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When communicating, individuals alter their language to fulfill a myriad of social functions. In particular, linguistic convergence and divergence are fundamental in establishing and maintaining group identity. Quantitatively characterizing linguistic convergence is important when testing hypotheses surrounding language, including interpersonal and group communication. We provide a quantitative interpretation of linguistic convergence grounded in information theory. We then construct a computational model, built on top of a neural network model of language, that can be deployed to measure and test hypotheses about linguistic convergence in "big data." We demonstrate the utility of our convergence measurement in two case studies: (1) showing that our measurement is indeed sensitive to linguistic convergence across turns in dyadic conversation, and (2) showing that our convergence measurement is sensitive to social factors that mediate convergence in Internet-based communities (specifically, r/MensRights and r/MensLib). Our measurement also captures differences in which social factors influence web-based communities. We conclude by discussing methodological and theoretical implications of this semantic convergence analysis., (© 2023. The Psychonomic Society, Inc.)

The article explores the use of theory in empirical papers presented at the ISIC: The Information Behavior Conference, from 1996 to 2020. The study examines the inclusion or lack of theory, the level of theory use, the specific theories used, and the disciplines from which these theories originated. The results indicate that most papers include theory, with many using theory substantially, but there is a significant decrease in theory use between the early and recent years of the conference.

To conclude, we believe that this special issue accurately portrays the scientific features of the CLADAG community nowadays and supports the CLADAG mission of facilitating the exchange of ideas in Classification and Data Analysis. This special issue of Statistical Analysis and Data Mining contains a selection of the papers presented at the 13th Scientific Meeting of the Classification and Data Analysis Group (CLADAG), scheduled for September 9-11, 2021 in Florence, Italy. The articles cover a broad range of data analysis topics: gender gap analysis, income clustering, structural equation modeling, multivariate nonparametric methods, and classifier selection. [Extracted from the article]

The evaluation of irrigation suitability plays a crucial role for the socio-economic development of the society, especially in the region of Sundarban. For sustainable agricultural practices, groundwater quality must be suitable for irrigation; otherwise, it can degrade soil and diminish crop yield. The entropy information theory, several irrigational indices, multivariate statistics, GIS, and geostatistics are used in this work to evaluate the geographical distribution and quality of groundwater in the Indian Sundarban region. In total, 33 groundwater samples were collected in 2018 (April and May), and they were evaluated for major cations, anions, as well as other parameters like electrical conductivity (EC), soluble sodium percentage (SSP), potential salinity (PS), total dissolved solids (TDS), Kelly ratio (KR), sodium absorption ratio (SAR), permeability index (PI), residual sodium carbonate (RSC), magnesium hazard (MH), and residual sodium bicarbonate (RSBC). The overall trend of the principal cations and anions is in the sequence of Na +  ≥ Mg 2+  ≥ Ca 2+  ≥ K 2+ and HCO 3 -  ≥ Cl -  ≥ NO 3 -  ≥ SO 4 2-  ≥ F - , respectively, whereas the spatial variation of %Na, SAR, RSBC, and MH demonstrate very poor irrigation water quality, and spatial variation of KR, RSC, SSP, PI, and PS signifies that the irrigation water quality is excellent to good. In order to identify the specific association and potential source of the dissolved chemical in the groundwater, statistical techniques like correlation and principal component analysis were also employed. The hydrochemical facies indicates that mixed type makes up the bulk (51.51%) of the water samples. Following the Wilcox plot, more than 75% of the water samples are good to doubtful; however, by the US salinity hazard map, roughly 60.60% of the samples had high salinity (C3-S1 zone). The EWQII reports that no samples fall into the very good (no restriction) category, whereas 30.30%, 30.30%, and 39.40% of the sample wells record good (low restriction), average (moderate restriction), and poor (severe restriction) irrigation water quality, respectively. Based on this study, the bulk of the groundwater samples taken from the study area are unsuitable for cultivation. The findings of this study will also help decision-makers develop adequate future plans for irrigation and groundwater resource management., (© 2023. The Author(s), under exclusive licence to Springer Nature Switzerland AG.)

