Mixture model description of the T-, P dependence of the refractive index of water

2001 ◽  
Vol 114 (7) ◽  
pp. 3157-3162 ◽  
Author(s):  
C. H. Cho ◽  
J. Urquidi ◽  
Gregory I. Gellene ◽  
G. Wilse Robinson
Entropy ◽  
2019 ◽  
Vol 21 (7) ◽  
pp. 702
Author(s):  
Takafumi Kanamori ◽  
Naoya Osugi

The quality of online services highly depends on the accuracy of the recommendations they can provide to users. Researchers have proposed various similarity measures based on the assumption that similar people like or dislike similar items or people, in order to improve the accuracy of their services. Additionally, statistical models, such as the stochastic block models, have been used to understand network structures. In this paper, we discuss the relationship between similarity-based methods and statistical models using the Bernoulli mixture models and the expectation-maximization (EM) algorithm. The Bernoulli mixture model naturally leads to a completely positive matrix as the similarity matrix. We prove that most of the commonly used similarity measures yield completely positive matrices as the similarity matrix. Based on this relationship, we propose an algorithm to transform the similarity matrix to the Bernoulli mixture model. Such a correspondence provides a statistical interpretation to similarity-based methods. Using this algorithm, we conduct numerical experiments using synthetic data and real-world data provided from an online dating site, and report the efficiency of the recommendation system based on the Bernoulli mixture models.


Author(s):  
W. E. Lee

An optical waveguide consists of a several-micron wide channel with a slightly different index of refraction than the host substrate; light can be trapped in the channel by total internal reflection.Optical waveguides can be formed from single-crystal LiNbO3 using the proton exhange technique. In this technique, polished specimens are masked with polycrystal1ine chromium in such a way as to leave 3-13 μm wide channels. These are held in benzoic acid at 249°C for 5 minutes allowing protons to exchange for lithium ions within the channels causing an increase in the refractive index of the channel and creating the waveguide. Unfortunately, optical measurements often reveal a loss in waveguiding ability up to several weeks after exchange.


Author(s):  
Walter C. McCrone

An excellent chapter on this subject by V.D. Fréchette appeared in a book edited by L.L. Hench and R.W. Gould in 1971 (1). That chapter with the references cited there provides a very complete coverage of the subject. I will add a more complete coverage of an important polarized light microscope (PLM) technique developed more recently (2). Dispersion staining is based on refractive index and its variation with wavelength (dispersion of index). A particle of, say almandite, a garnet, has refractive indices of nF = 1.789 nm, nD = 1.780 nm and nC = 1.775 nm. A Cargille refractive index liquid having nD = 1.780 nm will have nF = 1.810 and nC = 1.768 nm. Almandite grains will disappear in that liquid when observed with a beam of 589 nm light (D-line), but it will have a lower refractive index than that liquid with 486 nm light (F-line), and a higher index than that liquid with 656 nm light (C-line).


VASA ◽  
2008 ◽  
Vol 37 (Supplement 73) ◽  
pp. 26-32 ◽  
Author(s):  
Schlattmann ◽  
Höhne ◽  
Plümper ◽  
Heidrich

Background: In order to analyze the prevalence of Raynaud’s syndrome in diseases such as scleroderma and Sjögren’s syndrom – a meta-analysis of published data was performed. Methods: The PubMed data base of the National Library of Medicine was used for studies dealing with Raynaud’s syndrome and scleroderma or Raynaud’s syndroem and Sjögren’s syndrom respectively. The studies found provided data sufficient to estimate the prevalence of Raynaud’s syndrome. The statistical analysis was based on methods for a fixed effects meta-analysis and finite mixture model for proportions. Results: For scleroderma a pooled prevalence of 80.9% and 95% CI (0.78, 0.83) was obtained. A mixture model analysis found four latent classes. We identified a class with a very low prevalence of 11%, weighted with 0.15. On the other hand there is a class with a very high prevalence of 96%. Analysing the association with Sjögren’s syndrome, the pooled analysis leads to a prevalence of Raynaud’s syndrome of 32%, 95% CI(26.7%, 37.7%). A mixture model finds a solution with two latent classes. Here, 38% of the studies show a prevalence of 18.8% whereas 62% observe a prevalence of 38.3%. Conclusion: There is strong variability of studies reporting the prevalence of Raynaud’s syndrome in patients suffering from scleroderma or Sjögren’s syndrome. The available data are insufficient to perform a proper quantitative analysis of the association of Raynaud’s phenomenon with scleroderma or Sjögren’s syndrome. Properly planned and reported epidemiological studies are needed in order to perform a thorough quantitative analysis of risk factors for Raynaud’s syndrome.


1992 ◽  
Vol 139 (2) ◽  
pp. 163 ◽  
Author(s):  
M.R. Shenoy ◽  
R.M. de la Rue
Keyword(s):  

Sign in / Sign up

Export Citation Format

Share Document