The quality of word embeddings depends on the input corpora, model architectures, and hyper-parameter settings. Using the state-of-the-art neural embedding tool word2vec and both intrinsic and extrinsic evaluations, we present a comprehensive study of how the quality of embeddings changes according to these features. Apart from identifying the most influential hyper-parameters, we also observe ...

In many regards, tuning deep-learning networks is still more an art than it is a technique. Choosing the correct hyper-parameters and getting a complex network to learn properly can be daunting to people not well versed in that art. Taking the example of entailment classification, this work will analyze different methods and tools that can be used in order to diagnose learning problems. It will...

In the problem of parametric statistical inference with a nite parameter space, we study some simple rules for deening posterior upper and lower probabilities directly from the observed likelihood function, without using any prior probabilities. The rules satisfy the likelihood principle and a basic consistency principle (\avoiding sure loss"), they produce vacuous inferences when the likelihoo...

The aim of this study was to formulate and evaluate microencapsulated controlled release preparations of a highly water/soluble drug, salbutamol sulphate by (water in oil) in oil emulsion technique using ethyl cellulose as the retardant material. Various processing and formulation parameters such as drug/polymer ratio, stirring speed, volume of processing medium were optimized to maximize the e...

Gibbs sampling requires a vector of parameters of interest that are initially unknown. These parameters will be denoted by the vectorΦ . Nuisance parameters, Θ , are also initially unknown. The goal of Gibbs sampling is to find estimates for the parameters of interest in order to determine how well the observable data fits the model of interest, and also whether or not data independent of the o...

In the problem of parametric statistical inference with a ®nite parameter space, we propose some simple rules for de®ning posterior upper and lower probabilities directly from the observed likelihood function, without using any prior information. The rules satisfy the likelihood principle and a basic consistency principle (`avoiding sure loss'), they produce vacuous inferences when the likeliho...

The design, analysis, and demonstration of a digital-correlation microwave polarimeter for use in earth remote sensing is presented. We begin with an analysis of threelevel digital correlation and develop the correlator transfer function and radiometric sensitivity. A fifth-order polynomial regression is derived for inverting the digital correlation coefficient into the analog statistic. In add...

palm date leaves were processed with lime in a processing reactor in a factorial arrangement of treatments (3 × 3) with 3 levels of temperatures (40, 80 and 100°c) and time periods (80, 160, and 240 minutes) in a completely randomized design. fermentation parameters measured using gas production technique. with increasing pretreatment time and temperature lignin content reduced from 13.0 percen...

This paper proposes robust algorithms to perform deconvolution and inversion of the heat equation starting from 1D and 2D discrete noised data. The solutions are provided as splines that minimize some parameterized quadratic functionals. Parameters choice determines the trade-off between the regularity of the solution and the approximation of the initial data. The solutions are derived in an ex...

This paper describes a small-scale, coarse-grained parallel Prolog-based Principles-and-Parameters parser that is derived essentially “for free”, modulo minor control flow changes, from an existing serial implementation. The grammar remains unchanged. The system is designed to operate as efficiently as possible under the severe constraints imposed by loosely-coupled processors. We demonstrate s...