In this paper, we study some non-local measurements of quantum correlations in extended gravities with higher-order curvature terms, including conformal gravity. Precisely, we consider higher-curvature correction on holographic MUTUAL INFORMATION in conformal gravity. There is in fact one deformation in the states because of the higher-curvature corrections. Here by making use of the holographic methods, we study the deformation in the holographic MUTUAL INFORMATION due to the higher-curvature terms. We also address the change in the quantum phase transition due to these deformations.

Among all measures of independence between random variables, MUTUAL INFORMATION is the only one that is based on INFORMATION theory. MUTUAL INFORMATION takes into account of all kinds of dependencies between variables, i.e., both the linear and non-linear dependencies. In this paper we have classified some well-known bivariate distributions into two classes of distributions based on their MUTUAL INFORMATION. The distributions within each class have the same MUTUAL INFORMATION. These distributions have been used extensively as survival distributions of two component systems in reliability theory.

Today, feature selection, as a technique to improve the performance of the classification methods, has been widely considered by the computer scientists. As the dimensions of a matrix has a huge impact on the performance of processing on it, reducing the number of features by choosing the best subset of all the features. It will affect the performance of the algorithms. Finding the best subset by comparing all the possible subsets, even when n is small, is an intractable process, and hence, many research works have approached to the heuristic methods to find a near-optimal solutions. In this paper, we introduce a novel feature selection technique that selects the most informative features and omits the redundant or irrelevant ones. Our method is embedded in PSO (Particle Swarm Optimization). In order to omit the redundant or irrelevant features, it is necessary to figure out the relationship between different features. There are many correlation functions that can reveal this relationship. In our proposed method, to find this relationship, we use the MUTUAL INFORMATION technique. We evaluate the performance of our method on three classification benchmarks: Glass, Vowel, and Wine. Comparing the results obtained with four state-of-the-art methods demonstrates its superiority over them.

If teamwork is the key to effective organizations, INFORMATION technology is the key to effective teamwork. The first part of this paper introduces the concept of using teams and INFORMATION technology to effect organizational performance. Each element on the left side of this "equation" (the teams and the INFORMATION technology) is of equal importance in bringing about the "result" (organizational performance); in fact, the synergistic relationship between teams and INFORMATION technology makes them far more effective agents of change together than either could be apart. In the second part of this paper a quantitative model is presented that makes clear the significance of this relationship and presents the basic components of the process intended to exploit it: the MUTUAL design of teams and INFORMATION technology.      

In this research, the relation between INFORMATION asymmetry and earnings management within the accepted MUTUAL funds by Tehran stock exchange was examined. This is an applied research in terms of objective and since it relies on relevant historical INFORMATION of the past so is considered retrospective and the study concludes inductively as a correlated research.The present study contains one main hypothesis and four Sub –hypothesis. The present case study consists of accepted MUTUAL funds in Tehran stock exchange during five-year period (1388 –1392) finally data relating to 79 firms gathered in spite he existed limitations, then 60 companies were chosen in simple random sampling method.Several software including SPSS and Eviews was made use of to analyze questions and hypotheses For documentation of data analysis and to present ultimate solution.Research hypothesis are: 1. there is a direct relation between INFORMATION asymmetry and earnings management of great MUTUAL funds.2. there is a reverse relation between INFORMATION asymmetry and earnings management of small MUTUAL funds.3. there is a direct relation between INFORMATION asymmetry and earnings management of MUTUAL funds having a high financial leverage.4. There's a reverse relation between INFORMATION asymmetry and earnings management of MUTUAL funds having a low financial leverage.

This study aims to investigate the correlation between the diversification and accruals quality (AQ) in Iran’ s MUTUAL funds considering two main hypotheses and four sub-hypotheses. This research investigates the effects of cases such as beta of the company, the company's return on assets, debt ratio of company, firm's size, and accrual quality on the company's cost of capital and considers the effect of MUTUAL funds’ diversification on decreasing INFORMATION risk calculated through accruals quality in Tehran Stock Exchange (TSE) and Iran Farabourse listed companies. This research investigates 42 MUTUAL funds from 2009 to 2013. Furthermore, the financial data of companies is considered for 20 years up to 2013 in order to calculate the accruals quality. The research results indicate that the factors such as the company's beta, the company's return on assets, and the ratio of firm's debt have direct correlation with cost of capital and this indicates that the increased risk in the form of beta and debt ratio increases the investors' expected return. However, the firm's size is inversely correlated with the cost of capital indicating that the increased firm's size provides the possibility of borrowing and bargaining at lower costs for companies. Furthermore, diversification in MUTUAL funds results in lowering INFORMATION risk caused by low accrual quality. Accordingly, the result of this research can help the MUTUAL funds’ managers and investment companies to better manage their investments.

