SENATUS: An Approach to Joint Traffic Anomaly Detection and Root Cause Analysis

In this paper, we propose a novel approach, called SENATUS, for joint traffic anomaly detection and root-cause analysis. Inspired from the concept of a senate, the key idea of the proposed approach is divided into three stages: election, voting and decision. At the election stage, a small number of \nop{traffic flow sets (termed as senator flows)}senator flows are chosen\nop{, which are used} to represent approximately the total (usually huge) set of traffic flows. In the voting stage, anomaly detection is applied on the senator flows and the detected anomalies are correlated to identify the most possible anomalous time bins. Finally in the decision stage, a machine learning technique is applied to the senator flows of each anomalous time bin to find the root cause of the anomalies. We evaluate SENATUS using traffic traces collected from the Pan European network, GEANT, and compare against another approach which detects anomalies using lossless compression of traffic histograms. We show the effectiveness of SENATUS in diagnosing anomaly types: network scans and DoS/DDoS attacks

Exploring the Morphology of RAVE Stellar Spectra

The RAdial Velocity Experiment (RAVE) is a medium resolution R~7500 spectroscopic survey of the Milky Way which already obtained over half a million stellar spectra. They present a randomly selected magnitude-limited sample, so it is important to use a reliable and automated classification scheme which identifies normal single stars and discovers different types of peculiar stars. To this end we present a morphological classification of 350,000 RAVE survey stellar spectra using locally linear embedding, a dimensionality reduction method which enables representing the complex spectral morphology in a low dimensional projected space while still preserving the properties of the local neighborhoods of spectra. We find that the majority of all spectra in the database ~90-95% belong to normal single stars, but there is also a significant population of several types of peculiars. Among them the most populated groups are those of various types of spectroscopic binary and chromospherically active stars. Both of them include several thousands of spectra. Particularly the latter group offers significant further investigation opportunities since activity of stars is a known proxy of stellar ages. Applying the same classification procedure to the sample of normal single stars alone shows that the shape of the projected manifold in two dimensional space correlates with stellar temperature, surface gravity and metallicity.Comment: 28 pages, 11 figures, accepted for publication in ApJ

A Cluster Randomised Trial Evaluation of the Media Initiative for Children: Respecting Difference Programme

Evaluates trial outcomes of a preschool program designed to raise awareness of diversity issues, increase empathy, and promote inclusive behaviors among children, early childhood practitioners, and parents. Considers implications for further development

Classification methods for noise transients in advanced gravitational-wave detectors

Noise of non-astrophysical origin will contaminate science data taken by the Advanced Laser Interferometer Gravitational-wave Observatory (aLIGO) and Advanced Virgo gravitational-wave detectors. Prompt characterization of instrumental and environmental noise transients will be critical for improving the sensitivity of the advanced detectors in the upcoming science runs. During the science runs of the initial gravitational-wave detectors, noise transients were manually classified by visually examining the time-frequency scan of each event. Here, we present three new algorithms designed for the automatic classification of noise transients in advanced detectors. Two of these algorithms are based on Principal Component Analysis. They are Principal Component Analysis for Transients (PCAT), and an adaptation of LALInference Burst (LIB). The third algorithm is a combination of an event generator called Wavelet Detection Filter (WDF) and machine learning techniques for classification. We test these algorithms on simulated data sets, and we show their ability to automatically classify transients by frequency, SNR and waveform morphology

A difference boosting neural network for automated star-galaxy classification

In this paper we describe the use of a new artificial neural network, called the difference boosting neural network (DBNN), for automated classification problems in astronomical data analysis. We illustrate the capabilities of the network by applying it to star galaxy classification using recently released, deep imaging data. We have compared our results with classification made by the widely used Source Extractor (SExtractor) package. We show that while the performance of the DBNN in star-galaxy classification is comparable to that of SExtractor, it has the advantage of significantly higher speed and flexibility during training as well as classification.Comment: 9 pages, 1figure, 7 tables, accepted for publication in Astronomy and Astrophysic

Insight into the Evolution of Anuran Foot Flag Displays: A Comparative Study of Color and Kinematics

Understanding how complex animal displays evolve is a major goal of evolutionary organismal biology. Here, we study this topic by comparing convergently evolved gestural displays in two unrelated species of frog (Bornean Rock Frog, Staurois parvus, and Kottigehara Dancing Frog, Micrixalus kottigeharensis). This behavior, known as a foot flag, is produced when a male ?waves\u27 his hindlimb at another male during bouts of competition for access to mates. We assess patterns of variation in the color of frog feet and the kinematics of the display itself to help pinpoint similarities and differences of the visual signal elements. We find clear species differences in the color of foot webbing, which is broadcast to receivers during specific phases of the display. Analyses of foot-trajectory duration and geometry also reveal clear species differences in display speed and shape - S. parvus generates a faster and more circular visual signal, while M. kottigeharensis generates a much slower and more elliptical one. These data are consistent with the notion that color, speed, and shape likely encode species identity. However, we also found that foot flag speed shows significant among-individual variation, particularly the phase of the display in which foot webbings are visible. This result is consistent with the idea that frogs alter temporal signal components, which may showcase individual condition, quality, or motivation. Overall, our comparative study helps elucidate the variability of foot flagging behavior in a manner that informs how we understand the design principles that underlie its function as a signal in intraspecific communication