supervised and self-supervised methods but also overcomes overfitting problems caused by data imbalance. Some audio samples can be found on our demo website

GABP transcription factor is required for development of chronic myelogenous leukemia via its control of PRKD2

Hematopoietic stem cells (HSCs) are the source of all blood lineages, and HSCs must balance quiescence, self-renewal, and differentiation to meet lifelong needs for blood cell development. Transformation of HSCs by the breakpoint cluster region-ABL tyrosine kinase (BCR-ABL) oncogene causes chronic myelogenous leukemia (CML). The E-twenty six (ets) transcription factor GA binding protein (GABP) is a tetrameric transcription factor complex that contains GABPalpha and GABPbeta proteins. Deletion in bone marrow of Gabpa, the gene that encodes the DNA-binding component, caused cell cycle arrest in HSCs and profound loss of hematopoietic progenitor cells. Loss of Gabpalpha prevented development of CML, although mice continued to generate BCR-ABL-expressing Gabpalpha-null cells for months that were serially transplantable and contributed to all lineages in secondary recipients. A bioinformatic screen identified the serine-threonine kinase protein kinase D2 (PRKD2) as a potential effector of GABP in HSCs. Prkd2 expression was markedly reduced in Gabpalpha-null HSCs and progenitor cells. Reduced expression of PRKD2 or pharmacologic inhibition decreased cell cycling, and PRKD2 rescued growth of Gabpalpha-null BCR-ABL-expressing cells. Thus, GABP is required for HSC cell cycle entry and CML development through its control of PRKD2. This offers a potential therapeutic target in leukemia