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

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

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

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

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.

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

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

The 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]

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]

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]

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]

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]

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]

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]

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]

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

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

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]

Scale-free brain activity, linked with learning, the integration of different time scales, and the formation of mental models, is correlated with a metastable cognitive basis. The spectral slope, a key aspect of scale-free dynamics, was proposed as a potential indicator to distinguish between different sleep stages. Studies suggest that brain networks maintain a consistent scale-free structure across wakefulness, anesthesia, and recovery. Although differences in anesthetic sensitivity between the sexes are recognized, these variations are not evident in clinical electroencephalographic recordings of the cortex. Recently, changes in the slope of the power law exponent of neural activity were found to correlate with changes in Rényi entropy, an extended concept of Shannon's information entropy. These findings establish quantifiers as a promising tool for the study of scale-free dynamics in the brain. Our study presents a novel visual representation called the Rényi entropy-complexity causality space, which encapsulates complexity, permutation entropy, and the Rényi parameter q. The main goal of this study is to define this space for classical dynamical systems within theoretical bounds. In addition, the study aims to investigate how well different time series mimicking scale-free activity can be discriminated. Finally, this tool is used to detect dynamic features in intracranial electroencephalography (iEEG) signals. To achieve these goals, the study implementse the Bandt and Pompe method for ordinal patterns. In this process, each signal is associated with a probability distribution, and the causal measures of Rényi entropy and complexity are computed based on the parameter q. This method is a valuable tool for analyzing simulated time series. It effectively distinguishes elements of correlated noise and provides a straightforwardmeans of examining differences in behaviors, characteristics, and classifications. For the iEEG experimental data, the REM state showed a greater number of significant sex-based differences, while the supramarginal gyrus region showed themost variation across differentmodes and analyzes. Exploring scale-free brain activity with this framework could provide valuable insights into cognition and neurological disorders. The results may have implications for understanding differences in brain function between the sexes and their possible relevance to neurological disorders. [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]

With the advancement of technology, signal modulation types are becoming increasingly diverse and complex. The phenomenon of signal time–frequency overlap during transmission poses significant challenges for the classification and recognition of mixed signals, including poor recognition capabilities and low generality. This paper presents a recognition model for the fine-grained analysis of mixed signal characteristics, proposing a Geometry Coordinate Attention mechanism and introducing a low-rank bilinear pooling module to more effectively extract signal features for classification. The model employs a residual neural network as its backbone architecture and utilizes the Geometry Coordinate Attention mechanism for time–frequency weighted analysis based on information geometry theory. This analysis targets multiple-scale features within the architecture, producing time–frequency weighted features of the signal. These weighted features are further analyzed through a low-rank bilinear pooling module, combined with the backbone features, to achieve fine-grained feature fusion. This results in a fused feature vector for mixed signal classification. Experiments were conducted on a simulated dataset comprising 39,600 mixed-signal time–frequency plots. The model was benchmarked against a baseline using a residual neural network. The experimental outcomes demonstrated an improvement of 9% in the exact match ratio and 5% in the Hamming score. These results indicate that the proposed model significantly enhances the recognition capability and generalizability of mixed signal classification. [ABSTRACT FROM AUTHOR]

In this paper, we introduce a new method to solve the low-entropy problem in a quasi-static environment. We utilize a simultaneous transmission and reflection reconfigurable intelligent surface (STAR-RIS) that transmits and reflects incoming signals to users located on different sides of the surface. Specifically, we design the flow of multi-user channel probing when STAR-RIS is assisted, analyze and derive the analytical expression of the secret key capacity (SKC) of STAR-RIS-assisted system from the perspective of information theory, and finally analyze how to maximize the SKC and sum of all users under three different working protocols of STAR-RIS. The simulation results show that: (1) STAR-RIS can achieve better SKC performance than the conventional reflection/transmission RIS; and (2) the SKC is determined by the number of STAR-RIS elements, correlation coefficient, pilot length and RIS-reflected channel quality, and the time switching protocol can obtain higher SKC than the energy splitting protocol and mode switching protocol. [ABSTRACT FROM AUTHOR]

