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Contribution: This research illuminates information entropy's efficacy as a pivotal educational tool in programming, enabling the precise quantification of algorithmic complexity and student abstraction levels for solving problems. This approach can provide students quantitative, comparative insights into the differences between optimal and student implemented solution, and allowing educators to offer targeted feedback, thereby optimizing the learning and abstraction processes in algorithm design through deliberate practice. Background: Abstraction is considered one of the most important skills in problem solving. Many studies in programming have shown that higher abstraction capability can significantly simplify problems, reduce program complexity and improve efficiency. However, it is difficult to develop criteria to measure the level of abstraction, and there is still a lack of relevant systematic research. Research Questions: 1) How can students' abstraction ability in programming be effectively measured? and 2) How to develop programming education and training methods based on the measurement of abstraction ability? Methodology: Forty-six grade 10 students participated in the experiment, divided into two groups for programming training using information-entropy-based assessment and traditional learning methods. Their level of computational thinking, algorithmic efficiency improvements, and test scores were used to measure performance and to analyze the effectiveness of the training methods. Findings: Through empirical research, this article finds that information-entropy-based assessment can reflect the differences in problem solving among students possessing varying capabilities. Information entropy can be crucial for evaluating and improving students' abstraction performance and algorithm efficiency.

Online discussion research has mainly been conducted using case methods. This article proposes a method for comparative analysis based on network metrics such as information entropy and global network efficiency as more holistic measures characterizing social learning group dynamics. We applied social learning optimization analysis of networks (SLOAN) to a data set consisting of Coursera courses from a range of disciplines. We examined the relationship of discussion forum uses and measures of network efficiency, characterized by the information flow through the network. Discussion forums vary greatly in size and in use. Courses with a greater prevalence of subject-related versus procedural talk differed significantly in seeking but not disseminating behaviors in massive open online course discussion forums. Subject-related talk was related to higher network efficiency and had higher seeking and disseminating scores overall. We discuss the value of SLOAN for social learning and argue for the experimental study of online discussion optimization using a discussion post recommendation system for maximizing social learning.

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.

A study of online search, especially search performance, is vital because people heavily rely on the Internet nowadays. The purposes of this study are thus to examine the gratification needs affecting Internet users' intention to search information online, to explore the antecedents of online search performance, and to investigate online information-seeking behavior according to each search purpose. A survey of Internet users in Thailand is carried out. Data are collected extensively both from students and workers. Four hundred fifty-three questionnaires are collected online. Individuals use the Internet to search for information differently, for academic/ working achievement, problem-solving, and personal information needs. The main gratifications are information seeking and Internet ambiance. Internet search intention is a strong determinant of a user's online search performance and corresponding behavior. The theoretical implication is that researchers could apply the research framework to other contexts such as the specific information search. Information source providers could use the findings to modify online resources and their presentations for users effectively. Teachers/ managers could guide their students/ workers to search effectively. This study combines the U&G and the TPB theories. The novel aspects of gratifications i.e., antitraditional media sentiment and Internet ambiance are also explored.

While limited research on the information behaviors of community college students exists, the theories of Elfreda Chatman have not been used to develop understanding of this population. Community college students are more likely to be people of color and/or lower income than their peers in four-year colleges, making them potentially more likely to experience information poverty. This study explored if and how Chatman's theories explain the information behaviors of students at a highly diverse, urban community college. Thirteen students were interviewed using open-ended, qualitative prompts to describe their information seeking and use as it pertains to their college experiences, specifically advisement, financial aid, and understanding course requirements. Students indicated that they used interpersonal and online resources in some combination for all of their information needs for school and in everyday life. The students did not describe the information poverty theorized in many of Chatman's studies. However, it became clear from their responses that the students' information seeking and use, as well as their understanding of their own information behaviors, support a view of the community college as a small world that privileges information seeking as a normative behavior.

Communication theory can be challenging to teach in an engaging manner. The following original teaching activity suggests a way to incorporate the phenomenon of escape rooms into your theory classroom. Courses: Communication Theory, Introduction to Communication. Objectives: In this activity, students will apply the basic tenets of several communication theories--emotional contagion, cultivation theory, exemplification theory, and politeness theory--in solving a puzzle. Students will also apply theory by examining their own group processes during the activity through the lens of the functional perspective on group decision making.

The study of communication through the framework of established theories is central to communication education, and most undergraduate communication programs include a course dedicated to surveying theoretical perspectives. This activity guides students to review and synthesize theoretical knowledge in a March Madness-style end-of-semester competition. The purpose is threefold. First, students revisit theories covered over the semester, providing a valuable review opportunity. Second, students gain experience evaluating strengths and weaknesses of individual theories in comparison with others. Third, by considering theories in competition, students engage with broader metatheoretical questions in the study of communication. Courses: Communication Theory, Advanced Interpersonal Communication, Organizational Communication Theory. Objectives: The purpose of this end-of-semester activity is to demonstrate (1) students' ability to compare and contrast communication theories, (2) students' competency in evaluating communication theories, and (3) students' proficiency in discussing broader theoretical issues in the study of communication.

The measurement of knowledge contribution in collaborative knowledge building is an important research topic in computer-supported collaborative learning. The information measures of knowledge contribution based on information theory are proposed in this study, which includes two measures: amount of information and information gain. Discourse data collected from a collaborative knowledge building activity were analyzed to validate these measures. The results showed that our information measures can complement the traditional behavioral. With the help of the two measures, community-level variation tendency and individual-level knowledge contribution characteristics could be analyzed in collaborative knowledge building activities. A log function was used to fit the community knowledge variation tendency to measure the convergence of knowledge building. Students were clustered into five types according to their behaviors and contributions in collaborative knowledge building. Both teachers and researchers can benefit from these two information measures by using them in practice.

