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Medieval paper, a handmade product, is made with a mould which leaves an indelible imprint on the sheet of paper. This imprint includes chain lines, laid lines and watermarks which are often visible on the sheet. Extracting these features allows the identification of the paper stock and gives information about the chronology, localisation and movement of manuscripts and people. Most computational work for feature extraction of paper analysis has so far focused on radiography or transmitted light images. While these imaging methods provide clear visualisation of the features of interest, they are expensive and time consuming in their acquisition and not feasible for smaller institutions. However, reflected light images of medieval paper manuscripts are abundant and possibly cheaper in their acquisition. In this paper, we propose algorithms to detect and extract the laid and chain lines from reflected light images. We tackle the main drawback of reflected light images, that is, the low contrast attenuation of chain and laid lines and intensity jumps due to noise and degradation, by employing the spectral total variation decomposition and develop methods for subsequent chain and laid line extraction. Our results clearly demonstrate the feasibility of using reflected light images in paper analysis. This work enables feature extraction for paper manuscripts that have otherwise not been analysed due to a lack of appropriate images. We also open the door for paper stock identification at scale. [ABSTRACT FROM AUTHOR]

Compared to traditional partial differential equation modeling methods, Markov switching models can accurately capture the abrupt changes or jumps that complex systems often experience in the real world. In this paper, we propose a novel wave equation model with Markovian switching to represent complex systems with state‐jumping phenomena better, and the well‐posedness of the model is proved. In addition, a numerical method with non‐uniform grids is also proposed for the proposed model to simulate the data in realistic situations, which is based on the use of finite element discretization in space and central difference discretization in time. Finally, we conduct several experiments to analyze the errors and stability of the proposed model and the traditional model. The results show that the Markov switching model proposed in this paper has a smaller error than the traditional models while ensuring stability and can more accurately simulate the state jump phenomena of real‐world systems. [ABSTRACT FROM AUTHOR]

This paper discusses estimation and limited‐information goodness‐of‐fit test statistics in factor models for binary data using pairwise likelihood estimation and sampling weights. The paper extends the applicability of pairwise likelihood estimation for factor models with binary data to accommodate complex sampling designs. Additionally, it introduces two key limited‐information test statistics: the Pearson chi‐squared test and the Wald test. To enhance computational efficiency, the paper introduces modifications to both test statistics. The performance of the estimation and the proposed test statistics under simple random sampling and unequal probability sampling is evaluated using simulated data. [ABSTRACT FROM AUTHOR]

Uncertain regression analysis is a powerful analytical tool to model the relationships between explanatory variables and the response variable by uncertainty theory. One of the core problems in uncertain regression analysis is to estimate the unknown parameters of an uncertain regression model and the uncertain disturbance term. In this paper, the moment estimation of uncertain regression model is proposed, which can determine both the uncertain regression model and the disturbance term at one time. After that, the uncertain hypothesis test is used to test whether the estimated uncertain regression model is appropriate. Furthermore, a real-world example of factors analysis of grain yield is provided to illustrate the moment estimation. Finally, as a byproduct, this paper also indicates that the stochastic regression model cannot model the agriculture data. [ABSTRACT FROM AUTHOR]

This paper introduces and thoroughly examines the Exponentiated Gompertz Inverse Rayleigh (EtGoIr) Distribution, a four-parameter extension of the Gompertz Inverse Rayleigh distribution. The primary focus is on its application to biomedical datasets, shedding light on its mathematical and statistical properties. Some properties of the distribution that were derived include the quantile function, median, moments, incomplete moments, Rényi entropy, and probability weighted moments. The model parameters were estimated using the method of maximum likelihood. A simulation study was conducted to investigate the consistency of the proposed model. The outcome of the investigation revealed that the model demonstrates consistency, as evidenced by the reduction in both root mean square error (RMSE) and bias as sample sizes increase. To showcase the practical relevance of the EtGoIr distribution, the paper applies the model to three distinct biomedical datasets. The results highlight its enhanced flexibility, demonstrating superior fit compared to its counterpart. [ABSTRACT FROM AUTHOR]

Counts of a specific characteristic were obtained within regions defined on an object that was manufactured in a proprietary setting. The count regions were altered during production and resulted in misaligned or overlapping count data. A closed-formula maximum likelihood estimator (MLE) of the new region means is derived using all of the available count data and an independent Poisson model. The MLE is shown to be preferable to estimators constructed using generalized linear models for the overlapping data setting. This closed-form estimator extends to over-dispersed overlapping count data as the quasi-MLE and also performs well with correlated overlapping count data. Standard errors for the estimator are approximated and are validated with a simulation study. Additionally, the methods are extended to overlapping multinomial data. Illustrative examples of the methods are provided throughout the paper and are reproducible with the supplemental R code. Proofs of the paper's results are also included in the supplemental material. [ABSTRACT FROM AUTHOR]

Although deep learning (DL) algorithms have been proved to be effective in diverse research domains, their application in developing models for tabular data remains limited. Models trained on tabular data demonstrate higher efficacy using traditional machine learning models than DL models, which are largely attributed to the size and structure of tabular datasets and the specific application contexts in which they are utilized. Thus, the primary objective of this paper is to propose a method to use the supremacy of Stacked Bidirectional LSTM (Long Short-Term Memory) deep learning algorithms in pattern discovery incorporating tabular data with customized 3D tensor modeling in feeding neural networks. Our findings are empirically validated using six diverse, publicly available datasets each varying in size and learning objectives. This paper proves that the proposed model based on time-sequence DL algorithms, which were generally described as inadequate when dealing with tabular data, yields satisfactory results and competes effectively with other algorithms specifically designed for tabular data. An additional benefit of this approach is its ability to preserve simplicity while ensuring fast model training also with large datasets. Even with extremely small datasets, models can be applied to achieve exceptional predictive results and fully utilize their capacity. [ABSTRACT FROM AUTHOR]

