Hasil untuk "Information theory"

K. Malterud, V. Siersma, A. D. Guassora

Wynne W. Chin, Abhijit Gopal, William David Salisbury

V. Grover, M. Cheon, J. Teng

S. Robertson

Saul A. Kripke

E. Swanson

Rick Lambert

西森 秀稔

Paul L. Williams, R. Beer

Of the various attempts to generalize information theory to multiple variables, the most widely utilized, interaction information, suffers from the problem that it is sometimes negative. Here we reconsider from first principles the general structure of the information that a set of sources provides about a given variable. We begin with a new definition of redundancy as the minimum information that any source provides about each possible outcome of the variable, averaged over all possible outcomes. We then show how this measure of redundancy induces a lattice over sets of sources that clarifies the general structure of multivariate information. Finally, we use this redundancy lattice to propose a definition of partial information atoms that exhaustively decompose the Shannon information in a multivariate system in terms of the redundancy between synergies of subsets of the sources. Unlike interaction information, the atoms of our partial information decomposition are never negative and always support a clear interpretation as informational quantities. Our analysis also demonstrates how the negativity of interaction information can be explained by its confounding of redundancy and synergy.

M.R. Bell

Marie-E. Zubler, (née Godefroid), Julian Koch, Ralf Plattfaut

Non-governmental organizations (NGOs) typically have restrained information and communication technology (ICT) budgets and resources. At the same time, they face high pressure to reduce administrative costs. A possible solution to the resulting conundrum could be user-driven technology. This term describes a selection of technologies, including intelligent process automation, low-code platforms, and business intelligence tools that push innovation and user-centricity by letting operational employees directly deploy comparably cheap solutions without the need for central ICT support. Practitioner literature indicates, however, that user-driven technologies are lagging in the social sector despite evidence from some individual success stories published by researchers. Thus, a systematic assessment of user-driven technologies within NGOs and of potential challenges in their introduction is necessary. To close this research gap, we employ the method of computationally intensive theory construction, combining data mining with qualitative interviews. Results indicate that user-driven technologies are indeed lagging and that forming a problem-mindset and creating adequate governance structures are the main challenges to their introduction within NGOs.

Huanhuan Ma, Ran Ding, Junwen Wang et al.

BackgroundIn recent years, tryptophan metabolism has gained increasing attention for its pivotal role in shaping the tumor immune microenvironment and promoting cancer progression. As a result, it has become a central topic in cancer metabolism and tumor immunology. This study applies a comprehensive bibliometric approach to analyze global research trends in tryptophan metabolism within the context of cancer. By identifying emerging hotspots, leading contributors, and patterns of international collaboration, this work aims to provide meaningful insights to guide future therapeutic strategies targeting metabolic pathways in oncology.MethodsA systematic literature search was performed using the Web of Science Core Collection to retrieve publications related to tryptophan metabolism in cancer from 2005 to 2024. Bibliometric and visual analyses were conducted using CiteSpace, VOSviewer, and Python to examine publication trends, national and institutional contributions, author productivity, journal influence, co-citation networks, and keyword co-occurrence patterns.ResultsA total of 1,927 publications were identified, authored by 11,134 researchers from 70 countries and published in 781 academic journals. The volume of publications showed a steady increase, peaking in 2021. The United States and China emerged as the dominant contributors, excelling in both research output and international collaboration. Dietmar Fuchs was identified as the most prolific author, with 61 publications. The Medical University of Innsbruck was the leading institution, with 144 publications. Frontiers in Immunology demonstrated strong citation performance and academic impact. Co-citation and keyword analysis revealed key research themes, including “IDO (indoleamine 2,3-dioxygenase),” “tryptophan catabolism,” “cancer,” and “dendritic cells,” as well as emerging topics such as “gut microbiota,” “tumor microenvironment,” “aryl hydrocarbon receptor,” and “cancer immunotherapy.”ConclusionThis study highlights the growing significance of tryptophan metabolism research in cancer, underlining the complex interactions between metabolic pathways and immune responses. Further investigations are needed to explore the therapeutic potential of these metabolic pathways, which could lead to novel cancer treatment strategies.

