The estimation of the covariance matrix is a key concern in the analysis of longitudinal data. When data consists of multiple groups, it is often assumed the covariance matrices are either equal across groups or are completely distinct. We seek methodology to allow borrowing of strength across potentially similar groups to improve estimation. To that end, we introduce a covariance partition prior which proposes a partition of the groups at each measurement time. Groups in the same set of the partition share dependence parameters for the distribution of the current measurement given the preceding ones, and the sequence of partitions is modeled as a Markov chain to encourage similar structure at nearby measurement times. This approach additionally encourages a lower-dimensional structure of the covariance matrices by shrinking the parameters of the Cholesky decomposition toward zero. We demonstrate the performance of our model through two simulation studies and the analysis of data from a depression study. This article includes Supplementary Material available online.

Many complex diseases are caused by a variety of both genetic and environmental factors acting in conjunction. To help understand these relationships, nonparametric methods that use aggregate learning have been developed such as random forests and conditional forests. Molinaro et al. (2010) described a powerful, single model approach called partDSA that has the advantage of producing interpretable models. We propose two extensions to the partDSA algorithm called bagged partDSA and boosted partDSA. These algorithms achieve higher prediction accuracies than individual partDSA objects through aggregating over a set of partDSA objects. Further, by using partDSA objects in the ensemble, each base learner creates decision rules using both “and” and “or” statements, which allows for natural logical constructs. We also provide four variable ranking techniques that aid in identifying the most important individual factors in the models. In the regression context, we compared bagged partDSA and boosted partDSA to random forests and conditional forests. Using simulated and real data, we found that bagged partDSA had lower prediction error than the other methods if the data were generated by a simple logic model, and that it performed similarly for other generating mechanisms. We also found that boosted partDSA was effective for a particularly complex case. Taken together these results suggest that the new methods are useful additions to the ensemble learning toolbox. We implement these algorithms as part of the partDSA R package. Supplementary materials for this article are available online.

Computer models are used as replacements for physical experiments in a large variety of applications. Nevertheless, direct use of the computer model for the ultimate scientific objective is often limited by the complexity and cost of the model. Gaussian process regression has been the almost ubiquitous choice for a fast statistical emulator for such a computer model, due to its flexible form and analytical expressions for measures of predictive uncertainty. However, even this statistical emulator can be computationally intractable for large designs, due to computing time increasing with the cube of the design size. Multiple methods have been proposed for addressing this problem. We discuss several of them, and compare their predictive and computational performance in several scenarios. We propose solving this problem using a new method, adaptive design and analysis via partitioning trees (ADAPT). The new approach is motivated by the idea that most computer models are only complex in particular regions of the input space. By taking a data-adaptive approach to the development of a design, and choosing to partition the space in the regions of highest variability, we obtain a higher density of points in these regions and hence accurate prediction. Supplemental files for this article are available online.

Disk Partitioning Software Market Outlook

According to our latest research, the global disk partitioning software market size reached USD 1.92 billion in 2024. The market is anticipated to expand at a robust CAGR of 11.1% during the forecast period from 2025 to 2033. By 2033, the disk partitioning software market is projected to attain a value of USD 5.04 billion. This growth is primarily driven by the increasing adoption of advanced storage management solutions across enterprises and individual users, the rising demand for efficient data management, and the proliferation of cloud computing environments globally.

One of the most significant growth factors propelling the disk partitioning software market is the exponential increase in data generation across various industries. Organizations are facing unprecedented volumes of data due to digital transformation initiatives, Internet of Things (IoT) deployments, and the growing reliance on big data analytics. As a result, there is a heightened need for effective storage management solutions that can optimize the use of storage resources, improve system performance, and ensure data integrity. Disk partitioning software enables organizations to create, modify, and manage disk partitions efficiently, thereby enhancing data accessibility and minimizing downtime. The integration of user-friendly interfaces and automation features in modern disk partitioning tools further accelerates their adoption among both technical and non-technical users.