How do children learn to communicate, and what do they learn? Traditionally, most theories have taken an associative, compositional approach to these questions, supposing children acquire an inventory of form-meaning associations, and procedures for composing / decomposing them; into / from messages in production and comprehension. This paper presents an alternative account of human communication and its acquisition based on the systematic, discriminative approach embodied in psychological and computational models of learning, and formally described by communication theory. It describes how discriminative learning theory offers an alternative perspective on the way that systems of semantic cues are conditioned onto communicative codes, while information theory provides a very different view of the nature of the codes themselves. It shows how the distributional properties of languages satisfy the communicative requirements described in information theory, enabling language learners to align their expectations despite the vastly different levels of experience among language users, and to master communication systems far more abstract than linguistic intuitions traditionally assume. Topics reviewed include morphological development, the acquisition of verb argument structures, and the functions of linguistic systems that have proven to be stumbling blocks for compositional theories: grammatical gender and personal names.

The Internet of Things has received enormous attention. Despite the lucrative potential, this study tries to look beyond the hype to understand how IoT impacts firm performance. Based on the information processing theory, we intend to explore how IoT influences the financial performance of firms. Firms, which realise the urgent need to adopt IoT might encounter a diverse set of challenges, like whether adoption will be profitable, the level of commitment they should devote, and whether to adopt as a first or second-mover. We want to comprehend such adoption challenges on the Indian manufacturing firm's financial performance. This study uses panel data from BSE 500 listed manufacturing firms and conducts multivariate analysis to understand the relationship between the IoT implementation proxies: IoT adoption, first-mover advantage, and firm performance in the light of IoT commitment and experts. The result indicates that IoT implementation has an insignificant effect on manufacturing firm performance. Meanwhile, the consideration of IoT commitment and experts jointly can enable a manufacturing firm to reap significantly positive financial outcomes. In other words, the IoT commitment and expertise positively moderate the relationship. The finding is useful to the decision-makers to address the firm-level adjustment before the mere introduction of IoT. [ABSTRACT FROM AUTHOR]

1 In the setting where participants are asked multiple similar possibly subjective multi-choice questions (e.g., Do you like Panda Express? Y/N; Do you like Chick-fil-A? Y/N), a series of peer prediction mechanisms have been designed to incentivize honest reports and some of them achieve dominantly truthfulness: Truth-telling is a dominant strategy and strictly dominates other "non-permutation strategy" with some mild conditions. However, those mechanisms require the participants to perform an infinite number of tasks. When the participants perform a finite number of tasks, these mechanisms only achieve approximated dominant truthfulness. The existence of a dominantly truthful multi-task peer prediction mechanism that only requires a finite number of tasks remains to be an open question that may have a negative result, even with full prior knowledge. This article answers this open question by proposing a family of mechanisms, VMI-Mechanisms, that are dominantly truthful with a finite number of tasks. A special case of this family, DMI-Mechanism, only requires ≥ 2C tasks where C is the number of choices for each question (C=2 for binary-choice questions). The implementation of these mechanisms does not require any prior knowledge (detail-free) and only requires ≥ 2 participants. To the best of our knowledge, any mechanism of the family is the first dominantly truthful peer prediction mechanism that works for a finite number of tasks. The core of these new mechanisms is a new family of information-monotone information measures: volume mutual information (VMI). VMI is based on a simple geometric information measure design method, the volume method. The volume method measures the informativeness of an object by "counting" the number of objects that are less informative than it. In other words, the more objects that the object of interest dominates, the more informative it is considered to be. Finally, in the setting where agents need to invest efforts to obtain their private signals, we show how to select the mechanism to optimally incentivize efforts among a proper set of VMI-Mechanisms. [ABSTRACT FROM AUTHOR]

The remarkable signal-detection capabilities of the auditory and vestibular systems have been studied for decades. Much of the conceptual framework that arose from this research has suggested that these sensory systems rest on the verge of instability, near a Hopf bifurcation, in order to explain the detection specifications. However, this paradigm contains several unresolved issues. Critical systems are not robust to stochastic fluctuations or imprecise tuning of the system parameters. Further, a system poised at criticality exhibits a phenomenon known in dynamical systems theory as critical slowing down, where the response time diverges as the system approaches the critical point. An alternative description of these sensory systems is based on the notion of chaotic dynamics, where the instabilities inherent to the dynamics produce high temporal acuity and sensitivity to weak signals, even in the presence of noise. This alternative description resolves the issues that arise in the criticality picture. We review the conceptual framework and experimental evidence that supports the use of chaos for signal detection by these systems, and propose future validation experiments.

We introduce an approach for calculating perturbative corrections to the ring-polymer instanton approximation to tunneling splittings (RPI+PC) by computing higher-order terms in the asymptotic expansion in ℏ. The resulting method goes beyond standard instanton theory by using information on the third and fourth derivatives of the potential along the tunneling path to include additional anharmonic effects. This leads to significant improvements both in systems with low barriers and in systems with anharmonic modes. We demonstrate the applicability of RPI+PC to molecular systems by computing the tunneling splitting in full-dimensional malonaldehyde and a deuterated derivative. Comparing to both experiment and recent quantum mechanical benchmark results, we find that our perturbative correction reduces the error from −11% to 2% for hydrogen transfer and performs even better for the deuterated case. This makes our approach more accurate than previous calculations using diffusion Monte Carlo and path-integral molecular dynamics while being more computationally efficient. [ABSTRACT FROM AUTHOR]

Based on Social Information Processing Theory, this paper examines the mechanism of secure-base leadership's influence on employee radical innovation, especially the mediating role of employee boundary-spanning behavior and the moderating role of environmental uncertainty through questionnaire survey method (Study 1) and experimental method (Study 2). The results of the study show that secure-base leadership has a positive influence on employee radical innovation; employee boundary-spanning behavior plays a partial mediating role between secure-base leadership and employee radical innovation; environmental uncertainty moderates the relationship between secure-base leadership and employee boundary-spanning behavior positively; environmental uncertainty moderates the mediating role of employee boundary-spanning behavior between secure-base leadership and employee radical innovation. [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]

