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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]

Striking progress has been made in understanding cognition by analyzing how the brain is engaged in different modes of information processing. For instance, so-called synergistic information (information encoded by a set of neurons but not by any subset) plays a key role in areas of the human brain linked with complex cognition. However, two questions remain unanswered: (a) how and why a cognitive system can become highly synergistic; and (b) how informational states map onto artificial neural networks in various learning modes. Here we employ an information-decomposition framework to investigate neural networks performing cognitive tasks. Our results show that synergy increases as networks learn multiple diverse tasks, and that in tasks requiring integration of multiple sources, performance critically relies on synergistic neurons. Overall, our results suggest that synergy is used to combine information from multiple modalities—and more generally for flexible and efficient learning. These findings reveal new ways of investigating how and why learning systems employ specific information-processing strategies, and support the principle that the capacity for general-purpose learning critically relies on the system's information dynamics. Author summary: What is the informational basis of learning in humans, animals, or, indeed, artificial neural networks (ANN)? Furthermore, how can these systems learn to solve multiple tasks simultaneously? These fundamental questions are, surprisingly, still not fully understood. One advantage of studying ANNs is that we can precisely probe learning-related changes. Here we draw on a recent branch of information theory, partial information decomposition, to examine how different types of information support different learning goals, and where, in ANNs. We show that adding noise to an ANN encourages it to keep copies of information at multiple nodes, promoting robustness. In contrast, whenever flexible learning is required, for instance when facing varied stimulus types or diverse tasks, individual neurons work together to represent information more abstractly. This work sheds light on how systems encode information differently according to their learning pressures, which can help us better understand how and why the human brain uses particular forms of information processing. [ABSTRACT FROM AUTHOR]

Hyperdimensional computing (HDC) is an emerging computational paradigm for representing compositional information as high-dimensional vectors and has a promising potential in applications ranging from machine learning to neuromorphic computing. One of the long-standing challenges in HDC is factoring a compositional representation to its constituent factors, also known as the recovery problem. In this article, we take a novel approach to solve the recovery problem and propose the use of random linear codes. These codes are subspaces over the Boolean field and are a well-studied topic in information theory with various applications in digital communication. We begin by showing that hyperdimensional encoding using random linear codes retains favorable properties of the prevalent (ordinary) random codes; hence, HD representations using the two methods have comparable information storage capabilities. We proceed to show that random linear codes offer a rich subcode structure that can be used to form key-value stores, which encapsulate the most used cases of HDC. Most important, we show that under the framework we develop, random linear codes admit simple recovery algorithms to factor (either bundled or bound) compositional representations. The former relies on constructing certain linear equation systems over the Boolean field, the solution to which reduces the search space dramatically and strictly outperforms exhaustive search in many cases. The latter employs the subspace structure of these codes to achieve provably correct factorization. Both methods are strictly faster than the state-of-the-art resonator networks, often by an order of magnitude. We implemented our techniques in Python using a benchmark software library and demonstrated promising experimental results. [ABSTRACT FROM AUTHOR]

The quantitative structure-property relationship (QSPR) technique is used to gauge the n-octanol/water partition coefficient (log KOW) and enthalpy of vaporization (∆vapHm) of 133 Polychlorinated Biphenyls (PCBs) using conceptual density functional theory (CDFT)-based global reactivity and information-theory (IT) based parameters. Regression models are established using linear and multi-linear relationships to correlate the observed physicochemical properties of PCBs with the predicted ones. The study explored the significance of CDFT and IT descriptors, and based on the calculation of Pearson correlation coefficient values, the selection of suitable descriptors is made for successful QSPR models of selected PCBs. It is found that some of the CDFT parameters are highly correlated with the IT parameters, as suggested by their high Pearson correlation coefficient values for PCB systems. The regression model generated using the descriptors IG, g1, g2, EA, η for predicting log KOW and IF, g3, η, SS, SGBP for predicting ∆vapHm gives R2 value of 0.9342 and 0.8662, respectively, for the selected 133 PCB congeners. Furthermore, to verify the descriptor selection, a machine learning approach is also used to develop QSPR models in this study. QSPR modelling using CDFT and information theory-based descriptors for predicting n-octanol/water partition coefficient and enthalpy of vaporization for the selected PCBs [ABSTRACT FROM AUTHOR]

Collisions involving space objects pose significant threats to space sustainability. Within these collisions, catastrophic collisions which generate a larger amount of debris are particularly harmful. The quantity and characteristics of fragments generated from a collision are usually modeled using the NASA standard breakup model (SBM). However, on several occasions, the NASA SBM has incorrectly estimated the effects of collisions, including the Yunhai 1-02 breakup event. Using outer probability measure theory and information theory, we develop a correction methodology to estimate the Yunhai 1-02 event based on NASA SBM. This methodology successfully passes a statistical test using historical data. After correction, the characteristic diameter and relative velocity distributions of fragments generated from this event exhibit greater similarity with observations. The work in this paper facilitates the analysis of the properties of the debris cloud resulting from collisions with limited information, improving the predictions of future collisions. [Display omitted] • We condense and analyze the historical collision data. • We obtain a corrected parameter to adjust the SBM result, and this adjusted result passes the statistical test. • We improve the precision of characteristic diameter and relative velocity of fragments generated from Yunhai event. • This work is important to estimate the effect of collisions with limited information. [ABSTRACT FROM AUTHOR]

Emergence can be described as a relationship between entities at different levels of organization, that looks especially puzzling at the transitions between the major levels of matter, life, cognition and culture. Indeed, each major level is dependent on the lower one not just for its constituents, but in some more formal way. A passage by François Jacob suggests that all such evolutionary transitions are associated with the appearance of some form of memory–genetic, neural or linguistic respectively. This implies that they have an informational nature. Based on this idea, we propose a general model of informational systems understood as combinations of modules taken from a limited inventory. Some informational systems are "semantic" models, that is reproduce features of their environment. Among these, some are also "informed", that is have a pattern derived from a memory subsystem. The levels and components of informed systems can be listed to provide a general framework for knowledge organization, of relevance in both philosophical ontology and applied information services. [ABSTRACT FROM AUTHOR]

