293 research outputs found
Contagion and the beginning of the crisis - Pre-Lehman period
This paper provides an overview of the antecedents, main drivers and spillover mechanisms of the turbulence emanating from the US sub-prime credit market in the summer of 2007. Its primary goal is to discuss the facts and interrelationships featured in the various analyses and statistics in a uniform, non-standard approach, to separate the 'centre' from the 'periphery' in terms of the impact of contagion, and to understand the causes and consequences in the pre-Lehman period. The paper concludes that the primary causes of the turmoil were a persistently low international interest rate environment and financial imbalances engendered by globalisation. The combination of accelerating house price inflation and rapid financial asset price rises due to sub-prime mortgage credit securitisations (the originate-and-distribute model) as well as thebursting of asset price bubbles collectively were responsible for the magnitude of the distress. The spillover from the turmoil, in turn, was the consequence of increased international financial integration. One innovation of the paper is a detailed analysis of the channel of contagion within financial integration: a confidence crisis, coupled with turbulence in the interbank markets, played a major role in the centre, while on the periphery the triggers were internal vulnerability, rises in risk premia and reduced access to credit
Machine Learning in Falls Prediction; A cognition-based predictor of falls for the acute neurological in-patient population
Background Information: Falls are associated with high direct and indirect
costs, and significant morbidity and mortality for patients. Pathological falls
are usually a result of a compromised motor system, and/or cognition. Very
little research has been conducted on predicting falls based on this premise.
Aims: To demonstrate that cognitive and motor tests can be used to create a
robust predictive tool for falls.
Methods: Three tests of attention and executive function (Stroop, Trail
Making, and Semantic Fluency), a measure of physical function (Walk-12), a
series of questions (concerning recent falls, surgery and physical function)
and demographic information were collected from a cohort of 323 patients at a
tertiary neurological center. The principal outcome was a fall during the
in-patient stay (n = 54). Data-driven, predictive modelling was employed to
identify the statistical modelling strategies which are most accurate in
predicting falls, and which yield the most parsimonious models of clinical
relevance.
Results: The Trail test was identified as the best predictor of falls.
Moreover, addition of any others variables, to the results of the Trail test
did not improve the prediction (Wilcoxon signed-rank p < .001). The best
statistical strategy for predicting falls was the random forest (Wilcoxon
signed-rank p < .001), based solely on results of the Trail test. Tuning of the
model results in the following optimized values: 68% (+- 7.7) sensitivity, 90%
(+- 2.3) specificity, with a positive predictive value of 60%, when the
relevant data is available.
Conclusion: Predictive modelling has identified a simple yet powerful machine
learning prediction strategy based on a single clinical test, the Trail test.
Predictive evaluation shows this strategy to be robust, suggesting predictive
modelling and machine learning as the standard for future predictive tools
Evaluation of Hungarian Wines for Resveratrol by Overpressured Layer Chromatography
A method, including solid phase extraction sample preparation, overpressured layer chromatographic separation and subsequent densitometric evaluation, was developed for measurement of total resveratrol (cis- and trans-isomers) content of wine. The amount of resveratrol was determined in wine samples from different winemaking regions of Hungary. The total resveratrol was high in Hungarian red wines (3.6–11 mg/L), and much lower in white ones (0.04–1.5 mg/L)
The Trail Making test : a study of its ability to predict falls in the acute neurological in-patient population
Objective:
To determine whether tests of cognitive function and patient-reported outcome measures of motor function can be used to create a machine learning-based predictive tool for falls.
Design:
Prospective cohort study.
Setting:
Tertiary neurological and neurosurgical center.
Subjects:
In all, 337 in-patients receiving neurosurgical, neurological, or neurorehabilitation-based care.
Main Measures:
Binary (Y/N) for falling during the in-patient episode, the Trail Making Test (a measure of attention and executive function) and the Walk-12 (a patient-reported measure of physical function).
Results:
The principal outcome was a fall during the in-patient stay (n = 54). The Trail test was identified as the best predictor of falls. Moreover, addition of other variables, did not improve the prediction (Wilcoxon signed-rank P < 0.001). Classical linear statistical modeling methods were then compared with more recent machine learning based strategies, for example, random forests, neural networks, support vector machines. The random forest was the best modeling strategy when utilizing just the Trail Making Test data (Wilcoxon signed-rank P < 0.001) with 68% (± 7.7) sensitivity, and 90% (± 2.3) specificity.
Conclusion:
This study identifies a simple yet powerful machine learning (Random Forest) based predictive model for an in-patient neurological population, utilizing a single neuropsychological test of cognitive function, the Trail Making test
Maximal height statistics for 1/f^alpha signals
Numerical and analytical results are presented for the maximal relative
height distribution of stationary periodic Gaussian signals (one dimensional
interfaces) displaying a 1/f^alpha power spectrum. For 0<alpha<1 (regime of
decaying correlations), we observe that the mathematically established limiting
distribution (Fisher-Tippett-Gumbel distribution) is approached extremely
slowly as the sample size increases. The convergence is rapid for alpha>1
(regime of strong correlations) and a highly accurate picture gallery of
distribution functions can be constructed numerically. Analytical results can
be obtained in the limit alpha -> infinity and, for large alpha, by
perturbation expansion. Furthermore, using path integral techniques we derive a
trace formula for the distribution function, valid for alpha=2n even integer.
From the latter we extract the small argument asymptote of the distribution
function whose analytic continuation to arbitrary alpha > 1 is found to be in
agreement with simulations. Comparison of the extreme and roughness statistics
of the interfaces reveals similarities in both the small and large argument
asymptotes of the distribution functions.Comment: 17 pages, 8 figures, RevTex
Dynamical masses, absolute radii and 3D orbits of the triply eclipsing star HD 181068 from Kepler photometry
HD 181068 is the brighter of the two known triply eclipsing hierarchical triple stars in the
Kepler field. It has been continuously observed for more than 2 yr with the Kepler space
telescope. Of the nine quarters of the data, three have been obtained in short-cadence mode,
that is one point per 58.9 s. Here we analyse this unique data set to determine absolute physical
parameters (most importantly the masses and radii) and full orbital configuration using a
sophisticated novel approach. We measure eclipse timing variations (ETVs), which are then
combined with the single-lined radial velocity measurements to yield masses in a manner
equivalent to double-lined spectroscopic binaries. We have also developed a new light-curve
synthesis code that is used to model the triple, mutual eclipses and the effects of the changing
tidal field on the stellar surface and the relativistic Doppler beaming. By combining the stellar
masses from the ETV study with the simultaneous light-curve analysis we determine the
absolute radii of the three stars. Our results indicate that the close and the wide subsystems
revolve in almost exactly coplanar and prograde orbits. The newly determined parameters
draw a consistent picture of the system with such details that have been beyond reach before
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