26,059 research outputs found
Histogram-based models on non-thin section chest CT predict invasiveness of primary lung adenocarcinoma subsolid nodules.
109 pathologically proven subsolid nodules (SSN) were segmented by 2 readers on non-thin section chest CT with a lung nodule analysis software followed by extraction of CT attenuation histogram and geometric features. Functional data analysis of histograms provided data driven features (FPC1,2,3) used in further model building. Nodules were classified as pre-invasive (P1, atypical adenomatous hyperplasia and adenocarcinoma in situ), minimally invasive (P2) and invasive adenocarcinomas (P3). P1 and P2 were grouped together (T1) versus P3 (T2). Various combinations of features were compared in predictive models for binary nodule classification (T1/T2), using multiple logistic regression and non-linear classifiers. Area under ROC curve (AUC) was used as diagnostic performance criteria. Inter-reader variability was assessed using Cohen's Kappa and intra-class coefficient (ICC). Three models predicting invasiveness of SSN were selected based on AUC. First model included 87.5 percentile of CT lesion attenuation (Q.875), interquartile range (IQR), volume and maximum/minimum diameter ratio (AUC:0.89, 95%CI:[0.75 1]). Second model included FPC1, volume and diameter ratio (AUC:0.91, 95%CI:[0.77 1]). Third model included FPC1, FPC2 and volume (AUC:0.89, 95%CI:[0.73 1]). Inter-reader variability was excellent (Kappa:0.95, ICC:0.98). Parsimonious models using histogram and geometric features differentiated invasive from minimally invasive/pre-invasive SSN with good predictive performance in non-thin section CT
Of `Cocktail Parties' and Exoplanets
The characterisation of ever smaller and fainter extrasolar planets requires
an intricate understanding of one's data and the analysis techniques used.
Correcting the raw data at the 10^-4 level of accuracy in flux is one of the
central challenges. This can be difficult for instruments that do not feature a
calibration plan for such high precision measurements. Here, it is not always
obvious how to de-correlate the data using auxiliary information of the
instrument and it becomes paramount to know how well one can disentangle
instrument systematics from one's data, given nothing but the data itself. We
propose a non-parametric machine learning algorithm, based on the concept of
independent component analysis, to de-convolve the systematic noise and all
non-Gaussian signals from the desired astrophysical signal. Such a `blind'
signal de-mixing is commonly known as the `Cocktail Party problem' in
signal-processing. Given multiple simultaneous observations of the same
exoplanetary eclipse, as in the case of spectrophotometry, we show that we can
often disentangle systematic noise from the original light curve signal without
the use of any complementary information of the instrument. In this paper, we
explore these signal extraction techniques using simulated data and two data
sets observed with the Hubble-NICMOS instrument. Another important application
is the de-correlation of the exoplanetary signal from time-correlated stellar
variability. Using data obtained by the Kepler mission we show that the desired
signal can be de-convolved from the stellar noise using a single time series
spanning several eclipse events. Such non-parametric techniques can provide
important confirmations of the existent parametric corrections reported in the
literature, and their associated results. Additionally they can substantially
improve the precision exoplanetary light curve analysis in the future.Comment: ApJ accepte
Intraday forecasts of a volatility index: Functional time series methods with dynamic updating
As a forward-looking measure of future equity market volatility, the VIX
index has gained immense popularity in recent years to become a key measure of
risk for market analysts and academics. We consider discrete reported intraday
VIX tick values as realisations of a collection of curves observed sequentially
on equally spaced and dense grids over time and utilise functional data
analysis techniques to produce one-day-ahead forecasts of these curves. The
proposed method facilitates the investigation of dynamic changes in the index
over very short time intervals as showcased using the 15-second high-frequency
VIX index values. With the help of dynamic updating techniques, our point and
interval forecasts are shown to enjoy improved accuracy over conventional time
series models.Comment: 29 pages, 5 figures, To appear at the Annals of Operations Researc
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