Motif Recognition

The problem of recognizing motifs from biological data has been well-studied and numerous algorithms, both exact and approximate, have been proposed to address the underlying issue. We strongly believe that open availability and ease of accessibility of quality implementations for such algorithms are critical to the research community, in order to directly reproduce and utilize the results from other studies, so as not to reinvent the wheel. Moreover, it is also important for the implementation to be as generic as possible so that any researcher can to extend it with minimal effort to test a newly implemented algorithmic extension or heuristic. With this motivation, we choose to focus an existing algorithm, PatternBranching and, to a lesser degree, Yang2004. We analyze these approaches for minor heuristical changes & speed-ups by adjusting certain thresholds, and finally, implement the variant in high-level language (Java) using thought through programming practices and generic, extensible interfaces. We also analyze the performance of PatternBranching using a synthetically generated test-suite for a variety of sequence lengths and report the results. Code from this project will be made freely available online to the research community

Improving SIEM for critical SCADA water infrastructures using machine learning

Network Control Systems (NAC) have been used in many industrial processes. They aim to reduce the human factor burden and efficiently handle the complex process and communication of those systems. Supervisory control and data acquisition (SCADA) systems are used in industrial, infrastructure and facility processes (e.g. manufacturing, fabrication, oil and water pipelines, building ventilation, etc.) Like other Internet of Things (IoT) implementations, SCADA systems are vulnerable to cyber-attacks, therefore, a robust anomaly detection is a major requirement. However, having an accurate anomaly detection system is not an easy task, due to the difficulty to differentiate between cyber-attacks and system internal failures (e.g. hardware failures). In this paper, we present a model that detects anomaly events in a water system controlled by SCADA. Six Machine Learning techniques have been used in building and evaluating the model. The model classifies different anomaly events including hardware failures (e.g. sensor failures), sabotage and cyber-attacks (e.g. DoS and Spoofing). Unlike other detection systems, our proposed work helps in accelerating the mitigation process by notifying the operator with additional information when an anomaly occurs. This additional information includes the probability and confidence level of event(s) occurring. The model is trained and tested using a real-world dataset

Tests of Modified Gravity with Dwarf Galaxies

In modified gravity theories that seek to explain cosmic acceleration, dwarf galaxies in low density environments can be subject to enhanced forces. The class of scalar-tensor theories, which includes f(R) gravity, predict such a force enhancement (massive galaxies like the Milky Way can evade it through a screening mechanism that protects the interior of the galaxy from this "fifth" force). We study observable deviations from GR in the disks of late-type dwarf galaxies moving under gravity. The fifth-force acts on the dark matter and HI gas disk, but not on the stellar disk owing to the self-screening of main sequence stars. We find four distinct observable effects in such disk galaxies: 1. A displacement of the stellar disk from the HI disk. 2. Warping of the stellar disk along the direction of the external force. 3. Enhancement of the rotation curve measured from the HI gas compared to that of the stellar disk. 4. Asymmetry in the rotation curve of the stellar disk. We estimate that the spatial effects can be up to 1 kpc and the rotation velocity effects about 10 km/s in infalling dwarf galaxies. Such deviations are measurable: we expect that with a careful analysis of a sample of nearby dwarf galaxies one can improve astrophysical constraints on gravity theories by over three orders of magnitude, and even solar system constraints by one order of magnitude. Thus effective tests of gravity along the lines suggested by Hui et al (2009) and Jain (2011) can be carried out with low-redshift galaxies, though care must be exercised in understanding possible complications from astrophysical effects.Comment: 26 pages, 9 figure

Calculations of periodicity from H<i>α</i> profiles of Proxima Centauri

We investigate retrieval of the stellar rotation signal for Proxima Centauri. We make use of high-resolution spectra taken with UVES and HARPS of Proxima Centauri over a 13-yr period as well as photometric observations of Proxima Centauri from ASAS and HST. We measure the Hα equivalent width and Hα index, skewness and kurtosis and introduce a method that investigates the symmetry of the line, the peak ratio, which appears to return better results than the other measurements. Our investigations return a most significant period of 82.6 ± 0.1 days, confirming earlier photometric results and ruling out a more recent result of 116.6 days which we conclude to be an alias induced by the specific HARPS observation times. We conclude that whilst spectroscopic Hα measurements can be used for period recovery, in the case of Proxima Centauri the available photometric measurements are more reliable. We make 2D models of Proxima Centauri to generate simulated Hα, finding that reasonable distributions of plage and chromospheric features are able to reproduce the equivalent width variations in observed data and recover the rotation period, including after the addition of simulated noise and flares. However the 2D models used fail to generate the observed variety of line shapes measured by the peak ratio. We conclude that only 3D models which incorporate vertical motions in the chromosphere can achieve this

