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We provide a detailed exposition of our computational framework designed for the accurate calculation of real-frequency dynamical correlation functions of the single-impurity Anderson model in the regime of weak to intermediate coupling. Using quantum field theory within the Keldysh formalism to directly access the self-energy and dynamical susceptibilities in real frequencies, as detailed in our recent publication [Ge et al., Phys. Rev. B 109, 115128 (2024)], the primary computational challenge is the full three-dimensional real-frequency dependence of the four-point vertex. Our codebase provides a fully MPI+OpenMP parallelized implementation of the functional renormalization group (fRG) and the self-consistent parquet equations within the parquet approximation. It leverages vectorization to handle the additional complexity imposed by the Keldysh formalism, using optimized data structures and highly performant integration routines. Going beyond the results shown in the previous publication, the code includes functionality to perform fRG calculations in the multiloop framework, up to arbitrary loop order, including self-consistent self-energy iterations. Moreover, implementations of various regulators, such as hybridization, interaction, frequency, and temperature, are supplied. [ABSTRACT FROM AUTHOR]

Multipoint evaluation is the computational task of evaluating a polynomial given as a list of coefficients at a given set of inputs. Besides being a natural and fundamental question in computer algebra on its own, fast algorithms for this problem are also closely related to fast algorithms for other natural algebraic questions such as polynomial factorization and modular composition. And while nearly linear time algorithms have been known for the univariate instance of multipoint evaluation for close to five decades due to a work of Borodin and Moenck [7], fast algorithms for the multivariate version have been much harder to come by. In a significant improvement to the state-of-the-art for this problem, Umans [25] and Kedlaya & Umans [16] gave nearly linear time algorithms for this problem over field of small characteristic and over all finite fields, respectively, provided that the number of variables n is at most do(1) where the degree of the input polynomial in every variable is less than d. They also stated the question of designing fast algorithms for the large variable case (i.e., n x do(1)) as an open problem. In this work, we show that there is a deterministic algorithm for multivariate multipoint evaluation over a field Fq of characteristic p, which evaluates an n-variate polynomial of degree less than d in each variable on N inputs in time provided that p is at most do(1), and q is at most (exp(exp(exp(· · · (exp(d))))), where the height of this tower of exponentials is fixed. When the number of variables is large (e.g., n x do(1)), this is the first nearly linear time algorithm for this problem over any (large enough) field. Our algorithm is based on elementary algebraic ideas, and this algebraic structure naturally leads to the following two independently interesting applications: --We show that there is an algebraic data structure for univariate polynomial evaluation with nearly linear space complexity and sublinear time complexity over finite fields of small characteristic and quasipolynomially bounded size. This provides a counterexample to a conjecture of Miltersen [21] who conjectured that over small finite fields, any algebraic data structure for polynomial evaluation using polynomial space must have linear query complexity. We also show that over finite fields of small characteristic and quasipolynomially bounded size, Vandermonde matrices are not rigid enough to yield size-depth tradeoffs for linear circuits via the current quantitative bounds in Valiant's program [26]. More precisely, for every fixed prime p, we show that for every constant e > 0, and large enough n, the rank of any n × n Vandermonde matrix V over the field Fpa can be reduced to (n/exp(O(poly(e)log0.53 n))) by changing at most nT(e) entries in every row of V, provided a = poly(logn). Prior to this work, similar upper bounds on rigidity were known only for special Vandermonde matrices. For instance, the Discrete Fourier Transform matrices and Vandermonde matrices with generators in a geometric progression [9]. [ABSTRACT FROM AUTHOR]

Estimating ranks, quantiles, and distributions over streaming data is a central task in data analysis and monitoring. Given a stream of n items from a data universe equipped with a total order, the task is to compute a sketch (data structure) of size polylogarithmic in n. Given the sketch and a query item y, one should be able to approximate its rank in the stream, i.e., the number of stream elements smaller than or equal to y. Most works to date focused on additive εn error approximation, culminating in the KLL sketch that achieved optimal asymptotic behavior. This article investigates multiplicative (1±ε )-error approximations to the rank. Practical motivation for multiplicative error stems from demands to understand the tails of distributions, and hence for sketches to be more accurate near extreme values. The most space-efficient algorithms due to prior work store either O(log(ε²n)/ε²) or O(log³ (εn)/ε ) universe items. We present a randomized sketch storing O(log1.5 (εn)/ε ) items that can (1 ± ε )-approximate the rank of each universe item with high constant probability; this space bound is within an O(√log(εn)) factor of optimal. Our algorithm does not require prior knowledge of the stream length and is fully mergeable, rendering it suitable for parallel and distributed computing environments. [ABSTRACT FROM AUTHOR]

Graph analytics attract much attention from both research and industry communities. Due to its linear time complexity, the k-core decomposition is widely used in many real-world applications such as biology, social networks, community detection, ecology, and information spreading. In many such applications, the data graphs continuously change over time. The changes correspond to edge insertion and removal. Instead of recomputing the k-core, which is time-consuming, we study how to maintain the k-core efficiently. That is, when inserting or deleting an edge, we need to identify the affected vertices by searching for more vertices. The state-of-the-art order-based method maintains an order, the so-called k-order, among all vertices, which can significantly reduce the searching space. However, this order-based method is complicated to understand and implement, and its correctness is not formally discussed. In this work, we propose a simplified order-based approach by introducing the classical Order Data Structure to maintain the k-order, which significantly improves the worst-case time complexity for both edge insertion and removal algorithms. Also, our simplified method is intuitive to understand and implement; it is easy to argue the correctness formally. Additionally, we discuss a simplified batch insertion approach. The experiments evaluate our simplified method over 12 real and synthetic graphs with billions of vertices. Compared with the existing method, our simplified approach achieves high speedups up to 7.7× and 9.7× for edge insertion and removal, respectively. [ABSTRACT FROM AUTHOR]

This document is a correction to a previous article on gas-phase metallicities in MaNGA. The correction addresses an error in the calculation of O3N2-based calibrations, resulting in incorrect metallicity values. The correction decreases the raw metallicity values for these calibrations and slightly increases the number of overall spaxels with metallicities. The document also includes additional tables showing the number of metallicity values per calibration and the scatter of emission-line permutations between calibrations. The update on the polynomial fits used in the study shows a shift in the fits when converting between different calibrations, with a median magnitude of 0.032 dex. Some figures in the original study are affected, but most are not visibly different. The emission-line data used in the study is publicly available, and metallicity values can be obtained upon request. [Extracted from the article]

