Stationarity and regularity of infinite collections of sets. Applications to infinitely constrained optimization

This article continues the investigation of stationarity and regularity properties of infinite collections of sets in a Banach space started in Kruger and López (J. Optim. Theory Appl. 154(2), 2012), and is mainly focused on the application of the stationarity criteria to infinitely constrained optimization problems. We consider several settings of optimization problems which involve (explicitly or implicitly) infinite collections of sets and deduce for them necessary conditions characterizing stationarity in terms of dual space elements—normals and/or subdifferentials.

Spatiotemporal characteristics of the Huangtupo landslide in the Three Gorges region (China) constrained by radar interferometry

The Huangtupo landslide is one of the largest in the Three Gorges region, China. The county-seat town of Badong, located on the south shore between the Xiling and Wu gorges of the Yangtze River, was moved to this unstable slope prior to the construction of the Three Gorges Project, since the new Three Gorges reservoir completely submerged the location of the old city. The instability of the slope is affecting the new town by causing residential safety problems. The Huangtupo landslide provides scientists an opportunity to understand landslide response to fluctuating river water level and heavy rainfall episodes, which is essential to decide upon appropriate remediation measures. Interferometric Synthetic Aperture Radar (InSAR) techniques provide a very useful tool for the study of superficial and spatially variable displacement phenomena. In this paper, three sets of radar data have been processed to investigate the Huangtupo landslide. Results show that maximum displacements are affecting the northwest zone of the slope corresponding to Riverside slumping mass I#. The other main landslide bodies (i.e. Riverside slumping mass II#, Substation landslide and Garden Spot landslide) exhibit a stable behaviour in agreement with in situ data, although some active areas have been recognized in the foot of the Substation landslide and Garden Spot landslide. InSAR has allowed us to study the kinematic behaviour of the landslide and to identify its active boundaries. Furthermore, the analysis of the InSAR displacement time-series has helped recognize the different displacement patterns on the slope and their relationships with various triggering factors. For those persistent scatterers, which exhibit long-term displacements, they can be decomposed into a creep model (controlled by geological conditions) and a superimposed recoverable term (dependent on external factors), which appears closely correlated with reservoir water level changes close to the river's edge. These results, combined with in situ data, provide a comprehensive analysis of the Huangtupo landslide, which is essential for its management.

Optimisation of analytical methods for the characterisation of oranges, clementines and citrus hybrids cultivated in Spain on the basis of their composition in ascorbic acid, citric acid and major sugars

Three HPLC methods were optimised for the determination of citric acid, succinic acid and ascorbic acid using a photodiode array detector and fructose, glucose and sucrose using a refractive index in twenty eight citrus juices. The analysis was completed in <16 min. Two different harvests were taken into account for this study. For the season 2011, ascorbic acid content was comprised between 19.4 and 59 mg vitamin C/100 mL; meanwhile for the season 2012, the content was slightly higher for most of the samples ranging from 33.5 to 85.3 mg vitamin C/100 mL. Moreover, the citric acid content in orange juices ranged between 9.7 and 15.1 g L−1, while for clementines the content was clearly lower (i.e. from 3.5 to 8.4 g L−1). However, clementines showed the highest sucrose content with values near to 6 g/100 mL. Finally, a cluster analysis was applied to establish a classification of the citrus species.

Feature selection by multi-objective optimisation: Application to network anomaly detection by hierarchical self-organising maps

Feature selection is an important and active issue in clustering and classification problems. By choosing an adequate feature subset, a dataset dimensionality reduction is allowed, thus contributing to decreasing the classification computational complexity, and to improving the classifier performance by avoiding redundant or irrelevant features. Although feature selection can be formally defined as an optimisation problem with only one objective, that is, the classification accuracy obtained by using the selected feature subset, in recent years, some multi-objective approaches to this problem have been proposed. These either select features that not only improve the classification accuracy, but also the generalisation capability in case of supervised classifiers, or counterbalance the bias toward lower or higher numbers of features that present some methods used to validate the clustering/classification in case of unsupervised classifiers. The main contribution of this paper is a multi-objective approach for feature selection and its application to an unsupervised clustering procedure based on Growing Hierarchical Self-Organising Maps (GHSOMs) that includes a new method for unit labelling and efficient determination of the winning unit. In the network anomaly detection problem here considered, this multi-objective approach makes it possible not only to differentiate between normal and anomalous traffic but also among different anomalies. The efficiency of our proposals has been evaluated by using the well-known DARPA/NSL-KDD datasets that contain extracted features and labelled attacks from around 2 million connections. The selected feature sets computed in our experiments provide detection rates up to 99.8% with normal traffic and up to 99.6% with anomalous traffic, as well as accuracy values up to 99.12%.

