ON THE INITIAL VALUE OF PAGERANK

This paper provides the first systematic epistemological account of simulated data in empirical science. We focus on the epistemic issues modelers face when they generate simulated data to solve problems with empirical datasets, research tools, or experiments. We argue that for simulated data to count as epistemically reliable, a simulation model does not have to mimic its target. Instead, some models take empirical data as a target, and simulated data may successfully mimic such a target even if the model does not. We show how to distinguish between simulated and empirical data, and we also offer a definition of simulation that can accommodate Monte Carlo models. We shed light on the epistemology of simulated data by providing a taxonomy of four different mimicking relations that differ concerning the nature of the relation or relata. We illustrate mimicking relations with examples from different sciences. Our main claim is that the epistemic evaluation of simulated data should start with recognizing the diversity of mimicking relations rather than presuming that only one relation existed.

Functional connectivity (FC) has been invaluable for understanding the brain’s communication network, with strong potential for enhanced FC approaches to yield additional insights. Unlike with the fMRI field-standard method of pairwise correlation, theory suggests that partial correlation can estimate FC without confounded and indirect connections. However, partial correlation FC can also display low repeat reliability, impairing the accuracy of individual estimates. We hypothesized that reliability would be increased by adding regularization, which can reduce overfitting to noise in regression-based approaches like partial correlation. We therefore tested several regularized alternatives – graphical lasso, graphical ridge, and principal component regression – against unregularized partial and pairwise correlation, applying them to empirical resting-state fMRI and simulated data. As hypothesized, regularization vastly improved reliability, quantified using between-session similarity and intraclass correlation. This enhanced reliability then granted substantially more accurate individual FC estimates when validated against structural connectivity (empirical data) and ground truth networks (simulations). Graphical lasso showed especially high accuracy among regularized approaches, seemingly by maintaining more valid underlying network structures. We additionally found graphical lasso to be robust to noise levels, data quantity, and subject motion – common fMRI error sources. Lastly, we demonstrated that resting-state graphical lasso FC can effectively predict fMRI task activations and individual differences in behavior, further establishing its reliability, external validity, and ability to characterize task-related functionality. We recommend graphical lasso or similar regularized methods for calculating FC, as they can yield more valid estimates of unconfounded connectivity than field-standard pairwise correlation, while overcoming the poor reliability of unregularized partial correlation.

Functional connectivity (FC) has been invaluable for understanding the brain’s communication network, with strong potential for enhanced FC approaches to yield additional insights. Unlike with the fMRI field-standard method of pairwise correlation, theory suggests that partial correlation can estimate FC without confounded and indirect connections. However, partial correlation FC can also display low repeat reliability, impairing the accuracy of individual estimates. We hypothesized that reliability would be increased by adding regularization, which can reduce overfitting to noise in regression-based approaches like partial correlation. We therefore tested several regularized alternatives—graphical lasso, graphical ridge, and principal component regression—against unregularized partial and pairwise correlation, applying them to empirical resting-state fMRI and simulated data. As hypothesized, regularization vastly improved reliability, quantified using between-session similarity and intraclass correlation. This enhanced reliability then granted substantially more accurate individual FC estimates when validated against structural connectivity (empirical data) and ground truth networks (simulations). Graphical lasso showed especially high accuracy among regularized approaches, seemingly by maintaining more valid underlying network structures. We additionally found graphical lasso to be robust to noise levels, data quantity, and subject motion—common fMRI error sources. Lastly, we demonstrated that resting-state graphical lasso FC can effectively predict fMRI task activations and individual differences in behavior, further establishing its reliability, external validity, and ability to characterize task-related functionality. We recommend graphical lasso or similar regularized methods for calculating FC, as they can yield more valid estimates of unconfounded connectivity than field-standard pairwise correlation, while overcoming the poor reliability of unregularized partial correlation.

Development of a Coordinate Transformation method for direct georeferencing in map projection frames

The historical signal in nucleotide sequences becomes eroded over time by substitutions occurring repeatedly at the same sites. This phenomenon, known as substitution saturation, is recognized as one of the primary obstacles to deep-time phylogenetic inference using genome-scale data sets. We present a new test of substitution saturation and demonstrate its performance in simulated and empirical data. For some of the 36 empirical phylogenomic data sets that we examined, we detect substitution saturation in around 50% of loci. We found that saturation tends to be flagged as problematic in loci with highly discordant phylogenetic signals across sites. Within each data set, the loci with smaller numbers of informative sites are more likely to be flagged as containing problematic levels of saturation. The entropy saturation test proposed here is sensitive to high evolutionary rates relative to the evolutionary timeframe, while also being sensitive to several factors known to mislead phylogenetic inference, including short internal branches relative to external branches, short nucleotide sequences, and tree imbalance. Our study demonstrates that excluding loci with substitution saturation can be an effective means of mitigating the negative impact of multiple substitutions on phylogenetic inferences. [Phylogenetic model performance; phylogenomics; substitution model; substitution saturation; test statistics.]

