Intrinsic symmetry of the existing protocols of quantum dialogue are explored. It is shown that if we have a set of mutually orthogonal n-qubit states {|φ 0 , |φ 1 , ...., |φ i , ..., |φ 2 n −1 } and a set of m − qubit (m ≤ n) unitary operators {U 0 , U 1 , U 2 , ..., U 2 n −1 } : U i |φ 0 = |φ i and {U 0 , U 1 , U 2 , ..., U 2 n −1 } forms a group under multiplication then it would be sufficient to construct a quantum dialogue protocol using this set of quantum states and this group of unitary operators. The sufficiency condition is used to provide a generalized protocol of quantum dialogue. Further the basic concepts of group theory and quantum mechanics are used here to systematically generate several examples of possible groups of unitary operators that may be used for implementation of quantum dialogue. A large number of examples of quantum states that may be used to implement the generalized quantum dialogue protocol using these groups of unitary operators are also obtained. For example, it is shown that GHZ state, GHZ-like state, W state, 4 and 5 qubit Cluster states, Ω state, Brown state, Q4 state and Q5 state can be used for implementation of quantum dialogue protocol. The security and efficiency of the proposed protocol is appropriately analyzed. It is also shown that if a group of unitary operators and a set of mutually orthogonal states are found to be suitable for quantum dialogue then they can be used to provide solutions of socialist millionaire problem.
In this work, we present a study that traces the technical and cognitive processes in two visual analytics applications to a common theoretic model of soft knowledge that may be added into a visual analytics process for constructing a decision-tree model. Both case studies involved the development of classification models based on the "bag of features" approach. Both compared a visual analytics approach using parallel coordinates with a machine-learning approach using information theory. Both found that the visual analytics approach had some advantages over the machine learning approach, especially when sparse datasets were used as the ground truth. We examine various possible factors that may have contributed to such advantages, and collect empirical evidence for supporting the observation and reasoning of these factors. We propose an information-theoretic model as a common theoretic basis to explain the phenomena exhibited in these two case studies. Together we provide interconnected empirical and theoretical evidence to support the usefulness of visual analytics.
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Machine learning, particularly deep learning, is being increasing utilised in space applications, mirroring the groundbreaking success in many earthbound problems. Deploying a space device, e.g. a satellite, is becoming more accessible to small actors due to the development of modular satellites and commercial space launches, which fuels further growth of this area. Deep learning's ability to deliver sophisticated computational intelligence makes it an attractive option to facilitate various tasks on space devices and reduce operational costs. In this work, we identify deep learning in space as one of development directions for mobile and embedded machine learning. We collate various applications of machine learning to space data, such as satellite imaging, and describe how on-device deep learning can meaningfully improve the operation of a spacecraft, such as by reducing communication costs or facilitating navigation. We detail and contextualise compute platform of satellites and draw parallels with embedded systems and current research in deep learning for resource-constrained environments.
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