Deep Learning (DL) has emerged as a pivotal modelling tool in various domains because of its proficiency in learning distributed representations. Numerous DL algorithms have recently been proposed and applied to non-destructive testing (NDT) methods in agriculture. This study aimed to review the state-of-the-art applications of DL algorithms in NDT by analysing the application of DL to specific NDT applications and highlighting their contributions and challenges. It first presents a comprehensive overview of various NDT techniques that have been combined with DL in agricultural product evaluation, and then briefly describes their applications in diverse NDT tasks, such as image classification, object detection, image retrieval, and semantic segmentation. Second, this study addresses the ongoing challenges associated with data collection and fusion, model complexity, computational requirements, and robustness. Finally, future research directions are examined, underscoring the potential of novel neural network architectures and cross-disciplinary collaborations. This review aims to provide a clear understanding of the current state of DL-based NDT in agricultural product examinations and its prospects for the future. © 2017 Elsevier Inc. All rights reserved. Detailed visual map of the survey, illustrating the depth and breadth of the research field and the survey's comprehensive approach to the topic. The central concept: deep learning-based non-destructive testing (NDT) in agriculture branches out into multiple sections. The sections represent the technical aspects, such as deep learning techniques and NDT methods, the challenges in the field, future research directions, the potential impacts on agriculture sector, and the ethical implications of AI use in agriculture. [Display omitted] • Data structures of non-destructive methods input into neural network are summarised. • Metrics for the deployment of deep learning models are discussed. • Tailored DL architectures and cross-disciplinary collaboration are key direction. • Data issues, model complexity, and model robustness are main challenges. [ABSTRACT FROM AUTHOR]

Red–black (RB) trees are one of the most efficient variants of balanced binary search trees. However, they have often been criticized for being too complicated, hard to explain, and unsuitable for pedagogical purposes, particularly their delete operation. Sedgewick (in: Dagstuhl Workshop on Data Structures, 2008. https://sedgewick.io/wp-content/themes/sedgewick/papers/2008LLRB.pdf) identified the length of code as the root of the problems and introduced left-leaning red–black (LLRB) trees. The delete operation of LLRB trees has a compact recursive code. Unfortunately, it may perform many unnecessary operations. The crux of the deletion algorithm is dealing with a "deficient" subtree, that is one whose black-height has become one less than that of its sibling subtree. In this paper, we revisit 2–3 red–black trees and propose a parity-seeking delete algorithm with the basic idea of making a deficient subtree on a par with its sibling: either by fixing the deficient subtree or by turning the sibling deficient as well, ascending deficiency to the parent node. Interestingly, the proposed parity-seeking delete algorithm also works for 2–3–4 RB trees. Our experiments show that the proposed parity-seeking delete algorithm is as efficient as the best preceding RB trees. The proposed parity-seeking delete algorithm is easily understandable and suitable for pedagogical and practical purposes. [ABSTRACT FROM AUTHOR]

The knowledge graph emerges as powerful data structures that provide a deep representation and understanding of the knowledge presented in networks. In the pursuit of representation learning of the knowledge graph, entities and relationships undergo an embedding process, where they are mapped onto a vector space with reduced dimensions. These embeddings are progressively used to extract their information for a multitude of tasks in machine learning. Nevertheless, the increase data in knowledge graph has introduced a challenge, especially as knowledge graph embedding now encompass millions of nodes and billions of edges, surpassing the capacities of existing knowledge representation learning systems. In response to these challenge, this paper presents DistKGE, a distributed learning approach of knowledge graph embedding based on a new partitioning technique. In our experimental evaluation, we illustrate that the proposed approach improves the scalability of distributed knowledge graph learning with respect to graph size compared to existing methods in terms of runtime performances in the link prediction task aimed at identifying new links between entities within the knowledge graph. [ABSTRACT FROM AUTHOR]

Heterogeneous Graph (HG) is a data structure composed of various types of nodes and rich relational information, which can accurately show complex application scenarios in the real world. Although heterogeneous graph neural networks (HGNNs) have been widely applied to model HGs, there are still some issues that need to be addressed. On the one hand, most of HGNNs ignore the fine-grained information when modeling HGs, such as attribute and topology overcoupling due to the accumulation of multi-source heterogeneous information in message passing. On the other hand, HGNNs are designed from a single view (based on metapath or relation awareness), which undoubtedly leads to information loss and makes it difficult to fully extract potential interactions in HGs. To tackle the aforementioned limitations, a Multi-view fusion based Heterogeneous Graph Neural Network (MHGNN) is proposed, which is modeled from node view, network schema view, and semantics view to mine the information from different granularity in HGs. MHGNN extracts the fine-gained information of nodes, heterogeneous interaction of neighboring nodes, and mutual influence between different semantics from three views respectively. Then, the model integrates these information as the final node representation. To prove the effectiveness of this work, extensive experiments are conducted on four real-world datasets, and comparisons are made with seven competitive baselines. The results demonstrate that the proposed MHGNN significantly outperforms state-of-the-art methods. Source codes are available at https://github.com/ZZY-GraphMiningLab/MHGNN. [ABSTRACT FROM AUTHOR]

Maximum Variance Unfolding (MVU) is among the first methods in nonlinear dimensionality reduction for data visualization and classification. It aims to preserve local data structure and in the meantime push the variance among data as big as possible. However, MVU in general remains a computationally challenging problem and this may explain why it is less popular than other leading methods such as Isomap and t-SNE. In this paper, based on a key observation that the structure-preserving term in MVU is actually the squared stress in Multi-Dimensional Scaling (MDS), we replace the term with the stress function from MDS, resulting in a model that is usable. The property of the usability guarantees the "crowding phenomenon" will not happen in the dimension reduced results. The new model also allows us to combine label information and hence we call it the supervised MVU (SMVU). We then develop a fast algorithm that is based on Euclidean distance matrix optimization. By making use of the majorization-mininmization technique, the algorithm at each iteration solves a number of one-dimensional optimization problems, each having a closed-form solution. This strategy significantly speeds up the computation. We demonstrate the advantage of SMVU on some standard data sets against a few leading algorithms including Isomap and t-SNE. [ABSTRACT FROM AUTHOR]

In order to explore the construction and application of building BIM models, the construction industry is actively exploring a method that can quickly reshape the 3D information model of existing buildings in the wave of digital twins and smart cities. Starting from the perspective of deep learning 3D object detection algorithms, the author starts with the generation of large-scale building datasets and the theory of point cloud deep learning, analyzes the input data types required for point cloud deep learning frameworks, and focuses on the creation process of 3D bounding boxes and 3D point clouds for various building components. The author compares different point cloud datasets with the same data structure and implements an object detection algorithm based on the ScanNet dataset, furthermore, a feasible technology route for automatic generation of BIM models from 3D point clouds based on deep learning is integrated. Through this technology route, the trained neural network can input unknown building 3D point clouds and output BIM model parameters. [ABSTRACT FROM AUTHOR]

In the pharmaceutical industry, there is an abundance of regulatory documents used to understand the current regulatory landscape and proactively make project decisions. Due to the size of these documents, it is helpful for project teams to have informative summaries. We propose a novel solution, MedicoVerse, to summarize such documents using advanced machine learning techniques. MedicoVerse uses a multi-stage approach, combining word embeddings using the SapBERT model on regulatory documents. These embeddings are put through a critical hierarchical agglomerative clustering step, and the clusters are organized through a custom data structure. Each cluster is summarized using the bart-large-cnn-samsum model, and each summary is merged to create a comprehensive summary of the original document. We compare MedicoVerse results with established models T5, Google Pegasus, Facebook BART, and large language models such as Mixtral 8 × 7b instruct, GPT 3.5, and Llama-2-70b by introducing a scoring system that considers four factors: ROUGE score, BERTScore, business entities and the Flesch Reading Ease. Our results show that MedicoVerse outperforms the compared models, thus producing informative summaries of large regulatory documents. [ABSTRACT FROM AUTHOR]

