Rank-One Matrix Completion with Automatic Rank Estimation via L1-Norm Regularization

Completing a matrix from a small subset of its entries, i.e., matrix completion is a challenging problem arising from many real-world applications, such as machine learning and computer vision. One popular approach to solve the matrix completion problem is based on low-rank decomposition/factorization. Low-rank matrix decomposition-based methods often require a prespecified rank, which is difficult to determine in practice. In this paper, we propose a novel low-rank decomposition-based matrix completion method with automatic rank estimation. Our method is based on rank-one approximation, where a matrix is represented as a weighted summation of a set of rank-one matrices. To automatically determine the rank of an incomplete matrix, we impose L1-norm regularization on the weight vector and simultaneously minimize the reconstruction error. After obtaining the rank, we further remove the L1-norm regularizer and refine recovery results. With a correctly estimated rank, we can obtain the optimal solution under certain conditions. Experimental results on both synthetic and real-world data demonstrate that the proposed method not only has good performance in rank estimation, but also achieves better recovery accuracy than competing methods

Tensor Rank Estimation and Completion via CP-based Nuclear Norm

Tensor completion (TC) is a challenging problem of recovering missing entries of a tensor from its partial observation. One main TC approach is based on CP/Tucker decomposition. However, this approach often requires the determination of a tensor rank a priori. This rank estimation problem is difficult in practice. Several Bayesian solutions have been proposed but they often under/overestimate the tensor rank while being quite slow. To address this problem of rank estimation with missing entries, we view the weight vector of the orthogonal CP decomposition of a tensor to be analogous to the vector of singular values of a matrix. Subsequently, we define a new CP-based tensor nuclear norm as the L1-norm of this weight vector. We then propose Tensor Rank Estimation based on L1-regularized orthogonal CP decomposition (TREL1) for both CP-rank and Tucker-rank. Specifically, we incorporate a regularization with CP-based tensor nuclear norm when minimizing the reconstruction error in TC to automatically determine the rank of an incomplete tensor. Experimental results on both synthetic and real data show that: 1) Given sufficient observed entries, TREL1 can estimate the true rank (both CP-rank and Tucker-rank) of incomplete tensors well; 2) The rank estimated by TREL1 can consistently improve recovery accuracy of decomposition-based TC methods; 3) TREL1 is not sensitive to its parameters in general and more efficient than existing rank estimation methods

Bilinear Probabilistic Canonical Correlation Analysis via Hybrid Concatenations

Canonical Correlation Analysis (CCA) is a classical technique for two-view correlation analysis, while Probabilistic CCA (PCCA) provides a generative and more general viewpoint for this task. Recently, PCCA has been extended to bilinear cases for dealing with two-view matrices in order to preserve and exploit the matrix structures in PCCA. However, existing bilinear PCCAs impose restrictive model assumptions for matrix structure preservation, sacrificing generative correctness or model flexibility. To overcome these drawbacks, we propose BPCCA, a new bilinear extension of PCCA, by introducing a hybrid joint model. Our new model preserves matrix structures indirectly via hybrid vector-based and matrix-based concatenations. This enables BPCCA to gain more model flexibility in capturing two-view correlations and obtain close-form solutions in parameter estimation. Experimental results on two real-world applications demonstrate the superior performance of BPCCA over competing methods

Dislocation constriction and cross-slip in Al and Ag: an ab initio study

A novel model based on the Peierls framework of dislocations is developed. The new theory can deal with a dislocation spreading at more than one slip planes. As an example, we study dislocation cross-slip and constriction process of two fcc metals, Al and Ag. The energetic parameters entering the model are determined from ab initio calculations. We find that the screw dislocation in Al can cross-slip spontaneously in contrast with that in Ag, which splits into partials and cannot cross-slip without first being constricted. The dislocation response to an external stress is examined in detail. We determine dislocation constriction energy and critical stress for cross-slip, and from the latter, we estimate the cross-slip energy barrier for the straight screw dislocations

Modeling and Optimization of the Dilute Sulfuric Acid Treatment on Corn Stover at Low Temperature

Corn stover was hydrolyzed using dilute sulfuric acid at concentrations of 2, 4, and 6% over reaction times up to 300 minutes at 80oC. The concentrations of sugars (xylose and glucose) and degradation product (furfural) were determined and the kinetic parameters of mathematical models for predicting them in the hydrolysates were obtained. According to the models, an optimal condition for hydrolysis was achieved which was 5% H2SO4 at 80°C for 240min and the liquor contained up to 13.21g/l xylose, 5.07g /l glucose and 0.80g/l furfural. The hydrolysates obtained from corn stover can be used to produce hydrogen and methane by anaerobic fermentation process. The models could be used successfully to predict the concentrations of xylose, glucose and furfural within 0-300min under experimental acid concentration

Restructuring of colloidal aggregates in shear flow: Coupling interparticle contact models with Stokesian dynamics

A method to couple interparticle contact models with Stokesian dynamics (SD) is introduced to simulate colloidal aggregates under flow conditions. The contact model mimics both the elastic and plastic behavior of the cohesive connections between particles within clusters. Owing to this, clusters can maintain their structures under low stress while restructuring or even breakage may occur under sufficiently high stress conditions. SD is an efficient method to deal with the long-ranged and many-body nature of hydrodynamic interactions for low Reynolds number flows. By using such a coupled model, the restructuring of colloidal aggregates under stepwise increasing shear flows was studied. Irreversible compaction occurs due to the increase of hydrodynamic stress on clusters. Results show that the greater part of the fractal clusters are compacted to rod-shaped packed structures, while the others show isotropic compaction.Comment: A simulation movie be found at http://www-levich.engr.ccny.cuny.edu/~seto/sites/colloidal_aggregates_shearflow.htm

Detecting Current Noise with a Josephson Junction in the Macroscopic Quantum Tunneling Regime

We discuss the use of a hysteretic Josephson junction to detect current fluctuations with frequencies below the plasma frequency of the junction. These adiabatic fluctuations are probed by switching measurements observing the noise-affected average rate of macroscopic quantum tunneling of the detector junction out of its zero-voltage state. In a proposed experimental scheme, frequencies of the noise are limited by an on-chip filtering circuit. The third cumulant of current fluctuations at the detector is related to an asymmetry of the switching rates.Comment: 26 pages, 10 figures. To appear in Journal of Low Temperature Physics in the proceedings of the ULTI conference organized in Lammi, Finland (2006

OmOm Diagnostic for Dilaton Dark Energy

$Om$ diagnostic can differentiate between different models of dark energy without the accurate current value of matter density. We apply this geometric diagnostic to dilaton dark energy(DDE) model and differentiate DDE model from LCDM. We also investigate the influence of coupled parameter $\alpha$ on the evolutive behavior of $Om$ with respect to redshift $z$. According to the numerical result of $Om$, we get the current value of equation of state $\omega_{\sigma0}$=-0.952 which fits the WMAP5+BAO+SN very well.Comment: 6 pages and 6 figures