In this paper, we demonstrate how to identify whether different groups of students (classified by their major) provide different quantities of information in their student ratings of instruction (SROIs). As a corollary, we identified specific groups of students who provided a greater/lesser quantity of information in their responses. All calculations were undertaken using Microsoft Excel, and no prior statistical training was required to create or interpret our information measures. We used SROI data taken from a first-year logical reasoning course for health professions majors and found that the quantity of information provided by pharmacy and other health majors in their SROIs exceeded the quantity of information provided by nursing majors for every single SROI question. We also found that specific majors gave relatively greater quantities of information (relative to other majors) for specific types of SROI items.

The papers range from the theoretical ones, the paper applying the models, to the papers providing software tools for causal inference. B Instrumental variable in causal inference. b Papers [[1], [3]] investigate models using instrumental variable in causal inference. The present Special Issue of Entropy, entitled "Causal Inference for Heterogeneous Data and Information Theory", covers various aspects of causal inference. In the first step, the treatment and outcome are modeled to incorporate the potential confounders, and in the second step, the predictions are inserted into the ATE estimators such as the Augmented Inverse Probability Weighting (AIPW) estimator, based on neural networks (NN). [Extracted from the article]

Many discussions on serendipitous research discovery stress its unfortunate immeasurability. This unobservability may be due to paradoxes that arise out of the usual conceptualizations of serendipity, such as "accidental" versus "goal-oriented" discovery, or "useful" versus "useless" finds. Departing from a different distinction drawn from information theory—bibliometric redundancy and bibliometric variety—this paper argues otherwise: Serendipity is measurable, namely with the help of altmetrics, but only if the condition of highest bibliometric variety, or randomness, obtains. Randomness means that the publication is recommended without any biases of citation counts, journal impact, publication year, author reputation, semantic proximity, etc. Thus, serendipity must be at play in a measurable way if a paper is recommended randomly, and if users react to that recommendation (observable via altmetrics). A possible design for a serendipity-measuring device would be a Twitter bot that regularly recommends a random scientific publication from a huge corpus to capture the user interactions via altmetrics. Other than its implications for the concept of serendipity, this paper also contributes to a better understanding of altmetrics' use cases: not only do altmetrics serve the measurement of impact, the facilitation of impact, and the facilitation of serendipity, but also the measurement of serendipity. [ABSTRACT FROM AUTHOR]

The continuous expansion of the construction scale of smart city has reconstructed the urban information pattern. How to maintain the stability of information security while giving full play to the role of information sharing is a practical problem that must be solved for the sustainable development of smart city. Based on the information ecology theory, this paper construct the smart city information security risk evaluation system from six aspects. Then, zGT2FSs is established based on type-2 fuzzy set theory and IAA method, which fully considers the internal and external uncertainty of expert decision-making. According to the calculation results, the key influencing factors of information security risk of smart city are analyzed to provide suggestions and guidance for the formulation of information security control in the process of smart city construction in China. [ABSTRACT FROM AUTHOR]

In order to overcome the uncertainty in hyperspectral estimation of soil organic matter content, this paper aim to establish a grey information relational estimation model of soil organic matter content based on hyperspectral data and grey information theory. Based on 76 samples in Zhangqiu District of Jinan City, Shandong province of China, the spectral data are first transformed by the nine methods such as square root, first order differentiation of the logarithm reciprocal, and so on, the correlation coefficient is calculated, and the estimation factors are selected by using the principle of great maximum correlation. Then, according to the principle of increasing information and taking maximum method, the spectral estimation factors of each sample are sorted from small to large, and the grey information sequence is formed, and the grey relational estimation model of soil organic matter content is constructed based on the information chain. Finally, the estimation results based on different information chains are fused twice, and compared with the commonly used estimation methods. The results of the method in this paper show that the average relative error of the 12 test samples is 5.576%, and the determination coefficient R2 is 0.934, and the estimation accuracy is higher than that of commonly used methods such as multiple linear regression, BP neural network and support vector machine. The results show that the grey information relational estimation model using hyperspectral data proposed in this paper is feasible and effective, and it provides a new way for hyperspectral estimation of soil organic matter and other soil properties. [ABSTRACT FROM AUTHOR]

The paper discusses how modern theories of information converge with the Biblical concept of Logos. We begin with a condensed review of the various concepts of information. We then present some informational interpretations of Biblical Logos and compare them with modern theories of information, specifically the general theory of information (GTI). In closing, we point out some aspects of this study that warrant further analysis. Our informational interpretations of Biblical Logos are based on a paper by Henrik Gregersen. [ABSTRACT FROM AUTHOR]

