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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]

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]

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]

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]

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]

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]

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]

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]

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]

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]

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]

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]

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]

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]

Clique-width is a well-studied graph parameter owing to its use in understanding algorithmic traceability, and in this paper, we study the class of bounded clique-width graphs through the lens of succinct data structures. A data structure is said to be succinct if the amount of space used by the data structure is information-theoretically optimal up to lower-order additive terms. More specifically, we design a succinct data structure for graphs on n vertices whose clique-width is at most k ≤ ϵ log n for some constant 0 < ϵ < 1 / 6 , that supports degree, adjacency, and neighborhood queries efficiently. This resolves an open problem of Kamali (Algorithmica-2018). As an application of our main technique, we also propose succinct data structures for distance-hereditary and Ptolemaic graphs, which are subclasses of the class of graphs with clique-width at most 3. [ABSTRACT FROM AUTHOR]

A fundamental challenge in mass spectrometry-based proteomics is the identification of the peptide that generated each acquired tandem mass spectrum. Approaches that leverage known peptide sequence databases cannot detect unexpected peptides and can be impractical or impossible to apply in some settings. Thus, the ability to assign peptide sequences to tandem mass spectra without prior information—de novo peptide sequencing—is valuable for tasks including antibody sequencing, immunopeptidomics, and metaproteomics. Although many methods have been developed to address this problem, it remains an outstanding challenge in part due to the difficulty of modeling the irregular data structure of tandem mass spectra. Here, we describe Casanovo, a machine learning model that uses a transformer neural network architecture to translate the sequence of peaks in a tandem mass spectrum into the sequence of amino acids that comprise the generating peptide. We train a Casanovo model from 30 million labeled spectra and demonstrate that the model outperforms several state-of-the-art methods on a cross-species benchmark dataset. We also develop a version of Casanovo that is fine-tuned for non-enzymatic peptides. Finally, we demonstrate that Casanovo's superior performance improves the analysis of immunopeptidomics and metaproteomics experiments and allows us to delve deeper into the dark proteome. Identification of the peptide that generates each acquired tandem mass spectrum is a fundamental challenge in mass spectrometry-based proteomics. Here, the authors present Casanovo, a machine learning model that translates the sequence of peaks in a tandem mass spectrum into the sequence of amino acids that comprise the generating peptide. [ABSTRACT FROM AUTHOR]

Summary: The breadth‐first search procedure is an algorithm that traverses the vertices of a graph, determining the distance from each vertex to the initial vertex. The distance is infinite for a non‐reachable vertex from the starting vertex. Despite having an efficient serial version, this important algorithm is irregular, making its effective parallel implementation a daunting task. This paper shows the results of an OpenMP‐based implementation of the breadth‐first search procedure using the bag data structure. Furthermore, the code relied on the C++ programming language. This paper reimplements an existing proposal coded using the Cilk++ programming language. The experiments relied on 32 strongly connected graphs and 31 disconnected graphs in executions performed on two machines. The first machine contained 28 cores and two threads per core. The second machine comprised 48 processing cores, with hyperthreading disabled. Regarding the serial version, the parallel implementation yielded a speedup of up to 20× when using 28 processing cores and up to 25× when using 56 threads in tests performed on a machine with the first generation of Intel® Xeon® Scalable processors. Furthermore, the new parallel implementation yielded speedups of up to 45× when using 48 cores in experiments performed on a machine with the second generation of Intel® Xeon® Scalable processors. [ABSTRACT FROM AUTHOR]

We describe the following shared data from N = 103 healthy adults who completed a broad set of cognitive tasks, surveys, and neuroimaging measurements to examine the construct of self-regulation. The neuroimaging acquisition involved task-based fMRI, resting state fMRI, and structural MRI. Each subject completed the following ten tasks in the scanner across two 90-minute scanning sessions: attention network test (ANT), cued task switching, Columbia card task, dot pattern expectancy (DPX), delay discounting, simple and motor selective stop signal, Stroop, a towers task, and a set of survey questions. The dataset is shared openly through the OpenNeuro project, and the dataset is formatted according to the Brain Imaging Data Structure (BIDS) standard. [ABSTRACT FROM AUTHOR]