In network services, there are often conflicts among privacy protection, trust, and service quality, which greatly reduces the regulatory effectiveness of networks. Using the basic method of information theory, this paper considers the optimization mechanism of trust and the balance between privacy and service utility from the perspective of quantifying information flow. First, we build a trust model based on multiple factors, such as direct trust, service trust, and trust cloud network. The weight of trust factors is determined by category diversity and information entropy theory. Second, based on statistical knowledge and information entropy, we propose a new privacy measurement model with hierarchical weights. Third, we establish a trust privacy service relationship model to realize the trade-off between privacy, service, and trust in networks. The demander can choose to establish trust, protect privacy, or obtain high-quality services according to personal preferences and needs. Fourth, we propose a privacy trust mapping relationship model and a privacy disclosure selection model. Finally, we design different experimental schemes. These simulation results show that our research can not only provide better quality of service but also better protect privacy and help interactive parties in networks establish a strong trust relationship. This research will improve the effectiveness of networks and can be applied to class II vaccine multi-agent collaborative supervision based on technology. [ABSTRACT FROM AUTHOR]

This article investigates the trade‐off between privacy and security in cyber‐physical systems, with the goal of designing a privacy‐preserving mechanism based on information theory. Considering the unreliability of the communication channel, we assume that the private data is vulnerable to eavesdropping and bias injection attacks. To maintain privacy, the system is equipped with a privacy‐preserving mechanism achieved by injecting Gaussian‐type privacy noise into transmitted data, which inevitably leads to degraded detecting performance. Therefore, we investigate the trade‐off between privacy level and detection performance, where the privacy level and the detection performance are measured by mutual information and Kullback–Leibler divergence, respectively. Then, the optimal privacy noise is obtained by solving a convex optimization problem for maximizing the privacy degree and constraining a bound on detection performance degradation. Furthermore, to optimize the detection performance, another convex optimization problem is proposed to minimize both the false alarm rate and the missed alarm rate while guaranteeing a level of the privacy. Finally, a numerical example of the vehicle tracking problem is adopted to illustrate the effectiveness of the designed framework. [ABSTRACT FROM AUTHOR]

Protein evolution is guided by structural, functional, and dynamical constraints ensuring organismal viability. Pseudogenes are genomic sequences identified in many eukaryotes that lack translational activity due to sequence degradation and thus over time have undergone "devolution." Previously pseudogenized genes sometimes regain their protein-coding function, suggesting they may still encode robust folding energy landscapes despite multiple mutations. We study both the physical folding landscapes of protein sequences corresponding to human pseudogenes using the Associative Memory, Water Mediated, Structure and Energy Model, and the evolutionary energy landscapes obtained using direct coupling analysis (DCA) on their parent protein families. We found that generally mutations that have occurred in pseudogene sequences have disrupted their native global network of stabilizing residue interactions, making it harder for them to fold if they were translated. In some cases, however, energetic frustration has apparently decreased when the functional constraints were removed. We analyzed this unexpected situation for Cyclophilin A, Profilin-1, and Small Ubiquitin-like Modifier 2 Protein. Our analysis reveals that when such mutations in the pseudogene ultimately stabilize folding, at the same time, they likely alter the pseudogenes' former biological activity, as estimated by DCA. We localize most of these stabilizing mutations generally to normally frustrated regions required for binding to other partners. [ABSTRACT FROM AUTHOR]

Although information theory resolves the inconsistencies (known in the form of famous enigmas) of the traditional approach of thermostatistics, its place in the corresponding literature is not what it deserves. This article supports the idea that this is mainly due to epistemological rather than scientific reasons: the subjectivity introduced into physics is perceived as a problem. Here is an attempt to expose and clarify where exactly this subjectivity lies: in the representation of reality and in probabilistic inference, two aspects that have been integrated into the practice of science for a long time and which should no longer frighten anyone but have become explicit with information theory. [ABSTRACT FROM AUTHOR]

Objective: Information theory has been successfully employed to identify optimal pathway networks, mutual information (MI), and entropy as a dynamic response in statistical methods and estimate input and output information in systems biology. This research aims to investigate potentially integrated gene signatures for bone metastasis using graph-based information theory from the dynamic interaction interphase. Methods: The expression dataset with the series ID GSE26964 for bone metastasis from prostate cancer was retrieved. The dataset was segregated for differentially expressed genes (DEGs) using the Human Cancer Metastasis Database. MI was considered to capture non-linear connections to classify the key DEGs from the collected dataset using gene-gene statistical analysis and then a protein-protein interaction network (PPIN). The PPIN was used to calculate centrality metrics, bottlenecks, and functional annotations. Results: A total of 531 DEGs were identified. Thirteen genes were classified as highly correlated based on their gene expression data matrix. The extended PPIN of the 13 genes comprised 53 nodes and 372 edges. A total of four DEGs were identified as hubs. One novel gene was identified with strong network connectivity. Conclusion: The novel biomarkers for metastasis may provide information on cancer metastasis to the bone by implying MI and information theory. [ABSTRACT FROM AUTHOR]

Voltage‐clamp experiments are commonly utilised to characterise cellular ion channel kinetics. In these experiments, cells are stimulated using a known time‐varying voltage, referred to as the voltage protocol, and the resulting cellular response, typically in the form of current, is measured. Parameters of models that describe ion channel kinetics are then estimated by solving an inverse problem which aims to minimise the discrepancy between the predicted response of the model and the actual measured cell response. In this paper, a novel framework to evaluate the information content of voltage‐clamp protocols in relation to ion channel model parameters is presented. Additional quantitative information metrics that allow for comparisons among various voltage protocols are proposed. These metrics offer a foundation for future optimal design frameworks to devise novel, information‐rich protocols. The efficacy of the proposed framework is evidenced through the analysis of seven voltage protocols from the literature. By comparing known numerical results for inverse problems using these protocols with the information‐theoretic metrics, the proposed approach is validated. The essential steps of the framework are: (i) generate random samples of the parameters from chosen prior distributions; (ii) run the model to generate model output (current) for all samples; (iii) construct reduced‐dimensional representations of the time‐varying current output using proper orthogonal decomposition (POD); (iv) estimate information‐theoretic metrics such as mutual information, entropy equivalent variance, and conditional mutual information using non‐parametric methods; (v) interpret the metrics; for example, a higher mutual information between a parameter and the current output suggests the protocol yields greater information about that parameter, resulting in improved identifiability; and (vi) integrate the information‐theoretic metrics into a single quantitative criterion, encapsulating the protocol's efficacy in estimating model parameters. [ABSTRACT FROM AUTHOR]