Purpose: The aim of this paper is to provide a review of state-of-the-art literature on information sharing in the context of African perishable agri-food supply chains (AFSCs). In doing so, the authors hope to stimulate further research and advance both theory and practice on African perishable AFSCs, which is a relevant, but under-investigated context. Design/methodology/approach: The authors' systematic literature review covers a period of 21 years (2000–2021). After providing the bibliometric and methodological insights related to this sample of literature, the authors provide a detailed analysis and discussion of the key aspects of information sharing in African perishable AFSCs, based on a review framework grounded in the information sharing literature. Findings: The authors' review revealed that information sharing in African AFSCs is still in its nascent stage. Findings are based on four themes of (1) why share information (mainly to gain market access), (2) what information is shared (price and market information) (3) how it is shared (still traditional communication, with limited adoption of digital technologies?) and (4) antecedents, drivers and barriers (technology adoption and socio-economic background of Africans). Research limitations/implications: This paper outlines a research agenda for advancing the theory on information sharing in AFSCs. Furthermore, the review highlights the importance of context, supply chain structure, relationships, product characteristics and culture in studying AFSCs. Originality/value: A review on information sharing in African perishable AFSCs does not appear to exist in operations and supply chain management (O&SCM) and agribusiness journals. [ABSTRACT FROM AUTHOR]

Algebraic Side‐Channel Attacks (ASCAs), first proposed by Renauld and Standaert in 2009, are a potent cryptanalysis method against block ciphers. In this paper, the authors initially utilize ASCAs to analyze the security of the Trivium stream cipher, given its concise algebraic structure. Considering its efficiency in both hardware and software implementations, the authors deploy ASCAs to target Trivium implemented both in application specific integrated circuit (ASIC) under the Hamming Distance Leakage Model (HDLM) (noted as CASE 1) and in microcontrollers of various buses (i.e. some common 8‐bit, 16‐bit, and 32‐bit architectures, noted as CASE 2, CASE 3, and CASE 4, respectively) under the Hamming Weight Leakage Model (HWLM). Here, the authors' attacks are conducted on power‐simulated targets and not on real devices. For a single power consumption trace without measurement errors, this paper presents experimental results using MiniSat 2.0. Unfortunately, the authors were unable to break the ASIC implementation of Trivium under HDLM (CASE 1) with a time complexity of 2109 s or so, which is worse than the exhaustive key attack. For CASEs 2 to 4, the authors can find the complete 288‐bit state of Trivium within a reasonable timeframe. Specially, the success rate can reach 100% with an average solving time of less than 1 s when only measuring the leakages of the first eight consecutive rounds for CASE 2. Furthermore, the authors can still successfully recover the internal state even when obtaining leakages of the first 41 rounds with a random loss rate. In fact, it can tolerate a 74% random loss rate for the first 223 rounds. With regard to the potential errors in the measurements, the authors mitigate them using Tolerant ASCA (TASCA). Similarly, CASE 1 cannot be compromised even in error‐free situations, while the authors can still successfully recover the internal state of CASEs 2 to 4 from a single power trace, even with a high error rate, including 100% incorrect measurements. Surprisingly, for CASEs 2 to 4, the authors can recover the internal state with a 100% success rate, regardless of the error rate. As a result, the security of Trivium will not be enhanced when transitioning from a smaller 8‐bit platform to a larger 32‐bit platform. In the end, the authors will consider some more abstract attack models. The results can provide us with additional insights into the security of Trivium from a different perspective. [ABSTRACT FROM AUTHOR]

The preface to "Geometry and Topology with Applications" highlights the historical significance and evolution of geometry as a research field in mathematics. It discusses the contributions of prominent mathematicians like Euclid, Euler, Gauss, Lobachevsky, Riemann, Hilbert, Poincaré, Thurston, and Gromov to the development of various geometries and topologies. The document also focuses on the work of Thurston and Gromov in geometric topology and geometric group theory, respectively, and their impact on modern mathematics. The Special Issue attracted submissions from researchers worldwide, with 12 papers accepted for publication, including survey papers on discrete groups and topology in the fourth dimension. [Extracted from the article]