Another crucial factor contributing to the expansion of the disk partitioning software market is the rapid shift towards cloud-based infrastructure and virtualization. As businesses migrate their critical workloads to cloud environments, the demand for software capable of managing virtual disks and partitions has surged. Cloud-based disk partitioning solutions offer scalability, flexibility, and centralized control, allowing enterprises to manage storage resources across geographically dispersed data centers with ease. Additionally, the increasing adoption of hybrid and multi-cloud strategies among large enterprises is fueling the need for advanced partitioning tools that can seamlessly operate across heterogeneous IT environments. This trend is expected to remain a key driver for market growth throughout the forecast period.

The proliferation of digital devices and the growing number of individual users seeking efficient data management solutions are also driving the disk partitioning software market. With the rise in remote work, personal computing, and digital content creation, individual users require reliable tools to manage storage on their devices, recover lost data, and optimize disk usage. Disk partitioning software caters to these needs by providing functionalities such as partition resizing, disk cloning, and data migration. Furthermore, the increasing frequency of cyber threats and data breaches has heightened awareness about the importance of data backup and recovery, further boosting the adoption of advanced partitioning solutions among both enterprises and individual users.

From a regional perspective, North America currently dominates the disk partitioning software market, owing to the presence of major technology companies, high IT spending, and early adoption of advanced storage management solutions. However, the Asia Pacific region is expected to witness the fastest growth during the forecast period, driven by rapid digitalization, expanding IT infrastructure, and increasing investments in cloud technologies. Europe also holds a significant share of the market, supported by stringent data protection regulations and the widespread adoption of enterprise storage solutions. The Middle East & Africa and Latin America are emerging markets with substantial growth potential as organizations in these regions increasingly recognize the benefits of efficient disk partitioning and data management.

Component Analysis

The component segment of the disk partitioning software market is bifurcated into software and services. The software segment currently accounts for the largest market share, driven by the widespread adoption of standalone disk partitioning applications across diverse platforms and operating systems. These software solutions are designed to provide users with comprehensive tools for creating, resizing, merging, and deleting disk partitions, catering to both individual and enterprise requirements. The c

