Measure Of Information Content Research Articles

An Information Content Measurement Model of the Implementation Factors in E-commerce

In this study, aiming at the influences brought by multiple factors in implementation process, the factors that lead to successful e-commerce are analyzed, and an information content measurement model of implementation factors in e-commerce based on information axiom is put forward. This model firstly analyzes the factors that influence the implementation process in e-commerce, and proposes the factors of successful implementation based upon independence of information. Then by building different information content measurement models, the overall information content of the implementation process in e-commence could be obtained. Via the information content, the judgment whether an e-commence is successful can be realized. Finally, the validity of the model is verified via application example.

On the distribution of interspecies correlation for Markov models of character evolution on Yule trees

Efforts to reconstruct phylogenetic trees and understand evolutionary processes depend fundamentally on stochastic models of speciation and mutation. The simplest continuous-time model for speciation in phylogenetic trees is the Yule process, in which new species are “born” from existing lineages at a constant rate. Recent work has illuminated some of the structural properties of Yule trees, but it remains mostly unknown how these properties affect sequence and trait patterns observed at the tips of the phylogenetic tree. Understanding the interplay between speciation and mutation under simple models of evolution is essential for deriving valid phylogenetic inference methods and gives insight into the optimal design of phylogenetic studies. In this work, we derive the probability distribution of interspecies covariance under Brownian motion and Ornstein–Uhlenbeck models of phenotypic change on a Yule tree. We compute the probability distribution of the number of mutations shared between two randomly chosen taxa in a Yule tree under discrete Markov mutation models. Our results suggest summary measures of phylogenetic information content, illuminate the correlation between site patterns in sequences or traits of related organisms, and provide heuristics for experimental design and reconstruction of phylogenetic trees.

Phase/current information descriptors and equilibrium states in molecules

Quantum‐generalized entropic descriptors of the complex electronic states and their information distances are reexamined and applied to the phase‐equilibria in molecules. The relation between densities of the ordinary Fisher and Shannon measures of information content is used in determining their supplements due to phases/currents. These nonclassical terms complement the familiar classical (probability) functionals of information theory in the resultant information descriptors. The nonclassical Shannon entropy measures the average magnitude of the system phase distribution, while the current term in the related Fisher measure accounts for the gradient content of the state phase. The density constrained (vertical) and unconstrained (horizontal) equilibria in molecules are distinguished. The consistency requirement that the extreme entropic principles in terms of both these resultant measures have common solutions calls for the modified, negative sign of the nonclassical Fisher indeterminicity term. The equilibrium criteria are shown to give rise to the unitary phase‐transformation of molecular states in a “thermodynamic” representation of quantum‐mechanical description. Possible applications of this generalized description are discussed and thermodynamical analogies are commented upon. A separation of the density (modulus) and current (phase) factors of general many‐electron states is effected using the Harriman–Zumbach–Maschke construction of antisymmetric states yielding the specified electron density. A phenomenological description of molecular subsystems is outlined, which accounts for both the density and phase degrees‐of‐freedom of electronic states, and the current promotion of molecular fragments is explored. © 2014 Wiley Periodicals, Inc.

Comparison of different techniques in atmospheric temperature-humidity sensing from space

Numerical closed-loop experiments on retrieving atmospheric temperature and humidity profiles by high-resolution measurements of the outgoing thermal infrared (IR) radiation using a Russian Fourier spectrometer (IRFS-2) were performed. Three techniques were used: multiple linear regression (MLR), the iterative physical-mathematical approach (IPMA), and artificial neural networks (ANNs). The MLR technique gives significant root mean square (RMS) errors in the retrieval of the temperature profile, especially in the troposphere region; these errors may be as great as 2–3 K. The ANN and IPMA techniques are considerably more accurate, giving approximately equal RMS errors of 1.0–1.5 K at altitudes of 2–30 km. For all interpretation techniques, a growth of errors of retrieval of temperature in the lower troposphere is observed and is especially substantial (up to 3 K for the near-surface temperature) in thermal sensing over land. The systematic errors of temperature retrieval for the ANN technique are practically zero, and for the other two techniques, they do not exceed 0.4 K. The differences in thermal sensing of the atmosphere over water and land manifest themselves in the appearance of an additional five determined coefficients of expansion of the spectral dependence of the IR emissivity of land in principal components. This leads to increased errors on thermal sensing in the lower troposphere, up to ~0.5 K for all interpretation techniques. The information content of the IRFS-2 device measurements with regard to the atmospheric humidity profile is relatively small because of the values of the errors of measurements of the outgoing radiation in the shortwave range, and in particular, in the water vapour absorption band 6.3 µm. The ANN technique makes it possible to determine relative humidity in the troposphere with RMS errors of 10–15%. In the case of observations over water, the mean errors of the ANN technique are practically equal to zero, and for the MLR and IPMA techniques, they are of an approximately equal order of magnitude, namely 2–4% of relative humidity. The IPMA and MLR techniques give RMS humidity errors of 15–20% and up to 40%, respectively.