The article discusses the challenges and best practices of using in vivo-generated data in data science applications, such as statistical analyses and machine learning. It emphasizes the importance of aggregating and organizing data from multiple studies, normalizing the data to reduce variability, and transforming serial data into discrete values. The article also highlights the need for domain expertise in determining biological relevance and selecting appropriate ML algorithms. It concludes by advocating for the sharing of aggregated data sets to promote transparency, reproducibility, and scientific advancement. [Extracted from the article]

Background and objectives: Comprehending the research dataset is crucial for obtaining reliable and valid outcomes. Health analysts must have a deep comprehension of the data being analyzed. This comprehension allows them to suggest practical solutions for handling missing data, in a clinical data source. Accurate handling of missing values is critical for producing precise estimates and making informed decisions, especially in crucial areas like clinical research. With data's increasing diversity and complexity, numerous scholars have developed a range of imputation techniques. To address this, we conducted a systematic review to introduce various imputation techniques based on tabular dataset characteristics, including the mechanism, pattern, and ratio of missingness, to identify the most appropriate imputation methods in the healthcare field. Materials and methods: We searched four information databases namely PubMed, Web of Science, Scopus, and IEEE Xplore, for articles published up to September 20, 2023, that discussed imputation methods for addressing missing values in a clinically structured dataset. Our investigation of selected articles focused on four key aspects: the mechanism, pattern, ratio of missingness, and various imputation strategies. By synthesizing insights from these perspectives, we constructed an evidence map to recommend suitable imputation methods for handling missing values in a tabular dataset. Results: Out of 2955 articles, 58 were included in the analysis. The findings from the development of the evidence map, based on the structure of the missing values and the types of imputation methods used in the extracted items from these studies, revealed that 45% of the studies employed conventional statistical methods, 31% utilized machine learning and deep learning methods, and 24% applied hybrid imputation techniques for handling missing values. Conclusion: Considering the structure and characteristics of missing values in a clinical dataset is essential for choosing the most appropriate data imputation technique, especially within conventional statistical methods. Accurately estimating missing values to reflect reality enhances the likelihood of obtaining high-quality and reusable data, contributing significantly to precise medical decision-making processes. Performing this review study creates a guideline for choosing the most appropriate imputation methods in data preprocessing stages to perform analytical processes on structured clinical datasets. Highlights: • The evidence map emphasized the importance of considering missing data characteristics when choosing an imputation method, providing insights for researchers in method selection. • The distinction between statistical and learning-based approaches highlighted the strengths and considerations of each method based on data structure. • Understanding missing data structures can enhance the quality and reliability of data imputation techniques, and improve medical decision-making accuracy. • Simulation studies are crucial for validating imputation techniques and enhancing their robustness in practical applications. • Considering missing data mechanisms, patterns, and ratios can aid researchers in making informed decisions on selecting appropriate imputation methods, leading to high-quality, and reusable data for precise medical decision-making. [ABSTRACT FROM AUTHOR]

Introduction: This study aimed to investigate the effectiveness of data augmentation to improve dementia risk prediction using machine learning models. Recent studies have shown that basic blood tests are cost-effective in predicting cognitive function. However, developing models that address various conditions poses challenges due to constraints associated with blood test results and cognitive assessments, including high costs, limited sample sizes, and missing data from tests not performed in certain facilities. Despite being often limited by small sample sizes, periodontal examination data have also emerged as a cost-effective screening tool. Methods: To address these challenges, this study explored the effectiveness of data augmentation using the Synthetic Minority Over-sampling Technique for Regression with Gaussian noise (SMOGN), a Generative Adversarial Network (GAN), and a Conditional Tabular GAN (CTGAN) on periodontal examination and blood test data. The datasets included parameters such as cognitive assessment results from the Mini-Mental State Examination (MMSE), demographic characteristics, periodontal examination data, and blood test results. Linear regression models, random forests, and deep neural networks were used to evaluate the effectiveness of the synthesized data. Results: This study used measured data from 108 participants and the synthesized data generated from the measured data. External validity was evaluated using a different dataset of 41 participants with missing items. The results suggested that normal GANs have the advantage of investigating models in data diversity, whereas CTGANs preserve the data structure and linear relationships in tabular data from the measured data, which drastically improves linear regression models. Discussion: Importantly, by interpolating sparse areas in the distribution, such as age, the synthesized models maintained prediction accuracy for test data with extreme inputs. These findings suggest that GAN-synthesized data can effectively address regression problems and improve dementia risk prediction. [ABSTRACT FROM AUTHOR]

Background: S. pneumoniae (SPN) is the most common cause of pneumonia. The disease can be effectively prevented through immunisation. Since December 2020, the Malaysian Government has included the 10-valent pneumococcal conjugate vaccine (PCV10) for all infants born on or after 1 January 2020 as part of the National Immunisation Programme (NIP). However, the epidemiology of pneumonia remains poorly understood. To fill the knowledge gap, we established a multicentre surveillance study to understand the burden of pneumococcal pneumonia among young children in Peninsular Malaysia. Methods: MY-Pneumo is a multicentre prospective case–control study conducted in three sentinel sites located in three different states of Peninsular Malaysia – Kuala Lumpur, Pahang, and Kelantan. A cohort of at least 500 incident cases and 500 controls is enrolled beginning in October 2021 and matched for age. Cases are hospitalised children < 5 years with radiologically confirmed pneumonia, and the controls are children without any features suggestive of pneumonia. Clinical samples, including nasopharyngeal swabs (NPS) and urine, are collected according to the study protocol. Biological fluids such as blood, cerebrospinal fluid (CSF) and pleural fluid are obtained from invasive pneumonia disease (IPD) patients, if available. All children are tested for SPN using polymerase chain reaction (PCR) and pneumococcal urine antigen test (PUAT) using BinaxNow. Discussion: Surveillance data, including carriage rate, serotype variations and the phylogeny data structure of SPN among young children in Malaysia during PCV implementation, will be generated from this study. Trends and patterns of pneumococcal serotypes by different regions are important for targeted public health strategies. Our data will provide baseline information for estimating the impact of PCV10 implementation and will influence policymakers' decisions regarding the upgrade from PCV10 to a higher-valency conjugate vaccine in Malaysia. Trial registration: This project was registered at ClinicalTrials.gov (NCT04923035) on 2021, June 11. The study protocol was approved by the International Medical University Joint-Committee on Research & Ethics (4.15/JCM-216/2021) and the Institutional Review Board at sentinel sites (USM/JEPeM/21020190, IREC 2021–114, MREC ID No: 2021128–9769) and University of Southampton's Ethics and Research Governance (ERGo II 64844). [ABSTRACT FROM AUTHOR]