This dataset contains data files for a research paper, "Ethnicity-Based Name Partitioning for Author Name Disambiguation Using Supervised Machine Learning," published in the Journal of the Association for Information Science and Technology.Four zipped files are uploaded.Each zipped file contains five data files: signatures_train.txt, signatures_test.txt, records.txt, clusters_train.txt, and clusters_test.txt.1. 'Signatures' files contain lists of name instances. Each name instance (a row) is associated with information as follows. - 1st column: instance id (numeric): unique id assigned to a name instance - 2nd column: paper id (numeric): unique id assigned to a paper in which the name instance appears as an author name - 3rd column: byline position (numeric): integer indicating the position of the name instance in the authorship byline of the paper - 4th column: author name (string): name string formatted as surname, comma, and forename(s) - 5th column: ethnic name group (string): name ethnicity assigned by Ethnea to the name instance - 6th column: affiliation (string): affiliation associated with the name instance, if available in the original data - 7th column: block (string): simplified name string of the name instance to indicate its block membership (surname and first forename initial) - 8th column: author id (string): unique author id (i.e., author label) assigned by the creators of the original data2. 'Records' files contain lists of papers. Each paper is associated with information as follows. -1st column: paper id (numeric): unique paper id; this is the unique paper id (2nd column) in Signatures files -2nd column: year (numeric): year of publication * Some papers may have wrong publication years due to incorrect indexing or delayed updates in original data -3rd column: venue (string): name of journal or conference in which the paper is published * Venue names can be in full string or in a shortened format according to the formats in original data -4th column: authors (string; separated by vertical bar): list of author names that appear in the paper's byline * Author names are formatted into surname, comma, and forename(s) -5th column: title words (string; separated by space): words in a title of the paper. * Note that common words are stop-listed and each remaining word is stemmed using Porter's stemmer.3. 'Clusters' files contain lists of clusters. Each cluster is associated with information as follows. -1st column: cluster id (numeric): unique id of a cluster -2nd column: list of name instance ids (Signatures - 1st column) that belong to the same unique author id (Signatures - 8th column). Signatures and Clusters files consist of two subsets - train and test files - of original labeled data which are randomly split into 50%-50% by the authors of this study.Original labeled data for AMiner.zip, KISTI.zip, and GESIS.zip came from the studies cited below.If you use one of the uploaded data files, please cite them accordingly.[AMiner.zip]Tang, J., Fong, A. C. M., Wang, B., & Zhang, J. (2012). A Unified Probabilistic Framework for Name Disambiguation in Digital Library. IEEE Transactions on Knowledge and Data Engineering, 24(6), 975-987. doi:10.1109/Tkde.2011.13Wang, X., Tang, J., Cheng, H., & Yu, P. S. (2011). ADANA: Active Name Disambiguation. Paper presented at the 2011 IEEE 11th International Conference on Data Mining.[KISTI.zip]Kang, I. S., Kim, P., Lee, S., Jung, H., & You, B. J. (2011). Construction of a Large-Scale Test Set for Author Disambiguation. Information Processing & Management, 47(3), 452-465. doi:10.1016/j.ipm.2010.10.001Note that the original KISTI data contain errors and duplicates. This study reuses the revised version of KISTI reported in a study below.Kim, J. (2018). Evaluating author name disambiguation for digital libraries: A case of DBLP. Scientometrics, 116(3), 1867-1886. doi:10.1007/s11192-018-2824-5[GESIS.zip]Momeni, F., & Mayr, P. (2016). Evaluating Co-authorship Networks in Author Name Disambiguation for Common Names. Paper presented at the 20th international Conference on Theory and Practice of Digital Libraries (TPDL 2016), Hannover, Germany.Note that this study reuses the 'Evaluation Set' among the original GESIS data which was added titles by a study below.Kim, J., & Kim, J. (2020). Effect of forename string on author name disambiguation. Journal of the Association for Information Science and Technology, 71(7), 839-855. doi:10.1002/asi.24298[UM-IRIS.zip]This labeled dataset was created for this study. For description about the labeling method, please see 'Method' in the paper below.Kim, J., Kim, J., & Owen-Smith, J. (In print). Ethnicity-based name partitioning for author name disambiguation using supervised machine learning. Journal of the Association for Information Science and Technology. doi:10.1002/asi.24459.For details on the labeling method and limitations, see the paper below.Kim, J., & Owen-Smith, J. (2021). ORCID-linked labeled data for evaluating author name disambiguation at scale. Scientometrics. doi:10.1007/s11192-020-03826-6

The global partition management software market is booming, projected to reach $6.2 billion by 2033 with a 12% CAGR. Discover key trends, drivers, and restraints shaping this rapidly evolving sector, including cloud adoption, data management needs, and the rise of AI-powered tools. Learn about leading companies and regional market shares.

Classic evolutionary theory suggests that sexual dimorphism evolves primarily via sexual and fecundity selection. However, theory and evidence is beginning to accumulate suggesting that resource competition can drive the evolution of sexual dimorphism, via ecological character displacement between sexes. A key prediction of this hypothesis is that the extent of ecological divergence between sexes will be associated with the extent of sexual dimorphism.

As the stable isotope ratios of animal tissues provide a quantitative measure of various aspects of ecology, we carried out a meta-analysis examining associations between the extent of isotopic divergence between sexes and the extent of body size dimorphism. Our models demonstrate that large amounts of between-study variation in isotopic (ecological) divergence between sexes is non-random and may be associated with the traits of study subjects. We therefore completed meta-regressions to examine whether the extent of isotopic divergence between sexes is associated with the extent of sexual size dimorphism.

We found modest but significantly positive associations across species between size dimorphism and ecological differences between sexes, that increased in strength when the ecological opportunity for dietary divergence between sexes was greatest. Our results therefore provide further evidence that ecologically mediated selection, not directly related to reproduction, can contribute to the evolution of sexual dimorphism.

