Publications from our Researchers

Several of our current PhD candidates and fellow researchers at the Data Science Institute have published, or in the proccess of publishing, papers to present their research.  

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  • Journal article
    Strege C, Bertone G, Besjes GJ, Caron S, Ruiz de Austri R, Strubig A, Trotta Ret al., 2014,

    Profile likelihood maps of a 15-dimensional MSSM

    , Journal of High Energy Physics, Vol: 2014, ISSN: 1126-6708

    We present statistically convergent profile likelihood maps obtained via globalfits of a phenomenological Minimal Supersymmetric Standard Model with 15 free parameters(the MSSM-15), based on over 250M points. We derive constraints on the modelparameters from direct detection limits on dark matter, the Planck relic density measurementand data from accelerator searches. We provide a detailed analysis of the richphenomenology of this model, and determine the SUSY mass spectrum and dark matterproperties that are preferred by current experimental constraints. We evaluate the impactof the measurement of the anomalous magnetic moment of the muon (g −2) on our results,and provide an analysis of scenarios in which the lightest neutralino is a subdominant componentof the dark matter. The MSSM-15 parameters are relatively weakly constrained bycurrent data sets, with the exception of the parameters related to dark matter phenomenology(M1, M2, µ), which are restricted to the sub-TeV regime, mainly due to the relic densityconstraint. The mass of the lightest neutralino is found to be < 1.5 TeV at 99% C.L., butcan extend up to 3 TeV when excluding the g − 2 constraint from the analysis. Low-massbino-like neutralinos are strongly favoured, with spin-independent scattering cross-sectionsextending to very small values, ∼ 10−20 pb. ATLAS SUSY null searches strongly impacton this mass range, and thus rule out a region of parameter space that is outside the reachof any current or future direct detection experiment. The best-fit point obtained after inclusionof all data corresponds to a squark mass of 2.3 TeV, a gluino mass of 2.1 TeV and a130 GeV neutralino with a spin-independent cross-section of 2.4×10−10 pb, which is withinthe reach of future multi-ton scale direct detection experiments and of the upcoming LHCrun at increased centre-of-mass energy.

  • Journal article
    Martin J, Ringeval C, Trotta R, Vennin Vet al., 2014,

    Compatibility of Planck and BICEP2 results in light of inflation

    , PHYSICAL REVIEW D, Vol: 90, ISSN: 1550-7998
  • Book chapter
    Guo Y, He S, Guo L, 2014,

    Elastic Application Container System: Elastic Web Applications Provisioning

    Cloud applications have been gaining popularity in recent years for their flexibility in resource provisioning according to Web application demands. The Elastic Application Container (EAC) system is a technology that delivers a lightweight virtual resource unit for better resource efficiency and more scalable Web applications in the Cloud. It allows multiple application providers to concurrently run their Web applications on this technology without worrying the demand change of their Web applications. This is because the EAC system constantly monitors the resource usage of all hosting Web applications and automatically reacts to the resource usage change of Web applications (i.e. it automatically handles resource provisioning of the Web applications, such as scaling of the Web applications according to the demand). In the chapter, the authors firstly describe the architecture, its components of the EAC system, in order to give a brief overview of technologies involved in the system. They then present and explain resource-provisioning algorithms and techniques used in the EAC system for demand-driven Web applications. The resource-provisioning algorithms are presented, discussed, and evaluated so as to give readers a clear picture of resource-provisioning algorithms in the EAC system. Finally, the authors compare this EAC system technology with other Cloud technologies in terms of flexibility and resource efficiency.

  • Conference paper
    Guo Y, He S, Guo L, 2014,

    Cloud Resource Monitoring for Intrusion Detection

    We present a novel security monitoring framework for intrusion detection in IaaS cloud infrastructures. The framework uses statistical anomaly detection techniques over data monitored both inside and outside each Virtual Machine instance. We present the architecture of our monitoring framework and describe the implementation of the real-time monitors and detectors. We also describe how the framework is used in three different attack scenarios. For each of the three attack scenarios, we describe how the attack itself works and how it could be detected. We describe what data is monitored in our framework and how the detection is conducted using anomaly detection methods. We also present evaluation of the detection using synthetic and real data sets. Our experimental evaluation across all three scenarios shows that our tools perform well in practical situations and provide a promising direction for future research.

