nu-Anomica: A Fast Support Vector Based Novelty Detection Technique

In this paper we propose nu-Anomica, a novel anomaly detection technique that can be trained on huge data sets with much reduced running time compared to the benchmark one-class Support Vector Machines algorithm. In -Anomica, the idea is to train the machine such that it can provide a close approximation to the exact decision plane using fewer training points and without losing much of the generalization performance of the classical approach. We have tested the proposed algorithm on a variety of continuous data sets under different conditions. We show that under all test conditions the developed procedure closely preserves the accuracy of standard one-class Support Vector Machines while reducing both the training time and the test time by 5 - 20 times

Fast and Flexible Multivariate Time Series Subsequence Search

Multivariate Time-Series (MTS) are ubiquitous, and are generated in areas as disparate as sensor recordings in aerospace systems, music and video streams, medical monitoring, and financial systems. Domain experts are often interested in searching for interesting multivariate patterns from these MTS databases which often contain several gigabytes of data. Surprisingly, research on MTS search is very limited. Most of the existing work only supports queries with the same length of data, or queries on a fixed set of variables. In this paper, we propose an efficient and flexible subsequence search framework for massive MTS databases, that, for the first time, enables querying on any subset of variables with arbitrary time delays between them. We propose two algorithms to solve this problem (1) a List Based Search (LBS) algorithm which uses sorted lists for indexing, and (2) a R*-tree Based Search (RBS) which uses Minimum Bounding Rectangles (MBR) to organize the subsequences. Both algorithms guarantee that all matching patterns within the specified thresholds will be returned (no false dismissals). The very few false alarms can be removed by a post-processing step. Since our framework is also capable of Univariate Time-Series (UTS) subsequence search, we first demonstrate the efficiency of our algorithms on several UTS datasets previously used in the literature. We follow this up with experiments using two large MTS databases from the aviation domain, each containing several millions of observations. Both these tests show that our algorithms have very high prune rates (>99%) thus needing actual disk access for only less than 1% of the observations. To the best of our knowledge, MTS subsequence search has never been attempted on datasets of the size we have used in this paper

Multiple Kernel Learning for Heterogeneous Anomaly Detection: Algorithm and Aviation Safety Case Study

The world-wide aviation system is one of the most complex dynamical systems ever developed and is generating data at an extremely rapid rate. Most modern commercial aircraft record several hundred flight parameters including information from the guidance, navigation, and control systems, the avionics and propulsion systems, and the pilot inputs into the aircraft. These parameters may be continuous measurements or binary or categorical measurements recorded in one second intervals for the duration of the flight. Currently, most approaches to aviation safety are reactive, meaning that they are designed to react to an aviation safety incident or accident. In this paper, we discuss a novel approach based on the theory of multiple kernel learning to detect potential safety anomalies in very large data bases of discrete and continuous data from world-wide operations of commercial fleets. We pose a general anomaly detection problem which includes both discrete and continuous data streams, where we assume that the discrete streams have a causal influence on the continuous streams. We also assume that atypical sequence of events in the discrete streams can lead to off-nominal system performance. We discuss the application domain, novel algorithms, and also discuss results on real-world data sets. Our algorithm uncovers operationally significant events in high dimensional data streams in the aviation industry which are not detectable using state of the art method

Fast Multivariate Search on Large Aviation Datasets

Multivariate Time-Series (MTS) are ubiquitous, and are generated in areas as disparate as sensor recordings in aerospace systems, music and video streams, medical monitoring, and financial systems. Domain experts are often interested in searching for interesting multivariate patterns from these MTS databases which can contain up to several gigabytes of data. Surprisingly, research on MTS search is very limited. Most existing work only supports queries with the same length of data, or queries on a fixed set of variables. In this paper, we propose an efficient and flexible subsequence search framework for massive MTS databases, that, for the first time, enables querying on any subset of variables with arbitrary time delays between them. We propose two provably correct algorithms to solve this problem (1) an R-tree Based Search (RBS) which uses Minimum Bounding Rectangles (MBR) to organize the subsequences, and (2) a List Based Search (LBS) algorithm which uses sorted lists for indexing. We demonstrate the performance of these algorithms using two large MTS databases from the aviation domain, each containing several millions of observations Both these tests show that our algorithms have very high prune rates (>95%) thus needing actua

