scholarly journals Orbital Conflict: Cutting Planes for Symmetric Integer Programs

2021 ◽  
pp. ijoo.2019.0044
Author(s):  
Jeff Linderoth ◽  
José Núñez Ares ◽  
James Ostrowski ◽  
Fabrizio Rossi ◽  
Stefano Smriglio
Keyword(s):  
Integer Programming ◽  
Cutting Planes ◽  
Stable Set ◽  
Conflict Graph ◽  
Two Phase ◽  
Integer Programs ◽  
Ternary Codes ◽  
Covering Designs ◽  
First Time ◽  
Improve State

Cutting planes have been an important factor in the impressive progress made by integer programming (IP) solvers in the past two decades. However, cutting planes have had little impact on improving performance for symmetric IPs. Rather, the main breakthroughs for solving symmetric IPs have been achieved by cleverly exploiting symmetry in the enumeration phase of branch and bound. In this work, we introduce a hierarchy of cutting planes that arise from a reinterpretation of symmetry-exploiting branching methods. There are too many inequalities in the hierarchy to be used efficiently in a direct manner. However, the lowest levels of this cutting-plane hierarchy can be implicitly exploited by enhancing the conflict graph of the integer programming instance and by generating inequalities such as clique cuts valid for the stable set relaxation of the instance. We provide computational evidence that the resulting symmetry-powered clique cuts can improve state-of-the-art symmetry-exploiting methods. The inequalities are then employed in a two-phase approach with high-throughput computations to solve heretofore unsolved symmetric integer programs arising from covering designs, establishing for the first time the covering radii of two binary-ternary codes.

scholarly journals Deciphering the effects of kosmotrope and chaotrope salts during aqueous two phase extraction (ATPE) of polyphenolic compounds and glycoalkaloids from the leaves of a nutraceutical plant, Solanum retroflexum, with the aid of UPLC-QTOF-MS

2021 ◽  
Vol 64 (1) ◽  
Author(s):  
Tebogo Mokgehle ◽  
Ntakadzeni Madala ◽  
Wilson Gitari ◽  
Nikita Tavengwa
Keyword(s):  
Polar Compounds ◽  
Ultra Performance Liquid Chromatography ◽  
Polyphenolic Compounds ◽  
Phase Extraction ◽  
Two Phase ◽  
Solanum Retroflexum ◽  
Chaotropic Salts ◽  
Almost All ◽  
First Time ◽  
Aqueous Two Phase Extraction

AbstractSolanum plants (Solanaceae) are renowned source of nutraceuticals and have widely been explored for their phytochemical constituents. This work investigated the effects of kosmotropic and chaotropic salts on the number of phytochemicals extracted from the leaves of a nutraceutical plant, Solanum retroflexum, and analyzed on the ultra-performance liquid chromatography hyphenated to a quadrupole time of flight mass spectrometer (UPLC-QTOF-MS) detector. Here, a total of 20 different compounds were putatively characterized. The majority of the identified compounds were polyphenols and glycoalkaloids. Another compound, caffeoyl malate was identified for the first time in this plant. Glycoalkaloids such as solanelagnin, solamargine, solasonine, β-solanine (I) and β-solanine (II) were found to be extracted by almost all the salts used herein. Kosmotrope salts, overall, were more efficient in extracting polar compounds with 4 more polyphenolic compounds extracted compared to the chaotropes. Chaotropes were generally more selective for the extraction of less polar compounds (glycoalkaloids) with 3 more extracted than the kosmotropes. The chaotrope and the kosmotrope that extracted the most metabolites were NaCl and Na2SO4, respectively, with 12 metabolites extracted for each salt. This work demonstrated that a comprehensive metabolome of S. retroflexum, more than what was previously reported on the same plant, can be achieved by application of kosmotropes and chaotropes as extractants with the aid of the Aqueous Two Phase Extraction approach. The best-performing salts, Na2SO4 or NaCl, could potentially be applied on a commercial scale, to meet the ever-growing demand of the studied metabolites. The Aqueous Two Phase Extraction technique was found to be efficient in simultaneous extraction of multiple metabolites which can be applied in metabolomics.

scholarly journals Determining Accurate Vaccination Coverage Rates for Adolescents: The National Immunization Survey-Teen 2006