As a promising strategy to mitigate carbon emissions, hydrogen enrichment of conventional fuels is gaining increasing interest, but can lead to increased propensity to damaging thermoacoustic instability. This paper demonstrates the ability of machine learning algorithms to reliably predict a key flame behavior relevant to thermoacoustic stability prediction – the flame's nonlinear response to upstream acoustic forcing. Using computational simulations of a laminar premixed flame under difference hydrogen enrichment and equivalence ratio conditions, training data for machine learning are generated. The machine learning models are then used to predict the flame response under new test conditions, including extrapolating into new hydrogen enrichment regimes. The effect of algorithm choice, data structure, data size, and extrapolation distance on the performance of different machine learning models is investigated with particular attention to performance with sparse training data. It is found that a type of neural network known as the multi-layer perceptron (MLP) model outperforms the Gaussian process and random forest models in both interpolation and extrapolation tasks. A physics-informed preprocessing strategy, involving extracting the time delay of the flame response (which can be deduced from geometry and flow parameters) prior to applying machine learning, is found to remarkably improve the accuracy of machine learning models, especially in extrapolation tasks. When the extrapolation into new hydrogen content levels is strong enough, the MLP model no longer performs well. However, for small and intermediate extrapolation, the MLP model shows a commendable level of prediction, even with small data sets, fulfilling needs relevant to thermoacoustic stability prediction. Extending the MLP model to experimental data from turbulent flames, the gain fall-off frequency and phase lag are accurately predicted but not the secondary periodic fluctuations in gain. • AI-assisted prediction of flame response in new hydrogen enrichment regimes. • A physics-informed strategy extracting the time delay improves model performance. • The effects of algorithm choice, data structure and data size are studied. • MLP model shows good predictions in small extrapolation tasks with sparse data. • Extending to turbulent flames, the MLP model captures fall-off frequency in gain. [ABSTRACT FROM AUTHOR]

Background: Given the geographical sparsity of Rare Diseases (RDs), assembling a cohort is often a challenging task. Common data models (CDM) can harmonize disparate sources of data that can be the basis of decision support systems and artificial intelligence-based studies, leading to new insights in the field. This work is sought to support the design of large-scale multi-center studies for rare diseases. Methods: In an interdisciplinary group, we derived a list of elements of RDs in three medical domains (endocrinology, gastroenterology, and pneumonology) according to specialist knowledge and clinical guidelines in an iterative process. We then defined a RDs data structure that matched all our data elements and built Extract, Transform, Load (ETL) processes to transfer the structure to a joint CDM. To ensure interoperability of our developed CDM and its subsequent usage for further RDs domains, we ultimately mapped it to Observational Medical Outcomes Partnership (OMOP) CDM. We then included a fourth domain, hematology, as a proof-of-concept and mapped an acute myeloid leukemia (AML) dataset to the developed CDM. Results: We have developed an OMOP-based rare diseases common data model (RD-CDM) using data elements from the three domains (endocrinology, gastroenterology, and pneumonology) and tested the CDM using data from the hematology domain. The total study cohort included 61,697 patients. After aligning our modules with those of Medical Informatics Initiative (MII) Core Dataset (CDS) modules, we leveraged its ETL process. This facilitated the seamless transfer of demographic information, diagnoses, procedures, laboratory results, and medication modules from our RD-CDM to the OMOP. For the phenotypes and genotypes, we developed a second ETL process. We finally derived lessons learned for customizing our RD-CDM for different RDs. Discussion: This work can serve as a blueprint for other domains as its modularized structure could be extended towards novel data types. An interdisciplinary group of stakeholders that are actively supporting the project's progress is necessary to reach a comprehensive CDM. Conclusion: The customized data structure related to our RD-CDM can be used to perform multi-center studies to test data-driven hypotheses on a larger scale and take advantage of the analytical tools offered by the OHDSI community. [ABSTRACT FROM AUTHOR]

Analysis of integrated data often requires record linkage in order to join together the data residing in separate sources. In case linkage errors cannot be avoided, due to the lack a unique identity key that can be used to link the records unequivocally, standard statistical techniques may produce misleading inference if the linked data are treated as if they were true observations. In this paper, we propose methods for categorical data analysis based on linked data that are not prepared by the analyst, such that neither the match‐key variables nor the unlinked records are available. The adjustment is based on the proportion of false links in the linked file and our approach allows the probabilities of correct linkage to vary across the records without requiring that one is able to estimate this probability for each individual record. It accommodates also the general situation where unmatched records that cannot possibly be correctly linked exist in all the sources. The proposed methods are studied by simulation and applied to real data. [ABSTRACT FROM AUTHOR]

Dysphagia, a common result of other medical conditions, is caused by malfunctions in swallowing physiology resulting in difficulty eating and drinking. The Modified Barium Swallow Study (MBSS), the most commonly used diagnostic tool for evaluating dysphagia, can be assessed using the Modified Barium Swallow Impairment Profile (MBSImP™). The MBSImP assessment tool consists of a hierarchical grouped data structure with multiple domains, a set of components within each domain which characterize specific swallowing physiologies, and a set of tasks scored on a discrete scale within each component. We lack sophisticated approaches to extract patterns of physiologic swallowing impairment from the MBSImP task scores within a component while still recognizing the nested structure of components within a domain. We propose a Bayesian hierarchical profile regression model, which uses a Bayesian profile regression model in conjunction with a hierarchical Dirichlet process mixture model to (1) cluster subjects into impairment profile patterns while respecting the hierarchical grouped data structure of the MBSImP, and (2) simultaneously determine associations between latent profile cluster membership for all components and the outcome of dysphagia severity. We apply our approach to a cohort of patients referred for an MBSS and assessed using the MBSImP. Our research results can be used to inform appropriate intervention strategies, and provide tools for clinicians to make better multidimensional management and treatment decisions for patients with dysphagia. [ABSTRACT FROM AUTHOR]

The practical application of traditional data‐driven techniques for process monitoring encounters significant challenges due to the inherent nonlinear and dynamic nature of most industrial processes. Aiming at the problem of nonlinear dynamic process monitoring, a novel fault detection method based on dynamic kernel principal component analysis combined with weighted structural difference (DKPCA‐WSD) is proposed in this paper. Initially, the proposed method leverages a sophisticated nonlinear transformation to project the augmented matrix of the original input data into a high‐dimensional feature space, thereby facilitating the establishment of a DKPCA model. Subsequently, the WSD statistic is computed, utilizing a widely known sliding window technique, to quantify the mean and standard deviation differences across data structures. Ultimately, the WSD statistic is utilized for fault detection, completing the process monitoring task. By integrating the capability of DKPCA to capture nonlinear dynamic characteristics with the effectiveness of the WSD statistic in mitigating the impact of non‐Gaussian data distributions, DKPCA‐WSD significantly enhances the monitoring performance of traditional DKPCA in nonlinear dynamic processes. The proposed method is evaluated through a numerical case exhibiting nonlinear dynamic behaviors and a simulation model of a continuous stirred tank reactor. A comparative analysis with conventional methods, including principal component analysis (PCA), dynamic principal component analysis, KPCA, PCA similarity factor (SPCA), DKPCA, and moving window KPCA (MWKPCA), demonstrates that DKPCA‐WSD outperforms traditional fault detection techniques in nonlinear dynamic processes, offering a substantial improvement in monitoring performance. [ABSTRACT FROM AUTHOR]

