Up to now, many properties of macro instructions of SCMFSA are described by the parahalting concepts. However, many practical programs are not always halting while they are halting for initialization states. For this reason, we propose initialization halting concepts. That a program is initialization halting (called ”InitHalting” for short) means it is halting for initialization states.In order...

In this paper we propose a new feature extraction algorithm based on nonlinear prediction: the Neural Predictive Coding model which is an extension of the classical LPC one. This model is applied to speaker verification by the Arithmetic-Harmonic Sphericity (AHS) method. Two different initialization methods are proposed for the coding method based on the Neural Predictive Coding (NPC): classica...

We present a new approach for Cluster Analysis based on a Greedy Randomized Adaptive Search Procedure (GRASP), with the objective of overcoming the convergence to a local solution. It uses a probabilistic greedy Kaufman initialization to get initial solutions and K-Means as a local search algorithm. The approach is a new initialization one for K-Means. Hence, we compare it with some typical ini...

We consider the modified Moran process on graphs to study the spread of genetic and cultural mutations on structured populations. An initial mutant arises either spontaneously (aka uniform initialization), or during reproduction (aka temperature initialization) in a population of n individuals, and has a fixed fitness advantage r > 1 over the residents of the population. The fixation probabilit...

We present information-theoretically secure bit commitment, zero-knowledge and multi-party computation based on the assistance of an initialization server. In the initialization phase, the players interact with the server to gather resources that are later used to perform useful protocols. This initialization phase does not depend on the input of the protocol it will later enable. Once the init...

Proper initialization is one of the most important prerequisites for fast convergence of feed-forward neural networks like high order and multilayer perceptrons. This publication aims at determining the optimal value of the initial weight v ariance (or range), which is the principal parameter of random weight initialization methods for both types of neural networks. An overview of random weight...