Seismological support for the metastable superplume model, sharp features, and phase changes within the lower mantle

Recently, a metastable thermal-chemical convection model was proposed to explain the African Superplume. Its bulk tabular shape remains relatively stable while its interior undergoes significant stirring with low-velocity conduits along its edges and down-welling near the middle. Here, we perform a mapping of chemistry and temperature into P and S velocity variations and replace a seismically derived structure with this hybrid model. Synthetic seismogram sections generated for this 2D model are then compared directly with corresponding seismic observations of P (P, PCP, and PKP) and S (S, SCS, and SKS) phases. These results explain the anticorrelation between the bulk velocity and shear velocity and the sharpness and level of SKS travel time delays. In addition, we present evidence for the existence of a D" triplication (a putative phase change) beneath the down-welling structure

Tree level spontaneous R-symmetry breaking in O'Raifeartaigh models

We show that in O'Raifeartaigh models of spontaneous supersymmetry breaking, R-symmetries can be broken by non-zero values of fields at tree level, rather than by vacuum expectation values of pseudomoduli at loop level. As a complement of the recent result by Shih, we show that there must be a field in the theory with R-charge different from zero and two in order for R-symmetry breaking to occur, no matter whether the breaking happens at tree or loop level. We review the example by CDFM, and construct two types of tree level R-symmetry breaking models with a wide range of parameters and free of runaway problem. And the R-symmetry is broken everywhere on the pseudomoduli space in these models. This provides a rich set of candidates for SUSY model building and phenomenology.Comment: 8 pages; v2: major revision to section 6; v3: minor revision and typos; v4: typos, published version; v5: fix Latex syntax error, published versio

Operator fidelity susceptibility: an indicator of quantum criticality

We introduce the operator fidelity and propose to use its susceptibility for characterizing the sensitivity of quantum systems to perturbations. Two typical models are addressed: one is the transverse Ising model exhibiting a quantum phase transition, and the other is the one dimensional Heisenberg spin chain with next-nearest-neighbor interactions, which has the degeneracy. It is revealed that the operator fidelity susceptibility is a good indicator of quantum criticality regardless of the system degeneracy.Comment: Four pages, two figure

Task-Driven Dictionary Learning for Hyperspectral Image Classification with Structured Sparsity Constraints

Sparse representation models a signal as a linear combination of a small number of dictionary atoms. As a generative model, it requires the dictionary to be highly redundant in order to ensure both a stable high sparsity level and a low reconstruction error for the signal. However, in practice, this requirement is usually impaired by the lack of labelled training samples. Fortunately, previous research has shown that the requirement for a redundant dictionary can be less rigorous if simultaneous sparse approximation is employed, which can be carried out by enforcing various structured sparsity constraints on the sparse codes of the neighboring pixels. In addition, numerous works have shown that applying a variety of dictionary learning methods for the sparse representation model can also improve the classification performance. In this paper, we highlight the task-driven dictionary learning algorithm, which is a general framework for the supervised dictionary learning method. We propose to enforce structured sparsity priors on the task-driven dictionary learning method in order to improve the performance of the hyperspectral classification. Our approach is able to benefit from both the advantages of the simultaneous sparse representation and those of the supervised dictionary learning. We enforce two different structured sparsity priors, the joint and Laplacian sparsity, on the task-driven dictionary learning method and provide the details of the corresponding optimization algorithms. Experiments on numerous popular hyperspectral images demonstrate that the classification performance of our approach is superior to sparse representation classifier with structured priors or the task-driven dictionary learning method