Scalable and fault tolerant failure detection and consensus

Future extreme-scale high-performance computing systems will be required to work under frequent component failures. The MPI Forum's User Level Failure Mitigation proposal has introduced an operation, MPI_Comm_shrink, to synchronize the alive processes on the list of failed processes, so that applications can continue to execute even in the presence of failures by adopting algorithm-based fault tolerance techniques. This MPI_Comm_shrink operation requires a fault tolerant failure detection and consensus algorithm. This paper presents and compares two novel failure detection and consensus algorithms. The proposed algorithms are based on Gossip protocols and are inherently fault-tolerant and scalable. The proposed algorithms were implemented and tested using the Extreme-scale Simulator. The results show that in both algorithms the number of Gossip cycles to achieve global consensus scales logarithmically with system size. The second algorithm also shows better scalability in terms of memory and network bandwidth usage and a perfect synchronization in achieving global consensus.

Equatorial waves in the lower stratosphere. I: A novel detection method

Wavenumber-frequency spectral analysis and linear wave theory are combined in a novel method to quantitatively estimate equatorial wave activity in the tropical lower stratosphere. The method requires temperature and velocity observations that are regularly spaced in latitude, longitude and time; it is therefore applied to the ECMWF 15-year re-analysis dataset (ERA-15). Signals consistent with idealized Kelvin and Rossby-gravity waves are found at wavenumbers and frequencies in agreement with previous studies. When averaged over 1981-93, the Kelvin wave explains approximately 1 K-2 of temperature variance on the equator at 100 hPa, while the Rossby-gravity wave explains approximately 1 m(2)s(-2) of meridional wind variance. Some inertio-gravity wave and equatorial Rossby wave signals are also found; however the resolution of ERA-15 is not sufficient for the method to provide an accurate climatology of waves with high meridional structure.

A novel approach to the detection of synchronisation in EEG based on empirical mode decomposition

Transient neural assemblies mediated by synchrony in particular frequency ranges are thought to underlie cognition. We propose a new approach to their detection, using empirical mode decomposition (EMD), a data-driven approach removing the need for arbitrary bandpass filter cut-offs. Phase locking is sought between modes. We explore the features of EMD, including making a quantitative assessment of its ability to preserve phase content of signals, and proceed to develop a statistical framework with which to assess synchrony episodes. Furthermore, we propose a new approach to ensure signal decomposition using EMD. We adapt the Hilbert spectrum to a time-frequency representation of phase locking and are able to locate synchrony successfully in time and frequency between synthetic signals reminiscent of EEG. We compare our approach, which we call EMD phase locking analysis (EMDPL) with existing methods and show it to offer improved time-frequency localisation of synchrony.

The tagged microarray marker (TAM) method for high-throughput detection of single nucleotide and indel polymorphisms

The tagged microarray marker (TAM) method allows high-throughput differentiation between predicted alternative PCR products. Typically, the method is used as a molecular marker approach to determining the allelic states of single nucleotide polymorphisms (SNPs) or insertion-deletion (indel) alleles at genomic loci in multiple individuals. Biotin-labeled PCR products are spotted, unpurified, onto a streptavidin-coated glass slide and the alternative products are differentiated by hybridization to fluorescent detector oligonucleotides that recognize corresponding allele-specific tags on the PCR primers. The main attractions of this method are its high throughput (thousands of PCRs are analyzed per slide), flexibility of scoring (any combination, from a single marker in thousands of samples to thousands of markers in a single sample, can be analyzed) and flexibility of scale (any experimental scale, from a small lab setting up to a large project). This protocol describes an experiment involving 3,072 PCRs scored on a slide. The whole process from the start of PCR setup to receiving the data spreadsheet takes 2 d.

Mapping the landscape of the lymphocytic choriomeningitis virus stable signal peptide reveals novel functional domains

The stable signal peptide (SSP) of the lymphocytic choriomeningitis virus surface glycoprotein precursor has several unique characteristics. The SSP is unusually long, at 58 amino acids, and contains two hydrophobic domains, and its sequence is highly conserved among both Old and New World arenaviruses. To better understand the functions of the SSP, a panel of point and deletion mutants was created by in vitro mutagenesis to target the highly conserved elements within the SSP. We were also able to confirm critical residues required for separate SSP functions by trans-complementation. Using these approaches, it was possible to resolve functional domains of the SSP. In characterizing our SSP mutants, we discovered that the SSP is involved in several distinct functions within the viral life cycle, beyond translocation of the viral surface glycoprotein precursor into the endoplasmic reticulum lumen. The SSP is required for efficient glycoprotein expression, posttranslational maturation cleavage of GP1 and GP2 by SKI-1/S1P protease, glycoprotein transport to the cell surface plasma membrane, formation of infectious virus particles, and acid pH-dependent glycoprotein-mediated cell fusion.

