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This paper revisits the problem of rate distortion optimization (RDO) with focus on inter-picture dependence. A joint RDO framework which incorporates the Lagrange multiplier as one of parameters to be optimized is proposed. Simplification strategies are demonstrated for practical applications. To make the problem tractable, we consider an approach where prediction residuals of pictures in a video sequence are assumed to be emitted from a finite set of sources. Consequently the RDO problem is formulated as finding optimal coding parameters for a finite number of sources, regardless of the length of the video sequence. Specifically, in cases where a hierarchical prediction structure is used, prediction residuals of pictures at the same prediction layer are assumed to be emitted from a common source. Following this approach, we propose an iterative algorithm to alternatively optimize the selections of quantization parameters (QPs) and the corresponding Lagrange multipliers. Based on the results of the iterative algorithm, we further propose two practical algorithms to compute QPs and the Lagrange multipliers for the RA(random access) hierarchical video coding the first practical algorithm uses a fixed formula to compute QPs and the Lagrange multipliers, and the second practical algorithm adaptively adjusts both QPs and the Lagrange multipliers. Experimental results show that these three algorithms, integrated into the HM 16.20 reference software of HEVC, can achieve considerable RD improvements over the standard HM 16.20 encoder, in the common RA test configuration.In recent years, the field of object detection has made significant progress. The success of most of the state-of-the-art object detectors is derived from the use of feature pyramid and the carefully designed anchor boxes. However, the current methods of constructing feature pyramid usually blindly integrate multi-scale representations on each feature hierarchy. Furthermore, these detectors also suffer from some drawbacks brought by the hand-designed anchors. To mitigate the adverse effects caused thereby, we introduce a one-stage object detector, named as the semi-anchor-free network with enhanced feature pyramid (SAFNet). Specifically, to better construct feature pyramid, we propose a novel enhanced feature pyramid generation paradigm, which mainly consists of two modules, i.e., adaptive feature fusion module (AFFM) and self-enhanced module (SEM). The paradigm adaptively integrates multi-scale representations in a non-linear method meanwhile suppress the redundant semantic information for each pyramid level, such that a clean and enhanced feature pyramid could be obtained. In addition, an adaptive anchor generator (AAG) is designed to yield fewer but more suitable anchor boxes for each input image. Benefiting from the enhanced feature pyramid, AAG is capable of generating more accurate anchor boxes by introducing few priors. Thus, AAG has the ability to alleviate the drawbacks caused by the preset anchor hyper-parameters and helps to decrease the computation cost. Extensive experiments demonstrate the effectiveness of our approach. Profited from the proposed modules, SAFNet significantly boosts the detection performance, i.e., achieving 2 points and 2.1 points higher Average Precision (AP) than RetinaNet (our baseline) on PASCAL VOC and MS COCO respectively. Codes will be publicly available soon.A single-chip Gaussian monocycle pulse (GMP) transceiver was developed for radar-based microwave imaging by the use of 65-nm complementary metal oxide semiconductor (CMOS) technology. A transmitter (TX) generates GMP signals, whose pulse widths and -3 dB bandwidths are 192 ps and 5.9 GHz, respectively. A 102.4 GS/s equivalent time sampling receiver (RX) performs the minimum jitter, input referred noise, signal-to-nose-ratio (SNR), signal-to-noise and distortion ratio (SNDR) effective number of bits (ENOB) of 0.58 ps, 0.24 mVrms, 28.4 dB, 26.6 dB and 4.1 bits, respectively. The SNR for the bandwidth of 3.6 GHz is 36.3 dB. The power dissipations of transmitter and receiver circuits are 19.79 mW and 48.87 mW, respectively. The GMP transceiver module can differentiate two phantom targets with the size of 1 cm and the spacing of 1 cm by confocal imaging.Photoplethysmographic (PPG) measurements from ambulatory subjects may suffer from unreliability due to body movements and missing data segments due to loosening of sensor. This paper describes an on-device reliability assessment from PPG measurements using a stack denoising autoencoder (SDAE) and multilayer perceptron neural network (MLPNN). Tigecycline in vivo The missing segments were predicted by a personalized convolutional neural network (CNN) and long-short term memory (LSTM) model using a short history of the same channel data. Forty sets of volunteers' data, consisting of equal share of healthy and cardiovascular subjects were used for validation and testing. The PPG reliability assessment model (PRAM) achieved over 95% accuracy for correctly identifying acceptable PPG beats out of total 5000 using expert annotated data. Disagreement with experts' annotation was nearly 3.5%. The missing segment prediction model (MSPM) achieved a root mean square error (RMSE) of 0.22, and mean absolute error (MAE) of 0.11 for 40 missing beats prediction using only four beat history from the same channel PPG. The two models were integrated in a standalone device based on quad-core ARM Cortex-A53, 1.2 GHz, with 1 GB RAM, with 130 MB memory requirement and latency ∼0.35 s per beat prediction with a 30 s frame. The present method also provides improved performance with published works on PPG quality assessment and missing data prediction using two public datasets, CinC and MIMIC-II under PhysioNet.Recently, substantial research effort has focused on how to apply CNNs or RNNs to better capture temporal patterns to improve the accuracy of video classification. In this paper, we investigate the potential of a purely attention-based local feature integration. Accounting for the characteristics of such features in video classification, we first propose Basic Attention Clusters(BAC), which concatenates the output of multiple attention units applied in parallel and introduce a shifting operation to capture more diverse signals. Experiments show that BAC can achieve excellent results on multiple datasets. However, BAC treats all feature channels as an indivisible whole, which is suboptimal for achieving a finer-grained local feature integration over the channel dimension. Additionally, it treats the entire local feature sequence as an unordered set, thus ignoring the sequential relationships. To improve over BAC, we further propose the channel pyramid attention schema by splitting features into sub-features at multiple scales for coarse-to-fine sub-feature interaction modeling and propose the temporal pyramid attention schema by dividing the feature sequences into ordered sub-sequences of multiple lengths to account for the sequential order.