Methods We collated peer-reviewed literature available in the Web of Science Core Collection. The stable isotope literature is large, with the search term “stable isotope” returning ~76 500 studies at the time of writing. To constrain the search, we combined the following specific terms, using the default publication year range of 1900-2020, on 10/11/2020: Isotop* Nich; Isotop Nich* Male; Isotop* Nich* Female; Isotop* Nich* Male Female; Isotop* Nich* Sex Diff*; Isotop Nich* Dimorph; Isotop Dimorph*. Our searches returned 3489 studies, which we placed into a spreadsheet to highlight duplicates for manual removal. Removing duplicates resulted in 2807 studies for title and abstract screening. At this stage, we made the decision to constrain our analysis to the nitrogen and carbon stable isotope systems, due to the relatively small number of studies using other systems that were returned by our search terms. We also rejected studies during title and abstract screening if they did not use bulk stable isotope analysis, used samples of human, museum, archeological or palaeontological origin, were review, comment, or method papers, or if the animals sampled were not wild, not adults, not vertebrates or if data were not available for both sexes. We then searched the remaining 1279 studies using the ctrl+F search function and, separately, the terms “sex”, “male” and “female”, excluding studies if they contained none of these terms, under the assumption that they did not contain stable isotope ratios for each sex and, if at least one term was present, checking for the presence of the required data. Additional reasons for exclusion were if the full text was inaccessible without purchase or contacting authors, presented incomplete data (mean, error or sample size missing), was not in English or was a paper correction. We then attempted to extract data from the remaining 210 studies. Additional reasons for exclusion at this stage were if raw data was presented as images with >50 rows, if data were from an earlier study already included or if data extraction from figures was not possible. We extracted data from figures using a mouse pointer to individually select data points from an image of the figure, with the image calibrated to the axis values from the original figure; therefore, too much point overlap made this process inaccurate, because not all points could be selected for inclusion. The entire process provided 173 studies in which mean, standard deviation and sample sizes for each sex were presented in the manuscript, or could be calculated from raw data, or could be taken from model outputs, or extracted from figures. We collected data for any vertebrate species, from any global location and, if stable isotope ratios for each sex were presented for more than one tissue type, we entered each tissue as a separate row in our database.

Data Availability: All data supporting the findings of this study, including methodology examples, raw images and z-stack scans, statistical assessments as well as insect species information are all available through Edmond, the Open Access Data Repository of the Max Planck Society, or via the online version of the publication.

Acyclic digraphs are the underlying representation of Bayesian networks, a widely used class of probabilistic graphical models. Learning the underlying graph from data is a way of gaining insights about the structural properties of a domain. Structure learning forms one of the inference challenges of statistical graphical models. Markov chain Monte Carlo (MCMC) methods, notably structure MCMC, to sample graphs from the posterior distribution given the data are probably the only viable option for Bayesian model averaging. Score modularity and restrictions on the number of parents of each node allow the graphs to be grouped into larger collections, which can be scored as a whole to improve the chain’s convergence. Current examples of algorithms taking advantage of grouping are the biased order MCMC, which acts on the alternative space of permuted triangular matrices, and nonergodic edge reversal moves. Here, we propose a novel algorithm, which employs the underlying combinatorial structure of DAGs to define a new grouping. As a result convergence is improved compared to structure MCMC, while still retaining the property of producing an unbiased sample. Finally, the method can be combined with edge reversal moves to improve the sampler further. Supplementary materials for this article are available online.