In order to create higher benefits for enterprises in the university supply chain and make them meet the requirements of sustainable development, this study uses information ecology theory to study the cognitive management and transformation path of the green supply chain. Firstly, the related concepts of university supply chain management and green supply chain management are expounded, and the differences are mainly discussed. Then, the evaluation of supply chain greenness is studied. The fuzzy hierarchical comprehensive evaluation method combining quantitative and qualitative is used to establish the evaluation model. Finally, the proposed evaluation model is tested. The test results show that Suzhou University's greenness fuzzy evaluation score is 58.2 points, which is a medium level. The resource factor scored 61 points, the energy factor scored 52.9 points, the environmental factors scored 59.8 points, the economic factor scored 62.7 points, and the social factor scored 55.5 points. The company has obvious advantages in terms of resources and economy, but the level is not very high. The higher the cost synergy between enterprises and customers, the greater the environmental cost synergy. Therefore, manufacturing enterprises should strengthen communication and cooperation with customers. The higher the cost synergy control of manufacturing and waste recycling enterprises, the greater the synergy effect of environmental cost control. Therefore, increasing cooperation with waste recycling enterprises is conducive to developing environmental cost control and coordinated benefits. The proposed scheme provides some ideas for applying information ecology theory in the university supply chain. [ABSTRACT FROM AUTHOR]

Similarities and differences between the features related to the productivity of onshore and offshore wind energy are developed with the aid of information theory techniques complemented by normal statistics. The data comes from the 13‐year period between 2010 and 2022 for the registered turbines in Germany (practically all). The information content of the generated power is dynamically measured by the mutability of the files storing the information. Monthly statistics show that in spite of the Summer months being relatively unproductive, the corresponding mutability shows the possibility of making use of short periods of intermediate productivity in the case of offshore plants. Favorable conditions for wind energy generation in Wintertime are reached for both onshore and offshore production, although the latter is favored. More homogeneity and stability in the data are still necessary to generalize algorithms, protocols, and criteria. This general study shows the success of the information theory techniques in describing wind ramps. Applications to specific zones could improve the efficiency and capacity factor of particular wind turbines depending on their exposure to wind streams or the blocking of nearby mountains and forestry. The information theory techniques presented here allow for a different and novel viewpoint to detect favorable and unfavorable wind energy periods. [ABSTRACT FROM AUTHOR]

Purpose: The article extends the distinction of semantic from syntactic labour to comprehend all forms of mental labour. It answers a critique from de Fremery and Buckland, which required envisaging mental labour as a differentiated spectrum. Design/methodology/approach: The paper adopts a discursive approach. It first reviews the significance and extensive diffusion of the distinction of semantic from syntactic labour. Second, it integrates semantic and syntactic labour along a vertical dimension within mental labour, indicating analogies in principle with, and differences in application from, the inherited distinction of intellectual from clerical labour. Third, it develops semantic labour to the very highest level, on a consistent principle of differentiation from syntactic labour. Finally, it reintegrates the understanding developed of semantic labour with syntactic labour, confirming that they can fully and informatively occupy mental labour. Findings: The article further validates the distinction of semantic from syntactic labour. It enables to address Norbert Wiener's classic challenge of appropriately distributing activity between human and computer. Research limitations/implications: The article transforms work in progress into knowledge for diffusion. Practical implications: It has practical implications for determining what tasks to delegate to computational technology. Social implications: The paper has social implications for the understanding of appropriate human and machine computational tasks and our own distinctive humanness. Originality/value: The paper is highly original. Although based on preceding research, from the late 20th century, it is the first separately published full account of semantic and syntactic labour. [ABSTRACT FROM AUTHOR]

Rank‐reduction‐based simultaneous random noise attenuation and three‐dimensional seismic‐data interpolation has recently become a hot topic in reflection seismology. However, the rank of traditional methods is fixed without considering the variation of signal‐to‐noise ratio on different frequency components, leading to serious residual noise and further affecting the following processing and interpretation tasks. In addition, traditional methods also heavily rely on the application of singular value decomposition technique for rank reduction, which is proven to be computationally expensive for large‐scale data. Thus, a fast‐adaptive rank‐reduction method is proposed in this study. First, the information entropy theory is introduced to adaptively select the optimal rank at various frequencies by calculating the increment of singular entropy. Second, we propose a fast Random Block Krylov algorithm and a subspace multiplexing technique to replace the singular value decomposition algorithm used in traditional methods. The proposed method can significantly improve computational efficiency and yield better seismic‐data reconstruction performance than traditional methods. Applications of the proposed approach on both synthetic and field seismic data demonstrate its superior performance over a well‐known rank‐reduction‐based method, that is the random multi‐channel singular spectrum analysis, in terms of recovered signal‐to‐noise ratio and visual view. [ABSTRACT FROM AUTHOR]

Mainstream research on information theory within the field of living systems involves the application of analytical tools to understand a broad range of life processes. This paper is dedicated to an opposite problem: it explores the information theory and communication engineering methods that have counterparts in the data transmission process by way of DNA structures and neural fibers. Considering the requirements of modern multimedia, transmission methods chosen by nature may be different, suboptimal, or even far from optimal. However, nature is known for rational resource usage, so its methods have a significant advantage: they are proven to be sustainable. Perhaps understanding the engineering aspects of methods of nature can inspire a design of alternative green, stable, and low-cost transmission. [ABSTRACT FROM AUTHOR]