Traditional machine learning-based entity extraction methods rely heavily on feature engineering by experts, and the generalization ability of the model is poor. Prototype networks, on the other hand, can effectively use a small amount of labeled data to train models while using category prototypes to enhance the generalization ability of the models. Therefore, this paper proposes a prototype network-based named entity recognition (NER) method, namely the FSPN-NER model, to solve the problem of difficult recognition of sensitive data in data-sparse text. The model utilizes the positional coding model (PCM) to pre-train the data and perform feature extraction, then computes the prototype vectors to achieve entity matching, and finally introduces a boundary detection module to enhance the performance of the prototype network in the named entity recognition task. The model in this paper is compared with LSTM, BiLSTM, CRF, Transformer and their combination models, and the experimental results on the test dataset show that the model outperforms the comparative models with an accuracy of 84.8%, a recall of 85.8% and an F1 value of 0.853. [ABSTRACT FROM AUTHOR]

In its regulatory enterprise to improve the conditions of data sharing and reuse, the European Union has enacted new legislation: the Data Governance Act (DGA). The DGA envisages new forms of sharing public sector data (PSD). Based on a legal analysis of the DGA and an in-depth study of data governance literature, this paper highlights what is at stake in the new regulatory framework and argues that more than the mere openness of more PSD will be necessary to ensure that the European Union policy goals are achieved, especially those concerning enhancing innovation for the common good. From this perspective, the paper argues that the public data trust model of data governance and the Responsible Research and Innovation approach offer two powerful tools for public sector data governance. In this context, this paper contributes to the debate about new data governance models and discusses tools and frameworks enabling the use of data for the common good. It also provides insights to public administration practitioners aiming to implement a framework for increased and sustainable PSD sharing. [ABSTRACT FROM AUTHOR]

Introduction: Behaviourally informed soft policies, such as nudges, have become popular in areas like health, environment, and energy use as cost-effective instruments to change behaviour and decision-making. However, the effectiveness of soft policies in the transport sector is modest at best. One reason for this relative ineffectiveness might be their one-size-fits-all nature, and personalizing soft interventions has been suggested to increase their effectiveness. The Trans-theoretical Model (TTM) suggests that people progress through five stages of behavioural change, from pre-contemplating a behaviour to maintaining the behaviour, and behavioural interventions could be designed for specific stages. However, it is not always feasible to conduct surveys to place people at different stages of the TTM. Methods: This paper explores whether it is possible to use multimodality data taken from a travel diary to place people at different stages of the TTM. The analysis uses an existing dataset from 826 respondents that includes self-reported TTM stages regarding cycling and data on multimodality. In the analysis, the multimodality data are used to allocate respondents to categories and assign them to TTM stages. The performances of the stage assignment approaches are evaluated using the self-reported TTM data and confusion matrices. Findings: The accuracy of the allocation of participants to TTM stages using multimodality data is approximately 75%. The accuracy is higher for early stages (pre-contemplation) and later stages (maintenance) of the TTM. A data-driven approach to dealing with multimodality data performs slightly better than an approach that relies on pre-defined categorization. Conclusion: The paper suggests that it will be possible in the future to personalise behavioural interventions according to the stages of the TTM even in the absence of self-reported survey data that classifies people to TTM stages if objective multimodality data are available. [ABSTRACT FROM AUTHOR]

This paper employs the instrumental variable method and the intermediary model to explore the influence of monocentric and polycentric spatial structures of innovation agglomeration on energy efficiency. This paper uses data from 2010 to 2021 in China, and also panel data regression models were used. The findings indicate that the monocentric spatial structure of provincial system innovation agglomeration is primarily concentrated in northeastern and western regions of China, whereas the polycentric spatial structure is mainly distributed in eastern coastal areas. A monocentric spatial structure inhibits the enhancement of energy efficiency, but there is an environmental paradox of excessive resource agglomeration. In contrast, a polycentric spatial structure and its strengthening tendency facilitate the improvement of energy efficiency. A monocentric spatial structure has an inhibitory impact on the improvement of energy efficiency in the three sectors of agriculture, industry, and construction, while a polycentric spatial structure aids it. A monocentric spatial structure further hinders the improvement of energy efficiency by impeding the upgrading of the industrial structure, while the upgrading of the industrial structure driven by the polycentric spatial structure plays a role in enhancing energy efficiency. The policy recommendations of this paper are intended to help coordinate the pace of innovation development between cities, adjust the spatial structure of innovation agglomeration between regions, and promote the upgrading of the comprehensive industrial structure, thereby improving overall energy efficiency. [ABSTRACT FROM AUTHOR]

The aim of this paper is twofold. First, it serves as a comprehensive tutorial on Data‐Driven Soft Independent Modelling of Class Analogy (SIMCA) (DD‐SIMCA) method for one‐class classification. It covers all practical aspects of developing, validation, and application of DD‐SIMCA models, using a set of simple examples. Second, it introduces web application that implements the main DD‐SIMCA functionality. This application is freely available for everyone and does not require registration or installation. All calculations run locally in a browser without sending any information on a server, hence removing any obstacles to the dissemination of the data and models. The paper serves as a comprehensive tutorial on Data‐Driven SIMCA method for one class classification. It covers all practical aspects of developing, validation, and application of DD‐SIMCA models, using a set of simple examples. It also introduces web application that implements the main DD‐SIMCA functionality. [ABSTRACT FROM AUTHOR]

