1,729 research outputs found

    Minkowski Brane in Asymptotic dS5_5 Spacetime without Fine-tuning

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    We discuss properties of a 3-brane in an asymptotic 5-dimensional de-Sitter spacetime. It is found that a Minkowski solution can be obtained without fine-tuning. In the model, the tiny observed positive cosmological constant is interpreted as a curvature of 5-dimensional manifold, but the Minkowski spacetime, where we live, is a natural 3-brane perpendicular to the fifth coordinate axis.Comment: 6 pages, Latex fil

    Quantum state transmission via a spin ladder as a robust data bus

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    We explore the physical mechanism to coherently transfer the quantum information of spin by connecting two spins to an isotropic antiferromagnetic spin ladder system as data bus. Due to a large spin gap existing in such a perfect medium, the effective Hamiltonian of the two connected spins can be archived as that of Heisenberg type, which possesses a ground state with maximal entanglement. We show that the effective coupling strength is inversely proportional to the distance of the two spins and thus the quantum information can be transferred between the two spins separated by a longer distance, i.e. the characteristic time of quantum state transferring linearly depends on the distance.Comment: 7 pages, 5 figures, 1 tabl

    Ensuring data confidentiality via plausibly deniable encryption and secure deletion – a survey

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    Ensuring confidentiality of sensitive data is of paramount importance, since data leakage may not only endanger dataowners’ privacy, but also ruin reputation of businesses as well as violate various regulations like HIPPA andSarbanes-Oxley Act. To provide confidentiality guarantee, the data should be protected when they are preserved inthe personal computing devices (i.e.,confidentiality duringtheirlifetime); and also, they should be rendered irrecoverableafter they are removed from the devices (i.e.,confidentiality after their lifetime). Encryption and secure deletion are usedto ensure data confidentiality during and after their lifetime, respectively.This work aims to perform a thorough literature review on the techniques being used to protect confidentiality of thedata in personal computing devices, including both encryption and secure deletion. Especially for encryption, wemainly focus on the novel plausibly deniable encryption (PDE), which can ensure data confidentiality against both acoercive (i.e., the attacker can coerce the data owner for the decryption key) and a non-coercive attacker

    "An Econometric Analysis of SARS and Avian Flu on International Tourist Arrivals to Asia"

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    This paper compares the impacts of SARS and human deaths arising from Avian Flu on international tourist arrivals to Asia. The effects of SARS and human deaths from Avian Flu will be compared directly according to human deaths. The nature of the short run and long run relationship is examined empirically by estimating a static line fixed effect model and a difference transformation dynamic model, respectively. Empirical results from the static fixed effect and difference transformation dynamic models are consistent, and indicate that both the short run and long run SARS effect have a more significant impact on international tourist arrivals than does Avian Flu. In addition, the effects of deaths arising from both SARS and Avian Flu suggest that SARS is more important to international tourist arrivals than is Avian Flu. Thus, while Avian Flu is here to stay, its effect is currently not as significant as that of SARS.

    On compression rate of quantum autoencoders: Control design, numerical and experimental realization

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    Quantum autoencoders which aim at compressing quantum information in a low-dimensional latent space lie in the heart of automatic data compression in the field of quantum information. In this paper, we establish an upper bound of the compression rate for a given quantum autoencoder and present a learning control approach for training the autoencoder to achieve the maximal compression rate. The upper bound of the compression rate is theoretically proven using eigen-decomposition and matrix differentiation, which is determined by the eigenvalues of the density matrix representation of the input states. Numerical results on 2-qubit and 3-qubit systems are presented to demonstrate how to train the quantum autoencoder to achieve the theoretically maximal compression, and the training performance using different machine learning algorithms is compared. Experimental results of a quantum autoencoder using quantum optical systems are illustrated for compressing two 2-qubit states into two 1-qubit states
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