Quantum State Discrimination on Reconfigurable Noise-Robust Quantum Networks

A fundamental problem in Quantum Information Processing is the discrimination amongst a set of quantum states of a system. In this paper, we address this problem on an open quantum system described by a graph, whose evolution is defined by a Quantum Stochastic Walk. In particular, the structure of the graph mimics those of neural networks, with the quantum states to discriminate encoded on input nodes and with the discrimination obtained on the output nodes. We optimize the parameters of the network to obtain the highest probability of correct discrimination. Numerical simulations show that after a transient time the probability of correct decision approaches the theoretical optimal quantum limit. These results are confirmed analytically for small graphs. Finally, we analyze the robustness and reconfigurability of the network for different set of quantum states, and show that this architecture can pave the way to experimental realizations of our protocol as well as novel quantum generalizations of deep learning

Matching Image Sets via Adaptive Multi Convex Hull

Traditional nearest points methods use all the samples in an image set to construct a single convex or affine hull model for classification. However, strong artificial features and noisy data may be generated from combinations of training samples when significant intra-class variations and/or noise occur in the image set. Existing multi-model approaches extract local models by clustering each image set individually only once, with fixed clusters used for matching with various image sets. This may not be optimal for discrimination, as undesirable environmental conditions (eg. illumination and pose variations) may result in the two closest clusters representing different characteristics of an object (eg. frontal face being compared to non-frontal face). To address the above problem, we propose a novel approach to enhance nearest points based methods by integrating affine/convex hull classification with an adapted multi-model approach. We first extract multiple local convex hulls from a query image set via maximum margin clustering to diminish the artificial variations and constrain the noise in local convex hulls. We then propose adaptive reference clustering (ARC) to constrain the clustering of each gallery image set by forcing the clusters to have resemblance to the clusters in the query image set. By applying ARC, noisy clusters in the query set can be discarded. Experiments on Honda, MoBo and ETH-80 datasets show that the proposed method outperforms single model approaches and other recent techniques, such as Sparse Approximated Nearest Points, Mutual Subspace Method and Manifold Discriminant Analysis.Comment: IEEE Winter Conference on Applications of Computer Vision (WACV), 201

Cause for hope or despair? Evaluating race discrimination law as an access to justice mechanism for Aboriginal and Torres Strait Islander people

This thesis explores the contribution that racial discrimination laws have made and might make to addressing race discrimination against Indigenous Australians, who still experience this problem at disproportionately high levels despite introduction over four decades ago of racial discrimination legislation in Australia. The research investigates whether this legislation has failed to make appropriate contributions to reduction of race discrimination because of problems associated with Indigenous access to justice. It demonstrates that Aboriginal and Torres Strait Islander peoples are using processes of dispute resolution in this area to a limited degree, relative to the extent to which they encounter race-based discrimination. Informed by Indigenous methodologies, the research employs a mixed method design: utilising historical, qualitative and quantitative social science and legal approaches. This provides opportunity for distinctive analysis of the current limitations associated with Indigenous access to justice in the area of race discrimination. Also identified is whether Aboriginal and Torres Strait Islander peoples see value in enhancing Indigenous access to justice through race discrimination law and how this might be achieved. The thesis presents evidence that indicates that access to justice is seen, including by Aboriginal and Torres Strait Islander peoples, as an important right in itself and as essential to the assertion of all other rights, encompassing the right to equality or non-discrimination. It is argued, however, that to be effective the concept of access to justice must be appropriately expanded to incorporate Indigenous perspectives on 'justice' and how this might be attained. Formal equality of access to justice can lead to discriminatory outcomes, including limitations in terms of the extent to which Indigenous people are able to draw benefit from race discrimination law. The thesis also argues that Indigenous people do not see the law as providing a complete solution to the problem of race discrimination. Key non-legal strategies are identified, including those that empower Indigenous people to respond to discrimination without recourse to the law and that place responsibility for reduction of race discrimination targeting Indigenous people upon the wider community and government. The research makes a novel contribution to analysis of the effectiveness of race discrimination law in Australia. By prioritising Indigenous historical and contemporary perspectives throughout, it presents new perspectives on race discrimination law and access to justice for Aboriginal and Torres Strait Islander peoples

Support Vector Machine Implementations for Classification & Clustering

BACKGROUND: We describe Support Vector Machine (SVM) applications to classification and clustering of channel current data. SVMs are variational-calculus based methods that are constrained to have structural risk minimization (SRM), i.e., they provide noise tolerant solutions for pattern recognition. The SVM approach encapsulates a significant amount of model-fitting information in the choice of its kernel. In work thus far, novel, information-theoretic, kernels have been successfully employed for notably better performance over standard kernels. Currently there are two approaches for implementing multiclass SVMs. One is called external multi-class that arranges several binary classifiers as a decision tree such that they perform a single-class decision making function, with each leaf corresponding to a unique class. The second approach, namely internal-multiclass, involves solving a single optimization problem corresponding to the entire data set (with multiple hyperplanes). RESULTS: Each SVM approach encapsulates a significant amount of model-fitting information in its choice of kernel. In work thus far, novel, information-theoretic, kernels were successfully employed for notably better performance over standard kernels. Two SVM approaches to multiclass discrimination are described: (1) internal multiclass (with a single optimization), and (2) external multiclass (using an optimized decision tree). We describe benefits of the internal-SVM approach, along with further refinements to the internal-multiclass SVM algorithms that offer significant improvement in training time without sacrificing accuracy. In situations where the data isn't clearly separable, making for poor discrimination, signal clustering is used to provide robust and useful information – to this end, novel, SVM-based clustering methods are also described. As with the classification, there are Internal and External SVM Clustering algorithms, both of which are briefly described

Stochastic Feature Selection with Distributed Feature Spacing for Hyperspectral Data

Feature subset selection is a well studied problem in machine learning. One short-coming of many methods is the selection of highly correlated features; a characteristic of hyperspectral data. A novel stochastic feature selection method with three major components is presented. First, we present an optimized feature selection method that maximizes a heuristic using a simulated annealing search which increases the chance of avoiding locally optimum solutions. Second, we exploit local cross correlation pair-wise amongst classes of interest to select suitable features for class discrimination. Third, we adopt the concept of distributed spacing from the multi-objective optimization community to distribute features across the spectrum in order to select less correlated features. The classification performance of our semi-embedded feature selection and classification method is demonstrated on a 12-class textile hyperspectral classification problem under several noise realizations. These results are compared with a variety of feature selection methods that cover a broad range of approaches. Abstract © IEE