In this paper, a fractional active disturbance rejection control (FADRC) scheme is proposed for remotely operated vehicles (ROVs) to enhance high-precision positioning and docking control in the presence of ocean current disturbances and model uncertainties. The scheme comprises a double closed-loop fractional-order PI λ D μ controller (DFOPID) and a model-assisted finite-time sliding-mode extended state observer (MFSESO). Among them, DFOPID effectively compensates for non-matching disturbances, while its fractional-order term enhances the dynamic performance and steady-state accuracy of the system. MFSESO contributes to enhancing the estimation accuracy through the integration of sliding-mode technology and model information, ensuring the finite-time convergence of observation errors. Numerical simulations and pool experiments have shown that the proposed control scheme can effectively resist disturbances and successfully complete high-precision tasks in the absence of an accurate model. This underscores the independence of this control scheme on accurate model data of an operational ROV. Meanwhile, it also has the advantages of a simple structure and easy parameter tuning. The FADRC scheme presented in this paper holds practical significance and can serve as a valuable reference for applications involving ROVs. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this paper is to study a feasible visualization of large-size three-dimension (3D) color models which are beyond the maximum print size of newest paper-based 3D printer used 3D cutting-bonding frame (3D-CBF) and evaluate the effects of cutting angle and layout method on printing time of designed models. Design/methodology/approach: Sixteen models, including cuboid model, cylinder model, hole model and sphere model with different shape features, were divided into two symmetric parts and printed by the Mcor IRIS HD 3D printer. Before printing, two sub-parts were rearranged in one of three layout methods. Nine scaled sizes of original models were printed to find the quantitative relationship between printing time and scale values in each type. For the 0.3 times of original models, six cutting angles were evaluated in detail. Findings: The correlation function about colorization time and printed pages was proposed. Based on 3D-CBF, the correlation between printing time and scale size is statistically defined. Optimization parameters of designed parts visualization about cutting angel and layout method were found, even if their statistical results were difficult to model their effects on printing time of specimens. Research limitations/implications: The research is comparative and limited to the special models and used procedures. Originality/value: The paper provides a feasible visualization and printing speed optimization methods for the further industrialization of 3D paper-based printing technology in cultural creative field. [ABSTRACT FROM AUTHOR]

Application-oriented papers provide an important way to invigorate and cross-pollinate the visualization field, but the exact criteria for judging an application paper's merit remain an open question. This article builds on a panel at the 2016 IEEE Visualization Conference entitled "Application Papers: What Are They, and How Should They Be Evaluated?" that sought to gain a better understanding of prevalent views in the visualization community. This article surveys current trends that favor application papers, reviews the benefits and contributions of this paper type, and discusses their assessment in the review process. It concludes with recommendations to ensure that the visualization community is more inclusive to application papers. [ABSTRACT FROM PUBLISHER]

Summary: In this paper, a prescribed performance model‐free adaptive platooning sliding mode control (PP‐MFAP‐SMC) problem for the nonlinear vehicular platooning systems (VPSs) under denial‐of‐service (DoS) attacks is studied. Firstly, the partial form dynamic linearization (PFDL) technique is employed to convert the nonlinear VPSs into an equivalent linear data model, in which the nonlinear features of the VPSs are compressed into an unknown time‐varying pseudo gradient (PG) vector. Then, an observer is devised to acquire the estimation value of the unknown time‐varying PG vector. To lower the complication of the design, the constrained tracking error is converted into the unconstrained one. Based on which, the sliding mode control (SMC) strategy is proposed to enhance the robustness of the VPSs. Further, a PP‐MFAP‐SMC algorithm with an attack compensation mechanism is developed to ensure that the vehicular tracking errors of the position and velocity can converge to the predefined regions, respectively. Eventually, the effectiveness of the developed algorithm is demonstrated by an actual VPS with the comparisons. [ABSTRACT FROM AUTHOR]

Aiming at the problem of imprecise reputation evaluation caused by too much subjective and qualitative evaluation of the traditional reputation evaluation model in data transactions, An Evaluation Entity-Based Reputation Evaluation model (EEBRE) is proposed. In order to realize multi-dimensional reputation evaluation, the structure of the evaluation entity is proposed to dynamically obtain the reputation data of the trading parties; a reputation evaluation assembly method combining objective indicators and feedback rating indicators is proposed, which replaces the qualitative scoring of data set quality indicators with quantitative scoring, introduces the static individual information indicators as well as transaction context attribute indicators, such as transaction amount, time, etc., and retains the qualitative evaluation of the feedback ratings to provide an interactive experience feedback pathway for the data requesting party. The experimental results show that the EEBRE model proposed in this paper can safely and effectively evaluate the seller's reputation. [ABSTRACT FROM AUTHOR]

The application of network meta‐analysis is becoming increasingly widespread, and for a successful implementation, it requires that the direct comparison result and the indirect comparison result should be consistent. Because of this, a proper detection of inconsistency is often a key issue in network meta‐analysis as whether the results can be reliably used as a clinical guidance. Among the existing methods for detecting inconsistency, two commonly used models are the design‐by‐treatment interaction model and the side‐splitting models. While the original side‐splitting model was initially estimated using a Bayesian approach, in this context, we employ the frequentist approach. In this paper, we review these two types of models comprehensively as well as explore their relationship by treating the data structure of network meta‐analysis as missing data and parameterizing the potential complete data for each model. Through both analytical and numerical studies, we verify that the side‐splitting models are specific instances of the design‐by‐treatment interaction model, incorporating additional assumptions or under certain data structure. Moreover, the design‐by‐treatment interaction model exhibits robust performance across different data structures on inconsistency detection compared to the side‐splitting models. Finally, as a practical guidance for inconsistency detection, we recommend utilizing the design‐by‐treatment interaction model when there is a lack of information about the potential location of inconsistency. By contrast, the side‐splitting models can serve as a supplementary method especially when the number of studies in each design is small, enabling a comprehensive assessment of inconsistency from both global and local perspectives. [ABSTRACT FROM AUTHOR]

