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Electronic mail has become cast and embedded in our everyday lives. Billions of legitimate emails are sent on a daily basis. The widely established underlying infrastructure, its widespread availability as well as its ease of use have all acted as catalysts to such pervasive proliferation. Unfortunately, the same can be alleged about unsolicited bulk email, or rather spam. Various methods, as well as enabling architectures are available to try to mitigate spam permeation. In this respect, this dissertation compliments existing survey work in this area by contributing an extensive literature review of traditional and emerging spam filtering approaches. Techniques, approaches and architectures employed for spam filtering are appraised, critically assessing respective strengths and weaknesses. Velocity, volume and variety are key characteristics of the spam challenge. MapReduce (M/R) has become increasingly popular as an Internet scale, data intensive processing platform. In the context of machine learning based spam filter training, support vector machine (SVM) based techniques have been proven effective. SVM training is however a computationally intensive process. In this dissertation, a M/R based distributed SVM algorithm for scalable spam filter training, designated MRSMO, is presented. By distributing and processing subsets of the training data across multiple participating computing nodes, the distributed SVM reduces spam filter training time significantly. To mitigate the accuracy degradation introduced by the adopted approach, a Resource Description Framework (RDF) based feedback loop is evaluated. Experimental results demonstrate that this improves the accuracy levels of the distributed SVM beyond the original sequential counterpart. Effectively exploiting large scale, ‘Cloud’ based, heterogeneous processing capabilities for M/R in what can be considered a non-deterministic environment requires the consideration of a number of perspectives. In this work, gSched, a Hadoop M/R based, heterogeneous aware task to node matching and allocation scheme is designed. Using MRSMO as a baseline, experimental evaluation indicates that gSched improves on the performance of the out-of-the box Hadoop counterpart in a typical Cloud based infrastructure. The focal contribution to knowledge is a scalable, heterogeneous infrastructure and machine learning based spam filtering scheme, able to capitalize on collaborative accuracy improvements through RDF based, end user feedback. MapReduce based RDF Assisted Distributed SVM for High Throughput Spam Filtering

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Hydrogen, being the simplest and most abundant element in the Universe, is of fundamental importance to condensed matter sciences. Through advances in high pressure experimental technique, hydrogen (and its isotope deuterium) has been contained and studied using in situ optical spectroscopy to 315 (275 GPa) at 300 K, pressure and temperature conditions previously thought to be inaccessible. At 200 GPa, hydrogen undergoes a phase transformation, attributed to phase III, previously observed only at low temperatures. This is succeeded at 220 GPa by a reversible transformation to a new phase, IV, characterized by the simultaneous appearance of the second vibrational fundamental mode, new low-frequency phonon excitations, and a dramatic softening and broadening of the first vibrational fundamental mode. To impose constraints on the P-T phase diagram, the temperature stability of phase IV is investigated through a series of low temperature experiments, where the phase IV-III transformation is observed. Analysis of the Raman spectra suggests that phase IV is a mixture of graphene-like layers, consisting of elongated H2 dimers experiencing large pairing fluctuations, and unbound H2 molecules. Isotopic comparisons reveal spectral differences between the phase IV-III transition of hydrogen and deuterium, which strongly indicates the presence of proton tunnelling in phase IV. Optical transmission spectra of phase IV reveals an overall increase of absorption and a closing band gap reaching 1.8 eV at 315 GPa. No differences between the isotopes were observed in absorption studies, resulting in identical values for the band gap. Extrapolation of the band gap yields 375 GPa as the minimum transition pressure to a metallic state of hydrogen (deuterium).

<p> A search for vector-like quarks (VLQs) decaying to a Z boson using multi-stage machine learning was compared to a search using a standard square cuts search strategy. VLQs are predicted by several new theories beyond the Standard Model. The searches used 20.3 inverse femtobarns of proton-proton collisions at a center-of-mass energy of 8 TeV collected with the ATLAS detector in 2012 at the CERN Large Hadron Collider. CLs upper limits on production cross sections of vector-like top and bottom quarks were computed for VLQs produced singly or in pairs, <i>T_single, B_single, T_pair, </i> and <i>B_pair.</i> The two stage machine learning classification search strategy did not provide any improvement over the standard square cuts strategy, but for <i>T_pair, B_pair,</i> and <i>T_single,</i> a third stage of machine learning regression was able to lower the upper limits of high signal masses by as much as 50%. Additionally, new test statistics were developed for use in the Neyman construction of confidence regions in order to address deficiencies in current frequentist methods, such as the generation of empty set confidence intervals. A new method for treating nuisance parameters was also developed that may provide better coverage properties than current methods used in particle searches. Finally, significance ratio functions were derived that allow a more nuanced interpretation of the evidence provided by measurements than is given by confidence intervals alone.</p>

Enormous Ly alpha nebulae (ELANe), unique tracers of galaxy density peaks, are predicted to lie at the nodes and intersections of cosmic filamentary structures. Previous successful searches for ELANe have focused on wide-field narrowband surveys or have targeted known sources such as ultraluminous quasi-stellar objects (QSOs) or radio galaxies. Utilizing groups of coherently strong Ly alpha absorptions, we have developed a new method to identify high-redshift galaxy overdensities and have identified an extremely massive overdensity, BOSS1441, at z = 2-3. In its density peak, we discover an ELAN that is associated with a relatively faint continuum. To date, this object has the highest diffuse Ly alpha nebular luminosity of L-nebula = 5.1 +/- 0.1 x 10(44) erg s(-1). Above the 2 sigma surface brightness limit of SBLy alpha = 4.8 x 10(-18) erg s(-1) cm(-2) arcsec(-2), this nebula has an end-to-end spatial extent of 442 kpc. This radio-quiet source also has extended C IV lambda 1549 and He II lambda 1640 emission on greater than or similar to 30 kpc scales. Note that the Ly alpha, He II, and C IV emissions all have double-peaked line profiles. Each velocity component has an FWHM of approximate to 700-1000 km s(-1). We argue that this Lya nebula could be powered by shocks due to an active galactic nucleus-driven outflow or photoionization by a strongly obscured source.