The storage, transmission, and processing of data become significant problems when large digital data files or databases are involved, as in the case of decentralized online global databases such as blockchain. Here, we propose a novel method that allows for the scalability of digital assets, including blockchain databases in the download, validation, and confidentiality processes, by developing a lightweight blockchain technology called Entropic Blockchain. This is a computer-implemented mathematical method by which to generate an information-entropic numerical barcode representation of a digital asset. Using this technique, a 1–2 Mb block of digital data can be represented by a few bytes, significantly reducing the size of a blockchain. The entropic barcode file can be utilized on its own or as an optically machine-readable entropic barcode for secure data transmission, processing, labeling, identification, and one-way encryption, as well as for compression, validation, and digital tamper-proof checks. The mathematics of this process and all the steps involved in its implementation are discussed in detail in this article. [ABSTRACT FROM AUTHOR]

Exploring the antecedents that affect the team innovation performance can better promote the organization to research the potential factors to enhance the organizational innovation competitiveness. Drawing upon information processing theory, we develop a moderated mediation model to examine the relationship between team pro-social rule breaking and team innovation performance. A three-wave field study is constructed from two large manufacturing enterprises from 82 team leaders and their 382 subordinates in Shanghai, China. The results reveal that team pro-social rule breaking is positively related to team innovation performance through team reflexivity. In addition, the relationship between team pro-social rule breaking and team innovation performance via team reflexivity is positive only when team learning orientation is high. The implications, limitations, and future research directions of these findings are discussed. [ABSTRACT FROM AUTHOR]

The current state of the art in artificial intelligence is impressive, especially in terms of mastery of language, but not so much in terms of mathematical reasoning. What could be missing? Can we learn something useful about that gap from how the brains of mathematicians go about their craft? This essay builds on the idea that current deep learning mostly succeeds at system 1 abilities—which correspond to our intuition and habitual behaviors—but still lacks something important regarding system 2 abilities—which include reasoning and robust uncertainty estimation. It takes an information-theoretical posture to ask questions about what constitutes an interesting mathematical statement, which could guide future work in crafting an AI mathematician. The focus is not on proving a given theorem but on discovering new and interesting conjectures. The central hypothesis is that a desirable body of theorems better summarizes the set of all provable statements, for example, by having a small description length while at the same time being close (in terms of number of derivation steps) to many provable statements. [ABSTRACT FROM AUTHOR]

Seismic full-waveform inversion (FWI) or waveform inversion (WI) has gained extensive attention as a cutting-edge imaging method, which is expected to reveal the high-resolution images of complex geological structures. In this paper, we regard each 1-D signal in the inversion system as a 1-D probability distribution, then use the Jensen–Shannon divergence from information theory to measure the discrepancy between the predicted and observed signals, and finally implement a novel 2-D multiparameter shallow-seismic WI (MSWI). Essentially, the novel approach achieves an implicit weighting along the time-axis for each 1-D adjoint source defined by the classical WI (CWI), thus enhancing the extra illumination for a deeper medium compared with the CWI. By evaluating the inversion results of the two-layer model and fault model, the reconstruction accuracy for S -wave velocity and density of the new method is increased by about 30 and 20 per cent compared with that of the CWI under the same conditions, respectively. The reconstruction performance for P -wave velocity of these two methods is almost equal. In addition, the new 2-D MSWI is also resilient to white Gaussian noise in the data. Numerically, the inversion system has almost the strongest sensitivities to the S -wave velocity and density, performing the poorest sensitivity to the P -wave velocity. Finally, we test the novel method with a detection case for a power tunnel. [ABSTRACT FROM AUTHOR]

How do cognitive pressures shape the lexicons of natural languages? Here, we reframe George Kingsley Zipf's proposed "law of abbreviation" within a more general framework that relates it to cognitive pressures that affect speakers and listeners. In this new framework, speakers' drive to reduce effort (Zipf's proposal) is counteracted by the need for low‐frequency words to have word forms that are sufficiently distinctive to allow for accurate recognition by listeners. To support this framework, we replicate and extend recent work using the prevalence of subword phonemic sequences (phonotactic probability) to measure speakers' production effort in place of Zipf's measure of length. Across languages and corpora, phonotactic probability is more strongly correlated with word frequency than word length. We also show this measure of ease of speech production (phonotactic probability) is strongly correlated with a measure of perceptual difficulty that indexes the degree of competition from alternative interpretations in word recognition. This is consistent with the claim that there must be trade‐offs between these two factors, and is inconsistent with a recent proposal that phonotactic probability facilitates both perception and production. To our knowledge, this is the first work to offer an explanation why long, phonotactically improbable word forms remain in the lexicons of natural languages. [ABSTRACT FROM AUTHOR]