This paper investigates the influence of specialization, urbanization, and diversification externalities on the dynamics of tourism employment in Brazilian microregions. We use a dynamic panel data model for the 2006–2019 period. The location quotient, population density and the inverse of the Hirschman–Herfindahl index, proxies for specialization, urbanization, and diversification, respectively, positively affect tourism employment in the long run. Based on the estimated long‐run elasticities, the specialization externality produces the strongest influence on tourism employment after a permanent increase of one standard deviation. [ABSTRACT FROM AUTHOR]

In recent years, there has been a proliferation of weakly supervised methods in the field of video anomaly detection. Despite significant progress in existing research, these efforts have primarily focused on addressing this issue within Euclidean space. Conducting weakly supervised video anomaly detection in Euclidean space imposes a fundamental limitation by constraining the ability to model complex patterns due to the dimensionality constraints of the embedding space and lacking the capacity to model long-term contextual information. This inadequacy can lead to misjudgments of anomalous events due to insufficient video representation. However, hyperbolic space has shown significant potential for modeling complex data, offering new insights. In this paper, we rethink weakly supervised video anomaly detection with a novel perspective: transforming video features from Euclidean space into hyperbolic space may enable the network to learn implicit relationships in normal and anomalous videos, thereby enhancing its ability to effectively distinguish between them. Finally, to validate our approach, we conducted extensive experiments on the UCF-Crime and XD-Violence datasets. Experimental results show that our method not only has the lowest number of parameters but also achieves state-of-the-art performance on the XD-Violence dataset using only RGB information. [ABSTRACT FROM AUTHOR]

This paper is concerned with the finite element discretization of the data driven approach according to Kirchdoerfer and Ortiz (Comput Methods Appl Mech Eng 304:81–101, 2016) for the solution of PDEs with a material law arising from measurement data. To simplify the setting, we focus on a scalar diffusion problem instead of a problem in elasticity. It is proven that the data convergence analysis from Conti et al. (Arch Ration Mech Anal 229(1):79–123, 2018) carries over to the finite element discretization as long as H (div) -conforming finite elements such as the Raviart–Thomas element are used. As a corollary, minimizers of the discretized problems converge in data in the sense of Conti et al., as the mesh size tends to zero and the approximation of the local material data set gets more and more accurate. We moreover present several heuristics for the solution of the discretized data driven problems, which is equivalent to a quadratic semi-assignment problem and therefore NP-hard. We test these heuristics by means of three examples and it turns out that the "classical" alternating projection method according to Kirchdoerfer and Ortiz is superior w.r.t. the ratio of accuracy and computational time. [ABSTRACT FROM AUTHOR]

In this paper, a novel model-free adaptive bipartite consensus control scheme is proposed for unknown heterogeneous nonlinear multi-agent systems (MASs) with different input delays. Firstly, an equivalent data model is established for each agent by using the dynamic linearisation method. Secondly, based on an established equivalent data model and constructed predictive compensation algorithm, the distributed model-free adaptive bipartite consensus control scheme (DMFABCC) is designed by only utilising the measured input/output data rather than the state-space model of MASs. Next, by selecting an appropriate Lyapunov function, the convergence of bipartite tracking error is rigorously proved. Finally, numerical simulations are conducted to demonstrate the effectiveness of the proposed DMFABCC scheme for achieving the bipartite consensus target. [ABSTRACT FROM AUTHOR]

Saturation nonlinearity exists widely in various practical control systems. Modeling and parameter estimation of systems with saturation nonlinearity are of great importance for analyzing their characteristics and controller design. This paper focuses on the identification issue of the input nonlinear Box–Jenkins systems with saturation nonlinearity. The input saturation nonlinearity is presented as a linear parametric expression through the application of a switching function, then the identification model of the system is derived by using the key term separation technique. Based on this model and the data filtering technique, the filtering identification model of the system is given by changing the system structure without changing the relationship between the input and output, which can reduce the interference of the colored noise and improve the identification accuracy. Then a data filtering-based maximum likelihood gradient-based iterative algorithm is proposed to estimate the unknown parameters. The maximum likelihood gradient-based iterative algorithm is provided for comparison. The feasibility and superiority of the proposed approach are emphasized by a simulation example. [ABSTRACT FROM AUTHOR]

Due to the multiple physics involved and their mutual and complex interactions, nuclear engineers and researchers are constantly working on developing highly accurate Multi-Physics models, focusing in particular on the core coupling of Neutronics and Thermal-Hydraulics. Nevertheless, the development of accurate and stable models remains a challenging task despite the advancements in computational hardware and software. This work investigates the possibility of combining the available mathematical model with data collected on physical systems, with a two-fold goal: improvement of the performance of the former from the computational point of view without sacrificing accuracy and performing model bias correction with the knowledge coming in real-time from the system. In particular, two Data-Driven Reduced Order Modelling techniques, the Generalised Empirical Interpolation Method and the Parametrised-Background Data-Weak formulation, are applied to literature benchmark nuclear case studies, as they were observed to be quite well suited for the chosen cases. The main goal of this work is to assess the possibility of using external data to perform model bias correction: starting from a purposefully less accurate, but computationally cheaper base model, then using high-fidelity data to update and correct the model efficiently. Indeed, the numerical results obtained in this paper are promising, confirming the feasibility of this approach to develop computationally cheap and accurate multi-physics models; furthermore, investigation of Data-Driven Reduced Order Modelling approach to nuclear industrial cases, in the context of model bias correction, is foreseen. • State estimation using Data-Driven Reduced Order Modelling. • Solution of low-fidelity Multi-Physics models embedding Reduced Order Modelling approaches. • Update/correction of Multi-Physics models using higher-fidelity data. • Enhancing Multi-Physics modelling of nuclear reactors with data from different fidelity levels. [ABSTRACT FROM AUTHOR]

