Weighted Frechet Means as Convex Combinations in Metric Spaces: Properties and Generalized Median Inequalities

In this short note, we study the properties of the weighted Frechet mean as a convex combination operator on an arbitrary metric space, (Y,d). We show that this binary operator is commutative, non-associative, idempotent, invariant to multiplication by a constant weight and possesses an identity element. We also treat the properties of the weighted cumulative Frechet mean. These tools allow us to derive several types of median inequalities for abstract metric spaces that hold for both negative and positive Alexandrov spaces. In particular, we show through an example that these bounds cannot be improved upon in general metric spaces. For weighted Frechet means, however, such inequalities can solely be derived for weights equal or greater than one. This latter limitation highlights the inherent difficulties associated with working with abstract-valued random variables.Comment: 7 pages, 1 figure. Submitted to Probability and Statistics Letter

Hypothesis Testing For Network Data in Functional Neuroimaging

In recent years, it has become common practice in neuroscience to use networks to summarize relational information in a set of measurements, typically assumed to be reflective of either functional or structural relationships between regions of interest in the brain. One of the most basic tasks of interest in the analysis of such data is the testing of hypotheses, in answer to questions such as "Is there a difference between the networks of these two groups of subjects?" In the classical setting, where the unit of interest is a scalar or a vector, such questions are answered through the use of familiar two-sample testing strategies. Networks, however, are not Euclidean objects, and hence classical methods do not directly apply. We address this challenge by drawing on concepts and techniques from geometry, and high-dimensional statistical inference. Our work is based on a precise geometric characterization of the space of graph Laplacian matrices and a nonparametric notion of averaging due to Fr\'echet. We motivate and illustrate our resulting methodologies for testing in the context of networks derived from functional neuroimaging data on human subjects from the 1000 Functional Connectomes Project. In particular, we show that this global test is more statistical powerful, than a mass-univariate approach. In addition, we have also provided a method for visualizing the individual contribution of each edge to the overall test statistic.Comment: 34 pages. 5 figure

Operability and Results of Retro and On-Going Commission Tools Applied to an Existing Building

Several tools in the scope of Annex 40 (PECI Model Commissioning Plan and Guide specification, Emma-CTA, IPMVP) have been used to realise the retro and the on-going commissioning of an existing building. The aim of the work was to evaluate operability, consumed time, results of these tools used by HVAC operation technicians. Analysis of making use of the different tools in a common framework is proposed, giving feedback information to creative authors

The Infrared Continuum Sizes of Be Star Disks

We present an analysis of the near-infrared continuum emission from the circumstellar gas disks of Be stars using a radiative transfer code for a parametrized version of the viscous decretion disk model. This isothermal gas model creates predicted images that we use to estimate the HWHM emission radius along the major axis of the projected disk and the spatially integrated flux excess at wavelengths of 1.7, 2.1, 4.8, 9, and 18 ?m. We discuss in detail the effect of the disk base density, inclination angle, stellar effective temperature, and other physical parameters on the derived disk sizes and color excesses. We calculate color excess estimates relative to the stellar V -band flux for a sample of 130 Be stars using photometry from 2MASS and the AKARI infrared camera all-sky survey. The color excess relations from our models make a good match of the observed color excesses of Be stars. We also present our results on the projected size of the disk as a function of wavelength for the classical Be star ? Tauri, and we show that the model predictions are consistent with interferometric observations in the H, K', and 12 \mu m bands

Astrometric orbits of SB9 stars

Hipparcos Intermediate Astrometric Data (IAD) have been used to derive astrometric orbital elements for spectroscopic binaries from the newly released Ninth Catalogue of Spectroscopic Binary Orbits (SB9). Among the 1374 binaries from SB9 which have an HIP entry, 282 have detectable orbital astrometric motion (at the 5% significance level). Among those, only 70 have astrometric orbital elements that are reliably determined (according to specific statistical tests discussed in the paper), and for the first time for 20 systems, representing a 10% increase relative to the 235 DMSA/O systems already present in the Hipparcos Double and Multiple Systems Annex. The detection of the astrometric orbital motion when the Hipparcos IAD are supplemented by the spectroscopic orbital elements is close to 100% for binaries with only one visible component, provided that the period is in the 50 - 1000 d range and the parallax is larger than 5 mas. This result is an interesting testbed to guide the choice of algorithms and statistical tests to be used in the search for astrometric binaries during the forthcoming ESA Gaia mission. Finally, orbital inclinations provided by the present analysis have been used to derive several astrophysical quantities. For instance, 29 among the 70 systems with reliable astrometric orbital elements involve main sequence stars for which the companion mass could be derived. Some interesting conclusions may be drawn from this new set of stellar masses, like the enigmatic nature of the companion to the Hyades F dwarf HIP 20935. This system has a mass ratio of 0.98 but the companion remains elusive.Comment: Astronomy & Astrophysics, in press (16 pages, 12 figures); also available at http://www.astro.ulb.ac.be/Html/ps.html#Astrometr