We study stochastic linear contextual bandits (CB) where the agent observes a noisy version of the true context through a noise channel with unknown channel parameters. Our objective is to design an action policy that can "approximate" that of a Bayesian oracle that has access to the reward model and the noise channel parameter. We introduce a modified Thompson sampling algorithm and analyze its Bayesian cumulative regret with respect to the oracle action policy via information-theoretic tools. For Gaussian bandits with Gaussian context noise, our information-theoretic analysis shows that under certain conditions on the prior variance, the Bayesian cumulative regret scales as O ˜ (m T) , where m is the dimension of the feature vector and T is the time horizon. We also consider the problem setting where the agent observes the true context with some delay after receiving the reward, and show that delayed true contexts lead to lower regret. Finally, we empirically demonstrate the performance of the proposed algorithms against baselines. [ABSTRACT FROM AUTHOR]

A central challenge in hypothesis testing (HT) lies in determining the optimal balance between Type I (false positive) and Type II (non-detection or false negative) error probabilities. Analyzing these errors' exponential rate of convergence, known as error exponents, provides crucial insights into system performance. Error exponents offer a lens through which we can understand how operational restrictions, such as resource constraints and impairments in communications, affect the accuracy of distributed inference in networked systems. This survey presents a comprehensive review of key results in HT, from the foundational Stein's Lemma to recent advancements in distributed HT, all unified through the framework of error exponents. We explore asymptotic and non-asymptotic results, highlighting their implications for designing robust and efficient networked systems, such as event detection through lossy wireless sensor monitoring networks, collective perception-based object detection in vehicular environments, and clock synchronization in distributed environments, among others. We show that understanding the role of error exponents provides a valuable tool for optimizing decision-making and improving the reliability of networked systems. [ABSTRACT FROM AUTHOR]

Over the past decade and a half, dynamic functional imaging has revealed low-dimensional brain connectivity measures, identified potential common human spatial connectivity states, tracked the transition patterns of these states, and demonstrated meaningful transition alterations in disorders and over the course of development. Recently, researchers have begun to analyze these data from the perspective of dynamic systems and information theory in the hopes of understanding how these dynamics support less easily quantified processes, such as information processing, cortical hierarchy, and consciousness. Little attention has been paid to the effects of psychiatric disease on these measures, however. We begin to rectify this by examining the complexity of subject trajectories in state space through the lens of information theory. Specifically, we identify a basis for the dynamic functional connectivity state space and track subject trajectories through this space over the course of the scan. The dynamic complexity of these trajectories is assessed along each dimension of the proposed basis space. Using these estimates, we demonstrate that schizophrenia patients display substantially simpler trajectories than demographically matched healthy controls and that this drop in complexity concentrates along specific dimensions. We also demonstrate that entropy generation in at least one of these dimensions is linked to cognitive performance. Overall, the results suggest great value in applying dynamic systems theory to problems of neuroimaging and reveal a substantial drop in the complexity of schizophrenia patients' brain function. [ABSTRACT FROM AUTHOR]

This document is an editorial from the journal "Entropy" titled "Information Theory in Emerging Wireless Communication Systems and Networks." It discusses the rapid evolution of wireless communication systems and networks to meet the increasing demands for higher data rates, reliability, and connectivity. The editorial highlights various new technologies and communication techniques being proposed, such as autonomous aerial networks, terahertz communications, intelligent reflecting surfaces, and quantum communications. It emphasizes the importance of information theory in studying the performance limits of these emerging wireless systems and networks. The editorial also provides an overview of seven contributions in this special issue, covering topics such as full-duplex communications, intelligent reflecting surfaces, polar coding, artificial intelligence techniques, and wireless system security. The conclusion suggests that multiple-antenna communications and AI technologies will continue to shape the future of wireless networks. [Extracted from the article]