Complex neutrosophic soft groups represent a significant advancement in handling uncertainty by integrating the concepts of fuzzy logic, soft sets, and neutrosophic logic. These groups generalize complex fuzzy soft groups and introduce an additional dimension through neutrosophic membership functions, namely truth, indeterminacy, and falsity. This creates a richer framework for dealing with uncertainty and ambiguity, making it well-suited for managing complex data structures in real-world applications. We explore some important definitions and theoretical frameworks surrounding complex neutrosophic soft groups, highlighting the unique aspect of neutrosophic membership functions. Additionally, we present an overview of neutrosophic soft groups, exploring some of their key operations and fundamental properties. We then examine the basics of homogeneous complex neutrosophic soft sets and their roles in establishing complex neutrosophic soft groups. [ABSTRACT FROM AUTHOR]

Interrupted time series (ITS) designs are increasingly used for estimating the effect of shocks in natural experiments. Currently, ITS designs are often used in scenarios with many time points and simple data structures. This research investigates the performance of ITS designs when the number of time points is limited and with complex data structures. Using a Monte Carlo simulation study, we empirically derive the performance—in terms of power, bias and precision- of the ITS design. Scenarios are considered with multiple interventions, a low number of time points and different effect sizes based on a motivating example of the learning loss due to COVID school closures. The results of the simulation study show the power of the step change depends mostly on the sample size, while the power of the slope change depends on the number of time points. In the basic scenario, with both a step and a slope change and an effect size of 30% of the pre-intervention slope, the required sample size for detecting a step change is 1,100 with a minimum of twelve time points. For detecting a slope change the required sample size decreases to 500 with eight time points. To decide if there is enough power researchers should inspect their data, hypothesize about effect sizes and consider an appropriate model before applying an ITS design to their research. This paper contributes to the field of methodology in two ways. Firstly, the motivation example showcases the difficulty of employing ITS designs in cases which do not adhere to a single intervention. Secondly, models are proposed for more difficult ITS designs and their performance is tested. [ABSTRACT FROM AUTHOR]

ABSTRACT The performance of multivariate calibration models ŷ = f(x) for the prediction of a numerical property y from a set of x‐variables depends on the type of scaling of the x‐variables. Common scaling methods are autoscaling (dividing the centered x by its standard deviation s) and Pareto scaling (dividing the centered x by sP with p = 0.5). The adjusted Pareto scaling presented here varies the exponent P between 0 (no scaling) and 1 (autoscaling) with the aim of obtaining an optimum prediction performance for ŷ. Related scaling methods based on the variable spread are range scaling and vast scaling; while level scaling is based on the location (central value) of the variable. These scaling methods and robust versions are compared for models created by partial least‐squares (PLS) regression. The applied strategy repeated double cross validation (rdCV) evaluates the model performance for test set objects and considers its variability. Results with three data sets from chemistry show: (a) the efficacy of the different scaling methods depends on the data structure; (b) optimization of the Pareto exponent P is recommended; (c) range scaling or vast scaling may be better than adjusted Pareto scaling; (d) in general a heuristic search for the best scaling method is advisable. Overall, the consideration of different variants of scaling allow for a flexible adjustment of the variable contributions to the calibration model. [ABSTRACT FROM AUTHOR]

With the growing complexity of data structures, which include high-dimensional and multilabel datasets, the significance of feature selection has become more emphasized. Multilabel feature selection endeavors to identify a subset of features that concurrently exhibit relevance across multiple labels. Owing to the impracticality of performing exhaustive searches to obtain the optimal feature subset, conventional approaches in multilabel feature selection often resort to a heuristic search process. In this context, memetic multilabel feature selection has received considerable attention because of its superior search capability; the fitness of the feature subset created by the stochastic search is further enhanced through a refinement process predicated on the employed multilabel feature filter. Thus, it is imperative to employ an effective refinement process that frequently succeeds in improving the target feature subset to maximize the benefits of hybridization. However, the refinement process in conventional memetic multilabel feature selection often overlooks potential biases in feature scores and compatibility issues between the multilabel feature filter and the subsequent learner. Consequently, conventional methods may not effectively identify the optimal feature subset in complex multilabel datasets. In this study, we propose a new memetic multilabel feature selection method that addresses these limitations by incorporating the pruning of features and labels into the refinement process. The effectiveness of the proposed method was demonstrated through experiments on 14 multilabel datasets. [ABSTRACT FROM AUTHOR]

Effective data exchange holds the potential to bridge information gaps between organizations, creating essential prerequisites for enhanced collaboration. However, in cross-organizational database systems, ensuring strong consistency is often challenging. Inconsistent query results may be returned from different databases if data records are not timely synchronized, degrading mutual trust and collaboration efficiency among organizations. To address this issue, a novel cross-organizational data exchange approach is proposed. Specifically, consortium blockchain is leveraged as a Write-Ahead Log, enabling different organizations to trace relevant requests failed to be executed in real-time, thereby promptly providing consistent query results for users. Detailed settings on protocol level are designed for the blockchain platform to facilitate data exchange regulation, including data structures, consensus algorithms and an access control mechanism. Extensive simulation experiments are conducted to evaluate the performance of the proposed approach and investigate the impact of different parameters on data consistency and system availability. [ABSTRACT FROM AUTHOR]

This paper proposes a learning-based model for the resource-constrained edge nodes in the blockchain-enabled Internet of Medical Things (IoMT) systems to realize efficient querying. Three layers are designed in the new model: data evaluation layer, data storage layer and data distribution layer. The data evaluation layer extracts the features from medical data and evaluates their values based on the Extreme Learning Machine (ELM) method. Then, in the data storage layer, according to the value of medical data, a novelty data structure called Merkle–Huffman tree (M-H tree) is established. Compared with the Merkle tree, high-value data (frequently accessed data) in M-H tree is saved closer to the root node and can be found faster. In the data distribution layer, the sharding-based blockchain model is adopted to increase the storage scalability of the IoMT system. Finally, the experimental results show that the new learning-based model can effectively improve the query speed of the blockchain-enabled medical system by about 3.5% and free up large amounts of storage space on IoMT devices. [ABSTRACT FROM AUTHOR]

Summary The tidyCoverage R package provides a framework for intuitive investigation of collections of genomic tracks over genomic features, relying on the principle of tidy data manipulation. It defines two data structures, CoverageExperiment and AggregatedCoverage classes, directly extending the SummarizedExperiment fundamental class, and introduces a principled approach to exploring genome-wide data. This infrastructure facilitates the extraction and manipulation of genomic coverage track data across individual or multiple sets of thousands of genomic loci. This allows the end user to rapidly visualize track coverage at individual genomic loci or aggregated coverage profiles over sets of genomic loci. tidyCoverage seamlessly combines with the existing Bioconductor ecosystem to accelerate the integration of genome-wide track data in epigenomic analysis workflows. tidyCoverage emerges as a valuable tool, contributing to the advancement of epigenomics research by promoting consistency, reproducibility, and accessibility in data analysis. Availability and implementation tidyCoverage is an R package freely available from Bioconductor ≥ 3.19 (https://www.bioconductor.org/packages/tidyCoverage) for R ≥ 4.4. The software is distributed under the MIT License and is accompanied by example files and data. [ABSTRACT FROM AUTHOR]