Considering biological systems as information processing entities and analyzing their organizational structure via information-theoretic measures has become an established approach in life sciences. We transfer this framework to a field of broad general interest, the human gut microbiome. We use BacArena, a software combining agent-based modelling and flux-balance analysis, to simulate a simplified human intestinal microbiome (SIHUMI). In a first step, we derive information theoretic measures from the simulated abundance data, and, in a second step, relate them to the metabolic processes underlying the abundance data. Our study provides further evidence on the role of active information storage as an indicator of unexpected structural change in the observed system. Besides, we show that information transfer reflects coherent behavior in the microbial community, both as a reaction to environmental changes and as a result of direct effective interaction. In this sense, purely abundance-based information theoretic measures can provide meaningful insight on metabolic interactions within bacterial communities. Furthermore, we shed light on the important however little noticed technical aspect of distinguishing immediate and delayed effects in the interpretation of local information theoretical measures. Author summary: The idea of considering biological systems as information processing systems has recently gained increasing attention in life sciences. Following this approach, one assumes that every agent of a living system stores and transfers information and that those operations reflect biological processes. Consequently, one should be able to gain knowledge on a system's biological processes by measuring its information processing. An established mathematical framework to quantify information processing based on observational data is given by information theory. We explore this idea in the context of bacterial community analysis in the human gut habitat. This complex system is a common subject of ongoing research due to its impact on immunity and health. To keep complexity manageable and allow for controlled intervention, we simulate metabolic interaction of seven key species, representing the essential metabolic potential of the human gut microbiome. However, our approach can likewise be directly applied to in vivo/in vitro data. Estimating information storage and transfer from the resulting abundance data, we first identify a technical artefact, whose disregard can lead to strong misinterpretations of information processing. Second, making use of the metabolic data that the simulation provides, we show that information storage and transfer indeed relate to environmental conditions and species' interactions in a meaningful way. We derive general principals for the interpretation of these measures, that allow us to infer patterns of bacterial interaction without the need to measure chemical processes. In this sense, our approach could provide an alternative way to study impacts of dietary plans or medical interventions on the community level. [ABSTRACT FROM AUTHOR]

In this paper, we study algorithmic questions concerning products of matrices and their consequences for recognition algorithms for polyhedra. The 1- product of matrices S 1 ∈ R m 1 × n 1 , S 2 ∈ R m 2 × n 2 is a matrix in R (m 1 + m 2) × (n 1 n 2) whose columns are the concatenation of each column of S 1 with each column of S 2. The k - product generalizes the 1-product, by taking as input two matrices S 1 , S 2 together with k − 1 special rows of each of those matrices, and outputting a certain composition of S 1 , S 2 . Our first result is a polynomial time algorithm for the following problem: given a matrix S , is S a k -product of some matrices, up to permutation of rows and columns? Our algorithm is based on minimizing a symmetric submodular function that expresses mutual information from information theory. Our study is motivated by a close link between the 1-product of matrices and the Cartesian product of polytopes, and more generally between the k -product of matrices and the glued product of polytopes. These connections rely on the concept of a slack matrix, which gives an algebraic representation of classes of affinely equivalent polytopes. The slack matrix recognition problem is the problem of determining whether a given matrix is a slack matrix. This is an intriguing problem whose complexity is unknown. Our algorithm reduces the problem to instances which cannot be expressed as k -products of smaller matrices. In the second part of the paper, we give a combinatorial interpretation of k -products for two well-known classes of polytopes: 2-level matroid base polytopes and stable set polytopes of perfect graphs. We also show that the slack matrix recognition problem is polynomial-time solvable for such polytopes. Those two classes are special cases of 2-level polytopes, for which we conjecture that the slack matrix recognition problem is polynomial-time solvable. [ABSTRACT FROM AUTHOR]

The social context of the workplace influences attendance decisions. Regardless of personal and job factors, employees may choose to engage in sickness presenteeism behaviour (i.e., working when unwell) because of perceived pressure from the organization. Using Social Information Processing Theory, we introduce the construct of presenteeism pressure to capture this perception that an organization normalizes and expects employees to engage in presenteeism. Through a scale development study of working adults (N = 219), we create and refine the 11‐item Presenteeism Pressure Scale. Next, we provide evidence of convergent and discriminant validity of the scale in an independent sample of working adults (N = 248). We then concurrently examine presenteeism pressure's place in a nomological network of constructs within the presenteeism and broader organizational literature, in another sample (N = 764). Finally, we increase the rigour of our validation efforts by conducting an additional two‐wave study (N = 350) and expanding the nomological network of presenteeism pressure to include relevant work outcomes. Our results position presenteeism pressure as a unique and promising contributor to the understanding of presenteeism behaviours and work behaviours more generally. We conclude with suggestions for integrating presenteeism pressure into existing theory and better‐informed organizational attendance procedures. [ABSTRACT FROM AUTHOR]

The objective in the study of reliability and survival theory is to have precision in the extracted information. The measure of dispersion in the information content has been newly introduced in the information theory, known as varentropy. Studying the variability in the uncertainty measure is found productive for stochastic systems conditional on survival. In this paper, we provide a novel stochastic order based on the notion of residual varentropy and study its properties. Further on the basis of the residual varentropy, novel classes of life distributions are identified and investigated in detail. Moreover, some applications of the proposed order are produced at the end. [ABSTRACT FROM AUTHOR]

AbstractThe search for one-step alternatives to the generalized method of moment (GMM) has identified broad classes of potential estimators such as generalized empirical likelihoods (GEL), empirical cressie-read (ECR), exponentially tilted empirical likelihood (ETEL), and minimum discrepancy (MD) estimators. While empirical likelihood (EL) dominates other ECR estimators in terms of higher-order asymptotics, it lacks robustness to model misspecification. ETEL was shown to combine higher-order efficiency and robustness to misspecification but demands strong moment generating function existence conditions. We show, both theoretically and via simulations, how to achieve the same goal under weaker moment existence conditions within the class of MD estimators. [ABSTRACT FROM AUTHOR]