Arguments inspired by algorithmic information theory predict an inverse relation between the probability and complexity of output patterns in a wide range of input–output maps. This phenomenon is known as simplicity bias. By viewing the parameters of dynamical systems as inputs, and the resulting (digitised) trajectories as outputs, we study simplicity bias in the logistic map, Gauss map, sine map, Bernoulli map, and tent map. We find that the logistic map, Gauss map, and sine map all exhibit simplicity bias upon sampling of map initial values and parameter values, but the Bernoulli map and tent map do not. The simplicity bias upper bound on the output pattern probability is used to make a priori predictions regarding the probability of output patterns. In some cases, the predictions are surprisingly accurate, given that almost no details of the underlying dynamical systems are assumed. More generally, we argue that studying probability–complexity relationships may be a useful tool when studying patterns in dynamical systems. [ABSTRACT FROM AUTHOR]

The physical roots, interpretation, controversies, and precise meaning of the Landauer principle are surveyed. The Landauer principle is a physical principle defining the lower theoretical limit of energy consumption necessary for computation. It states that an irreversible change in information stored in a computer, such as merging two computational paths, dissipates a minimum amount of heat k B T l n 2 per a bit of information to its surroundings. The Landauer principle is discussed in the context of fundamental physical limiting principles, such as the Abbe diffraction limit, the Margolus–Levitin limit, and the Bekenstein limit. Synthesis of the Landauer bound with the Abbe, Margolus–Levitin, and Bekenstein limits yields the minimal time of computation, which scales as τ m i n ~ h k B T . Decreasing the temperature of a thermal bath will decrease the energy consumption of a single computation, but in parallel, it will slow the computation. The Landauer principle bridges John Archibald Wheeler's "it from bit" paradigm and thermodynamics. Experimental verifications of the Landauer principle are surveyed. The interrelation between thermodynamic and logical irreversibility is addressed. Generalization of the Landauer principle to quantum and non-equilibrium systems is addressed. The Landauer principle represents the powerful heuristic principle bridging physics, information theory, and computer engineering. [ABSTRACT FROM AUTHOR]

Traffic state classification and relevance calculation at intersections are both difficult problems in traffic control. In this paper, we propose an intersection relevance model based on a temporal graph attention network, which can solve the above two problems at the same time. First, the intersection features and interaction time of the intersections are regarded as input quantities together with the initial labels of the traffic data. Then, they are inputted into the temporal graph attention (TGAT) model to obtain the classification accuracy of the target intersections in four states—free, stable, slow moving, and congested—and the obtained neighbouring intersection weights are used as the correlation between the intersections. Finally, it is validated by VISSIM simulation experiments. In terms of classification accuracy, the TGAT model has a higher classification accuracy than the three traditional classification models and can cope well with the uneven distribution of the number of samples. The information gain algorithm from the information entropy theory was used to derive the average delay as the most influential factor on intersection status. The correlation from the TGAT model positively correlates with traffic flow, making it interpretable. Using this correlation to control the division of subareas improves the road network's operational efficiency more than the traditional correlation model does. This demonstrates the effectiveness of the TGAT model's correlation. [ABSTRACT FROM AUTHOR]

Using information-theoretic quantities in practical applications with continuous data is often hindered by the fact that probability density functions need to be estimated in higher dimensions, which can become unreliable or even computationally unfeasible. To make these useful quantities more accessible, alternative approaches such as binned frequencies using histograms and k-nearest neighbors (k-NN) have been proposed. However, a systematic comparison of the applicability of these methods has been lacking. We wish to fill this gap by comparing kernel-density-based estimation (KDE) with these two alternatives in carefully designed synthetic test cases. Specifically, we wish to estimate the information-theoretic quantities: entropy, Kullback–Leibler divergence, and mutual information, from sample data. As a reference, the results are compared to closed-form solutions or numerical integrals. We generate samples from distributions of various shapes in dimensions ranging from one to ten. We evaluate the estimators' performance as a function of sample size, distribution characteristics, and chosen hyperparameters. We further compare the required computation time and specific implementation challenges. Notably, k-NN estimation tends to outperform other methods, considering algorithmic implementation, computational efficiency, and estimation accuracy, especially with sufficient data. This study provides valuable insights into the strengths and limitations of the different estimation methods for information-theoretic quantities. It also highlights the significance of considering the characteristics of the data, as well as the targeted information-theoretic quantity when selecting an appropriate estimation technique. These findings will assist scientists and practitioners in choosing the most suitable method, considering their specific application and available data. We have collected the compared estimation methods in a ready-to-use open-source Python 3 toolbox and, thereby, hope to promote the use of information-theoretic quantities by researchers and practitioners to evaluate the information in data and models in various disciplines. [ABSTRACT FROM AUTHOR]

Research indicates that effective learner-centred feedback requires learner agency, impact and sensemaking. While scholars are focusing on supporting agency and impact, limited research has addressed sensemaking. This is problematic, because if learners fail to understand feedback, impact is likely to be reduced. In response, this study examines (non) alignment between teacher intent and student sensemaking of authentic feedback comments. The sample included four teachers and eighteen students from two Australian universities. Data were collected via stimulated recall interviews and a feedback coding task. The results suggest that sensemaking of strength-based comments, critiques and actionable information was aided when the comments were clear and specific. On the other hand, sensemaking was limited when comments were designed to mitigate against negative affect, overloaded with multiple intentions, or overly brief. This study informs theory around learner-centred feedback design which, in turn, improves the likelihood that teacher comments will be interpreted accurately by learners. [ABSTRACT FROM AUTHOR]