Kaiwei Liu, Bing Yuan, Jiang Zhang

After coarse-graining a complex system, the dynamics of its macro-state may exhibit more pronounced causal effects than those of its micro-state. This phenomenon, known as causal emergence, is quantified by the indicator of effective information. However, two challenges confront this theory: the absence of well-developed frameworks in continuous stochastic dynamical systems and the reliance on coarse-graining methodologies. In this study, we introduce an exact theoretic framework for causal emergence within linear stochastic iteration systems featuring continuous state spaces and Gaussian noise. Building upon this foundation, we derive an analytical expression for effective information across general dynamics and identify optimal linear coarse-graining strategies that maximize the degree of causal emergence when the dimension averaged uncertainty eliminated by coarse-graining has an upper bound. Our investigation reveals that the maximal causal emergence and the optimal coarse-graining methods are primarily determined by the principal eigenvalues and eigenvectors of the dynamic system’s parameter matrix, with the latter not being unique. To validate our propositions, we apply our analytical models to three simplified physical systems, comparing the outcomes with numerical simulations, and consistently achieve congruent results.

Michael J. Casey, Jörg Fliege, Rubén J. Sánchez-García et al.

Abstract Background Single-cell sequencing (sc-Seq) experiments are producing increasingly large data sets. However, large data sets do not necessarily contain large amounts of information. Results Here, we formally quantify the information obtained from a sc-Seq experiment and show that it corresponds to an intuitive notion of gene expression heterogeneity. We demonstrate a natural relation between our notion of heterogeneity and that of cell type, decomposing heterogeneity into that component attributable to differential expression between cell types (inter-cluster heterogeneity) and that remaining (intra-cluster heterogeneity). We test our definition of heterogeneity as the objective function of a clustering algorithm, and show that it is a useful descriptor for gene expression patterns associated with different cell types. Conclusions Thus, our definition of gene heterogeneity leads to a biologically meaningful notion of cell type, as groups of cells that are statistically equivalent with respect to their patterns of gene expression. Our measure of heterogeneity, and its decomposition into inter- and intra-cluster, is non-parametric, intrinsic, unbiased, and requires no additional assumptions about expression patterns. Based on this theory, we develop an efficient method for the automatic unsupervised clustering of cells from sc-Seq data, and provide an R package implementation.

Jonathan W. Kelly, Taylor A. Doty, Lucia A. Cherep et al.

Virtual reality users are susceptible to disorientation, particularly when using locomotion interfaces that lack self-motion cues. Environmental cues, such as boundaries defined by walls or a fence, provide information to help the user remain oriented. This experiment evaluated whether the type of boundary impacts its usefulness for staying oriented. Participants wore a head-mounted display and performed a triangle completion task in virtual reality by traveling two outbound path segments before attempting to point to the path origin. The task was completed with two teleporting interfaces differing in the availability of rotational self-motion cues, and within five virtual environments differing in the availability and type of boundaries. Pointing errors were highest in an open field without environmental cues, and lowest in a classroom with walls and landmarks. Environments with a single square boundary defined by a fence, drop-off, or floor texture discontinuity led to errors in between the open field and the classroom. Performance with the floor texture discontinuity was similar to that with navigational barriers (i.e., fence and drop-off), indicating that an effective barrier need not be a navigational impediment. These results inform spatial cognitive theory about boundary-based navigation and inform application by specifying the types of environmental and self-motion cues that designers of virtual environments should include to reduce disorientation in virtual reality.

Emmanuel Asafo-Adjei, Anokye Mohammed Adam, Peterson Owusu Junior et al.