Background: A variant can be pathogenic or benign with relation to a human disease. Current classification categories from benign to pathogenic reflect a probabilistic summary of the current understanding. A primary metric of clinical utility for multiplexed assays of variant effect (MAVE) is the number of variants that can be reclassified from uncertain significance (VUS). However, a gap in this measure of utility is that it underrepresents the information gained from MAVEs. The aim of this study was to develop an improved quantification metric for MAVE utility. We propose adopting an information content approach that includes data that does not reclassify variants will better reflect true information gain. We adopted an information content approach to evaluate the information gain, in bits, for MAVEs of BRCA1, PTEN, and TP53. Here, one bit represents the amount of information required to completely classify a single variant starting from no information. Results: BRCA1 MAVEs produced a total of 831.2 bits of information, 6.58% of the total missense information in BRCA1 and a 22-fold increase over the information that only contributed to VUS reclassification. PTEN MAVEs produced 2059.6 bits of information which represents 32.8% of the total missense information in PTEN and an 85-fold increase over the information that contributed to VUS reclassification. TP53 MAVEs produced 277.8 bits of information which represents 6.22% of the total missense information in TP53 and a 3.5-fold increase over the information that contributed to VUS reclassification. Conclusions: An information content approach will more accurately portray information gained through MAVE mapping efforts than by counting the number of variants reclassified. This information content approach may also help define the impact of guideline changes that modify the information definitions used to classify groups of variants. [ABSTRACT FROM AUTHOR]

The relation between electroencephalography (EEG) rhythms, brain functions, and behavioral correlates is well-established. Some physiological mechanisms underlying rhythm generation are understood, enabling the replication of brain rhythms in silico. This offers a pathway to explore connections between neural oscillations and specific neuronal circuits, potentially yielding fundamental insights into the functional properties of brain waves. Information theory frameworks, such as Integrated information Decomposition (Φ-ID), relate dynamical regimes with informational properties, providing deeper insights into neuronal dynamic functions. Here, we investigate wave emergence in an excitatory/inhibitory (E/I) balanced network of integrate and fire neurons with short-term synaptic plasticity. This model produces a diverse range of EEG-like rhythms, from low δ waves to high-frequency oscillations. Through Φ-ID, we analyze the network's information dynamics and its relation with different emergent rhythms, elucidating the system's suitability for functions such as robust information transfer, storage, and parallel operation. Furthermore, our study helps to identify regimes that may resemble pathological states due to poor informational properties and high randomness. We found, e.g., that in silicoβ and δ waves are associated with maximum information transfer in inhibitory and excitatory neuron populations, respectively, and that the coexistence of excitatory θ, α, and β waves is associated to information storage. Additionally, we observed that high-frequency oscillations can exhibit either high or poor informational properties, potentially shedding light on ongoing discussions regarding physiological versus pathological high-frequency oscillations. In summary, our study demonstrates that dynamical regimes with similar oscillations may exhibit vastly different information dynamics. Characterizing information dynamics within these regimes serves as a potent tool for gaining insights into the functions of complex neuronal networks. Finally, our findings suggest that the use of information dynamics in both model and experimental data analysis, could help discriminate between oscillations associated with cognitive functions and those linked to neuronal disorders. Author summary: Electroencephalography (EEG) records cortical brain activity and is widely used in neuroscience for identifying cognitive states and diagnosing brain pathologies. However, the relationship between functional brain states and specific rhythms is sometimes unclear. Traditional methods combined with computational models often fail to link dynamical regimes to their possible functions. To address this, we used a computational model that generates in silico EEG-like signals in a neuron population. Instead of only analyzing spectral features, we focused our study on information flow in the neuron population between small groups of inhibitory and excitatory neurons during the emergence of different rhythms. We found that in some regimes, the system exhibits enhanced computational properties, with excitatory neurons maintaining parallel processing capacities, inhibitory neurons showing high robustness, or populations maximizing information transfer. In other regimes, low information flow results in more random behavior. Our work highlights the utility of informational dynamic analysis for understanding the relationship between emerging neuronal waves and functions in in silico neuronal populations, a fact that stimulates extending the present study to neuronal cultures and in vivo EEG time series. [ABSTRACT FROM AUTHOR]

High-throughput chromosome conformation capture (Hi-C) technology captures spatial interactions of DNA sequences into matrices, and software tools are developed to identify topologically associating domains (TADs) from the Hi-C matrices. With structural information theory, SuperTAD adopted a dynamic programming approach to find the TAD hierarchy with minimal structural entropy. However, the algorithm suffers from high time complexity. To accelerate this algorithm, we design and implement an approximation algorithm with a theoretical performance guarantee. We implemented a package, SuperTAD-Fast. Using Hi-C matrices and simulated data, we demonstrated that SuperTAD-Fast achieved great runtime improvement compared with SuperTAD. SuperTAD-Fast shows high consistency and significant enrichment of structural proteins from Hi-C data of human cell lines in comparison with the existing six hierarchical TADs detecting methods. [ABSTRACT FROM AUTHOR]

Despite the adage "hope for the best, plan for the worst," site-level proactive plans produced by construction planning meetings show planning for the best. Planning meetings comprise both proactive and reactive discussions. Although not all the information provided during meetings is useful, limited efforts have been made to measure the amount of useful information embedded in reactive versus proactive discussions. This research aims to determine the appropriate trade-offs between the number of reactive and proactive discussions at each site-planning level. By utilizing information theory and the Chow–Liu tree, the authors demonstrate to what extent and in what order meeting participants should address site-planning level reactivity and proactivity to obtain more useful information effectively through a case study. The results indicate that proactive planning discussions improve percent plan complete (PPC) 20% more than reactive ones in general. However, missing information due to not undertaking reactive planning could put PPC at greater risk (2% more) than proactive planning, implying high uncertainty at the lowest level of site planning. This research contributes to the body of knowledge by innovatively developing an information theory-based approach to quantify the uncertainty in proactive and reactive discussions for construction planning. Using the information theory approach, this research enables project managers to quantify the uncertainty in reactive and proactive planning discussions. By adjusting the framework and parameters, the approach can have general applications in planning meetings. [ABSTRACT FROM AUTHOR]