This article presents the critique of cybernetics as central to the history of one of the twentieth century’s most infamous avant-garde movements: the Situationist International (SI). Bringing together and analysing a series of seemingly marginal events in the build-up to May ’68, this article shows how the SI’s portrait of cybernetics as an emerging form of social power and control foreshadows later developments in French radical thought. This little-noted trajectory in the situationist movement also highlights the ways in which aesthetics, theory, and politics are inextricably tied together in the events of May ’68. [ABSTRACT FROM AUTHOR]

The trade distance is an important constraining factor underpinning the emergence of social and economic interactions of complex systems. However, agent-based studies supported by the granular analysis of distances are limited. Here, we present a complexity method that places the actual geographical locations of individual firms in Japan at the epicentre of our research. By combining methods derived from network science together with information theory measures, and by using a comprehensive dataset of Japanese inter-firm business transactions, we evaluate the effects of spatial features on the structural patterns of the economy. We find that the normalised probability distributions of the distances between interacting firms obey a power law like decay concomitant with the sizes of firms and regions. Furthermore, small firms would reach large distances to become customers of large firms, while trading between either only small firms or only large firms tends to be at smaller distances. Furthermore, a time evolution analysis suggests a reduction in the overall average trading distances in last 20 years. Lastly, our analysis concerning the trading dynamics among prefectures indicates that the preference to trade with neighbouring prefectures tends to be more pronounced at rural regions as opposed to the larger central conurbations. [ABSTRACT FROM AUTHOR]

(1) Background: Swallowing is a complex process that comprises well-timed control of oropharyngeal and laryngeal structures to achieve airway protection and swallowing efficiency. To understand its temporality, previous research adopted adherence measures and revealed obligatory pairs in healthy swallows and the effect of aging and bolus type on the variability of event timing and order. This study aimed to (i) propose a systemic conceptualization of swallowing physiology, (ii) apply sequence analyses, a set of information-theoretic and bioinformatic methods, to quantify and characterize swallowing temporality, and (iii) investigate the effect of aging and dysphagia on the quantified variables using sequence analyses measures. (2) Method: Forty-three participants (17 young adults, 15 older adults, and 11 dysphagic adults) underwent B-mode ultrasound swallowing examinations at the mid-sagittal plane of the submental region. The onset, maximum, and offset states of hyoid bone displacement, geniohyoid muscle contraction, and tongue base retraction were identified and sorted to form sequences which were analyzed using an inventory of sequence analytic techniques; namely, overlap coefficients, Shannon entropy, and longest common subsequence algorithms. (3) Results: The concurrency of movement sequence was found to be significantly impacted by aging and dysphagia. Swallowing sequence variability was also found to be reduced with age and the presence of dysphagia (H(2) = 52.253, p < 0.001, η 2 = 0.260). Four obligatory sequences were identified, and high adherence was also indicated in two previously reported pairs. These results provided preliminary support for the validity of sequence analyses for quantifying swallowing sequence temporality. (4) Conclusions: A systemic conceptualization of human deglutition permits a multi-level quantitative analysis of swallowing physiology. Sequence analyses are a set of promising quantitative measurement techniques for point-of-care ultrasound (POCUS) swallowing examinations and outcome measures for swallowing rehabilitation and evaluation of associated physiological conditions, such as sarcopenia. Findings in the current study revealed physiological differences among healthy young, healthy older, and dysphagic adults. They also helped lay the groundwork for future AI-assisted dysphagia assessment and outcome measures using POCUSs. Arguably, the proposed conceptualization and analyses are also modality-independent measures that can potentially be generalized for other instrumental swallowing assessment modalities. [ABSTRACT FROM AUTHOR]

The concept of entropy and information gain of BE-algebras in scientific disciplines such as information theory, data science, supply chain and machine learning assists us to calculate the uncertanity of the scientific processes of phenomena. In this respect the notion of filter entropy for a transitive BE-algebra is introduced and its properties are investigated. The notion of a dynamical system on a transitive BE-algebra is introduced. The concept of the entropy for a transitive BE-algebra dynamical system is developed and, its characteristics are considered. The notion of equivalent transitive BE-algebra dynamical systems is defined, and it is proved the fact that two equivalent BE-algebra dynamical systems have the same entropy. Theorems to help calculate the entropy are given. Specifically, a new version of Kolmogorov– Sinai Theorem has been proved. The study introduces the concept of information gain of a transitive BE-algebra with respect to its filters and investigates its properties. This study proposes the use of filter entropy to approximate the level of risk introduced by a BE-algebra dynamical system. This aim is reached by defining the information gain with respect to the filters of a BE-algebra. This methodology is well developed for use in engineering, especially in industrial networks. This paper proposes a novel approach to assess the quantity of uncertainty, and the impact of information gain of a BE-algebra dynamical system. [ABSTRACT FROM AUTHOR]

The paper applies the methods of information theory to the study of the interplanetary magnetic field and its variations as a result of solar activity. The statistical regularities of the projections of the vectors of the interplanetary magnetic field and the speed of the flow of solar wind particles do not carry information about the order of realization for the available states of the studied physical system. At the same time, such characteristics can be obtained from phase diagrams or phase portraits created on the basis of experimental samples in subspaces of the phase space, which display both the values of vector quantities and the sequence order in a particular time series. The paper proposes a method for synthesizing vector graphs in the phase subspace of the interplanetary magnetic field (IMF). Results are considered of the reconstruction and analysis of implemented graphs based on the time series of satellite monitoring of the state of the IMF, provided by the database of the NASA Goddard Space Flight Center since the beginning of 2023. The graph is constructed on the basis of experimental samples for projections of magnetic field vectors. Field vectors converge and diverge at the nodes of the graph, the edges of the graph allow one to control the analyzed trajectory of the system in the phase subspace and restore the transition tree for a particular vector field. The concept of a spherical reference surface of a vector graph is introduced, which allows one to bring the compared implementations of graphs to a single linear scale and a single curvature of the reference surface. Examples are considered under the action of various external factors associated with the solar magnetic field and coronal mass ejections. [ABSTRACT FROM AUTHOR]

