Model selection is the problem of distinguishing competing models, perhaps featuring different numbers of parameters. The statistics literature contains two distinct sets of tools, those based on information theory such as the Akaike Information Criterion (AIC), and those on Bayesian inference such as the Bayesian evidence and Bayesian Information Criterion (BIC). The Deviance Information Criterion combines ideas from both heritages; it is readily computed from Monte Carlo posterior samples and, unlike the AIC and BIC, allows for parameter degeneracy. I describe the properties of the information criteria, and as an example compute them from Wilkinson Microwave Anisotropy Probe 3-yr data for several cosmological models. I find that at present the information theory and Bayesian approaches give significantly different conclusions from that data.
In these lectures I give an introduction to the quantum physics of black holes, including recent developments based on quantum information theory such as the rewall paradox and its various cousins. I also give an introduction to holography and the AdS/CFT correspondence, focusing on those aspects which are relevant for the black hole information problem.
<b>Objectives</b>: Based on social cognitive theory, this study aims to explore the associated factors of and whether and how health information literacy was correlated to health behavior and glycemic control among individuals with type 2 diabetes and metabolic syndrome. <b>Methods</b>: Following convenient sampling, this cross-sectional, correlational study was conducted among 225 patients with type 2 diabetes and metabolic syndrome from an outpatient clinic in a suburban area of Beijing, China. Hierarchical multiple regression and mediation analysis were performed to explore the effect of health information literacy on self-management practice and hemoglobin A1c in this sample. The STROBE guidelines for cross-sectional studies were followed. <b>Results</b>: The findings showed incompetent health information literacy, inadequate self-management behavior, and suboptimal glycemic control in a sample of patients with type 2 diabetes and metabolic syndrome. Based on social cognitive theory, the results of regression analysis indicated that self-management attitude, health problem-solving, and chronic illness resources were correlated with self-management practice, and health problem-solving and health information evaluation were correlated with hemoglobin A1c. Mediation analysis revealed that self-management attitude, health problem-solving, and chronic disease resources fully mediated the effect of health information literacy on self-management practice. There was an indirect effect of health information literacy on hemoglobin A1c through health problem-solving. <b>Conclusions</b>: The findings demonstrated that health information literacy has significant indirect and direct effects on self-management behavior and glycemic control through self-management attitude, health problem-solving, and chronic disease resources in a sample of patients with type 2 diabetes and metabolic syndrome.
Based on the psychological traits of executives, this study investigates the impact and mechanism of Chief Executive Officer (CEO) narcissism on corporate Environmental, Social and Governance (ESG) performance. Based on the upper echelons theory, we measure the degree of CEO narcissism by the size of their handwritten signatures and empirically examine whether and how CEO narcissism affects corporate ESG performance. Using the data of Chinese A-share listed companies during 2009–2022 as the research sample, this paper finds that CEO narcissism is significantly negatively correlated with corporate ESG performance. Further, the mechanism test indicates that CEO narcissism reduces firms' ESG performance mainly through three paths: lowering internal control quality, lowering information transparency and exacerbating financing constraints. The heterogeneity test finds that the negative effect of CEO narcissism on firms' ESG performance is more significant in firms with shorter CEO tenure, lower institutional ownership, firms in non-heavy pollution industries and lower media attention. In addition, the economic consequence test finds that the negative effect of CEO narcissism on ESG performance ultimately leads to a decrease in corporate performance. We adopt a CEO narcissism perspective to innovatively deconstruct the micro-level driving mechanisms behind corporate ESG performance in the Chinese context. It provides a theoretical basis for firms to improve their governance structures to constrain the adverse behaviors of narcissistic CEOs, while empowering regulatory authorities to identify the underlying psychological causes behind differences in corporate ESG performance. This has strategic value for promoting substantive ESG investments by firms.
Information inequalities govern the ultimate limitations in information theory and as such play an pivotal role in characterizing what values the entropy of multipartite states can take. Proving an information inequality, however, quickly becomes arduous when the number of involved parties increases. For classical systems, [Yeung, IEEE Trans. Inf. Theory (1997)] proposed a framework to prove Shannon-type inequalities via linear programming. Here, we derive an analogous framework for quantum systems, based on the strong sub-additivity and weak monotonicity inequalities for the von-Neumann entropy. Importantly, this also allows us to handle constrained inequalities, which - in the classical case - served as a crucial tool in proving the existence of non-standard, so-called non-Shannon-type inequalities [Zhang & Yeung, IEEE Trans. Inf. Theory (1998)]. Our main contribution is the Python package qITIP, for which we present the theory and demonstrate its capabilities with several illustrative examples
This book provides the reader with the mathematical framework required to fully explore the potential of small quantum information processing devices. As decoherence will continue to limit their size, it is essential to master the conceptual tools which make such investigations possible. A strong emphasis is given to information measures that are essential for the study of devices of finite size, including Rnyi entropies and smooth entropies. The presentation is self-contained and includes rigorous and concise proofs of the most important properties of these measures. The first chapters will introduce the formalism of quantum mechanics, with particular emphasis on norms and metrics for quantum states. This is necessary to explore quantum generalizations of Rnyi divergence and conditional entropy, information measures that lie at the core of information theory. The smooth entropy framework is discussed next and provides a natural means to lift many arguments from information theory to the quantum setting. Finally selected applications of the theory to statistics and cryptography are discussed. The book is aimed at graduate students in Physics and Information Theory. Mathematical fluency is necessary, but no prior knowledge of quantum theory is required.
