We consider linear regression models where both input data (the values of independent variables) and output data (the observations of the dependent variable) are affected by loss of information caused by uncertainty, indeterminacy, rounding or censoring. Some or all of the crisp data, which are unavailable, are replaced by known intervals. We introduce a possibilistic generalization of the leas...

ABSTRACT Proto ols or part of proto ols are frequently reused through proje ts if they are suÆ iently generi . For instan e, the Contra t Net proto ol an be used verbatim or in other proto ols su h as the supply hain management. This reusability might be diÆ ult to do due to the la k of reusability in urrent intera tion proto ol formalisms. In this paper, we present a new approa h based on a mo...

Linear models are often built to understand how a set of input data affects output data and predict its value. A linear regression model assumes that that regression function E(Y |X) is linear in the inputsX1, . . . ,Xp. The output is represented as a weighted sum of the input variables (or features), where the weights are called the regression coefficients. The regressions coefficients are asc...

We address the exact recovery of the support of a k-sparse vector with Orthogonal Matching Pursuit (OMP) and Orthogonal Least Squares (OLS) in a noiseless setting. We consider the scenario where OMP/OLS have selected good atoms during the first l iterations (l < k) and derive a new sufficient and worst-case necessary condition for their success in k steps. Our result is based on the coherence μ...

Nitric oxide has been implicated in the pathogenesis of multiple sclerosis. However, it is still unclear whether nitric oxide plays a protective role or is deleterious. We have previously shown that peroxynitrite, a reaction product of nitric oxide and superoxide, is toxic to mature oligodendrocytes (OLs). The toxicity is mediated by intracellular zinc release, phosphorylation of extracellular ...

We consider the Orthogonal Least-Squares (OLS) algorithm for the recovery of a m-dimensional k-sparse signal from a low number of noisy linear measurements. The Exact Recovery Condition (ERC) in bounded noisy scenario is established for OLS under certain condition on nonzero elements of the signal. The new result also improves the existing guarantees for Orthogonal Matching Pursuit (OMP) algori...

This paper covers new optical packet switching router technologies, system architectures, and the future photonic Internet. In particular, we will discuss all-optical label switching routers scalable to 42 Petabit/sec switching capacity, experimental demonstrations of 11 hop cascaded operation, and a 477 km fiber link field trial. Introduction Optical packet switching routers allow integration ...

The adaptive lasso is a model selection method shown to be both consistent in variable selection and asymptotically normal in coefficient estimation. The actual variable selection performance of the adaptive lasso depends on the weight used. It turns out that the weight assignment using the OLS estimate (OLS-adaptive lasso) can result in very poor performance when collinearity of the model matr...

PURPOSE To propose a more accurate method to predict the soft tissue changes after orthognathic surgery. PATIENTS AND METHODS The subjects included 69 patients who had undergone surgical correction of Class III mandibular prognathism by mandibular setback. Two multivariate methods of forming prediction equations were examined using 134 predictor and 36 soft tissue response variables: the ordi...