Robust variable selection for nonlinear models with diverging number of parameters

We focus on the problem of simultaneous variable selection and estimation for nonlinear models based on modal regression (MR), when the number of coefficients diverges with sample size. With appropriate selection of the tuning parameters, the resulting estimator is shown to be consistent and to enjoy the oracle properties

Exotic Haldane Superfluid Phase of Soft-Core Bosons in Optical Lattices

We propose to realize an exotic Haldane superfluid (HSF) phase in an extended Bose-Hubbard model on the two-leg ladder (i.e., a two-species mixture of interacting bosons). The proposal is confirmed by means of large-scale quantum Monte Carlo simulations, with a significant part of the ground-state phase diagram being revealed. Most remarkably, the newly discovered HSF phase features both superfluidity and the non-local topological Haldane order. The effects induced by varying the number of legs are furthermore explored. Our results shed light on how topological superfluid emerges in bosonic systems.Comment: 5 pages, 6 figures; accepted for publication in Physical Review B (April 29, 2016

Robust variable selection in partially varying coefficient single-index model

By combining basis function approximations and smoothly clipped absolute deviation (SCAD) penalty, this paper proposes a robust variable selection procedure for a partially varying coefficient single-index model based on modal regression. The proposed procedure simultaneously selects significant variables in the parametric components and the nonparametric components. With appropriate selection of the tuning parameters, we establish the theoretical properties of our procedure, including consistency in variable selection and the oracle property in estimation. Furthermore, we also discuss the bandwidth selection and propose a modified expectation-maximization (EM)-type algorithm for the proposed estimation procedure. The finite sample properties of the proposed estimators are illustrated by some simulation examples.The research of Zhu is partially supported by National Natural Science Foundation of China (NNSFC) under Grants 71171075, 71221001 and 71031004. The research of Yu is supported by NNSFC under Grant 11261048

Influence of the rotational sense of two colliding laser beams on the radiation of an ultrarelativistic electron

With analytical treatment, the classical dynamics of an ultrarelativistic electron in two counter-propagating circularly polarized strong laser beams with either co-rotating or counter-rotating direction are considered. Assuming that the particle energy is the dominant scale in the setup, an approximate solution is derived and the influence of the rotational sense on the dynamics is analyzed. Qualitative differences in both electron energy and momentum are found for the laser beams being co-rotating or counter-rotating and are confirmed by the exact numerical solution of the classical equation of motion. Despite of these differences in the electron trajectory, the radiation spectra of the electron do not deviate qualitatively from each other for configurations with varying rotational directions of the laser beams. Here, the radiation of an ultrarelativistic electron interacting with counterpropagating laser beams is given in the framework of the Baier-Katkov semi-classical approximation. Several parameter regimes are considered and the spectra resulting from the two scenarios all have the same shape and only differ quantitatively by a few percent.Comment: 13 pages, 8 figure

FLASH: Randomized Algorithms Accelerated over CPU-GPU for Ultra-High Dimensional Similarity Search

We present FLASH (\textbf{F}ast \textbf{L}SH \textbf{A}lgorithm for \textbf{S}imilarity search accelerated with \textbf{H}PC), a similarity search system for ultra-high dimensional datasets on a single machine, that does not require similarity computations and is tailored for high-performance computing platforms. By leveraging a LSH style randomized indexing procedure and combining it with several principled techniques, such as reservoir sampling, recent advances in one-pass minwise hashing, and count based estimations, we reduce the computational and parallelization costs of similarity search, while retaining sound theoretical guarantees. We evaluate FLASH on several real, high-dimensional datasets from different domains, including text, malicious URL, click-through prediction, social networks, etc. Our experiments shed new light on the difficulties associated with datasets having several million dimensions. Current state-of-the-art implementations either fail on the presented scale or are orders of magnitude slower than FLASH. FLASH is capable of computing an approximate k-NN graph, from scratch, over the full webspam dataset (1.3 billion nonzeros) in less than 10 seconds. Computing a full k-NN graph in less than 10 seconds on the webspam dataset, using brute-force ($n^2D$), will require at least 20 teraflops. We provide CPU and GPU implementations of FLASH for replicability of our results