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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]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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]

Information theory has proven to be a worthwhile tool for investigating the implications of the Higgs sector in the Next‐to‐Minimal Supersymmetric Standard Model (NMSSM) using Higgs information at the Large Hadron Collider (LHC) assessed through the entropy constructed by means of the branching ratios of decay channels of the Higgs boson. The present article focuses on the parameter space of supersymmetric extension with an extra term of singlet in light of various experimental constraints. Our findings show the most preferred values of m0, m1/2, A0, tanβ, λ, μeff, neutralino lightest supersymmetric particle (LSP) mχ~10, lighter chargino mχ~1±, singlino mχ~50, and gluino mg~ to be around 1.93 TeV, 1.78 TeV, −3.62 TeV, 27.5, 0.012, 665.7 GeV, 740 GeV, 790 GeV, 11.24 TeV, and 3.70 TeV, respectively, that is compatible with the relic density of dark matter. [ABSTRACT FROM AUTHOR]

Infectious diseases risk is directly related to human life safety. After the COVID-19 pandemic, people have paid unprecedented attention to the risk of infectious diseases. Compared with treatment after the outbreak of the epidemic, identifying the influencing factors of infectious disease risk and quantitatively analyzing and assessing infectious disease risk before the outbreak of the epidemic plays an equally important role. This article focuses on the risk of irregular outbreaks of infectious diseases. On the one hand, a method based on information gain is proposed to calculate the weight of environmental factors directly related to infectious disease risk, to clarify the correlation between environmental factors and infectious disease risk. On the other hand, the risk calculation method based on risk weight number is proposed to calculate the risk level of different infectious diseases under the influence of specific environmental factors. Finally, the effectiveness and feasibility of the proposed method are verified through case analysis and discussion. By comparing it with other risk assessment methods, the advantages and disadvantages of the proposed method are demonstrated. [ABSTRACT FROM AUTHOR]