We consider the purity-based entanglement detection scheme introduced in [C. Moura Alves and D. Jaksch, Phys. Rev. Lett. 93, 110501 (2004)]. We describe how it could be implemented in an optical lattice using two-atom loss, and prove that in this form it detects all pure entangled states even without any spatial resolution. We then prove that correcting for certain reasonable types of experimental error is possible, and practical for error rates up to the order of one over the number of lattice sites considered. Limited spatial resolution similarly becomes a significant improvement over no spatial resolution only at nearly single site level. We also show how to use this process for state parameter estimation and collapse-revival evidence of entanglement, for which it remains useful even when the error rate is too high to permit unambiguous entanglement detection. We also consider an optical lattice bosonic analogue of the fractional quantum Hall (FQH) effect. This system can reach high “magnetic fields” very difficult to attain in the solid state FQH system, where the discrete nature of the lattice becomes important. Near simple rational numbers l/n of flux quanta per lattice cell, we find that the single particle states become nearly periodic with period n lattice sites, and have an n fold degeneracy which leads to FQH states resembling those of n-internal-state particles. Standard time of flight expansion would reveal this periodicity and be able to distinguish FQH states from vortex lattice or Mott insulator states. Shot noise correlation would provide further information on the nature of the FQH states.

This thesis studies rhythm from an evolutionary computation perspective. Rhythm is the most fundamental dimension of music and can be used as a ground to describe the evolution of music. More specifically, the main goal of the thesis is to investigate how complex rhythmic structures evolve, subject to the cultural transmission between individuals in a society. The study is developed by means of computer modelling and simulations informed by evolutionary computation and artificial life (A-Life). In this process, self-organisation plays a fundamental role. The evolutionary process is steered by the evaluation of rhythmic complexity and by the exposure to rhythmic material. In this thesis, composers and musicologists will find the description of a system named A-Rhythm, which explores the emerged behaviours in a community of artificial autonomous agents that interact in a virtual environment. The interaction between the agents takes the form of imitation games. A set of necessary criteria was established for the construction of a compositional system in which cultural transmission is observed. These criteria allowed the comparison with related work in the field of evolutionary computation and music. In the development of the system, rhythmic representation is discussed. The proposed representation enabled the development of complexity and similarity based measures, and the recombination of rhythms in a creative manner. A-Rhythm produced results in the form of simulation data which were evaluated in terms of the coherence of repertoires of the agents. The data shows how rhythmic sequences are changed and sustained in the population, displaying synchronic and diachronic diversity. Finally, this tool was used as a generative mechanism for composition and several examples are presented.

This work is the result of the definition, design and evaluation of a novel method to interconnect the computational elements - commonly known as Configurable Analogue Blocks (CABs) - of a programmable analogue array. This method is proposed for total or partial replacement of the conventional methods due to serious limitations of the latter in terms of scalability. With this method, named Asynchronous Spike Event Coding (ASEC) scheme, analogue signals from CABs outputs are encoded as time instants (spike events) dependent upon those signals activity and are transmitted asynchronously by employing the Address Event Representation (AER) protocol. Power dissipation is dependent upon input signal activity and no spike events are generated when the input signal is constant. On-line, programmable computation is intrinsic to ASEC scheme and is performed without additional hardware. The ability of the communication scheme to perform computation enhances the computation power of the programmable analogue array. The design methodology and a CMOS implementation of the scheme are presented together with test results from prototype integrated circuits (ICs).

International Telemetering Conference Proceedings / October 17-20, 1994 / Town & Country Hotel and Conference Center, San Diego, California / In many signal processing and telemetry applications only a portion of the Discrete Fourier Transform (DFT) of a data sequence is of interest. This paper develops an algorithm which enables computation of the FFT only over the frequency values of interest, reducing the computational complexity. As will be shown, the algorithm is also very modular which lends to efficient parallel processing implementation. This paper will begin by developing the frequency selective FFT algorithm, and conclude with a comparative analysis of the computational complexity of the algorithm with respect to the traditional FFT.

Approved for public release; distribution is unlimited / This thesis investigates the viability of a new method for numerically computing the input impedance and the currents on simple antenna structures. This technique considers the antenna between two ground planes and uses multiregion cylindrical harmonic expansions with tangential field continuity to obtain the surface currents and input impedance. The computed results are compared to the results obtained from the Numerical Electromagnetics Code for various physical parameters to assess computational accuracy. / http://archive.org/details/computationofmon00hurl / Lieutenant, United States Navy

This paper explores the potential applications of existing spectral clustering algorithms to real life problems through experiments on existing road traffic data. The analysis begins with an overview of previous unsupervised machine learning techniques and constructs an effective spectral clustering algorithm that demonstrates the analytical power of the method. The paper focuses on the spectral embedding method’s ability to project non-linearly separable, high dimensional data into a more manageable space that allows for accurate clustering. The key step in this method involves solving a normalized eigenvector problem in order to construct an optimal representation of the original data. While this step greatly enhances our ability to analyze the relationships between data points and identify the natural clusters within the original dataset, it is difficult to comprehend the eigenvalue representation of the data in terms of the original input variables. The later sections of this paper will explore how the careful framing of questions with respect to available data can help researchers extract tangible decision driving results from real world data through spectral clustering analysis.

