Publications

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
    Bertone G, Calore F, Caron S, Austri RRD, Kim JS, Trotta R, Weniger Cet al., 2016,

    Global analysis of the pMSSM in light of the Fermi GeV excess: prospects for the LHC Run-II and astroparticle experiments

    , Journal of Cosmology and Astroparticle Physics, Vol: 2016, ISSN: 1475-7516
  • Journal article
    Ma ZB, Yang Y, Liu YX, Bharath AAet al., 2016,

    Recurrently decomposable 2-D convolvers for FPGA-based digital image processing

    , IEEE Transactions on Circuits and Systems, Vol: 63, Pages: 979-983, ISSN: 1549-7747

    Two-dimensional (2-D) convolution is a widely used operation in image processing and computer vision, characterized by intensive computation and frequent memory accesses. Previous efforts to improve the performance of field-programmable gate array (FPGA) convolvers focused on the design of buffering schemes and on minimizing the use of multipliers. A recently proposed recurrently decomposable (RD) filter design method can reduce the computational complexity of 2-D convolutions by splitting the convolution between an image and a large mask into a sequence of convolutions using several smaller masks. This brief explores how to efficiently implement RD based 2-D convolvers using FPGA. Three FPGA architectures are proposed based on RD filters, each with a different buffering scheme. The conclusion is that RD based architectures achieve higher area efficiency than other previously reported state-of-the-art methods, especially for larger convolution masks. An area efficiency metric is also suggested, which allows the most appropriate architecture to be selected.
  • Journal article
    Creswell A, Bharath AA, 2016,

    Task Specific Adversarial Cost Function

    The cost function used to train a generative model should fit the purpose ofthe model. If the model is intended for tasks such as generating perceptuallycorrect samples, it is beneficial to maximise the likelihood of a sample drawnfrom the model, Q, coming from the same distribution as the training data, P.This is equivalent to minimising the Kullback-Leibler (KL) distance, KL[Q||P].However, if the model is intended for tasks such as retrieval or classificationit is beneficial to maximise the likelihood that a sample drawn from thetraining data is captured by the model, equivalent to minimising KL[P||Q]. Thecost function used in adversarial training optimises the Jensen-Shannon entropywhich can be seen as an even interpolation between KL[Q||P] and KL[P||Q]. Here,we propose an alternative adversarial cost function which allows easy tuning ofthe model for either task. Our task specific cost function is evaluated on adataset of hand-written characters in the following tasks: Generation,retrieval and one-shot learning.
  • Journal article
    de Montjoye YKJV,

    Privacy by design in big data: An overview of privacy enhancing technologies in the era of big data analytics

    , arXiv

    The extensive collection and processing of personal information in big data analytics has given rise to serious privacy concerns, related to wide scale electronic surveillance, profiling, and disclosure of private data. To reap the benefits of analytics without invading the individuals' private sphere, it is essential to draw the limits of big data processing and integrate data protection safeguards in the analytics value chain. ENISA, with the current report, supports this approach and the position that the challenges of ...
  • Book chapter
    de Montjoye YKJV, 2015,

    Modeling and UnderstandingIntrinsic Characteristics of Human Mobility

    , Social Phenomena From Data Analysis to Models, Publisher: Springer, ISBN: 9783319140117

    Humans are intrinsically social creatures and our mobility is central to understanding how our societies grow and function. Movement allows us to congregate with our peers, access things we need, and exchange information. Human mobility has huge impacts on topics like urban and transportation planning, social and biologic spreading, and economic outcomes. So far, modeling these processes has been hindered by a lack of data. This is radically changing with the rise of ubiquitous devices. In this chapter, we discuss recent progress deriving insights from the massive, high resolution data sets collected from mobile phone and other devices. We begin with individual mobility, where empirical evidence and statistical models have shown important intrinsic and universal characteristics about our movement: we, as human, are fundamentally slow to explore new places, relatively predictable, and mostly unique. We then explore methods of modeling aggregate movement of people from place to place and discuss how these estimates can be used to understand and optimize transportation infrastructure. Finally, we highlight applications of these findings to the dynamics of disease spread, social networks, and economic outcomes.
  • Journal article
    Rivera-Rubio J, Alexiou I, Bharath AA, 2015,

    Appearance-based indoor localization: a comparison of patch descriptor performance

    , Pattern Recognition Letters, Vol: 66, Pages: 109-117, ISSN: 1872-7344

    Vision is one of the most important of the senses, and humans use it extensively during navigation. We evaluated different types of image and video frame descriptors that could be used to determine distinctive visual landmarks for localizing a person based on what is seen by a camera that they carry. To do this, we created a database containing over 3 km of video-sequences with ground-truth in the form of distance travelled along different corridors. Using this database, the accuracy of localization—both in terms of knowing which route a user is on—and in terms of position along a certain route, can be evaluated. For each type of descriptor, we also tested different techniques to encode visual structure and to search between journeys to estimate a user’s position. The techniques include single-frame descriptors, those using sequences of frames, and both color and achromatic descriptors. We found that single-frame indexing worked better within this particular dataset. This might be because the motion of the person holding the camera makes the video too dependent on individual steps and motions of one particular journey. Our results suggest that appearance-based information could be an additional source of navigational data indoors, augmenting that provided by, say, radio signal strength indicators (RSSIs). Such visual information could be collected by crowdsourcing low-resolution video feeds, allowing journeys made by different users to be associated with each other, and location to be inferred without requiring explicit mapping. This offers a complementary approach to methods based on simultaneous localization and mapping (SLAM) algorithms.
  • Conference paper
    Rivera-Rubio J, Alexiou I, Bharath AA, 2015,

    Associating Locations Between Indoor Journeys from Wearable Cameras

    , 13th European Conference on Computer Vision (ECCV), Publisher: SPRINGER-VERLAG BERLIN, Pages: 29-44, ISSN: 0302-9743
  • Conference paper
    Tauheed F, Heinis T, Ailamaki A, 2015,

    THERMAL-JOIN: A Scalable Spatial Join for Dynamic Workloads

    , Pages: 939-950
  • Conference paper
    Heinis T, Ailamaki A, 2015,

    Reconsolidating Data Structures

    , Pages: 665-670
  • Conference paper
    Karpathiotakis M, Alagiannis I, Heinis T, Branco M, Ailamaki Aet al., 2015,

    Just-In-Time Data Virtualization: Lightweight Data Management with ViDa
  • Conference paper
    Rivera-Rubio J, Alexiou I, Bharath AA, 2015,

    Indoor Localisation with Regression Networks and Place Cell Models.

    , Publisher: BMVA Press, Pages: 147.1-147.1
  • Journal article
    Heinis T, Ham DA, 2015,

    On-the-Fly Data Synopses: Efficient Data Exploration in the Simulation Sciences

    , SIGMOD Record, Vol: 44, Pages: 23-28
  • Journal article
    Wang S, Pandis I, Johnson D, Emam I, Guitton F, Oehmichen A, Guo Yet al., 2014,

    Optimising Correlation Matrix Calculations on Gene Expression Data

    , BMC Bioinformatics, Vol: 15, ISSN: 1471-2105
  • 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

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