An Efficient Source Model Selection Framework in Model Databases

With the explosive increase of big data, training a Machine Learning (ML) model becomes a computation-intensive workload, which would take days or even weeks. Thus, reusing an already trained model has received attention, which is called transfer learning. Transfer learning avoids training a new model from scratch by transferring knowledge from a source task to a target task. Existing transfer learning methods mostly focus on how to improve the performance of the target task through a specific source model, and assume that the source model is given. Although many source models are available, it is difficult for data scientists to select the best source model for the target task manually. Hence, how to efficiently select a suitable source model in a model database for model reuse is an interesting but unsolved problem. In this paper, we propose SMS, an effective, efficient, and flexible source model selection framework. SMS is effective even when the source and target datasets have significantly different data labels, and is flexible to support source models with any type of structure, and is efficient to avoid any training process. For each source model, SMS first vectorizes the samples in the target dataset into soft labels by directly applying this model to the target dataset, then uses Gaussian distributions to fit for clusters of soft labels, and finally measures the distinguishing ability of the source model using Gaussian mixture-based metric. Moreover, we present an improved SMS (I-SMS), which decreases the output number of the source model. I-SMS can significantly reduce the selection time while retaining the selection performance of SMS. Extensive experiments on a range of practical model reuse workloads demonstrate the effectiveness and efficiency of SMS

Effect of Recession on the Re-entry Capsule Aerodynamic Characteristic

AbstractNumerical simulation and analysis of aerodynamic characteristics of Soyuz ablation shape is carried out in this paper for the adverse influence coming from recession. The result indicates that the shape change caused by the recession will increase absolute value of trim angle of attack and trim lift-drag ratio. The conclusion offers reference for the aerodynamic layout design and improve of the Soyuz re-entry capsule

Evaluation of Ecosystem Services in Strategic Environmental Assessment for River Basin Planning

Water Resources Planning and Managemen

Genomic structure of human lysosomal glycosylasparaginase

AbstractThe gene structure of the human lysosomal enzyme glycosylasparaginase was determined. The gene spans 13 kb and consists of 9 exons. Both 5′ and 3′ untranslated regions of the gene are uninterrupted by introns. A number of transcriptional elements were identified in the 5′ upstream sequence that includes two putative CAAT boxes followed by TATA-like sequences together with two AP-2 binding sites and one for Sp1. A 100 bp CpG island and several ETF binding sites were also found. Additional AP-2 and Sp1 binding sites are present in the first intron. Two polyadenylation sites are present and appear to be functional. The major known glycosylasparaginase gene defect G488→C, which causes the lysosomal storage disease aspartylglycosaminuria (AGU) in Finland, is located in exon 4. Exon 5 encodes the post-translational cleavage site for the formation of the mature α/β subunits of the enzyme as well as a recently proposed active site threonine, Thr206