Stress-induced changes in the Schizosaccharomyces pombe proteome using two-dimensional difference gel electrophoresis, mass spectrometry and a novel integrated robotics platform

Robotic and manual methods have been used to obtain identification of significantly changing proteins regulated when Schizosaccharomyces pombe is exposed to oxidative stress. Differently treated S. pombe cells were lysed, labelled with CyDye and analysed by two-dimensional difference gel electrophoresis. Gel images analysed off-line, using the DeCyder image analysis software [GE Healthcare, Amersham, UK] allowed selection of significantly regulated proteins. Proteins displaying differential expression were excised robotically for manual digestion and identified by matrix-assisted laser desorption/ionisation - mass spectrometry (MALDI-MS). Additionally the same set of proteins displaying differential expression were automatically cut and digested using a prototype robotic platform. Automated MALDI-MS, peak label assignment and database searching were utilised to identify as many proteins as possible. The results achieved by the robotic system were compared to manual methods. The identification of all significantly altered proteins provides an annotated peroxide stress-related proteome that can be used as a base resource against which other stress-induced proteomic changes can be compared.

Cyclins, cyclin-dependent kinases, and cyclin-dependent kinase inhibitors: detection methods and activity measurements

The cyclin/cyclin-dependent kinase (Cdk) complexes and the Cdk inhibitors (CDKI) are crucial regulators of cell cycle progression in all eukaryotic cells. Using rat cardiac myocytes as a model system, this chapter provides a detailed account of methods that can be employed to measure both cyclin/Cdk activity in cells and the extent of CDKI inhibitory activity present in a particular cell type.

Identification of novel expressed sequences, up-regulated in the leucocytes of chronic fatigue syndrome patients

BACKGROUND: Chronic fatigue syndrome (CFS) is an increasing medical phenomenon of unknown aetiology leading to high levels of chronic morbidity. Of the many hypotheses that purport to explain this disease, immune system activation, as a central feature, has remained prominent but unsubstantiated. Supporting this, a number of important cytokines have previously been shown to be over-expressed in disease subjects. The diagnosis of CFS is highly problematic since no biological markers specific to this disease have been identified. The discovery of genes relating to this condition is an important goal in seeking to correctly categorize and understand this complex syndrome. OBJECTIVE: The aim of this study was to screen for changes in gene expression in the lymphocytes of CFS patients. METHODS: 'Differential Display' is a method for comparing mRNA populations for the induction or suppression of genes. In this technique, mRNA populations from control and test subjects can be 'displayed' by gel electrophoresis and screened for differing banding patterns. These differences are indicative of altered gene expression between samples, and the genes that correspond to these bands can be cloned and identified. Differential display has been used to compare expression levels between four control subjects and seven CFS patients. RESULTS: Twelve short expressed sequence tags have been identified that were over-expressed in lymphocytes from CFS patients. Two of these correspond to cathepsin C and MAIL1 - genes known to be upregulated in activated lymphocytes. The expression level of seven of the differentially displayed sequences have been verified by quantifying relative level of these transcripts using TAQman quantitative PCR. CONCLUSION: Taken as a whole, the identification of novel gene tags up-regulated in CFS patients adds weight to the idea that CFS is a disease characterized by subtle changes in the immune system.

