674 research outputs found
One Thing After Another: Why the Passage of Time Is Not an Illusion
Does time seem to pass, even though it doesn’t, really? Many philosophers think the answer is ‘Yes’—at least when ‘time’s passing’ is understood in a particular way. They take time’s passing to be a process by which each time in turn acquires a special status, such as the status of being the only time that exists, or being the only time that is present. This chapter suggests that, on the contrary, all we perceive is temporal succession, one thing after another, a notion to which modern physics is not inhospitable. The contents of perception are best described in terms of ‘before’ and ‘after’, rather than ‘past’, ‘present, and ‘future’
Spherical Slepian functions and the polar gap in geodesy
The estimation of potential fields such as the gravitational or magnetic
potential at the surface of a spherical planet from noisy observations taken at
an altitude over an incomplete portion of the globe is a classic example of an
ill-posed inverse problem. Here we show that the geodetic estimation problem
has deep-seated connections to Slepian's spatiospectral localization problem on
the sphere, which amounts to finding bandlimited spherical functions whose
energy is optimally concentrated in some closed portion of the unit sphere.
This allows us to formulate an alternative solution to the traditional damped
least-squares spherical harmonic approach in geodesy, whereby the source field
is now expanded in a truncated Slepian function basis set. We discuss the
relative performance of both methods with regard to standard statistical
measures as bias, variance and mean-square error, and pay special attention to
the algorithmic efficiency of computing the Slepian functions on the region
complementary to the axisymmetric polar gap characteristic of satellite
surveys. The ease, speed, and accuracy of this new method makes the use of
spherical Slepian functions in earth and planetary geodesy practical.Comment: 14 figures, submitted to the Geophysical Journal Internationa
Generative discriminative models for multivariate inference and statistical mapping in medical imaging
This paper presents a general framework for obtaining interpretable
multivariate discriminative models that allow efficient statistical inference
for neuroimage analysis. The framework, termed generative discriminative
machine (GDM), augments discriminative models with a generative regularization
term. We demonstrate that the proposed formulation can be optimized in closed
form and in dual space, allowing efficient computation for high dimensional
neuroimaging datasets. Furthermore, we provide an analytic estimation of the
null distribution of the model parameters, which enables efficient statistical
inference and p-value computation without the need for permutation testing. We
compared the proposed method with both purely generative and discriminative
learning methods in two large structural magnetic resonance imaging (sMRI)
datasets of Alzheimer's disease (AD) (n=415) and Schizophrenia (n=853). Using
the AD dataset, we demonstrated the ability of GDM to robustly handle
confounding variations. Using Schizophrenia dataset, we demonstrated the
ability of GDM to handle multi-site studies. Taken together, the results
underline the potential of the proposed approach for neuroimaging analyses.Comment: To appear in MICCAI 2018 proceeding
Moving Beyond Noninformative Priors: Why and How to Choose Weakly Informative Priors in Bayesian Analyses
Throughout the last two decades, Bayesian statistical methods have proliferated throughout ecology and evolution. Numerous previous references established both philosophical and computational guidelines for implementing Bayesian methods. However, protocols for incorporating prior information, the defining characteristic of Bayesian philosophy, are nearly nonexistent in the ecological literature. Here, I hope to encourage the use of weakly informative priors in ecology and evolution by providing a ‘consumer\u27s guide’ to weakly informative priors. The first section outlines three reasons why ecologists should abandon noninformative priors: 1) common flat priors are not always noninformative, 2) noninformative priors provide the same result as simpler frequentist methods, and 3) noninformative priors suffer from the same high type I and type M error rates as frequentist methods. The second section provides a guide for implementing informative priors, wherein I detail convenient ‘reference’ prior distributions for common statistical models (i.e. regression, ANOVA, hierarchical models). I then use simulations to visually demonstrate how informative priors influence posterior parameter estimates. With the guidelines provided here, I hope to encourage the use of weakly informative priors for Bayesian analyses in ecology. Ecologists can and should debate the appropriate form of prior information, but should consider weakly informative priors as the new ‘default’ prior for any Bayesian model
Quality management in heavy duty manufacturing industry: TQM vs. Six Sigma
‘Is TQM a management fad?’ This question has been extensively documented in the quality management literature; and will be tackled in this research though a critical literature review on the area. ‘TQM versus Six-Sigma’ debate, which has also been a fundamental challenge in this research filed, is addressed by a thematic and chronological review on the peer papers. To evaluate this challenge in practice, a primary research in heavy duty machinery production industry have been conducted using a case-study on, J C Bamford Excavators Ltd (JCB), the largest European construction machinery producer. The result highlights that TQM is a natural foundation to build up Six-Sigma upon; and not surprisingly the quality yield in a TQM approach complemented by Six-sigma is far higher and more stable than when TQM with no Six-Sigma focus is being put in place; thus presenting the overall finding that TQM and Six Sigma are compliments, not substitutes. The study will be concluded with an overview on quality management approaches in the heavy duty manufacturing industry to highlight the way forward for the industry