Machine learning based IoT Intrusion Detection System:an MQTT case study (MQTT-IoT-IDS2020 Dataset)

The Internet of Things (IoT) is one of the main research fields in the Cybersecurity domain. This is due to (a) the increased dependency on automated device, and (b) the inadequacy of general-purpose Intrusion Detection Systems (IDS) to be deployed for special purpose networks usage. Numerous lightweight protocols are being proposed for IoT devices communication usage. One of the distinguishable IoT machine-to-machine communication protocols is Message Queuing Telemetry Transport (MQTT) protocol. However, as per the authors best knowledge, there are no available IDS datasets that include MQTT benign or attack instances and thus, no IDS experimental results available. In this paper, the effectiveness of six Machine Learning (ML) techniques to detect MQTT-based attacks is evaluated. Three abstraction levels of features are assessed, namely, packet-based, unidirectional flow, and bidirectional flow features. An MQTT simulated dataset is generated and used for the training and evaluation processes. The dataset is released with an open access licence to help the research community further analyse the accompanied challenges. The experimental results demonstrated the adequacy of the proposed ML models to suit MQTT-based networks IDS requirements. Moreover, the results emphasise on the importance of using flow-based features to discriminate MQTT-based attacks from benign traffic, while packet-based features are sufficient for traditional networking attacks

Implications of the Small Spin Changes Measured for Large Jupiter-Family Comet Nuclei

Rotational spin-up due to outgassing of comet nuclei has been identified as a possible mechanism for considerable mass-loss and splitting. We report a search for spin changes for three large Jupiter-family comets (JFCs): 14P/Wolf, 143P/Kowal-Mrkos, and 162P/Siding Spring. None of the three comets has detectable period changes, and we set conservative upper limits of 4.2 (14P), 6.6 (143P) and 25 (162P) minutes per orbit. Comparing these results with all eight other JFCs with measured rotational changes, we deduce that none of the observed large JFCs experiences significant spin changes. This suggests that large comet nuclei are less likely to undergo rotationally-driven splitting, and therefore more likely to survive more perihelion passages than smaller nuclei. We find supporting evidence for this hypothesis in the cumulative size distributions of JFCs and dormant comets, as well as in recent numerical studies of cometary orbital dynamics. We added 143P to the sample of 13 other JFCs with known albedos and phase-function slopes. This sample shows a possible correlation of increasing phase-function slopes for larger geometric albedos. Partly based on findings from recent space missions to JFCs, we hypothesise that this correlation corresponds to an evolutionary trend for JFCs. We propose that newly activated JFCs have larger albedos and steeper phase functions, which gradually decrease due to sublimation-driven erosion. If confirmed, this could be used to analyse surface erosion from ground and to distinguish between dormant comets and asteroids

First-Year Sloan Digital Sky Survey-II (SDSS-II) Supernova Results: Constraints on Non-Standard Cosmological Models

We use the new SNe Ia discovered by the SDSS-II Supernova Survey together with additional supernova datasets as well as observations of the cosmic microwave background and baryon acoustic oscillations to constrain cosmological models. This complements the analysis presented by Kessler et al. in that we discuss and rank a number of the most popular non-standard cosmology scenarios. When this combined data-set is analyzed using the MLCS2k2 light-curve fitter, we find that more exotic models for cosmic acceleration provide a better fit to the data than the Lambda-CDM model. For example, the flat DGP model is ranked higher by our information criteria tests than the standard model. When the dataset is instead analyzed using the SALT-II light-curve fitter, the standard cosmological constant model fares best. Our investigation also includes inhomogeneous Lemaitre-Tolman-Bondi (LTB) models. While our LTB models can be made to fit the supernova data as well as any other model, the extra parameters they require are not supported by our information criteria analysis.Comment: ApJ in press, updated reference