Say it Again Sam: The Idiosyncratic Information Content of Corporate Conference Calls

This paper examines the idiosyncratic information-content of corporate conference calls. It studies the determinants, and the consequences, of idiosyncratic information production. To facilitate this study, I develop a novel measure of information-content which analyzes every (idiosyncratic) word choice made by management during both sessions of the call. In a sample of S&P 1500 firms from 2001 to 2012, this new measure of information-content explains cumulative abnormal returns. It shows how CEOs produce (suppress) information during the conference call. It shows how analyst participation in the call improves information production. It shows that a differential value is placed on information conditioned on the market segment of the firm. I contrast the effectiveness of this new measure to that of the conventional methodologies of tone and word-counting. I provide evidence that this new information-content measure is better suited to conference calls than are the other two.

ChemInform Abstract: Maximizing Information Content of Single‐Molecule FRET Experiments: Multi‐Color FRET and FRET Combined with Force or Torque

AbstractReview: 48 refs.

Quantum information descriptors and communications in molecules

Several concepts of information theory (IT) are extended to cover the complex probability amplitudes (wave functions) of molecular quantum mechanics. The classical and non-classical aspects of the electronic structure are revealed by the electronic probability and phase distributions, respectively. The information terms due to the probability and current distributions are accounted for in the complementary Shannon and Fisher measures of the resultant information content of quantum states. Similar generalization of the information-distance descriptors is also established. The superposition principle (SP) of quantum mechanics, which introduces the conditional probabilities between quantum states, is used to generate a network of quantum communications in molecules, and to identify the non-additive contributions to physical and information quantities. The phase-relations in two-orbital model are explored. The orbital communication theory of the chemical bond introduces the entropic bond multiplicities and their partition into IT covalent/ionic components. The conditional probabilities between atomic orbitals, propagated via the network of the occupied molecular orbitals, which define the bond system and orbital communications in molecules, are generated from the bond-projected SP. In the one-determinantal representation of the molecular ground state the communication amplitudes are then related to elements of the charge and bond-order matrix. Molecular equilibria are reexamined and parallelism between the vertical (density-constrained) energy or entropy/information principles of IT and the corresponding thermodynamic criteria is emphasized.

Hadamard multiplexed fluorescence tomography

Depth-resolved three-dimensional (3D) reconstruction of fluorophore-tagged inclusions in fluorescence tomography (FT) poses a highly ill-conditioned problem as depth information must be extracted from boundary data. Due to the ill-posed nature of the FT inverse problem, noise and errors in the data can severely impair the accuracy of the 3D reconstructions. The signal-to-noise ratio (SNR) of the FT data strongly affects the quality of the reconstructions. Additionally, in FT scenarios where the fluorescent signal is weak, data acquisition requires lengthy integration times that result in excessive FT scan periods. Enhancing the SNR of FT data contributes to the robustness of the 3D reconstructions as well as the speed of FT scans. A major deciding factor in the SNR of the FT data is the power of the radiation illuminating the subject to excite the administered fluorescent reagents. In existing single-point illumination FT systems, the source power level is limited by the skin maximum radiation exposure levels. In this paper, we introduce and study the performance of a multiplexed fluorescence tomography system with orders-of-magnitude enhanced data SNR over existing systems. The proposed system allows for multi-point illumination of the subject without jeopardizing the information content of the FT measurements and results in highly robust reconstructions of fluorescent inclusions from noisy FT data. Improvements offered by the proposed system are validated by numerical and experimental studies.