This paper examines the relationship between environmental violations and stock price crash risk, using the sample of Chinese A-shared listed firms and a comprehensive range of Chinese listed heavily polluting companies covering 2012 to 2021. We provide strong and robust evidence that environmental violations significantly increase future stock price crash risk for Chinese listed heavily polluting companies. Through the mechanism of internal control, our paper finds that environmental violations lead to the weakening of internal control, thereby exacerbating future stock price crash risk. We further find that the legal environment has a negatively moderating impact on the relationship. Additionally, heterogeneity analysis shows that the influence of environmental violations on stock price crash risk is more prominent when firms are smaller, have non-state ownership, and have a lower level of corporate governance and external monitoring. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this study is twofold: first, to provide a nuanced understanding of the information concept that encapsulates both its universal foundations and context-dependent intricacies, and second, to propose a theoretical framework that integrates these diverse viewpoints, thereby contributing to the theoretical discourse and practical applications in information science/studies (IS). Design/methodology/approach: The aim of this paper is to explore the relationship between universalism and pluralism in connection with semiotics. The paper adopts a conceptual approach based on semiotics to address the long-standing debate over the definition of information in IS. By engaging with two foundational semiotic theories—C.S. Peirce's pragmatic semiotics and Ferdinand de Saussure's structuralist semiotics – the research seeks to bridge the conflicting perspectives of universalism and pluralism within the field. Findings: The findings reveal that Peirce's semiotics, with its universalist core, views information as dynamic and shaped by context, while Saussure's approach, which is rooted in linguistics, emphasizes the relational aspects of sign systems and connects them to pluralism. This comparative analysis facilitates a broad understanding of information that transcends the limitations of singular perspectives. In conclusion, the paper seeks to demonstrate that the integration of universalist and pluralist perspectives through semiotics offers a more holistic approach to the study of information in IS. It contributes to the field by suggesting that the tension between these perspectives is not only reconcilable but also essential for a richer and more complete understanding of information. This theoretical foundation paves the way for future research and practical advancements in IS and advocates for approaches that recognize the complexity and multifaceted nature of information. Research limitations/implications: The study engages with theoretical frameworks, predominantly semiotic theories by C.S. Peirce and Ferdinand de Saussure. While this provides a deep conceptual understanding, the lack of empirical data may limit the practical applicability of the findings. The research focuses on two semiotic perspectives, which, although foundational, do not represent the entirety of semiotics or information theory. Other significant semiotic theories and perspectives could provide alternative or complementary insights. The concepts of universalism and pluralism in information science are highly intricate and multifaceted. This study's approach to synthesizing these concepts, while comprehensive, might oversimplify some aspects of these complex paradigms. Originality/value: The paper's originality lies in its unique integration of semiotic theories into the discourse of IS. This integration offers a novel perspective on understanding the concept of information, bridging the gap between universalist and pluralist approaches. The study presents a new conceptual framework for understanding information that combines elements of universalism (consistent, objective understanding of information) and pluralism (context-dependent, subjective interpretations). This dual approach is relatively unexplored in IS literature, providing fresh insights into the complex nature of information. [ABSTRACT FROM AUTHOR]

Throughout, the effects of related-party transactions (RPTs) have been a hot topic in financial markets and corporate governance research. This paper analyzes the theoretical foundation of the effects of RPTs and constructs a new indicator, the quasi-profit margin, to assess the effects of RPTs by studying their impact on the quasi-profit margin. Based on the information asymmetry between transaction parties and the information screening theory, the paper proposes the buying inferior to selling theory, systematically explaining the impact of transaction direction on the effects of RPTs. Subsequently, using panel data from Chinese A-share listed companies from 2016 to 2021, the paper constructs fixed-effects models and conducts empirical studies from both exogenous and endogenous perspectives, employing estimation methods such as high dimensional fixed effects method, two-stage least squares method, and three-stage least squares method. The research indicates that RPTs of Chinese A-share listed companies generally exhibit a tunneling effect, and the transaction direction significantly affects the effects of RPTs. The higher the proportion of RPTs conducted as sellers to the total RPTs, the smaller the overall tunneling effect of the RPTs. This study has implications for reducing the tunneling risk of RPTs and improving corporate governance for listed companies, as well as providing some references for financial regulatory authorities to identify and rectify illegal RPTs. [ABSTRACT FROM AUTHOR]

