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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]

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]

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]

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]

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]

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]

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]

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]

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]

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]

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]

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]

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]

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]

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]

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]

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In this paper, we use the generalized version of convex functions, known as strongly convex functions, to derive improvements to the Jensen–Mercer inequality. We achieve these improvements through the newly discovered characterizations of strongly convex functions, along with some previously known results about strongly convex functions. We are also focused on important applications of the derived results in information theory, deducing estimates for χ -divergence, Kullback–Leibler divergence, Hellinger distance, Bhattacharya distance, Jeffreys distance, and Jensen–Shannon divergence. Additionally, we prove some applications to Mercer-type power means at the end. [ABSTRACT FROM AUTHOR]

Influence maximization is a fundamental problem in social network analysis. This problem refers to the identification of a set of influential users as initial spreaders to maximize the spread of a message in a network. When such a message is spread, some users may be influenced by it. A common assumption of existing work is that the impact of a message is essentially binary: A user is either influenced (activated) or not influenced (non-activated). However, how strongly a user is influenced by a message may play an important role in this user's attempt to influence subsequent users and spread the message further; existing methods may fail to model accurately the spreading process and identify influential users. In this article, we propose a novel approach to model a social network as a fuzzy graph where a fuzzy variable is used to represent the extent to which a user is influenced by a message (user's activation level). By extending a diffusion model to simulate the spreading process in such a fuzzy graph, we conceptually formulate the fuzzy influence maximization problem for which three methods are proposed to identify influential users. Experimental results demonstrate the accuracy of the proposed methods in determining influential users in social networks. [ABSTRACT FROM AUTHOR]

Selecting appropriate Global Climate Models (GCMs) presents a significant challenge for accurate climate projections. To address this, a novel framework based on information theory based minimum redundancy and maximum relevancy (MRMR) method identifies top-performing GCMs across the entire study region using multicriteria decision analysis methodology. A subset of the ten best-performing models out of twenty-two GCMs is chosen for multi-model ensemble analysis. Five MME methods are selected to assess the ensemble performance of the ten selected GCMs, categorized into simple, regression-based, geometric-based, and machine learning ensembles. This study evaluates the effectiveness of the MME method based on a comprehensive index called the extended distance between indices of simulation and observation. An Adaptive Multimodel Standardized Drought Index (AMSDI) has been developed based on the optimal MME method. For the application of the framework and the proposed index, historical precipitation data from 1950 to 2014 were utilized from 28 grid points in the Punjab province of Pakistan as the reference dataset. Additionally, simulations from 22 models of the Coupled Model Intercomparison Project phase 6, both past and future, were employed for the estimation procedure. In AMSDI indicator, we used improved multimodel ensemble of precipitation for future drought characterization under various future scenarios. Outcome associated with this research show that AMSDI effectively have ability to effectively identifiy extreme drought events for all three future scenarios. In conclusion, the AMSDI method is shown to be effective and flexible, improving accuracy in monitoring droughts. [ABSTRACT FROM AUTHOR]

Self-organization theory is an interdisciplinary scientific direction to improve the quality of acquired knowledge and skills. The article aims to study the development of interdisciplinary research activities among students based on self-organization theory. The study was conducted using an integrated approach, methods of analysis and synthesis, modeling, pedagogical experiments, and mathematical statistics. An elective course, “synergetic-interdisciplinary scientific theory”, and a model for preparing students for interdisciplinary research activities were developed and implemented. The knowledge and skills of 272 physics students in the Republic of Kazakhstan universities in control and experimental groups improved after the training. The knowledge highest level is observed among the experimental group students while studying the natural experiments’ theoretical aspects necessary for researching the natural objects’ sustainability. Experimental group students demonstrated better results in completing the task based on the acquired knowledge and skills. They rated the education quality significantly better on a short educational assessment scale. The results-developed course and the students’ preparing models for interdisciplinary research can be used in advanced courses for different specialties educators. Research perspectives could potentially contribute to the development of integrated subjects and interdisciplinary programs in natural sciences and humanities. [ABSTRACT FROM AUTHOR]

