42 research outputs found
The ROSAT-ESO Flux-Limited X-Ray (REFLEX) Galaxy Cluster Survey III: The Power Spectrum
We present a measure of the power spectrum on scales from 15 to 800 Mpc/h
using the ROSAT-ESO Flux-Limited X-Ray(REFLEX) galaxy cluster catalogue. The
REFLEX survey provides a sample of the 452 X-ray brightest southern clusters of
galaxies with the nominal flux limit S=3.0 10^{-12}erg/s/cm2 for the ROSAT
energy band (0.1-2.4)keV. Several tests are performed showing no significant
incompletenesses of the REFLEX clusters with X-ray luminosities brighter than
10^{43}erg/s up to scales of about 800 Mpc/h. They also indicate that cosmic
variance might be more important than previous studies suggest. We regard this
as a warning not to draw general cosmological conclusions from cluster samples
with a size smaller than REFLEX. Power spectra, P(k), of comoving cluster
number densities are estimated for flux- and volume-limited subsamples. The
most important result is the detection of a broad maximum within the comoving
wavenumber range 0.022<k<0.030 h/Mpc. The data suggest an increase of the power
spectral amplitude with X-ray luminosity. Compared to optically selected
cluster samples the REFLEX P(k)is flatter for wavenumbers k<0.05 h/Mpc thus
shifting the maximum of P(k) to larger scales. The smooth maximum is not
consistent with the narrow peak detected at k=0.05 h/Mpc using the Abell/ACO
richness data. In the range 0.02<k<0.4 h/Mpc general agreement is found
between the slope of the REFLEX P(k) and those obtained with optically selected
galaxies. A semi-analytic description of the biased nonlinear power spectrum in
redshift space gives the best agreement for low-density Cold Dark Matter models
with or without a cosmological constant.Comment: 22 pages, 20 figures, (A&A accepted), also available at
http://www.xray.mpe.mpg.de/theorie/REFLEX
Community-Based Production of Open Source Software: What Do We Know About the Developers Who Participate?
This paper seeks to close an empirical gap regarding the motivations, personal attributes and behavioral patterns among free/libre and open source (FLOSS) developers, especially those involved in community-based production, and its findings on the existing literature and the future directions for research. Respondents to an extensive web-survey’s (FLOSS-US 2003) questions about their reasons for work on FLOSS are classified according to their distinct “motivational profiles” by hierarchical cluster analysis. Over half of them also are matched to projects of known membership sizes, revealing that although some members from each of the clusters are present in the small, medium and large ranges of the distribution of project sizes, the mixing fractions for the large and the very small project ranges are statistically different. Among developers who changed projects, there is a discernable flow from the bottom toward the very small towards to large projects, some of which is motivated by individuals seeking to improve their programming skills. It is found that the profile of early motivation, along with other individual attributes, significantly affects individual developers’ selections of projects from different regions of the size range.Open source software, FLOSS project, community-based peer production, population heterogeneity, micro-motives, motivational profiles, web-cast surveys, hierarchical cluster analysis
Irrigated lands assessment for water management: Technique test
A procedure for estimating irrigated land using full frame LANDSAT imagery was demonstrated. Relatively inexpensive interpretation of multidate LANDSAT photographic enlargements was used to produce a map of irrigated land in California. The LANDSAT and ground maps were then linked by regression equations to enable precise estimation of irrigated land area by county, basin, and statewide. Land irrigated at least once in California in 1979 was estimated to be 9.86 million acres, with an expected error of less than 1.75% at the 99% level of confidence. To achieve the same level of error with a ground-only sample would have required 3 to 5 times as many ground sample units statewide. A procedure for relatively inexpensive computer classification of LANDSAT digital data to irrigated land categories was also developed. This procedure is based on ratios of MSS band 7 and 5, and gave good results for several counties in the Central Valley
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Ground vibration from underground railways: how simplifying assumptions limit prediction accuracy
Noise and vibration from underground railways is a documented disturbance to individuals living or working near subways. Much work has been done to understand and simulate the dynamic interactions between the train, track, tunnel and soil resulting in numerical models which can predict ground-borne vibration around the tunnels and at the soil surface. However, all such numerical models rely on simplifying assumptions to make the problems trackable: soil is assumed homogenous, tunnels are assumed long and straight, the soil is assumed to be in perfect contact with the tunnel, etc. This dissertation is concerned with quantifying the uncertainty associated with some of these simplifying assumptions to provide a better estimation of the prediction accuracy when numerical models are used for "real world" applications.
The first section investigates the effect of voids at the tunnel-soil interface. The Pipe-in-Pipe model is extended to allow finite-sized voids at the interface by deriving the discrete transfer functions for the tunnel and soil from the continuous solution. The results suggest that relatively small voids can significantly affect the rms velocity predictions at higher frequencies (100-200Hz) and moderately effect predictions at lower frequencies (15-100Hz). The results are also found to be sensitive to void length and void sector angle.
The second section investigates issues associated with assuming the soil is homogeneous: the effect of inclined soil layers; the effect of a subsiding soil layer; the effect of soil inhomogeneity. The thin-layer method approach is utilized as its semi-analytical formulation allows for accurate predictions with relatively short run times. The results from the three investigations suggest that slight inclination of soil layers and typical levels of soil inhomogeneity can result in significant variation in surface results compared to the homogeneous assumption. The geometric effect of a subsiding soil layer has a less significant effect on surface vibration.