This paper bridges contemporary neuroscience theories and Hegelian philosophy, centering on Karl Friston's Free Energy Principle (FEP). Neuroscience models like the Bayesian brain hypothesis and predictive coding depict the brain as a predictive machine, echoing Hermann von Helmholtz's concept of unconscious inference, where perception is shaped by prior knowledge. The FEP, rooted in information theory and statistical physics, suggests organisms minimize sensory surprise through unconscious and active inference, providing a model for behavior and explaining the purposiveness of biological systems. Some scholars assert that Georg W. F. Hegel's view of living beings in his Philosophy of Nature aligns with the FEP, portraying them as purposive and enactive systems. This paper extends this idea, proposing that Hegel's 'System of Science' in the Phenomenology of Spirit functions as a free energy-minimizing system. It discusses predictive coding and the FEP, establishing criteria for a system that minimizes free energy, and applies these criteria to Hegel's work. The paper argues that the dialectical narrative in the Phenomenology operates as a reflective system driven to minimize logical or conceptual free energy, ultimately advancing the spirit towards absolute spirit. This Hegelian predictive model generates expectations essential for dialectical progression. [ABSTRACT FROM AUTHOR]

Ontologies are used to semantically enrich different types of information systems (IS), ensure a reasoning on their content and integrate heterogeneous IS at the semantical level. On the other hand, fuzzy theory is employed in IS for handling the uncertainty and fuzziness of their attributes, resulting in a fully fuzzy IS. As such, ontology- and fuzzy-based IS (i.e. ontology and fuzzy IS) are being developed. So, in this paper, we present a bibliometric analysis of the ontology and fuzzy IS concept to grasp its main ideas, and to increase its body of knowledge by providing a concept map for ontology and fuzzy IS. The main results obtained show that by adding ontologies and fuzzy theory to traditional ISs, they evolve into intelligent ISs capable of managing fuzzy and semantically rich (ontological) information and ensuring knowledge recognition in various fields of application. This bibliometric analysis would enable practitioners and researchers gain a comprehensive understanding of the ontology and fuzzy IS concept that they can eventually adopt for development of intelligent IS in their work. [ABSTRACT FROM AUTHOR]

Purpose: In the actual livestreaming background, to obtain more income, some broadcasters will transform their original single role orientation into mixed one. This research study aims to conduct an empirical study on the influence of the broadcasters' role orientation transformation on the viewers' tipping behavior. Design/methodology/approach: The authors collect data from Kuai, a leading online live streaming service provider in China. The dataset includes 175,701 live streaming data from 971 broadcasters in 7 months. To avoid unobservable factors, the authors adopt two difference-in-differences (DID) models to estimate the effect of two kinds of broadcaster's role orientation transformation on the broadcaster's direct income separately. And the authors use the Heckman-type correction to solve broadcasters' self-selected problem. Findings: The authors evaluated that there is a U-shape relationship between the broadcasters' role orientation transformation and their direct income. The broadcasters' direct income experienced a sharp decline for a short period of time after transformation and followed by a rise after a period of adaptation. And for broadcasters with different genders and amounts of fans, the influence degree of role orientation transformation is various. Originality/value: This paper provides a fresh usage of the regulatory engagement theory in the brand new information communication technology. And it also explores the boundary effect of the participating object's self-factors in the regulatory engagement theory. Besides, this paper expands the research of livestreaming into natural background. Such results also provide operable suggestions for the livestream platform, the broadcaster himself and the enterprises who want to employ some broadcasters to recommend their products. [ABSTRACT FROM AUTHOR]

Informed trading, driven by information asymmetry and market imperfections, varies in presence across markets. This form of trading not only distorts market transaction prices and hinders resource allocation but also initiates adverse selection transactions, increasing liquidity risks and potentially precipitating market crashes, thereby impeding the market's healthy development. Utilizing information asymmetry theory and principal-agent theory, this paper analyzes data from A-share listed companies from 2011 to 2022. Employing a fixed-effect model, it empirically examines the influence of enterprise digital transformation on the likelihood of informed trading. The findings demonstrate that enterprise digital transformation markedly reduces the likelihood of informed trading. Further analysis of heterogeneity indicates that, compared to state-owned, non-high-tech enterprises and enterprises in the western region, the inhibitory effect on informed trading is more pronounced in non-state-owned, high-tech enterprises and enterprises in the eastern and central regions. Additionally, the chain mediation effect underscores that digital transformation weakens information asymmetry and strengthens internal controls, thereby reducing informed trading. Finally, employing a dynamic panel threshold model we find that digital transformation can only significantly inhibit the informed transactions when enterprises have reached a certain level of technological and asset accumulation. [ABSTRACT FROM AUTHOR]

