Multiscale blind source separation.

We provide a new methodology for statistical recovery of single linear mixtures of piecewise constant signals (sources) with unknown mixing weights and change points in a multiscale fashion. We show exact recovery within an epsilon-neighborhood of the mixture when the sources take only values in a known finite alphabet. Based on this we provide the SLAM (Separates Linear Alphabet Mixtures) estimators for the mixing weights and sources. For Gaussian error, we obtain uniform confidence sets and optimal rates (up to log-factors) for all quantities. SLAM is efficiently computed as a nonconvex optimization problem by a dynamic program tailored to the finite alphabet assumption. Its performance is investigated in a simulation study. Finally, it is applied to assign copy-number aberrations from genetic sequencing data to different clones and to estimate their proportions

Scale space consistency of piecewise constant least squares estimators -- another look at the regressogram

We study the asymptotic behavior of piecewise constant least squares regression estimates, when the number of partitions of the estimate is penalized. We show that the estimator is consistent in the relevant metric if the signal is in $L^2([0,1])$, the space of c\`{a}dl\`{a}g functions equipped with the Skorokhod metric or $C([0,1])$ equipped with the supremum metric. Moreover, we consider the family of estimates under a varying smoothing parameter, also called scale space. We prove convergence of the empirical scale space towards its deterministic target.Comment: Published at http://dx.doi.org/10.1214/074921707000000274 in the IMS Lecture Notes Monograph Series (http://www.imstat.org/publications/lecnotes.htm) by the Institute of Mathematical Statistics (http://www.imstat.org

Neuronal avalanches recorded in the awake and sleeping monkey do not show a power law but can be reproduced by a self-organized critical model

Poster presentation: Self-organized critical (SOC) systems are complex dynamical systems that may express cascades of events, called avalanches [1]. The SOC state was proposed to govern brain function, because of its activity fluctuations over many orders of magnitude, its sensitivity to small input and its long term stability [2,3]. In addition, the critical state is optimal for information storage and processing [4]. Both hallmark features of SOC systems, a power law distribution f(s) for the avalanche size s and a branching parameter (bp) of unity, were found for neuronal avalanches recorded in vitro [5]. However, recordings in vivo yielded contradictory results [6]. Electrophysiological recordings in vivo only cover a small fraction of the brain, while criticality analysis assumes that the complete system is sampled. We hypothesized that spatial subsampling might influence the observed avalanche statistics. In addition, SOC models can have different connectivity, but always show a power law for f(s) and bp = 1 when fully sampled. This may not be the case under subsampling, however. Here, we wanted to know whether a state change from awake to asleep could be modeled by changing the connectivity of a SOC model without leaving the critical state. We simulated a SOC model [1] and calculated f(s) and bp obtained from sampling only the activity of a set of 4 × 4 sites, representing the electrode positions in the cortex. We compared these results with results obtained from multielectrode recordings of local field potentials (LFP) in the cortex of behaving monkeys. We calculated f(s) and bp for the LFP activity recorded while the monkey was either awake or asleep and compared these results to results obtained from two subsampled SOC model with different connectivity. f(s) and bp were very similar for both the experiments and the subsampled SOC model, but in contrast to the fully sampled model, f(s) did not show a power law and bp was smaller than unity. With increasing the distance between the sampling sites, f(s) changed from "apparently supercritical" to "apparently subcritical" distributions in both the model and the LFP data. f(s) and bp calculated from LFP recorded during awake and asleep differed. These changes could be explained by altering the connectivity in the SOC model. Our results show that subsampling can prevent the observation of the characteristic power law and bp in SOC systems, and misclassifications of critical systems as sub- or supercritical are possible. In addition, a change in f(s) and bp for different states (awake/asleep) does not necessarily imply a change from criticality to sub- or supercriticality, but can also be explained by a change in the effective connectivity of the network without leaving the critical state

On reconciling ground-based with spaceborne normalized radar cross section measurements

©2002 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.This study examines differences in the normalized radar cross section, derived from ground-based versus spaceborne radar data. A simple homogeneous half-space model, indicates that agreement between the two improves as 1) the distance from the scatterer is increased; and/or 2) the extinction coefficient increases

Design, theory, and measurement of a polarization insensitive absorber for terahertz imaging

We present the theory, design, and realization of a polarization-insensitive metamaterial absorber for terahertz frequencies. We derive geometrical-independent conditions for effective medium absorbers in general, and for resonant metamaterials specically. Our fabricated design reaches and absorptivity of 78% at 1.145 ThzComment: 6 Pages, 5 figures; figures update

Extraintestinal pathogenic <i>Escherichia coli</i> are associated with intestinal inflammation in patients with ulcerative colitis