Species trees from consensus single nucleotide polymorphism (SNP) data: Testing phylogenetic approaches with simulated and empirical data

POD curves estimations are based on statistical studies of empirical data obtained from inspection results. In order to achieve statistical significance, requirements are that 60 to 80 mock-ups containing “realistic” flaws must be inspected by a set of inspectors. This is a costly and time consuming process and the effort must be done for each NDT configuration which requires POD estimation. One way to achieve cost reduction is to replace some of the required experimental data with numerical simulation results. This idea follows the concept of Model Assisted POD (MAPOD). POD curves are no longer estimated from a fully empirical dataset but rather from a mix of experimental and simulated data. Simulations are performed using physics-based models, whose predictions are validated for the considered application case. In order to make the approach suitable for industrial needs, it is required that uncertainties introduced in the process thru the merging of simulation and experimental data are assessed. In this paper, a statistical method based on Bayesian updating is proposed, which mixes numerical simulations and information brought by the measurements. A practical implementation of the approach to a high frequency eddy current inspection for fatigue cracks is presented.

A comparative study between simulated residential electricity demand data and metered data from the UK Household Electricity Survey is presented. For this study, a high-resolution probabilistic model was used to test whether this increasingly widely used modelling approach provides an adequate representation of the statistical characteristics the most comprehensive dataset of metered electricity demand available in the UK. Both the empirical and simulated electricity consumption data have been analysed on an aggregated level, paying special attention to the mean daily load profiles, the distribution of households with respect to the total annual demands, and the distributions of the annual demands of particular appliances. A thorough comparison making use of both qualitative and quantitative methods was made between simulated datasets and it's metered counterparts. Significant discrepancies were found in the distribution of households with respect to both overall electricity consumption and consumption of individual appliances. Parametric estimates of the distributions of metered data were obtained, and the analytic expressions for both the density function and cumulative distribution are given. These can be incorporated into new and existent modelling frameworks, as well as used as tools for further analysis.

Software for realistically simulating complex population genomic processes is revolutionizing our understanding of evolutionary processes, and providing novel opportunities for integrating empirical data with simulations. However, the integration between standalone simulation software and R is currently not well developed. Here, we present slimr, an R package designed to create a seamless link between standalone software SLiM >3.0, one of the most powerful population genomic simulation frameworks, and the R development environment, with its powerful data manipulation and analysis tools. We show how slimr facilitates smooth integration between genetic data, ecological data and simulation in a single environment. The package enables pipelines that begin with data reading, cleaning and manipulation, proceed to constructing empirically based parameters and initial conditions for simulations, then to running numerical simulations and finally to retrieving simulation results in a format suitable for comparisons with empirical data - aided by advanced analysis and visualization tools provided by R. We demonstrate the use of slimr with an example from our own work on the landscape population genomics of desert mammals, highlighting the advantage of having a single integrated tool for both data analysis and simulation. slimr makes the powerful simulation ability of SLiM directly accessible to R users, allowing integrated simulation projects that incorporate empirical data without the need to switch between software environments. This should provide more opportunities for evolutionary biologists and ecologists to use realistic simulations to better understand the interplay between ecological and evolutionary processes.

World Wide Web (WWW) is a platform that explores a wide range of information used for the development of web applications. Some examples of these applications include social network analysis, personalized item recommendations, and web page classification and ranking. Among these applications, search engines and web page ranking are particularly important as they consistently index and store billions of web pages on the internet. The main objective of this paper is to create an innovative framework for the classification and re-ranking of web pages using intelligent techniques. The framework is structured into two key phases: classification and re-ranking-based retrieval. In the initial classification phase, a series of pre-processing steps are implemented, including the elimination of HTML tags, punctuation, stop words, and the application of stemming. After pre-processing, a word-to-vector conversion is performed, followed by feature extraction utilizing Principal Component Analysis (PCA). This sequence of actions leads to optimal feature selection, which is vital for the precise classification of web pages. Given the multitude of features present in web pages that can compromise classification accuracy, this study employs a novel meta-heuristic algorithm, the Opposition Based-Tunicate Swarm Algorithm (O-TSA), to facilitate optimal feature selection. The refined features are subsequently processed through the Enhanced Convolutional-Recurrent Neural Network (E-CRNN), enhanced by O-TSA, resulting in the effective classification of diverse web page categories. In the second phase, the re-ranking process is executed using O-TSA, which establishes the objective function based on a similarity function (correlation) for URL matching, leading to optimal re-ranking of web.

The estimation of migration rates using molecular markers is an important aspect of many population genetic studies. Several different methods are available for estimating migration, but most of these make multiple limiting assumptions. One method that is relatively free from assumptions is BAYESASS, which uses assignment methods in a Bayesian framework. However, when tested using simulated data, this method was found to have problems with the convergence of the Markov chain Monte Carlo. Here, I perform a literature study to test whether these convergence problems are also present when BAYESASS is used to estimate migration rates from empirical data. A review of 100 studies that have used BAYESASS shows that this is indeed the case. The estimated proportions of nonmigrants were mostly either close to 2/3 or 1, indicating that the MCMC tends to get trapped near the bounds of the prior distribution. In addition, I found that the quality of the inference was negatively affected by the number of sampled populations, but increased with increasing numbers of sampled individuals and with the strength of the population structure as measured by FST . Based on these results, I give several recommendations that should help to reduce problems when using BAYESASS with empirical data. Most importantly, I argue that researchers should be more realistic about inferences of migration rates and that BAYESASS will give optimal results only when the experiment has been especially designed around its use.