Aiming at the diversity requirements of cognitive radar monitoring tasks, a joint optimization design criterion that comprehensively considers the mutual information (MI) and signal-to-interference-to-noise ratio (SINR) between the target and the echo is proposed. In view of the challenges brought by the traditional water-filling algorithm, this paper further studies how to effectively solve the new optimization criteria to improve the overall performance of the system. Specifically, this paper proposes a PCMA-ES algorithm that combines an adaptive penalty function with the Covariance Matrix Adaptive Evolutionary Strategy (CMA-ES) algorithm. The penalty function aims to prioritize feasible solutions by assigning them the highest fitness. For infeasible solutions with lower constraint violations, the fitness is slightly lower, allowing for better utilization of information from infeasible solutions. The simulation results show that the PCMA-ES algorithm has lower time complexity or better performance than the traditional water-filling algorithm, and can solve more complex transmission waveforms. In addition, the waveform designed with a joint optimization criterion outperforms that based on a single optimization criterion. The radar detection focus can be adjusted to meet the specific requirements of diverse detection tasks. [ABSTRACT FROM AUTHOR]

Simple Summary: The evolutionary process can be seen as a process of acquisition, storage, and updating of information by a population about the environment in which it lives. In this paper, I propose a model that starts with the distribution of mating that occurs according to mutual mating fitness and ends with the distribution of viable adult genotypes obtained after this mating. The result of the evolutionary dynamics associated with each stage of the model can be described in terms of information. This informational description facilitates the connection between cause and effect, as well as the development of statistics to test the null model of zero information, i.e., random mating and/or no effect of natural selection. Incorporating the informational perspective into the mathematical formalism of population genetics/genomics contributes to clarifying, expanding, and deepening the mathematical description of evolutionary theory. Population genetics describes evolutionary processes, focusing on the variation within and between species and the forces shaping this diversity. Evolution reflects information accumulated in genomes, enhancing organisms' adaptation to their environment. In this paper, I propose a model that begins with the distribution of mating based on mutual fitness and progresses to viable adult genotype distribution. At each stage, the changes result in different measures of information. The evolutionary dynamics at each stage of the model correspond to certain aspects of interest, such as the type of mating, the distribution of genotypes in regard to mating, and the distribution of genotypes and haplotypes in the next generation. Changes to these distributions are caused by variations in fitness and result in Jeffrey's divergence values other than zero. As an example, a model of hybrid sterility is developed of a biallelic locus, comparing the information indices associated with each stage of the evolutionary process. In conclusion, the informational perspective seems to facilitate the connection between cause and effect and allows the development of statistical tests to perform hypothesis testing against zero-information null models (random mating, no selection, etc.). The informational perspective could contribute to clarify, deepen, and expand the mathematical foundations of evolutionary theory. [ABSTRACT FROM AUTHOR]

Negation provides a novel perspective for the representation of information. However, current research seldom addresses the issue of negation within the random permutation set theory. Based on the concept of belief reassignment, this paper proposes a method for obtaining the negation of permutation mass function in the of random set theory. The convergence of proposed negation is verified, the trends of uncertainty and dissimilarity after each negation operation are investigated. Furthermore, this paper introduces a negation-based uncertainty measure, and designs a multi-source information fusion approach based on the proposed measure. Numerical examples are used to verify the rationality of proposed method. [ABSTRACT FROM AUTHOR]

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This paper uses the theory of information imperfections to examine the relationship between income inequality and financial development in a dual financial system comprising traditional finance and microfinance. It uses panel data for 97 developing countries over a period characterized by declining income inequality from 2000 to 2017. Traditional finance is measured through domestic credit relative to GDP and the IMF Financial Development Index. Microfinance is measured through the size of microfinance gross loan portfolio relative to GDP and the number of active microfinance borrowers to the total population. Controlling for various measures of macroeconomic and socioeconomic variables and using different econometric specifications including a random-effects linear regression model with endogenous sample selection, the results show that microfinance consistently predicts a lower income inequality measured by the Gini coefficient, while traditional finance shows no impact. The findings of this paper draw clear policy implications regarding the role of microfinance as a tool for income equality in developing countries. [ABSTRACT FROM AUTHOR]