We model a mixture of asymmetric and nonlinear bidirectional and unidirectional causality between four macroeconomic variables (exchange rate, GDP, global economic policy uncertainty, and relative CPI) and stock returns of BRICS economies in the frequency-domain using the information flow theory. The Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN)-based Rényi effective transfer entropy approach is used to establish dynamic flow of information between macroeconomic variables and stock returns of BRICS. The original return series suggested insignificant information flow between most macroeconomic variables and stock returns. However, we reveal both asymmetric and tail dependent analyses at diverse scales between macroeconomic variables and stock returns of BRICS economies. Moreover, we find negative significant flow of information between the variables, in that knowing the history of one variable (either stock or macroeconomic variable), in this case, indicates considerably more uncertainty than knowing the history of only the other variable (either stock or macroeconomic variable). We also observe that global economic policy uncertainty has the most significant adverse causal relationship with stock returns of BRICS, especially in the long term. These results have important implications that investors and policymakers should take into account. Regulators should consider instituting sound policy actions geared towards minimising long-term effects of external shocks and uncertainties.

I.V. Bychkov, G.M. Ruzhnikov, R.K. Fedorov et al.

The paper considers a problem of classifying Sentinel-2 multispectral satellite images for environmental monitoring of the Baikal Natural Territory (BNT). The specificity of the BNT required the creation of a new set of 12 classes, which takes into account current problems. The set was formed in such a way that the areas corresponding to these classes completely covered the BNT. A training dataset was formed using a web interface based on Sentinel-2 satellite images. The classification of satellite images was carried out using Random Forest algorithms and the ResNet50 neural network. The accuracy of the calculations showed that the classification results can be used to solve actual problems of the Baikal natural territory, in particular, to analyze changes in the forestland, assess the impact of climate change on the landscape, analyze the dynamics of development activities, create farmland inventory, etc.

Damien Lekkas, Joseph A Gyorda, George D Price et al.

BackgroundThe digital era has ushered in an unprecedented volume of readily accessible information, including news coverage of current events. Research has shown that the sentiment of news articles can evoke emotional responses from readers on a daily basis with specific evidence for increased anxiety and depression in response to coverage of the recent COVID-19 pandemic. Given the primacy and relevance of such information exposure, its daily impact on the mental health of the general population within this modality warrants further nuanced investigation. ObjectiveUsing the COVID-19 pandemic as a subject-specific example, this work aimed to profile and examine associations between the dynamics of semantic affect in online local news headlines and same-day online mental health term search behavior over time across the United States. MethodsUsing COVID-19–related news headlines from a database of online news stories in conjunction with mental health–related online search data from Google Trends, this paper first explored the statistical and qualitative affective properties of state-specific COVID-19 news coverage across the United States from January 23, 2020, to October 22, 2020. The resultant operationalizations and findings from the joint application of dictionary-based sentiment analysis and the circumplex theory of affect informed the construction of subsequent hypothesis-driven mixed effects models. Daily state-specific counts of mental health search queries were regressed on circumplex-derived features of semantic affect, time, and state (as a random effect) to model the associations between the dynamics of news affect and search behavior throughout the pandemic. Search terms were also grouped into depression symptoms, anxiety symptoms, and nonspecific depression and anxiety symptoms to model the broad impact of news coverage on mental health. ResultsExploratory efforts revealed patterns in day-to-day news headline affect variation across the first 9 months of the pandemic. In addition, circumplex mapping of the most frequently used words in state-specific headlines uncovered time-agnostic similarities and differences across the United States, including the ubiquitous use of negatively valenced and strongly arousing language. Subsequent mixed effects modeling implicated increased consistency in affective tone (SpinVA β=–.207; P<.001) as predictive of increased depression-related search term activity, with emotional language patterns indicative of affective uncontrollability (FluxA β=.221; P<.001) contributing generally to an increase in online mental health search term frequency. ConclusionsThis study demonstrated promise in applying the circumplex model of affect to written content and provided a practical example for how circumplex theory can be integrated with sentiment analysis techniques to interrogate mental health–related associations. The findings from pandemic-specific news headlines highlighted arousal, flux, and spin as potentially significant affect-based foci for further study. Future efforts may also benefit from more expansive sentiment analysis approaches to more broadly test the practical application and theoretical capabilities of the circumplex model of affect on text-based data.