The Sludge Dewaterability in Advanced Wastewater Treatment: A Survey of Four Different Membrane BioReactor Pilot Plants

The wasted activated sludge dewaterability represents a major concern for Wastewater Treatment Plants (WWTPs) managers. Indeed, whereas the dewatered sludge could represents a re-usable matrix, the principal drawback related to the wasted sludge dewaterability is the high water content due to the presence of extracellular polymeric substances (EPS) that allow the trapping of water molecules within the bio sludge flocs. In order to provide an outlook of the dewaterability features of activated sludge derived from advanced WWTP, the present research reports a long term survey (over two years) aimed at assessing the principal dewaterability parameters of the sludge wasted from different Membrane BioReactor pilot plants

An ant colony-based semi-supervised approach for learning classification rules

Semi-supervised learning methods create models from a few labeled instances and a great number of unlabeled instances. They appear as a good option in scenarios where there is a lot of unlabeled data and the process of labeling instances is expensive, such as those where most Web applications stand. This paper proposes a semi-supervised self-training algorithm called Ant-Labeler. Self-training algorithms take advantage of supervised learning algorithms to iteratively learn a model from the labeled instances and then use this model to classify unlabeled instances. The instances that receive labels with high confidence are moved from the unlabeled to the labeled set, and this process is repeated until a stopping criteria is met, such as labeling all unlabeled instances. Ant-Labeler uses an ACO algorithm as the supervised learning method in the self-training procedure to generate interpretable rule-based models—used as an ensemble to ensure accurate predictions. The pheromone matrix is reused across different executions of the ACO algorithm to avoid rebuilding the models from scratch every time the labeled set is updated. Results showed that the proposed algorithm obtains better predictive accuracy than three state-of-the-art algorithms in roughly half of the datasets on which it was tested, and the smaller the number of labeled instances, the better the Ant-Labeler performance

Nitrogen-limited mangrove ecosystems conserve N through dissimilatory nitrate reduction to ammonium

Earlier observations in mangrove sediments of Goa, India have shown denitrification to be a major pathway for N loss1. However, percentage of total nitrate transformed through complete denitrification accounted for <0–72% of the pore water nitrate reduced. Here, we show that up to 99% of nitrate removal in mangrove sediments is routed through dissimilatory nitrate reduction to ammonium (DNRA). The DNRA process was 2x higher at the relatively pristine site Tuvem compared to the anthropogenically-influenced Divar mangrove ecosystem. In systems receiving low extraneous nutrient inputs, this mechanism effectively conserves and re-circulates N minimizing nutrient loss that would otherwise occur through denitrification. In a global context, the occurrence of DNRA in mangroves has important implications for maintaining N levels and sustaining ecosystem productivity. For the first time, this study also highlights the significance of DNRA in buffering the climate by modulating the production of the greenhouse gas nitrous oxide

Decoding Unattended Fearful Faces with Whole-Brain Correlations: An Approach to Identify Condition-Dependent Large-Scale Functional Connectivity

Processing of unattended threat-related stimuli, such as fearful faces, has been previously examined using group functional magnetic resonance (fMRI) approaches. However, the identification of features of brain activity containing sufficient information to decode, or “brain-read”, unattended (implicit) fear perception remains an active research goal. Here we test the hypothesis that patterns of large-scale functional connectivity (FC) decode the emotional expression of implicitly perceived faces within single individuals using training data from separate subjects. fMRI and a blocked design were used to acquire BOLD signals during implicit (task-unrelated) presentation of fearful and neutral faces. A pattern classifier (linear kernel Support Vector Machine, or SVM) with linear filter feature selection used pair-wise FC as features to predict the emotional expression of implicitly presented faces. We plotted classification accuracy vs. number of top N selected features and observed that significantly higher than chance accuracies (between 90–100%) were achieved with 15–40 features. During fearful face presentation, the most informative and positively modulated FC was between angular gyrus and hippocampus, while the greatest overall contributing region was the thalamus, with positively modulated connections to bilateral middle temporal gyrus and insula. Other FCs that predicted fear included superior-occipital and parietal regions, cerebellum and prefrontal cortex. By comparison, patterns of spatial activity (as opposed to interactivity) were relatively uninformative in decoding implicit fear. These findings indicate that whole-brain patterns of interactivity are a sensitive and informative signature of unattended fearful emotion processing. At the same time, we demonstrate and propose a sensitive and exploratory approach for the identification of large-scale, condition-dependent FC. In contrast to model-based, group approaches, the current approach does not discount the multivariate, joint responses of multiple functional connections and is not hampered by signal loss and the need for multiple comparisons correction