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The present paper studies continuity of generalized entropy functions and relative entropies defined using the notion of a deformed logarithmic function. In particular, two distinct definitions of relative entropy are discussed. As an application, all considered entropies are shown to satisfy Lesche's stability condition. The entropies of Tsallis' non-extensive thermostatistics are taken as examples.

This paper presents a method for evaluating concept similarity within Fuzzy Formal Concept Analysis. In the perspective of developing the Semantic Web, such a method can be helpful when the digital resources found on the Internet cannot be treated equally and the integration of fuzzy data becomes fundamental for the search and discovery of information in the Web.

This study examines the impact of increasing pre-trade transparency using intraday data from the Taiwanese stock market, which has recently experienced gradually increasing transparency. The analytical results indicate the disclosed quotes are more informative than the accompanied depths, and the orders of institutional traders are more informative than those of individual traders. Additionally, the best quotes of unexecuted orders for individual traders always contain more information than the average quotes from Steps 2 to 5, whereas this does not apply for institutional investors. The feature is more obvious for the sub-samples with high and medium turnover rate, but not for the sub-samples with low turnover rate.

We investigate whether defined benefit (DB) pension contributions convey information about earnings quality proxied by different measurements of discretionary accruals (DA). We find that greater DA are negatively is associated with discretionary pension contributions. Since greater DA is associated with lower earnings quality, the result implies a positive relationship between earnings quality and voluntary pension contributions. In contrast, our evidence does not suggest a similar relationship holds for earnings quality and mandatory pension contributions. In addition, while our analysis identifies a negative relation between DA and total pension contributions, the relation is statistically weaker than that between DA and voluntary pension contribution. Robustness tests are conducted and results are found to continue to hold. Our results are consistent with the theoretical argument that voluntary pension contributions are indicative of firms' earnings quality since both the voluntary pension contributions and earnings quality result from the same set of incentives behind managerial discretions. Our study sheds light on the management's motivation for making voluntary pension contributions and improves our understanding of firms' consideration in funding strategies for DB pension plans.

In this paper, we employ the information theory to analyze the development of brain as the newborn ages. We compute the Shannon entropy of Electroencephalography (EEG) signal during sleep for 10 groups of newborns who are aged 36 weeks to 45 weeks (first to the last group). Based on the obtained results, EEG signals for newborns in 36 weeks have the lowest information content, whereas EEG signals for newborns in 45 weeks show the greatest information content. Therefore, we concluded that the information content of EEG signal increases as the age of newborn increases. Th result of statistical analysis demonstrated that the influence of increment of age of newborn on the variations of informant content of their EEG signals was significant.

Our eyes are always in search of exploring our surrounding environment. The brain controls our eyes’ activities through the nervous system. Hence, analyzing the correlation between the activities of the eyes and brain is an important area of research in vision science. This paper evaluates the coupling between the reactions of the eyes and the brain in response to different moving visual stimuli. Since both eye movements and EEG signals (as the indicator of brain activity) contain information, we employed Shannon entropy to decode the coupling between them. Ten subjects looked at four moving objects (dynamic visual stimuli) with different information contents while we recorded their EEG signals and eye movements. The results demonstrated that the changes in the information contents of eye movements and EEG signals are strongly correlated ($r=0.7084$), which indicates a strong correlation between brain and eye activities. This analysis could be extended to evaluate the correlation between the activities of other organs versus the brain.

This paper investigates the impact of Chinese Treasury bond (CTB) futures on the information content of interest rate swap (IRS) from a multifractality perspective. We first use multifractal detrended fluctuation analysis (MF-DFA) method and show that the swap rate and the CTB yield exhibit strong multifractality. In addition, employing multifractal detrended cross-correlation analysis (MF-DCCA) method, we find that cross-correlations between the swap rate and the CTB yield are multifractally persistent. Moreover, after the reintroduction of Treasury bond futures, the persistence of cross-correlation between the series is weaker. Our results indicate that the information content of IRS decreased after the re-launch of CTB futures.