Context: The electron localization is a concept that allows scientists to better understand the physical and chemical properties of electronic systems. It is associated with the propensity of electron pairs with opposite spins to accumulate as well as with their response to external perturbations. This paper contains a detailed description of the design and implementation of the program KLD, which was primarily developed in our research group to elucidate electron localization in molecular systems by evaluating the information content of electron-pair density functions. KLD employs two information-based functions as a real space measure of the Fermi and Coulomb holes for same-spin electrons and shows a better resolution as compared to other methods (i.e., ELF). Information about the acceleration of the code is also included in the present work, being noticeable the reduction of wall-time calculation and the error calculation between versions. Methods: KLD was designed to be easy to use, extend, and maintain; thus, many principles of modern software development, extensive testing, and package management were adopted. The latest version of the KLD program was created utilizing the Compute Unified Device Architecture (CUDA) version, which allows it to use the computational capacity of NVIDIA Graphics Processing Units (GPUs) for processing purposes. The electron-pair conditional density was calculated from the canonical molecular orbitals obtained at the HF/6-31G(2df,p) level, or alternatively the natural orbitals in the case of explicit correlated wavefunctions computed at the MP2/6-31G(2df,p)//HF/6-31G(2df,p) level. [ABSTRACT FROM AUTHOR]

To understand the connection among the brain and facial muscles, we decoded the correlation of facial muscles and brain activities. We applied information theory (Shannon entropy) to EEG and EMG signals recorded from 12 subjects (7 M, 5 F) during rest and stimulation using three odors (pineapple, banana, vanilla). The results indicated that a larger variation in the molecular complexity of odors causes a larger variation in the information of EMG and EEG signals. Therefore, facial muscles' activity is strongly coupled (r = −0.8399) to the brain's activity. Our analysis method could be further employed to examine the relationship between the brain and other organs by comparing other physiological signals with the brain signals. [ABSTRACT FROM AUTHOR]

The Kullback–Leibler divergence is a measure of the divergence between two probability distributions, often used in statistics and information theory. However, exact expressions for it are not known for multivariate or matrix-variate distributions apart from a few cases. In this paper, exact expressions for the Kullback–Leibler divergence are derived for over twenty multivariate and matrix-variate distributions. The expressions involve various special functions. [ABSTRACT FROM AUTHOR]

Modern information theory pioneered by Shannon provides the mathematical foundation of information transmission and compression. However, the physical (and especially the energetic) nature of the information has been elusive. While the processing of information incurs inevitable energy dissipation, it is possible for communication systems to harness information to perform useful work. In this article, we prove that the thermodynamic cost (that is, the entropy production of the communication system) is at least equal to the information transmitted. Based on this result, a model of a communication heat engine is proposed, which can extract work from the heat bath by utilizing the transmission of information. The communication heat engine integrates the manipulation of both energy and information so that both information and power may be transmitted in parallel. The information transmission rate and the information power of the communication heat engine are derived from a pure thermodynamics argument. We find that the information power of the communication heat engine can be increased by increasing the number of communication channels, but the absolute energy efficiency of the heat engine first increases and then decreases after the number of channels of the system exceeds a threshold. The proposed model and definitions provide a new way to think of a classical communication system from a thermodynamic perspective. [ABSTRACT FROM AUTHOR]

Without proven causal power, consciousness cannot be integrated with physics except as an epiphenomenon, hence the term 'hard problem'. Integrated Information Theory (IIT) side-steps the issue by stating that subjective experience must be identical to informational physical structures whose cause-and-effect power is greater than the sum of their parts. But the focus on spatially oriented structures rather than events in time introduces a deep conceptual flaw throughout its entire structure, including the measure of integrated information, known as Φ (phi). However, the problem can be corrected by incorporating the temporal feature of consciousness responsible for the hard problem, which can ultimately resolve it, namely, that experiencer and experienced are not separated in time but exist simultaneously. Simultaneous causation is not possible in physics, hence the hard problem, and yet it can be proven deductively that consciousness does have causal power because of this phenomenological simultaneity. Experiencing presence makes some facts logically possible that would otherwise be illogical. Bypassing the hard problem has caused much of the criticism that IIT has attracted, but by returning to its roots in complexity theory, it can repurpose its model to measure causal connections that are temporally rather than spatially related. [ABSTRACT FROM AUTHOR]