This document is a response to a comment made by Robertas Damaševičius regarding an article on info-autopoiesis and artificial general intelligence (AGI). The response addresses specific points raised in the comment and clarifies the author's position. It emphasizes that info-autopoiesis, the self-creation of information, is fundamental only to humans and not achievable by artificial creations. The article argues for the impossibility of achieving AGI using info-autopoiesis and suggests that resources should be directed towards more achievable goals in AI. The response also discusses the relationship between biological aspects of human intelligence and AI research, highlighting the use of neural networks and considering the architecture of AGI. It concludes by stating that info-autopoiesis sets a limit to human knowledge based on fundamental principles and that all human creations are syntactic and incapable of semantic interpretation. The text discusses the concept of info-autopoiesis and its application to AI research. Info-autopoiesis is a framework that aims to explain how living organisms function, and the paper explores its potential implications for AI and AGI development. The author acknowledges the limitations of info-autopoiesis and emphasizes the need for interdisciplinary collaboration and ethical considerations in AI research. The text concludes by suggesting areas for further research and expressing gratitude for the comments and suggestions provided by Robertas Damaševičius. [Extracted from the article]

Fuzzy extractors convert noisy signals from the physical world into reliable cryptographic keys. Fuzzy min-entropy measures the limit of the length of key that a fuzzy extractor can derive from a distribution (Fuller et al. in IEEE Trans Inf Theory 66(8):5282–5298, 2020). In general, fuzzy min-entropy that is superlogarithmic in the security parameter is required for a noisy distribution to be suitable for key derivation. There is a wide gap between what is possible with respect to computational and information-theoretic adversaries. Under the assumption of general-purpose obfuscation, keys can be securely derived from all distributions with superlogarithmic entropy. Against information-theoretic adversaries, however, it is impossible to build a single fuzzy extractor that works for all distributions (Fuller et al. 2020). A weaker information-theoretic goal is building a fuzzy extractor for each probability distribution. This is the approach taken by Woodage et al. (in: Advances in Cryptology—CRYPTO, Springer, pp 682–710, 2017). Prior approaches use the full description of the probability mass function and are inefficient. We show this is inherent: for a quarter of distributions with fuzzy min-entropy and 2 k points there is no secure fuzzy extractor that uses less 2 Θ (k) bits of information about the distribution. We show an analogous result with stronger parameters for information-theoretic secure sketches. Secure sketches are frequently used to construct fuzzy extractors. [ABSTRACT FROM AUTHOR]

Determining the modulus of elasticity for soil is crucial in geotechnical engineering when conducting stress-deformation analyses. However, due to the difficulty involved in calculating this parameter, as well as the fact that the modulus of elasticity for soil is nonlinear in nature, there is often uncertainty surrounding its value. A study was therefore conducted to investigate these uncertainties and their impact on geotechnical analyses and plans. The study involved modeling and numerically analyzing a deep drilling guard structure using the anchoring method. To obtain the necessary information, two projects – depth and guard structure, which were both undertaken by Jahan Mall and Baran - in Mashhad were selected as case studies. In this study, the Jahan Mall project pit was analyzed using both two- and three-dimensional numerical models. Four different models were used: Moore-Coulomb (MC), hardening soil (HS), hardening soil with small strain stiffness (Cysoil HSS), and a newly developed nonlinear model based on the Moore-Coulomb model. To carry out the analysis, information obtained from barometric tests, standard penetration tests, and shear wave propagation was used. The results of the analysis were compared with each other and with the monitoring data. It was found that for the Moore-Coulomb behavior model, the pressure modulus (Ep) should be corrected to three to five times its original value in order to obtain accurate results. However, for the Cysoil HSS, HS models, and the newly developed model, there was no need to correct the pressurometric data or shear wave propagation. Additionally, it was determined that while no correction is necessary when using standard penetration numbers, an appropriate relationship should be used to convert them into the modulus of elasticity. Based on these findings, the Gud Baran project was analyzed, and it was concluded that the newly developed model and method for converting standard penetration numbers can be applied broadly and produce desirable results. [ABSTRACT FROM AUTHOR]