Experimental investigation of the equatorial ionospheric anomaly in Africa in the period of solar minimum

Equatorial ionization anomaly of F layer in Africa during period of solar minimu

Naturalness in testable type II seesaw scenarios

New physics coupling to the Higgs sector of the Standard Model can lead to dangerously large corrections to the Higgs mass. We investigate this problem in the type II seesaw model for neutrino mass, where a weak scalar triplet is introduced. The interplay of direct and indirect constraints on the type II seesaw model with its contribution to the Higgs mass is analyzed. The focus lies on testable triplet masses and (sub) eV-scale triplet vacuum expectation values. We identify scenarios that are testable in collider and/or lepton flavor violation experiments, while satisfying the Higgs naturalness criterion.Comment: 18 pages, 8 figures, 2 table

Expectation-maximization Gaussian-mixture approximate message passing

Abstract—When recovering a sparse signal from noisy compressive linear measurements, the distribution of the signal’s non-zero coefficients can have a profound effect on recovery mean-squared error (MSE). If this distribution was aprioriknown, then one could use computationally efficient approximate message passing (AMP) techniques for nearly minimum MSE (MMSE) recovery. In practice, however, the distribution is unknown, motivating the use of robust algorithms like LASSO—which is nearly minimax optimal—at the cost of significantly larger MSE for non-least-favorable distributions. As an alternative, we propose an empirical-Bayesian technique that simultaneously learns the signal distribution while MMSE-recovering the signal—according to the learned distribution—using AMP. In particular, we model the non-zero distribution as a Gaussian mixture and learn its parameters through expectation maximization, using AMP to implement the expectation step. Numerical experiments on a wide range of signal classes confirm the state-of-the-art performance of our approach, in both reconstruction error and runtime, in the high-dimensional regime, for most (but not all) sensing operators. Index Terms—Compressed sensing, belief propagation, expectation maximization algorithms, Gaussian mixture model. I

Optimization and evaluation of variability in the programming window of a flash cell with molecular metal-oxide storage

We report a modeling study of a conceptual nonvolatile memory cell based on inorganic molecular metal-oxide clusters as a storage media embedded in the gate dielectric of a MOSFET. For the purpose of this paper, we developed a multiscale simulation framework that enables the evaluation of variability in the programming window of a flash cell with sub-20-nm gate length. Furthermore, we studied the threshold voltage variability due to random dopant fluctuations and fluctuations in the distribution of the molecular clusters in the cell. The simulation framework and the general conclusions of our work are transferrable to flash cells based on alternative molecules used for a storage media