Stratospheric aerosol particle size information in Odin-OSIRIS limb scatter spectra

Abstract. The Optical Spectrograph and InfraRed Imaging System (OSIRIS) onboard the Odin satellite has now taken over a decade of limb scatter measurements that have been used to retrieve the version 5 stratospheric aerosol extinction product. This product is retrieved using a representative particle size distribution to calculate scattering cross sections and scattering phase functions for the forward model calculations. In this work the information content of OSIRIS measurements with respect to stratospheric aerosol is systematically examined for the purpose of retrieving particle size information along with the extinction coefficient. The benefit of using measurements at different wavelengths and scattering angles in the retrieval is studied, and it is found that incorporation of the 1530 nm radiance measurement is key for a robust retrieval of particle size information. It is also found that using OSIRIS measurements at the different solar geometries available on the Odin orbit simultaneously provides little additional benefit. Based on these results, an improved aerosol retrieval algorithm is developed that couples the retrieval of aerosol extinction and mode radius of a log-normal particle size distribution. Comparison of these results with coincident measurements from SAGE III shows agreement in retrieved extinction to within approximately 10% over the bulk of the aerosol layer, which is comparable to version 5. The retrieved particle size, when converted to Ångström coefficient, shows good qualitative agreement with SAGE II measurements made at somewhat shorter wavelengths.

Finding the Most Distant Quasars Using Bayesian Selection Methods

Quasars, the brightly glowing disks of material that can form around the super-massive black holes at the centres of large galaxies, are amongst the most luminous astronomical objects known and so can be seen at great distances. The most distant known quasars are seen as they were when the Universe was less than a billion years old (i.e., $\sim\!7%$ of its current age). Such distant quasars are, however, very rare, and so are difficult to distinguish from the billions of other comparably-bright sources in the night sky. In searching for the most distant quasars in a recent astronomical sky survey (the UKIRT Infrared Deep Sky Survey, UKIDSS), there were $\sim\!10^3$ apparently plausible candidates for each expected quasar, far too many to reobserve with other telescopes. The solution to this problem was to apply Bayesian model comparison, making models of the quasar population and the dominant contaminating population (Galactic stars) to utilise the information content in the survey measurements. The result was an extremely efficient selection procedure that was used to quickly identify the most promising UKIDSS candidates, one of which was subsequently confirmed as the most distant quasar known to date.

The effect of measurement timing on the information content of customer satisfaction measures

We use two sets of customer satisfaction measures obtained from a homebuilding company to examine the effect of measurement timing on the association between customer satisfaction measures and future financial performance. The research site employs two separate consulting firms that measure customer satisfaction at different times from the same homebuyer population. A national consulting firm captures customer satisfaction at a fixed time in the year following purchase (the “NF” measure), whereas an industry-focused, boutique consulting firm captures customer satisfaction at three specific points in time (30 days, 5 months, 11 months) after purchase (the “BF” measures). We analyze data for the period 2002–2004 and have the following findings: first, customers’ satisfaction varies over a homebuyer's consumption period. Comparing across the three BF measures, we find that on average a homebuyer is most satisfied 30 days after purchase and least satisfied 11 months after purchase. Second, we compare the NF measure with the BF measures and find significant differences in their predictive abilities for future financial performance. The BF measures are significant leading indicators of future financial performance, as measured by higher revenues and profits and lower warranty costs, but the NF measure is not. Additional analyses indicate that the relatively higher predictive ability of the BF measures is due to the more precise timing of those measures, rather than differences in measurement content. Finally, we find that the point of diminishing returns to improvements in customer satisfaction varies across customer satisfaction measures obtained at different points in the consumption period. We conclude that timing has a significant impact on the information content of customer satisfaction measures, at least for goods and services that are consumed over extended period of time.

Omitting correlated variables

Data collected on the physical, biological or man-made world are often highly correlated, posing the question of whether fewer variables would contain almost as much information. A crude solution is simply to look at the Pearson correlation matrix and omit one of a pair of highly correlated variables. A more systematic method is to condition on one or more variables, and observe the resulting partial covariance matrix. If the variables have little variance after the conditioning, then the conditioning variables contain most of the information of all the original variables. Paralleling the usual tests applied in judging how many principal components are sufficient to represent all the data, we can use the amount of variance explained by the conditioning variable (s), as a measure of information content. The paper references earlier work in this area, explains the computation and includes examples using published data sets. The approach is found to be highly competitive with using principal components, and has the obvious advantage over principal components of simply omitting some of the original variables from further consideration. The method has been coded in Visual-Basic add-ins to an Excel spreadsheet.

