179 research outputs found

    MaxSAT Evaluation 2020 : Solver and Benchmark Descriptions

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    MaxSAT Evaluation 2020 : Solver and Benchmark Descriptions

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    Investigations of cellular automata-based stream ciphers

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    In this thesis paper, we survey the literature arising from Stephan Wolfram\u27s original paper, “Cryptography with Cellular Automata” [WOL86] that first suggested stream ciphers could be constructed with cellular automata. All published research directly and indirectly quoting this paper are summarized up until the present. We also present a novel stream cipher design called Sum4 that is shown to have good randomness properties and resistance to approximation using linear finite shift registers. Sum4 is further studied to determine its effective strength with respect to key size given that an attack with a SAT solver is more efficient than a bruteforce attack. Lastly, we give ideas for further research into improving the Sum4 cipher

    Spark-based Cloud Data Analytics using Multi-Objective Optimization

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    International audienceData analytics in the cloud has become an integral part of enterprise businesses. Big data analytics systems, however, still lack the ability to take user performance goals and budgetary constraints for a task, collectively referred to as task objectives, and automatically configure an analytic job to achieve these objectives. This paper presents a data analytics optimizer that can automatically determine a cluster configuration with a suitable number of cores as well as other system parameters that best meet the task objectives. At a core of our work is a principled multi-objective optimization (MOO) approach that computes a Pareto optimal set of job configurations to reveal tradeoffs between different user objectives, recommends a new job configuration that best explores such tradeoffs, and employs novel optimizations to enable such recommendations within a few seconds. We present efficient incremental algorithms based on the notion of a Progressive Frontier for realizing our MOO approach and implement them into a Spark-based prototype. Detailed experiments using benchmark workloads show that our MOO techniques provide a 2-50x speedup over existing MOO methods, while offering good coverage of the Pareto frontier. When compared to Ottertune, a state-of-the-art performance tuning system, our approach recommends configurations that yield 26%-49% reduction of running time of the TPCx-BB benchmark while adapting to different application preferences on multiple objectives

    An Eigenanalysis and Synthesis of Unitary Operators used in Quantum Computing Algorithms

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    In this work we tackle the challenge of designing quantum unitary operators which represent solutions to optimization problems. We start with a novel method which combines an evolutionary algorithm known as an Evolution Strategy (ES) with a method to randomly generate unitary operators. With this new method, a quantum operator is represented for the first time using real-valued vectors and can be evolved or designed to meet certain target criteria. This criteria could be the solution to an optimization problem. With the ability to evolve quantum operators, we attempt to evolve various known single and multi-qubit quantum gates as well as quantum oracles. We evolve quantum operators which solve instance problems of a known NP-Hard problem and even attempt to evolve a generalized solution operator. We evolve multiple operators with varying size and investigate their properties through eigenanalysis methods as well as by synthesizing them into quantum logic gates using the quantum compiler Qubiter. We also present a new quantum logic algebra which offers a new way to represent quantum circuits and demonstrate its immediate uses in quantum computing

    Automatically Defined Templates for Improved Prediction of Non-stationary, Nonlinear Time Series in Genetic Programming

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    Soft methods of artificial intelligence are often used in the prediction of non-deterministic time series that cannot be modeled using standard econometric methods. These series, such as occur in finance, often undergo changes to their underlying data generation process resulting in inaccurate approximations or requiring additional human judgment and input in the process, hindering the potential for automated solutions. Genetic programming (GP) is a class of nature-inspired algorithms that aims to evolve a population of computer programs to solve a target problem. GP has been applied to time series prediction in finance and other domains. However, most GP-based approaches to these prediction problems do not consider regime change. This paper introduces two new genetic programming modularity techniques, collectively referred to as automatically defined templates, which better enable prediction of time series involving regime change. These methods, based on earlier established GP modularity techniques, take inspiration from software design patterns and are more closely modeled after the way humans actually develop software. Specifically, a regime detection branch is incorporated into the GP paradigm. Regime specific behavior evolves in a separate program branch, implementing the template method pattern. A system was developed to test, validate, and compare the proposed approach with earlier approaches to GP modularity. Prediction experiments were performed on synthetic time series and on the S&P 500 index. The performance of the proposed approach was evaluated by comparing prediction accuracy with existing methods. One of the two techniques proposed is shown to significantly improve performance of time series prediction in series undergoing regime change. The second proposed technique did not show any improvement and performed generally worse than existing methods or the canonical approaches. The difference in relative performance was shown to be due to a decoupling of reusable modules from the evolving main program population. This observation also explains earlier results regarding the inferior performance of genetic programming techniques using a similar, decoupled approach. Applied to financial time series prediction, the proposed approach beat a buy and hold return on the S&P 500 index as well as the return achieved by other regime aware genetic programming methodologies. No approach tested beat the benchmark return when factoring in transaction costs

    Image Processing Pipeline Based on Coupled Oscillator Models

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    For decades, researchers have been developing algorithms for image processing pipelines. Image Processing Pipelines (IPPs) are algorithmic constructions built to iteratively modify an input image into a series of abstractions for the purposes of decoding its contents into a higher level representation. There have been many proposed IPPs, varying in both physical construction, and in algorithmic paradigm, but by and large these propositions have been based in Boolean computation and arithmetic. Studies and trends have shown that Boolean computers are hitting a theoretical ceiling on their performance in terms of transistor size, energy consumption/heat dissipation, clock rates, and by extension computational time. Due to these issues, researchers have proposed using non-Boolean approaches, where possible, for various computations in common algorithms. One of the emerging technologies in the field of non-Boolean computation has been the use of coupled oscillators. A proposed use of coupled oscillators is for pattern matching, which can also be interpreted as a high-dimensional distance measurement. Using an approach based on the use of coupled oscillators as a basic computational primitive, this work aims to utilize the benefits gained from this new computational paradigm to gain performance in terms of both speed and power with respect to IPPs, without decreasing the accuracy of their algorithms
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