Volatile Loss and Classification of Kuiper Belt Objects

Observations indicate that some of the largest Kuiper Belt Objects (KBOs) have retained volatiles in the gas phase, which implies the presence of an atmosphere that can affect their reflectance spectra and thermal balance. Volatile escape rates driven by solar heating of the surface were estimated by Schaller and Brown (2007) (SB) and Levi and Podolak (2009)(LP) using Jeans escape from the surface and a hydrodynamic model respectively. Based on recent molecular kinetic simulations these rates can be hugely in error (e.g., a factor of $\sim 10^{16}$ for the SB estimate for Pluto). In this paper we estimate the loss of primordial N$_2$ for several large KBOs guided by recent molecular kinetic simulations of escape due to solar heating of the surface and due to UV/EUV heating of the upper atmosphere. For the latter we extrapolate simulations of escape from Pluto (Erwin et al. 2013) using the energy limited escape model recently validated for the KBOs of interest by molecular kinetic simulations (Johnson et al. 2013). Unless the N$_2$ atmosphere is thin ($\lesssim 10^{18}$ N$_2$/cm$^2$) and/or the radius small ($\lesssim 200-300$ km), we find that escape is primarily driven by the UV/EUV radiation absorbed in the upper atmosphere rather than the solar heating of the surface. This affects the previous interpretations of the relationship between atmospheric loss and the observed surface properties. The long-term goal is to connect detailed atmospheric loss simulations with a model for volatile transport (e.g., Young, 2014) for individual KBOs.Comment: 20 pages, 5 figures; submitted to Ap

Erasing Quantum Distinguishability via Single-Mode Filtering

Erasing quantum-mechanical distinguishability is of fundamental interest and also of practical importance, particularly in subject areas related to quantum information processing. We demonstrate a method applicable to optical systems in which single-mode filtering is used with only linear optical instruments to achieve quantum indistinguishability. Through "heralded" Hong-Ou-Mandel interference experiments we measure and quantify the improvement of indistinguishability between single photons generated via spontaneous four-wave mixing in optical fibers. The experimental results are in excellent agreement with predictions of a quantum-multimode theory we develop for such systems, without the need for any fitting parameter.Comment: 5 pages, 5 figure

Mathematical modelling of polyamine metabolism in bloodstream-form trypanosoma brucei: An application to drug target identification

© 2013 Gu et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are creditedThis article has been made available through the Brunel Open Access Publishing Fund.We present the first computational kinetic model of polyamine metabolism in bloodstream-form Trypanosoma brucei, the causative agent of human African trypanosomiasis. We systematically extracted the polyamine pathway from the complete metabolic network while still maintaining the predictive capability of the pathway. The kinetic model is constructed on the basis of information gleaned from the experimental biology literature and defined as a set of ordinary differential equations. We applied Michaelis-Menten kinetics featuring regulatory factors to describe enzymatic activities that are well defined. Uncharacterised enzyme kinetics were approximated and justified with available physiological properties of the system. Optimisation-based dynamic simulations were performed to train the model with experimental data and inconsistent predictions prompted an iterative procedure of model refinement. Good agreement between simulation results and measured data reported in various experimental conditions shows that the model has good applicability in spite of there being gaps in the required data. With this kinetic model, the relative importance of the individual pathway enzymes was assessed. We observed that, at low-to-moderate levels of inhibition, enzymes catalysing reactions of de novo AdoMet (MAT) and ornithine production (OrnPt) have more efficient inhibitory effect on total trypanothione content in comparison to other enzymes in the pathway. In our model, prozyme and TSHSyn (the production catalyst of total trypanothione) were also found to exhibit potent control on total trypanothione content but only when they were strongly inhibited. Different chemotherapeutic strategies against T. brucei were investigated using this model and interruption of polyamine synthesis via joint inhibition of MAT or OrnPt together with other polyamine enzymes was identified as an optimal therapeutic strategy.The work was carried out under a PhD programme partly funded by Prof. Ray Welland, School of Computing Science, University of Glasgo

Electron Impact Excitation Cross Sections for Hydrogen-Like Ions

We present cross sections for electron-impact-induced transitions n --> n' in hydrogen-like ions C 5+, Ne 9+, Al 12+, and Ar 17+. The cross sections are computed by Coulomb-Born with exchange and normalization (CBE) method for all transitions with n < n' < 7 and by convergent close-coupling (CCC) method for transitions with n 2s and 1s --> 2p are presented as well. The CCC and CBE cross sections agree to better than 10% with each other and with earlier close-coupling results (available for transition 1 --> 2 only). Analytical expression for n --> n' cross sections and semiempirical formulae are discussed.Comment: RevTeX, 5 pages, 13 PostScript figures, submitted to Phys. Rev.

olaparib maintenance therapy in patients pts with platinum sensitive relapsed psr ovarian cancer oc and stable disease sd following platinum based chemotherapy

n/

Endometrial Carcinoma: A Review of Chemotherapy, Drug Resistance, and the Search for New Agents

The article examines current treatment options in patients with endometrial carcinoma, the role of drug resistance, and the rationale for the use of epothilones in treating this disease