Software defect prediction helps to optimize testing resources allocation by identifying defect-prone modules prior to testing. Most existing models build their prediction capability based on a set of historical data, presumably from the same or similar project settings as those under prediction. However, such historical data is not always available in practice. One potential way of predicting defects in projects without historical data is to learn predictors from data of other projects. This paper investigates defect predictions in the cross-project context focusing on the selection of training data. We conduct three large-scale experiments on 34 data sets obtained from 10 open source projects. Major conclusions from our experiments include: (1) in the best cases, training data from other projects can provide better prediction results than training data from the same project; (2) the prediction results obtained using Z. He ( ) · training data from other projects meet our criteria for acceptance on the average level, defects in 18 out of 34 cases were predicted at a Recall greater than 70% and a Precision greater than 50%; (3) results of cross-project defect predictions are related with the distributional characteristics of data sets which are valuable for training data selection. We further propose an approach to automatically select suitable training data for projects without historical data. Prediction results provided by the training data selected by using our approach are comparable with those provided by training data from the same project.

Path loss prediction is of great significance for the performance optimization of wireless networks. With the development and deployment of the fifth-generation (5G) mobile communication systems, new path loss prediction methods with high accuracy and low complexity should be proposed. In this paper, the principle and procedure of machine-learning-based path loss prediction are presented. Measured data are used to evaluate the performance of different models such as artificial neural network, support vector regression, and random forest. It is shown that these machine-learning-based models outperform the log-distance model. In view of the fact that the volume of measured data sometimes cannot meet the requirements of machine learning algorithms, we propose two mechanisms to expand the training dataset. On one hand, old measured data can be reused in new scenarios or at different frequencies. On the other hand, the classical model can also be utilized to generate a number of training samples based on the prior information obtained from measured results. Measured data are employed to verify the feasibility of these data expansion mechanisms. Finally, some issues for future research are discussed.

No abstract

No abstract

ZrTe 5 is a newly discovered topological material. Shortly after a single layer ZrTe 5 had been predicted to be a two-dimensional topological insulator, a handful of experiments have been carried out on bulk ZrTe 5 crystals, which however suggest that its bulk form may be a three-dimensional topological Dirac semimetal. We report the first transport study on ultra thin ZrTe 5 flakes down to 10 nm. A significant modulation of the characteristic resistivity maximum in the temperature dependence by thickness has been observed. Remarkably, the metallic behavior, occurring only below about 150 K in bulk, persists to over 320 K for flakes less than 20 nm thick. Furthermore, the resistivity maximum can be greatly tuned by ionic gating. Combined with the Hall resistance, we identify contributions from a semiconducting and a semimetallic bands. The enhancement of the metallic state in thin flakes are consequence of shifting of the energy bands. Our results suggest that the band structure sensitively depends on the film thickness, which may explain the divergent experimental observations on bulk materials.

No abstract

No abstract

No abstract