The Experts below are selected from a list of 24 Experts worldwide ranked by ideXlab platform
Keiichi Akama - One of the best experts on this subject based on the ideXlab platform.
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quark gluon cluster a Composite Entity just due to color confinement
arXiv: General Physics, 2020Co-Authors: Keiichi AkamaAbstract:We incorporate confining effects into statistical formulation of quantum chromodynamics (QCD). It implies that quark-gluon clusters existed in the early universe, triggering the information accumulation in nature. The confinement-driven clustering would explain the reason why the quark-gluon plasma in the heavy-ion ollision experiments looks like liquid unlike the QCD expectation.
Floyd Marinescu - One of the best experts on this subject based on the ideXlab platform.
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ejb design patterns advanced patterns processes and idioms
2002Co-Authors: Floyd MarinescuAbstract:Foreword. Introduction. Acknowledgments. About the Contributors. Part One: EJB Pattern Language. Chapter 1 EJB Layer Architectural Patterns. Session Facade. Message Facade. EJB Command. Data Transfer Object Factory. Generic Attribute Access. Business Interface. Chapter 2 Inter--Tier Data Transfer Patterns. Data Transfer Object. Domain Data Transfer Object. Custom Data Transfer Objects. Data Transfer HashMap. Data Transfer RowSet.Chapter 3 Transaction and Persistence Patterns. Version Number. JDBC for Reading. Data Access Command Beans. Dual Persistent Entity Bean. Chapter 4 Client--Side EJB Interaction Patterns. EJBHomeFactory. Business Delegate. Chapter 5 Primary Key Generation Strategies. Sequence Blocks. UUID for EJB. Stored Procedures for Autogenerated Keys. Part Two: Best Practices for EJB Design and Implementation. Chapter 6 From Requirements to Pattern--Driven Design. TheServerSide's Forum Messaging System Use Cases. A Quick Referesher on Design Issues and Terminology What Is a Domain Model? Understanding the Layers in a J2EE System. Pattern--Driven EJB Architectures. Domain and Persistence Layer Patterns. Services Layer Patterns. Asychronous Use Cases. Synchronous Use Cases. Other Services Layer Patterns. Inter--Tier Data Transfer Patterns. Application Layer Patterns. Summary. Chapter 7 EJB Development Process: Building with Ant and Unit Testing with Junit. Order of Development. Layer--Independent Code. Domain First. Persistence Second. Services Third. Clients Last. Automating Environment Administration with Ant. What Is a J2EE Application Environment? What Does It Mean to Administer a J2EE Application Environment? Using Ant. Unit Testing with JUnit. Summary. Chapter 8 Alternatives to Entity Beans. Entity Beans Features. Entity Beans and Cognitive Dissonance. In Defense of Entity Beans. Alternatives to Entity Beans. Use Straight JDBC/Stored Procedures. Use a Third Party O/R Mapping Product. Build a Custom Persistence Framework. Use Java Data Objects. An EJB Developer's Introduction to Java Data Objects. Class Requirements and Dependencies. Build and Deployment Processes. Inheritance. Client APIs. Dynamic versus Static Discovery Mechanisms. An EJB Developer's Guide to Using JDO. Preparing Your EJB Environment. Configuring Session Beans. Executing Use Cases and Transaction Management. Container--Managed Transactions. Bean--Managed Transactions. Caching/Lazy Loading and Reference Navigation. Finding Java Data Objects. Inter--Tier Data Transfer. Summary. Chapter 9 EJB Design Strategies, Idioms, and Tips. Don't Use the Composite Entity Bean Pattern. Use a Field--Naming Convention to Allow for Validation in EJB 2.0 CMP Entity Beans. Don't Get and Set Value/Data Transfer Objects on Entity Beans.Using Java Singletons Is OK If They're Used Correctly. Prefer Scheduled Updates to Real--Time Computation. Use a Serialized Java Class to Add Compiler Type Checking to Message--Driven Bean Interactions. Always Call setRollbackOnly when Application Exceptions Occur. Limit Parameters to ejbCreate. Don't Use Data Transfer Objects in ejbCreate. Don't Use XML to Communicate as a DTO Mechanism Unless You Really, Really Have To. Appendix: Pattern Code Listing. References. Index.
Shahin Shafei - One of the best experts on this subject based on the ideXlab platform.
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registered images single Composite Entity classified algorithm into linear superposition pyramid decomposition
Research & Reviews: Journal of Engineering and Technology, 2014Co-Authors: Shahin ShafeiAbstract:For keeping the signal exponents at the singular points of the underlying signal and lifting its smoothness at all the remaining points. An extension of the non-linear smoothness constrained filter to image processing is studied at this paper. Numerical experiments demonstrated that our approach is effective and efficient. there exists a polynomial of degree such that is a piecewise smooth signal, infinitely differentiable and there exists a polynomial of degree the definition of H¨older exponent, conclude that admits for all except the singular points. Then we can rewrite the above equation by theorem. it admits as its exponent
S Bohner - One of the best experts on this subject based on the ideXlab platform.
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support vector machines to weight voters in a voting system of Entity extractors
International Joint Conference on Neural Network, 2006Co-Authors: Deborah Vakas Duong, J Venuto, B Goertzel, Ryan Richardson, S BohnerAbstract:Support vector machines are used to combine the outputs of multiple Entity extractors, thus creating a Composite Entity extraction system. The Composite system has a significantly higher f-measure than any of the component systems. Compared to a standard voting technique for combining the results of multiple Entity extractors, the SVM approach produces comparable precision and recall statistics but tends to utilize fewer of the component Entity extractors, thus providing superior computational efficiency, which is critical in practical applications. In this paper, we present our experimental results of comparing a standard voting technique with SVM that each aggregate four Entity extractors. We also describe our future plans of integrating agent-based technology into our experimental testbed where we examine the evolution of Composite techniques as part of the analysis stream. Given that much of the improvement comes from tuning the algorithms to the data stream with a human-in-the-loop, we are considering the merits of employing cognitive agents that are strategically embedded in the workflow for processing data. As we tune the algorithms for better performance on the data streams, we envision agents learning the patterns of data streams and apply the appropriate tuning to ensure optimality.
Deborah Vakas Duong - One of the best experts on this subject based on the ideXlab platform.
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support vector machines to weight voters in a voting system of Entity extractors
International Joint Conference on Neural Network, 2006Co-Authors: Deborah Vakas Duong, J Venuto, B Goertzel, Ryan Richardson, S BohnerAbstract:Support vector machines are used to combine the outputs of multiple Entity extractors, thus creating a Composite Entity extraction system. The Composite system has a significantly higher f-measure than any of the component systems. Compared to a standard voting technique for combining the results of multiple Entity extractors, the SVM approach produces comparable precision and recall statistics but tends to utilize fewer of the component Entity extractors, thus providing superior computational efficiency, which is critical in practical applications. In this paper, we present our experimental results of comparing a standard voting technique with SVM that each aggregate four Entity extractors. We also describe our future plans of integrating agent-based technology into our experimental testbed where we examine the evolution of Composite techniques as part of the analysis stream. Given that much of the improvement comes from tuning the algorithms to the data stream with a human-in-the-loop, we are considering the merits of employing cognitive agents that are strategically embedded in the workflow for processing data. As we tune the algorithms for better performance on the data streams, we envision agents learning the patterns of data streams and apply the appropriate tuning to ensure optimality.