The SAFEE on-board threat detection system

Under the framework of the European Union Funded SAFEE project(1), this paper gives an overview of a novel monitoring and scene analysis system developed for use onboard aircraft in spatially constrained environments. The techniques discussed herein aim to warn on-board crew about pre-determined indicators of threat intent (such as running or shouting in the cabin), as elicited from industry and security experts. The subject matter experts believe that activities such as these are strong indicators of the beginnings of undesirable chains of events or scenarios, which should not be allowed to develop aboard aircraft. This project aimes to detect these scenarios and provide advice to the crew. These events may involve unruly passengers or be indicative of the precursors to terrorist threats. With a state of the art tracking system using homography intersections of motion images, and probability based Petri nets for scene understanding, the SAFEE behavioural analysis system automatically assesses the output from multiple intelligent sensors, and creates. recommendations that are presented to the crew using an integrated airborn user interface. Evaluation of the system is conducted within a full size aircraft mockup, and experimental results are presented, showing that the SAFEE system is well suited to monitoring people in confined environments, and that meaningful and instructive output regarding human actions can be derived from the sensor network within the cabin.

Design and evaluation of intelligent classifier based intrusion detection system

We have discovered a novel approach of intrusion detection system using an intelligent data classifier based on a self organizing map (SOM). We have surveyed all other unsupervised intrusion detection methods, different alternative SOM based techniques and KDD winner IDS methods. This paper provides a robust designed and implemented intelligent data classifier technique based on a single large size (30x30) self organizing map (SOM) having the capability to detect all types of attacks given in the DARPA Archive 1999 the lowest false positive rate being 0.04 % and higher detection rate being 99.73% tested using full KDD data sets and 89.54% comparable detection rate and 0.18% lowest false positive rate tested using corrected data sets.

Enhancing change detection in low-quality surveillance footage using markov random fields

Urban surveillance footage can be of poor quality, partly due to the low quality of the camera and partly due to harsh lighting and heavily reflective scenes. For some computer surveillance tasks very simple change detection is adequate, but sometimes a more detailed change detection mask is desirable, eg, for accurately tracking identity when faced with multiple interacting individuals and in pose-based behaviour recognition. We present a novel technique for enhancing a low-quality change detection into a better segmentation using an image combing estimator in an MRF based model.

Empirical mode decomposition: a novel technique for the study of tremor time series

Tremor is a clinical feature characterized by oscillations of a part of the body. The detection and study of tremor is an important step in investigations seeking to explain underlying control strategies of the central nervous system under natural (or physiological) and pathological conditions. It is well established that tremorous activity is composed of deterministic and stochastic components. For this reason, the use of digital signal processing techniques (DSP) which take into account the nonlinearity and nonstationarity of such signals may bring new information into the signal analysis which is often obscured by traditional linear techniques (e.g. Fourier analysis). In this context, this paper introduces the application of the empirical mode decomposition (EMD) and Hilbert spectrum (HS), which are relatively new DSP techniques for the analysis of nonlinear and nonstationary time-series, for the study of tremor. Our results, obtained from the analysis of experimental signals collected from 31 patients with different neurological conditions, showed that the EMD could automatically decompose acquired signals into basic components, called intrinsic mode functions (IMFs), representing tremorous and voluntary activity. The identification of a physical meaning for IMFs in the context of tremor analysis suggests an alternative and new way of detecting tremorous activity. These results may be relevant for those applications requiring automatic detection of tremor. Furthermore, the energy of IMFs was visualized as a function of time and frequency by means of the HS. This analysis showed that the variation of energy of tremorous and voluntary activity could be distinguished and characterized on the HS. Such results may be relevant for those applications aiming to identify neurological disorders. In general, both the HS and EMD demonstrated to be very useful to perform objective analysis of any kind of tremor and can therefore be potentially used to perform functional assessment.

A novel shape feature for fast region-based pedestrian recognition

A new class of shape features for region classification and high-level recognition is introduced. The novel Randomised Region Ray (RRR) features can be used to train binary decision trees for object category classification using an abstract representation of the scene. In particular we address the problem of human detection using an over segmented input image. We therefore do not rely on pixel values for training, instead we design and train specialised classifiers on the sparse set of semantic regions which compose the image. Thanks to the abstract nature of the input, the trained classifier has the potential to be fast and applicable to extreme imagery conditions. We demonstrate and evaluate its performance in people detection using a pedestrian dataset.

Near-far resistant detection of CDMA signals via isolation bit insertion

This paper presents a novel scheme for near-far resistant CDMA detection: isolation bit insertion (IBI). At the transmitter, isolation bits are inserted into the information bit sequence before modulation, and a practical linear decorrelating detector (LDD) is obtained at the receiver. All the advantages that an LDD theoretically offers are retained and realised in practice.