Methods used in topological data analysis naturally capture higher-order interactions in point cloud data embedded in a metric space. This methodology was recently extended to data living in an information space, by which we mean a space measured with an information theoretical distance. One such setting is a finite collection of discrete probability distributions embedded in the probability simplex measured with the relative entropy (Kullback–Leibler divergence). More generally, one can work with a Bregman divergence parameterized by a different notion of entropy. While theoretical algorithms exist for this setup, there is a paucity of implementations for exploring and comparing geometric-topological properties of various information spaces. The interest of this work is therefore twofold. First, we propose the first robust algorithms and software for geometric and topological data analysis in information space. Perhaps surprisingly, despite working with Bregman divergences, our design reuses robust libraries for the Euclidean case. Second, using the new software, we take the first steps towards understanding the geometric-topological structure of these spaces. In particular, we compare them with the more familiar spaces equipped with the Euclidean and Fisher metrics. [ABSTRACT FROM AUTHOR]

We present contrast information, a novel application of some specific cases of relative entropy, designed to be useful for the cognitive modelling of the sequential perception of continuous signals. We explain the relevance of entropy in the cognitive modelling of sequential phenomena such as music and language. Then, as a first step to demonstrating the utility of constrast information for this purpose, we empirically show that its discrete case correlates well with existing successful cognitive models in the literature. We explain some interesting properties of constrast information. Finally, we propose future work toward a cognitive architecture that uses it. [ABSTRACT FROM AUTHOR]

Recently, research interest in the field of infrastructure attack and defense scenarios has increased. Numerous methods have been proposed for studying strategy interactions that combine complex network theory and game theory. However, the unavoidable effect of constrained strategies in complex situations has not been considered in previous studies. This study introduces a novel approach to analyzing these interactions by including the effects of constrained strategies, a factor often neglected in traditional analyses. First, we introduce the rule of constraints on strategies, which depends on the average distance between selected nodes. As the average distance increases, the probability of choosing the corresponding strategy decreases. Second, we establish an attacker–defender game model with constrained strategies based on the above rule and using information theory to evaluate the uncertainty of these strategies. Finally, we present a method for solving this problem and conduct experiments based on a target network. The results highlight the unique characteristics of the Nash equilibrium when setting constraints, as these constraints influence decision makers' Nash equilibria. When considering the constrained strategies, both the attacker and the defender tend to select strategies with lower average distances. The effect of the constraints on their strategies becomes less apparent as the number of attackable or defendable nodes increases. This research advances the field by introducing a novel framework for examining strategic interactions in infrastructure defense and attack scenarios. By incorporating strategy constraints, our work offers a new perspective on the critical area of infrastructure security. [ABSTRACT FROM AUTHOR]

Equilibrium density fluctuations at the molecular level produce cavities in a liquid and can be analyzed to shed light on the statistics of the number of molecules occupying observation volumes of increasing radius. An information theory approach led to the conclusion that these probabilities should follow a Gaussian distribution. Computer simulations confirmed this prediction across various liquid models if the size of the observation volume is not large. The reversible work required to create a cavity and the chance of finding no molecules in a fixed observation volume are directly correlated. The Gaussian formula for the latter probability is scrutinized to derive the changes in enthalpy and entropy, which arise from the cavity creation. The reversible work of cavity creation has a purely entropic origin as a consequence of the solvent-excluded volume effect produced by the inaccessibility of a region of the configurational space. The consequent structural reorganization leads to a perfect compensation of enthalpy and entropy changes. Such results are coherent with those obtained from Lee in his direct statistical mechanical study. [ABSTRACT FROM AUTHOR]

Despite the importance of digital transformation in improving sustainable innovation performance, the mechanisms and boundary conditions through which digital transformation affects sustainable innovation performance is unclear. Using the lens of information technology‐enabled organizational capabilities, this study develops a moderated mediation model based on new economic growth theory and information asymmetry theory to examine the impact of digital transformation on sustainable innovation performance. Using a sample of Chinese agriculture firms, our findings reveal that digital transformation positively affects sustainable innovation performance through digital technology‐business alignment, and this indirect effect is stronger when environmental uncertainty is lower. Moreover, environmental uncertainty negatively moderates the relationship between digital transformation and digital technology‐business alignment. Our theoretical explanations and empirical evidence provide insights into how digital transformation initiatives can be leveraged to improve sustainable innovation performance, thus making an important contribution to the related literature. [ABSTRACT FROM AUTHOR]

