Network Filtering for Big Data: Triangulated Maximally Filtered Graph

We propose a network-filtering method, the Triangulated Maximally Filtered Graph (TMFG), that provides an approximate solution to the WEIGHTED MAXIMAL PLANAR GRAPH problem. The underlying idea of TMFG consists in building a triangulation that maximizes a score function associated with the amount of information retained by the network.TMFG uses as weights any arbitrary similarity measure to arrange data into a meaningful network structure that can be used for clustering, community detection and modelling. The method is fast, adaptable and scalable to very large datasets; it allows online updating and learning as new data can be inserted and deleted with combinations of local and non-local moves. Further, TMFG permits readjustments of the network in consequence of changes in the strength of the similarity measure. The method is based on local topological moves and can therefore take advantage of parallel and GPUs computing. We discuss how this network-filtering method can be used intuitively and efficiently for big data studies and its significance from an information-theoretic perspective

Nonlinear characterisation of a silicon integrated Bragg waveguide filter

Bragg waveguides are promising optical filters for pump suppression in spontaneous Four-Wave Mixing (FWM) photon sources. In this work, we investigate the generation of unwanted photon pairs in the filter itself. We do this by taking advantage of the relation between spontaneous and classical FWM, which allows for the precise characterisation of the nonlinear response of the device. The pair generation rate estimated from the classical measurement is compared with the theoretical value calculated by means of a full quantum model of the filter, which also allows to investigate the spectral properties of the generated pairs. We find a good agreement between theory and experiment, confirming that stimulated FWM is a valuable approach to characterise the nonlinear response of an integrated filter, and that the pairs generated in a Bragg waveguide are not a serious issue for the operation of a fully integrated nonclassical source

Parsimonious modeling with information filtering networks

We introduce a methodology to construct parsimonious probabilistic models. This method makes use of information filtering networks to produce a robust estimate of the global sparse inverse covariance from a simple sum of local inverse covariances computed on small subparts of the network. Being based on local and low-dimensional inversions, this method is computationally very efficient and statistically robust, even for the estimation of inverse covariance of high-dimensional, noisy, and short time series. Applied to financial data our method results are computationally more efficient than state-of-the-art methodologies such as Glasso producing, in a fraction of the computation time, models that can have equivalent or better performances but with a sparser inference structure. We also discuss performances with sparse factor models where we notice that relative performances decrease with the number of factors. The local nature of this approach allows us to perform computations in parallel and provides a tool for dynamical adaptation by partial updating when the properties of some variables change without the need of recomputing the whole model. This makes this approach particularly suitable to handle big data sets with large numbers of variables. Examples of practical application for forecasting, stress testing, and risk allocation in financial systems are also provided

Rumen fluid, a new diagnostic matrix in dairy cattle farms?

Production diseases of dairy cows are considered man-made problems caused by the inability of cowsto achieve a sufficient feed energy intake (Mulligan, 2008).A correct management of production diseases demands early diagnostic and prognostic parameters, inorder to improve the management system and reduce the prevalence of clinical cases (Ingvartsen,2003).A previous study of our group indicated that forestomachs walls express immune receptors andcytokines, and the rumen liquor contains leukocytes able to produce IFN-γ (Trevisi, 2014).Our working hypothesis implied that ruminal fluids could be a source of diagnostic information for theidentification of herds at risk for production diseases.We first demonstrated that the diet can influence the immune response in forestomachs. Diverseleukocyte populations at low concentrations and IFN-γ were revealed in some samples of rumen fluids,with a clear inhibition of the response observed in the animals fed the maize-supplemented diet,compared to a normal and a soy-supplemented diet.We better characterized the leukocytes subpopulations in the rumen liquor, isolating B cells, monocytesand γδT cells.Finally we performed a field survey in order to find correlation among the immune profile of the rumenliquor. Clinically healthy animals showed a farm specific immunologic pattern of the rumen liquor: lowCD45 mRNA expression, low IFN-γ, few/absent B-cells.We can conclude that the epithelial cells of ruminant forestomachs can react to different stresses(metabolic, infectious, inflammatory) and the inflammatory response can be sustained by infiltratingleukocytes.Our data points into the idea that dairy farms could be ranked according to a risk score using theinflammatory markers in rumen fluids, in addition to the traditional analysis.

Parsimonious modeling with information filtering networks

We introduce a methodology to construct parsimonious probabilistic models. This method makes use of information filtering networks to produce a robust estimate of the global sparse inverse covariance from a simple sum of local inverse covariances computed on small subparts of the network. Being based on local and low-dimensional inversions, this method is computationally very efficient and statistically robust, even for the estimation of inverse covariance of high-dimensional, noisy, and short time series. Applied to financial data our method results are computationally more efficient than state-of-the-art methodologies such as Glasso producing, in a fraction of the computation time, models that can have equivalent or better performances but with a sparser inference structure. We also discuss performances with sparse factor models where we notice that relative performances decrease with the number of factors. The local nature of this approach allows us to perform computations in parallel and provides a tool for dynamical adaptation by partial updating when the properties of some variables change without the need of recomputing the whole model. This makes this approach particularly suitable to handle big data sets with large numbers of variables. Examples of practical application for forecasting, stress testing, and risk allocation in financial systems are also provided

Beyond two-point statistics: using the minimum spanning tree as a tool for cosmology

Cosmological studies of large-scale structure have relied on two-point statistics, not fully exploiting the rich structure of the cosmic web. In this paper we show how to capture some of this cosmic web information by using the minimum spanning tree (MST), for the first time using it to estimate cosmological parameters in simulations. Discrete tracers of dark matter such as galaxies, N-body particles or haloes are used as nodes to construct a unique graph, the MST, that traces skeletal structure. We study the dependence of the MST on cosmological parameters using haloes from a suite of COmoving Lagrangian Acceleration (COLA) simulations with a box size of 250 h(-1) Mpc, varying the amplitude of scalar fluctuations (A(s)), matter density (Omega(m)), and neutrino mass (Sigma m(nu)). The power spectrum P and bispectrum B are measured for wavenumbers between 0.125 and 0.5 h Mpc(-1), while a corresponding lower cut of similar to 12.6 h(-1) Mpc is applied to the MST. The constraints from the individual methods are fairly similar but when combined we see improved 1 sigma constraints of similar to 17 per cent (similar to 12 per cent) on Omega(m) and similar to 12 per cent (similar to 10 per cent) on A(s) with respect to P (P + B) thus showing the MST is providing additional information. The MST can be applied to current and future spectroscopic surveys (BOSS, DESI, Euclid, PSF, WFIRST, and 4MOST) in 3D and photometric surveys (DES and LSST) in tomographic shells to constrain parameters and/or test systematics

A scalable middleware-based infrastructure for energy management and visualization in city districts

Following the Smart City views, citizens, policy makers and energy distribution companies need a reliable and scalable infrastructure to manage and analyse energy consumption data in a city district context. In order to move forward this view, a city district model is needed, which takes into account different data-sources such as Building Information Models, Geographic Information Systems and real-time information coming from heterogeneous devices in the district. The Internet of Things paradigm is creating new business opportunities for low-cost, low-power and high-performance devices. Nevertheless, because of the "smart devices" heterogeneity, in order to provide uniform access to their functionalities, an abstract point of view is needed. Therefore, we propose an distributed software infrastructure, exploiting service-oriented middleware and ontology solutions to cope with the management, simulation and visualization of district energy data