2009 ◽  
Vol 124 (5) ◽  
pp. 642-651 ◽  
Author(s):  
Nidhi Jain ◽  
James A. Singleton ◽  
Margrethe Montgomery ◽  
Benjamin Skalland
Keyword(s):  
Vaccination Coverage ◽  
National Level ◽  
National Sample ◽  
Two Phase ◽  
National Immunization ◽  
Level Data ◽  
Control And Prevention ◽  
Coverage Rates ◽  
First Time ◽  
National Immunization Survey

Since 1994, the Centers for Disease Control and Prevention has funded the National Immunization Survey (NIS), a large telephone survey used to estimate vaccination coverage of U.S. children aged 19–35 months. The NIS is a two-phase survey that obtains vaccination receipt information from a random-digit-dialed survey, designed to identify households with eligible children, followed by a provider record check, which obtains provider-reported vaccination histories for eligible children. In 2006, the survey was expanded for the first time to include a national sample of adolescents aged 13–17 years, called the NIS-Teen. This article summarizes the methodology used in the NIS-Teen. In 2008, the NIS-Teen was expanded to collect state-specific and national-level data to determine vaccination coverage estimates. This survey provides valuable information to guide immunization programs for adolescents.

Strong lift-and-project cutting planes for the stable set problem

2012 ◽  
Vol 141 (1-2) ◽  
pp. 165-192 ◽  
Author(s):  
Monia Giandomenico ◽  
Fabrizio Rossi ◽  
Stefano Smriglio
Keyword(s):  
Cutting Planes ◽  
Stable Set ◽  
Strong Lift ◽  
Stable Set Problem

scholarly journals Deep Learning to Ternary Hash Codes by Continuation

2021 ◽  
Author(s):  
Mingrui Chen ◽  
Weiyu Li ◽  
weizhi lu
Keyword(s):  
Deep Learning ◽  
Image Retrieval ◽  
Continuation Method ◽  
Binary Codes ◽  
Hard Thresholding ◽  
Joint Learning ◽  
Ternary Codes ◽  
First Time ◽  
Hash Codes ◽  
Discrete Functions

Recently, it has been observed that $\{0,\pm1\}$-ternary codes which are simply generated from deep features by hard thresholding, tend to outperform $\{-1, 1\}$-binary codes in image retrieval. To obtain better ternary codes, we for the first time propose to jointly learn the features with the codes by appending a smoothed function to the networks. During training, the function could evolve into a non-smoothed ternary function by a continuation method, and then generate ternary codes. The method circumvents the difficulty of directly training discrete functions and reduces the quantization errors of ternary codes. Experiments show that the proposed joint learning indeed could produce better ternary codes.

scholarly journals Deep Learning to Ternary Hash Codes by Continuation

2021 ◽  
Author(s):  
Mingrui Chen ◽  
Weiyu Li ◽  
weizhi lu
Keyword(s):  
Deep Learning ◽  
Image Retrieval ◽  
Continuation Method ◽  
Binary Codes ◽  
Hard Thresholding ◽  
Joint Learning ◽  
Ternary Codes ◽  
First Time ◽  
Hash Codes ◽  
Discrete Functions

Recently, it has been observed that $\{0,\pm1\}$-ternary codes which are simply generated from deep features by hard thresholding, tend to outperform $\{-1, 1\}$-binary codes in image retrieval. To obtain better ternary codes, we for the first time propose to jointly learn the features with the codes by appending a smoothed function to the networks. During training, the function could evolve into a non-smoothed ternary function by a continuation method, and then generate ternary codes. The method circumvents the difficulty of directly training discrete functions and reduces the quantization errors of ternary codes. Experiments show that the proposed joint learning indeed could produce better ternary codes.

Two-phase systems developed with hydrophilic and hydrophobic deep eutectic solvents for simultaneously extracting various bioactive compounds with different polarities

2018 ◽  
Vol 20 (8) ◽  
pp. 1879-1886 ◽  
Author(s):  
Jun Cao ◽  
Luyao Chen ◽  
Mohan Li ◽  
Fuliang Cao ◽  
Linguo Zhao ◽  
...  
Keyword(s):  
Bioactive Compounds ◽  
Deep Eutectic Solvents ◽  
Two Phase ◽  
Phase Systems ◽  
First Time

Two-phase systems developed with hydrophilic deep eutectic solvents (DESs) and hydrophobic DESs were prepared in this study for the first time.