We present a data structure that, given a graph G of n vertices and m edges, and a suitable pair of nested r-divisions of G, preprocesses G in O (m + n) time and handles any series of edge-deletions in O(m) total time while answering queries to pairwise biconnectivity in worst-case O(1) time. In case the vertices are not biconnected, the data structure can return a cutvertex separating them in worst-case O(1) time. As an immediate consequence, this gives optimal amortized decremental biconnectivity, 2-edge connectivity, and connectivity for large classes of graphs, including planar graphs and other minor free graphs. [ABSTRACT FROM AUTHOR]

Persistent homology barcodes and diagrams are a cornerstone of topological data analysis that capture the "shape" of a wide range of complex data structures, such as point clouds, networks, and functions. However, their use in statistical settings is challenging due to their complex geometric structure. In this paper, we revisit the persistent homology rank function, which is mathematically equivalent to a barcode and persistence diagram, as a tool for statistics and machine learning. Rank functions, being functions, enable the direct application of the statistical theory of functional data analysis (FDA)—a domain of statistics adapted for data in the form of functions. A key challenge they present over barcodes in practice, however, is their lack of stability—a property that is crucial to validate their use as a faithful representation of the data and therefore a viable summary statistic. In this paper, we fill this gap by deriving two stability results for persistent homology rank functions under a suitable metric for FDA integration. We then study the performance of rank functions in functional inferential statistics and machine learning on real data applications, in both single and multiparameter persistent homology. We find that the use of persistent homology captured by rank functions offers a clear improvement over existing non‐persistence‐based approaches. [ABSTRACT FROM AUTHOR]

The problem of constructing the internal Voronoi diagram of a polygonal figure—a polygon with polygonal holes—is considered. A method based on the plane sweep paradigm is proposed. Direct construction of only the internal part of the Voronoi diagram allows us to reduce the amount of computations and increase robustness compared to known solutions. Another factor in reducing the computational complexity is the use of the property of pairwise incidence of linear segments formed by the sides of the polygonal figure. To take these features into account, it is proposed to build a data structure called sweep linestatus in the form of an ordered set of sites' areas of responsibility. This structure is implemented as a combination of a balanced tree and a bidirectional list. Computational experiments illustrate the numerical reliability and efficiency of the proposed method. [ABSTRACT FROM AUTHOR]

Neuroimaging data acquired using multiple scanners or protocols are increasingly available. However, such data exhibit technical artifacts across batches which introduce confounding and decrease reproducibility. This is especially true when multi‐batch data are analyzed using complex downstream models which are more likely to pick up on and implicitly incorporate batch‐related information. Previously proposed image harmonization methods have sought to remove these batch effects; however, batch effects remain detectable in the data after applying these methods. We present DeepComBat, a deep learning harmonization method based on a conditional variational autoencoder and the ComBat method. DeepComBat combines the strengths of statistical and deep learning methods in order to account for the multivariate relationships between features while simultaneously relaxing strong assumptions made by previous deep learning harmonization methods. As a result, DeepComBat can perform multivariate harmonization while preserving data structure and avoiding the introduction of synthetic artifacts. We apply this method to cortical thickness measurements from a cognitive‐aging cohort and show DeepComBat qualitatively and quantitatively outperforms existing methods in removing batch effects while preserving biological heterogeneity. Additionally, DeepComBat provides a new perspective for statistically motivated deep learning harmonization methods. [ABSTRACT FROM AUTHOR]

The k shortest paths problem finds applications in multiple fields. Of particular interest in the transportation field is the variant of finding k simple shortest paths (KSSP), which has a higher complexity. This research presents a novel label-setting algorithm for the multi-destination KSSP problem in directed networks that obviates repeated applications of the algorithm to each destination (necessary in existing deviation-based algorithms), resulting in a significant computational speedup. It is shown that the proposed algorithm is exact and flexible enough to handle several variants of the problem by appropriately modifying the termination condition. Theoretically, it is also shown to be faster than state-of-the-art algorithms in sparse and dense networks whenever the number of labels created is sub-polynomial in network size. A heuristic method and optimized data structures are proposed to improve the algorithm's scalability and worst-case performance. The computational results show that the proposed heuristic provides two to three orders of magnitude computational time speedups (29–1416 times across different networks) with negligible loss in solution quality (maximum average deviation of 0.167% from the optimal solution). Finally, a practical application of the proposed method is illustrated to determine the gravity of an edge (relative structural importance) in a network. [ABSTRACT FROM AUTHOR]

Manifold learning-based approaches have emerged as prominent techniques for dimensionality reduction. Among these methods, t-Distributed Stochastic Neighbor Embedding (t-SNE) and Uniform Manifold Approximation and Projection (UMAP) stand out as two of the most widely used and effective approaches. While both methods share similar underlying procedures, empirical observations indicate two distinctive properties: global data structure preservation and computational efficiency. However, the underlying mathematical principles behind these distinctions remain elusive. To address this gap, this study presents a comparative analysis of the subprocesses involved in these methods, aiming to elucidate the mathematical mechanisms underlying the observed distinctions. By meticulously examining the equation formulations, the mathematical mechanisms contributing to global data structure preservation and computational efficiency are elucidated. To validate the theoretical analysis, data are collected through a laboratory experiment, and an open-source dataset is utilized for validation across different datasets. The consistent alignment of results obtained from both balanced and unbalanced datasets robustly confirms the study's findings. The insights gained from this study provide a deeper understanding of the mathematical underpinnings of t-SNE and UMAP, enabling more informed and effective use of these dimensionality reduction techniques in various applications, such as anomaly detection, natural language processing, and bioinformatics. [ABSTRACT FROM AUTHOR]

Optimization of 4D modeling through the application of flexible linking rules using the example project "Weser crossing A281" The implementation of 4D modeling in construction projects faces significant challenges due to a static approach and the absence of established standards. In practice, implementation is mostly manual or relies on rigid, rule‐based linkages. Additionally, standards for establishing these linkage rules are lacking. This article addresses coping with this issue and introduces the application of flexible linkage rules through a case study. Flexible 4D modeling involves creating a customized code for each scheduling process to consider all project hierarchy levels. Based on a cross‐trade project information structure, a tailored filtering of geometric objects is conducted for each process. The result is a specific selection of geometric objects tailored to each process, interacting flexibly with it. The presented method of interdisciplinary and flexible linking rules for 4D modeling aims to increase efficiency and accuracy in construction projects by allowing different disciplines and trades, as well as their specific processes, to be flexibly accommodated while ensuring a consistent data structure simultaneously. This approach can contribute to optimizing the planning and execution of construction projects and promoting collaboration among project stakeholders by conducting 4D modeling based on a project information structure. [ABSTRACT FROM AUTHOR]