This paper presents an analysis of how three interlocutors sequentially organize and accomplish mutual understanding in naturally occurring audiovisual recordings of therapy sessions. The analysis is in keeping with microanalysis of face-to-face dialog (MFD) and follows operational definitions of three-step micro-processes that interlocutors use when they calibrate new information; that is, how they agree that they have understood each other's words and actions well enough for current practical purposes. Pointing to some of the complexities that characterize triadic interactions, the analysis contributes with new documentations of 'suspended', 'nested', 'branched', 'multi-paced', and 'mixed interpretations' calibrations. The analysis also demonstrates how interlocutors may calibrate the 'tone' of an utterance before the topical content is mutually understood. The results and their implications may be relevant to practitioners of institutional talks at large, where the quality and outcome of, for instance, assessments and interventions largely rely on accomplishing mutual understanding. [ABSTRACT FROM AUTHOR]

When communicating, individuals alter their language to fulfill a myriad of social functions. In particular, linguistic convergence and divergence are fundamental in establishing and maintaining group identity. Quantitatively characterizing linguistic convergence is important when testing hypotheses surrounding language, including interpersonal and group communication. We provide a quantitative interpretation of linguistic convergence grounded in information theory. We then construct a computational model, built on top of a neural network model of language, that can be deployed to measure and test hypotheses about linguistic convergence in "big data." We demonstrate the utility of our convergence measurement in two case studies: (1) showing that our measurement is indeed sensitive to linguistic convergence across turns in dyadic conversation, and (2) showing that our convergence measurement is sensitive to social factors that mediate convergence in Internet-based communities (specifically, r/MensRights and r/MensLib). Our measurement also captures differences in which social factors influence web-based communities. We conclude by discussing methodological and theoretical implications of this semantic convergence analysis. [ABSTRACT FROM AUTHOR]

Information is not a monolithic entity, but can be decomposed into synergistic, unique, and redundant components. Relative predominance of synergy and redundancy in the human brain follows a unimodal–transmodal organisation and reflects underlying structure, neurobiology, and dynamics. Brain regions navigate trade-offs between these components to combine the flexibility of synergy for higher cognition and the robustness of redundancy for key sensory and motor functions. Redundancy appears stable across primate evolution, whereas synergy is selectively increased in humans and especially in human-accelerated regions. Computational studies offer new insights into the causal relationship between synergy, redundancy, and cognitive capabilities. To explain how the brain orchestrates information-processing for cognition, we must understand information itself. Importantly, information is not a monolithic entity. Information decomposition techniques provide a way to split information into its constituent elements: unique, redundant, and synergistic information. We review how disentangling synergistic and redundant interactions is redefining our understanding of integrative brain function and its neural organisation. To explain how the brain navigates the trade-offs between redundancy and synergy, we review converging evidence integrating the structural, molecular, and functional underpinnings of synergy and redundancy; their roles in cognition and computation; and how they might arise over evolution and development. Overall, disentangling synergistic and redundant information provides a guiding principle for understanding the informational architecture of the brain and cognition. [ABSTRACT FROM AUTHOR]

Using the theory of motivated information management (TMIM), this study tested the degree to which sexual minorities' uncertainty discrepancy about their partner's communication about their relationship to their family of origin predicted their information management strategies and relational quality (i.e., closeness and satisfaction). Participants included 111 sexual minorities involved in a monogamous romantic relationship. Results supported the theoretic logic of the TMIM, as uncertainty discrepancy positively predicted negative emotions, which in turn negatively predicted SMs' outcome expectancies and efficacy assessments but positively predicted direct and indirect information seeking. Indirect effects emerged for uncertainty discrepancy on direct information seeking and information avoidance, as well as for relational closeness and satisfaction, via the mechanisms specified by the TMIM. Importantly, this study reveals relational ambivalence that sexual minorities may experience as a result of managing their uncertainty about their partner's relationship disclosures to their partner's family-of-origin. [ABSTRACT FROM AUTHOR]

This article reports on the third phase of a project studying the integration of information behavior theories and concepts into professional education for reference and information service (RIS). It also provides a synthesis of the results of all studies in the project, including a syllabus study, an interview study, and the survey. Results of the project show that Kuhlthau's information search process model, along with several other key theories, models, and concepts, is perceived by instructors of introductory RIS courses in North America as important for RIS practice. Instructors feel that students are generally receptive to conceptual content in the course, although most teach a more practice-focused course or a course with a balance of conceptual and practical content. Still, 10-31% of introductory RIS courses do not include any information behavior content, suggesting that the relevance of theory to practice remains an ongoing debate. [ABSTRACT FROM AUTHOR]

The health status of the service sector workforce is a significant unknown in the field of medical geography. While spatial epidemiology has made progress in predicting the relationship between human health and the environment, there are still important challenges that remain unsolved. The main issue lies in the inability to statistically determine and visually represent all spatial concepts, as there is a need to cover a wide range of service activities while also considering the impact of numerous traditional medical variables and emerging risk factors, such as those related to socioeconomic and bioclimatic factors. This study aims to address the needs of health professionals by defining, prioritizing, and visualizing multiple occupational health risk factors that contribute to the well-being of workers. To achieve this, a methodological approach based on the synergy of Bayesian machine learning and geostatistics is proposed. Extensive data from occupational health surveillance tests were collected in Spain, along with socioeconomic and bioclimatic covariates, to assess potential social and climate impacts on health. This integrated approach enabled the identification of relevant patterns related to risk factors. A three-step geostatistical modeling process, including variography, ordinary kriging, and G clustering, was used to generate national distribution maps for various factors such as annual mean temperature, annual rainfall, spine health, limb health, cholesterol, age, and sleep quality. These maps considered four target activities—administration, finances, education, and hospitality. Remarkably, bioclimatic variables were found to contribute approximately 9% to the overall health status of workers. [ABSTRACT FROM AUTHOR]