  • Journal article
    Guo Y, He S, Guo L, 2014,

    Enhancing Cloud Resource Utilisation using Statistical Analysis

    Resource provisioning based on virtual machine (VM) has been widely accepted and adopted in cloud computing environments. A key problem resulting from using static scheduling approaches for allocating VMs on different physical machines (PMs) is that resources tend to be not fully utilised. Although some existing cloud reconfiguration algorithms have been developed to address the problem, they normally result in high migration costs and low resource utilisation due to ignoring the multi-dimensional characteristics of VMs and PMs. In this paper we present and evaluate a new algorithm for improving resource utilisation for cloud providers. By using a multivariate probabilistic model, our algorithm selects suitable PMs for VM re-allocation which are then used to generate a reconfiguration plan. We also describe two heuristics metrics which can be used in the algorithm to capture the multi-dimensional characteristics of VMs and PMs. By combining these two heuristics metrics in our experiments, we observed that our approach improves the resource utilisation level by around 8% for cloud providers, such as IC Cloud, which accept user-defined VM configurations and 14% for providers, such as Amazon EC2, which only provide limited types of VM configurations.

  • Journal article
    Yang X, Guo Y, Guo L, 2014,

    An iterative parameter estimation method for biological systems and its parallel implementation

    , CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE, Vol: 26, Pages: 1249-1267, ISSN: 1532-0626
  • Journal article
    Nie L, Yang X, Adcock I, Xu Z, Guo Yet al., 2014,

    Inferring cell-scale signalling networks via compressive sensing

    , PLOS One, Vol: 9, ISSN: 1932-6203
  • Journal article
    Wang M, Zhang W, Ding W, Dai D, Zhang H, Xie H, Chen L, Guo Y, Xie Jet al., 2014,

    Parallel Clustering Algorithm for Large-Scale Biological Data Sets

    , PLOS ONE, Vol: 9, ISSN: 1932-6203
  • Journal article
    Martin J, Ringeval C, Trotta R, Vennin Vet al., 2014,

    The best inflationary models after Planck

    , Journal of Cosmology and Astroparticle Physics, Vol: 2014, ISSN: 1475-7516

    We compute the Bayesian evidence and complexity of 193 slow-roll single-field models of inflation using the Planck 2013 Cosmic Microwave Background data, with the aim of establishing which models are favoured from a Bayesian perspective. Our calculations employ a new numerical pipeline interfacing an inflationary effective likelihood with the slow-roll library ASPIC and the nested sampling algorithm MultiNest. The models considered represent a complete and systematic scan of the entire landscape of inflationary scenarios proposed so far. Our analysis singles out the most probable models (from an Occam's razor point of view) that are compatible with Planck data, while ruling out with very strong evidence 34% of the models considered. We identify 26% of the models that are favoured by the Bayesian evidence, corresponding to 15 different potential shapes. If the Bayesian complexity is included in the analysis, only 9% of the models are preferred, corresponding to only 9 different potential shapes. These shapes are all of the plateau type.

  • Journal article
    Han R, Ghanem MM, Guo L, Guo Y, Osmond M, Han R, Ghanem M, Guo L, Guo Y, Osmond Met al., 2014,

