We report the chloroplast (cp) genome sequence of tartary buckwheat (Fagopyrum tataricum) obtained by next-generation sequencing technology and compared this with the previously reported common buckwheat (F. esculentum ssp. ancestrale) cp genome. The cp genome of F. tataricum has a total sequence length of 159,272 bp, which is 327 bp shorter than the common buckwheat cp genome. The cp gene content, order, and orientation are similar to those of common buckwheat, but with some structural variation at tandem and palindromic repeat frequencies and junction areas. A total of seven InDels (around 100 bp) were found within the intergenic sequences and the ycf1 gene. Copy number variation of the 21-bp tandem repeat varied in F. tataricum (four repeats) and F. esculentum (one repeat), and the InDel of the ycf1 gene was 63 bp long. Nucleotide and amino acid have highly conserved coding sequence with about 98% homology and four genes—rpoC2, ycf3, accD, and clpP—have high synonymous (Ks) value. PCR based InDel markers were applied to diverse genetic resources of F. tataricum and F. esculentum, and the amplicon size was identical to that expected in silico. Therefore, these InDel markers are informative biomarkers to practically distinguish raw or processed buckwheat products derived from F. tataricum and F. esculentum.
The variety of existing cloud services creates a challenge for service providers to enforce reasonable Software Level Agreements (SLA) stating the Quality of Service (QoS) and penalties in case QoS is not achieved. To avoid such penalties at the same time that the infrastructure operates with minimum energy and resource wastage, constant monitoring and adaptation of the infrastructure is needed. We refer to Software-Defined Cloud Computing, or simply Software-Defined Clouds (SDC), as an approach for automating the process of optimal cloud configuration by extending virtualization concept to all resources in a data center. An SDC enables easy reconfiguration and adaptation of physical resources in a cloud infrastructure, to better accommodate the demand on QoS through a software that can describe and manage various aspects comprising the cloud environment. In this paper, we present an architecture for SDCs on data centers with emphasis on mobile cloud applications. We present an evaluation, showcasing the potential of SDC in two use cases-QoS-aware bandwidth allocation and bandwidthaware, energy-efficient VM placement-and discuss the research challenges and opportunities in this emerging area.
In this paper, we present techniques for automatically classifying players and tracking ball movements in basketball game video clips under poor conditions, where the camera angle dynamically shifts and changes. In the core of our system lies Yolo, a realtime object detection system. Given the ground truth boxes collected by our data specialists, Yolo is trained to detect the presence of objects in every video frame. In addition, Yolo uses Darknet that implements convolution neural networks to classify a detected object to a player and to recognize its jersey numbers of specific movements. By identifying players and ball possessions, we can automatically compute ball distributions that are reflected on complex networks. With original Yolo system, player movement can be interrupted, when the players move out of the frame due to camera shift and when players overlap each other on a two-dimensional frame. We have adapted Yolo to keep track of players even under such poor condition by considering contextual information available from the framework preceding and/or succeeding problematic video frames. In addition to the novel movement inference method, we provide a framework for analyzing the pass networks in various perspectives to help the managing staff to reveal critical determinants of team performance and to design better game strategies. We assess the performance of our system in terms of accuracy by making a comparison with the analytical reports generated by human experts.INDEX TERMS Sports analytics, object detection, complex networks, deep learning, video processing.
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