The findings from this study suggest that employing simplifying assumptions for the cases investigated can reasonably result in uncertainty bands of +/-5dB. Considering all the simplifying assumptions used in numerical models of ground vibration from underground railways it would not be unreasonable to conclude that the prediction accuracy for such a model may be limited to +/-10dB
Neural Coding and Organization Principles in the Drosophila Olfactory System
Sensory systems receive and process external stimuli to allow an organism to perceive and react to the environment. How is sensory information subsequently represented, transformed, and interpreted in the neural system? In this dissertation, I have investigated this fundamental question using the fruit fly (Drosophila melanogaster) olfactory system.Chemical cues are transduced into neural signals in the insect antenna by the olfactory receptor neurons (ORNs). The ORNs send their axons to the antennal lobe (AL), with each ORN type innervating a specific neuropil (glomerulus), where they synapse onto excitatory and inhibitory projection neurons (ePNs and iPNs). The ePNs project their axons to the 3rd order stages, the calyx (CL) and lateral horn (LH). On the other hand, the iPNs only innervate the LH. In this dissertation, I first examined how well the peripheral neural activities evoked by an odorant could predict the final behavioral output. As the stimulus intensity increases, a fly’s preference for some odorants switch from attraction to aversion. Behavior assay suggested this phenomenon may help the fly evade harmful environment. Our results indicate that at the level of ORNs, increases in stimulus intensity could result in oscillatory extracellular field potentials that arise entirely due to abrupt changes in cell excitability. Notably, combining the activity of a few ORNs was sufficient to predict intensity-dependent preference changes with odor intensity. How is the sensory input organized in the downstream neural circuit, the insect antennal lobe? Odor-evoked signals from sensory neurons (ORNs) triggered neural responses that were patterned over space and time in cholinergic ePNs and GABAergic iPNs within the antennal lobe. The dendritic-axonal (I/O) response mapping was complex and diverse, and the axonal organization was region-specific (mushroom body vs. lateral horn). In the lateral horn, feed-forward excitatory and inhibitory axonal projections matched ‘odor tuning’ in a stereotyped, dorsal-lateral locus, but mismatched in most other locations. In the temporal dimension, ORN, ePN, and iPN odor-evoked responses had similar encoding features, such as information refinement over time and divergent ON and OFF responses. Notably, analogous spatial and temporal coding principles were observed in all flies, and the latter emerged from idiosyncratic neural processing approaches. In sum, these results provide key insights necessary for understanding how sensory information is organized along spatial and temporal dimensions
The use and application of performance metrics with regional climate models
Abstract
This thesis aims to assess and develop objective and robust approaches to evaluate
regional climate model (RCM) historical skill using performance metrics and to
provide guidance to relevant groups as to how best utilise these metrics. Performance
metrics are quantitative, scalar measures of the numerical distance, or
’error’, between historical model simulations and observations. Model evaluation
practice tends to involve ad hoc approaches with little consideration to the underlying
sensitivity of the method to small changes in approach. The main questions
that arise are to what degree are the outputs, and subsequent applications, of these
performance metrics robust?
ENSEMBLES and CORDEX RCMs covering Europe are used with E-OBS
observational data to assess historical and future simulation characteristics using a
range of performance metrics. Metric sensitivity is found in some cases to be low,
such as differences between variable types, with extreme indices often producing
redundant information. In other cases sensitivity is large, particularly for temporal
statistics, but not for spatial pattern statistics. Assessments made over a single
decade are found to be robust with respect to the full 40-year time period.
Two applications of metrics are considered: metric combinations and exploration
of the stationarity of historical RCM bias characteristics. The sensitivity of
metric combination procedure is found to be low with respect to the combination
method and potentially high for the type of metric included, but remains uncertain
for the number of metrics included. Stationarity of biases appears to be highly
dependent on the potential for underlying causes of model bias to change substantially
in the future, such as the case of surface albedo in the Alps.
It is concluded that performance metrics and their applications can and should
be considered more systematically using a range of redundancy and stationarity
tests as indicators of historical and future robustness
Continuous regression: a functional regression approach to facial landmark tracking
Facial Landmark Tracking (Face Tracking) is a key step for many Face Analysis systems, such as Face Recognition, Facial Expression Recognition, or Age and Gender Recognition, among others. The goal of Facial Landmark Tracking is to locate a sparse set of points defining a facial shape in a video sequence. These typically include the mouth, the eyes, the contour, or the nose tip. The state of the art method for Face Tracking builds on Cascaded Regression, in which a set of linear regressors are used in a cascaded fashion, each receiving as input the output of the previous one, subsequently reducing the error with respect to the target locations.
Despite its impressive results, Cascaded Regression suffers from several drawbacks, which are basically caused by the theoretical and practical implications of using Linear Regression. Under the context of Face Alignment, Linear Regression is used to predict shape displacements from image features through a linear mapping. This linear mapping is learnt through the typical least-squares problem, in which a set of random perturbations is given. This means that, each time a new regressor is to be trained, Cascaded Regression needs to generate perturbations and apply the sampling again. Moreover, existing solutions are not capable of incorporating incremental learning in real time. It is well-known that person-specific models perform better than generic ones, and thus the possibility of personalising generic models whilst tracking is ongoing is a desired property, yet to be addressed.
This thesis proposes Continuous Regression, a Functional Regression solution to the least-squares problem, resulting in the first real-time incremental face tracker. Briefly speaking, Continuous Regression approximates the samples by an estimation based on a first-order Taylor expansion yielding a closed-form solution for the infinite set of shape displacements. This way, it is possible to model the space of shape displacements as a continuum, without the need of using complex bases. Further, this thesis introduces a novel measure that allows Continuous Regression to be extended to spaces of correlated variables. This novel solution is incorporated into the Cascaded Regression framework, and its computational benefits for training under different configurations are shown. Then, it presents an approach for incremental learning within Cascaded Regression, and shows its complexity allows for real-time implementation. To the best of my knowledge, this is the first incremental face tracker that is shown to operate in real-time. The tracker is tested in an extensive benchmark, attaining state of the art results, thanks to the incremental learning capabilities