Abstract. The symmetry preserving singular value decomposition (SPSVD) produces the best symmetric (low rank) approximation to a set of data. These symmetric approximations are characterized via an invariance under the action of a symmetry group on the set of data. The symmetry groups of interest consist of all the non-spherical symmetry groups in three dimensions. This set includes the rotatio...

this research aimed to investigate the learning organization characteristics in environmental non-governmental organizations (engos) in tehran city using the watkins and marsick’s seven dimensional learning organization model. the research method was descriptive- correlation and used survey method. the population consisted of all active members on engos in tehran city.  using bartlett’s (2011) ...

We propose a new additive decomposition of probability tables – tensor rank-one decomposition. The basic idea is to decompose a probability table into a series of tables, such that the table that is the sum of the series is equal to the original table. Each table in the series has the same domain as the original table but can be expressed as a product of one-dimensional tables. Entries in table...

The deep connection between the Burrows-Wheeler transform (BWT) and the socalled rank and select data structures for symbol sequences is the basis of most successful approaches to compressed text indexing. Rank of a symbol at a given position equals the number of times the symbol appears in the corresponding prefix of the sequence. Select is the inverse, retrieving the positions of the symbol o...

We propose a new additive decomposition of probability tables tensor rank-one decomposition. The basic idea is to decompose a probability table into a series of tables, such that the table that is the sum of the series is equal to the original table. Each table in the series has the same domain as the original table but can be expressed as a product of one-dimensional tables. Entries in tables ...

We present a new method based on functional tensor decomposition and dynamic approximation to compute the solution of high-dimensional time-dependent nonlinear partial differential equation (PDE). The idea is project time derivative PDE onto tangent space low-rank manifold at each time. Such projection can be computed by minimizing convex energy over space. This minimization problem yields uniq...