    Enabling cost-aware and adaptive elasticity of multi-tier cloud applications

    , Future Generation Computer Systems, Vol: n/a, ISSN: 0167-739X

    Elasticity (on-demand scaling) of applications is one of the most important features of cloud computing. This elasticity is the ability to adaptively scale resources up and down in order to meet varying application demands. To date, most existing scaling techniques can maintain applications’ Quality of Service (QoS) but do not adequately address issues relating to minimizing the costs of using the service. In this paper, we propose an elastic scaling approach that makes use of cost-aware criteria to detect and analyse the bottlenecks within multi-tier cloud-based applications. We present an adaptive scaling algorithm that reduces the costs incurred by users of cloud infrastructure services allowing them to scale their applications only at bottleneck tiers and present the design of an intelligent platform that automates the scaling process. Our approach is generic for a wide class of multi-tier applications, and we demonstrate its effectiveness against other approaches by studying the behaviour of an example e-commerce application using a standard workload benchmark. â?� Elasticity enables adaptively scaling up and down cloud applications to meet run-time requirements. â?� We propose an approach for achieving cost-effective elasticity. â?� Cost-aware criteria are introduced. â?� Changing workloads are adapted by scaling up or down only the bottleneck components in multi-tier applications.

  • Journal article
    Heinis T, 2014,

    Data analysis: Approximation aids handling of big data

    , Nature, Vol: 515, Pages: 198-198
  • Conference paper
    Wang S, Pandis I, Emam I, Johnson D, Guitton F, Oehmichen A, Guo Yet al., 2014,

    DSIMBench: A benchmark for Microarray Data using R

    , the 40th International Conference on Very Large Databases (VLDB 2014)
  • Journal article
    Wang S, Pandis I, Wu C, He S, Johnson D, Emam I, Guitton F, Guo Yet al., 2014,

    High Dimensional Biological Data Retrieval Optimization with NoSQL Technology

    , BMC Genomics, ISSN: 1471-2164
  • Conference paper
    Lee C-H, Birch D, Wu C, Silva D, Tsinalis O, Li Y, Yan S, Ghanem M, Guo Yet al., 2013,

    Building a Generic Platform for Big Sensor Data Applications

    , 1st IEEE International Conference on Big Data (BigData2013)

    The drive toward smart cities alongside the rising adoption of personal sensors is leading to a torrent of sensor data. While systems exist for storing and managing sensor data, the real value of such data is the insight which can be generated from it. However there is currently no platform which enables sensor data to be taken from collection, through use in models to produce useful data products. The architecture of such a platform is a current research question in the field of Big Data and Smart Cities. In this paper we explore five key challenges in this field and provide a response through a sensor data platform “Concinnity” which can take sensor data from collection to final product via a data repository and workflow system. This will enable rapid development of applications built on sensor data using data fusion and the integration and composition of models to form novel workflows. We summarize the key features in our approach, exploring how this enables value to be derived from sensor data efficiently.

  • Journal article
    Strege C, Bertone G, Feroz F, Fornasa M, Ruiz de Austri R, Trotta Ret al., 2013,

    Global fits of the cMSSM and NUHM including the LHC Higgs discovery and new XENON100 constraints


    We present global fits of the constrained Minimal Supersymmetric Standard Model (cMSSM) and the Non-Universal Higgs Model (NUHM), including the most recent CMS constraint on the Higgs boson mass, 5.8 fb−1 integrated luminosity null Supersymmetry searches by ATLAS, the new LHCb measurement of BR(bar Bs → μ+μ−) and the 7-year WMAP dark matter relic abundance determination. We include the latest dark matter constraints from the XENON100 experiment, marginalising over astrophysical and particle physics uncertainties. We present Bayesian posterior and profile likelihood maps of the highest resolution available today, obtained from up to 350M points. We find that the new constraint on the Higgs boson mass has a dramatic impact, ruling out large regions of previously favoured cMSSM and NUHM parameter space. In the cMSSM, light sparticles and predominantly gaugino-like dark matter with a mass of a few hundred GeV are favoured. The NUHM exhibits a strong preference for heavier sparticle masses and a Higgsino-like neutralino with a mass of 1 TeV. The future ton-scale XENON1T direct detection experiment will probe large portions of the currently favoured cMSSM and NUHM parameter space. The LHC operating at 14 TeV collision energy will explore the favoured regions in the cMSSM, while most of the regions favoured in the NUHM will remain inaccessible. Our best-fit points achieve a satisfactory quality-of-fit, with p-values ranging from 0.21 to 0.35, so that none of the two models studied can be presently excluded at any meaningful significance level.

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