Determining the manipulation unit of working memory is one of the fundamental questions in understanding how working memory functions. The prevalent object-based theory in cognitive research predicts that memory manipulation is performed on the level of objects. Here we show instead that the basic units of working memory manipulation are Boolean maps, a data structure describing what can be perceived in an instant. We developed four new manipulation tasks (with data from 80 adults) and showed that manipulation times only increased when the number of Boolean maps manipulated increased. Increasing the number of orientations manipulated did not induce longer manipulation times, consistent with a key prediction of the Boolean map theory. Our results show that Boolean maps are the manipulation unit of working memory. [ABSTRACT FROM AUTHOR]

The mode is a fundamental descriptive statistic in data analysis, signifying the most frequent element within a dataset. The range mode query (RMQ) problem expands upon this concept by preprocessing an array A containing n natural numbers. This allows for the swift determination of the mode within any subarray A[a..b], thus optimizing the computation of the mode for a multitude of range queries. The efficacy of this process bears considerable importance in data analytics and retrieval across diverse platforms, including but not limited to online shopping experiences and financial auditing systems. This study is dedicated to exploring and benchmarking different algorithms and data structures designed to tackle the RMQ problem. The goal is to not only address the theoretical aspects of RMQ but also to provide practical solutions that can be applied in real-world scenarios, such as the optimization of an online shopping platform's understanding of customer preferences, enhancing the efficiency and effectiveness of data retrieval in large datasets. [ABSTRACT FROM AUTHOR]

Background: Effective learning in computer programming courses has been a constant challenge for university teachers and has become a relevant competence for current professionals. The literature on gamification in learning presents mixed results, mainly due to problems in instructional design and inconsistency in gamification. Studies with positive empirical results on computer programming formative activities and showing under what conditions gamification is effective still need to be included. Objectives: This study aimed to quantify the effects of gamification techniques in software tools that support computer programming learning applied to data‐structures university students. The study focused on determining whether there are differences in learning when using gamification techniques in online exercising formative activities. The effects of gamification on learning objectives with diverse complexity are studied. Methods: The study was conducted on computer engineering university students following a pretest‐intervention‐posttest design. They were divided into control and experimental groups, where both used a bank of exercises in Moodle to support learning. The experimental group considered gamification techniques, including points, leaderboards, and badges. Results and Conclusions: Gamification techniques significantly outperformed in learning gains in exercising activities without gamification. Regarding learning complexity, increasing taxonomic complexity did not negatively affect gamification learning effects. Lay Description: What is currently known about the subject matter?: Gamification has been explored as a strategy to enhance learning motivation in various educational contexts, including computer programmingLiterature shows mixed quantitative results on learning improvements under gamification.Few studies cover formative exercises, deal with computer programming tasks, or use a LMS like Moodle.Lack of literature exploring the conditions for effective gamification use, and involving learning objective complexity. What does this paper add to the existing body of research?: Quantifies the impact of gamification techniques on learning outcomes, specifically focusing on computer‐programming in university.Provides empirical evidence on gamification significantly improving learning gains compared to non‐gamified data‐structures exercisesGamification is effective in tasks of diverse levels of taxonomic complexity, with consistent effectiveness regardless of learning difficulty. Implications of study findings for practitioners: Gamification techniques, can be used in higher education to enhance student engagement and learning outcomes in computer programmingThe gamification information (e.g. points and ranking) do not require an automatic update, can be processed manually and published daily.It is effective to use gamification for learning tasks of medium‐high difficulty in Moodle on computer programming topics.It is key a thoughtful instructional design when applying gamification, ensuring alignment with learning objectives and task complexity. [ABSTRACT FROM AUTHOR]

Periodic high utility sequential pattern mining (PHUSPM) has attracted much attention because it can find more practical regular patterns in time series. However, existing PHUSPM algorithms struggle to effectively handle incremental updates and overlook the downward closure property and complexity of the algorithm in large-scale data. To solve this problem, this paper proposed an IncPUS-Miner algorithm, which effectively realized the incremental mining of periodic high-utility sequential patterns (PHUSPs). IncPUS-Miner introduced a novel data structure called pu-tree. Each tree node corresponded to an updated utility list(UUL) to store the auxiliary information of the corresponding sequence. When incremental data was added, this structure allowed flexible updates to project information, thereby enhancing the dynamic adaptability and scalability of the algorithm. In addition, this paper proposed two new upper bounds of sequence utility, PUB and EUB, and two corresponding pruning strategies, which effectively reduced the computational burden. The experimental results show that the IncPUS-Miner algorithm effectively realizes the incremental mining of PHUSPs on real data, and shows superior performance compared with other algorithms. [ABSTRACT FROM AUTHOR]

Big data technique is now a prevalent concentration for specialist and academics due to the development of information technology, cloud computing, and Internet of Things technologies. A fiscal risk controlling model using MSHDS-RS, was innovatively proposed to deal with the current situation of unreasonable design of features in risk controlling technique. This model's innovation is that the model utilizes a normalized sparse approach for optimizing feature fusion after drawing loan customer information sources' hard and soft features, thereby forming integrated features. Then, the feature subset derived from probability sampling is trained as a base classifier, and the results of the base classifier are fused and optimized using evidence reasoning rules. MSHDS-RS's accuracy improvement rate was about 3.0% and 3.6% higher than existing PMB-RS methods', respectively, by observing MSHDS-RS's operating results in different feature sets with soft and integrated feature indicators. Therefore, the proposed optimization fusion method is reliable and feasible. This research contributes to the control of internet financial risks and has certain value in making effective decisions on loan platforms. [ABSTRACT FROM AUTHOR]

Many time series data mining algorithms work by reasoning about the relationships the conserved shapes of subsequences. To facilitate this, the Matrix Profile is a data structure that annotates a time series by recording each subsequence's Euclidean distance to its nearest neighbor. In recent years, the community has shown that using the Matrix Profile it is possible to discover many useful properties of a time series, including repeated behaviors (motifs), anomalies, evolving patterns, regimes, etc. However, the Matrix Profile is limited to representing the relationship between the subsequence's shapes. It is understood that, for some domains, useful information is conserved not in the subsequence's shapes, but in the subsequence's features. In recent years, a new set of features for time series called catch22 has revolutionized feature-based mining of time series. Combining these two ideas seems to offer many possibilities for novel data mining applications; however, there are two difficulties in attempting this. A direct application of the Matrix Profile with the catch22 features would be prohibitively slow. Less obviously, as we will demonstrate, in almost all domains, using all twenty-two of the catch22 features produces poor results, and we must somehow select the subset appropriate for the domain. In this work, we introduce novel algorithms to solve both problems and demonstrate that, for most domains, the proposed C22MP is a state-of-the-art anomaly detector. [ABSTRACT FROM AUTHOR]

