Positive estimation of the between-group variance component in one-way anova and meta-analysis

Positive estimators of the between-group (between-study) variance are proposed. Explicit variance formulae for the estimators are given and approximate confidence intervals for the between-group variance are constructed, as our proposal to a long outstanding problem. By Monte Carlo simulation, the bias and standard deviation of the proposed estimators are compared with the truncated versions of the maximum likelihood (ML) estimator, restricted maximum likelihood (REML) estimator and a (lately) standard estimator in meta-analysis. Attained confidence coefficients of the constructed confidence intervals are also presented

Spectrofluorimetric Determination of Aliskiren in Tablets and Spiked Human Plasma through Derivatization with Dansyl Chloride

A simple and sensitive method has been developed and validated for the determination of aliskiren (ALS) in its dosage forms and spiked plasma. The method was based on the reaction of the drug with dansyl chloride in the presence of bicarbonate solution of pH 10.5 to give a highly fluorescent derivative which was measured at 501 nm with excitition at 378 nm in dichloromethane. Different experimental parameters affecting the development of the method and stability were carefully studied and optimized. The calibration curves were linear over the concentration ranges of 100–700 and 50–150 ng/mL for standard solution and plasma, respectively. The limits of detection were 27.52 ng/mL in standard solution, 4.91 ng/mL in plasma. The developed method was successfully applied to the analysis the drug in the commercial tablets and spiked plasma samples. The mean recovery of ALS from tablets and plasma was 100.10 and 97.81%, respectively. A proposal of the reaction pathway was presented

Spectrophotometric determination of nicradipine and isradipine in pharmaceutical formulations

A sensitive spectrophotometric method was developed for the determination of some 1,4-dihydropyridine compounds namely, nicardipine and isradipine either in pure form or in pharmaceutical preparations. The method is based on the reduction of nicardipine and isradipine with zinc powder and calcium chloride followed by further reduction with sodium pentacyanoaminoferrate (II) to give violet and red products having the absorbance maximum at 546 and 539 nm with nicardipine and isradipine, respectively. Beer’s law was obeyed over the concentration range 8.0–180 μg/ml with the detection limit of 1.67 μg/ml for nicardipine and 8.0–110 μg/ml with the detection limit of 1.748 μg/ml for isradipine. The analytical parameters and their effects on the reported methods were investigated. The molar absorptivity, quantization limit, standard deviation of intercept (Sa), standard deviation of slope (Sb) and standard deviation of the residuals (Sy/x) were calculated. The composition of the result compounds were found 1:1 for nicardipine and 1:2 for isradipine by Job’s method and the conditional stability constant (Kf) and the free energy changes (ΔG) were calculated for compounds formed. The proposed method was applied successfully for the determination of nicardipine and isradipine in their dosage forms. The results obtained were in good agreement with those obtained using the reference or official methods. A proposal of the reaction pathway was presented

Automated Machine Learning for Entity Matching Tasks

The paper studies the application of automated machine learning approaches (AutoML) for addressing the problem of Entity Matching (EM). This would make the existing, highly effective, Machine Learning (ML) and Deep Learning based approaches for EM usable also by non-expert users, who do not have the expertise to train and tune such complex systems. Our experiments show that the direct application of AutoML systems to this scenario does not provide high quality results. To address this issue, we introduce a new component, the EM adapter, to be pipelined with standard AutoML systems, that preprocesses the EM datasets to make them usable by automated approaches. The experimental evaluation shows that our proposal obtains the same effectiveness as the state-of-the-art EM systems, but it does not require any skill on ML to tune it

Development of a ML-based model-independent analysis strategy at the LHC

earching for New Physics is the primary goal of the CMS experiment at the LHC. Performing such search without relying on a specific theory extending the Standard Model (SM) is of paramount importance but at the same time highly non-trivial. Recently, several proposal have been made in that sense, in particular exploiting the power of modern ML techniques. The goal of this thesis is to apply such model-independent strategies to concrete physics cases, aiming at detecting signals solely by comparing the collision data with what predicted by the SM

Multilevel Markov Chain Monte Carlo Method for High-Contrast Single-Phase Flow Problems

In this paper we propose a general framework for the uncertainty quantification of quantities of interest for high-contrast single-phase flow problems. It is based on the generalized multiscale finite element method (GMsFEM) and multilevel Monte Carlo (MLMC) methods. The former provides a hierarchy of approximations of different resolution, whereas the latter gives an efficient way to estimate quantities of interest using samples on different levels. The number of basis functions in the online GMsFEM stage can be varied to determine the solution resolution and the computational cost, and to efficiently generate samples at different levels. In particular, it is cheap to generate samples on coarse grids but with low resolution, and it is expensive to generate samples on fine grids with high accuracy. By suitably choosing the number of samples at different levels, one can leverage the expensive computation in larger fine-grid spaces toward smaller coarse-grid spaces, while retaining the accuracy of the final Monte Carlo estimate. Further, we describe a multilevel Markov chain Monte Carlo method, which sequentially screens the proposal with different levels of approximations and reduces the number of evaluations required on fine grids, while combining the samples at different levels to arrive at an accurate estimate. The framework seamlessly integrates the multiscale features of the GMsFEM with the multilevel feature of the MLMC methods following the work in \cite{ketelson2013}, and our numerical experiments illustrate its efficiency and accuracy in comparison with standard Monte Carlo estimates.Comment: 29 pages, 6 figure