This study introduces a novel method for classifying sets of images, called Riemannian geodesic discriminant analysis–minimum Riemannian mean distance (RGDA-MRMD). This method first converts image data into symmetric positive definite (SPD) matrices, which capture important features related to the variability within the data. These SPD matrices are then mapped onto simpler, flat spaces (tangent spaces) using a mathematical tool called the logarithm operator, which helps to reduce their complexity and dimensionality. Subsequently, regularized local Fisher discriminant analysis (RLFDA) is employed to refine these simplified data points on the tangent plane, focusing on local data structures to optimize the distances between the points and prevent overfitting. The optimized points are then transformed back into a complex, curved space (SPD manifold) using the exponential operator to enhance robustness. Finally, classification is performed using the minimum Riemannian mean distance (MRMD) algorithm, which assigns each data point to the class with the closest mean in the Riemannian space. Through experiments on the ETH-80 (Eidgenössische Technische Hochschule Zürich-80 object category), AFEW (acted facial expressions in the wild), and FPHA (first-person hand action) datasets, the proposed method demonstrates superior performance, with accuracy scores of 97.50%, 37.27%, and 88.47%, respectively. It outperforms all the comparison methods, effectively preserving the unique topological structure of the SPD matrices and significantly boosting image set classification accuracy. [ABSTRACT FROM AUTHOR]

ABSTRACT Biomarker‐guided therapy is a growing area of research in medicine. To optimize the use of biomarkers, several study designs including the biomarker‐strategy design (BSD) have been proposed. Unlike traditional designs, the emphasis here is on comparing treatment strategies and not on treatment molecules as such. Patients are assigned to either a biomarker‐based strategy (BBS) arm, in which biomarker‐positive patients receive an experimental treatment that targets the identified biomarker, or a non‐biomarker‐based strategy (NBBS) arm, in which patients receive treatment regardless of their biomarker status. We proposed a simulation method based on a partially clustered frailty model (PCFM) as well as an extension of Freidlin formula to estimate the sample size required for BSD with multiple targeted treatments. The sample size was mainly influenced by the heterogeneity of treatment effect, the proportion of biomarker‐negative patients, and the randomization ratio. The PCFM is well suited for the data structure and offers an alternative to traditional methodologies. [ABSTRACT FROM AUTHOR]

This paper analyzes several covariance matrix U‐tests, which are constructed by modifying the classical John‐Nagao and Ledoit‐Wolf tests, under the elliptically distributed data structure. We study the limiting distributions of these location‐invariant test statistics as the data dimension p$$ p $$ may go to infinity in an arbitrary way as the sample size n$$ n $$ does. We find that they tend to have unsatisfactory size performances for general elliptical population. This is mainly because such population often possesses high‐order correlations among their coordinates. Taking such kind of dependency into consideration, we propose necessary corrections for these tests to cope with elliptically high‐dimensional data. For computational efficiency, alternative forms of the new test statistics are also provided. We derive the universal (n,p)$$ \left(n,p\right) $$ asymptotic null distributions of the proposed test statistics under elliptical distributions and beyond. The powers of the proposed tests are further investigated. The accuracy of the tests is demonstrated by simulations and an empirical study. [ABSTRACT FROM AUTHOR]

The Software Heritage (SWH) dataset represents an invaluable source of open-source code as it aims to collect, preserve, and share all publicly available software in source code form ever produced by humankind. Although designed to archive deduplicated small files thanks to the use of a Merkle tree as the underlying data structure, querying the SWH dataset presents challenges due to the nature of these structures, which organize content based on hash values rather than any locality principle. The magnitude of the repository, coupled with the resource-intensive nature of the download process, highlights the need for specialized infrastructure and computational resources to effectively handle and study the extensive dataset housed within SWH. Currently, there is a lack of infrastructures specifically tailored for running analytics on the SWH dataset, leaving users to handle these issues manually. To address these challenges, we implemented the SWH-Analytics (SWHA) framework, a development environment that transparently runs custom analytic applications on publicly available software data preserved over time by SWH. Specifically, this work shows how SWHA can be effectively exploited to study usage patterns of free and open-source software licenses, highlighting the need to improve license literacy among developers. [ABSTRACT FROM AUTHOR]