With high analytical capability, Artificial Intelligence (AI) provides insights suitable to the context that add value to decision making and finds its applications in different sectors. Researchers envisage sustainable agriculture for meeting rising food demand of growing population with the use of AI. Using an algorithmic approach, this paper identifies key determinants influencing adoption of AI in agriculture through a systematic review of literature and a framework is proposed based on well-known theories of Information Systems. The identified factors were classified into five categories – individual characteristics, environmental factors, structural factors, technology factors and demographic factors. The authors indicate need for empowering facilitators for increasing adoption rate and highlight the importance of building trust among farmers for using new applications. The findings of this paper will enrich the understanding of researchers, AI solution developers and service-providers to customise the solution and devise communication strategies for increasing reachability to improve its adoption. [ABSTRACT FROM AUTHOR]

This paper proposes a new entropy-sensitive based data coding algorithm for the secure communication of image information between transceiver systems. The proposed algorithm utilizes chaos theory and the image information content of the reference image to create sensitivity on the decoding side for a high level of secrecy. It successfully recovers secret images at the receiver's side using secret code series derived from both the secret and reference images, instead of direct transmission of secret image. The image information can be retrieved only through the same reference image, the same system parameters and identical code series using the proper decoding technique at the receiver. Quantitative results indicate that the average coding time for 128x128 images is approximately 0.27 seconds, while the extraction time averages 0.19 seconds, yielding impressive rates of 0.487 Mbps and 0.677 Mbps, respectively. Moreover, according to qualitative results, even a single-bit change in the reference image leads to a complete inability to decode the secret image, highlighting the robustness and security of the algorithm. Experimental results on various images show that the proposed algorithm is reliable, fast and effective in securing confidential image information. [ABSTRACT FROM AUTHOR]

The Gaussian signaling strategy with power control for the Gaussian Z-interference channel with weak interference is reviewed in this paper. In particular, we study the various communication strategies that may arise at various points of the capacity region and identify the locations of the phase transitions between the various strategies. The Gaussian Z-interference channel with weak interference is known to have two critical points in its capacity region, where the slope of the region shows a sudden change. They occur at the points of the unconditional maximum rate for one of the users and the maximum rate that can be accommodated by the other user. In this paper, we discuss additional critical points (locations of phase transitions) in the achievable region of this channel. These turn out to be second-order phase transitions, i.e., we do not observe a discontinuous slope in the achievable rate region, but there is a discontinuity in the second derivative of the rate contour of the achievable region. This review paper is mainly based on some of our ITA (Information Theory and Applications Workshop, UCSD, San Diego, CA, USA) papers since 2011. [ABSTRACT FROM AUTHOR]

The famous hypercontractive estimate discovered independently by Gross, Bonami and Beckner has had a great impact on combinatorics and theoretical computer science since it was first used in this setting in a seminal paper by Kahn, Kalai and Linial. The usual proofs of this inequality begin with two-point space, where some elementary calculus is used and then generalised immediately by introducing another dimension using submultiplicativity (Minkowski's integral inequality). In this paper, we prove this inequality using information theory. We compare the entropy of a pair of correlated vectors in { 0 , 1 } n to their separate entropies, analysing them bit by bit (not as a figure of speech, but as the bits are revealed) using the chain rule of entropy. [ABSTRACT FROM AUTHOR]

Assuming the underlying statistical distribution of data is critical in information theory, as it impacts the accuracy and efficiency of communication and the definition of entropy. The real-world data are widely assumed to follow the normal distribution. To better comprehend the skewness of the data, many models more flexible than the normal distribution have been proposed, such as the generalized alpha skew-t (GAST) distribution. This paper studies some properties of the GAST distribution, including the calculation of the moments, and the relationship between the number of peaks and the GAST parameters with some proofs. For complex probability distributions, representative points (RPs) are useful due to the convenience of manipulation, computation and analysis. The relative entropy of two probability distributions could have been a good criterion for the purpose of generating RPs of a specific distribution but is not popularly used due to computational complexity. Hence, this paper only provides three ways to obtain RPs of the GAST distribution, Monte Carlo (MC), quasi-Monte Carlo (QMC), and mean square error (MSE). The three types of RPs are utilized in estimating moments and densities of the GAST distribution with known and unknown parameters. The MSE representative points perform the best among all case studies. For unknown parameter cases, a revised maximum likelihood estimation (MLE) method of parameter estimation is compared with the plain MLE method. It indicates that the revised MLE method is suitable for the GAST distribution having a unimodal or unobvious bimodal pattern. This paper includes two real-data applications in which the GAST model appears adaptable to various types of data. [ABSTRACT FROM AUTHOR]