In complex networks, contrastive learning has emerged as a crucial technique for acquiring discriminative representations from graph data. Maximizing the similarity among relevant sample pairs while minimizing that among irrelevant pairs is pivotal in contrastive learning. Therefore, careful consideration must be given to the design of sample pairs in contrastive learning. However, existing node-level self-supervised contrastive learning often treats the enhanced representation of a central node as a positive sample, while considering representations of all other nodes as negative samples. This approach can lead to conflicts in downstream tasks on some graph data, as nodes of the same class are treated as negatives during learning. Precision in sample pair design is essential for enhancing the performance of contrastive learning. To address this issue, this paper introduces a negative sample debiased sampling contrastive learning (NDSCL), specifically tailored for node classification tasks. In particular, this method integrates contrastive learning with semi-supervised learning. A trained classifier assigns pseudo-labels to unlabeled data, and debiased sampling is applied to negative samples. Unlike other methods that focus on negative sample selection, NDSCL also addresses the imbalance in pseudo-label distribution by employing debiasing techniques. Finally, in conjunction with diffusion augmentation, the model is provided with diverse views as inputs to maximize the retention of underlying semantic information. Experimental results demonstrate that the proposed model significantly outperforms baseline models in node-level classification tasks across multiple network datasets. Moreover, the model not only enhances accuracy but also improves computational speed and memory requirements for handling large-scale graph data structures. [ABSTRACT FROM AUTHOR]

Rough fuzzy clustering algorithms have received extensive attention due to the excellent ability to handle overlapping and uncertainty of data. However, existing rough fuzzy clustering algorithms generally consider single view clustering, which neglects the clustering requirements of multiple views and results in the failure to identify diverse data structures in practical applications. In addition, rough fuzzy clustering algorithms are always sensitive to the initialized cluster centers and easily fall into local optimum. To solve the above problems, the multi-view and transfer learning are introduced into rough fuzzy clustering and a robust multi-view knowledge transfer-based rough fuzzy c-means clustering algorithm (MKT-RFCCA) is proposed in this paper. First, multiple distance metrics are adopted as multiple views to effectively recognize different data structures, and thus positively contribute to clustering. Second, a novel multi-view transfer-based rough fuzzy clustering objective function is constructed by using fuzzy memberships as transfer knowledge. This objective function can fully explore and utilize the potential information between multiple views and characterize the uncertainty information. Then, combining the statistical information of color histograms, an initialized centroids selection strategy is presented for image segmentation to overcome the instability and sensitivity caused by the random distribution of the initialized cluster centers. Finally, to reduce manual intervention, a distance-based adaptive threshold determination mechanism is designed to determine the threshold parameter for dividing the lower approximation and boundary region of rough fuzzy clusters during the iteration process. Experiments on synthetic datasets, real-world datasets, and noise-contaminated Berkeley and Weizmann images show that MKT-RFCCA obtains favorable clustering results. Especially, it provides satisfactory segmentation results on images with different types of noise and preserves more specific detail information of images. [ABSTRACT FROM AUTHOR]

This paper explores the problem of automated robot program generation from limited historical data when neither accurate geometric environmental models nor online vision feedback are available. The Program Reuse Framework (PRF) is developed, which uses expert-defined motion classes, a novel data structure introduced in this work, to learn affordances, workspaces, and skills from historical data. Historical data comprise raw robot joint trajectories and descriptions of the robot task being completed. Given new tasks, motion classes are then used again to formulate an optimization problem capable of generating new open-loop, skill-based programs to complete the tasks. To cope with a lack of geometric models, a technique to learn safe workspaces from demonstrations is developed, allowing the risk of new programs to be estimated before execution. A new learnable motion primitive for redundant manipulators is introduced, called a redundancy dynamical movement primitive, which enables new end-effector goals to be reached while mimicking the whole-arm behavior of a demonstration. A mobile manipulator part transportation task is used throughout to illustrate each step of the framework. [ABSTRACT FROM AUTHOR]

Snakebite envenomations (SBE) are a significant global public health threat due to their morbidity and mortality. This is a neglected public health issue in many tropical and subtropical countries. Brazil is in the top ten countries affected by SBE, with 32,160 cases reported only in 2020, posing a high burden for this population. In this paper, we describe the data structure of snakebite records from 2007 to 2020 in the Notifiable Disease Information System (SINAN), made available by the Brazilian Ministry of Health (MoH). In addition, we also provide R scripts that allow a quick and automatic updating of data from the SINAN according to its availability. The data presented in this work are related to clinical and demographic information on SBE cases. Also, data on outcomes, laboratory results, and treatment are available. The dataset is available and freely accessible; however, preprocessing, adjustments, and standardization are necessary due to incompleteness and inconsistencies. Regardless of these limitations, it provides a solid basis for assessing different aspects and the national burden of envenoming. Dataset: The dataset is available at https://github.com/LabdataManaus/describe%5fsnakebite/tree/main. Accessed on 26 April 2024. Dataset License: CC BY-NC-SA [ABSTRACT FROM AUTHOR]

Network visualization, in mathematics often referred to as graph visualization, has evolved significantly over time, offering various methods to effectively represent complex data structures. New methods and devices advance the possibilities of visualization both from the point of view of the quality of displayed information and of the possibilities of visualizing a larger amount of data. Immersive visualization includes the user directly in presented visual representation but requires a native 3D environment for direct interaction with visualized information. This article describes an approach to creating a force-directed immersive 3D network visualization algorithm available for application in immersive environments, such as a cave automatic virtual environment or virtual reality. The algorithm aims to address the challenge of creating visually appealing and easily interpretable visualizations by utilizing 3D space and the Unity engine. The results show successfully visualized data and developed interactive visualization methods, overcoming limitations of basic force-directed implementations. The main contribution of the presented research is the force-directed algorithm with springs and controlled placement as an immersive visualization technique that combines the use of springs and attractive forces to stabilize a network in a 3D environment. [ABSTRACT FROM AUTHOR]

BRIDGES is a software framework for creating engaging assignments for required courses such as data structures and algorithms. It provides students with a simplified API that populates their own data structure implementations with live and real-world data, and provides the ability for students to easily visualize the data structures they create as part of routine classroom exercises. The objective is to use the infrastructure to promote a better understanding of the data structure and its underlying algorithms. This report describes the BRIDGES infrastructure and provides evaluation data collected over the first five years of the project. In the first 2 years, as we were developing the BRIDGES projects, our focus was on gathering data to assess whether the addition of the BRIDGES exercises had an effect on student retention of core concepts in data structures; and throughout the 5-year duration of the project, student interest and faculty feedback were collected online and anonymously. A mixed method design was used to evaluate the project impact. A quasiexperimental design compared student cohorts who were enrolled in comparable course sections that used BRIDGES with those that did not. Qualitative and quantitative measures were developed and used together with course grades and grade point averages. Interest and relevance in BRIDGES programming assignments was assessed with additional survey data from students and instructors. Results showed that students involved in BRIDGES projects demonstrated larger gains in knowledge of data structures compared to students enrolled in comparable course sections, as well as long-term benefits in their performance in four follow-on required courses. Survey responses indicated that some investment of time was needed to use BRIDGES, but the extra efforts were associated with several notable outcomes. Students and instructors had positive perceptions of the value of engaging in BRIDGES projects. BRIDGES can become a tool to get students more engaged in critical foundational courses, demonstrating relevance and context to today's computational challenges. [ABSTRACT FROM AUTHOR]