Person localization using sensor information fusion

Nowadays the incredible grow of mobile devices market led to the need for location-aware applications. However, sometimes person location is di cult to obtain, since most of these devices only have a GPS (Global Positioning System) chip to retrieve location. In order to sup- press this limitation and to provide location everywhere (even where a structured environment doesn't exist) a wearable inertial navigation sys- tem is proposed, which is a convenient way to track people in situations where other localization systems fail. The system combines pedestrian dead reckoning with GPS, using widely available, low-cost and low-power hardware components. The system innovation is the information fusion and the use of probabilistic methods to learn persons gait behavior to correct, in real-time, the drift errors given by the sensors.This work is part-funded by ERDF - European Regional Development Fund through the COMPETE Programme (operational programme for competitiveness) and by National Funds through the FCT Fundao para a Cincia e a Tecnologia (Portuguese Foundation for Science and Technology) within project FCOMP-01-0124-FEDER- 028980 (PTDC/EEI-SII/1386/2012). Ricardo also acknowledge FCT for the support of his work through the PhD grant (SFRH/DB/70248/2010)

Does J/ψ→π+π−J/\psi \rightarrow \pi^{+} \pi^{-} fix the Electromagnetic Form Factor Fπ(t)F_{\pi}(t) at t=MJ/ψ2t=M_{J/\psi}^2?

We show that the $J/\psi \rightarrow \pi^{+} \pi^{-}$ decay is a reliable source of information for the electromagnetic form factor of the pion at $t=M_{J/\psi}^2=9.6 {\rm GeV}^2$ by using general arguments to estimate, or rather, put upper bounds on, the background processes that could spoil this extraction. We briefly comment on the significance of the resulting $F_{\pi}(M_{J/\psi}^2)$.Comment: 10 pages revtex manuscript, one figure--not included, U. of MD PP #94-00

DADA: Degree-Aware Algorithms for Network-Based Disease Gene Prioritization

<p>Abstract</p> <p>Background</p> <p>High-throughput molecular interaction data have been used effectively to prioritize candidate genes that are linked to a disease, based on the observation that the products of genes associated with similar diseases are likely to interact with each other heavily in a network of protein-protein interactions (PPIs). An important challenge for these applications, however, is the incomplete and noisy nature of PPI data. Information flow based methods alleviate these problems to a certain extent, by considering indirect interactions and multiplicity of paths.</p> <p>Results</p> <p>We demonstrate that existing methods are likely to favor highly connected genes, making prioritization sensitive to the skewed degree distribution of PPI networks, as well as ascertainment bias in available interaction and disease association data. Motivated by this observation, we propose several statistical adjustment methods to account for the degree distribution of known disease and candidate genes, using a PPI network with associated confidence scores for interactions. We show that the proposed methods can detect loosely connected disease genes that are missed by existing approaches, however, this improvement might come at the price of more false negatives for highly connected genes. Consequently, we develop a suite called D<smcaps>A</smcaps>D<smcaps>A</smcaps>, which includes different uniform prioritization methods that effectively integrate existing approaches with the proposed statistical adjustment strategies. Comprehensive experimental results on the Online Mendelian Inheritance in Man (OMIM) database show that D<smcaps>A</smcaps>D<smcaps>A</smcaps> outperforms existing methods in prioritizing candidate disease genes.</p> <p>Conclusions</p> <p>These results demonstrate the importance of employing accurate statistical models and associated adjustment methods in network-based disease gene prioritization, as well as other network-based functional inference applications. D<smcaps>A</smcaps>D<smcaps>A</smcaps> is implemented in Matlab and is freely available at <url>http://compbio.case.edu/dada/</url>.</p

Generalized Relativistic Meson Wave Function

We study the most general, relativistic, constituent $q{\overline q}$ meson wave function within a new covariant framework. We find that by including a tensor wave function component, a pure valence quark model is now capable of reproducing not only all static pion data ($f_\pi$, $\langle r_\pi^2 \rangle$) but also the distribution amplitude, form factor $(F_\pi(Q^2))$, and structure functions. Further, our generalized spin wave function provides a much better detailed description of meson properties than models using a simple relativistic extension of the $S=L=0$ nonrelativistic wave function.Comment: 17 pages, REXTeX 3.0 file, (uuencoded postscript files of 8 figures appended

Personal navigation via high-resolution gait-corrected inertial measurement units

In this paper, a personal micronavigation system that uses high-resolution gait-corrected inertial measurement units is presented. The goal of this paper is to develop a navigation system that uses secondary inertial variables, such as velocity, to enable long-term precise navigation in the absence of Global Positioning System (GPS) and beacon signals. In this scheme, measured zero-velocity duration from the ground reaction sensors is used to reset the accumulated integration errors from accelerometers and gyroscopes in position calculation. With the described system, an average position error of 4 m is achieved at the end of half-hour walks. © 2010 IEEE

NeAT: a toolbox for the analysis of biological networks, clusters, classes and pathways

