Online Supplement to `Efficient Simulation Resource Sharing and Allocation for Selecting the Best'

This is the online supplement to the article by the same authors, "Efficient Simulation Resource Sharing and Allocation for Selecting the Best," published in the IEEE Transactions on Automatic Control

Online Appendix for “Gradient-Based Myopic Allocation Policy: An Efficient Sampling Procedure in a Low-Confidence Scenario”

This is the online appendix, which includes theoretical and numerical supplements containing some technical details and three additional numerical examples, which could not fit in the main body due to page limits by the journal for a technical note. The abstract for the main body is as follows: In this note, we study a simulation optimization problem of selecting the alternative with the best performance from a finite set, or a so-called ranking and selection problem, in a special low-confidence scenario. The most popular sampling allocation procedures in ranking and selection do not perform well in this scenario, because they all ignore certain induced correlations that significantly affect the probability of correct selection in this scenario. We propose a gradient-based myopic allocation policy (G-MAP) that takes the induced correlations into account, reflecting a trade-off between the induced correlation and the two factors (mean-variance) found in the optimal computing budget allocation formula. Numerical experiments substantiate the efficiency of the new procedure in the low-confidence scenario.This work was supported in part by the National Science Foundation (NSF) under Grants CMMI-0856256, CMMI- 1362303, CMMI-1434419, by the National Natural Science Foundation of China (NSFC) under Grants 71571048, by the Air Force of Scientific Research (AFOSR) under Grant FA9550-15-10050, and by the Science and Technology Agency of Sichuan Province under Grant 2014GZX0002

Building quantum neural networks based on swap test

Artificial neural network, consisting of many neurons in different layers, is an important method to simulate humain brain. Usually, one neuron has two operations: one is linear, the other is nonlinear. The linear operation is inner product and the nonlinear operation is represented by an activation function. In this work, we introduce a kind of quantum neuron whose inputs and outputs are quantum states. The inner product and activation operator of the quantum neurons can be realized by quantum circuits. Based on the quantum neuron, we propose a model of quantum neural network in which the weights between neurons are all quantum states. We also construct a quantum circuit to realize this quantum neural network model. A learning algorithm is proposed meanwhile. We show the validity of learning algorithm theoretically and demonstrate the potential of the quantum neural network numerically.Comment: 10 pages, 13 figure

Isolation and Identification of miRNAs in Jatropha curcas

MicroRNAs (miRNAs) are small noncoding RNAs that play crucial regulatory roles by targeting mRNAs for silencing. To identify miRNAs in Jatropha curcas L, a bioenergy crop, cDNA clones from two small RNA libraries of leaves and seeds were sequenced and analyzed using bioinformatic tools. Fifty-two putative miRNAs were found from the two libraries, among them six were identical to known miRNAs and 46 were novel. Differential expression patterns of 15 miRNAs in root, stem, leave, fruit and seed were detected using quantitative real-time PCR. Ten miRNAs were highly expressed in fruit or seed, implying that they may be involved in seed development or fatty acids synthesis in seed. Moreover, 28 targets of the isolated miRNAs were predicted from a jatropha cDNA library database. The miRNA target genes were predicted to encode a broad range of proteins. Sixteen targets had clear BLASTX hits to the Uniprot database and were associated with genes belonging to the three major gene ontology categories of biological process, cellular component, and molecular function. Four targets were identified for JcumiR004. By silencing JcumiR004 primary miRNA, expressions of the four target genes were up-regulated and oil composition were modulated significantly, indicating diverse functions of JcumiR004