Jayavani Vankara , Muddada Murali Krishna , Sekharamahanti S. Nandhini
et al.
Due to the lack of precise medical testing for autism, such as blood tests to detect the illness, diagnosing autism spectrum disorder (ASD) has proven to be challenging. The prevalence of restrictive and/or repetitive behaviors and difficulties and impairments in social communication are hallmarks of autism spectrum disorders. This behavioral condition has been identified. Doctors assess the child's developmental history and behavior to make a diagnosis. Research results. This research used a hybrid Multi Label-Graph Convolutional Network (ML-GCN) with label-attentive neighborhood convolution to categorize the autism spectrum disorder. It offers a clear and effective graph wrapper module in particular for collecting the local attribute data of a specific node to produce a logical representation of node functioning. Additionally, the homeopathic theory recommends developing a taxonomy for attention-related terms. Furthermore, developed an adaptive graph technique that allows the model to learn the kernel for each layer dynamically and uniquely, allowing the model to acquire more valuable and efficient features. On three frequently used reference datasets, including customized and non-specialized networks, comprehensive tests were conducted to validate the neural network-based approach to multi-label classification.
A change in behavior that results from experience and is relatively permanent in nature can be defined as Learning. Behaviorism, a dominant theory used to explain learning, sought to measure only observable behaviors. In AI training, supervised and reinforcement learning methods can help machines learn from feedback and improve their performance and this process influences or biases the learner when he adopts AI as a tool for learning and when it is in their zone of understanding. Exposure to AI technologies in higher education prepares students for future careers, as they gain experience with tools and skills that are increasingly relevant in the workforce. To identify the implications of AI interaction in learning we have taken the experiences of 38 students and 15 educators from different fields. We have identified different levels of AI interactions in scaffolding, individualization, challenges, and stress. The design and implementation of different learning platforms aligned with the Zone of Proximal Development (ZPD) in higher education can also be associated with several learning theories. therefore, the features of such a platform also correspond to different learning theories.
This paper introduces the concept of accessibility from the field of transportation planning and adopts it within the context of Information Retrieval (IR). An analogy is drawn between the fields, which motivates the development of document accessibility measures for IR systems. Considering the accessibility of documents within a collection given an IR System provides a different perspective on the analysis and evaluation of such systems which could be used to inform the design, tuning and management of current and future IR systems.
The gravity method is a measurement of relatively noticeable variations in the Earth’s gravitational field caused by lateral variations in rock's density. In the current research, a new technique is applied on the previous Bouguer map of gravity surveys (conducted from 1940–1950) of the last century, by selecting certain areas in the South-Western desert of Iraqi-territory within the provinces' administrative boundary of Najaf and Anbar. Depending on the theory of gravity inversion where gravity values could be reflected to density-contrast variations with the depths; so, gravity data inversion can be utilized to calculate the models of density and velocity from four selected depth-slices 9.63 Km, 1.1 Km, 0.682 Km and 0.407 Km. The depths were selected using the power spectrum analysis technique of gravity data. Gravity data are inverted based on gravitational anomalies for each depth slice or level and the extracted equivalent depth data from available wells using a connection curve between densities and velocities, which were mostly compatible with Nafe and Drake's standard curve. The inverted gravity data images highlight the behavior of anomalies/structures in the model and domain of density/velocity, which can be utilized in the processing of the recorded seismic data and time to depth conversion, in parallel with available well's data information within the intended study area of South-Western Iraq.
Simon Caron-Huot, Frank Coronado, Anh-Khoi Trinh
et al.