The global office modular partition systems market, valued at $640 million in 2025, is projected to experience robust growth, driven by a Compound Annual Growth Rate (CAGR) of 5.5% from 2025 to 2033. This expansion is fueled by several key factors. The increasing demand for flexible and adaptable workspace solutions in modern offices is a primary driver. Companies are increasingly prioritizing efficient space utilization and the ability to quickly reconfigure layouts to meet evolving business needs. Furthermore, the growing adoption of modular systems in various sectors, including healthcare (hospitals, clinics) and education (schools, universities), is significantly contributing to market growth. The ease of installation, cost-effectiveness compared to traditional construction, and sustainability benefits of modular partitions are also key selling points. While challenges such as the initial investment costs and potential limitations in design flexibility compared to traditional partitions exist, the overall market outlook remains positive. The market segmentation, with a diverse range of applications (office buildings, hospitals, schools) and material types (glass, metal), indicates a broad appeal and multiple avenues for future growth. Geographical expansion, particularly in developing economies experiencing rapid urbanization and infrastructure development, will likely fuel further market expansion in the coming years. The presence of established players like Steelcase and emerging companies such as Avanti Systems signifies a competitive yet dynamic market landscape. The market's growth trajectory indicates strong potential for investors and businesses involved in the manufacturing, distribution, and installation of office modular partition systems. Further growth is expected to be fueled by technological advancements leading to more sustainable and aesthetically pleasing partition solutions. The focus on creating healthier and more productive workspaces will drive adoption within the office sector, while the increasing demand for flexible learning environments will boost demand from the education sector. This sustained growth will likely see a shift towards more specialized and technologically advanced modular partition systems, offering features like improved acoustics, enhanced security, and integrated technology solutions. Competitive pressures will necessitate continuous innovation and adaptation to meet the evolving needs of a diverse customer base. This comprehensive report provides an in-depth analysis of the global office modular partition systems market, encompassing historical data (2019-2024), current estimates (2025), and future projections (2025-2033). The market is projected to reach several million units by 2033, driven by a range of factors explored within this report. This study offers crucial insights for businesses involved in manufacturing, distribution, and installation of modular partitions, as well as for investors seeking opportunities in this rapidly evolving sector. Key search terms like "modular office partitions," "office partition systems," "glass office partitions," "metal office partitions," and "modular wall systems" are strategically integrated throughout the report to maximize its online visibility.

Marine communities undergo rapid changes because of human-induced ecosystem pressures. The Baltic Sea pelagic food web has experienced several regime shifts during the past century, resulting in a system where competition between planktivorous mesopredators is assumed to be high. While the two clupeids sprat and herring reveal signs of competition, the stickleback population has increased drastically during the past decades. Here, we investigate diet overlap between the three dominating planktivorous fish in the Baltic Sea, utilizing DNA metabarcoding on the 18S rRNA gene and the COI gene, targeted qPCR, and microscopy. Our results show niche differentiation between clupeids and stickleback and that rotifers play an important function in niche partitioning of stickleback, as a resource that is not being used, neither by the clupeids nor by other zooplankton. We further show that all the diet assessment methods used in this study are consistent but DNA metabarcoding describes the plankton-fish link at the highest taxonomic resolution. This study suggests that rotifers and other understudied soft-bodied prey may have an important function in the pelagic food web and that the growing population of pelagic stickleback is supported by the unutilized feeding niche offered by the rotifers. Methods The methods for the collection of data can be found in the respective manuscript and the data processing steps are explained in the README.

Momentum methods have been shown to accelerate the convergence of the standard gradient descent algorithm in practice and theory. In particular, the random partition based minibatch gradient descent methods with momentum (MGDM) are widely used to solve large-scale optimization problems with massive datasets. Despite the great popularity of the MGDM methods in practice, their theoretical properties are still underexplored. To this end, we investigate the theoretical properties of MGDM methods based on the linear regression models. We first study the numerical convergence properties of the MGDM algorithm and derive the conditions for faster numerical convergence rate. In addition, we explore the relationship between the statistical properties of the resulting MGDM estimator and the tuning parameters. Based on these theoretical findings, we give the conditions for the resulting estimator to achieve the optimal statistical efficiency. Finally, extensive numerical experiments are conducted to verify our theoretical results. Supplementary materials for this article are available online.

Safety Partitioned Hypervisor Deployments Market Outlook

According to our latest research, the global Safety Partitioned Hypervisor Deployments market size reached USD 1.37 billion in 2024, and the market is projected to grow at a robust CAGR of 11.2% from 2025 to 2033. By 2033, the market is expected to attain a value of USD 3.55 billion. This growth trajectory is primarily driven by the rising need for secure, isolated, and reliable virtualization solutions across critical sectors such as automotive, aerospace, industrial automation, and healthcare. The increasing adoption of safety partitioned hypervisors for mission-critical and safety-sensitive applications is a key market driver, as organizations seek to mitigate cyber threats and streamline compliance with stringent regulatory standards.

