The axial vector and tensor charge, defined as the first moments of the forward nucleon matrix elements of corresponding quark currents, are essential for characterizing the spin structure of the nucleon. There are no definitive theoretical predictions for the tensor charge, aside from several model-dependent calculations. We present a new approach that exploits the approximate mass degeneracy ...

This paper characterizes the necessary and sufficient conditions for tensegrity equilibria. Static models of tensegrity structures are reduced to linear algebra problems, after first characterizing the problem in a vector space where direction cosines are not needed. This is possible by describing the components of all member vectors. While our approach enlarges (by a factor of 3) the vector sp...

Example 3. If X ⊆ RN is a vector space then it is a vector subspace of RN . Example 4. R1 is a vector subspace of R2. But the set [−1, 1] is not a vector subspace because it is not closed under either vector addition or scalar multiplication (for example, 1 + 1 = 2 6∈ [−1, 1]). Geometrically, a vector space in RN looks like a line, plane, or higher dimensional analog thereof, through the origin...

Hierarchical classification is critical to knowledge management and exploration, as in gene function prediction and document categorization. In hierarchical classification, an input is classified according to a structured hierarchy. In a situation as such, the central issue is how to effectively utilize the inter-class relationship to improve the generalization performance of flat classificatio...

Vulnerability assessment of structures encounter many uncertainties like seismic excitations intensity and response of structures. The most common approach adopted to deal with these uncertainties is vulnerability assessment through fragility functions. Fragility functions exhibit the probability of exceeding a state namely performance-level as a function of seismic intensity. A common approach...

The feature map represented by the set of weight vectors of the basic SOM (Self-Organizing Map) provides a good approximation to the input space from which the sample vectors come. But the timedecreasing learning rate and neighborhood function of the basic SOM algorithm reduce its capability to adapt weights for a varied environment. In dealing with non-stationary input distributions and changi...

Disease-related protein-coding genes have been widely studied, but disease-related non-coding genes remain largely unknown. This work introduces a new vector to represent diseases, and applies the newly vectorized data for a positive-unlabeled learning algorithm to predict and rank disease-related long non-coding RNA (lncRNA) genes. This novel vector representation for diseases consists of two ...

Abs t r ac t . J . F . Benders devised a clever approach for exploiting the structure of mathematical programming problems with complicating variables (variables which, when temporarily fixed, render the remaining optimization problem considerably more tractable). For the class of problems specifically considered by Benders, fixing the values of the complicating variables reduces the given prob...

In this work, we first review some general properties of dense granular materials. We are particularly concerned with a statistical description of these materials, and it is in this light that we briefly describe results from four representative studies. These are: experiment 1: determining local force statistics, vector forces, force distributions and correlations for static granular systems; ...