Measuring the stellar wind parameters in IGR J17544-2619 and Vela X-1 constrains the accretion physics in supergiant fast X-ray transient and classical supergiant X-ray binaries

Context. Classical supergiant X-ray binaries (SGXBs) and supergiant fast X-ray transients (SFXTs) are two types of high-mass X-ray binaries (HMXBs) that present similar donors but, at the same time, show very different behavior in the X-rays. The reason for this dichotomy of wind-fed HMXBs is still a matter of debate. Among the several explanations that have been proposed, some of them invoke specific stellar wind properties of the donor stars. Only dedicated empiric analysis of the donors’ stellar wind can provide the required information to accomplish an adequate test of these theories. However, such analyses are scarce. Aims. To close this gap, we perform a comparative analysis of the optical companion in two important systems: IGR J17544-2619 (SFXT) and Vela X-1 (SGXB). We analyze the spectra of each star in detail and derive their stellar and wind properties. As a next step, we compare the wind parameters, giving us an excellent chance of recognizing key differences between donor winds in SFXTs and SGXBs. Methods. We use archival infrared, optical and ultraviolet observations, and analyze them with the non-local thermodynamic equilibrium (NLTE) Potsdam Wolf-Rayet model atmosphere code. We derive the physical properties of the stars and their stellar winds, accounting for the influence of X-rays on the stellar winds. Results. We find that the stellar parameters derived from the analysis generally agree well with the spectral types of the two donors: O9I (IGR J17544-2619) and B0.5Iae (Vela X-1). The distance to the sources have been revised and also agree well with the estimations already available in the literature. In IGR J17544-2619 we are able to narrow the uncertainty to d = 3.0 ± 0.2 kpc. From the stellar radius of the donor and its X-ray behavior, the eccentricity of IGR J17544-2619 is constrained to e< 0.25. The derived chemical abundances point to certain mixing during the lifetime of the donors. An important difference between the stellar winds of the two stars is their terminal velocities (ν∞ = 1500 km s-1 in IGR J17544-2619 and ν∞ = 700 km s-1 in Vela X-1), which have important consequences on the X-ray luminosity of these sources. Conclusions. The donors of IGR J17544-2619 and Vela X-1 have similar spectral types as well as similar parameters that physically characterize them and their spectra. In addition, the orbital parameters of the systems are similar too, with a nearly circular orbit and short orbital period. However, they show moderate differences in their stellar wind velocity and the spin period of their neutron star which has a strong impact on the X-ray luminosity of the sources. This specific combination of wind speed and pulsar spin favors an accretion regime with a persistently high luminosity in Vela X-1, while it favors an inhibiting accretion mechanism in IGR J17544-2619. Our study demonstrates that the relative wind velocity is critical in class determination for the HMXBs hosting a supergiant donor, given that it may shift the accretion mechanism from direct accretion to propeller regimes when combined with other parameters.

Constrained self-organizing feature map to preserve feature extraction topology

In many classification problems, it is necessary to consider the specific location of an n-dimensional space from which features have been calculated. For example, considering the location of features extracted from specific areas of a two-dimensional space, as an image, could improve the understanding of a scene for a video surveillance system. In the same way, the same features extracted from different locations could mean different actions for a 3D HCI system. In this paper, we present a self-organizing feature map able to preserve the topology of locations of an n-dimensional space in which the vector of features have been extracted. The main contribution is to implicitly preserving the topology of the original space because considering the locations of the extracted features and their topology could ease the solution to certain problems. Specifically, the paper proposes the n-dimensional constrained self-organizing map preserving the input topology (nD-SOM-PINT). Features in adjacent areas of the n-dimensional space, used to extract the feature vectors, are explicitly in adjacent areas of the nD-SOM-PINT constraining the neural network structure and learning. As a study case, the neural network has been instantiate to represent and classify features as trajectories extracted from a sequence of images into a high level of semantic understanding. Experiments have been thoroughly carried out using the CAVIAR datasets (Corridor, Frontal and Inria) taken into account the global behaviour of an individual in order to validate the ability to preserve the topology of the two-dimensional space to obtain high-performance classification for trajectory classification in contrast of non-considering the location of features. Moreover, a brief example has been included to focus on validate the nD-SOM-PINT proposal in other domain than the individual trajectory. Results confirm the high accuracy of the nD-SOM-PINT outperforming previous methods aimed to classify the same datasets.