Analysis of the correlation among the activities of the eyes and brain is an important research area in physiological science. In this paper, we analyzed the correlation between the reactions of eyes and the brain during rest and while watching different visual stimuli. Since every external stimulus transfers information to the human brain, and on the other hand, eye movements and EEG signals contain information, we utilized Shannon entropy to evaluate the coupling between them. In the experiment, 10 subjects looked at 4 images with different information contents while we recorded their EEG signals and eye movements simultaneously. According to the results, the information contents of eye fluctuations, EEG signals, and visual stimuli are coupled, which reflect the coupling between the brain and eye activities. Similar analyses could be performed to evaluate the correlation among the activities of other organs versus the brain.

One popular approach to prediction of binding motifs of transcription factors is to model the problem as to search for a group of l-mers (motifs), for some l > 0, one from each of the provided promoter regions of a group of co-expressed genes, that exhibit high information content when aligned without gaps. In our current work, we assume that these desired l-mers have evolved from a common ancestor, each of which has mutations in at most k-positions from the common ancestor, where k is substantially smaller than l. This implies that these l-mers should belong to the k-neighborhood of their common ancestor, measured in terms of Hamming distance. If the ancestor is given, then the problem for finding these l-mers becomes trivial. Unfortunately, the problem of identifying the unknown ancestor is probably as hard as the problem of predicting the motifs themselves. Our goal is to identify a set of l-mers that slightly violate the k-neighborhood of a putative ancestor, but capture all the desired motifs, which will lead to an efficient way for identification of the desired motifs. The main contributions of this paper are in four aspects: (a) we have derived nontrivial lower and upper bounds of information content for a set of l-mers that differ from an unknown ancestor in no more than k positions; (b) we have defined a new distance between two sequences and a k-pseudo-neighborhood, based on the new distance, that contains the k-neighborhood, defined by Hamming distance, of the to-be-defined ancestor; (c) we have developed an algorithm to minimize the sum of all the distances between a predicted ancestor motif and a group of l-mers from the provided promoter regions, using the new distance; and (d) we have tested PROMOCO and compared its prediction results performance with two other prediction programs.

The algorithm, implemented as a computer software program PROMOCO, has been used to find all conserved motifs in a set of provided promoter sequences. Our preliminary application of PROMOCO shows that it achieves better or comparable prediction results, when compared to popular programs for identification of cis regulatory binding motifs. A limitation of the algorithm is that it does not work well when the size of the set of provided promoter sequences is too small or when desired motifs appear in only small portion of the given sequences.

This study examines the effect of a security regulation that occurs simultaneously with International Financial Reporting Standards (IFRS) adoption on the information content of earnings announcements in Italy. To identify the effect of this regulation, we use a treatment and a control sample of IFRS countries that vary in the adoption of the security regulation, but are similar along a set of accounting and institutional dimensions (Italy versus France, Belgium, and Portugal). We find that the increase in information content of earnings announcements is more pronounced in Italy (treatment sample). Further, we analyze non-earnings disclosures using 2106 earnings announcements and find that the inclusion of IFRS-based detailed financial statements in earnings announcements contributes to the increased informativeness of IFRS earnings announcements. Our results provide support to the notion that regulatory changes concurrent with IFRS adoption are necessary to yield capital-market benefits.

We examine the information content of strategic-plans’ long-term growth targets (SPLTG) and of strategic-plans’ forecast horizons (SPFH). Using a sample of 224 strategic plan presentations by Italian listed companies during the period 2002–2018, we provide evidence that the SPLTG conveys credible and useful information to investors. We also assume that longer forecast horizons are more uncertain and we find that stock price reaction is negatively associated with long-term forecast horizons. Then, we investigate whether SPLTG presented in conjunction with long-term SPFH are perceived as less credible. The findings document that investors perceive long-term growth targets as credible regardless of the SPFH length. Our study contributes to the current debate on the use of strategic plans as comprehensive disclosure able to provide credible and useful information.

The similarity of two gene products can be used to solve many problems in information biology. Since one gene product corresponds to several GO (Gene Ontology) terms, one way to calculate the gene product similarity is to use the similarity of their GO terms. This GO term similarity can be defined as the semantic similarity on the GO graph. There are many kinds of similarity definitions of two GO terms, but the information of the GO graph is not used efficiently. This paper presents a new way to mine more information of the GO graph by regarding edge as information content and using the information of negation on the semantic graph. A simple experiment is conducted and, as a result, the accuracy increased by 8.3 percent in average, compared with the traditional method which uses node as information source.