Application of Wray-Agarwal turbulence model for numerical simulation of gas-solid flows in CFB risers

2021 ◽  
pp. 1-25
Author(s):  
Yali Shao ◽  
Ramesh K. Agarwal ◽  
Xudong Wang ◽  
Baosheng Jin
Keyword(s):  
Turbulence Model ◽  
Circulating Fluidized Bed ◽  
Volume Fraction ◽  
Solid Phase ◽  
Solid Volume Fraction ◽  
Turbulence Models ◽  
Two Phase ◽  
Efficient Simulation ◽  
Pressure Profiles ◽  
First Time

Abstract In recent decades, increasing attention has been focused on accurate modeling of circulating fluidized bed (CFB) risers to provide valuable guidance to design, optimization and operation of reactors. Turbulence model plays an important role in accurate prediction of complex gas-solid flows. Recently developed Wray-Agarwal (WA) model is a one-equation turbulence model with the advantages of high computational efficiency and competitive accuracy with two-equation models. In this paper for the first time, Eulerian-Eulerian approach coupled with different turbulence models including WA model, standard κ-ε model and shear stress transport (SST) κ-ω model is employed to simulate two-phase flows of gas phase and solid phase in two CFB risers, in order to assess accuracy and efficiency of WA model compared to other well-known two-equation models. Predicted gas-solid flow dynamic characteristics including the gas-solid volume fraction distributions in radial and axial directions, pressure profiles and solid mass flux distributions are compared with data obtained from experiment in detail. The results demonstrate WA model is very promising for accurate and efficient simulation of gas-solid multiphase flows.

Machine Learning Enhancing Adaptivity of Multimodal Mobile Systems

2012 ◽  
pp. 969-985
Author(s):  
Floriana Esposito ◽  
Teresa M.A. Basile ◽  
Nicola Di Mauro ◽  
Stefano Ferilli
Keyword(s):  
Machine Learning ◽  
Mobile Device ◽  
Pattern Discovery ◽  
User Interaction ◽  
User Model ◽  
Mobile Systems ◽  
Machine Learning Techniques ◽  
Two Phase ◽  
Learning Techniques ◽  
First Time

One of the most important features of a mobile device concerns its flexibility and capability to adapt the functionality it provides to the users. However, the main problems of the systems present in literature are their incapability to identify user needs and, more importantly, the insufficient mappings of those needs to available resources/services. In this paper, we present a two-phase construction of the user model: firstly, an initial static user model is built for the user connecting to the system the first time. Then, the model is revised/adjusted by considering the information collected in the logs of the user interaction with the device/context in order to make the model more adequate to the evolving user’s interests/ preferences/behaviour. The initial model is built by exploiting the stereotype concept, its adjustment is performed exploiting machine learning techniques and particularly, sequence mining and pattern discovery strategies.

Machine Learning Enhancing Adaptivity of Multimodal Mobile Systems

2011 ◽  
pp. 121-138
Author(s):  
Floriana Esposito ◽  
Teresa M.A. Basile ◽  
Nicola Di Mauro ◽  
Stefano Ferilli
Keyword(s):  
Machine Learning ◽  
Mobile Device ◽  
Pattern Discovery ◽  
User Interaction ◽  
User Model ◽  
Mobile Systems ◽  
Machine Learning Techniques ◽  
Two Phase ◽  
Learning Techniques ◽  
First Time

One of the most important features of a mobile device concerns its flexibility and capability to adapt the functionality it provides to the users. However, the main problems of the systems present in literature are their incapability to identify user needs and, more importantly, the insufficient mappings of those needs to available resources/services. In this paper, we present a two-phase construction of the user model: firstly, an initial static user model is built for the user connecting to the system the first time. Then, the model is revised/adjusted by considering the information collected in the logs of the user interaction with the device/context in order to make the model more adequate to the evolving user’s interests/ preferences/behaviour. The initial model is built by exploiting the stereotype concept, its adjustment is performed exploiting machine learning techniques and particularly, sequence mining and pattern discovery strategies.

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