Estimating Level of Engagement from Ocular Landmarks

E-learning offers many advantages like being economical, flexible and customizable, but also has challenging aspects such as lack of – social-interaction, which results in contemplation and sense of remoteness. To overcome these and sustain learners’ motivation, various stimuli can be incorporated. Nevertheless, such adjustments initially require an assessment of engagement level. In this respect, we propose estimating engagement level from facial landmarks exploiting the facts that (i) perceptual decoupling is promoted by blinking during mentally demanding tasks; (ii) eye strain increases blinking rate, which also scales with task disengagement; (iii) eye aspect ratio is in close connection with attentional state and (iv) users’ head position is correlated with their level of involvement. Building empirical models of these actions, we devise a probabilistic estimation framework. Our results indicate that high and low levels of engagement are identified with considerable accuracy, whereas medium levels are inherently more challenging, which is also confirmed by inter-rater agreement of expert coders

Qualitative interpretation of galaxy spectra

We describe a simple step-by-step guide to qualitative interpretation of galaxy spectra. Rather than an alternative to existing automated tools, it is put forward as an instrument for quick-look analysis, and for gaining physical insight when interpreting the outputs provided by automated tools. Though the recipe is of general application, it was developed for understanding the nature of the Automatic Spectroscopic K-means based (ASK) template spectra. They resulted from the classification of all the galaxy spectra in the Sloan Digital Sky Survey data release 7 (SDSS-DR7), thus being a comprehensive representation of the galaxy spectra in the local universe. Using the recipe, we give a description of the properties of the gas and the stars that characterize the ASK classes, from those corresponding to passively evolving galaxies, to HII galaxies undergoing a galaxy-wide starburst. The qualitative analysis is found to be in excellent agreement with quantitative analyses of the same spectra. A number of byproducts follow from the analysis. There is a tight correlation between the age of the stellar population and the metallicity of the gas, which is stronger than the correlations between galaxy mass and stellar age, and galaxy mass and gas metallicity. The galaxy spectra are known to follow a 1-dimensional sequence, and we identify the luminosity-weighted mean stellar age as the affine parameter that describes the sequence. All ASK classes happen to have a significant fraction of old stars, although spectrum-wise they are outshined by the youngest populations. Old stars are metal rich or metal poor depending on whether they reside in passive galaxies or in star-forming galaxies.Comment: Simple step-by-step guide to interpreting galaxy spectra. Accepted for publication in ApJ. 17 pages with 21 figure

First-year Sloan Digital Sky Survey-II (SDSS-II) supernova results: consistency and constraints with other intermediate-redshift datasets

We present an analysis of the luminosity distances of Type Ia Supernovae from the Sloan Digital Sky Survey-II (SDSS-II) Supernova Survey in conjunction with other intermediate redshift (z<0.4) cosmological measurements including redshift-space distortions from the Two-degree Field Galaxy Redshift Survey (2dFGRS), the Integrated Sachs-Wolfe (ISW) effect seen by the SDSS, and the latest Baryon Acoustic Oscillation (BAO) distance scale from both the SDSS and 2dFGRS. We have analysed the SDSS-II SN data alone using a variety of "model-independent" methods and find evidence for an accelerating universe at >97% level from this single dataset. We find good agreement between the supernova and BAO distance measurements, both consistent with a Lambda-dominated CDM cosmology, as demonstrated through an analysis of the distance duality relationship between the luminosity (d_L) and angular diameter (d_A) distance measures. We then use these data to estimate w within this restricted redshift range (z<0.4). Our most stringent result comes from the combination of all our intermediate-redshift data (SDSS-II SNe, BAO, ISW and redshift-space distortions), giving w = -0.81 +0.16 -0.18(stat) +/- 0.15(sys) and Omega_M=0.22 +0.09 -0.08 assuming a flat universe. This value of w, and associated errors, only change slightly if curvature is allowed to vary, consistent with constraints from the Cosmic Microwave Background. We also consider more limited combinations of the geometrical (SN, BAO) and dynamical (ISW, redshift-space distortions) probes.Comment: 13 pages, 7 figures, accepted for publication in MNRA