Embodiment has been incorporated in communication studies researching the experiencing self, motivated cognitive information processing, and embodied medium theory. This article highlights another factor—past bodily experiences—as important for understanding the impact of embodiment on communication processes. Expanding schema to a construct spanning multiple levels of the neural hierarchy, we propose embodied schema as a minimal framework to capture the idea that all mental structures are grounded in the body. Based on the function of embodied schemas, we describe an Embodied Schema Information Processing Theory (ESIPT) that includes an embodied dual-process theory, which offers a more coherent account of the automatic cue- or heuristic-based processing mode, and a model that describes the influence of the environment and bodily state on high-level cognitive processing. This article systematically explores the role of past bodily experiences and provides a general account of embodied information processing that can inform a wide range of communication studies. [ABSTRACT FROM AUTHOR]

The causal relationship between vegetation and temperature serves as a driving factor for global warming in the climate system. However, causal relationships are typically characterized by complex facets, particularly within natural systems, necessitating the ongoing development of robust approaches capable of addressing the challenges inherent in causality analysis. Various causality approaches offer distinct perspectives on understanding causal structures, even when experiments are meticulously designed with a specific target. Here, we use the complex vegetation–climate interaction to demonstrate some of the many facets of causality analysis by applying three different causality frameworks including (i) the kernel Granger causality (KGC), a nonlinear extension of the Granger causality (GC), to understand the nonlinearity in the vegetation–climate causal relationship; (ii) the Peter and Clark momentary conditional independence (PCMCI), which combines the Peter and Clark (PC) algorithm with the momentary conditional independence (MCI) approach to distinguish the feedback and coupling signs in vegetation–climate interaction; and (iii) the Liang–Kleeman information flow (L-K IF), a rigorously formulated causality formalism based on the Liang–Kleeman information flow theory, to reveal the causal influence of vegetation on the evolution of temperature variability. The results attempt to capture a fuller understanding of the causal interaction of leaf area index (LAI) on air temperature (T) during 1981–2018, revealing the characteristics and differences in distinct climatic tipping point regions, particularly in terms of nonlinearity, feedback signals, and variability sources. This study demonstrates that realizing a more holistic causal structure of complex problems like the vegetation–climate interaction benefits from the combined use of multiple models that shed light on different aspects of its causal structure, thus revealing novel insights that are missing when we rely on one single approach. This prompts the need to move toward a multimodel causality analysis that could reduce biases and limitations in causal interpretations. [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]

Treated as one of the popular measures in information theory, fuzzy mutual information quantifies the amount of information that one random variable has about another one. Different from standard mutual information, fuzzy mutual information can deal with not only discrete-valued but also real-valued variables. Therefore, fuzzy mutual information has been recently used in evolutionary filter feature selection approaches to measure the correlation between the classes and the features, and the dependencies within a feature set. Typically, this way can be considered as computationally efficient but sometimes it may not contribute to the performance of a classification algorithm. To address this issue, an improved evolutionary wrapper-filter approach which integrates an initialisation scheme and a local search module based on fuzzy mutual information in differential evolution is proposed. According to a number of experiments conducted on several real-world benchmark datasets, the proposed approach does not only significantly improve the computational efficiency of an evolutionary computation technique but also the performance of a classification algorithm. [ABSTRACT FROM AUTHOR]

Ambient Intelligence deployments are very vulnerable to Cyber-Physical attacks. In these attacking strategies, intruders try to manipulate the behavior of the global system by affecting some key elements within the deployment. Typically, attackers inject false information, integrate malicious devices within the deployment, or infect communications among sensor nodes, among other possibilities. To protect Ambient Intelligence deployments against these attacks, complex data analysis algorithms are usually employed in the cloud to remove anomalous information from historical series. However, this approach presents two main problems. First, it requires all Ambient Intelligence systems to be networked and connected to the cloud. But most new applications for Ambient Intelligence are supported by isolated systems. And second, they are computationally heavy and not compatible with new decentralized architectures. Therefore, in this paper we propose a new decentralized security solution, based on a Blockchain ledger, to protect isolated Ambient Intelligence deployments. In this ledger, new sensing data are considered transactions that must be validated by edge managers, which operate a Blockchain network. This validation is based on reputation metrics evaluated by sensor nodes using historical network data and identity parameters. Through information theory, the coherence of all transactions with the behavior of the historical deployment is also analyzed and considered in the validation algorithm. The relevance of edge managers in the Blockchain network is also weighted considering the knowledge they have about the deployment. An experimental validation, supported by simulation tools and scenarios, is also described. Results show that up to 93% of Cyber-Physical attacks are correctly detected and stopped, with a maximum delay of 37 s. [ABSTRACT FROM AUTHOR]