Measuring the relevance of the microstructure noise in financial data

Logical character dependency is a major conceptual and methodological problem in phylogenetic inference of morphological data sets, as it violates the assumption of character independence that is common to all phylogenetic methods. It is more frequently observed in higher-level phylogenies or in data sets characterizing major evolutionary transitions, as these represent parts of the tree of life where (primary) anatomical characters either originate or disappear entirely. As a result, secondary traits related to these primary characters become "inapplicable" across all sampled taxa in which that character is absent. Various solutions have been explored over the last three decades to handle character dependency, such as alternative character coding schemes and, more recently, new algorithmic implementations. However, the accuracy of the proposed solutions, or the impact of character dependency across distinct optimality criteria, has never been directly tested using standard performance measures. Here, we utilize simple and complex simulated morphological data sets analyzed under different maximum parsimony optimization procedures and Bayesian inference to test the accuracy of various coding and algorithmic solutions to character dependency. This is complemented by empirical analyses using a recoded data set on palaeognathid birds. We find that in small, simulated data sets, absent coding performs better than other popular coding strategies available (contingent and multistate), whereas in more complex simulations (larger data sets controlled for different tree structure and character distribution models) contingent coding is favored more frequently. Under contingent coding, a recently proposed weighting algorithm produces the most accurate results for maximum parsimony. However, Bayesian inference outperforms all parsimony-based solutions to handle character dependency due to fundamental differences in their optimization procedures-a simple alternative that has been long overlooked. Yet, we show that the more primary characters bearing secondary (dependent) traits there are in a data set, the harder it is to estimate the true phylogenetic tree, regardless of the optimality criterion, owing to a considerable expansion of the tree parameter space. [Bayesian inference, character dependency, character coding, distance metrics, morphological phylogenetics, maximum parsimony, performance, phylogenetic accuracy.].

Crosstalk plays a critical role in prevention research to promote purposeful behavior change through randomized controlled trials. However, two challenges prevent researchers from assessing crosstalk between subjects in the intervention and the control conditions that may contaminate an intervention trial. First, it is very hard if not impossible to identify who in the intervention group have talked with whom in the control group; therefore the crosstalk effect cannot be statistically evaluated. Second no method is readily available to quantify crosstalk even if we know who has talked with whom. To overcome the challenges, we devised the random-pairing bootstrap (RPB) method based on statistical principles and adapted the mutual information (MI) technique from the information sciences. The established RPB method provides a novel approach for researchers to identify participants in the intervention and the control groups who might have talked with each other; the MI itself is an analytical method capable of quantifying both linear and nonlinear relationships on a variable between two groups of subjects who might have experienced information exchange. An MI measure therefore provides evidence supporting the effect from crosstalk on a target variable with data generated through RPB. To establish the PRB-MI methodology, we first conducted a systematic test with simulated data. We then analyzed empirical data from a randomized controlled trial (n=1360) funded by the National Institute of Health. Analytical results with simulated data indicate that RBP-MI method can effectively detect a known crosstalk effect with different effect sizes. Analytical results with empirical data show that effects from within-group crosstalk are greater than those of between-group crosstalk, which is within our expectation. These findings suggest the validity and utility of the RBP-MI method in behavioral intervention research. Further research is needed to improve the method.

Plant-specific human reliability analysis (HRA) tailors human error probabilities (HEPs) to a facility's unique contexts. The diversity of digital human-machine interfaces and operational cultures necessitates methods grounded in empirical data rather than generic databases to ensure true plant-specificity. Advancing beyond prior fragmented data applications, this study establishes a systematic pipeline integrating the HuREX (Human Reliability data Extraction) database with the EMBRACE (EMpirical data-Based crew Reliability Assessment and Cognitive Error analysis) method. By sharing a unified task taxonomy, this framework directly translates plant-specific simulator data into coherent HEP estimates. In an application of this framework to the APR1400 plant, time-dependent failure probabilities were derived using Bayesian inference on 30 simulator performance records. Concurrently, nominal primitive error probabilities (NPEPs) were estimated via logistic regression from 44,585 task records. Due to current data scarcity, performance shaping factor (PSF) multipliers were determined through structured expert elicitation. This data-method integration provides a replicable foundation for generating context-sensitive HEPs in digitalized control rooms, highlighting both empirical strengths and current data limitations. • A plant-specific HRA approach is developed by integrating HuREX with EMBRACE. • HuREX data support the estimation of the time and cognitive failure probabilities. • The proposed approach is applied to estimate HEPs for the APR1400 plant. • Limitations such as reliance on expert judgment are discussed.