LARS-like symptoms in the general population may suggest the significance of postoperative functional problems and emotional implications of rectal surgery

Background & Aim. Sphincter-saving rectal surgery is prone to cause changes in bowel function associated with Low Anterior Resection Syndrome (LARS). Our aim was to assess LARS-like symptoms within a population of 50-80-year old in order to understand the functional disturbances and emotional impact of LARS. Materials and methods: We used a questionnaire to evaluate LARS with the following categories of symptoms: flatulence control, anal incontinence, frequency, clustering and urgency of the stools, and the psycho-emotional impact created by the presence of these symptoms. We calculated the severity of LARS on 343 responders. Results. The average age of the responders (57.4% females) was 60 years. Overall, 48.1% of those questioned had no LARS-associated symptoms, while the rest presented either minor (39.9%) or major (12%) LARS-like symptomatology according to the assessment scale. Women have a higher relative risk (1.32) of having minor or major LARS. The frequency of stools did not correlate with the overall LARS score. The psycho-emotional impact was mostly influenced by the presence of incontinence (p=0.001) and urgency (p=0.05). Discussions. The study highlights the need to integrate the initial status of patients into the overall quantification of the effects of surgery on the quality of life. Age does not influence the prevalence of LARS, but symptoms seem more prevalent in women. The psycho-emotional impact is relevant to the general population, so explanations given during the informed consent and accurate description of potential consequences of surgical intervention increase compliance to ensure better post-operative control of the symptomatology. Conclusions. Deriving a normative LARS-like score may alter the interpretation and discussion of LARS scores for future rectal cancer patients, and it also provides a better understanding of the emotional impact of such symptoms on certain population subsets or cultural groups

Pathwise Least Angle Regression and a Significance Test for the Elastic Net

Least angle regression (LARS) by Efron et al. (2004) is a novel method for constructing the piece-wise linear path of Lasso solutions. For several years, it remained also as the de facto method for computing the Lasso solution before more sophisticated optimization algorithms preceded it. LARS method has recently again increased its popularity due to its ability to find the values of the penalty parameters, called knots, at which a new parameter enters the active set of non-zero coefficients. Significance test for the Lasso by Lockhart et al. (2014), for example, requires solving the knots via the LARS algorithm. Elastic net (EN), on the other hand, is a highly popular extension of Lasso that uses a linear combination of Lasso and ridge regression penalties. In this paper, we propose a new novel algorithm, called pathwise (PW-)LARS-EN, that is able to compute the EN knots over a grid of EN tuning parameter {\alpha} values. The developed PW-LARS-EN algorithm decreases the EN tuning parameter and exploits the previously found knot values and the original LARS algorithm. A covariance test statistic for the Lasso is then generalized to the EN for testing the significance of the predictors. Our simulation studies validate the fact that the test statistic has an asymptotic Exp(1) distribution.Comment: 5 pages, 25th European Signal Processing Conference (EUSIPCO 2017

Resampled Priors for Variational Autoencoders

We propose Learned Accept/Reject Sampling (LARS), a method for constructing richer priors using rejection sampling with a learned acceptance function. This work is motivated by recent analyses of the VAE objective, which pointed out that commonly used simple priors can lead to underfitting. As the distribution induced by LARS involves an intractable normalizing constant, we show how to estimate it and its gradients efficiently. We demonstrate that LARS priors improve VAE performance on several standard datasets both when they are learned jointly with the rest of the model and when they are fitted to a pretrained model. Finally, we show that LARS can be combined with existing methods for defining flexible priors for an additional boost in performance

