3164 Slices
Medium 9781601323163

Quantum-Intersection Equivalents of the Orthomodularity Law in Quantum Logic: Part 5

Hamid R. Arabnia, George A. Gravvanis, George Jandieri, Ashu M. G. Solo, and Fernando G. Tinetti CSREA Press PDF

Int'l Conf. Foundations of Computer Science | FCS'14 |

81

Quantum-Intersection Equivalents of the

Orthomodularity Law in Quantum Logic: Part 5

Jack K. Horner

P. O. Box 266

Los Alamos, New Mexico 87544 USA jhorner@cybermesa.com

FCS 2014

Abstract

The optimization of quantum computing circuitry and compilers at some level must be expressed in terms of quantum-mechanical behaviors and operations. In much the same way that the structure of conventional propositional (Boolean) logic (BL) is the logic of the description of the behavior of classical physical systems and is isomorphic to a Boolean algebra (BA), so also the algebra, C(H), of closed linear subspaces of (equivalently, the system of linear operators on (observables in)) a Hilbert space is a logic of the descriptions of the behavior of quantum mechanical systems and is a model of an ortholattice (OL). An

OL can thus be thought of as a kind of “quantum logic” (QL). C(H) is also a model of an orthomodular lattice, which is an OL conjoined with the orthomodularity axiom (OMA). The rationalization of the OMA as a claim proper to physics has proven problematic, motivating the question of whether the OMA and its equivalents are required in an adequate characterization of QL. Here I provide an automated deduction of a quantum-intersection-based equivalent of the OMA. The proof may be novel.

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Medium 9781601322500

Development of Outage Tolerant FSM Model for Fading Channels

Hamid R. Arabnia, Victor A. Clincy, Leonidas Deligiannidis, George Jandieri, Ashu M. G. Solo, and Fernando G. Tinetti CSREA Press PDF

220

Int'l Conf. Wireless Networks | ICWN'13 |

Development of Outage Tolerant FSM Model for Fading

Channels

Ms. Anjana Jain1

P. D. Vyavahare1

L. D. Arya2

Department of Electronics and Telecomm. Engg. , Shri G. S. Institute of Technology and Science, Indore,

M.P., India

2

Department of Electrical Engineering, Shri G. S. Institute of Technology and Science, Indore, M.P., India

1

Abstract - Finite State Markov (FSM) models for fading channels need to be revised for more realistic design of emerging mobile networks and their performance evaluation. In this paper a Outage Tolerant FSM Model

(OTFSM) is proposed based on concept of certain tolerable outage times, which are defined as ‘Tolerance time’. These are the short duration of outage time which is considered as satisfactory times over the channel. In this paper, a statistical approach is being presented for the development of OTFSM model and evaluation of its fading parameters such as Average Fade Duration (AFD), outage probability and outage frequency. Derived results may be used for higher layer performance evaluation and selection of physical layer parameters of wireless networks.

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Medium 9781601323118

Gene Expression Profile Classification in Random Feature Space

Hamid R. Arabnia, Quoc-Nam Tran, Mary Q. Yang, George Jandieri, Ashu M. G. Solo, Fernando G. Tinetti CSREA Press PDF

44

Int'l Conf. Bioinformatics and Computational Biology | BIOCOMP'14 |

Gene Expression Profile Classification in Random

Feature Space

1

X. Hang1

Department of Electrical & Computer Engineering, California State University, Northridge, California,

USA

Abstract - In this study, gene expression profile classification is done via sparse representation in the random feature Space, which is obtained by either random projection or nonlinear random mapping used in Extreme learning machine (ELM).

The numerical experiment shows that sparse representation has slightly better performance than ELM.

Compared with traditional feature selection methods, both random projection and nonlinear random mapping in

ELM are data independent and not affected by the quality of training datasets. In this study we investigate gene expression profile classification in random feature space. We use spare representation technique [9-10] for classifier, and compare the performance with ELM.

Keywords: Gene expression profile, Random feature, Sparse representation, Extreme learning machine

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Medium 9781601323132

Distributed Evolutionary Algorithm for Clustering Multi-Characteristic Social Networks

Robert Stahlbock, Gary M. Weiss, Mahmoud Abou-Nasr, Hamid R. Arabnia CSREA Press PDF

Int'l Conf. Data Mining | DMIN'14 |

17

Distributed Evolutionary Algorithm for Clustering

Multi-Characteristic Social Networks

Mustafa H. Hajeer

Department of computer science

Dipankar Dasgupta

Department of computer science

King-Ip Lin

Department of computer science

The University of Memphis

The University of Memphis

The University of Memphis

mhhajeer@memphis.edu

ddasgupta@memphis.edu

davidlin@memphis.edu

Abstract— In this information era, data from different sources (online activities) are in abundance. Social media are increasingly providing activities and data, relations and interactions (audio, video and texting) among social actors (people), due to increasing capabilities of mobile devices and the ease access to the Internet. More than a billion people are now involved in online social media, and analyzing these interactive structures is a huge data-analytic problem.

The primary focus of this work is to develop a clustering algorithm for multi-characteristic and dynamic online social networks. This work uses a combination of multi-objective evolutionary algorithms, distributed file systems and nested hybrid-indexing techniques to cluster the multi-characteristic dynamic social networks. Empirical results demonstrate that this adaptive clustering of dynamic social interactions can also provide a reliable distributed framework for BIG data analysis.

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Medium 9781601323125

Unsolved Problems In Computational Science: I

Hamid R. Arabnia, George A. Gravvanis, George Jandieri, Ashu M. G. Solo, and Fernando G. Tinetti CSREA Press PDF

162

Int'l Conf. Scientific Computing | CSC'14 |

Unsolved Problems In Computational Science: I

Shanzhen Gao, Keh-Hsun Chen

Department of Computer Science, College of Computing and Informatics

University of North Carolina at Charlotte, Charlotte, NC 28223, USA

Email: sgao3@uncc.edu, chen@uncc.edu

Abstract—We will talk about some interesting open problems in computational science. Most of them are new. These problems are related to number theory, geometry theory, combinatorics, graph theory, linear algebra and group theory. They are easy to state and understand although they are very difficult to be solved by researchers in mathematics or computer science. It seems to us that it is very challenging to find suitable mathematical methods or efficient algorithms to deal with them.

Keywords: Computational number theory, computational geometry, formula, integer sequence, algorithm

I. I NTRODUCTION

The development of computational science continues in a rapid rhythm, some open problems are made clear and simultaneously new open problems to be solved come out.

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