Deep learning (DL) has become one of the most successful machine learning techniques. To achieve the optimal development result, there are emerging requirements on the interoperability between DL frameworks that the trained model files and training/serving programs can be re-utilized. Faithful model conversion is a promising technology to enhance the framework interoperability in which a source model is transformed into the semantic equivalent in another target framework format. However, several major challenges need to be addressed. First, there are apparent discrepancies between DL frameworks. Second, understanding the semantics of a source model could be difficult due to the framework scheme and optimization. Lastly, there exist a large number of DL frameworks, bringing potential significant engineering efforts.

Big Data computing platform has evolved to be a multi-tenant service. The service quality matters because system failure or performance slowdown could adversely affect business and user experience. There is few study in literature on service quality issues of production Big Data computing platform. In this paper, we present an empirical study on the service quality issues of Microsoft ProductA, which is a company-wide multi-tenant Big Data computing platform, serving thousands of customers from hundreds of teams. ProductA has a well-defined incident management process, which helps customers report and mitigate service quality issues on 24/7 basis. This paper explores the common symptom, causes and mitigation of service quality issues in Big Data computing. We conduct an empirical study on 210 real service quality issues in ProductA. Our major findings include (1) 21.0% of escalations are caused by hardware faults; (2) 36.2% are caused by system side defects; (3) 37.2% are due to customer side faults. We also studied the general diagnosis process and the commonly adopted mitigation solutions. Our findings can help improve current development and maintenance practice of Big Data computing platform, and motivate tool support.

BACKGROUND: To utilize the large volume of gene expression information generated from different microarray experiments, several meta-analysis techniques have been developed. Despite these efforts, there remain significant challenges to effectively increasing the statistical power and decreasing the Type I error rate while pooling the heterogeneous datasets from public resources. The objective of this study is to develop a novel meta-analysis approach, Consistent Differential Expression Pattern (CDEP), to identify genes with common differential expression patterns across different datasets. RESULTS: We combined False Discovery Rate (FDR) estimation and the non-parametric RankProd approach to estimate the Type I error rate in each microarray dataset of the meta-analysis. These Type I error rates from all datasets were then used to identify genes with common differential expression patterns. Our simulation study showed that CDEP achieved higher statistical power and maintained low Type I error rate when compared with two recently proposed meta-analysis approaches. We applied CDEP to analyze microarray data from different laboratories that compared transcription profiles between metastatic and primary cancer of different types. Many genes identified as differentially expressed consistently across different cancer types are in pathways related to metastatic behavior, such as ECM-receptor interaction, focal adhesion, and blood vessel development. We also identified novel genes such as AMIGO2, Gem, and CXCL11 that have not been shown to associate with, but may play roles in, metastasis. CONCLUSIONS: CDEP is a flexible approach that borrows information from each dataset in a meta-analysis in order to identify genes being differentially expressed consistently. We have shown that CDEP can gain higher statistical power than other existing approaches under a variety of settings considered in the simulation study, suggesting its robustness and insensitivity to data variation commonly associated with microarray experiments. AVAILABILITY: CDEP is implemented in R and freely available at: http://genomebioinfo.musc.edu/CDEP/. CONTACT: [email protected].

The exploration of computer-supported cooperative work (CSCW) in military fields is how to enhance the combat efficiency via computer technology. Along with the appearance of advanced weapon and the increasing of campaign complexity, it is essential that the system must be capable of supporting the ability of high performance cooperation as well as possessing the dynamic and flexible processing ability. Thereby the dynamic workflow is introduced into the system based on the analysis of cooperative engagement. At the same time, aiming at cooperative engagement, the program management system architecture is proposed with which the prototype of cooperative engagement system is developed. This paper implements the application of CSCW in military fields by which the whole campaign efficiency will be improved maximally.