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We introduce libdlr, a library implementing the recently introduced discrete Lehmann representation (DLR) of imaginary time Green's functions. The DLR basis consists of a collection of exponentials chosen by the interpolative decomposition to ensure stable and efficient recovery of Green's functions from imaginary time or Matsubara frequency samples. The library provides subroutines to build the DLR basis and grids, and to carry out various standard operations. The simplicity of the DLR makes it straightforward to incorporate into existing codes as a replacement for less efficient representations of imaginary time Green's functions, and libdlr is intended to facilitate this process. libdlr is written in Fortran, provides a C header interface, and contains a Python module pydlr. We also introduce a stand-alone Julia implementation, Lehmann.jl. Program Title: libdlr CPC Library link to program files: https://doi.org/10.17632/56z594pzsj.1 Developer's repository link: https://github.com/jasonkaye/libdlr Licensing provisions: Apache-2.0 Programming language: Fortran, C, Python, Julia Nature of problem: Discretization and compression of functions (Green's functions and self-energies) with an imaginary time variable. Solution method: Explicit basis functions and discretization points obtained by low rank compression of the analytical continuation kernel. [ABSTRACT FROM AUTHOR]

Abstract: Robust estimation procedures for linear and mixture linear errors-in-variables regression models are proposed based on the relationship between the least absolute deviation criterion and maximum likelihood estimation in a Laplace distribution. The finite sample performance of the proposed procedures is evaluated by simulation studies. [Copyright &y& Elsevier]

Abstract: The secret sharing schemes in conventional visual cryptography are capable of sharing one secret image into a set of random transparencies (called shares) in the form of rectangles, which reveal the secret image to the human visual system when they are superimposed. Recently, visual secret sharing schemes involving multiple secrets have attracted much attention. By adopting rotations on one of the two encoded circle shares, more than two secrets could be shared. Yet, the encoding and decoding processes of circle shares need more sophisticated mechanisms than those of rectangular or square ones. In this paper, we explore the possibilities of visual multiple secret sharing using simply two rectangular or square shares. Specifically, we define some operations onto a transparency based upon turning over or flipping around. Then we propose visual cryptographic schemes that are able to encode two or four secrets into two rectangular shares and up to eight secrets into two square shares such that the secrets cannot be obtained from any single share, whereas they are revealed by stacking the two shares under various combinations of turning or flipping operations. The proposed schemes, which solidly elaborate the relationship between the encoded shares and the shared secrets, broaden the research scope and enrich the flexibility and applicability of visual cryptography or image encryption theoretically and practically. [Copyright &y& Elsevier]

Abstract: consider a method of classification using the Gibbs posterior which is directly constructed from the empirical classification errors. They propose an algorithm to sample from the Gibbs posterior which utilizes a smoothed approximation of the empirical classification error, via a Gibbs sampler with augmented latent variables. In this paper, we note some drawbacks of this algorithm and propose an alternative method for sampling from the Gibbs posterior, based on the Metropolis algorithm. The numerical performance of the algorithms is examined and compared via simulated data. We find that the Metropolis algorithm produces good classification results at an improved speed of computation. [Copyright &y& Elsevier]

A network-based method applied to collaborative filtering in recommender systems is introduced in this paper. Specifically, a novel mixed-membership stochastic block model with a conjugate prior from the exponential family is proposed for bipartite networks. The analytical expression of the model is derived, and a variational Bayesian algorithm that is computationally feasible for approximating the untractable posterior distributions is presented. Extensive simulations show that the proposed model provides more accurate inference than competing methods with the presence of outliers. The proposed model is also applied to a MovieLens dataset for a real data application. [ABSTRACT FROM AUTHOR]

Multivariate regression techniques are commonly applied to explore the associations between large numbers of outcomes and predictors. In real-world applications, the outcomes are often of mixed types, including continuous measurements, binary indicators, and counts, and the observations may also be incomplete. Building upon the recent advances in mixed-outcome modeling and sparse matrix factorization, generalized co-sparse factor regression (GOFAR) is proposed, which utilizes the flexible vector generalized linear model framework and encodes the outcome dependency through a sparse singular value decomposition (SSVD) of the integrated natural parameter matrix. To avoid the estimation of the notoriously difficult joint SSVD, GOFAR proposes both sequential and parallel unit-rank estimation procedures. By combining the ideas of alternating convex search and majorization–minimization, an efficient algorithm is developed to solve the sparse unit-rank problem and implemented in the R package gofar. Extensive simulation studies and two real-world applications demonstrate the effectiveness of the proposed approach. • Joint modeling of mixed outcomes in the large-dimensional problem. • Association learning via low-rank and sparse singular value decomposition. • Divide and conquer (sequential and parallel) approach for estimating model parameters. • Additional application: generalized low-rank and sparse matrix factorization; capable of handling missing entries in responses. [ABSTRACT FROM AUTHOR]