Multiple Testing and Variable Selection along Least Angle Regression's path

In this article, we investigate multiple testing and variable selection using Least Angle Regression (LARS) algorithm in high dimensions under the Gaussian noise assumption. LARS is known to produce a piecewise affine solutions path with change points referred to as knots of the LARS path. The cornerstone of the present work is the expression in closed form of the exact joint law of K-uplets of knots conditional on the variables selected by LARS, namely the so-called post-selection joint law of the LARS knots. Numerical experiments demonstrate the perfect fit of our finding. Our main contributions are three fold. First, we build testing procedures on variables entering the model along the LARS path in the general design case when the noise level can be unknown. This testing procedures are referred to as the Generalized t-Spacing tests (GtSt) and we prove that they have exact non-asymptotic level (i.e., Type I error is exactly controlled). In that way, we extend a work from (Taylor et al., 2014) where the Spacing test works for consecutive knots and known variance. Second, we introduce a new exact multiple false negatives test after model selection in the general design case when the noise level can be unknown. We prove that this testing procedure has exact non-asymptotic level for general design and unknown noise level. Last, we give an exact control of the false discovery rate (FDR) under orthogonal design assumption. Monte-Carlo simulations and a real data experiment are provided to illustrate our results in this case. Of independent interest, we introduce an equivalent formulation of LARS algorithm based on a recursive function.Comment: 62 pages; new: FDR control and power comparison between Knockoff, FCD, Slope and our proposed method; new: the introduction has been revised and now present a synthetic presentation of the main results. We believe that this introduction brings new insists compared to previous version

The Lyman Alpha Reference Sample: III. Properties of the Neutral ISM from GBT and VLA Observations

We present new H I imaging and spectroscopy of the 14 UV-selected star-forming galaxies in the Lyman Alpha Reference Sample (LARS), aimed for a detailed study of the processes governing the production, propagation, and escape of Ly$\alpha$ photons. New H I spectroscopy, obtained with the 100m Green Bank Telescope (GBT), robustly detects the H I spectral line in 11 of the 14 observed LARS galaxies (although the profiles of two of the galaxies are likely confused by other sources within the GBT beam); the three highest redshift galaxies are not detected at our current sensitivity limits. The GBT profiles are used to derive fundamental H I line properties of the LARS galaxies. We also present new pilot H I spectral line imaging of 5 of the LARS galaxies obtained with the Karl G. Jansky Very Large Array (VLA). This imaging localizes the H I gas and provides a measurement of the total H I mass in each galaxy. In one system, LARS 03 (UGC 8335 or Arp 238), VLA observations reveal an enormous tidal structure that extends over 160 kpc from the main interacting systems and that contains $>$10$^9$ M$_{\odot}$ of H I. We compare various H I properties with global Ly$\alpha$ quantities derived from HST measurements. The measurements of the Ly$\alpha$ escape fraction are coupled with the new direct measurements of H I mass and significantly disturbed H I velocities. Our robustly detected sample reveals that both total H I mass and linewidth are tentatively correlated with key Ly$\alpha$ tracers. Further, on global scales, these data support a complex coupling between Ly$\alpha$ propagation and the H I properties of the surrounding medium.Comment: Preprint form, 16 figures, accepted in Ap

On the access to an earth resources data processing system

The Purdue/LARS earth resources data processing system is briefly described. The considerations to which an organization would want to give attention before obtaining a remote terminal to this system are discussed. The support of such a terminal which Purdue/LARS is willing to propose is described

Reduced-order modeling using Dynamic Mode Decomposition and Least Angle Regression

Dynamic Mode Decomposition (DMD) yields a linear, approximate model of a system's dynamics that is built from data. We seek to reduce the order of this model by identifying a reduced set of modes that best fit the output. We adopt a model selection algorithm from statistics and machine learning known as Least Angle Regression (LARS). We modify LARS to be complex-valued and utilize LARS to select DMD modes. We refer to the resulting algorithm as Least Angle Regression for Dynamic Mode Decomposition (LARS4DMD). Sparsity-Promoting Dynamic Mode Decomposition (DMDSP), a popular mode-selection algorithm, serves as a benchmark for comparison. Numerical results from a Poiseuille flow test problem show that LARS4DMD yields reduced-order models that have comparable performance to DMDSP. LARS4DMD has the added benefit that the regularization weighting parameter required for DMDSP is not needed.Comment: 14 pages, 2 Figures, Submitted to AIAA Aviation Conference 201

Least angle regression for time series forecasting with many predictors.

Least Angle Regression(LARS)is a variable selection method with proven performance for cross-sectional data. In this paper, it is extended to time series forecasting with many predictors. The new method builds parsimonious forecast models,taking the time series dynamics into account. It is a exible method that allows for ranking the different predictors according to their predictive content. The time series LARS shows good forecast performance, as illustrated in a simulation study and two real data applications, where it is compared with the standard LARS algorithm and forecasting using diffusion indices.macro-econometrics; model selection; penalized regression; variable ranking;