The problem of quantifying the distance between distributions arises in various fields, including cryptography, information theory, communication networks, machine learning, and data mining. In this article, the analogy with the cumulative Jensen–Shannon divergence, defined in Nguyen and Vreeken (2015), we propose a new divergence measure based on the cumulative distribution function and call it the cumulative α-Jensen–Shannon divergence, denoted by CJS (α) . Properties of CJS (α) are studied in detail, and also two upper bounds for CJS (α) are obtained. The simplified results under the proportional reversed hazard rate model are given. Various illustrative examples are analyzed. [ABSTRACT FROM AUTHOR]

Geographic information systems (GIS) has been used in archaeology for four decades, and colloquially appears to have become a main tool in the geospatial aspects of archaeological practice. In this paper, we examine temporal trends in the use and/or mention of GIS in archaeological publications (books and journal articles), conference presentations, and websites. We gathered data through keyword searches and with formal sampling surveys and conducted both quantitative and qualitative analyses to characterize the changing nature and intensity of GIS use in archaeology over time, and then contextualize these trends with a narrative history of archaeological GIS. We show how archaeological GIS-use has grown from a few early adopters of the 1980s, through a slow initial integration phase in the 1990s, to a set of two major expansions in the 2000s and 2010s. While we find that applied use of GIS has grown to the point where it can be considered ubiquitous—if not completely universal—in the discipline, we also discovered that the major focus in archaeological GIS advancement is methodological rather than theoretical. We identify five roadblocks that we believe have hampered the development of a theory-rich archaeological GIS: (1) deficiencies in the archaeological GIS education model, (2) over-reliance on commercial software, (3) technical/technological barriers, (4) gaps in acceptance of GIS, and (5) the perception of GIS as "just a tool." We offer initial suggestions for ways forward to mitigate the effects of these roadblocks and build a more robust, theoretically sophisticated relationship with GIS in archaeology. [ABSTRACT FROM AUTHOR]

Situation awareness (SA) is a key indicator of operator task performance and behavioral safety in Human-Machine Interaction (HMI), and task difficulty is an important influencing factors in establishing and maintaining SA, especially in safety-critical scenarios such as flying and nuclear. However, the complexity and diversity of multitasking make it difficult to quantify task difficulty, which to a certain extent limits the research on cognitive research. To solve this problem, we proposed an approach to quantifying task difficulty in multitasking and analyzed the effect of task difficulty on different levels of SA through experiments in this paper. First, a task difficulty measure for a single meta-task was developed based on Shannon's information theory, including both discrete and continuous meta-tasks. Next, a three-dimensional attribute model of meta-tasks was proposed to measure the difficulty value added by concurrent tasks based on multiple resource theory. Finally, an experiment was conducted to measure SA at different levels based on SAGAT, and SA under different levels of task difficulty was analyzed. The results showed a strong negative correlation between SA and task difficulty. Specifically, compared to SA at the comprehension and prediction levels, SA at the perceptual level was more easily influenced by task difficulty. This study can provide some reference for the quantify of task difficulty in human factors experiments, the optimization of tasks in HMI, and the selection of operator attention allocation strategies. [ABSTRACT FROM AUTHOR]

Social learning is the updating of beliefs based on observation of others. Such observation can lead to efficient aggregation of information, but also to inaccurate decisions, fragility of mass behaviors, and, in the case of information cascades, to complete blockage of learning. We review the theory of information cascades and social learning and discuss important themes, insights, and applications of this literature as it has developed over the last 30 years. We also highlight open questions and promising directions for further theoretical and empirical exploration. (JEL D71, D82, D83, D91, Z13) [ABSTRACT FROM AUTHOR]

This study examines the information diffusion of "Airbnb discrimination" and related digital activism on Twitter. The model-based inferential network analysis shows a hub-and-spoke network that is loosely connected, indicating that the diffusion network of Airbnb discrimination includes various issues (e.g., antisemitism). The retweet network mirrors the existing offline relationship of users (e.g., those who support opinion leaders of antisemitism), demonstrating a core–periphery structure during the information diffusion process. This study not only empirically tested the retweet relationships but also provided theoretical explanations on how these relationships form through the notion of curative logic and two step information flow theory. The findings of this research challenge the widely accepted assumption about social media that "the more users share, the more people will see." [ABSTRACT FROM AUTHOR]

The research on the interpretation of mathematics as part of the sciences of mind, introduced as 'Geisteswissenschaften' in the German philosophy, reflects the relationship between mathematics and arts, including architecture. This study focuses on the early works of the philosopher Max Bense where he described the contours of an intellectual history of mathematics related to philosophy. He pointed out strong relationships between mathematics and all areas of human creation abilities. In particular, his investigations on the relationship between arts, including architecture, and mathematics in important historical phases reveal that the mathematics of an age is reflected aesthetically in the artistic styles or theories of art. His thesis posits that a high point in mathematics corresponds to a high point in art. Later on, Bense developed an aesthetic theory on the basis of mathematics, semiotics and information theory. [ABSTRACT FROM AUTHOR]

Ensemble learning has gotten a lot of interest because of its capacity to increase predictive accuracy by merging numerous models. However, redundant data and a high level of computing complexity frequently plague ensembles. To choose a subset of models while maintaining the accuracy and diversity of the ensemble, ensemble pruning techniques are used to address these problems. Accuracy and diversity must coexist, even though their goals are conflicting. This is why we formulate the issue of ensemble pruning as a dual-objective maximization problem using the idea from information theory. Then, we propose a Comprehensive Ensemble Pruning Framework (CEPF) based on the dual-objective maximization (DOM) trade-off metric. Extensive evaluation of our framework on the exclusively collected PhysioSense dataset demonstrates the superiority of our method compared to existing pruning techniques. PhysioSense dataset was collected after getting approval from the Institutional Human Ethics Committee (IHEC) of Panimalar Medical College Hospital and Research Institute, Chennai, Tamil Nadu (Protocol No: PMCHRI-IHEC-059). The proposed framework not only preserves or improves ensemble accuracy and diversity but also achieves a significant reduction in actual ensemble size. Furthermore, the proposed method provides valuable insights into the dual-objective trade-off between accuracy and diversity paving the way for further research and advancements in ensemble pruning techniques. [ABSTRACT FROM AUTHOR]