Credit scoring models are critical for financial institutions to assess borrower risk and maintain profitability. Although machine learning models have improved credit scoring accuracy, imbalanced class distributions remain a major challenge. The widely used Synthetic Minority Oversampling TEchnique (SMOTE) struggles with high-dimensional, non-linear data and may introduce noise through class overlap. Generative Adversarial Networks (GANs) have emerged as an alternative, offering the ability to model complex data distributions. Conditional Wasserstein GANs (cWGANs) have shown promise in handling both numerical and categorical features in credit scoring datasets. However, research on extracting latent features from non-linear data and improving model explainability remains limited. To address these challenges, this paper introduces the Non-parametric Oversampling Technique for Explainable credit scoring (NOTE). The NOTE offers a unified approach that integrates a Non-parametric Stacked Autoencoder (NSA) for capturing non-linear latent features, cWGAN for oversampling the minority class, and a classification process designed to enhance explainability. The experimental results demonstrate that NOTE surpasses state-of-the-art oversampling techniques by improving classification accuracy and model stability, particularly in non-linear and imbalanced credit scoring datasets, while also enhancing the explainability of the results. [ABSTRACT FROM AUTHOR]

One of the challenges in planning a digital edition is the selection of the encoding system and representation format that the edition will use to encode musical information. This decision affects the form of the edition not only through the descriptive capabilities of the encoding format, but also through the format's adherence to certain data structures such as tables, trees, and graphs. Far from being straightforward containers, each of these structures possess unique qualities that constrain the encoding process into considering the music within the parameters of that data model. Data that does not fit must be cajoled into the chosen model, with varying degrees of success. Recent work has developed arguments for the use of certain formats in specific cases based on features and interoperability, but a thorough review of the suitability and sustainability of the underlying data structures has yet to be conducted.This paper explores the problem of music representation in data structures from the perspective of musical domain. By encoding the same passages of music in multiple hypothetical formats it demonstrates the musical aspects and, particularly, the relationships that the data structures commonly used in music encoding variously excel at, privilege, or struggle with representing. I argue that encoding projects should consider the constraints that the data structures of a format impose on their encoding; whether there are instances that would be unsatisfactorily modelled, the data biases this creates, and therefore whether it would be advisable to extend the representation format to accommodate another data structure. [ABSTRACT FROM AUTHOR]

Min–max programming problems with addition–min constraints have been studied in the literature to model data transfer in BitTorrent-like peer-to-peer file-sharing systems. It is well known that the class of overlap functions contains various operators, including the "min" operator. The aim of this paper is to generalize the above min–max programming problem with addition–overlap function constraints. We demonstrate that this new optimization problem can be transformed into a simplified single-variable optimization problem, which makes it easier to find an optimal solution. The bisection method will be used to find this optimal solution. In addition, when the overlap function is explicitly specified, an iterative method is given to compute the optimal objective value with a polynomial time complexity. A numerical example is provided to illustrate the procedures. [ABSTRACT FROM AUTHOR]

This paper concerns the modeling of smart health sensors for data monitoring, where the data are eventually noisy, with correlated noise. In this work, we applied Clifford-based wavelets/multiwavelets for correlated noise in multi-sensors health data monitoring by estimating the set of sensor nodes minimizing an eventual error computed by the signal values at the selected nodes mostly caused by the correlated noise. Instead of directly minimizing the estimation error, we focused on evaluating a multi-level scheme based on multiwavelets for the estimation of the error between the parameter vector and its sub-vector of those nodes. Numerical simulations are provided with a comparison to some recent existing works. This model showed a performance and a fast time execution compared to those existing works. This model exceeds these models by the non-necessity to assume a priory structure of the data. Wavelets are capable to detect, localize, and eliminate the noise, even correlated, efficiently via the independent uncorrelated multiwavelets’ components. [ABSTRACT FROM AUTHOR]

The article presents the implications of personal data governance models for algorithmic discrimination. It reflects on the significance of engaging with data subjects to prevent or mitigate discrimination by comparing the effectiveness of the top-down and bottom-up approaches to data governance. The paper goes beyond the option to either decline or agree to terms of service while entering online platforms and argues that bottom-up data governance models of data trusts and information fiduciaries can be more inclusive than the top-down data governance. [ABSTRACT FROM AUTHOR]

The main aim of this paper is to consider a new risk metric that permits taking into account the spatial interactions of data. The considered risk metric explores the spatial tail-expectation of the data. Indeed, it is obtained by combining the ideas of expected shortfall regression with an expectile risk model. A spatio-functional Nadaraya–Watson estimator of the studied metric risk is constructed. The main asymptotic results of this work are the establishment of almost complete convergence under a mixed spatial structure. The claimed asymptotic result is obtained under standard assumptions covering the double functionality of the model as well as the data. The impact of the spatial interaction of the data in the proposed risk metric is evaluated using simulated data. A real experiment was conducted to measure the feasibility of the Spatio-Functional Expectile Shortfall Regression (SFESR) in practice. [ABSTRACT FROM AUTHOR]

In this paper, we propose an alternative distribution to model count data exhibiting uni/bimodality. It arises as a weighted version of the beta-binomial distribution, which is defined by a parametric weight function that admits up to two modes for the resulting probability mass function. Like the baseline beta-binomial distribution, the proposed distribution performs well in modeling overdispersed binomial data. Structural properties of the new distribution are studied. Raw moments are derived, which are used to describe the dispersion behavior relative to the mean and the skewness behavior. Parameter estimation is carried out using the maximum likelihood method. A simulation study is conducted in order to illustrate the behavior of the estimators. Finally, two applications illustrating the usefulness of the proposal are presented. [ABSTRACT FROM AUTHOR]

