A Verifiable Fully Homomorphic Encryption Scheme for Cloud Computing Security

Performing smart computations in a context of cloud computing and big data is highly appreciated today. Fully homomorphic encryption (FHE) is a smart category of encryption schemes that allows working with the data in its encrypted form. It permits us to preserve confidentiality of our sensible data and to benefit from cloud computing powers. Currently, it has been demonstrated by many existing schemes that the theory is feasible but the efficiency needs to be dramatically improved in order to make it usable for real applications. One subtle difficulty is how to efficiently handle the noise. This paper aims to introduce an efficient and verifiable FHE based on a new mathematic structure that is noise free

The Second Chvatal Closure Can Yield Better Railway Timetables

We investigate the polyhedral structure of the Periodic Event Scheduling Problem (PESP), which is commonly used in periodic railway timetable optimization. This is the first investigation of Chvatal closures and of the Chvatal rank of PESP instances. In most detail, we first provide a PESP instance on only two events, whose Chvatal rank is very large. Second, we identify an instance for which we prove that it is feasible over the first Chvatal closure, and also feasible for another prominent class of known valid inequalities, which we reveal to live in much larger Chvatal closures. In contrast, this instance turns out to be infeasible already over the second Chvatal closure. We obtain the latter result by introducing new valid inequalities for the PESP, the multi-circuit cuts. In the past, for other classes of valid inequalities for the PESP, it had been observed that these do not have any effect in practical computations. In contrast, the new multi-circuit cuts that we are introducing here indeed show some effect in the computations that we perform on several real-world instances - a positive effect, in most of the cases

A multi-resolution approximation for massive spatial datasets

Automated sensing instruments on satellites and aircraft have enabled the collection of massive amounts of high-resolution observations of spatial fields over large spatial regions. If these datasets can be efficiently exploited, they can provide new insights on a wide variety of issues. However, traditional spatial-statistical techniques such as kriging are not computationally feasible for big datasets. We propose a multi-resolution approximation (M-RA) of Gaussian processes observed at irregular locations in space. The M-RA process is specified as a linear combination of basis functions at multiple levels of spatial resolution, which can capture spatial structure from very fine to very large scales. The basis functions are automatically chosen to approximate a given covariance function, which can be nonstationary. All computations involving the M-RA, including parameter inference and prediction, are highly scalable for massive datasets. Crucially, the inference algorithms can also be parallelized to take full advantage of large distributed-memory computing environments. In comparisons using simulated data and a large satellite dataset, the M-RA outperforms a related state-of-the-art method.Comment: 23 pages; to be published in Journal of the American Statistical Associatio

Estimating small angular scale CMB anisotropy with high resolution N-body simulations: weak lensing

We estimate the impact of weak lensing by strongly nonlinear cosmological structures on the cosmic microwave background. Accurate calculation of large $\ell$ multipoles requires N-body simulations and ray-tracing schemes with both high spatial and temporal resolution. To this end we have developed a new code that combines a gravitational Adaptive Particle-Particle, Particle-Mesh (AP3M) solver with a weak lensing evaluation routine. The lensing deviations are evaluated while structure evolves during the simulation so that all evolution steps--rather than just a few outputs--are used in the lensing computations. The new code also includes a ray-tracing procedure that avoids periodicity effects in a universe that is modeled as a 3-D torus in the standard way. Results from our new simulations are compared with previous ones based on Particle-Mesh simulations. We also systematically investigate the impact of box volume, resolution, and ray-tracing directions on the variance of the computed power spectra. We find that a box size of $512 h^{-1}$ Mpc is sufficient to provide a robust estimate of the weak lensing angular power spectrum in the $\ell$-interval (2,000--7,000). For a reaslistic cosmological model the power $[\ell(\ell+1)C_{\ell}/2\pi]^{1/2}$ takes on values of a few $\mu K$ in this interval, which suggests that a future detection is feasible and may explain the excess power at high $\ell$ in the BIMA and CBI observations.Comment: 49 pages, 13 figures, accepted for publication in Ap

Investigating the electronic structure of a supported metal nanoparticle: Pd in SiCN

We investigate the electronic structure of a Palladium nanoparticle that is partially embedded in a matrix of silicon carbonitride. From classical molecular dynamics simulations we first obtain a representative atomic structure. This geometry then serves as input to density-functional theory calculations that allow us to access the electronic structure of the combined system of particle and matrix. In order to make the computations feasible, we devise a subsystem strategy for calculating the relevant electronic properties. We analyze the Kohn-Sham density of states and pay particular attention to d-states which are prone to be affected by electronic self-interaction. We find that the density of states close to the Fermi level is dominated by states that originate from the Palladium nanoparticle. The matrix has little direct effect on the electronic structure of the metal. Our results contribute to explaining why silicon carbonitride does not have detrimental effects on the catalytic properties of palladium particles and can serve positively as a stabilizing mechanical support