On the combination of omics data for prediction of binary outcomes
Enrichment of predictive models with new biomolecular markers is an important
task in high-dimensional omic applications. Increasingly, clinical studies
include several sets of such omics markers available for each patient,
measuring different levels of biological variation. As a result, one of the
main challenges in predictive research is the integration of different sources
of omic biomarkers for the prediction of health traits. We review several
approaches for the combination of omic markers in the context of binary outcome
prediction, all based on double cross-validation and regularized regression
models. We evaluate their performance in terms of calibration and
discrimination and we compare their performance with respect to single-omic
source predictions. We illustrate the methods through the analysis of two real
datasets. On the one hand, we consider the combination of two fractions of
proteomic mass spectrometry for the calibration of a diagnostic rule for the
detection of early-stage breast cancer. On the other hand, we consider
transcriptomics and metabolomics as predictors of obesity using data from the
Dietary, Lifestyle, and Genetic determinants of Obesity and Metabolic syndrome
(DILGOM) study, a population-based cohort, from Finland
Case study in six sigma methadology : manufacturing quality improvement and guidence for managers
This article discusses the successful implementation of Six Sigma methodology in a high precision and critical process in the manufacture of automotive products. The Six Sigma define–measure–analyse–improve–control approach resulted in a reduction of tolerance-related problems and improved the first pass yield from 85% to 99.4%. Data were collected on all possible causes and regression analysis, hypothesis testing, Taguchi methods, classification and regression tree, etc. were used to analyse the data and draw conclusions. Implementation of Six Sigma methodology had a significant financial impact on the profitability of the company. An approximate saving of US$70,000 per annum was reported, which is in addition to the customer-facing benefits of improved quality on returns and sales. The project also had the benefit of allowing the company to learn useful messages that will guide future Six Sigma activities
Differential expression analysis with global network adjustment
<p>Background: Large-scale chromosomal deletions or other non-specific perturbations of the transcriptome can alter the expression of hundreds or thousands of genes, and it is of biological interest to understand which genes are most profoundly affected. We present a method for predicting a gene’s expression as a function of other genes thereby accounting for the effect of transcriptional regulation that confounds the identification of genes differentially expressed relative to a regulatory network. The challenge in constructing such models is that the number of possible regulator transcripts within a global network is on the order of thousands, and the number of biological samples is typically on the order of 10. Nevertheless, there are large gene expression databases that can be used to construct networks that could be helpful in modeling transcriptional regulation in smaller experiments.</p>
<p>Results: We demonstrate a type of penalized regression model that can be estimated from large gene expression databases, and then applied to smaller experiments. The ridge parameter is selected by minimizing the cross-validation error of the predictions in the independent out-sample. This tends to increase the model stability and leads to a much greater degree of parameter shrinkage, but the resulting biased estimation is mitigated by a second round of regression. Nevertheless, the proposed computationally efficient “over-shrinkage” method outperforms previously used LASSO-based techniques. In two independent datasets, we find that the median proportion of explained variability in expression is approximately 25%, and this results in a substantial increase in the signal-to-noise ratio allowing more powerful inferences on differential gene expression leading to biologically intuitive findings. We also show that a large proportion of gene dependencies are conditional on the biological state, which would be impossible with standard differential expression methods.</p>
<p>Conclusions: By adjusting for the effects of the global network on individual genes, both the sensitivity and reliability of differential expression measures are greatly improved.</p>
Selection of tuning parameters in bridge regression models via Bayesian information criterion
We consider the bridge linear regression modeling, which can produce a sparse
or non-sparse model. A crucial point in the model building process is the
selection of adjusted parameters including a regularization parameter and a
tuning parameter in bridge regression models. The choice of the adjusted
parameters can be viewed as a model selection and evaluation problem. We
propose a model selection criterion for evaluating bridge regression models in
terms of Bayesian approach. This selection criterion enables us to select the
adjusted parameters objectively. We investigate the effectiveness of our
proposed modeling strategy through some numerical examples.Comment: 20 pages, 5 figure
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