This paper presents a new mesh data layout for parallel I/O of linear unstructured polyhedral meshes. The new mesh representation infers coherence across entities of different topological dimensions, i.e., grid cells, faces, and points. The coherence due to cell-to-face and face-to-point connectivities of the mesh is formulated as a tree data structure distributed across processors. The mesh distribution across processors creates consecutive and contiguous slices that render an optimized data access pattern for parallel I/O. A file format using the coherent mesh representation, developed and tested with OpenFOAM, enables the usability of the software at unprecedented scales. Further implications of the coherent and sliceable mesh representation arise due to simplifications in partitioning and diminished pre- and post-processing overheads. [ABSTRACT FROM AUTHOR]

In the context of 'energy shortage', developing a novel energy-based power system is essential for advancing the current power system towards low-carbon solutions. As the usage duration of lithium-ion batteries for energy storage increases, the nonlinear changes in their aging process pose challenges to accurately assess their performance. This paper focuses on the study LiFeO4(LFP), used for energy storage, and explores their performance degradation mechanisms. Furthermore, it introduces common battery models and data structures and algorithms, which used for predicting the correlation between electrode materials and physical parameters, applying to state of health assessment and thermal warning. This paper also discusses the establishment of digital management system. Compared to conventional battery networks, dynamically reconfigurable battery networks can realize real-time monitoring of lithium-ion batteries, and reduce the probability of fault occurrence to an acceptably low level. [ABSTRACT FROM AUTHOR]

This work presents a comprehensive study and methodology to cope the problem of multi-object tracking. The methodology proposed here aims to simplify the problem space by using RNN as a regression network for predicting current object tracker information from past data and using Siamese network for re-identification. The number of re-identification is reduced by doing suboptimal perfect matching using a tree data structure for geometric location data of objects in view port. [ABSTRACT FROM AUTHOR]

Three-dimensional (3D) geographic information systems (GIS) are vital for urban planning and architectural design, enabling detailed visualization and analysis of urban landscapes. However, the growing size of 3D city data and the transition to web environments cause performance challenges for real-time applications. This study addresses the need for optimizing 3D spatial analyses for the web using Quadtree data structures and multiprocessing tools. Optimization scenarios based on data size and hardware configurations are evaluated, and the best hosting plans for GIS applications on cloud services are also considered. [ABSTRACT FROM AUTHOR]

Machine learning algorithms have revolutionized data analysis by uncovering hidden patterns and structures. Clustering algorithms play a crucial role in organizing data into coherent groups. We focused on K-Means, hierarchical, and Self-Organizing Map (SOM) clustering algorithms for analyzing homogeneous datasets based on archaeological finds from the middle phase of Pre-Pottery B Neolithic in Southern Levant (10,500–9500 cal B.P.). We aimed to assess the repeatability of these algorithms in identifying patterns using quantitative and qualitative evaluation criteria. Thorough experimentation and statistical analysis revealed the pros and cons of each algorithm, enabling us to determine their appropriateness for various clustering scenarios and data types. Preliminary results showed that traditional K-Means may not capture datasets' intricate relationships and uncertainties. The hierarchical technique provided a more probabilistic approach, and SOM excelled at maintaining high-dimensional data structures. Our research provides valuable insights into balancing repeatability and interpretability for algorithm selection and allows professionals to identify ideal clustering solutions. [ABSTRACT FROM AUTHOR]

Polytomous categorical data are frequent in studies, that can be obtained with an individual or grouped structure. In both structures, the generalized logit model is commonly used to relate the covariates on the response variable. After fitting a model, one of the challenges is the definition of an appropriate residual and choosing diagnostic techniques. Since the polytomous variable is multivariate, raw, Pearson, or deviance residuals are vectors and their asymptotic distribution is generally unknown, which leads to difficulties in graphical visualization and interpretation. Therefore, the definition of appropriate residuals and the choice of the correct analysis in diagnostic tools is important, especially for nominal data, where a restriction of methods is observed. This paper proposes the use of randomized quantile residuals associated with individual and grouped nominal data, as well as Euclidean and Mahalanobis distance measures, as an alternative to reduce the dimension of the residuals. We developed simulation studies with both data structures associated. The half-normal plots with simulation envelopes were used to assess model performance. These studies demonstrated a good performance of the quantile residuals, and the distance measurements allowed a better interpretation of the graphical techniques. We illustrate the proposed procedures with two applications to real data. [ABSTRACT FROM AUTHOR]

