Dynamic selection scheme for cooperative relay under high mobility environment

The mobility of relay has great influence on the performance of a cooperative relay system.This paper proposes a dynamic selection scheme under the amplify-and-forward communication mode in high mobility environment,based on the estimation of channels and the power allocation for each relay node by comparing it with the pre-set threshold.This scheme is used to choose the cooperative relay dynamically for a multiple relay scenario.Simulation results show that this proposed relay selection scheme decreases the outage probability effectively,maintains system capacity well,and improves the performance of the relay system.

Dynamic ensemble selection of convolutional neural networks and its application in flower classification

In recent years,convolutional neural networks(CNNs)have achieved great success in image classification.However,CNN models usually have complex network structures that tend to cause some related problems,such as redundancy of network parameters,low training efficiency,overfitting,and weak generalization ability.To solve these problems and improve the accuracy of flower classification,the advantages of CNNs were combined with those of ensemble learning and a method was developed for the dynamic ensemble selection of CNNs.First,MobileNet models pre-trained on a public dataset were transferred to flower datasets to train thirteen different MobileNet classifiers,and a resampling strategy was used to enhance the diversity of individual models.Second,the thirteen classifiers were sorted by a classifier sorting algorithm,before ensemble selection,to avoid an exhaustive search.Finally,with the credibility of recognition results,a classifier subset was dynamically selected and integrated to identify the flower species from their images.To verify the effectiveness,the proposed method was used to classify the images of five flower species.The accuracy of the proposed method was 95.50%,an improvement of 1.62%,3.94%,22.04%,13.77%,and 0.44%,over those of MobileNet,Inception-v1,ResNet-50,Inception-ResNet-v2,and the linear ensemble method,respectively.In addition,the performance of the proposed method was compared with five other methods for flower classification.The experimental results demonstrated the accuracy and robustness of the proposed method.

Production of cold CN molecules by photodissociating ICN precursors in brute-force field

We theoretically investigate the production of cold CN molecules by photodissociating ICN precursors in a brute-force field. The energy shifts and adiabatic orientation of the rotational ICN precursors are first investigated as a function of the external field strength. The dynamical photofragmentation of ICN precursors is numerically simulated for cases with and without orienting field. The CN products are compared in terms of their velocity distributions. A small portion of the CN fragments are recoiled to near zero speed in the lab frame by appropriately selecting the photo energy for dissociation. With a precursor ICN molecular beam of - 1.5 K in rotational temperature, the production of low speed CN fragments can be improved by more than 5 times when an orienting electrical field of 100 k V/cm is present. The corresponding production rate for decelerated fragments with speeds ≤ 50 m/s is simulated to be about ～2.1×10^-4 and CN number densities of 10^8 –10^10 cm^-3 can be reached with precursor ICN densities of ～10^12 –10^14 cm^-3 from supersonic expansion.

A Static Greedy and Dynamic Adaptive Thread Spawning Approach for Loop-Level Parallelism

Thread-level speculation becomes more attractive for the exploitation of thread-level parallelism from irregular sequential applications. But it is common for speculative threads to fail to reach the expected parallel performance. The reason is that the performance of speculative threads is extremely complicated by the fact that it not only suffers from the imprecision of compiler-directed performance estimation due to ambiguous control and data dependences, but also depends on the underlying hardware configuration and program behaviors. Thus, this paper proposes a statically greedy and dynamically adaptive approach for loop-level speculation to dynamically determine the best loop level at runtime. It relies on the compiler to select and optimize all loop candidates greedily, which are then proceeded on the cost-benefit analysis of different loop nesting levels for the determination of the order of loop speculation. Under the runtime loop execution prediction, we dynamically schedule and update the order of loop speculation, and ensure the best loop level to be always parallelized. Two different policies are also examined to maximize overall performance. Compared with traditional static loop selection techniques, our approach （：an achieve comparable or better performance.

Time Consistent Multi-period Worst-Case Risk Measure in Robust Portfolio Selection

In this paper,we first construct a time consistent multi-period worst-case risk measure,which measures the dynamic investment risk period-wise from a distributionally robust perspective.Under the usually adopted uncertainty set,we derive the explicit optimal investment strategy for the multi-period robust portfolio selection problem under the multi-period worst-case risk measure.Empirical results demonstrate that the portfolio selection model under the proposed risk measure is a good complement to existing multi-period robust portfolio selection models using the adjustable robust approach.