scholarly journals Linguistic Mathematical Relationships Saved or Lost in Translating Texts: Extension of the Statistical Theory of Translation and Its Application to the New Testament

Information ◽  
2022 ◽  
Vol 13 (1) ◽  
pp. 20
Author(s):  
Emilio Matricciani
Keyword(s):  
Linear Regression ◽  
New Testament ◽  
Communication Theory ◽  
Signal To Noise Ratio ◽  
Small Sample Size ◽  
Technical Problem ◽  
Small Sample ◽  
The New Testament ◽  
Scientific Technical ◽  
Mathematical Relationships

The purpose of the paper is to extend the general theory of translation to texts written in the same language and show some possible applications. The main result shows that the mutual mathematical relationships of texts in a language have been saved or lost in translating them into another language and consequently texts have been mathematically distorted. To make objective comparisons, we have defined a “likeness index”—based on probability and communication theory of noisy binary digital channels-and have shown that it can reveal similarities and differences of texts. We have applied the extended theory to the New Testament translations and have assessed how much the mutual mathematical relationships present in the original Greek texts have been saved or lost in 36 languages. To avoid the inaccuracy, due to the small sample size from which the input data (regression lines) are calculated, we have adopted a “renormalization” based on Monte Carlo simulations whose results we consider as “experimental”. In general, we have found that in many languages/translations the original linguistic relationships have been lost and texts mathematically distorted. The theory can be applied to texts translated by machines. Because the theory deals with linear regression lines, the concepts of signal-to-noise-ratio and likenss index can be applied any time a scientific/technical problem involves two or more linear regression lines, therefore it is not limited to linguistic variables but it is universal.

Frequency Importance Functions in Quiet and Noise for Adults With Cochlear Implants

2015 ◽  
Vol 24 (4) ◽  
pp. 477-486 ◽  
Author(s):  
Douglas P. Sladen ◽  
Todd. A. Ricketts
Keyword(s):  
Cochlear Implants ◽  
Signal To Noise Ratio ◽  
Small Sample Size ◽  
Normal Hearing ◽  
Small Sample ◽  
Equal Weight ◽  
Signal To Noise ◽  
Speech Spectrum ◽  
Noisy Conditions ◽  
Listening In Noise

Purpose Several studies have been devoted to understanding the frequency information available to adult users of cochlear implants when listening in quiet. The objective of this study was to construct frequency importance functions for a group of adults with cochlear implants and a group of adults with normal hearing both in quiet and in a +10 dB signal-to-noise ratio. Method Two groups of adults, 1 with cochlear implants and 1 with normal hearing, were asked to identify nonsense syllables in quiet and in the presence of 6-talker babble while “holes” were systematically created in the speech spectrum. Frequency importance functions were constructed. Results Results showed that adults with normal hearing placed greater weight on bands 1, 3, and 4 than on bands 2, 5, and 6, whereas adults with cochlear implants placed equal weight on all bands. The frequency importance functions for each group did not differ between listening in quiet and listening in noise. Conclusions Adults with cochlear implants assign perceptual weight toward different frequency bands, though the weight assignment does not differ between quiet and noisy conditions. Generalizing these results to the broader population of adults with implants is constrained by a small sample size.

Exploring the Benefits of Transformations in Health Utility Mapping

2020 ◽  
Vol 40 (2) ◽  
pp. 183-197
Author(s):  
Nicholas Mitsakakis ◽  
Karen E. Bremner ◽  
George Tomlinson ◽  
Murray Krahn
Keyword(s):  
Quality Of Life ◽  
Linear Regression ◽  
Simulation Study ◽  
Small Sample Size ◽  
Real Data ◽  
Health Utility ◽  
Small Sample ◽  
Health Utilities ◽  
Utility Values

Background. Quality-of-life research and cost-effectiveness analyses frequently require data on health utility, a global measure of health-related quality of life. When utilities are unavailable, researchers have “mapped” descriptive instruments to utility instruments, using samples of responses to both instruments. Health utilities have an idiosyncratic distribution, with upper bound and probability mass at 1, left skewness, and kurtosis. Estimation of mean utility values conditional on covariates is of interest, particularly in health utility mapping applications. Traditional linear regression may be unsuitable because fundamental assumptions are violated. Complex statistical methods come with deficiencies that may outweigh their benefits. Aim. To investigate the benefits of transforming the health utility response variable before fitting a linear regression model. Methods. We compared log, logit, arcsin, and Box-Cox transformations with an untransformed model, using several measures of model accuracy. We made our evaluation by designing and conducting a simulation study and reanalyzing data from 2 published studies, which “mapped” a psychometric descriptive instrument to a utility instrument. Results. In the simulation study, log transformation with smearing estimator had in most cases the lowest bias but one of the highest variances, especially for estimating low utility values under small sample size. The untransformed model was outperformed by the transformed models. Findings were inconclusive for the analysis of real data, where arcsin gave the lowest error for one of the data sets, while the untransformed model had the best performance for the other. Conclusions. We identified the benefits of transformations and offered suggestions for future modeling of health utilities. However, the benefits were moderate and no single transformation appeared to be universally optimal, suggesting that selection requires examination on a case-by-case basis.

