A new 3-D mesh simplificatión algorithm

To simplify the 3D color head mesh ,it is more important to keep the boundary and quality of the head’s sense organs including eyes, eyebrows, nose and mouth. In this paper, we present a novel mesh simplification algorithm based on region segmentation. The algorithm can be divided into two stages: segmentation and simplification. After the automatic segmentation of 3D color head mesh into different head parts, vertices are classed into region-boundary vertices and region-inner vertices. Using iterative edge collapse and region-weighted error metric, the algorithm generates continuous levels of detail (LOD). Results of several experiments are shown, demonstrating the validity and efficiency of our method.Keywords: mesh simplification, level of detail, image segmentation, multi-resolution mode

Output Voltage Response Improvement and Ripple Reduction Control for Input-parallel Output-parallel High-Power DC Supply

A three-phase isolated AC-DC-DC power supply is widely used in the industrial field due to its attractive features such as high-power density, modularity for easy expansion and electrical isolation. In high-power application scenarios, it can be realized by multiple AC-DC-DC modules with Input-Parallel Output-Parallel (IPOP) mode. However, it has the problems of slow output voltage response and large ripple in some special applications, such as electrophoresis and electroplating. This paper investigates an improved Adaptive Linear Active Disturbance Rejection Control (A-LADRC) with flexible adjustment capability of the bandwidth parameter value for the high-power DC supply to improve the output voltage response speed. To reduce the DC supply ripple, a control strategy is designed for a single module to adaptively adjust the duty cycle compensation according to the output feedback value. When multiple modules are connected in parallel, a Hierarchical Delay Current Sharing Control (HDCSC) strategy for centralized controllers is proposed to make the peaks and valleys of different modules offset each other. Finally, the proposed method is verified by designing a 42V/12000A high-power DC supply, and the results demonstrate that the proposed method is effective in improving the system output voltage response speed and reducing the voltage ripple, which has significant practical engineering application value.Comment: Accepted by IEEE Transactions on Power Electronic

oneDNN Graph Compiler: A Hybrid Approach for High-Performance Deep Learning Compilation

With the rapid development of deep learning models and hardware support for dense computing, the deep learning workload characteristics changed significantly from a few hot spots on compute-intensive operations to a broad range of operations scattered across the models. Accelerating a few compute-intensive operations using the expert-tuned implementation of primitives does not fully exploit the performance potential of AI hardware. Various efforts have been made to compile a full deep neural network (DNN) graph. One of the biggest challenges is to achieve high-performance tensor compilation by generating expert level performance code for the dense compute-intensive operations and applying compilation optimization at the scope of DNN computation graph across multiple compute-intensive operations. We present oneDNN Graph Compiler, a tensor compiler that employs a hybrid approach of using techniques from both compiler optimization and expert-tuned kernels for high performance code generation of the deep neural network graph. oneDNN Graph Compiler addresses unique optimization challenges in the deep learning domain, such as low-precision computation, aggressive fusion of graph operations, optimization for static tensor shapes and memory layout, constant weight optimization, and memory buffer reuse. Experimental results demonstrate significant performance gains over existing tensor compiler and primitives library for performance-critical DNN computation graphs and end-to-end models on Intel Xeon Scalable Processors.Comment: 10 pages excluding reference, 9 figures, 1 tabl

Down-Regulation of MicroRNA-214 Contributed to the Enhanced Mitochondrial Transcription Factor A and Inhibited Proliferation of Colorectal Cancer Cells

Background/Aims: Colon cancer, also known as colorectal cancer (CRC), is one of the most common malignant tumors globally. Although significant advances have been made for developing novel therapeutics, the mechanisms of progression of colorectal cancer are still poorly understood. Methods: In this study, we identified down-regulation of microRNA-214 (miR-214) as the contributing factor for CRC. Mitochondrial transcription factor A (TFAM) and miR-214 expression in tumor samples from colorectal cancer patients and cancer cell lines were examined by reverse transcription and real-Time PCR (qPCR) or Western Blotting. Results: Our data demonstrated that miR-214 was significantly down-regulated in the tissue samples from CRC patients as well as CRC derived cell lines. TFAM overexpression was also observed in CRC patients and identified as a target for miR-214. Knockdown of TFAM by miR-214 mimics significantly inhibited the proliferation of CRC cell lines. Also, down-regulation of TFAM inhibited nuclear factor kappa-light-chain-enhancer of activated B cells (NF-κB) nuclear translocation and the expression of NF-κB depended genes. Conclusion: In conclusion, our data suggested that down-regulation of MiR-214 contributed to the enhanced TFAM expression and decreased proliferation of CRC cells

A prognostic index model for assessing the prognosis of ccRCC patients by using the mRNA expression profiles of AIF1L, SERPINC1 and CES1

Background: Kidney carcinoma is a major cause of carcinoma-related death, with the prognosis for advanced or metastatic renal cell carcinoma still very poor. The aim of this study was to investigate feasible prognostic biomarkers that can be used to construct a prognostic index model for clear cell renal cell carcinoma (ccRCC) patients. Methods: The mRNA expression profiles of ccRCC samples were downloaded from the The Cancer Genome Atlas (TCGA) dataset and the correlation of AIF1L with malignancy, tumor stage and prognosis were evaluated. Differentially expressed genes (DEGs) between AIF1L-low and AIF1L-high expression groups were selected. Those with prognostic value as determined by univariate and multivariate Cox regression analysis were then used to construct a prognostic index model capable of predicting the outcome of ccRCC patients. Results: The expression level of AIF1L was lower in ccRCC samples than in normal kidney samples. AIF1L expression showed an inverse correlation with tumor stage and a positive association with better prognosis. ccRCC samples were divided into high- and low-expression groups according to the median value of AIF1L expression. In the AIF1L-high expression group, 165 up-regulated DEGs and 601 down-regulated DEGs were identified. Three genes (AIF1L, SERPINC1 and CES1) were selected following univariate and multivariate Cox regression analysis. The hazard ratio (HR) and 95% confidence intervals (CI) for these genes were: AIF1L (HR = 0.83, 95% CI: 0.76–0.91), SERPINC1 (HR = 1.33, 95% CI: 1.12–1.58), and CES1 (HR = 0.87, 95% CI: 0.78–0.97). A prognostic index model based on the expression level of the three genes showed good performance in predicting ccRCC patient outcome, with an area under the ROC curve (AUC) of 0.671. Conclusion: This research provides a better understanding of the correlation between AIF1L expression and ccRCC. We propose a novel prognostic index model comprising AIF1L, SERPINC1 and CES1 expression that may assist physicians in determining the prognosis of ccRCC patients