Multi-fan is an analogous notion of fan in toric theory. Fan is a combinatorial object associated to a toric variety. Multi-fan is associated to an orbifold with an action of half the dimension of the orbifold. In this paper the equivariant elliptic genus and the equivariant orbifold elliptic genus of multi-fans are defined and their character formulas are exhibited. A vanishing theorem concern...

A divide and conquer algorithm is presented for computing arbitrary multi-dimensional discrete Fourier transforms. In contrast to standard approaches such as the row-column algorithm, this algorithm allows an arbitrary decomposition, based solely on the size of the transform independent of the dimension of the transform. Only minor modifications are required to compute transforms with different...

Responses of auditory neurons vary with many dimensions of acoustical stimuli. As a consequence, there is a difference between sensitivity to a particular dimension (e.g., ITD or level), which is assessed when only that dimension is varied while other dimensions are fixed (yielding tuning curves), and information about that dimension, which requires that all natural variability be considered. I...

In this paper, the local fuzzy fractal dimension (LFFD) is proposed to represent the feature of local image complexity. The definition of LFFD is an extension of the box-counting dimension of discrete sets by incorporating the fuzzy set. The relationship between LFFD and local intensity changing is investigated by experiments, which proves that LFFD is an important feature of edges and is insen...

With the pervasive availability of Web 2.0 and social networking sites, people can interact with each other easily through various social media. For instance, popular sites like Del.icio.us, Flickr, and YouTube allow users to comment shared content (bookmark, photos, videos), and users can tag their own favorite content. Users can also connect to each other, and subscribe to or become a fan or ...

Learning is the important property of Back Propagation Network (BPN) and finding the suitable weights and thresholds during training in order to improve training time as well as achieve high accuracy. Currently, data pre-processing such as dimension reduction input values and pre-training are the contributing factors in developing efficient techniques for reducing training time with high accura...