Improvement of Quantum Approximate Optimization Algorithm for Max-Cut Problems.

The objective of this short letter is to study the optimal partitioning of value stream networks into two classes so that the number of connections between them is maximized. Such kind of problems are frequently found in the design of different systems such as communication network configuration, and industrial applications in which certain topological characteristics enhance value-stream network resilience. The main interest is to improve the Max-Cut algorithm proposed in the quantum approximate optimization approach (QAOA), looking to promote a more efficient implementation than those already published. A discussion regarding linked problems as well as further research questions are also reviewed.

Insights into the prevalence of software project defects.

This paper analyses the effect of the effort distribution along the software development lifecycle on the prevalence of software defects. This analysis is based on data that was collected by the International Software Benchmarking Standards Group (ISBSG) on the development of 4,106 software projects. Data mining techniques have been applied to gain a better understanding of the behaviour of the project activities and to identify a link between the effort distribution and the prevalence of software defects. This analysis has been complemented with the use of a hierarchical clustering algorithm with a dissimilarity based on the likelihood ratio statistic, for exploratory purposes. As a result, different behaviours have been identified for this collection of software development projects, allowing for the definition of risk control strategies to diminish the number and impact of the software defects. It is expected that the use of similar estimations might greatly improve the awareness of project managers on the risks at hand.

TAO-robust backpropagation learning algorithm.

In several fields, as industrial modelling, multilayer feedforward neural networks are often used as universal function approximations. These supervised neural networks are commonly trained by a traditional backpropagation learning format, which minimises the mean squared error (mse) of the training data. However, in the presence of corrupted data (outliers) this training scheme may produce wrong models. We combine the benefits of the non-linear regression model tau-estimates [introduced by Tabatabai, M. A. Argyros, I. K. Robust Estimation and testing for general nonlinear regression models. Applied Mathematics and Computation. 58 (1993) 85-101] with the backpropagation algorithm to produce the TAO-robust learning algorithm, in order to deal with the problems of modelling with outliers. The cost function of this approach has a bounded influence function given by the weighted average of two psi functions, one corresponding to a very robust estimate and the other to a highly efficient estimate. The advantages of the proposed algorithm are studied with an example.