to calculate partial and total productivity of production factors in broiler farms in yazd province, 72 manufacturing units were selected based on simple random sampling method and their information and statistics were collected for one production period in the second half of 2013. to measure productivity, the cobb-douglas production function was estimated using classic and fuzzy regression met...

Normalized Mutual Information (NMI) has been widely used to evaluate accuracy of community detection algorithms. In this notes we show that NMI is seriously affected by systematic error due to finite size of networks, and may give wrong estimate of performance of algorithms in some cases. A simple expression for the estimate of this error is derived and tested numerically. We suggest to use a n...

Computing meaningful clusters of nodes is crucial to analyze large networks. In this paper, we present a pairwise node similarity measure that allows to extract roles, i.e. group of nodes sharing similar flow patterns within a network. We propose a low rank iterative scheme to approximate the similarity measure for very large networks. Finally, we show that our low rank similarity score success...

We study the classes of the form ALMOST-R, for R a reducibility. characterization of these classes in terms of reductions to n-random languages , a subclass of algorithmically random languages. We also discuss the possibility of characterizing the classes ALMOST-R in terms of resource bounded measure.

Representation learning and unsupervised learning are two central topics of machine learning and signal processing. Deep learning is one of the most effective unsupervised representation learning approach. The main contributions of this paper to the topics are as follows. (i) We propose to view the representative deep learning approaches as special cases of the knowledge reuse framework of clus...

This article gives a comprehensive description of the fractal geometry of conformally-invariant (CI) scaling curves, in the plane or half-plane. It focuses on deriving critical exponents associated with interacting random paths, by exploiting an underlying quantum gravity (QG) structure, which uses KPZ maps relating exponents in the plane to those on a random lattice, i.e., in a fluctuating met...

It has been shown [Van den Broeck and Kawai, 1990, Patarnello and Carnevali, 1987] that feedforward Boolean networks can learn to perform specific simple tasks and generalize well if only a subset of the learning examples is provided for learning. Here, we extend this body of work and show experimentally that random Boolean networks (RBNs), where both the interconnections and the Boolean transf...

It has been shown [7,16] that feedforward Boolean networks can learn to perform specific simple tasks and generalize well if only a subset of the learning examples is provided for learning. Here, we extend this body of work and show experimentally that random Boolean networks (RBNs), where both the interconnections and the Boolean transfer functions are chosen at random initially, can be evolve...