The network analysis tools (NeAT) (http://rsat.ulb.ac.be/neat/) provide a user-friendly web access to a collection of modular tools for the analysis of networks (graphs) and clusters (e.g. microarray clusters, functional classes, etc.). A first set of tools supports basic operations on graphs (comparison between two graphs, neighborhood of a set of input nodes, path finding and graph randomization). Another set of programs makes the connection between networks and clusters (graph-based clustering, cliques discovery and mapping of clusters onto a network). The toolbox also includes programs for detecting significant intersections between clusters/classes (e.g. clusters of co-expression versus functional classes of genes). NeAT are designed to cope with large datasets and provide a flexible toolbox for analyzing biological networks stored in various databases (protein interactions, regulation and metabolism) or obtained from high-throughput experiments (two-hybrid, mass-spectrometry and microarrays). The web interface interconnects the programs in predefined analysis flows, enabling to address a series of questions about networks of interest. Each tool can also be used separately by entering custom data for a specific analysis. NeAT can also be used as web services (SOAP/WSDL interface), in order to design programmatic workflows and integrate them with other available resources

Form Factors and QCD in Spacelike and Timelike Region

We analyze the basic hard exclusive processes: \pi\gamma*\gamma - transition, pion and nucleon electromagnetic form factors, and discuss the analytic continuation of QCD formulas from the spacelike q^2<0 to the timelike region q^2 >0 of the relevant momentum transfers. We describe the construction of the timelike version of the coupling constant \alpha_s. We show that due to the analytic continuation of the collinear logarithms each eigenfunction of the evolution equation acqiures a phase factor and investigate the resulting interference effects which are shown to be very small. We found no sources for the K-factor-type enhancements in the perturbative QCD contribution to the hadronic form factors. To study the soft part of the pion electromagnetic form factor, we use a QCD sum rule inspired model and show that there are non-canceling Sudakov double logarithms which result in a K-factor-type enhancement in the timelike region.Comment: 12 pages, LaTeX; a few typos corrected, references adde

NeAT: a toolbox for the analysis of biological networks, clusters, classes and pathways

The network analysis tools (NeAT) (http://rsat.ulb.ac.be/neat/) provide a user-friendly web access to a collection of modular tools for the analysis of networks (graphs) and clusters (e.g. microarray clusters, functional classes, etc.). A first set of tools supports basic operations on graphs (comparison between two graphs, neighborhood of a set of input nodes, path finding and graph randomization). Another set of programs makes the connection between networks and clusters (graph-based clustering, cliques discovery and mapping of clusters onto a network). The toolbox also includes programs for detecting significant intersections between clusters/classes (e.g. clusters of co-expression versus functional classes of genes). NeAT are designed to cope with large datasets and provide a flexible toolbox for analyzing biological networks stored in various databases (protein interactions, regulation and metabolism) or obtained from high-throughput experiments (two-hybrid, mass-spectrometry and microarrays). The web interface interconnects the programs in predefined analysis flows, enabling to address a series of questions about networks of interest. Each tool can also be used separately by entering custom data for a specific analysis. NeAT can also be used as web services (SOAP/WSDL interface), in order to design programmatic workflows and integrate them with other available resources

Measurement of the Charged Pion Electromagnetic Form Factor

Separated longitudinal and transverse structure functions for the reaction 1H(e,eprime pi+)n were measured in the momentum transfer region Q2=0.6-1.6 (GeV/c)**2 at a value of the invariant mass W=1.95 GeV. New values for the pion charge form factor were extracted from the longitudinal cross section by using a recently developed Regge model. The results indicate that the pion form factor in this region is larger than previously assumed and is consistent with a monopole parameterization fitted to very low Q2 elastic data.Comment: 5 pages, 3 figure

Supernova / Acceleration Probe: A Satellite Experiment to Study the Nature of the Dark Energy

The Supernova / Acceleration Probe (SNAP) is a proposed space-based experiment designed to study the dark energy and alternative explanations of the acceleration of the Universe's expansion by performing a series of complementary systematics-controlled measurements. We describe a self-consistent reference mission design for building a Type Ia supernova Hubble diagram and for performing a wide-area weak gravitational lensing study. A 2-m wide-field telescope feeds a focal plane consisting of a 0.7 square-degree imager tiled with equal areas of optical CCDs and near infrared sensors, and a high-efficiency low-resolution integral field spectrograph. The SNAP mission will obtain high-signal-to-noise calibrated light-curves and spectra for several thousand supernovae at redshifts between z=0.1 and 1.7. A wide-field survey covering one thousand square degrees resolves ~100 galaxies per square arcminute. If we assume we live in a cosmological-constant-dominated Universe, the matter density, dark energy density, and flatness of space can all be measured with SNAP supernova and weak-lensing measurements to a systematics-limited accuracy of 1%. For a flat universe, the density-to-pressure ratio of dark energy can be similarly measured to 5% for the present value w0 and ~0.1 for the time variation w'. The large survey area, depth, spatial resolution, time-sampling, and nine-band optical to NIR photometry will support additional independent and/or complementary dark-energy measurement approaches as well as a broad range of auxiliary science programs. (Abridged)Comment: 40 pages, 18 figures, submitted to PASP, http://snap.lbl.go