One of the primary growth factors for the Safety Partitioned Hypervisor Deployments market is the escalating demand for robust cybersecurity frameworks in industries handling sensitive and mission-critical data. As connected devices proliferate and the Industrial Internet of Things (IIoT) becomes more pervasive, organizations require advanced virtualization solutions that ensure strict isolation between different operational domains. Safety partitioned hypervisors offer the ability to run multiple operating systems on a single hardware platform, each within its own secure partition, thereby minimizing the risk of cross-domain attacks and system failures. This architecture is particularly vital in sectors like automotive, where functional safety and real-time performance are paramount, driving widespread adoption and market expansion.

Another significant factor propelling market growth is the rapid evolution of regulatory and compliance requirements globally. Industries such as aerospace, defense, and healthcare face increasingly rigorous safety and security standards, including ISO 26262, DO-178C, and IEC 62304. Safety partitioned hypervisors enable organizations to achieve and demonstrate compliance by providing deterministic behavior, fault isolation, and formal verification capabilities. The ability to securely consolidate workloads without compromising on functional safety or regulatory mandates is accelerating the deployment of these solutions, especially among enterprises seeking to optimize operational efficiency while maintaining the highest levels of safety assurance.

Technological advancements and the integration of artificial intelligence (AI) and machine learning (ML) into hypervisor architectures are further fueling the growth of the Safety Partitioned Hypervisor Deployments market. Modern hypervisors are leveraging AI-driven threat detection, automated resource allocation, and predictive maintenance capabilities to enhance system reliability and performance. The convergence of virtualization with edge computing and 5G connectivity is opening new avenues for real-time data processing and secure workload management across distributed environments. These innovations are not only expanding the application landscape but also attracting investments from both established enterprises and emerging startups, thereby creating a dynamic and competitive market ecosystem.

Regionally, North America continues to dominate the market, driven by the presence of leading technology providers, high cybersecurity awareness, and substantial investments in critical infrastructure. However, Asia Pacific is emerging as the fastest-growing region, propelled by rapid industrialization, increasing adoption of smart manufacturing, and government initiatives to bolster digital security frameworks. Europe remains a significant market, underpinned by stringent regulatory mandates and robust demand from the automotive and aerospace sectors. The Middle East & Africa and Latin America are also witnessing steady growth, supported by expanding digital transformation initiatives and the modernization of legacy systems.

Component Analysis

The Component segment of the Safety Partitioned Hypervisor Deployments market is broadly categorized into Software, Hardware, and Services. Software solutions form the core of the market, encompassing hypervisor platforms, management tools, and security modules that enable the creation and enforcement of isolated virtual environments. The demand for advanced software solutions is being driven by the need for flexibility, scalability, and seamless integration with existing IT infrastruc

Cache Partitioning for Mixed-Criticality Systems Market Outlook

According to our latest research, the global Cache Partitioning for Mixed-Criticality Systems market size reached USD 1.32 billion in 2024, demonstrating robust expansion driven by the proliferation of safety-critical and real-time applications across industries. The market is projected to grow at a CAGR of 13.8% from 2025 to 2033, reaching an estimated USD 4.12 billion by the end of the forecast period. This impressive growth trajectory is fueled by the increasing adoption of mixed-criticality systems in sectors such as automotive, aerospace, and healthcare, where the safe and predictable operation of embedded systems is paramount.

The growth factors propelling the cache partitioning for mixed-criticality systems market are multifaceted, with the most significant being the rising demand for real-time processing and deterministic performance in embedded systems. As industries like automotive and aerospace transition toward autonomous vehicles and advanced avionics, the need to guarantee that high-criticality tasks are isolated from lower-priority workloads has become critical. Cache partitioning, by enabling predictable memory access and reducing cache interference, ensures that critical applications meet stringent timing and safety requirements. This technological advantage is further amplified by the proliferation of multi-core processors, which are now standard in embedded systems, making cache management a focal point of system design and optimization.

Another major driver is the tightening of regulatory frameworks and safety standards globally. Standards such as ISO 26262 for automotive functional safety and DO-178C for avionics software require robust mechanisms to prevent interference between tasks of different criticality levels. Cache partitioning solutions, particularly those integrating dynamic and hybrid approaches, are increasingly being mandated to achieve certification and compliance. This regulatory push is encouraging OEMs and system integrators to invest in advanced cache management technologies, thus expanding the addressable market. The growing complexity of industrial automation systems and the integration of AI-driven functionalities in consumer electronics are also fueling the demand for sophisticated cache partitioning solutions.