One of the main applications of machine learning in bioinformatics is the construction of classification models which can accurately classify new instances using information gained from previous instances. With the help of machine learning algorithms (such as supervised classification and gene selection) new meaningful knowledge can be extracted from bioinformatics datasets that can help in disease diagnosis and prognosis as well as in prescribing the right treatment for a disease. One particular challenge encountered when analyzing bioinformatics datasets is data noise, which refers to incorrect or missing values in datasets. Noise can be introduced as a result of experimental errors (e.g. faulty microarray chips, insufficient resolution, image corruption, and incorrect laboratory procedures), as well as other errors (errors during data processing, transfer, and/or mining). A special type of data noise called class noise, which occurs when an instance/example is mislabeled. Previous research showed that class noise has a detrimental impact on machine learning algorithms (e.g. worsened classification performance and unstable feature selection). In addition to data noise, gene expression datasets can suffer from the problems of high dimensionality (a very large feature space) and class imbalance (unequal distribution of instances between classes). As a result of these inherent problems, constructing accurate classification models becomes more challenging. / Includes bibliography. / Dissertation (Ph.D.)--Florida Atlantic University, 2015. / FAU Electronic Theses and Dissertations Collection

In response to the massive amounts of data that make up a large number of bioinformatics datasets, it has become increasingly necessary for researchers to use computers to aid them in their endeavors. With difficulties such as high dimensionality, class imbalance, noisy data, and difficult to learn class boundaries, being present within the data, bioinformatics datasets are a challenge to work with. One potential source of assistance is the domain of data mining and machine learning, a field which focuses on working with these large amounts of data and develops techniques to discover new trends and patterns that are hidden within the data and to increases the capability of researchers and practitioners to work with this data. Within this domain there are techniques designed to eliminate irrelevant or redundant features, balance the membership of the classes, handle errors found in the data, and build predictive models for future data. / Includes bibliography. / Dissertation (Ph.D.)--Florida Atlantic University, 2015. / FAU Electronic Theses and Dissertations Collection

Nesta tese de doutoramento nós tivemos como principal objetivo desenvolver novos métodos para proteção da informação e computação quântica. Começamos, de forma introdutória, ilustrando os conceitos básicos e fundamentais da teoria da informação e computação quântica, como os bits quânticos (qubits), o operador densidade, o emaranhamento e as operações lógicas quânticas. Na seqüência, apresentamos os formalismos utilizados para tratar sistemas abertos, ou seja, sujeitos a erros, além das principais técnicas existentes a fim de proteger a informação quântica, como os códigos de correção de erros, os subespaços livres de erros e o desacoplamento dinâmico. Finalmente, baseando-nos na técnica de desacoplamento dinâmico, introduzimos um esquema de proteção para operações lógicas quânticas e o emaranhamentos entre qubits utilizando campos de alta freqüência. Ilustramos em detalhes a proteção da operação lógica quântica de Hadamard e do emaranhamento entre dois qubits, além de apresentarmos as principais diferenças e vantagens de nosso método quando comparado às técnicas tradicionais de desacoplamento dinâmico. / The main objective of this thesis is the development of a new procedure for quantum information and computation protection. We begin by briefly illustrating the basic concepts of quantum information and computation theory, such as quantum bits (qubits), density matrix operator, entanglement, and quantum logical operations. Subsequently, we present the formalism utilized to treat quantum open systems, i.e., systems subjected to errors, and the main strategies to protect quantum information, such as quantum error correction codes, decoherence-free subspaces, and dynamical decoupling. Finally, based on the dynamical decoupling strategies, we introduce a procedure to protect quantum logical operations and entanglement utilizing high-frequency continuous fields. We illustrate, in details, the protection of a Hadamard quantum gate and of entanglement between two qubits, and present the differences and advantages of our procedure when compared with traditional techniques of dynamical decoupling.

Thesis: S.M., Massachusetts Institute of Technology, Computation for Design and Optimization Program, 2016. / S.M. !c Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science 2016 / Cataloged from PDF version of thesis. / Includes bibliographical references (pages 83-88). / Cities today are strained by the exponential growth in population where they are homes to the majority of world's population. Understanding the complexities underlying the emerging behaviors of human travel patterns on the city level is essential toward making informed decision-making pertaining to urban transportation infrastructures This thesis includes several attempts towards modeling and understanding human mobility at the scales of individuals and the scale of aggregate population movement. The second chapter includes the development of a browser delivering visual insights of the aggregate behavior of populations in cities. The third chapter provides a computational framework for clustering regions in cities based on their attraction behavior and in doing so aids a predictive model in predicting inflows to newly developed regions. The fourth chapter investigates the patterns of individuals' movement at the city scale towards developing a predictive model for a persons' next visited location. The predictive accuracy is then increased by adding movement information of the population. The motivation behind the work of this thesis is derived from the demand of tools that provides fine-grained analysis of the complexity of human travel within cites. The approach takes advantage of the existing built infrastructures to sense the mobility of people eliminating the financial and temporal burdens of traditional methods. The outcomes of this work will assist both planners and the public in understanding the complexities of human mobility within their cities. / by Fahad Alhasoun. / S.M. / S.M. !c Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science