Biometric authentication methods have become increasingly popular for their ability to offer secure and convenient access control. However, concerns about the privacy and security of biometric data have arisen. In this study, we present a novel approach to address these concerns by proposing a cancellable biometric encryption technique for secure and format-preserving iris authentication. Our method leverages the Quotient Filter data structure to transform encrypted iris templates into cancellable templates while preserving their original format. We carefully select an appropriate format-preserving encryption algorithm for iris templates and design a mapping scheme to achieve cancellability. To assess the effectiveness and performance of our approach, extensive experiments are conducted. The quantitative results indicate the efficiency and efficacy of our cancellable biometric encryption technique using the Quotient Filter. Our innovation, the Iris Authentication for Multiple Cancelled Instances Using a Quotient Filter (IAMCIQF), demonstrates competitive performance across several key metrics. IAMCIQF achieves a high level of security strength and strikes a balance between security and efficiency in terms of key size, encryption and decryption speeds and storage efficiency when compared to other existing techniques. The quantitative outcomes underscore IAMCIQF’s potential as a promising solution for attaining secure and format-preserving iris authentication, addressing critical concerns about biometric data security. [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]

Multi-group data, which include the same set of variables on separate groups of samples, are commonly seen in practice. Such data structure consists of data from multiple groups and can be challenging to analyze due to data heterogeneity. We propose a novel Joint and Individual Component Regression (JICO) model to analyze multi-group data. Our proposed model decomposes the response into shared and group-specific components, which are driven by low-rank approximations of joint and individual structures from the predictors respectively. The joint structure has the same regression coefficients across multiple groups, whereas individual structures have group-specific regression coefficients. We formulate this framework under the representation of latent components and propose an iterative algorithm to solve for the joint and individual scores. We us the Continuum Regression (CR) to estimate the latent scores, which provides a unified framework that covers the Ordinary Least Squares (OLS), the Partial Least Squares (PLS), and the Principal Component Regression (PCR) as its special cases. We show that JICO attains a good balance between global and group-specific models and remains flexible due to the usage of CR. We conduct simulation studies and analysis of an Alzheimer's disease dataset to further demonstrate the effectiveness of JICO. R package of JICO is available online at . for this article are available online. [ABSTRACT FROM AUTHOR]

Motivation The Full-text index in Minute space (FM-index) is a memory-efficient data structure widely used in bioinformatics for solving the fundamental pattern-matching task of searching for short patterns within a long reference. With the demand for short query patterns, the k -ordered concept has been proposed for FM-indexes. However, few construction algorithms in the state of the art fully exploit this idea to achieve significant speedups in the pan-genome era. Results We introduce the k -ordered induced suffix sorting (kISS) for efficient construction and utilization of k -ordered FM-indexes. We present an algorithmic workflow for building k -ordered suffix arrays, incorporating two novel strategies to improve time and memory efficiency. We also demonstrate the compatibility of integrating k -ordered FM-indexes with locate operations in FMtree. Experiments show that kISS can improve the construction time, and the generated k -ordered suffix array can also be applied to FMtree without any additional in computation or memory usage. Availability and implementation https://github.com/jhhung/kISS. [ABSTRACT FROM AUTHOR]

Natural resources and geographic information big data are regarded as essential productive factors in the context of digital government, and an important component of the national integrated government big data system. Due to data dispersion and isolated application program existing in the various departments, it is difficult to realize data sharing and application across departments and businesses, leading to the low utilization rate of data. In response to the issues mentioned above, the digital government spatio-temporal data center was designed with the demands for natural resource and geographic information big data services. The critical components, including storage computation, data structure and application support were introduced clearly. By integrating the natural resources and geographic information database with the population comprehensive database and legal person comprehensive database, an organic composition of people, enterprises and geographic locations was established, which was explained by the specific application practices. [ABSTRACT FROM AUTHOR]