E. coli of the phylogenetic group B2 harbouring Extra intestinal Pathogenic Escherichia coli (ExPEC) genes are frequently seen as colonizers of the intestine in patients with active ulcerative colitis (UC). In this study, we describe the influence of E. coli Nissle (EcN) B2 as add-on treatment to conventional therapies in patients with active UC. For this study one hundred active UC patients were randomized to ciprofloxacin or placebo for 1 week followed by EcN or placebo for 7 weeks. Stool samples were collected at weeks 0, 1, 8, 12, where E. coli were characterized and fecal calprotectin was measured. We showed that in the active UC patient group receiving Placebo/EcN, fewer patients reached remission, in comparison to the patient group receiving Placebo/placebo (p < 0.05). Active UC patients initially colonized with E. coli B2 had increased fecal calprotectin values and Colitis Activity Index scores in comparison to patients colonized with E. coli A and D (p < 0.05*). In conclusion, treatment of UC patients with E. coli Nissle (B2) does not promote clinical remission and active UC patients colonized with E. coli B2 have an increased intestinal inflammation

Kolmogorov Similarity Hypotheses for Scalar Fields: Sampling Intermittent Turbulent Mixing in the Ocean and Galaxy

Kolmogorov's three universal similarity hypotheses are extrapolated to describe scalar fields like temperature mixed by turbulence. By the analogous Kolmogorov third hypothesis for scalars, temperature dissipation rates chi averaged over lengths r > L_K should be lognormally distributed with intermittency factors I that increase with increasing turbulence energy length scales L_O as I_chi-r = m_T ln(L_O/r). Tests of Kolmogorovian velocity and scalar universal similarity hypotheses for very large ranges of turbulence length and time scales are provided by data from the ocean and the Galactic interstellar medium. The universal constant for turbulent mixing intermittency m_T is estimated from oceanic data to be 0.44+-0.01, which is remarkably close to estimates for Kolmogorov's turbulence intermittency constant m_u of 0.45+-0.05 from Galactic as well as atmospheric data. Extreme intermittency complicates the oceanic sampling problem, and may lead to quantitative and qualitative undersampling errors in estimates of mean oceanic dissipation rates and fluxes. Intermittency of turbulence and mixing in the interstellar medium may be a factor in the formation of stars.Comment: 23 pages original of Proc. Roy. Soc. article, 8 figures; in "Turbulence and Stochastic Processes: Kolmogorov's ideas 50 years on", London The Royal Society, 1991, J.C.R. Hunt, O.M. Phillips, D. Williams Eds., pages 1-240, vol. 434 (no. 1890) Proc. Roy. Soc. Lond. A, PDF fil

A Comparative Study of Aerocapture Missions with a Mars Destination

Conventional interplanetary spacecraft use propulsive systems to decelerate into orbit. Aerocapture is an alternative approach for orbit capture, in which the spacecraft makes a single pass through a target destination's atmosphere. Although this technique has never been performed, studies show there are substantial benefits of using aerocapture for reduction of propellant mass, spacecraft size, and mission cost. The In-Space Propulsion (ISP) Program, part of NASA's Science Mission Directorate, has invested in aerocapture technology development since 2002. Aerocapture investments within ISP are largely driven by mission systems analysis studies, The purpose of this NASA-funded report is to identify and document the fundamental parameters of aerocapture within previous human and robotic Mars mission studies which will assist the community in identifying technology research gaps in human and robotic missions, and provide insight for future technology investments. Upon examination of the final data set, some key attributes within the aerocapture disciplines are identified

Drift estimation in sparse sequential dynamic imaging, with application to nanoscale fluorescence microscopy.

A major challenge in many modern superresolution fluorescence microscopy techniques at the nanoscale lies in the correct alignment of long sequences of sparse but spatially and temporally highly resolved images. This is caused by the temporal drift of the protein structure, e.g. due to temporal thermal inhomogeneity of the object of interest or its supporting area during the observation process. We develop a simple semiparametric model for drift correction in single-marker switching microscopy. Then we propose an M-estimator for the drift and show its asymptotic normality. This is used to correct the final image and it is shown that this purely statistical method is competitive with state of the art calibration techniques which require the incorporation of fiducial markers in the specimen. Moreover, a simple bootstrap algorithm allows us to quantify the precision of the drift estimate and its effect on the final image estimation. We argue that purely statistical drift correction is even more robust than fiducial tracking, rendering the latter superfluous in many applications. The practicability of our method is demonstrated by a simulation study and by a single-marker switching application. This serves as a prototype for many other typical imaging techniques where sparse observations with high temporal resolution are blurred by motion of the object to be reconstructed