Wrocław University of Technology

About: Wrocław University of Technology is a education organization based out in Wrocław, Poland. It is known for research contribution in the topics: Laser & Computer science. The organization has 13115 authors who have published 31279 publications receiving 338694 citations.

Three-fold rotational defects in two-dimensional transition metal dichalcogenides

Abstract: As defects frequently govern the properties of crystalline solids, the precise microscopic knowledge of defect atomic structure is of fundamental importance. We report a new class of point defects in single-layer transition metal dichalcogenides that can be created through 60° rotations of metal-chalcogen bonds in the trigonal prismatic lattice, with the simplest among them being a three-fold symmetric trefoil-like defect. The defects, which are inherently related to the crystal symmetry of transition metal dichalcogenides, can expand through sequential bond rotations, as evident from in situ scanning transmission electron microscopy experiments, and eventually form larger linear defects consisting of aligned 8-5-5-8 membered rings. First-principles calculations provide insights into the evolution of rotational defects and show that they give rise to p-type doping and local magnetic moments, but weakly affect mechanical characteristics of transition metal dichalcogenides. Thus, controllable introduction of rotational defects can be used to engineer the properties of these materials.

Probabilistic load forecasting via Quantile Regression Averaging on sister forecasts

Abstract: Majority of the load forecasting literature has been on point forecasting, which provides the expected value for each step throughout the forecast horizon. In the smart grid era, the electricity demand is more active and less predictable than ever before. As a result, probabilistic load forecasting, which provides additional information on the variability and uncertainty of future load values, is becoming of great importance to power systems planning and operations. This paper proposes a practical methodology to generate probabilistic load forecasts by performing Quantile Regression Averaging (QRA) on a set of sister point forecasts. There are two major benefits of the proposed approach: 1) it can leverage the development in the point load forecasting literature over the past several decades; and 2) it does not rely so much on high quality expert forecasts, which are rarely achievable in load forecasting practice. To demonstrate the effectiveness of the proposed approach and make the results reproducible to the load forecasting community, we construct a case study using the publicly available data from the Global Energy Forecasting Competition 2014. Comparing with the benchmark methods that utilize the variability of a selected individual forecast, the proposed approach leads to dominantly better performance as measured by the pinball loss function and the Winkler score.

Which process metrics can significantly improve defect prediction models? An empirical study

Abstract: The knowledge about the software metrics which serve as defect indicators is vital for the efficient allocation of resources for quality assurance. It is the process metrics, although sometimes difficult to collect, which have recently become popular with regard to defect prediction. However, in order to identify rightly the process metrics which are actually worth collecting, we need the evidence validating their ability to improve the product metric-based defect prediction models. This paper presents an empirical evaluation in which several process metrics were investigated in order to identify the ones which significantly improve the defect prediction models based on product metrics. Data from a wide range of software projects (both, industrial and open source) were collected. The predictions of the models that use only product metrics (simple models) were compared with the predictions of the models which used product metrics, as well as one of the process metrics under scrutiny (advanced models). To decide whether the improvements were significant or not, statistical tests were performed and effect sizes were calculated. The advanced defect prediction models trained on a data set containing product metrics and additionally Number of Distinct Committers (NDC) were significantly better than the simple models without NDC, while the effect size was medium and the probability of superiority (PS) of the advanced models over simple ones was high ( $$p=.016$$ p = . 016 , $$r=-.29$$ r = - . 29 , $$\hbox {PS}=.76$$ PS = . 76 ), which is a substantial finding useful in defect prediction. A similar result with slightly smaller PS was achieved by the advanced models trained on a data set containing product metrics and additionally all of the investigated process metrics ( $$p=.038$$ p = . 038 , $$r=-.29$$ r = - . 29 , $$\hbox {PS}=.68$$ PS = . 68 ). The advanced models trained on a data set containing product metrics and additionally Number of Modified Lines (NML) were significantly better than the simple models without NML, but the effect size was small ( $$p=.038$$ p = . 038 , $$r=.06$$ r = . 06 ). Hence, it is reasonable to recommend the NDC process metric in building the defect prediction models.

Nature Inspired Methods and Their Industry Applications—Swarm Intelligence Algorithms

Abstract: In this paper, we present the swarm intelligence (SI) concept and mention some metaheuristics belonging to the SI. We present the particle swarm optimization (PSO) algorithm and the ant colony optimization (ACO) method as the representatives of the SI approach. In recent years, researchers are eager to develop and apply a variety of these two methods, despite the development of many other newer methods as Bat or FireFly algorithms. Presenting the PSO and ACO we put their pseudocode, their properties, and intuition lying behind them. Next, we focus on their real-life applications, indicating many papers presented varieties of basic algorithms and the areas of their applications.