In the realm of machine learning, feature selection emerges as a prevalent data preprocessing technique, playing a crucial role in enhancing model performance across diverse downstream tasks such as fault diagnosis, biological recognition, and object detection. Nevertheless, the challenge of incomplete supervision, stemming from limited labeled data availability, poses a formidable obstacle in acquiring the optimal feature subset for model input. To address the problem that label scarcity may deteriorate the feature evaluation and selection, we introduce a novel semi-supervised feature selection algorithm termed Semi2MNR integrating the principles of Minimum Neighborhood Redundancy and Maximum Neighborhood Relevancy. Firstly, k-nearest neighborhood granulation is leveraged to construct a collection of neighborhood uncertainty measures from the perspective of information theory. Then, the neighborhood mutual information is expressed to assess the feature-to-label relevance based on labeled samples and feature-to-feature redundance based on unlabeled samples. Finally, as the evaluation criterion of min-neighborhood-redundancy and max-neighborhood-relevancy is constrained, a forward sequential searching algorithm is devised to identify the min-redundant and max-relevant features. The empirical findings from our experiments on 12 UCI data sets unequivocally demonstrate the superiority of Semi2MNR in the presence of partially labeled data with varying labeling rates. Comparative analysis against other feature selection algorithms suggests that CART, KNN, and SVM classifiers fed with features selected by Semi2MNR consistently yield optimal accuracies. [ABSTRACT FROM AUTHOR]

GNNs are powerful models based on node representation learning that perform particularly well in many machine learning problems related to graphs. The major obstacle to the deployment of GNNs is mostly a problem of societal acceptability and trustworthiness, properties which require making explicit the internal functioning of such models. Here, we propose to mine activation rules in the hidden layers to understand how the GNNs perceive the world. The problem is not to discover activation rules that are individually highly discriminating for an output of the model. Instead, the challenge is to provide a small set of rules that cover all input graphs. To this end, we introduce the subjective activation pattern domain. We define an effective and principled algorithm to enumerate activations rules in each hidden layer. The proposed approach for quantifying the interest of these rules is rooted in information theory and is able to account for background knowledge on the input graph data. The activation rules can then be redescribed thanks to pattern languages involving interpretable features. We show that the activation rules provide insights on the characteristics used by the GNN to classify the graphs. Especially, this allows to identify the hidden features built by the GNN through its different layers. Also, these rules can subsequently be used for explaining GNN decisions. Experiments on both synthetic and real-life datasets show highly competitive performance, with up to 200 % improvement in fidelity on explaining graph classification over the SOTA methods. [ABSTRACT FROM AUTHOR]

The rise of platforms since the Millennium has drastically reshaped human activities worldwide and transformed our physical world into what is known as platform societies, wherein social and economic interactions are profoundly mediated and defined by the digital infrastructure of platforms. The immense integration of platforms into daily human life prompts inquiry into their capacity to foster social good. This Special Issue presents a compilation of articles that scrutinize the intricate relationship between platforms and their potential to promote social good in the contexts of mainland China and Hong Kong. In both settings, platforms are subject to negotiation and co-creation among technological, commercial, and political logics, although the nuances differ. While social good can embody a wide range of public interests and common goods, this Special Issue specifically explores social good within the domains of promoting good health and well-being, advocating responsible production and consumption, fostering societal equality, advancing decent work and economic growth, and supporting justice and effective institutions. The collection illustrates that platforms may contribute to social good at both macro and micro levels. Platforms have the capacity to provide infrastructure for social and economic activities in the digital realm, driving transformations in business and social relations. Platforms can also offer individuals and collectives access to resources, empowering them to safeguard public interests. Understanding the role of platforms in upholding social good requires consideration of multifaceted forces and interests rooted in the market, state, and civil society. [ABSTRACT FROM AUTHOR]

Purpose: This study examines green absorptive capacity as an important intervening variable that elucidates the relationship between green supply chain management (GSCM) practices (specifically, green purchasing, customer cooperation and investment recovery) and firm performance. Design/methodology/approach: Drawing from the theoretical underpinnings of the natural-resource-based view theory and information processing theory, a research model is developed and tested using data obtained from 368 manufacturing firms in Ghana. Data analysis was conducted using structural equation modeling. Findings: The results indicate that green purchasing, customer cooperation and investment recovery have a direct positive and significant effect on firm performance. Additionally, green purchasing and customer cooperation have a positive and significant effect on green absorptive capacity but investment recovery does not. Further, the results show that the paths from green purchasing and customer cooperation to firm performance are positively mediated by green absorptive capacity. Practical implications: The study reveals to supply chain managers that green absorptive capacity is an important conduit through which firms can achieve enhanced firm performance from GSCM initiatives. Originality/value: This study makes a contribution by integrating the absorptive capacity literature and green management literature and establishes green absorptive capacity as a mechanism through which GSCM practices enhance firm performance. [ABSTRACT FROM AUTHOR]

AbstractPatent information platforms have become crucial for innovators to stay informed about technological advancements. These platforms, distinguished by their function-oriented nature, differ significantly from traditional systems, highlighting the need for research on user behavior impact. This study proposes a research model to investigate how information, system, and interface quality influence continuous use intention, with perceived pleasure and needs satisfaction as mediators. A survey with 468 users was conducted, followed by regression and structural equation modeling for path and mediation analyses. The findings indicate that information and system quality have positive but distinct impacts on perceived pleasure and needs satisfaction, while interface quality affects only perceived pleasure. Needs satisfaction mediates the impact of information and system quality on continuous use intention, whereas perceived pleasure does not. This study introduces a function-oriented scenario into information systems research and identifies the unique impact paths of information and system quality. For these function-oriented platforms, the influence of interface quality on needs satisfaction or perceived pleasure on continuous use intention is insignificant, establishing a new context relevant to pragmatism and push–pull–mooring theories. Practical insights are provided for managers to improve system efficiency and enhance user loyalty. [ABSTRACT FROM AUTHOR]