In the theory of special relativity, energy can be found in two forms: kinetic energy and rest mass. The potential energy of a body is actually stored in the form of rest mass, the interaction energy too, but temperature is not. Information acquired about a dynamical system can be potentially used to extract useful work from it. Hence, the "mass–energy–information equivalence principle" that has been recently proposed. In this paper, it is first recalled that for a thermodynamic system made of non-interacting entities at constant temperature, the internal energy is also constant. So, the energy involved in a variation in entropy ( T Δ S ) differs from a change in the potential energy stored or released and cannot be associated to a corresponding variation in mass of the system, even if it is expressed in terms of the quantity of information. This debate gives us the opportunity to deepen the notion of entropy seen as a quantity of information, to highlight the difference between logical irreversibility (a state-dependent property) and thermodynamical irreversibility (a path-dependent property), and to return to the nature of the link between energy and information that is dynamical. [ABSTRACT FROM AUTHOR]

We investigate the zero-error coding for computing problems with encoder side information. An encoder provides access to a source X and is furnished with side information g (Y) . It communicates with a decoder that possesses side information Y and aims to retrieve f (X , Y) with zero probability of error, where f and g are assumed to be deterministic functions. In previous work, we determined a condition that yields an analytic expression for the optimal rate R * (g) ; in particular, it covers the case where P X , Y is full support. In this article, we review this result and study the side information design problem, which consists of finding the best trade-offs between the quality of the encoder's side information g (Y) and R * (g) . We construct two greedy algorithms that give an achievable set of points in the side information design problem, based on partition refining and coarsening. One of them runs in polynomial time. [ABSTRACT FROM AUTHOR]

Controlling the time evolution of a probability distribution that describes the dynamics of a given complex system is a challenging problem. Achieving success in this endeavour will benefit multiple practical scenarios, e.g., controlling mesoscopic systems. Here, we propose a control approach blending the model predictive control technique with insights from information geometry theory. Focusing on linear Langevin systems, we use model predictive control online optimisation capabilities to determine the system inputs that minimise deviations from the geodesic of the information length over time, ensuring dynamics with minimum "geometric information variability". We validate our methodology through numerical experimentation on the Ornstein–Uhlenbeck process and Kramers equation, demonstrating its feasibility. Furthermore, in the context of the Ornstein–Uhlenbeck process, we analyse the impact on the entropy production and entropy rate, providing a physical understanding of the effects of minimum information variability control. [ABSTRACT FROM AUTHOR]

It is well known that in information theory—as well as in the adjacent fields of statistics, machine learning and artificial intelligence—it is essential to quantify the dissimilarity between objects of uncertain/imprecise/inexact/vague information; correspondingly, constrained optimization is of great importance, too. In view of this, we define the dissimilarity-measure-natured generalized φ–divergences between fuzzy sets, ν –rung orthopair fuzzy sets, extended representation type ν –rung orthopair fuzzy sets as well as between those fuzzy set types and vectors. For those, we present how to tackle corresponding constrained minimization problems by appropriately applying our recently developed dimension-free bare (pure) simulation method. An analogous program is carried out by defining and optimizing generalized φ–divergences between (rescaled) basic belief assignments as well as between (rescaled) basic belief assignments and vectors. [ABSTRACT FROM AUTHOR]

Aureochromes are unique blue light-responsive light-oxygen-voltage (LOV) photoreceptors cum basic leucine zipper (bZIP) transcription factors (TFs), present exclusively in photosynthetic marine stramenopiles. Considering the availability of the complete genome sequence, this study focuses on aureochromes from Ectocarpus siliculosus. Aureochromes mediate light-regulated developmental responses in these brown photosynthetic algae. Both the LOV sensor and the bZIP effector show overall sequence-structure conservation. The structurally similar LOV + bZIP modules of aureochrome homologs/paralogs prefer a dimeric state. Besides a heterogeneous linker connecting the sensor-effector and a flexible N-terminal region, the sequence composition of both domains is vital. Aureochromes execute diverse cellular responses in different photosynthetic stramenopiles – though their activities can vary even within a given algal species. Therefore, it is important to understand whether aureochromes select dimerization partners from the same family or interact with other bZIPs as well. To regulate multifarious biological activities, it is possible that aureochromes activate the global TF interaction network. Following homo/heterodimer modeling, we address the compatibility of dimerization partners by screening through heptad repeats. We evaluate the dimer interface area in terms of gain in solvation energy and the number of hydrogen bonds/salt bridge interactions. We further explore the relative stability of these structures from a graph-theoretic perspective through well-studied measures such as the energy of the graph, average participation coefficient, and betweenness centrality. Furthermore, we also conduct an information-theoretic analysis using hitherto understudied measures such as network information centrality and Kullback-Leibler divergence. We find that all our investigations into the relative stability of the dimers using diverse methods from bioinformatics, network science, and information theory are in harmonious agreement. Coupling preferences of monomers in aureochromes can be further translated to design novel optogenetic tools useful for understanding human development and disease. [ABSTRACT FROM AUTHOR]

Motivated by a paper of Dragomir, we give new refinements for both discrete and integral Jensen inequalities using the Jensen's gap. As applications, we give refinements of various inequalities verifiable by Jensen's inequality. Topics covered: norms, quasi-arithmetic means, Hölder's inequality and f-divergences in information theory. [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]

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]

Historically, economic models of memory have neglected a pivotal aspect: decay, the established notion that memories deteriorate over time, resulting in a loss of fidelity. In this paper we show that a framework of rational memory with decay can produce the recency effect and other intuitively appealing memory phenomena. We apply the framework to both finite time and dynamic programming settings, deriving a wide range of results with a focus on comparative statics and asymptotic behaviors. Many of our results can also be applied to multi-dimensional rational inattention settings where individuals have to split their attention across several information sources. [ABSTRACT FROM AUTHOR]