Abstract How much spectral information is needed to determine the correlation functions of a conformal theory? We study this question in the context of planar supersymmetric Yang-Mills theory, where integrability techniques accurately determine the single-trace spectrum at finite ’t Hooft coupling. Corresponding OPE coefficients are constrained by dispersive sum rules, which implement crossing symmetry. Focusing on correlators of four stress-tensor multiplets, we construct combinations of sum rules which determine one-loop correlators, and we study a numerical bootstrap problem that nonperturbatively bounds planar OPE coefficients. We observe interesting cusps at the location of physical operators, and we obtain a nontrivial upper bound on the OPE coefficient of the Konishi operator outside the perturbative regime.
Nuclear and particle physics. Atomic energy. Radioactivity
Virtual assistants are becoming increasingly important speech-driven Information Retrieval platforms that assist users with various tasks. We discuss open problems and challenges with respect to modeling spoken information queries for virtual assistants, and list opportunities where Information Retrieval methods and research can be applied to improve the quality of virtual assistant speech recognition. We discuss how query domain classification, knowledge graphs and user interaction data, and query personalization can be helpful to improve the accurate recognition of spoken information domain queries. Finally, we also provide a brief overview of current problems and challenges in speech recognition.
Through chaos theory, experimental data of hourly time series are analyzed. These time series consist of Radon concentration levels and meteorological variables of temperature, pressure, and relative humidity within the boundary layer and very close to the ground. Results were obtained in two urban dwellings for family use and for two different periods of time, of the order of one month and one month plus one week, respectively. Each time series was subjected to a chaotic analysis showing the existence of the characteristic chaotic parameters in the appropriate ranges: Lyapunov coefficient (λ), correlation dimension (Dc), Kolmogorov entropy (SK), Lempel-Ziv complexity (LZ), Hurst coefficient (H), maximum predictability time (τ), lost information (<ΔI>) and fractal dimension (D). The studied processes show to be irreversible. From the chaotic parameters, it is shown that the ratio between the entropy of each meteorological variable and the radon concentration is very sensitive to relative humidity. Likewise, the meteorological variables that most affect the concentration of Radon are relative humidity and temperature. The concordance between the results obtained and those delivered by analyzes carried out through other methodologies in longer periods is verified.
Abstract Water as an important assistant can alter the reactivity of atmospheric species. This project is designed to investigate the impact of a single water molecule on the atmospheric reactions of aromatic compounds that have not been attended to comprehensively. In the first part, the atmospheric oxidation mechanisms of thiophene initiated by hydroperoxyl radical through a multiwell-multichannel potential energy surface were studied to have useful information about the chemistry of the considered reaction. It was verified that for the thiophene plus HO2 reaction, the addition mechanism is dominant the same as other aromatic compounds. Due to the importance of the subject and the presence of water molecules in the atmosphere with a high concentration that we know as relative humidity, and also the lack of insight into the influence of water on the reactions of aromatic compounds with active atmospheric species, herein, the effect of a single water molecule on the addition pathways of the title reaction is evaluated. In another word, this research explores how water can change the occurrence of reactions of aromatic compounds in the atmosphere. For this, the presence of one water molecule is simulated by higher-level calculations (BD(T) method) through the main interactions with the stationary points of the most probable pathways. The results show that the mechanism of the reaction with water is more complicated than the bare reaction due to the formation of the ring-like structures. Also, water molecule decreases the relative energies of all addition pathways. Moreover, atoms in molecule theory (AIM) along with the kinetic study by the transition state (TST) and the Rice–Ramsperger–Kassel–Marcus (RRKM) theories demonstrate that the overall interactions of a path determine how the rate of that path changes. In this regard, our results establish that the interactions of water with HO2 (thiophene) in the initial complex 1WHA (1WTA or 1WTB) are stronger (weaker) than the sum of its interactions in transition states. Also, for the water-assisted pathways, the ratio of the partition function of the transition state to the partition functions of the reactants is similar to the respective bare reaction. Therefore, the reaction rates of the bare pathways are more than the water-assisted paths that include the 1WHA complex and are less than the paths that involve the 1WTA and 1WTB complexes.
We describe a rational approach to reduce the computational and communication complexities of lossless point-to-point compression for computation with side information. The traditional method relies on building a characteristic graph with vertices representing the source symbols and with edges that assign a source symbol to a collection of independent sets to be distinguished for the exact recovery of the function. Our approach uses fractional coloring for a b-fold coloring of characteristic graphs to provide a linear programming relaxation to the traditional coloring method and achieves coding at a fine-grained granularity. We derive the fundamental lower bound for compression, given by the fractional characteristic graph entropy, through generalizing the notion of Körner's graph entropy. We demonstrate the coding gains of fractional coloring over traditional coloring via a computation example. We conjecture that the integrality gap between fractional coloring and traditional coloring approaches the smallest b that attains the fractional chromatic number to losslessly represent the independent sets for a given characteristic graph, up to a linear scaling which is a function of the fractional chromatic number.