Link invariants, for 3-manifolds, are defined in the context of the RozanskyWitten theory. To each knot in the link one associates a holomorphic bundle over a holomorphic symplectic manifold X . The invariants are evaluated for b1(M) ≥ 1 and X Hyper-Kähler. To obtain invariants of Hyper-Kähler X one finds that the holomorphic vector bundles must be hyper-holomorphic. This condition is derived a...

The objective of the present paper is to study real hyper surfaces of a complex projective space with generalized recurrent second fundamental tensor and it is shown that such type real hyper surface exist. Also, we study real hyper surfaces of a complex projective space with generalized recurrent Ricci tensor. It is proved that a real hyper surfaces of complex projective space is generalized R...

We improve a recent result [A. Badr: Hyper-Minimization in O(n). In Proc. CIAA, LNCS 5148, 2008] for hyper-minimized finite automata. Namely, we present an O(n logn) algorithm that computes for a given finite deterministic automaton (dfa) an almost equivalent dfa that is as small as possible—such an automaton is called hyper-minimal. Here two finite automata are almost equivalent if and only if...

Markov distributions are used to describe multivariate data with conditional independence structure. Applications of Markov distributions arise in many fields including demography, flood prediction, and telecommunications. A hyper Markov law is a distribution over the space of all Markov distributions; such laws have been used as prior distributions for various types of graphical models. Dirich...

As hypertext systems become widely available and their popularity increases, attention has turned to converting existing textual documents into hypertextual form. An important issue in this area is the fully automatic production of hypertext for learning, teaching, training, or self-referencing. Although many studies have addressed the problem of producing hy-per-books, either manually or semi-...

We view hyper-graphs as incidence graphs, i.e. bipartite graphs with a set of nodes representing vertices and a set of nodes representing hyper-edges, with two nodes being adjacent if the corresponding vertex belongs to the corresponding hyper-edge. It defines a random hyper-multigraph specified by two distributions, one for the degrees of the vertices, and one for the sizes of the hyper-edges....

An iterative selection hyper-heuristic passes a solution through a heuristic selection process to decide on a heuristic to apply from a fixed set of low level heuristics and then a move acceptance process to accept or reject the newly created solution at each step. In this study, we introduce Robinhood hyper-heuristic whose heuristic selection component allocates equal share from the overall ex...

Hyper-bent functions as a subclass of bent functions attract much interest and it is elusive to completely characterize hyper-bent functions. Most of known hyper-bent functions are Boolean functions with Dillon exponents and they are often characterized by special values of Kloosterman sums. In this paper, we present a method for characterizing hyper-bent functions with Dillon exponents. A clas...

Hyper-minimization of deterministic nite automata (dfa) is a recently introduced state reduction technique that allows a nite change in the recognized language. A generalization of this lossy compression method to the weighted setting over semi elds is presented, which allows the recognized weighted language to di er for nitely many input strings. First, the structure of hyper-minimal determini...

Hyper-heuristics are high level improvement search methodologies exploring space of heuristics [4]. According to [5], hyper-heuristics can be categorized in many ways. A hyper-heuristic either selects from a set of available low level heuristics or generates new heuristics from components of existing low level heuristics to solve a problem, leading to a distinction between selection and generat...