This paper introduces bivariate log-symmetric models for analyzing the relationship between two variables, assuming a family of log-symmetric distributions. These models offer greater flexibility than the bivariate lognormal distribution, allowing for better representation of diverse distribution shapes and behaviors in the data. The log-symmetric distribution family is widely used in various scientific fields and includes distributions such as log-normal, log-Student-t, and log-Laplace, among others, providing several options for modeling different data types. However, there are few approaches to jointly model continuous positive and explanatory variables in regression analysis. Therefore, we propose a class of generalized linear model (GLM) regression models based on bivariate log-symmetric distributions, aiming to fill this gap. Furthermore, in the proposed model, covariates are used to describe its dispersion and correlation parameters. This study uses a dataset of anthropometric measurements of newborns to correlate them with various biological factors, proposing bivariate regression models to account for the relationships observed in the data. Such models are crucial for preventing and controlling public health issues. [ABSTRACT FROM AUTHOR]

In this paper, we propose an efficient method called the response-aided score-matching representative (RASMR) approach to facilitate massive data model selection and data analysis with generalized linear models (GLMs) and a predetermined data partition due to data localization. Similar to the original score-matching representative (SMR) approach, RASMR constructs an artificial data point, called the representative, for each data block. It then fits a GLM on the representative dataset, which provides not only an efficient approach for massive data analysis but also an ideal solution in response to privacy concerns by avoiding the transfer of sensitive data. By further splitting the data blocks according to the values of the response variables, RASMR can obtain more accurate parameter estimates than SMR. Furthermore, by theoretical justifications and simulation studies, we show that RASMR can be more efficiently utilized for model selection and variable selection for a massive dataset by approximating the Akaike information criterion (AIC) and the aggregated prediction errors for cross-validation, which are commonly used for choosing the most appropriate statistical model and drawing reliable conclusions. We also apply the proposed RASMR approach to the airline on-time performance data, which consists of 371 data files labeled by month, and show that RASMR can be successfully used for selecting the most appropriate model for real massive data analysis. [ABSTRACT FROM AUTHOR]

To facilitate a direct comparison of the differences in network resources among countries worldwide, this paper proposes a method for quantifying the relationship between autonomous systems and territorial networks from the perspective of network topology. Using global router-level network topology data as the foundational data for the network resources of various countries, we abstract the dual mapping information of router geographic distribution and operational ownership into a matrix-form mathematical model. By employing relevant indicators from both network scale and border connectivity, we compare matrix model data from different periods to quantitatively assess changes in the network structures of countries globally. The study results show that internet resources are concentrated in the United States, which owns 38.04% of the global routers, distributed across 87.88% of the countries, significantly impacting the global network. Compared to the average quantitative indicators of each country, 67.00% of the countries exhibit higher deployment consistency, 37.30% show higher border connection consistency, 23.81% perform prominently in terms of impact, and 46.20% have outstanding border node degrees. From a continental perspective, the analysis indicates that Asian and African countries have a closer relationship between AS and territorial networks, while Europe's connections are relatively sparse. Over time, we observe a slight decline in deployment consistency in Asia, Africa, and Europe, a slight increase in border connection consistency in Asia, Africa, and North America, and enhanced impact in Asia, Africa, Europe, and South America. These trends suggest that the integration between AS and territorial networks is intensifying in most countries. [ABSTRACT FROM AUTHOR]

The idea of experimental design has been widely used in subsampling algorithms to extract a small portion of big data that carries useful information for statistical modeling. Most existing subsampling algorithms of this kind are model-based and designed to achieve the corresponding optimality criteria for the model. However, data generating models are frequently unknown or complicated. Model-free subsampling algorithms are needed for obtaining samples that are robust under model misspecification and complication. This paper introduces two novel algorithms, called the Projection-Uniform Subsampling algorithm and its extension. Both algorithms aim to extract a subset of samples from big data that are space-filling in low-dimensional projections. We show that subdata obtained from our algorithms perform superiorly under the uniform projection criterion and centered L 2 -discrepancy. Comparisons among our algorithms, model-based and model-free methods are conducted through two simulation studies and two real-world case studies. We demonstrate the robustness of our proposed algorithms in building statistical models in scenarios involving model misspecification and complication. [ABSTRACT FROM AUTHOR]

Palmprint as biometrics has gained increasing attention recently due to its discriminative ability and robustness. However, existing methods mainly improve palmprint verification within one spectrum, which is challenging to verify across different spectrums. Additionally, in distributed server-client-based deployment, palmprint verification systems predominantly necessitate clients to transmit private data for model training on the centralized server, thereby engendering privacy apprehensions. To alleviate the above issues, in this paper, we propose a physics-driven spectrum-consistent federated learning method for palmprint verification, dubbed as PSFed-Palm. PSFed-Palm draws upon the inherent physical properties of distinct wavelength spectrums, wherein images acquired under similar wavelengths display heightened resemblances. Our approach first partitions clients into short- and long-spectrum groups according to the wavelength range of their local spectrum images. Subsequently, we introduce anchor models for short- and long-spectrum, which constrain the optimization directions of local models associated with long- and short-spectrum images. Specifically, a spectrum-consistent loss that enforces the model parameters and feature representation to align with their corresponding anchor models is designed. Finally, we impose constraints on the local models to ensure their consistency with the global model, effectively preventing model drift. This measure guarantees spectrum consistency while protecting data privacy, as there is no need to share local data. Extensive experiments are conducted to validate the efficacy of our proposed PSFed-Palm approach. The proposed PSFed-Palm demonstrates compelling performance despite only a limited number of training data. The codes have been released at https://github.com/Zi-YuanYang/PSFed-Palm. [ABSTRACT FROM AUTHOR]

