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Showing 1 to 12 of 12 for “"Random functions"”.

  1. Physical random functions

    … Teller Machines. To address this issue, Physical Random Functions are introduced. These are Random Functions that are physically tied to a particular device. To show that Physical Random Functions solve the initial problem, it must be shown that they can be made, and that it is possible to use …

    mit Repository record for Physical random functions (opens in a new tab)

  2. Pseudorandom functions with structure : extensions and implications

    … of this work, we introduce a new type of pseudo-random function for which "aggregate queries" over exponential-sized sets can be efficiently answered. We show how to use algebraic properties of underlying classical pseudo random functions, to construct such "aggregate pseudo-random functions" for …

    mit Repository record for Pseudorandom functions with structure : extensions and implications (opens in a new tab)

  3. AEGIS : a single-chip secure processor

    … be protected separately from the rest. Physical random functions provide a cheap and secure way of generating a unique secret key on each processor, which enables a remote party to authenticate the processor chip.

    mit Repository record for AEGIS : a single-chip secure processor (opens in a new tab)

  4. Comparison of Strategies for the Constraint Determination of Simulink Models

    … models are quite similar to mathematical functions and therefore optimization algorithms can be applied to constrain the outputs. Optimizations of simple mathematical functions paved the way for random functions and finally led to the development of two optimization algorithms. During the …

    montana-tech Repository record for Comparison of Strategies for the Constraint Determination of Simulink Models (opens in a new tab)

  5. Comparison of Strategies for the Constraint Determination of Simulink Models

    … models are quite similar to mathematical functions and therefore optimization algorithms can be applied to constrain the outputs. Optimizations of simple mathematical functions paved the way for random functions and finally led to the development of two optimization algorithms. During the …

    montana Repository record for Comparison of Strategies for the Constraint Determination of Simulink Models (opens in a new tab)

  6. Accuracy-aware privacy mechanisms for distributed computation

    … Sharing strategy that involves using correlated random functions to obfuscate private objective functions followed by using a standard distributed optimization algorithm. We characterize a tight graph connectivity condition for proving privacy via non-identifiability of local objective functions. …

    uiuc Repository record for Accuracy-aware privacy mechanisms for distributed computation (opens in a new tab)

  7. Multifield inflation in random potentials and the rapid-turn limit

    … the latter. Using a new technique for generating random functions with Gaussian random fields, which we also prove the validity of, we generate random potentials for as many as 100 fields for inflation. We look at the observables of these models and in particular compute the local non-Gaussianity. …

    cambridge Repository record for Multifield inflation in random potentials and the rapid-turn limit (opens in a new tab)

  8. Geostatistical spatiotemporal modelling with application to the western king prawn of the Shark Bay managed prawn fishery

    … viewing the data as realisations of space-time random functions. Traditional geostatistics aims to model the spatial variability of a process so, in order to incorporate a time dimension into a geostatistical model, the fundamental differences between the space and time dimensions must be …

    edithcowan Repository record for Geostatistical spatiotemporal modelling with application to the western king prawn of the Shark Bay managed prawn fishery (opens in a new tab)

  9. Some Models for Time Series of Counts

    … is addressed by employing theory from iterated random functions and coupling techniques. Using this theory, we are also able to obtain the asymptotic behavior of maximum likelihood estimates of the parameters. Extensions of the base model in several directions are considered. Inspired by the …

    columbia-diss Repository record for Some Models for Time Series of Counts (opens in a new tab)

  10. Modeling Spatial Variability of Field-Scale Solute Transport in the Vadose Zone

    … properties in each horizon are treated as random functions of zero transverse spatial correlation length, after accounting for any spatial trends. The spatially variable parameters were generated using the Latin hypercube sampling method, and the stochastic simulation of the model was …

    vt Repository record for Modeling Spatial Variability of Field-Scale Solute Transport in the Vadose Zone (opens in a new tab)

  11. Macroscopic behaviour of Lipschitz random surfaces

    A random field is a random function φ from the square lattice ℤᵈ to some fixed standard Borel space (E, ℰ). A random surface is a random field with the extra condition that E ∈ {ℤ, ℝ} where ℰ is the standard σ-algebra. For random surfaces, one often studies the gradient ∇φ of the random function of …

    cambridge Repository record for Macroscopic behaviour of Lipschitz random surfaces (opens in a new tab)