Additionally, the rapid evolution of the Internet of Things (IoT) and edge computing is creating new opportunities for cache partitioning technologies. As more devices perform critical operations closer to the data source, ensuring the reliability and safety of these operations becomes essential. Cache partitioning for mixed-criticality systems is becoming a key enabler for next-generation edge devices, supporting applications from smart manufacturing to remote healthcare monitoring. The increasing collaboration between hardware vendors and software developers to deliver integrated partitioning solutions is also accelerating market adoption and innovation, further boosting growth prospects.

From a regional perspective, North America leads the cache partitioning for mixed-criticality systems market in 2024, accounting for approximately 38% of the global market share, followed closely by Europe and Asia Pacific. The dominance of North America is attributed to the presence of major automotive and aerospace manufacturers, as well as a mature ecosystem of technology providers and regulatory agencies. Europe is witnessing significant growth, particularly in the automotive and industrial automation sectors, while Asia Pacific is emerging as a high-growth region, driven by rapid industrialization and the expansion of electronics manufacturing. Latin America and the Middle East & Africa, while smaller in market size, are expected to see steady adoption as industries in these regions modernize and digitize their operations.

Partitioning Technique Analysis

The partitioning technique segment within the cache parti

Sources: Statistical Yearbooks, Newspaper-Articles, Scientific Publications.

Accurate estimation of the change in crime over time is a critical first step toward better understanding of public safety in large urban environments. Bayesian hierarchical modeling is a natural way to study spatial variation in urban crime dynamics at the neighborhood level, since it facilitates principled “sharing of information” between spatially adjacent neighborhoods. Typically, however, cities contain many physical and social boundaries that may manifest as spatial discontinuities in crime patterns. In this situation, standard prior choices often yield overly smooth parameter estimates, which can ultimately produce mis-calibrated forecasts. To prevent potential over-smoothing, we introduce a prior that partitions the set of neighborhoods into several clusters and encourages spatial smoothness within each cluster. In terms of model implementation, conventional stochastic search techniques are computationally prohibitive, as they must traverse a combinatorially vast space of partitions. We introduce an ensemble optimization procedure that simultaneously identifies several high probability partitions by solving one optimization problem using a new local search strategy. We then use the identified partitions to estimate crime trends in Philadelphia between 2006 and 2017. On simulated and real data, our proposed method demonstrates good estimation and partition selection performance. Supplementary materials for this article are available online.

The computation of marginal posterior density in Bayesian analysis is essential in that it can provide complete information about parameters of interest. Furthermore, the marginal posterior density can be used for computing Bayes factors, posterior model probabilities, and diagnostic measures. The conditional marginal density estimator (CMDE) is theoretically the best for marginal density estimation but requires the closed-form expression of the conditional posterior density, which is often not available in many applications. We develop the partition weighted marginal density estimator (PWMDE) to realize the CMDE. This unbiased estimator requires only a single Markov chain Monte Carlo output from the joint posterior distribution and the known unnormalized posterior density. The theoretical properties and various applications of the PWMDE are examined in detail. The PWMDE method is also extended to the estimation of conditional posterior densities. We carry out simulation studies to investigate the empirical performance of the PWMDE and further demonstrate the desirable features of the proposed method with two real data sets from a study of dissociative identity disorder patients and a prostate cancer study, respectively. Supplementary materials for this article are available online.