In this paper, we present a data-driven modeling method for lateral motion control of unknown vehicle models. Vehicle's motion can be modeled linearly but this model has complex and nonlinear characteristic. Therefore, it is necessary to know the exact information of the car chassis and requires a knowledge and understanding of dynamics. To solve these drawbacks, we linearly represent full vehicle's lateral dynamics which include nonlinear behavior using dynamic mode decomposition (DMD), one of the data driven modeling methods. To determine the validity of the model obtained using the DMD method, we conducted a simulation of the comparison of the output states between the existing model and the model obtained through DMD modeling, using the scenario of a dynamic maneuver called a double line change during lateral motion of a vehicle. After determination of validation is completed, we designed a lane keeping system by applying a model predictive control to specifically evaluate the model of the proposed method. Performance was derived by comparing the error caused by the vehicle driving on the course with the controller of the simulation. The performance of the proposed approach has been evaluated through simulations and is useful when the model is inaccurate. [ABSTRACT FROM AUTHOR]

Reflectivity inversion is an important deconvolution problem in reflection seismology that helps to describe the subsurface structure. Generally, deconvolution techniques iteratively work on the seismic data for estimating reflectivity. Therefore, these techniques are computationally expensive and may be slow to converge. In this paper, a novel method for estimating reflectivity signals in seismic data using an approximate finite rate of innovation (FRI) framework, is proposed. The seismic data is modeled as a convolution between the Ricker wavelet and the FRI signal, a Dirac impulse train. Relaxing the accurate exponential reproduction limitation given by generalised Strang-Fix (GSF) conditions, we develop a suitable sampling kernel utilizing Ricker wavelet which allows us to estimate the reflectivity signal. The experimental results demonstrate that the proposed approximate FRI framework provides a better reflectivity estimation than the deconvolution technique for medium-to-high signal-to-noise ratio (SNR) regimes with nearly 18% of seismic data. [ABSTRACT FROM AUTHOR]

Monte Carlo simulation studies are among the primary scientific outputs contributed by methodologists, guiding application of various statistical tools in practice. Although methodological researchers routinely extend simulation study findings through follow-up work, few studies are ever replicated. Simulation studies are susceptible to factors that can contribute to replicability failures, however. This paper sought to conduct a meta-scientific study by replicating one highly cited simulation study (Curran et al., Psychological Methods, 1, 16-29, 1996) that investigated the robustness of normal theory maximum likelihood (ML)-based chi-square fit statistics under multivariate nonnormality. We further examined the generalizability of the original study findings across different nonnormal data generation algorithms. Our replication results were generally consistent with original findings, but we discerned several differences. Our generalizability results were more mixed. Only two results observed under the original data generation algorithm held completely across other algorithms examined. One of the most striking findings we observed was that results associated with the independent generator (IG) data generation algorithm vastly differed from other procedures examined and suggested that ML was robust to nonnormality for the particular factor model used in the simulation. Findings point to the reality that extant methodological recommendations may not be universally valid in contexts where multiple data generation algorithms exist for a given data characteristic. We recommend that researchers consider multiple approaches to generating a specific data or model characteristic (when more than one is available) to optimize the generalizability of simulation results. [ABSTRACT FROM AUTHOR]

In this paper, we propose a nonparametric estimator of ruin probability in the Wiener–Poisson risk model based on high-frequency data. The estimator is constructed via the Fourier-cosine method and the threshold technique, and the convergence rate is also studied for a large sample size. Finally, we verify the effectiveness of our estimator through some simulation studies. [ABSTRACT FROM AUTHOR]

Panel data model with fixed effects is widely used in economic and administrative applications. However, the presence of factors: measurement errors, data variability and outliers may potentially decrease the accuracy of the model prediction. In this paper, we use panel interval-valued data to represent measurement errors and data volatility of observations. Further, we propose a corresponding panel interval-valued data model with fixed effects, in which both the response and explanatory variables are interval-valued data. To reduce the impact of outliers on our model, we propose a robust estimation method based on the iterative weighted least squares technique. Later, Monte Carlo simulation and empirical application demonstrate that our model is a suitable tool for analyzing the behaviour of panel interval-valued data. [ABSTRACT FROM AUTHOR]

This paper introduces the modified Kies Topp-Leone (MKTL) distribution for modeling data on the (0, 1) or [0, 1] interval. The shapes of the density and hazard rate functions manifest desirable shapes, making the MKTL distribution suitable for modeling data with different characteristics at the unit interval. Twelve different estimation methods are utilized to estimate the distribution parameters, and Monte Carlo simulation experiments are executed to assess the performance of the methods. The simulation results suggest that the maximum likelihood method is the superior method. The usefulness of the new distribution is illustrated by utilizing three data sets, and its performance is juxtaposed with that of other competing models. The findings affirm the superiority of the MKTL distribution over the other candidate models. Applying the developed quantile regression model using the new distribution disclosed that it offers a competitive fit over other existing regression models. [ABSTRACT FROM AUTHOR]

Imitation learning enables synthesis of controllers for systems with complex objectives and uncertain plant models. However, ensuring an imitation learned controller is stable requires copious amounts of data and/or a known plant model. In this paper, we explore an input–output (IO) stability approach to imitation learning, which achieves stability with sparse data sets while only requiring coarse knowledge of the energy characteristics of the plant. A constrained optimization problem is developed, in which the controller learns to mimic expert data while maintaining stabilizing energy characteristics induced by the plant. While the learning objective is nonconvex, iterative convex overbounding (ICO) and projected gradient descent (PGD) are explored as methods to learn the controller. In numerical examples, it is shown that with little knowledge of the plant model and a small data set, the dissipativity constrained learned controller achieves closed loop stability and successfully mimics the behavior of the expert controller, while other methods often fail to maintain stability and achieve good performance. [ABSTRACT FROM AUTHOR]

