‎Let $X$ be a real normed  space, then  $C(subseteq X)$  is  functionally  convex  (briefly, $F$-convex), if  $T(C)subseteq Bbb R $ is  convex for all bounded linear transformations $Tin B(X,R)$; and $K(subseteq X)$  is  functionally   closed (briefly, $F$-closed), if  $T(K)subseteq Bbb R $ is  closed  for all bounded linear transformations $Tin B(X,R)$. We improve the    Krein-Milman theorem  ...

Parameterization of a 3D triangular mesh is a fundamental problem in mesh processing, such as texture mapping, multiresolution modeling, and smooth surface fitting. The convex combination approach is widely used for parameterization because it has good properties such as fast computation and little distortion of embedded triangles. However, the approach has one drawback: most boundary triangles...

We derive an upper bound on the generalization error of classi ers which can be represented as thresholded convex combinations of thresholded convex combinations of functions. Such classi ers include single hidden-layer threshold networks and voted combinations of decision trees (such as those produced by boosting algorithms). The derived bound depends on the proportion of training examples wit...

We investigate how different depth cues are combined when one cue is ambiguous. Convex and concave surfaces produce similar texture projections at large viewing distances. Our study considered unambiguous disparity information and its combination with ambiguous texture information. Specifically, we asked whether disparity and texture were processed separately, before linear combination of shape...

In this study, the combination of Phenergan C17H2ON2S, in which two aromatic rings are present, are attached to the rings at carbon position 5 and 13 halogens, and the changes in the asymmetric parameter, the convex constants, and the chemical displacement of the lateral carbon atoms are calculated. It shows that with the change of halogen based on the electrone...

We present an overview on piecewise linearization methods for MINLPs. This will include the concept of disjunctive constraints, which is necessary to define logarithmic reformulations of the so called disaggregated convex combination method and the convex combination method. For the case of a general univariate quadratic function we also calculate the linearization error and proof that equidist...