Robust 3D Action Recognition through Sampling Local Appearances and Global Distributions

3D action recognition has broad applications in human-computer interaction and intelligent surveillance. However, recognizing similar actions remains challenging since previous literature fails to capture motion and shape cues effectively from noisy depth data. In this paper, we propose a novel two-layer Bag-of-Visual-Words (BoVW) model, which suppresses the noise disturbances and jointly encodes both motion and shape cues. First, background clutter is removed by a background modeling method that is designed for depth data. Then, motion and shape cues are jointly used to generate robust and distinctive spatial-temporal interest points (STIPs): motion-based STIPs and shape-based STIPs. In the first layer of our model, a multi-scale 3D local steering kernel (M3DLSK) descriptor is proposed to describe local appearances of cuboids around motion-based STIPs. In the second layer, a spatial-temporal vector (STV) descriptor is proposed to describe the spatial-temporal distributions of shape-based STIPs. Using the Bag-of-Visual-Words (BoVW) model, motion and shape cues are combined to form a fused action representation. Our model performs favorably compared with common STIP detection and description methods. Thorough experiments verify that our model is effective in distinguishing similar actions and robust to background clutter, partial occlusions and pepper noise

Performance Analysis of the Unary Coding Aided SWIPT in a Single-User Z-Channel

Radio frequency (RF) signal based simultaneous wireless information and power transfer (SWIPT) has emerged as a promising technique for satisfying both the communication and charging requests of the massively deployed IoT devices. Different from the physical layer and the medium-access-control layer design for coordinating the SWIPT in the RF band, we study its coding-level control from the information theoretical perspective. Due to its practical implementation of the decoder and its flexibility on the codeword structure, the unary code is chosen as a potential joint information and energy encoder. By conceiving the classic Z-channel, the mutual information and the energy harvesting performance of the unary coding aided SWIPT transceiver is analysed. Furthermore, the optimal codeword distribution is obtained for maximising the mutual information, while satisfying the minimum energy harvesting requirement. Our theoretical analysis and the optimal coding design are demonstrated by the numerical results

Self-Refining Deep Symmetry Enhanced Network for Rain Removal

Rain removal aims to remove the rain streaks on rain images. The state-of-the-art methods are mostly based on Convolutional Neural Network~(CNN). However, as CNN is not equivariant to object rotation, these methods are unsuitable for dealing with the tilted rain streaks. To tackle this problem, we propose Deep Symmetry Enhanced Network~(DSEN) that is able to explicitly extract the rotation equivariant features from rain images. In addition, we design a self-refining mechanism to remove the accumulated rain streaks in a coarse-to-fine manner. This mechanism reuses DSEN with a novel information link which passes the gradient flow to the higher stages. Extensive experiments on both synthetic and real-world rain images show that our self-refining DSEN yields the top performance.Comment: Accepted by ICIP 19. Corresponding and contact author: Hanrong Y

Selective delivery of interleukine-1 receptor antagonist to inflamed joint by albumin fusion

BACKGROUND: Interleukin-1 receptor antagonist, a cytokine that is highly therapeutic to rheumatoid arthritis and several other inflammatory diseases, exhibits rapid blood clearance and poor retention time on the target in clinical application due to its small size and lack of specificity to target tissue. Albumin has been widely employed as macromolecular carrier for drug delivery purpose to extend the plasma half-life of therapeutic molecules and has been shown to selectively accumulate and to be metabolized in the inflamed joints of patients with rheumatoid arthritis. This suggests that genetic fusion of IL-1ra to albumin can probably overcome the drawbacks of in vivo application of IL-1ra. RESULT: A recombinant protein, engineered by fusing human serum albumin (HSA) to the carboxyl terminal of IL-1ra, was produced in Pichia pastoris and purified to homogeneity. The fusion protein retained the antagonist activity of IL-1ra and had a plasma half-life of approximately 30-fold more than that of IL-1ra in healthy mice. In vivo bio-distribution studies demonstrated that the fusion protein selectively accumulated in arthritic paws for a long period of time in mice with collagen-induced arthritis, showing low uptake rates in normal organs such as liver, kidney, spleen and lung in contrast to IL-1ra alone. Moreover, this fusion protein was able to significantly improve the therapeutic efficacy of IL-1ra in collagen-induced arthritis mouse model. CONCLUSIONS: The fusion protein described here, able to selectively deliver IL-1ra to inflamed tissue, could yield important contributions for the therapy of rheumatoid arthritis and other inflammatory diseases

RefineDetLite: A Lightweight One-stage Object Detection Framework for CPU-only Devices

Previous state-of-the-art real-time object detectors have been reported on GPUs which are extremely expensive for processing massive data and in resource-restricted scenarios. Therefore, high efficiency object detectors on CPU-only devices are urgently-needed in industry. The floating-point operations (FLOPs) of networks are not strictly proportional to the running speed on CPU devices, which inspires the design of an exactly "fast" and "accurate" object detector. After investigating the concern gaps between classification networks and detection backbones, and following the design principles of efficient networks, we propose a lightweight residual-like backbone with large receptive fields and wide dimensions for low-level features, which are crucial for detection tasks. Correspondingly, we also design a light-head detection part to match the backbone capability. Furthermore, by analyzing the drawbacks of current one-stage detector training strategies, we also propose three orthogonal training strategies---IOU-guided loss, classes-aware weighting method and balanced multi-task training approach. Without bells and whistles, our proposed RefineDetLite achieves 26.8 mAP on the MSCOCO benchmark at a speed of 130 ms/pic on a single-thread CPU. The detection accuracy can be further increased to 29.6 mAP by integrating all the proposed training strategies, without apparent speed drop.Comment: 16 pages, 8 figure