Some Transformation Methods on Probabilistic Model for Crowdsensing Networks

2015 ◽  
Author(s):  
Shuang Wu ◽  
Xiaofeng Gao ◽  
Guihai Chen
Keyword(s):  
Probabilistic Model ◽  
Transformation Methods

Probabilistic model for distributed generation expansion in distribution power network

2011 ◽  
pp. 1366-1371 ◽  
Author(s):  
C. Ponce Corral ◽  
H. Bludszuweit ◽  
J.A. Domínguez Navarro
Keyword(s):  
Distributed Generation ◽  
Probabilistic Model ◽  
Power Network ◽  
Generation Expansion ◽  
Distribution Power Network

Imputation Via Copula and Transformation Methods, With Applications to Financial and Economic Data

2010 ◽  
Author(s):  
Yangyong Zhang ◽  
Craig A. Friedman ◽  
Jinggang Huang ◽  
Wenbo Cao
Keyword(s):  
Economic Data ◽  
Transformation Methods

Assessment and Analysis of Credit Risk with Using of Logical and Probabilistic Model

2002 ◽  
Author(s):  
Vassilij Karassev ◽  
Andrey Roukine ◽  
E.D. Dmitrievich Solojentsev
Keyword(s):  
Credit Risk ◽  
Probabilistic Model

Microbial Bioconversion: A Regio-specific Method for Novel Drug Design and Toxicological Study of Metabolites

2019 ◽  
Vol 20 (14) ◽  
pp. 1156-1162
Author(s):  
Maria Yousuf ◽  
Waqas Jamil ◽  
Khayala Mammadova
Keyword(s):  
Microbial Transformation ◽  
Specific Method ◽  
Reaction Conditions ◽  
Small Organic Molecules ◽  
Toxicological Studies ◽  
Drug Designing ◽  
Transformation Methods ◽  
Novel Drug

The methods of chemical structural alteration of small organic molecules by using microbes (fungi, bacteria, yeast, etc.) are gaining tremendous attention to obtain structurally novel and therapeutically potential leads. The regiospecific mild environmental friendly reaction conditions with the ability of novel chemical structural modification in compounds categorize this technique; a distinguished and unique way to obtain medicinally important drugs and their in vivo mimic metabolites with costeffective and timely manner. This review article shortly addresses the immense pharmaceutical importance of microbial transformation methods in drug designing and development as well as the role of CYP450 enzymes in fungi to obtain in vivo drug metabolites for toxicological studies.

scholarly journals Joint Semantic Synthesis and Morphological Analysis of the Derived Word

2018 ◽  
Vol 6 ◽  
pp. 33-48 ◽  
Author(s):  
Ryan Cotterell ◽  
Hinrich Schütze
Keyword(s):  
Neural Networks ◽  
Recurrent Neural Networks ◽  
Probabilistic Model ◽  
Morphological Analysis ◽  
Semantic Representation ◽  
English Word ◽  
Word Formation ◽  
Additive Models ◽  
Segmentation Accuracy ◽  
Morphological Productivity

Much like sentences are composed of words, words themselves are composed of smaller units. For example, the English word questionably can be analyzed as question+ able+ ly. However, this structural decomposition of the word does not directly give us a semantic representation of the word’s meaning. Since morphology obeys the principle of compositionality, the semantics of the word can be systematically derived from the meaning of its parts. In this work, we propose a novel probabilistic model of word formation that captures both the analysis of a word w into its constituent segments and the synthesis of the meaning of w from the meanings of those segments. Our model jointly learns to segment words into morphemes and compose distributional semantic vectors of those morphemes. We experiment with the model on English CELEX data and German DErivBase (Zeller et al., 2013) data. We show that jointly modeling semantics increases both segmentation accuracy and morpheme F1 by between 3% and 5%. Additionally, we investigate different models of vector composition, showing that recurrent neural networks yield an improvement over simple additive models. Finally, we study the degree to which the representations correspond to a linguist’s notion of morphological productivity.

Sign in / Sign up

Export Citation Format

Share Document