707 research outputs found
Are there asymmetries in the effects of training on the conditional male wage distribution?
Recent studies have used quantile regression (QR) techniques to estimate the impact of education on the location, scale and shape of the conditional wage distribution. In our paper we investigate the degree to which work-related training – another important form of human capital – affects the location, scale and shape of the conditional wage distribution. Using the first six waves of the European Community Household Panel, we utilise both ordinary least squares and QR techniques to estimate associations between work-related training and wages for private sector men in ten European Union countries. Our results show that, for the majority of countries, there is a fairly uniform association between training and hourly wages across the conditional wage distribution. However, there are considerable differences across countries in mean associations between training and wages
Using texture analysis in the development of a potential radiomic signature for early identification of hepatic metastasis in colorectal cancer
Background: Radiomics allows information not readily available to the naked eye to be extracted from high resolution imaging modalities such as CT. Identifying that a cancer has already metastasised at the time of presentation through a radiomic signature will affect the treatment pathway. The ability to recognise the existence of metastases earlier will have a significant impact on the survival outcomes. / Aim: To create a novel radiomic signature using textural analysis in the evaluation of synchronous liver metastases in colorectal cancer. / Methods: CT images at baseline and subsequent surveillance over a 5-year period of patients with colorectal cancer were processed using textural analysis software. Comparison was made between those patients who developed liver metastases and those that remained disease free to detect differences in the ‘texture’ of the liver. / Results: A total of 24 patients were divided into two matched groups for comparison. Significant differences between the two groups scores when using the textural analysis programme were found on coarse filtration (p = 0.044). Patients that went on to develop metastases an average of 18 months after presentation had higher levels of hepatic heterogeneity on CT. / Conclusion: This initial study demonstrates the potential of using a textural analysis programme to build a radiomic signature to predict the development of hepatic metastases in rectal cancer patients otherwise thought to have clear staging CT scans at time of presentation
Computationally efficient solutions for tracking people with a mobile robot: an experimental evaluation of Bayesian filters
Modern service robots will soon become an essential part of modern society. As they have to move and act in human environments, it is essential for them to be provided with a fast and reliable tracking system that localizes people in the neighbourhood. It is therefore important to select the most appropriate filter to estimate the position of these persons.
This paper presents three efficient implementations of multisensor-human tracking based on different Bayesian estimators: Extended Kalman Filter (EKF), Unscented Kalman Filter (UKF) and Sampling Importance Resampling (SIR) particle filter. The system implemented on a mobile robot is explained, introducing the methods used to detect and estimate the position of multiple people. Then, the solutions based on the three filters are discussed in detail. Several real experiments are conducted to evaluate their performance, which is compared in terms of accuracy, robustness and execution time of the estimation. The results show that a solution based on the UKF can perform as good as particle filters and can be often a better choice when computational efficiency is a key issue
Video-Based Camera Tracking Using Rotation-Discriminative Template Matching
This paper presents a video-based camera tracker that combines marker-based and feature point-based cues in a particle filter framework. The framework relies on their complementary performance. Marker-based trackers can robustly recover camera position and orientation when a reference (marker) is available, but fail once the reference becomes unavailable. On the other hand, feature point tracking can still provide estimates given a limited number of feature points. However, these tend to drift and usually fail to recover when the reference reappears. Therefore, we propose a combination where the estimate of the filter is updated from the individual measurements of each cue. More precisely, the marker-based cue is selected when the marker is available whereas the feature point-based cue is selected otherwise. Feature points are dynamically found in scene and used for further tracking. Evaluations on real cases show that the fusion of these two approaches outperforms the individual tracking results. A critical aspect of the feature point-based cue is to robustly recognise the feature points depite rotations of the camera. A novelty of the proposed framework is the use of a rotation-discriminative method to match feature points
The linked survival prospects of siblings : evidence for the Indian states
This paper reports an analysis of micro-data for India that shows a high correlation in infant mortality
among siblings. In 13 of 15 states, we identify a causal effect of infant death on the risk of infant death of the
subsequent sibling (a scarring effect), after controlling for mother-level heterogeneity. The scarring effects
are large, the only other covariate with a similarly large effect being mother’s (secondary or higher)
education. The two states in which evidence of scarring is weak are Punjab, the richest, and Kerala, the
socially most progressive. The size of the scarring effect depends upon the sex of the previous child in three
states, in a direction consistent with son-preference. Evidence of scarring implies that policies targeted at
reducing infant mortality will have social multiplier effects by helping avoid the death of subsequent
siblings. Comparison of other covariate effects across the states offers some interesting new insights
Expression profile of nuclear receptors upon epstein — barr virus induced b cell transformation
Background: Infection of human B cells with Epstein—Barr virus (EBV) induces metabolic activation, morphological transformation, cell
proliferation and eventual immortalization. Aim: To identify the nuclear receptors, which are the cellular interaction partners of EBNAs,
that will help to elucidate the mechanism of B cell transformation. Methods: We have compared the nuclear receptor profile in the naïve
and EBV-transformed B-lymphocytes, using TaqMan LDA microfluidic card technology. Results: Out of 48 nuclear receptor, 17 showed
differential expression at the mRNA level. The expression of 5 genes was elevated in EBV-transformed cells, whereas 12 genes were downregulated
in lymphoblastoid cells (LCLs). 7 genes were studied at the protein level; 2 genes were up regulated (Nr2F2 and RARA) and
4 genes were down regulated (ERB, NUR77, PPARG, and VDR) in LCLs. Conclusion: The nuclear receptor profiling on EBV infected
B cells showed alterations of nuclear receptors expression at both mRNA and protein levels compared with non infected peripheral blood
cells. Further analysis on a possible role of each nuclear receptor in EBV induced cell transformation should be performed
Evaluating Data Assimilation Algorithms
Data assimilation leads naturally to a Bayesian formulation in which the
posterior probability distribution of the system state, given the observations,
plays a central conceptual role. The aim of this paper is to use this Bayesian
posterior probability distribution as a gold standard against which to evaluate
various commonly used data assimilation algorithms.
A key aspect of geophysical data assimilation is the high dimensionality and
low predictability of the computational model. With this in mind, yet with the
goal of allowing an explicit and accurate computation of the posterior
distribution, we study the 2D Navier-Stokes equations in a periodic geometry.
We compute the posterior probability distribution by state-of-the-art
statistical sampling techniques. The commonly used algorithms that we evaluate
against this accurate gold standard, as quantified by comparing the relative
error in reproducing its moments, are 4DVAR and a variety of sequential
filtering approximations based on 3DVAR and on extended and ensemble Kalman
filters.
The primary conclusions are that: (i) with appropriate parameter choices,
approximate filters can perform well in reproducing the mean of the desired
probability distribution; (ii) however they typically perform poorly when
attempting to reproduce the covariance; (iii) this poor performance is
compounded by the need to modify the covariance, in order to induce stability.
Thus, whilst filters can be a useful tool in predicting mean behavior, they
should be viewed with caution as predictors of uncertainty. These conclusions
are intrinsic to the algorithms and will not change if the model complexity is
increased, for example by employing a smaller viscosity, or by using a detailed
NWP model
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