Blind boxes have swiftly emerged as a prominent marketing strategy in tourism. Based on information gap theory, this research aims to explore how to optimize marketing effectiveness through varying levels of information gap. Findings show that a moderate-level information gap triggers stronger impulsive purchase intention than a high-level information gap, with curiosity and anxiety perception mediating the effect. Moreover, consumers with a low-risk propensity are particularly sensitive to the impact of the information gap level. These findings contribute theoretically and provide practical guidance for optimizing the marketing of plane ticket blind boxes. [ABSTRACT FROM AUTHOR]

Increasingly, manufacturing enterprises are compelled to pursue innovative solutions to environmental issues. Addressing such issues requires mindful leadership to support employees and organizations in maintaining awareness during complex situations, which then promotes environmental sustainability. Drawing on social information processing theory, this study theorizes and tests the relationships between mindful leadership, employee moral reflectiveness, environmental passion, and employee green creativity. To test the model, we utilize a two-wave multisource dataset comprising 215 workers from manufacturing firms in China. The findings demonstrate that mindful leadership has a positive relationship with employee green creativity. Additionally, our research reveals that both moral reflectiveness and environmental passion serve as mediators in linking mindful leadership to employee green creativity. Crucially, our empirical analysis suggests a serial mediation model, examining the sequential role of moral reflectiveness and environmental passion in the relationship between mindful leadership and employee green creativity. The findings provide a new viewpoint on employees' eco-friendly behaviors and have practical implications for improving environmental sustainability within organizations. [ABSTRACT FROM AUTHOR]

This study applies the social information processing theory to investigate how perceived high-performance work systems (HPWS) influence bootlegging behavior. Additionally, it explores the potential mediating role of willingness to take risks and creative self-efficacy in the association between perceived HPWS and bootlegging behavior. In addition, this study examines how human resource management (HRM) system strength acts as a cross-level moderator in the connection between perceived HPWS and willingness to take risks and creative self-efficacy. Based on a sample of 399 respondents from 80 firms, the results indicate a positive connection between perceived HPWS and bootlegging behavior. Moreover, the results reveal that willingness to take risks and creative self-efficacy mediate the connection between perceived HPWS and bootlegging behavior. Findings also reveal that cross-level HRM system strength moderates the influence of perceived HPWS on willingness to take risks and creative self-efficacy. The study also highlights its theoretical contributions and practical implications and proposes avenues for future research. [ABSTRACT FROM AUTHOR]

In rational decision-making processes, the information interaction among individual robots is a critical factor influencing system stability. We establish a game-theoretic model based on mutual information to address division of labor decision-making and stability issues arising from differential information interaction among swarm robots. Firstly, a mutual information model is employed to measure the information interaction among robots and analyze its influence on the behavior of individual robots. Secondly, employing the Cournot model and the Stackelberg model, we model the diverse decision-making behaviors of swarm robots influenced by discrepancies in mutual information. The intricate decision dynamics exhibited by the system under the disparity mutual information values during the game process, along with the stability of Nash equilibrium points, are analyzed. Finally, dynamic complexity simulations of the game models are simulated under the disparity mutual information values: (1) When ν1 of the game model varies within a certain range, the Nash equilibrium point loses stability and enters a chaotic state. (2) As I(X;Y) increases, the decision-making pattern of robots transitions gradually from the Cournot game to the Stackelberg game. Concurrently, the sensitivity of swarm robotics systems to changes in decision parameter decreases, reducing the likelihood of the system entering a chaotic state. [ABSTRACT FROM AUTHOR]

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Increasing urbanization is driving rapid growth in residential multi-owned properties (RMOPs) worldwide. Concerns about the quality of these buildings are also growing in many countries. Yet research into the quality of RMOPs (particularly apartments) remains scant and under-theorized. Addressing this knowledge gap, this paper reports the results of research which employed information asymmetry theory to provide new insights into the prevalence and risks of building defects in the Australian RMOP construction market. Using data about defects in 635 RMOPs built in Sydney between 2008 and 2017 and semi-structured interviews with sixty-six experienced practitioners, the results highlight the severity of the defects crisis. The research identified two types of information asymmetries which exacerbate the risk of buying into a defective building for RMOP customers: data fragmentation and data robustness. From a policy and practical perspective, this highlights the challenges faced by consumers in undertaking 'due diligence' and the need for reforms that address these information asymmetries. [ABSTRACT FROM AUTHOR]