It is widely believed that play and curiosity are key ingredients as children develop models of the world. There is also an emerging consensus that children are Bayesian learners who combine their structured prior beliefs with estimations of the likelihood of new evidence to infer the most probable model of the world. An influential school of thought within developmental psychology, rational constructivism, combines these two ideas to propose that children learn intuitive theories of how the world works in part by engaging in play activities that allow them to gather new information for testing their theories. There are still, however, at least two pieces missing from rational constructivist theories of development. First, rational constructivism has so far devoted little attention to explaining why children's preferred form of learning, play, feels so fun, enjoyable, and rewarding. Rational constructivism may suggest that children are curious and like to play because reducing uncertainty and learning better theories of the causal workings of the world is enjoyable. What remains unclear, however, is why reducing uncertainty in play is interesting, fun, and joyful, while doing so in other forms of learning can be frustrating or boring. Second, rational constructivism may have overlooked how children, during play, will take control of and manipulate their environment, sometimes in an effort to create ideal niches for surprise‐extraction, sometimes for developing strategies for making the world fit with their predictions. These missing elements from rational constructivism can be provided by understanding the contribution of play to development in terms of predictive processing, an influential framework in cognitive neuroscience that models many of the brain's cognitive functions as processes of model‐based, probabilistic prediction. [ABSTRACT FROM AUTHOR]

Over the last decade, automatic chemical design frameworks for discovering molecules with drug-like properties have significantly progressed. Among them, the variational autoencoder (VAE) is a cutting-edge approach that models the tractable latent space of the molecular space. In particular, the usage of a VAE along with a property estimator has attracted considerable interest because it enables gradient-based optimization of a given molecule. However, although successful results have been achieved experimentally, the theoretical background and prerequisites for the correct operation of this method have not yet been clarified. In view of the above, we theoretically analyze and rigorously reconstruct the entire framework. From the perspective of parameterized distribution and the information theory, we first describe how the previous model overcomes the limitations of the beta VAE in discovering molecules with the desired properties. Furthermore, we describe the prerequisites for training the above model. Next, from the log-likelihood perspective of each term, we reformulate the objectives for exploring latent space to generate drug-like molecules. The distributional constraints are defined in this study, which will break away from the invalid molecular search. We demonstrated that our model could discover a novel chemical compound for targeting BCL-2 family proteins in de novo approach. Through the theoretical analysis and practical implementation, the importance of the aforementioned prerequisites and constraints to operate the model was verified. [ABSTRACT FROM AUTHOR]

Cognitive scientists and neuroscientists typically understand the brain as a complex communication/information-processing system. A limitation of this framework is that it requires cognitive systems to have prior knowledge about their environment to successfully perform some of their basic functions, such as perceiving. It is unclear how the source of such knowledge can be explained from within this framework. Drawing on Dennett (1981), we refer to this as the loans of intelligence problem. Recent advances in machine learning have resulted in the development of a family of algorithms, including the class known as autoencoders, that seem to provide a way for the information-processing framework to avoid this problem: cognitive systems do not require loans of intelligence, but instead acquire the knowledge necessary for perception through a process of unsupervised learning. This paper argues that although autoencoders do avoid the loans of intelligence problem, how they do so should not be understood from within the information-processing framework. Instead, their success should be interpreted as a proof of concept of how neural networks can attune to Gibsonian information. We thus propose that autoencoders belong to a class of algorithms for modeling the brain that have recently been dubbed direct fit algorithms. [ABSTRACT FROM AUTHOR]

AbstractThe asymptotic equipartiton property (AEP) plays a crucial role in information theory, providing a theoretical foundation for understanding and analyzing aspects such as coding, compression, and the reliability of communication systems. In this article, we mainly study the generalized AEP of higher-order non homogeneous Markov information sources by establishing several strong deviation theorems. To achieve this, we first introduce the concepts of generalized sample divergence rate of mth-order non homogeneous Markov information sources. Meanwhile, we give a class of generalized strong deviation theorems for moving average of the functions of m+1 variables for mth-order non homogeneous Markov information sources, and also establish strong deviation theorems and strong limit theorems of the frequencies of occurrence of ordered tuples of states for this Markov information sources. Finally, the generalized AEP for mth-order non homogeneous Markov information sources are establized. Our results also generalize some known results. [ABSTRACT FROM AUTHOR]

We show that a set of thermally weakly coupled geometrically frustrated systems (GFSs), each of which is constraint to reside at negative Boltzmann temperatures, is in equilibrium cooler than its constituents. It may even exhibit positive temperatures at low energies. The challenge for the second law of thermodynamics arising from potential heat flow related to the gradient of temperatures between a GFS and its environment is resolved by considering the energy fluctuations above the ground state. They are comprised in the canonical temperature, derived from information theory. Whereas the gradient of Boltzmann temperatures gives the direction of the stochastic drift of the most probable state of a GFS within its environment, the canonical temperature gradient defines that of heat flow. [ABSTRACT FROM AUTHOR]

Whether individuals engage with or avoid health information and which sources are used is crucial for informed decision-making about mammography. Therefore, we aim to develop a typology of mammography-related information behaviors and explore their determinants, which were derived from the Theory of Motivated Information Management (TMIM). Based on a two-wave online survey of a sample of German women aged 40 to 69 years (N = 1,138), a cluster analysis resulted in five person-centric types of information behavior that cover respondents’ variety of engagement with mammography-related information: The seekers, the scanners, the avoiders, the involved, and the inactive. The types indicated that most individuals possess a high preference for one strategy of information behavior. Looking at the determinants of belonging to a particular type of information behavior, it becomes apparent that the factors of the TMIM have only limited explanatory power. The most relevant factors in distinguishing the types of information behaviors are benefit perceptions, worry, interest, avoidance efficacy, seeking, and avoidance-related outcome expectancies. Scanners and seekers had higher benefit perceptions. Seekers were further motivated by greater worries, while involved ones felt more interested and held stronger expectancies. Avoiders were more anxious and had a higher avoidance efficacy. The study suggests that inactive individuals and avoiders are highly prevalent and relevant target groups for health interventions that support informed decision-making. To facilitate informed decision-making, women should be encouraged to weigh benefits and harms of their options and to address negative emotions, such as anxiety, that may exist or arise during decision-making. [ABSTRACT FROM AUTHOR]