This paper presents the exponentiated alpha-power log-logistic (EAPLL) distribution, which extends the log-logistic distribution. The EAPLL distribution emphasizes its suitability for survival data modeling by providing analytical simplicity and accommodating both monotone and non-monotone failure rates. We derive some of its mathematical properties and test eight estimation methods using an extensive simulation study. To determine the best estimation approach, we rank mean estimates, mean square errors, and average absolute biases on a partial and overall ranking. Furthermore, we use the EAPLL distribution to examine three real-life survival data sets, demonstrating its superior performance over competing log-logistic distributions. This study adds vital insights to survival analysis methodology and provides a solid framework for modeling various survival data scenarios. [ABSTRACT FROM AUTHOR]

In order to study the dynamic performance of rail-track turnout more widely and deeply, this paper established a 3D refined model of turnout and calculated the condensation characteristics of rail-track through the application of harmonic load sweep frequency analysis. The relevant calculation results are verified by field experiments. It was found that the rail pin-pinned resonance mode in the stock rail and switch rail close position, and jacking block and heel block would be suppressed. The change of switch rail profile caused gradual variation of switch rail admittance characteristic, which showed as the low-frequency original point admittance gradually decreases in the frequency-spatial distribution. The special fastener system of guardrail has limited lateral constraint on the guide rail, resulting in the lateral pin-pinned resonance mode of the guide rail not being significantly observed in the measured results. At the same time, the variation of the vertical bending first-order frequency of the rail was analyzed to analyze the change of the vertical support stiffness in the switch zone. The reliability and shortage of the three-dimensional (3D) track model in the switch zone were also analyzed by comparing the experimental data with the model calculation results. [ABSTRACT FROM AUTHOR]

This paper proposes a novel generalized partial linear mixed models (GPLMMs) with the random effects distribution distributed as a multivariate skew-normal distribution for discrete longitudinal data over time. Under the Bayesian framework, we extended the Bayesian version of adaptive lasso (least absolute shrinkage and selection operator) for linear regression to the Bayesian adaptive lasso with the least squares approximation (LSA) method for generalized partial linear mixed models. A hybrid algorithm combining the Gibbs sampler and the Metropolis-Hastings algorithm in conjunction with the Bayesian adaptive lasso with LSA is used to simultaneously obtain the Bayesian estimates of unknown parameters, smoothing function and random effects, as well as select important covariates in GPLMMs. Several simulation studies and a real example are presented to illustrate the proposed methodologies. [ABSTRACT FROM AUTHOR]

In this paper, we present the unit-power half-normal distribution, derived from the power half-normal distribution, for data analysis in the open unit interval. The statistical properties of the unit-power half-normal model are described in detail. Simulation studies are carried out to evaluate the performance of the parameter estimators. Additionally, we implement the quantile regression for this model, which is applied to two real healthcare data sets. Our findings suggest that the unit power half-normal distribution provides a robust and flexible alternative for existing models for proportion data. [ABSTRACT FROM AUTHOR]

In natural language processing, word sense disambiguation (WSD) continues to be a major difficulty, especially for low-resource languages where linguistic variation and a lack of data make model training and evaluation more difficult. The goal of this comprehensive review and meta-analysis of the literature is to summarize the body of knowledge regarding WSD techniques for low-resource languages, emphasizing the advantages and disadvantages of different strategies. A thorough search of several databases for relevant literature produced articles assessing WSD methods in low-resource languages. Effect sizes and performance measures were extracted from a subset of trials through analysis. Heterogeneity was evaluated using pooled effect and estimates were computed by meta-analysis. The preferred reporting elements for systematic reviews and meta-analyses (PRISMA) were used to develop the process for choosing the relevant papers for extraction. The meta-analysis included 32 studies, encompassing a range of WSD methods and low-resourced languages. The overall pooled effect size indicated moderate effectiveness of WSD techniques. Heterogeneity among studies was high, with an I2 value of 82.29%, suggesting substantial variability in WSD performance across different studies. The (τ2) tau value of 5.819 further reflects the extent of between-study variance. This variability underscores the challenges in generalizing findings and highlights the influence of diverse factors such as language-specific characteristics, dataset quality, and methodological differences. The p-values from the meta-regression (0.454) and the meta-analysis (0.440) suggest that the variability in WSD performance is not statistically significantly associated with the investigated moderators, indicating that the performance differences may be influenced by factors not fully captured in the current analysis. The absence of significant p-values raises the possibility that the problems presented by low-resource situations are not yet well addressed by the models and techniques in use. [ABSTRACT FROM AUTHOR]

The presented paper introduces a novel path planning algorithm designed for generating low-cost trajectories that fulfill mission requirements expressed in Linear Temporal Logic (LTL). The proposed algorithm is particularly effective in environments where cost functions encompass the entire configuration space. A core contribution of this paper is the presentation of a refined approach to sampling-based path planning algorithms that aligns with the specified mission objectives. This enhancement is achieved through a multi-layered framework approach, enabling a simplified discrete abstraction without relying on mesh decomposition. This abstraction is especially beneficial in complex or high-dimensional environments where mesh decomposition is challenging. The discrete abstraction effectively guides the sampling process, influencing the selection of vertices for extension and target points for steering in each iteration. To further improve efficiency, the algorithm incorporates a deep learning-based extension, utilizing training data to accurately model the optimal trajectory distribution between two points. The effectiveness of the proposed method is demonstrated through simulated tests, which highlight its ability to identify low-cost trajectories that meet specific mission criteria. Comparative analyses also confirm the superiority of the proposed method compared to existing methods. [ABSTRACT FROM AUTHOR]