6,696 research outputs found
Implementation of standard testbeds for numerical relativity
We discuss results that have been obtained from the implementation of the
initial round of testbeds for numerical relativity which was proposed in the
first paper of the Apples with Apples Alliance. We present benchmark results
for various codes which provide templates for analyzing the testbeds and to
draw conclusions about various features of the codes. This allows us to sharpen
the initial test specifications, design a new test and add theoretical insight.Comment: Corrected versio
Bound and Conquer: Improving Triangulation by Enforcing Consistency
We study the accuracy of triangulation in multi-camera systems with respect
to the number of cameras. We show that, under certain conditions, the optimal
achievable reconstruction error decays quadratically as more cameras are added
to the system. Furthermore, we analyse the error decay-rate of major
state-of-the-art algorithms with respect to the number of cameras. To this end,
we introduce the notion of consistency for triangulation, and show that
consistent reconstruction algorithms achieve the optimal quadratic decay, which
is asymptotically faster than some other methods. Finally, we present
simulations results supporting our findings. Our simulations have been
implemented in MATLAB and the resulting code is available in the supplementary
material.Comment: 8 pages, 4 figures, Submitted to IEEE Transactions on Pattern
Analysis and Machine Intelligenc
A Machine Learning Approach for Plagiarism Detection
Plagiarism detection is gaining increasing importance due to requirements for integrity in education. The existing research has investigated the problem of plagrarim detection with a varying degree of success. The literature revealed that there are two main methods for detecting plagiarism, namely extrinsic and intrinsic.
This thesis has developed two novel approaches to address both of these methods. Firstly a novel extrinsic method for detecting plagiarism is proposed. The method is based on four well-known techniques namely Bag of Words (BOW), Latent Semantic Analysis (LSA), Stylometry and Support Vector Machines (SVM). The LSA application was fine-tuned to take in the stylometric features (most common words) in order to characterise the document authorship as described in chapter 4. The results revealed that LSA based stylometry has outperformed the traditional LSA application. Support vector machine based algorithms were used to perform the classification procedure in order to predict which author has written a particular book being tested. The proposed method has successfully addressed the limitations of semantic characteristics and identified the document source by assigning the book being tested to the right author in most cases.
Secondly, the intrinsic detection method has relied on the use of the statistical properties of the most common words. LSA was applied in this method to a group of most common words (MCWs) to extract their usage patterns based on the transitivity property of LSA. The feature sets of the intrinsic model were based on the frequency of the most common words, their relative frequencies in series, and the deviation of these frequencies across all books for a particular author.
The Intrinsic method aims to generate a model of author âstyleâ by revealing a set of certain features of authorship. The modelâs generation procedure focuses on just one author as an attempt to summarise aspects of an authorâs style in a definitive and clear-cut manner.
The thesis has also proposed a novel experimental methodology for testing the performance of both extrinsic and intrinsic methods for plagiarism detection. This methodology relies upon the CEN (Corpus of English Novels) training dataset, but divides that dataset up into training and test datasets in a novel manner. Both approaches have been evaluated using the well-known leave-one-out-cross-validation method. Results indicated that by integrating deep analysis (LSA) and Stylometric analysis, hidden changes can be identified whether or not a reference collection exists
Uniformisation techniques for stochastic simulation of chemical reaction networks
This work considers the method of uniformisation for continuous-time Markov
chains in the context of chemical reaction networks. Previous work in the
literature has shown that uniformisation can be beneficial in the context of
time-inhomogeneous models, such as chemical reaction networks incorporating
extrinsic noise. This paper lays focus on the understanding of uniformisation
from the viewpoint of sample paths of chemical reaction networks. In
particular, an efficient pathwise stochastic simulation algorithm for
time-homogeneous models is presented which is complexity-wise equal to
Gillespie's direct method. This new approach therefore enlarges the class of
problems for which the uniformisation approach forms a computationally
attractive choice. Furthermore, as a new application of the uniformisation
method, we provide a novel variance reduction method for (raw) moment
estimators of chemical reaction networks based upon the combination of
stratification and uniformisation
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