Search

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.

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.

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]

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]

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]

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]

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]

Anurans have several characteristics in their acoustic communication that distinguish them. These characteristics are influenced by body-related, environmental and social variables. However, the complexity of anuran repertoire and the effect of such variables on it are still understudied. Dendropsophus minutus is a Neotropical treefrog that has a wide repertoire composed of three notes, grouped in at least 71 unique combinations. To quantify complexity, we used the entropy measurement, since it measures the randomness of a sequence. Therefore, we could test if repertoire complexity in D. minutus is related with body condition, social context, and environmental variables. We recorded vocalisations of 80 males in three sites and found 44 unique note combinations not described before, increasing the species' repertoire to 115 combinations. We observed that the species has near maximum entropy for note frequency, but a low entropy for note sequencies, showing a clear a bias for some note combinations. We found a positive relationship between repertoire complexity and air temperature. Thus, based on our results, the complexity of this species' repertoire is an additional trait influenced by climate, and should be considered in future studies. [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]

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 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]

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]

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]

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]

Diverse explanations or theories of consciousness are arrayed on a roughly physicalist-to-nonphysicalist landscape of essences and mechanisms. Categories: Materialism Theories (philosophical, neurobiological, electromagnetic field, computational and informational, homeostatic and affective, embodied and enactive, relational, representational, language, phylogenetic evolution); Non-Reductive Physicalism; Quantum Theories; Integrated Information Theory; Panpsychisms; Monisms; Dualisms; Idealisms; Anomalous and Altered States Theories; Challenge Theories. There are many subcategories, especially for Materialism Theories. Each explanation is self-described by its adherents, critique is minimal and only for clarification, and there is no attempt to adjudicate among theories. The implications of consciousness explanations or theories are assessed with respect to four questions: meaning/purpose/value (if any); AI consciousness; virtual immortality; and survival beyond death. A Landscape of Consciousness, I suggest, offers perspective. [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]

Schizophrenia is a polygenic complex disease with a heritability as high as 80 %, yet the mechanism of polygenic interaction in its pathogenesis remains unclear. Studying the interaction and regulation of schizophrenia susceptibility genes is crucial for unraveling the pathogenesis of schizophrenia and developing antipsychotic drugs. Therefore, we developed a bioinformatics method named GRACI (Gene Regulation Analysis based on Causal Inference) based on the principles of information theory, a causal inference model, and high order chromatin 3D conformation. GRACI captures the interaction and regulatory relationships between schizophrenia susceptibility genes by analyzing genotyping data. Two datasets, comprising 1459 and 2065 samples respectively, were analyzed, and the gene networks from both datasets were constructed. GRACI showcased superior accuracy when compared to widely adopted methods for detecting gene-gene interactions and intergenic regulation. This alignment was further substantiated by its correlation with chromatin high-order conformation patterns. Using GRACI, we identified three potential genes— KCNN3 , KCNH1 , and KCND3 —that are directly associated with schizophrenia pathogenesis. Furthermore, the results of GRACI on the standalone dataset illustrated the method's applicability to other complex diseases. GRACI download: https://github.com/liuliangjie19/GRACI [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]

Addressing the issue of information asymmetry in crowdfunding, this paper developed a new model for predicting crowdfunding performance, based on the decision utility rules for processing uncertain information in prospect theory and combining the analysis of crowdfunding project information disclosure with investor utility. To tackle the issue of excessive feature selection in practical applications, it introduced a sparsity-based feature selection method using neural networks, which could help crowdfunding platforms to focus on core features for better understanding and predicting investor behavior. Empirical analysis of over 150 000 projects on the Kickstarter platform shows that models considering investors perception of risk and prospect utility have better predictive and explanatory power for crowdfunding performance. The research results not only provide a new perspective for the prediction and evaluation of crowdfunding projects, but also offer powerful tools for crowdfunding platforms and fundraisers to establish models for analyzing backers backing behavior. [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]

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]

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]

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]

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]