This paper presents rational inattention as a new, transdiagnostic theory of information seeking in neurodevelopmental conditions that have uneven cognitive and socio‐emotional profiles, including developmental language disorder (DLD), dyslexia, dyscalculia and autism. Rational inattention holds that the optimal solution to minimizing epistemic uncertainty is to avoid imprecise information sources. The key theoretical contribution of this report is to endogenize imprecision, making it a function of the primary neurocognitive difficulties that have been invoked to explain neurodivergent phenotypes, including deficits in auditory perception, working memory, procedural learning and the social brain network. We argue that disengagement with information sources with low endogenous precision (e.g. speech in DLD, orthography‐phonology mappings in dyslexia, numeric stimuli in dyscalculia and social signals in autism) constitutes resource‐rational behaviour. We demonstrate the strength of this account in a series of computational simulations. In experiment 1, we simulate information seeking in artificial agents mimicking an array of neurodivergent phenotypes, which optimally explore a complex learning environment containing speech, text, numeric stimuli and social cues. In experiment 2, we simulate optimal information seeking in a cross‐modal dual‐task paradigm and qualitatively replicate empirical data from children with and without DLD. Across experiments, simulated agents' only aim was to maximally reduce epistemic uncertainty, with no difference in reward across information sources. We show that rational inattention emerges naturally in specific neurodivergent phenotypes as a function of low endogenous precision. For instance, an agent mimicking the DLD phenotype disengages with speech (and preferentially engages with alternative precise information sources) because endogenous imprecision renders speech not conducive to information gain. Because engagement is necessary for learning, simulation demonstrates how optimal information seeking may paradoxically contribute negatively to an already delayed learning trajectory in neurodivergent children. Research Highlights: We present the first comprehensive theory of information seeking in neurodivergent children to date, centred on the notion of rational inattention.We demonstrate the strength of this account in a series of computational simulations involving artificial agents mimicking specific neurodivergent phenotypes that optimally explore a complex learning environment containing speech, text, numeric stimuli, and social cues.We show how optimal information seeking may, paradoxically, contribute negatively to an already delayed learning trajectory in neurodivergent children.This report advances our understanding of the factors shaping short‐term decision making and long‐term learning in neurodivergent children. [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]

One of the fundamental steps toward understanding a complex system is identifying variation at the scale of the system's components that is most relevant to behavior on a macroscopic scale. Mutual information provides a natural means of linking variation across scales of a system due to its independence of functional relationship between observables. However, characterizing the manner in which information is distributed across a set of observables is computationally challenging and generally infeasible beyond a handful of measurements. Here, we propose a practical and general methodology that uses machine learning to decompose the information contained in a set of measurements by jointly optimizing a lossy compression of each measurement. Guided by the distributed information bottleneck as a learning objective, the information decomposition identifies the variation in the measurements of the system state most relevant to specified macroscale behavior. We focus our analysis on two paradigmatic complex systems: a Boolean circuit and an amorphous material undergoing plastic deformation. In both examples, the large amount of entropy of the system state is decomposed, bit by bit, in terms of what is most related to macroscale behavior. The identification of meaningful variation in data, with the full generality brought by information theory, is made practical for studying the connection between micro- and macroscale structure in complex systems. [ABSTRACT FROM AUTHOR]

In information theory, the well-known log-sum inequality is a fundamental tool which indicates the non-negativity for the relative entropy. In this article, we establish a set of inequalities which are similar to the log-sum inequality involving two functions defined on scalars. The parametric extended log-sum inequalities are shown. We extend these inequalities for the commutative matrices. In addition, utilizing the Löwner partial order relation and the Hansen–Pedersen theory for non-commutative positive semi-definite matrices we demonstrate a number of matrix-inequalities analogous to the log-sum inequality. [ABSTRACT FROM AUTHOR]

Within the causal association paradigm, a method is proposed to assess the validity of a continuous outcome as a surrogate for a binary true endpoint. The methodology is based on a previously introduced information‐theoretic definition of surrogacy and has two main steps. In the first step, a new model is proposed to describe the joint distribution of the potential outcomes associated with the putative surrogate and the true endpoint of interest. The identifiability issues inherent to this type of models are handled via sensitivity analysis. In the second step, a metric of surrogacy new to this setting, the so‐called individual causal association is presented. The methodology is studied in detail using theoretical considerations, some simulations, and data from a randomized clinical trial evaluating an inactivated quadrivalent influenza vaccine. A user‐friendly R package Surrogate is provided to carry out the evaluation exercise. [ABSTRACT FROM AUTHOR]

In information theory, the entropy ergodic theorem is a fundamental one well-known to all. In this article, we will study the generalized entropy ergodic theorem for Markov chains indexed by a spherically symmetric tree. First, we give the definition of Markov chains indexed by a spherically symmetric tree. Meanwhile, the generalized strong law of large numbers for Markov chains indexed by a spherically symmetric tree is proved. Finally, we obtain the generalized entropy ergodic theorem for Markov chains indexed by a spherically symmetric tree. [ABSTRACT FROM AUTHOR]

Color naming in natural languages is not arbitrary: It reflects efficient partitions of perceptual color space [T. Regier, P. Kay, N. Khetarpal, Proc. Natl. Acad. Sci. U.S.A. 104, 1436-1441 (2007)] modulated by the relative needs to communicate about different colors [C. Twomey, G. Roberts, D. Brainard, J. Plotkin, Proc. Natl. Acad. Sci. U.S.A. 118, e2109237118 (2021)]. These psychophysical and communicative constraints help explain why languages around the world have remarkably similar, but not identical, mappings of colors to color terms. Languages converge on a small set of efficient representations.But languages also evolve, and the number of terms in a color vocabulary may change over time. Here we show that history, i.e. the existence of an antecedent color vocabulary, acts as a nonadaptive constraint that biases the choice of efficient solution as a language transitions from a vocabulary of size n to n + 1 terms. Moreover, as efficient vocabularies evolve to include more terms they explore a smaller fraction of all possible efficient vocabularies compared to equally sized vocabularies constructed de novo. This path dependence of the cultural evolution of color naming presents an opportunity. Historical constraints can be used to reconstruct ancestral color vocabularies, allowing us to answer long-standing questions about the evolutionary sequences of color words, and enabling us to draw inferences from phylogenetic patterns of language change. [ABSTRACT FROM AUTHOR]