2,394 research outputs found

    Unconditionally verifiable blind computation

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    Blind Quantum Computing (BQC) allows a client to have a server carry out a quantum computation for them such that the client's input, output and computation remain private. A desirable property for any BQC protocol is verification, whereby the client can verify with high probability whether the server has followed the instructions of the protocol, or if there has been some deviation resulting in a corrupted output state. A verifiable BQC protocol can be viewed as an interactive proof system leading to consequences for complexity theory. The authors, together with Broadbent, previously proposed a universal and unconditionally secure BQC scheme where the client only needs to be able to prepare single qubits in separable states randomly chosen from a finite set and send them to the server, who has the balance of the required quantum computational resources. In this paper we extend that protocol with new functionality allowing blind computational basis measurements, which we use to construct a new verifiable BQC protocol based on a new class of resource states. We rigorously prove that the probability of failing to detect an incorrect output is exponentially small in a security parameter, while resource overhead remains polynomial in this parameter. The new resource state allows entangling gates to be performed between arbitrary pairs of logical qubits with only constant overhead. This is a significant improvement on the original scheme, which required that all computations to be performed must first be put into a nearest neighbour form, incurring linear overhead in the number of qubits. Such an improvement has important consequences for efficiency and fault-tolerance thresholds.Comment: 46 pages, 10 figures. Additional protocol added which allows arbitrary circuits to be verified with polynomial securit

    Knowledge graph prediction of unknown adverse drug reactions and validation in electronic health records

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    Unknown adverse reactions to drugs available on the market present a significant health risk and limit accurate judgement of the cost/benefit trade-off for medications. Machine learning has the potential to predict unknown adverse reactions from current knowledge. We constructed a knowledge graph containing four types of node: drugs, protein targets, indications and adverse reactions. Using this graph, we developed a machine learning algorithm based on a simple enrichment test and first demonstrated this method performs extremely well at classifying known causes of adverse reactions (AUC 0.92). A cross validation scheme in which 10% of drug-adverse reaction edges were systematically deleted per fold showed that the method correctly predicts 68% of the deleted edges on average. Next, a subset of adverse reactions that could be reliably detected in anonymised electronic health records from South London and Maudsley NHS Foundation Trust were used to validate predictions from the model that are not currently known in public databases. High-confidence predictions were validated in electronic records significantly more frequently than random models, and outperformed standard methods (logistic regression, decision trees and support vector machines). This approach has the potential to improve patient safety by predicting adverse reactions that were not observed during randomised trials

    Percolative shunting on electrified surface

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    The surface discharge of electrified dielectrics at high humidity is considered. The percolative nature of charge transport in electrets is established. Particular attention is given to the phenomena of adsorption and nucleation of electrically conducting phase in the cause of percolation cluster growth on electrified surface. The critical index of the correlation lenght for percolation cluster is found, and its value is in good agreement with the known theoretical estimations.Comment: 4 pages with 1 figure, revtex, published in Tech. Phys. Lett. 25 (1999) 877-879 with one additional figur

    Equivalence of operator-splitting schemes for the integration of the Langevin equation

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    We investigate the equivalence of different operator-splitting schemes for the integration of the Langevin equation. We consider a specific problem, so called the directed percolation process, which can be extended to a wider class of problems. We first give a compact mathematical description of the operator-splitting method and introduce two typical splitting schemes that will be useful in numerical studies. We show that the two schemes are essentially equivalent through the map that turns out to be an automorphism. An associated equivalent class of operator-splitting integrations is also defined by generalizing the specified equivalence.Comment: 4 page

    Trajectories of dementia-related cognitive decline in a large mental health records derived patient cohort

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    BACKGROUND: Modeling trajectories of decline can help describe the variability in progression of cognitive impairment in dementia. Better characterisation of these trajectories has significant implications for understanding disease progression, trial design and care planning. METHODS: Patients with at least three Mini-mental State Examination (MMSE) scores recorded in the South London and Maudsley NHS Foundation Trust Electronic Health Records, UK were selected (N = 3441) to form a retrospective cohort. Trajectories of cognitive decline were identified through latent class growth analysis of longitudinal MMSE scores. Demographics, Health of Nation Outcome Scales and medications were compared across trajectories identified. RESULTS: Four of the six trajectories showed increased rate of decline with lower baseline MMSE. Two trajectories had similar initial MMSE scores but different rates of decline. In the faster declining trajectory of the two, a higher incidence of both behavioral problems and sertraline prescription were present. CONCLUSIONS: We find suggestive evidence for association of behavioral problems and sertraline prescription with rate of decline. Further work is needed to determine whether trajectories replicate in other datasets

    Diffuse continuum gamma rays from the Galaxy

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    A new study of the diffuse Galactic gamma-ray continuum radiation is presented, using a cosmic-ray propagation model which includes nucleons, antiprotons, electrons, positrons, and synchrotron radiation. Our treatment of the inverse Compton (IC) scattering includes the effect of anisotropic scattering in the Galactic interstellar radiation field (ISRF) and a new evaluation of the ISRF itself. Models based on locally measured electron and nucleon spectra and synchrotron constraints are consistent with gamma-ray measurements in the 30-500 MeV range, but outside this range excesses are apparent. A harder nucleon spectrum is considered but fitting to gamma rays causes it to violate limits from positrons and antiprotons. A harder interstellar electron spectrum allows the gamma-ray spectrum to be fitted above 1 GeV as well, and this can be further improved when combined with a modified nucleon spectrum which still respects the limits imposed by antiprotons and positrons. A large electron/IC halo is proposed which reproduces well the high-latitude variation of gamma-ray emission. The halo contribution of Galactic emission to the high-latitude gamma-ray intensity is large, with implications for the study of the diffuse extragalactic component and signatures of dark matter. The constraints provided by the radio synchrotron spectral index do not allow all of the <30 MeV gamma-ray emission to be explained in terms of a steep electron spectrum unless this takes the form of a sharp upturn below 200 MeV. This leads us to prefer a source population as the origin of the excess low-energy gamma rays.Comment: Final version accepted for publication in The Astrophysical Journal (vol. 537, July 10, 2000 issue); Many Updates; 20 pages including 49 ps-figures, uses emulateapj.sty. More details can be found at http://www.gamma.mpe-garching.mpg.de/~aws/aws.htm
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