scholarly journals Proximal Algorithms for Large-Scale Statistical Modeling and Sensor/Actuator Selection

2020 ◽  
Vol 65 (8) ◽  
pp. 3441-3456 ◽  
Author(s):  
Armin Zare ◽  
Hesameddin Mohammadi ◽  
Neil K. Dhingra ◽  
Tryphon T. Georgiou ◽  
Mihailo R. Jovanovic
Keyword(s):  
Statistical Modeling ◽  
Large Scale ◽  
Proximal Algorithms ◽  
Sensor Actuator

scholarly journals Semantic discrimination of technicality in French nominalizations

2020 ◽  
Vol 4 (2) ◽  
pp. 100-119
Author(s):  
Marine Wauquier ◽  
Nabil Hathout ◽  
Cécile Fabre
Keyword(s):  
Statistical Modeling ◽  
Large Scale ◽  
Distributional Analysis ◽  
Lexical Resources ◽  
Definition Of ◽  
Semantic Distinction

Abstract French suffixations in -age, -ion and -ment are considered roughly equivalent, yet some differences have been pointed out regarding the semantics of the resulting nominalizations. In this study, we confirm the existence of a semantic distinction between them on the basis of a large scale distributional analysis. We show that the distinction is partially determined by the degree of technicality of the denoted action: -age nominals tend to be more technical than -ion ones. We examine this hypothesis through the statistical modeling of technicality. To this end, we propose a linguistic definition of technicality, which we implement using empirical, quantitative criteria estimated in corpora and lexical resources. We show to what extent the differences with respect to these criteria adequately approximate technicality. Our study indicates that this definition of technicality, while amendable, provides new perspectives for the characterization of action nouns.

Statistical modeling of pressurized hot-water batch extraction (PHWE) to produce hemicelluloses with desired properties

Holzforschung ◽  
2016 ◽  
Vol 70 (7) ◽  
pp. 633-640 ◽  
Author(s):  
Marjatta Kleen ◽  
Andrey Pranovich ◽  
Stefan Willför
Keyword(s):  
Reaction Time ◽  
Statistical Modeling ◽  
Large Scale ◽  
Degree Of Polymerization ◽  
Hot Water ◽  
Extraction Temperature ◽  
Dissolved Solids ◽  
Average Molecular Mass ◽  
Extraction Apparatus ◽  
Independent Process

Abstract The pressurized hot-water extraction (PHWE) process of Norway spruce sawdust has been optimized aiming at the production of a hemicellulose-rich fraction consisting mainly of galactoglucomannans (GGM). The independent process parameters temperature, reaction time, and liquid-to-wood (L/W) ratio were in focus of the statistical modeling. The main target product properties were the average molecular mass (Mw) and the GGM content of the dissolved solids in the extracts and the yield of polymeric hemicelluloses with Mw larger than 4 kDa in the ethanol-water precipitate. According to the model, the highest Mw (>30 kDa) of the total dissolved solids in the extract can be obtained at a low extraction temperature (ET), a short extraction time (Et), and a low L/W ratio. The best result was 37 kDa, corresponding to a degree of polymerization (DP) about 230. The highest GGM content of the extract (>11% of the sawdust, which is about 70% of the GGM in sawdust) can be obtained with a high ET, a long Et and a high L/W ratio. According to the model, the PHWE process gives rise to the largest possible amount of polymeric hemicelluloses at 170°C, 11 min reaction time, and at L/W 5. Provided that a large-scale extraction apparatus works under these conditions with the same efficiency, it should be possible to produce around 60 g polymeric hemicelluloses (mainly GGM) with a Mw around 15 kDa from 1 kg spruce sawdust, which is roughly 25% of the original hemicelluloses in the sawdust.

Studying macromolecular interactions on the large scale : a bioinformatics approach

2014 ◽  
Author(s):  
◽  
Xingyan Kuang
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Statistical Modeling ◽  
Large Scale ◽  
Molecular Mechanisms ◽  
Mining Machine ◽  
Interaction Data ◽  
Macromolecular Interactions ◽  
Macromolecular Binding

[ACCESS RESTRICTED TO THE UNIVERSITY OF MISSOURI AT AUTHOR'S REQUEST.] Characterization of macromolecular interactions is not only critical for understanding how macromolecules perform their biological functions, such as promoting chemical reactions and acting as antibodies, but is also important for finding out molecular mechanisms behind the human diseases. Furthermore, the information of macromolecular binding is pivotal for elucidating metabolic, signal transduction, and other networks. Finally, our knowledge about macromolecular interactions may be critical in studying how complex genetic variations and alternative splicing affect the development and course of diseases such as cancer. Researchers are trying to understand the evolution and physics of macromolecular interactions by collecting and analyzing the interaction data, developing predictive models for characterization of macromolecular structure and function, and, applying the developed techniques to study specific biological systems or particular diseases. Some research methods like machine learning, statistical modeling and data mining based of the macromolecular interaction data derived from experimentally determined structures of macromolecule complexes are frequently used to discover the principle of interactions. Our work incorporates data mining, machine learning and statistical modeling methodology together into the location of macromolecular binding and establishes a comprehensive relational macromolecular database. Additionally, a sequence-based protein binding site prediction method was built using machine learning method and statistic model. This predictor intelligently integrates the information derived from the protein?s sequence and its homology model so that it can offer accurate predictions irrespective of the varying quality of comparative models. Our methods to analyze the mutations have been applied to studying the role of these interactions in diseases, like cancer.

scholarly journals Usage of statistical modeling techniques in surface and groundwater level prediction

2020 ◽  
Vol 69 (3) ◽  
pp. 248-265 ◽  
Author(s):  
Klemen Kenda ◽  
Jože Peternelj ◽  
Nikos Mellios ◽  
Dimitris Kofinas ◽  
Matej Čerin ◽  
...  
Keyword(s):  
Statistical Modeling ◽  
Large Scale ◽  
Heterogeneous Data ◽  
Data Driven ◽  
Gradient Boosting ◽  
Feature Generation ◽  
Modeling Techniques ◽  
Regression Techniques ◽  
Data Driven Modeling ◽  
Groundwater Level Prediction

Abstract The paper presents a thorough evaluation of the performance of different statistical modeling techniques in ground- and surface-level prediction scenarios as well as some aspects of the application of data-driven modeling in practice (feature generation, feature selection, heterogeneous data fusion, hyperparameter tuning, and model evaluation). Twenty-one different regression and classification techniques were tested. The results reveal that batch regression techniques are superior to incremental techniques in terms of accuracy and that among them gradient boosting, random forest and linear regression perform best. On the other hand, introduced incremental models are cheaper to build and update and could still yield good enough results for certain large-scale applications.

Sign in / Sign up

Export Citation Format

Share Document