Candecomp/Parafac with zero constraints at arbitrary positions in a loading matrix

When one interprets Candecomp/Parafac (CP) solutions for analyzing three-way data, small loadings are often ignored, that is, considered to be zero. Rather than just considering them zero, it seems better to actually model such values as zero. This can be done by successive modeling approaches as well as by a simultaneous modeling approach. This paper offers algorithms for three such approaches, and compares them on the basis of empirical data and a simulation study. The conclusion of the latter was that, under realistic circumstances, all approaches recovered the underlying structure well, when the number of values to constrain to zero was given. Whereas the simultaneous modeling approach seemed to perform slightly better, differences were very small and not substantial. Given that the simultaneous approach is far more time consuming than the successive approaches, the present study suggests that for practical purposes successive approaches for modeling zeros in the CP model seem to be indicated

Factor PD-Clustering

Factorial clustering methods have been developed in recent years thanks to the improving of computational power. These methods perform a linear transformation of data and a clustering on transformed data optimizing a common criterion. Factorial PD-clustering is based on Probabilistic Distance clustering (PD-clustering). PD-clustering is an iterative, distribution free, probabilistic, clustering method. Factor PD-clustering make a linear transformation of original variables into a reduced number of orthogonal ones using a common criterion with PD-Clustering. It is demonstrated that Tucker 3 decomposition allows to obtain this transformation. Factor PD-clustering makes alternatively a Tucker 3 decomposition and a PD-clustering on transformed data until convergence. This method could significantly improve the algorithm performance and allows to work with large dataset, to improve the stability and the robustness of the method

Cystatin C is not a reliable marker of residual glomerular filtration rate during continuous renal replacement therapy

Contains fulltext : 95779.pdf (publisher's version ) (Closed access

Effective Catalytic Oxidation of 1-Alkenes Using Palladium-nitro Complexes in the Presence of Amides.

The rate of oxidation and the catalyst stability in the oxidation of 1-alkenes with O2 using [PdCl(NO2)(MeCN)2] as catalyst is considerably improved by the use of amides as ligands or solvents.</p

Activation of sp3-CH Bonds in a Mono(pentamethylcyclopentadienyl)yttrium Complex. X-ray Crystal Structures and Dynamic Behavior of Cp*Y(o-C6H4CH2NMe2)2 and Cp*Y[o-C6H4CH2NMe(CH2-μ)][μ-o-C6H4CH2NMe(CH2-μ)]YCp*[THF]

Reaction of Y(o-C6H4CH2NMe2)3 (1) with Cp*H gives Cp*Y(o-C6H4CH2NMe2)2 (2), which crystallizes in the monoclinic space group P21/n (No. 14) with a = 18.607 (4) Å, b = 15.633 (3) Å, c = 8.861 (3) Å, β = 102.73 (3)°, and Z = 4. Least-squares refinement with 3006 independent reflections (F > 4.0σ(F)) led to a final RF (wR) of 0.053 (0.068). The molecular structure consists of monomeric Cp*Y(o-C6H4CH2NMe2)2 units with a regularly bonded Cp* ligand (Y-Ct = 2.367 (3) Å), equal Y-C(aryl) distances (2.479 (6) and 2.471 (6) Å), and both nitrogen atoms coordinated to yttrium (Y-N distances = 2.568 (5) and 2.506 (6) Å). Short intramolecular Y···H distances (Y···H(181) = 3.00 (6) Å, Y···H(183) = 3.13 (9) Å) indicate agostic interactions. The long N(2)-C(18) bond (1.55 (1) Å) and the short Y···C(18) distance (3.202 (8) Å) indicate an Y···C-N agostic interaction. Thermolysis of 2 in THF gives Cp*Y[o-C6H4CH2NMe(CH2-μ)][μ-o-C6H4CH2NMe(CH2-μ)]YCp*[THF] (3) and N,N-dimethylbenzylamine. Compound 3 crystallizes in the monoclinic space group P21/c (No. 14) with a = 17.004 (1) Å, b = 13.962 (1) Å, c = 20.129 (3) Å, β = 92.94 (1)°, and Z = 4. Least-squares refinement with 4578 independent reflections (F > 5.0σ(F)) led to a final RF (wR) of 0.065 (0.070). The molecule consists of two Cp*Y fragments (Y(1)-Ct(1) = 2.420 (6) Å, Y(2)-Ct(2) = 2.414 (5) Å), bridged by two methylene carbon atoms (Y(1)-C(9) = 2.591 (10) Å, Y(2)-C(9) = 2.527 (9) Å, Y(1)-C(18) = 2.622 (10) Å, Y(2)-C(18) = 2.532 (10) Å) and one aryl carbon atom (Y(1)-C(1) = 2.702 (8) Å, Y(2)-C(1) = 2.547 (9) Å). The remaining aryl group is not bridging (Y(1)-C(10) = 2.441 (8) Å). Asymmetry in 3 is caused by THF coordination (Y(2)-O = 2.446 (5) Å). Thermolysis of 2 can be explained by dissociation of an Y-N dative bond followed by activation of an agostic C-H bond

Algorithms and literate programs for weighted low-rank approximation with missing data

Linear models identification from data with missing values is posed as a weighted low-rank approximation problem with weights related to the missing values equal to zero. Alternating projections and variable projections methods for solving the resulting problem are outlined and implemented in a literate programming style, using Matlab/Octave's scripting language. The methods are evaluated on synthetic data and real data from the MovieLens data sets