INFORMATION theory is a branch of mathematics. INFORMATION theory is used in genetic and bioinformatics analyses and can be used for many analyses related to the biological structures and sequences. Bio-computational grouping of genes facilitates genetic analysis, sequencing and structural-based analyses. In this study, after retrieving gene and exon DNA sequences affecting milk yield in dairy cattle, the entropy in orders one to four for each gene and eta exons was calculated. In order to extract gene distances, MUTUAL INFORMATION method was calculated. The results of MUTUAL INFORMATION of DNA and exon sequences were entered as input into 7 general clustering algorithms. In order to aggregate the results of clustering, AdaBoost algorithm was used. Finally, the results of AdaBoost algorithm were investigated by GeneMANIA prediction server to explore the results from gene annotation point of view. Integrated result of each clustering algorithm due to AdaBoost algorithm, which implied as gene tree, indicated that proposed method biologically grouped set of genes as it was proved by their gene annotation using GeneMANI. We believe that the proposed method might be used with other DNA based clustering competitive methods and therefore, it can be used to group set of genes in other species.

Background: Myocardial infarction remains a leading cause of morbidity and mortality among cardiac disease. Cardiac wall thickening in patients withmyocardial infarction is less than healthy individuals. Accurate measurement of cardiac wall fractional thickening and path-length of myocardium points in healthy data and patients with myocardial infarction can help physicians in diagnosing the affected area. Patients and Methods: Epi/Endocardium of all slices in end-diastole frame were segmented, then more than 150 points in each slice were selected to track by weighted normalized MUTUAL INFORMATION algorithm over all frames. Weighted normalized MUTUAL INFORMATION was computed between two three-dimensional masks sized 3 3 3, pixel that were located in end-diastole and subsequent frames centroid of the selected points. Finally, by computing the distance between endocardium and epicardium in each slice over all frames, cardiac wall thickness and fractional thickening was measured. Moreover, the path-length of each data point during cardiac period was calculated and sketched in bulls-eye format. Evaluation of the method was done by ten healthy and twenty patients with myocardial infarction. Results: Cardiac wall kinesis was evaluated by normalized path length, which was presented in standard 17-segment bull’ s-eye format. Wall thickness and fractional wall thickening for all slices over all frames were extracted in order to determine the infarct region. Infarct regions had minimal fractional thickening and normalized path length. All evaluations demonstrated hypo-kinesis in the damaged region. Conclusion: Evaluation of obtained results showed significant difference between local parameters of healthy and infarcted myocardium. In all patients, the process was able to precisely determine the affected region that was all well matched with clinical evidence.

In a competitive electricity market, an accurate short term price forecasting is essential for all the participants in market as a risk management technique. For both spot markets and long-term contracts, price forecast is necessary to develop bidding strategies or negotiation skills in order to maximize benefit. This paper proposes an efficient tool for short-term electricity price forecasting with a simple model and acceptable computation time by combining several intelligent methods. Using inference, Adaptive Network-based Fuzzy Inference System (ANFIS) is used to determine the nonlinear relation between large quantities of input variables and forecasted price (output variable). To decrease the complexity and improve the accuracy, MUTUAL INFORMATION (MI) technique is used to efficiently select the best set of input variables which have important INFORMATION concerning forecasted price. Moreover, Particle Swarm Optimization (PSO) algorithm with new strategy in choosing the particles is adopted to tune ANFIS parameters more precisely. To evaluate the accuracy and performance, the proposed hybrid MUTUAL INFORMATION-ANFIS-PSO (MIAP) methodology is implemented on the real world case study of Spanish electricity market. The results show the great potential of this proposed method in fast and accurate short-term price forecasting in comparison with some of the previous price forecasting techniques.