The number of cell types, information content, and the evolution of complex multicellularity

The number of different cell types (NCT) characterizing an organism is often used to quantify organismic complexity. This method results in the tautology that more complex organisms have a larger number of different kinds of cells, and that organisms with more different kinds of cells are more complex. This circular reasoning can be avoided (and simultaneously tested) when NCT is plotted against different measures of organismic information content (e.g., genome or proteome size). This approach is illustrated by plotting the NCT of representative diatoms, green and brown algae, land plants, invertebrates, and vertebrates against data for genome size (number of base-pairs), proteome size (number of amino acids), and proteome functional versatility (number of intrinsically disordered protein domains or residues). Statistical analyses of these data indicate that increases in NCT fail to keep pace with increases in genome size, but exceed a one-to-one scaling relationship with increasing proteome size and with increasing numbers of intrinsically disordered protein residues. We interpret these trends to indicate that comparatively small increases in proteome (and not genome size) are associated with disproportionate increases in NCT, and that proteins with intrinsically disordered domains enhance cell type diversity and thus contribute to the evolution of complex multicellularity.

Maximizing information content of single-molecule FRET experiments: multi-color FRET and FRET combined with force or torque

Since its first demonstration about twenty years ago, single-molecule fluorescence resonance energy transfer (FRET) has undergone remarkable technical advances. In this tutorial review, we will discuss two technical advances that increase the information content of the single-molecule FRET measurements: single-molecule multi-color FRET and single-molecule FRET combined with force or torque. Our expectations for future developments will be briefly discussed at the end.

An experiment on selecting most informative variables in socio-economic data

In many studies where data are collected on several variables, there is a motivation to find if fewer variables would provide almost as much information. Variance of a variable about its mean is the common statistical measure of information content, and that is used here. We are interested whether the variability in one variable is sufficiently correlated with that in one or more of the other variables that the first variable is redundant. We wish to find one or more ‘principal variables’ that sufficiently reflect the information content in all the original variables. The paper explains the method of principal variables and reports experiments using the technique to see if just a few variables are sufficient to reflect the information in 11 socioeconomic variables on 130 countries from a World Bank (WB) database. While the method of principal variables is highly successful in a statistical sense, the WB data varies greatly from year to year, demonstrating that fewer variables wo uld be inadequate for this data.

INFORMATION CONTENT OF ASSOCIATION RULES

Database records can be often interpreted as state descriptions of some world, system or generic object, states of which occur independently and are de- scribed by binary properties. If records do not contain missing values, then there exists close relationship between association rules and propositions about state properties. In data mining we usually get a lot of association rules with large con- fidence and large support. Since their interpretation is often cumbersome, some quantitative measure of their informativeness would be very helpful. The main aim of the paper is to define a measure of the amount of infor- mation contained in an association rule. For this purpose we make use of the tight correspondence between association rules and logical implications. At first a quantitative measure of information content of logical formulas is introduced and studied. Information content of an association rule is then defined as information content of the corresponding logical implication in the situation when no knowl- edge about dependence among properties of world states is at our disposal. The intuitive meaning of the defined measure is that the association rule that allows more appropriate correction of the distribution of world states, acquired under un- fair assumption of independence of state properties, contains also larger amount of information. The more appropriate correction here means a correction of the current probability distribution of states that leads to the distribution that is closer to the true distribution in the sense of Kullback-Leibler divergence measure.

Development of a fuzzy entropy based method for detecting altered gene–gene interactions in carcinogenic state