Motivated by recent investigations relating the sharpness of the Jensen inequality, this paper concerns with the sharpness of the converse of the Jensen inequality. For this reason, the Jensen inequality has become one of the most discussed developmental inequalities in the current literature on mathematical inequalities. In this Special Issue, we present new results related to classical inequalities, such as the Jensen inequality, Jensen-Steffensen inequality, Jessen inequality, Grüss inequality, Chebyshev inequality, etc. [Extracted from the article]

With the vigorous development of data mining field, more and more algorithms have been proposed or improved. How to quickly select a data mining algorithm that is suitable for data sets in medical field is a challenge for some medical workers. The purpose of this paper is to study the comparative characteristics of the general medical data set and the general data sets in other fields, and find the applicability rules of the data mining algorithm suitable for the characteristics of the current research data set. The study quantified characteristics of the research data set with 26 indicators, including simple indicators, statistical indicators and information theory indicators. Eight machine learning algorithms with high maturity, low user involvement and strong family representation were selected as the base algorithms. The algorithm performances were evaluated by three aspects: prediction accuracy, running speed and memory consumption. By constructing decision tree and stepwise regression model to learn the above metadata, the algorithm applicability knowledge of medical data set is obtained. Through cross-verification, the accuracy of all the algorithm applicability prediction models is above 75%, which proves the validity and feasibility of the applicability knowledge. [ABSTRACT FROM AUTHOR]

The digital transfer of the mind to a computer system (i.e., mind uploading) requires representing the mind as a finite sequence of bits (1s and 0s). The classic "stored-program computer" paradigm, in turn, implies the equivalence between program and data, so that the sequence of bits themselves can be interpreted as a program, which will be algorithmically executed in the receiving device. Now, according to a previous proof, on which this paper is based, a computational or algorithmic machine, however complex, cannot be free (in the sense of 'self-determined'). Consequently, a finite sequence of bits cannot adequately represent a free mind and, therefore, a free mind cannot be digitally transferred, quod erat demonstrandum. The impossibility of making this transfer, as demonstrated here, should be a concern especially for those who wish to achieve it. Since we intend this to be a rigorous demonstration, we must give precise definitions and conditions of validity. The most important part of the paper is devoted to explaining the meaning and reasonableness of these definitions and conditions (for example that being truly free means being self-determined). Special attention is paid, also, to the philosophical implications of the demonstration. Finally, this thesis is distinguished from other closely related issues (such as other possible technological difficulties to "discretize" the mind; or, whether it is possible to transfer the mind from one material support to another one in a non-digital way). [ABSTRACT FROM AUTHOR]

Real-world adversarial patches were shown to be successful in compromising state-of-the-art models in various computer vision applications. Most existing defenses rely on analyzing input or feature level gradients to detect the patch. However, these methods have been compromised by recent GAN-based attacks that generate naturalistic patches. In this paper, we propose a new perspective to defend against adversarial patches based on the entropy carried by the input, rather than on its saliency. We present Jedi, a new defense against adversarial patches that tackles the patch localization problem from an information theory perspective; leveraging the high entropy of adversarial patches to identify potential patch zones, and using an autoencoder to complete patch regions from high entropy kernels. Jedi achieves high-precision adversarial patch localization and removal, detecting on average 90% of adversarial patches across different benchmarks, and recovering up to 94% of successful patch attacks. Since Jedi relies on an input entropy analysis, it is model-agnostic, and can be applied to off-the-shelf models without changes to the training or inference of the models. Moreover, we propose a comprehensive qualitative analysis that investigates the cases where Jedi fails, comparatively with related methods. Interestingly, we find a significant core failure cases among the different defenses share one common property: high entropy. We think that this work offers a new perspective to understand the adversarial effect under physical-world settings. We also leverage these findings to enhance Jedi's handling of entropy outliers by introducing Adaptive Jedi, which boosts performance by up to 9% in challenging images., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)