The optimal read strategy for strong consistent key-value applications is to enable the per-replica local reads that each replica has the ability to serve reads locally. Unfortunately, current schemes for the per-replica local reads are perplexed by two issues. First, some schemes have to violate the per-replica local reads when the workload is skewed, degrading the throughput. Second, most of current schemes rely on leases or a specialized hardware to guarantee the linearizability, bringing difficulties to the deployment.

Human leukocyte antigen (HLA) can encode the human major histocompatibility complex (MHC) proteins and play a key role in adaptive and innate immunity. Emerging clinical evidences suggest that the presentation of tumor neoantigens and neoantigen-specific T cell response associated with MHC class I molecules are of key importance to activate the adaptive immune systemin cancer immunotherapy. Therefore, accurate HLA typing is very essential for the clinical application of immunotherapy. In this study, we conducted performance evaluations of 4 widely used HLA typing tools (OptiType, Phlat, Polysolver and seq2hla) for predicting HLA class Ia genes from WES and RNA-seq data of 28 cancer patients. HLA genotyping data using PCR-SBT method was firstly obtained as the golden standard and was subsequently compared with HLA typing data by using NGS techniques. For both WES data and RNA-seq data, OptiType showed the highest accuracy for HLA-Ia typing than the other 3 programs at 2-digit and 4-digit resolution. Additionally, HLA typing accuracy from WES data was higher than from RNA-seq data (99.11% for WES data versus 96.42% for RNA-seq data). The accuracy of HLA-Ia typing by OptiType can reach 100% with the average depth of HLA gene regions >20x. Besides, the accuracy of 2-digit and 4-digit HLA-Ia typing based on control samples was higher than tumor tissues. In conclusion, OptiType by using WES data from control samples with the high average depth (>20x) of HLA gene regions can present a probably superior performance for HLA-Ia typing, enabling its application in cancer immunotherapy.

We propose a new method to refine the result of video annotation by exploiting the semantic and visual context of video. On one hand, semantic context mining is performed in a supervised way, using the manual concept labels of the training set. It is very useful for boosting video annotation performance, because semantic context is learned from labels given by people, indicating human intention. In this paper, we model the spatial and temporal context in video by using conditional random fields with different structures. Comparing with existing methods, our method could more accurately capture concept relationship in video and could more effectively improve the video annotation performance. On the other hand, visual context mining is performed in a semi-supervised way based on the visual similarities among video shots. It indicates the natural visual property of video, and could be considered as the compensation to semantic context, which generally could not be perfectly modeled. In this paper, we construct a graph based on the visual similarities among shots. Then a semi-supervised learning approach is adopt based on the graph to propagate probabilities of the reliable shots to others having similar visual features with them. Extensive experimental results on the widely used TRECVID datasets exhibit the effectiveness of our method for improving video annotation accuracy.

We propose a new method to boost the performance of video annotation by exploiting concept relationship in temporal context. The motivation of our idea mainly comes from the fact that temporally continuous shots in video are generally with consistent content, so that concepts in these shots should be semantically relevant. We utilize a temporal model to describe the contributions of relevant concepts to the presence of a target concept. By connecting our model with conditional random field and adopting the learning and inference approaches of it, we could obtain the refined probability of a concept occurring in the shot, which is the leverage of temporal context and initial output of video annotation. Experimental results on the widely used TRECVID dataset exhibit the effectiveness of our method for improving video annotation accuracy.

This paper proposes a new approach to refine video annotation by exploiting the inter-shot context. Our method is mainly novel in two ways. On one hand, to refine annotation result of the target concept, we model the sequence of shots in video as a conditional random field with chain structure. In this way, we can capture different kinds of concept relationships in inter-shot context to improve the annotation accuracy. On the other hand, to exploit inter-shot context for the target concept, we classify shots into different types according to their correlation to the target concept, which will be used to represent different kinds of concept relationships in inter-shot context. Experiments on the widely used TRECVID 2006 data set show that our method is effective for refining video annotation, achieving a significant performance improvement over several state of the art methods.

This paper proposes a new approach for the multiple frame integration of video, whose novelty mainly lies in three phases: Firstly, in the text-block group (TBG) identification, we identify the blocks with the same text by considering jointly the location, edge distribution and contrast of the text block. Then, in the TBG filtering, to avoid the bad effects of the blurred text on the result of integration, we measure the clarity of the text using the proposed text-intensity map, and select the blocks with the clear text for the integration.Finally, in the TBG integration, we integrate the text blocks by using the average and minimum integrations in the text and background of the image respectively,which can obtain the clean background and clear text with high contrast for the effective text recognition.The experimental results show our method can improve the performance of text recognition in video significantly.

This paper proposes a new approach for the text detection and extraction in image. The novelty of our approach mainly lies in the color-based clustering into two phases: In text detection phase, we consider jointly the two significant features of text regions in image: homogeneous color and sharp edges, and color-based clustering is employed to decompose the color edge map of image into several edge maps, which makes the text detection of image more accurate. In text extraction phase, on one hand, for effective text recognition, we consider the color difference between the text and background in image, and color-based clustering is utilized to remove image noise. Another hand, for effective binarization of text region, instead of performing binarization in a constant color plane as in the existing methods, our approach can adaptively select the best color plane according to the text contrast difference among color planes for binarization. Experimental results show our approach is better than the existing methods.