In this article, we propose a methodology for identifying the interactions among the genes in terms of dependencies (named as gene-gene interaction) that have altered quite significantly from normal stage to diseased stage with respect to their expression patterns. This idea leads to predict the disease mediating genes along with their altered interactions. The proposed methodology involves measuring information content of individual genes using fuzzy entropy, conditional fuzzy entropy of a gene on another, dependencies (interactions) of a pair of genes in both normal and diseased states, detecting the dependencies being deviated from normal to carcinogenic state and finally identifying the influential genes from altered dependencies. Thus the gene-gene interactions for normal state and diseased state are represented separately by the gene dependency networks (GDN). The altered interactions among the genes have been represented using a network, called altered gene dependency network (AGDN), in which each node represents a gene and a directed edge signifies altered dependency between a pair of nodes (genes). The methodology has been demonstrated on five gene expression data sets dealing with human lung cancer, colon cancer, sarcoma, breast cancer and leukemia. The results are appropriately validated, in terms of gene-gene interactions, using biochemical pathways, t-test, p-value, NCBI database and earlier investigations in terms of gene regulation. We have also used sensitivity to validate the results. For a comparative study, we have used some existing association rule mining algorithms and frequent pattern mining algorithms like Fuzzy Cluster-Based Association Rules, Apriori, T-Apriori in terms of gene-gene interactions. In addition, we have implemented Significance Analysis of Microarray, Signal-to-Noise Ratio, Neighborhood analysis, Bayesian regularization and frequent pattern mining algorithms for a comparison with AGDN in terms of ability to identify the important genes mediating the cancers.

Multimodal image/video fusion rule using generalized pixel significance based on statistical properties of the neighborhood

Image fusion has been receiving increasing attention in the research community with the aim of investigating general formal solutions to a wide spectrum of applications such as multifocus, multiexposure, multispectral ( $$IR$$ -visible) and multimodal medical (CT and MRI) image and video fusion. While there exist many fusion techniques for each of these applications, it is difficult to formulate a common fusion technique that works equally well for all these applications. This is mainly because of the different characteristics of the images involved in various applications and the correspondingly different requirements on the fused image. In this work, we propose a common generalized fusion framework for all these classes, based on the statistical properties of local neighborhood of a pixel. As the eigenvalue of the unbiased estimate of the covariance matrix of an image block depends on the strength of edges in that block, we propose to employ it to compute a quantity we call as the significance of a pixel. This generalized pixel significance in turn can be used as a measure of the useful information content in that block, and hence can be used in the fusion process. Several data sets were fused to compare the results with various recently published methods. The analysis shows that for all the image types into consideration, the proposed methods improve the quality of the fused image, both visually and quantitatively, by preserving all the relevant information.

An Information-Aware Framework for Exploring Multivariate Data Sets

Information theory provides a theoretical framework for measuring information content for an observed variable, and has attracted much attention from visualization researchers for its ability to quantify saliency and similarity among variables. In this paper, we present a new approach towards building an exploration framework based on information theory to guide the users through the multivariate data exploration process. In our framework, we compute the total entropy of the multivariate data set and identify the contribution of individual variables to the total entropy. The variables are classified into groups based on a novel graph model where a node represents a variable and the links encode the mutual information shared between the variables. The variables inside the groups are analyzed for their representativeness and an information based importance is assigned. We exploit specific information metrics to analyze the relationship between the variables and use the metrics to choose isocontours of selected variables. For a chosen group of points, parallel coordinates plots (PCP) are used to show the states of the variables and provide an interface for the user to select values of interest. Experiments with different data sets reveal the effectiveness of our proposed framework in depicting the interesting regions of the data sets taking into account the interaction among the variables.

An entropy approach for the optimization of cross-section spacing for river modelling

An accurate definition of river geometry is essential to implement one-dimensional (1D) hydraulic models and, in particular, appropriate spacing between cross-sections is key for capturing a river’s hydraulic behaviour. This work explores the potential of an entropy-based approach, as a complementary method to existing guidelines, to determine the optimal number of cross-sections to support 1D hydraulic modelling. To this end, given a redundant collection of existing cross-sections, a location subset is selected minimizing total correlation (as a measure of redundancy) and maximizing joint entropy (as a measure of information content). The problem is posed as a multi-objective optimization problem and solved using a genetic algorithm: the Non-dominated Sorting Genetic Algorithm (NSGA)-II. The proposed method is applied to a river reach of the Po River (Italy) and compared to standard guidelines for 1D hydraulic modelling. Cross-sections selected through the proposed methodology were found to provide an accurate description of the flood water profile, while optimizing computational efficiency.Editor D. KoutsoyiannisCitation Ridolfi, E., Alfonso, L., Di Baldassarre, G., Dottori, F., Russo, F., and Napolitano, F., 2013. An entropy approach for the optimization of cross-section spacing for river modelling. Hydrological Sciences Journal, 59 (1), 126–137.