Originally developed as a theory of consciousness, integrated information theory provides a mathematical framework to quantify the causal irreducibility of systems and subsets of units in the system. Specifically, mechanism integrated information quantifies how much of the causal powers of a subset of units in a state, also referred to as a mechanism, cannot be accounted for by its parts. If the causal powers of the mechanism can be fully explained by its parts, it is reducible and its integrated information is zero. Here, we study the upper bound of this measure and how it is achieved. We study mechanisms in isolation, groups of mechanisms, and groups of causal relations among mechanisms. We put forward new theoretical results that show mechanisms that share parts with each other cannot all achieve their maximum. We also introduce techniques to design systems that can maximize the integrated information of a subset of their mechanisms or relations. Our results can potentially be used to exploit the symmetries and constraints to reduce the computations significantly and to compare different connectivity profiles in terms of their maximal achievable integrated information. Author summary: Integrated Information Theory (IIT) offers a theoretical framework to quantify the causal irreducibilty of a system, subsets of the units in a system, and the causal relations among the subsets. For example, mechanism integrated information quantifies how much of the causal powers of a subset of units in a state cannot be accounted for by its parts. Here, we provide theoretical results on the upper bounds for this measure, how it is achieved, and why mechanisms with overlapping parts cannot all be maximally integrated. We also study the upper bounds for integrated information of causal relations among the mechanisms. The ideas introduced here can potentially pave the way to design systems with optimal causal irreducibility and to develop computationally lightweight exact or approximate measures for integrated information. [ABSTRACT FROM AUTHOR]

This paper presents a new method for evaluating vulnerability in several dimensions by combining Catastrophe Theory with Geographic Information Systems (GIS). This research investigates vulnerability in three districts in Selangor, Malaysia: Hulu Langat, Sepang, and Kuala Langat. The study analyzes vulnerability across six dimensions, namely social, economic, physical, institutional, environmental, and cultural. The evaluation utilizes ArcGIS 10.8 and a data-driven Multi-Criteria Decision Analysis (MCDA) methodology to generate geographical vulnerability maps. The methodology utilizes Catastrophe Theory, a mathematical framework proficient in understanding abrupt changes in intricate systems, and GIS, a potent tool for studying and visualizing geographic data. These tools work together to help identify important thresholds and tipping points in systems that are prone to disasters, which improves our understanding of how vulnerability changes over time. The studied area contains a historical record of many disasters, encompassing floods, landslides, storms, and forest fires. The 2021 flood, which was the most severe in the area's history, emphasized the necessity for a comprehensive risk assessment. The study's findings demonstrate the heterogeneity in vulnerability across the six categories, offering crucial insights for the management of disaster risk. The results illustrate the efficacy of this integrated approach in comprehending the multifaceted essence of vulnerability. The created multidimensional vulnerability maps provide valuable information for policymakers, planners, and emergency responders. These maps enable focused interventions and enhance resilience. The study emphasizes the significance of improving institutional capacities, economic robustness, and community readiness in order to minimize the effects of disasters. This research makes a substantial contribution to the field of disaster risk management by offering an innovative methodology for assessing vulnerability. This methodology has wideranging applications in the areas of disaster prevention and mitigation. [ABSTRACT FROM AUTHOR]

Encoding and decoding are the two key steps in information processing. In this work, we study the encoding and decoding capabilities of operational theories in the context of information-storability game, where the task is to freely choose a set of states from which one state is chosen at random and by measuring the state it must be identified; a correct guess results in as many utiles as the number of states in the chosen set and an incorrect guess means a penalty of a fixed number of utiles. We connect the optimal winning strategy of the game to the amount of information that can be stored in a given theory, called the information storability of the theory, and show that one must use so-called nondegradable sets of states and nondegradable measurements whose encoding and decoding properties cannot be reduced. We demonstrate that there are theories where the perfect discrimination strategy is not the optimal one so that the introduced game can be used as an operational test for super information storability. We further develop the concept of information storability by giving new useful conditions for calculating it in specific theories. [ABSTRACT FROM AUTHOR]

Psychopathology is vast and diverse. Across distinct disease states, individuals exhibit symptoms that appear counter to the standard view of rationality (expected utility maximization). We argue that some aspects of psychopathology can be described as resource-rational, reflecting a rational trade-off between reward and cognitive resources. We review work on two theories of this kind: rational inattention, where a capacity limit applies to perceptual channels, and policy compression, where the capacity limit applies to action channels. We show how these theories can parsimoniously explain many forms of psychopathology, including affective, primary psychotic, and neurodevelopmental disorders, as well as many effects of psychoactive medications on these disorders. While there are important disorder-specific differences and the theories are by no means universal, we argue that resource rationality offers a useful new perspective on psychopathology. By emphasizing the role of cognitive resource constraints, this approach offers a more inclusive picture of rationality. Some aspects of psychopathology may reflect rational trade-offs rather than the breakdown of rationality. [ABSTRACT FROM AUTHOR]

Living cells constantly sense and respond to environmental changes. Transcription, the process by which DNA is transcribed into RNA, serves as a critical bridge between external signals and gene expression, ultimately shaping cellular behavior. To unravel the transcription dynamics and the relationship between input signals and gene expression output, various transcription models have been developed. This review explores these common models, their computational frameworks, and the resulting distributions for mRNA number and transcriptional event duration, which offer valuable insights into input-output relationships and underlying response mechanisms. We further analyze how different promoter types, chromatin environments, and network motifs influence these relationships. Finally, we probe how information theory can be applied to systems with near-maximum channel capacity to reveal the dynamic range of t ranscription factor concent rat ions, input-output dynamics, and the link between these factors and gene expression distribution. Through these multifaceted analyses, we identify key regulators of dynamic input-output relationships and gain deeper insights into how genes respond to transcription factor signals. Quantitative studies of input-output relationships hold promises for identifying key regulatoiy factors, predicting changes in gene expression patterns, and designing interventions to manipulate cellular functions and behavior. [ABSTRACT FROM AUTHOR]