LS 4825: A blue supergiant on the far side of the galaxy

We present high-resolution spectroscopic observations of LS 4825, a V = 12 B-type star in the Galactic center direction. On the basis of its stellar and interstellar spectra, we infer that it is likely to be a young supergiant at a distance of 21 +/- 5 kpc, and hence lying on the far side of the 'Galaxy. Adopting this hypothesis, a differential abundance analysis shows LS 4825 to have a chemical composition that is consistent with local B-type supergiants. These observations therefore represent the first detailed investigation of a star on the far side of the Galactic center. We trace multiple interstellar components in Ca II K and Na I D spectra, with velocities -206 less than or equal to v(lst) less than or equal to +93 km s(-1). We consider the likely origin of this gas and find that some components appear to trace matter lying close to the Galactic center. We discuss the possible use of such sight lines in furthering our understanding both of the nature of gas around the Galactic center and of the abundance gradient of the Galaxy.

Adaptive Bacteria Colony Picking in Unstructured Environments Using Intensity Histogram and Unascertained LS-SVM Classifier

Features analysis is an important task which can significantly affect the performance of automatic bacteria colony picking. Unstructured environments also affect the automatic colony screening. This paper presents a novel approach for adaptive colony segmentation in unstructured environments by treating the detected peaks of intensity histograms as a morphological feature of images. In order to avoid disturbing peaks, an entropy based mean shift filter is introduced to smooth images as a preprocessing step. The relevance and importance of these features can be determined in an improved support vector machine classifier using unascertained least square estimation. Experimental results show that the proposed unascertained least square support vector machine (ULSSVM) has better recognition accuracy than the other state-of-the-art techniques, and its training process takes less time than most of the traditional approaches presented in this paper.

An Efficient LS-SVM-Based Method for Fuzzy System Construction

This paper proposes an efficient learning mechanism to build fuzzy rule-based systems through the construction of sparse least-squares support vector machines (LS-SVMs). In addition to the significantly reduced computational complexity in model training, the resultant LS-SVM-based fuzzy system is sparser while offers satisfactory generalization capability over unseen data. It is well known that the LS-SVMs have their computational advantage over conventional SVMs in the model training process; however, the model sparseness is lost, which is the main drawback of LS-SVMs. This is an open problem for the LS-SVMs. To tackle the nonsparseness issue, a new regression alternative to the Lagrangian solution for the LS-SVM is first presented. A novel efficient learning mechanism is then proposed in this paper to extract a sparse set of support vectors for generating fuzzy IF-THEN rules. This novel mechanism works in a stepwise subset selection manner, including a forward expansion phase and a backward exclusion phase in each selection step. The implementation of the algorithm is computationally very efficient due to the introduction of a few key techniques to avoid the matrix inverse operations to accelerate the training process. The computational efficiency is also confirmed by detailed computational complexity analysis. As a result, the proposed approach is not only able to achieve the sparseness of the resultant LS-SVM-based fuzzy systems but significantly reduces the amount of computational effort in model training as well. Three experimental examples are presented to demonstrate the effectiveness and efficiency of the proposed learning mechanism and the sparseness of the obtained LS-SVM-based fuzzy systems, in comparison with other SVM-based learning techniques.

LS-ADT: Lightweight and Scalable Anomaly Detection for Cloud Datacentres

Cloud data centres are implemented as large-scale clusters with demanding requirements for service performance, availability and cost of operation. As a result of scale and complexity, data centres typically exhibit large numbers of system anomalies resulting from operator error, resource over/under provisioning, hardware or software failures and security issus anomalies are inherently difficult to identify and resolve promptly via human inspection. Therefore, it is vital in a cloud system to have automatic system monitoring that detects potential anomalies and identifies their source. In this paper we present a lightweight anomaly detection tool for Cloud data centres which combines extended log analysis and rigorous correlation of system metrics, implemented by an efficient correlation algorithm which does not require training or complex infrastructure set up. The LADT algorithm is based on the premise that there is a strong correlation between node level and VM level metrics in a cloud system. This correlation will drop significantly in the event of any performance anomaly at the node-level and a continuous drop in the correlation can indicate the presence of a true anomaly in the node. The log analysis of LADT assists in determining whether the correlation drop could be caused by naturally occurring cloud management activity such as VM migration, creation, suspension, termination or resizing. In this way, any potential anomaly alerts are reasoned about to prevent false positives that could be caused by the cloud operator’s activity. We demonstrate LADT with log analysis in a Cloud environment to show how the log analysis is combined with the correlation of systems metrics to achieve accurate anomaly detection.

Parametric scans of HB and LS-RPA regimes employing Petawatt laser

By contrast to the Target Normal Sheath acceleration (TNSA) mechanism [1], Radiation Pressure Acceleration (RPA) is currently attracting a substantial amount of experimental [2,3] and theoretical [4-6] attention worldwide due to its superior scaling in terms of ion energy and laser-ion conversion efficiency. Employing Vulcan Petawatt lasers of the Rutherford Appleton Laboratory, UK, both the Hole-boring (HB) and the Light-Sail (LS) regimes of the RPA have been extensively explored. When the target thickness is of the order of hole-boring velocity times the laser pulse duration, highly collimated plasma jets of near solid density are ejected from the foil, lasting up to ns after the laser interaction. By changing the linear polarisation of the laser to circular, improved homogeneity in the jet's spatial density profile is achieved which suggests more uniform and sustained radiation pressure drive on target ions. By decreasing the target areal density or increasing irradiance on the target, the LS regime of the RPA is accessed where relatively high flux (~ 1012 particles/MeV/Sr) of ions are accelerated to ~ 10 MeV/nucleon energies in a narrow energy bandwidth. The ion energy scaling obtained from the parametric scans agrees well with theoretical estimation based on RPA mechanism and the narrow bandwidth feature in the ion spectra is studied by 2D particle-in-simulations.