Simple Summary: In scRNA-seq analysis, diffusion-based approaches help identify the connections between cells, allowing us to observe the progression of individual cells as they change phenotypes within a mathematical space known as a manifold. Recently, these approaches have been used as a reference for imputation, a technique that addresses missing data, a common challenge in scRNA-seq analysis. For example, MAGIC is a popular diffusion-based imputation method, and it has shown success in uncovering gene–gene interactions related to phenotypic transitions that would not be possible without imputation. However, previous evaluations have not adequately compared the impact of different parameter settings on MAGIC, particularly over-smoothing issues. To address this, we developed sc-PHENIX, which utilizes a similar diffusion approach as MAGIC but incorporates a PCA-UMAP initialization step, whereas MAGIC only uses PCA. We compared sc-PHENIX and MAGIC in terms of imputation accuracy, visualization, biological insights, and preservation of data structure. Our findings show that sc-PHENIX outperforms MAGIC across various common parameters such as "diffusion time" (t), the number of nearest neighbors (knn), and PCA dimensions. It effectively captures and preserves the global, local, and continuous data structures, leading to more reliable imputation and potentially uncovering new biological insights in diverse datasets. Single-cell transcriptomics (scRNA-seq) is revolutionizing biological research, yet it faces challenges such as inefficient transcript capture and noise. To address these challenges, methods like neighbor averaging or graph diffusion are used. These methods often rely on k-nearest neighbor graphs from low-dimensional manifolds. However, scRNA-seq data suffer from the 'curse of dimensionality', leading to the over-smoothing of data when using imputation methods. To overcome this, sc-PHENIX employs a PCA-UMAP diffusion method, which enhances the preservation of data structures and allows for a refined use of PCA dimensions and diffusion parameters (e.g., k-nearest neighbors, exponentiation of the Markov matrix) to minimize noise introduction. This approach enables a more accurate construction of the exponentiated Markov matrix (cell neighborhood graph), surpassing methods like MAGIC. sc-PHENIX significantly mitigates over-smoothing, as validated through various scRNA-seq datasets, demonstrating improved cell phenotype representation. Applied to a multicellular tumor spheroid dataset, sc-PHENIX identified known extreme phenotype states, showcasing its effectiveness. sc-PHENIX is open-source and available for use and modification. [ABSTRACT FROM AUTHOR]

Object‐space occluding contours of triangular meshes (a.k.a. mesh contours) are at the core of many methods in computer graphics and computational geometry. A number of hierarchical data‐structures have been proposed to accelerate their computation on the CPU, but they do not map well to the GPU for real‐time applications, such as video games. We show that a simple, flat data‐structure composed of patches bounded by a normal cone and a bounding sphere may reach this goal, provided it is constructed to maximize the probability for a patch to be culled over all viewpoints. We derive a heuristic metric to efficiently estimate this probability, and present a greedy, bottom‐up algorithm that constructs patches by grouping mesh edges according to this metric. In addition, we propose an effective way of computing their bounding sphere. We demonstrate through extensive experiments that this data‐structure achieves similar performance as the state‐of‐the‐art on the CPU but is also perfectly adapted to the GPU, leading to up to ×5 speedups. [ABSTRACT FROM AUTHOR]

Electronic health records (EHRs) contain a vast array of phenotypic data on large numbers of individuals, often collected over decades. Due to the wealth of information, EHR data have emerged as a powerful resource to make first discoveries and identify disparities in our healthcare system. While the number of EHR‐based studies has exploded in recent years, most of these studies are directed at associations with disease rather than pharmacotherapeutic outcomes, such as drug response or adverse drug reactions. This is largely due to challenges specific to deriving drug‐related phenotypes from the EHR. There is great potential for EHR‐based discovery in clinical pharmacology research, and there is a critical need to address specific challenges related to accurate and reproducible derivation of drug‐related phenotypes from the EHR. This review provides a detailed evaluation of challenges and considerations for deriving drug‐related data from EHRs. We provide an examination of EHR‐based computable phenotypes and discuss cutting‐edge approaches to map medication information for clinical pharmacology research, including medication‐based computable phenotypes and natural language processing. We also discuss additional considerations such as data structure, heterogeneity and missing data, rare phenotypes, and diversity within the EHR. By further understanding the complexities associated with conducting clinical pharmacology research using EHR‐based data, investigators will be better equipped to design thoughtful studies with more reproducible results. Progress in utilizing EHRs for clinical pharmacology research should lead to significant advances in our ability to understand differential drug response and predict adverse drug reactions. [ABSTRACT FROM AUTHOR]