scholarly journals Random Subspace Aggregation for Cancer Prediction with Gene Expression Profiles

2016 ◽  
Vol 2016 ◽  
pp. 1-10 ◽  
Author(s):  
Liying Yang ◽  
Zhimin Liu ◽  
Xiguo Yuan ◽  
Jianhua Wei ◽  
Junying Zhang
Keyword(s):  
Gene Expression ◽  
Nearest Neighbor ◽  
Signal To Noise Ratio ◽  
Small Sample Size ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Small Sample ◽  
Biological Data ◽  
Superior Performance ◽  
Random Subspaces

Background. Precisely predicting cancer is crucial for cancer treatment. Gene expression profiles make it possible to analyze patterns between genes and cancers on the genome-wide scale. Gene expression data analysis, however, is confronted with enormous challenges for its characteristics, such as high dimensionality, small sample size, and low Signal-to-Noise Ratio.Results. This paper proposes a method, termed RS_SVM, to predict gene expression profiles via aggregating SVM trained on random subspaces. After choosing gene features through statistical analysis, RS_SVM randomly selects feature subsets to yield random subspaces and training SVM classifiers accordingly and then aggregates SVM classifiers to capture the advantage of ensemble learning. Experiments on eight real gene expression datasets are performed to validate the RS_SVM method. Experimental results show that RS_SVM achieved better classification accuracy and generalization performance in contrast with single SVM,K-nearest neighbor, decision tree, Bagging, AdaBoost, and the state-of-the-art methods. Experiments also explored the effect of subspace size on prediction performance.Conclusions. The proposed RS_SVM method yielded superior performance in analyzing gene expression profiles, which demonstrates that RS_SVM provides a good channel for such biological data.

scholarly journals Predicting energy demand of residential buildings: A linear regression-based approach for a small sample size

2021 ◽  
Vol 16 (2) ◽  
pp. 67-85
Author(s):  
Soufiane Boukarta
Keyword(s):  
Linear Regression ◽  
Energy Demand ◽  
Residential Buildings ◽  
Small Sample Size ◽  
Building Design ◽  
Building Envelope ◽  
Small Sample ◽  
Design Strategies ◽  
Modeling Techniques ◽  
Thermal Simulations

Abstract The key design strategies that reduce the energy demand of buildings are not present in most thermal codes in many countries. Therefore, modeling techniques offer an alternative to combine the architects' modus operandi with the energy efficiency in the early stages of architectural design and with higher speed and precision. However, a review of the scientific literature using modeling techniques shows that most researchers use a relatively large sample of thermal simulations. This paper proposes a simplified method based on the linear regression modeling technique and considers a relatively smaller sample of thermal simulations. A total of 6 key building design strategies were identified, related to the urban context, building envelope, and shape factor. A simulation protocol containing 60 possible combinations was designed by random selection. In the present study, the Pleiades software was used to estimate the annual energy demand for heating and cooling for a typical dwelling in a humid climate zone. A parametric study and sensitivity analysis to identify the most efficient parameters was performed in SPSS 21. The resulting model predicts the annual energy demand with an accuracy of 93.7%, a root mean square error (RMSE) of 5.88, and a scatter index (SI) of 8.59%. The models performed could efficiently and quickly assist architects while designing the buildings in the architectural practice.

Probing the ultrastructure of the mitotic and meiotic spindle in eggs: An expeditious approach based on semi-thin (0.25 μm) serial sections

1983 ◽  
Vol 41 ◽  
pp. 552-555
Author(s):  
Conly L. Rieder ◽  
S. Bowser ◽  
R. Nowogrodzki ◽  
K. Ross ◽  
G. Sluder
Keyword(s):  
Small Sample Size ◽  
Small Sample ◽  
Meiotic Spindle ◽  
Serial Sections ◽  
Mitotic Apparatus ◽  
Thin Sections ◽  
Cell Cycles ◽  
Ultrastructural Studies

Eggs have long been a favorite material for studying the mechanism of karyokinesis in-vivo and in-vitro. They can be obtained in great numbers and, when fertilized, divide synchronously over many cell cycles. However, they are not considered to be a practical system for ultrastructural studies on the mitotic apparatus (MA) for several reasons, the most obvious of which is that sectioning them is a formidable task: over 1000 ultra-thin sections need to be cut from a single 80-100 μm diameter egg and of these sections only a small percentage will contain the area or structure of interest. Thus it is difficult and time consuming to obtain reliable ultrastructural data concerning the MA of eggs; and when it is obtained it is necessarily based on a small sample size.We have recently developed a procedure which will facilitate many studies concerned with the ultrastructure of the MA in eggs. It is based on the availability of biological HVEM's and on the observation that 0.25 μm thick serial sections can be screened at high resolution for content (after mounting on slot grids and staining with uranyl and lead) by phase contrast light microscopy (LM; Figs 1-2).

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