The HapMap (haplotype map) projects have produced valuable genetic resources in life science research communities, allowing researchers to investigate sequence variations and conduct genome-wide association study (GWAS) analyses. A typical HapMap project may require sequencing hundreds, even thousands, of individual lines or accessions within a species. Due to limitations in current sequencing technology, the genotype values for some accessions cannot be clearly called. Additionally, allelic heterozygosity can be very high in some lines, causing genetic and sometimes phenotypic segregation in their descendants. Genetic and phenotypic segregation degrades the original accession’s specificity and makes it difficult to distinguish one accession from another. Therefore, it is vitally important to determine and validate HapMap accessions before one conducts a GWAS analysis. However, to the best of our knowledge, there are no prior methodologies or tools that can readily distinguish or validate multiple accessions in a HapMap population. We devised a bioinformatics approach to distinguish multiple HapMap accessions using only a minimum number of genetic markers. First, we assign each candidate marker with a distinguishing score (DS), which measures its capability in distinguishing accessions. The DS score prioritizes those markers with higher percentages of homozygous genotypes (allele combinations), as they can be stably passed on to offspring. Next, we apply the “set-partitioning” concept to select optimal markers by recursively partitioning accession sets. Subsequently, we build a hierarchical decision tree in which a specific path represents the selected markers and the homogenous genotypes that can be used to distinguish one accession from others in the HapMap population. Based on these algorithms, we developed a web tool named MAD-HiDTree (Multiple Accession Distinguishment-Hierarchical Decision Tree), designed to analyze a user-input genotype matrix and construct a hierarchical decision tree. Using genetic marker data extracted from the Medicago truncatula HapMap population, we successfully constructed hierarchical decision trees by which the original 262 M. truncatula accessions could be efficiently distinguished. PCR experiments verified our proposed method, confirming that MAD-HiDTree can be used for the identification of a specific accession. MAD-HiDTree was developed in C/C++ in Linux. Both the source code and test data are publicly available at https://bioinfo.noble.org/MAD-HiDTree/.

Understanding the role of predators in food webs can be challenging in highly diverse predator/prey systems composed of small cryptic species. DNA based dietary analysis can supplement predator removal experiments and provide high resolution for prey identification. Here we use a metabarcoding approach to provide initial insights into the diet and functional role of coral-dwelling predatory fish feeding on small invertebrates. Fish were collected in Moorea (French Polynesia) where the BIOCODE project has generated DNA barcodes for numerous coral associated invertebrate species. Pyrosequencing data revealed a total of 292 Operational Taxonomic Units (OTU) in the gut contents of the arc-eye hawkfish (Paracirrhites arcatus), the flame hawkfish (Neocirrhites armatus) and the coral croucher (Caracanthus maculatus). One hundred forty-nine (51%) of them had species-level matches in reference libraries (>98% similarity) while 76 additional OTUs (26%) could be identified to higher taxonomic levels. Decapods that have a mutualistic relationship with Pocillopora and are typically dominant among coral branches, represent a minor contribution of the predators’ diets. Instead, predators mainly consumed transient species including pelagic taxa such as copepods, chaetognaths and siphonophores suggesting non random feeding behavior. We also identified prey species known to have direct negative interactions with stony corals, such as Hapalocarcinus sp, a gall crab considered a coral parasite, as well as species of vermetid snails known for their deleterious effects on coral growth. Pocillopora DNA accounted for 20.8% and 20.1% of total number of sequences in the guts of the flame hawkfish and coral croucher but it was not detected in the guts of the arc-eye hawkfish. Comparison of diets among the three fishes demonstrates remarkable partitioning with nearly 80% of prey items consumed by only one predator. Overall, the taxonomic resolution provided by the metabarcoding approach highlights a highly complex interaction web and demonstrates that levels of trophic partitioning among coral reef fishes have likely been underestimated. Therefore, we strongly encourage further empirical approaches to dietary studies prior to making assumptions of trophic equivalency in food web reconstruction.

physioDL: A dataset for geomorphic deep learning representing a scene classification task (predict physiographic region in which a hilshade occurs)Purpose: Datasets for geomorphic deep learning. Predict the physiographic region of an area based on a hillshade image. Terrain data were derived from the 30 m (1 arc-second) 3DEP product across the entirety of CONUS. Each chip has a spatial resolution of 30 m and 256 rows and columns of pixels. As a result, each chip measures 7,680 meters-by-7,680 meters. Two datasets are provided. Chips in the hs folder represent a multidirectional hillshade while chips in the ths folder represent a tinted multidirectional hillshade. Data are represented in 8-bit (0 to 255 scale, integer values). Data are projected to the Web Mercator projection relative to the WGS84 datum. Data were split into training, test, and validation partitions using stratified random sampling by region. 70% of the samples per region were selected for training, 15% for testing, and 15% for validation. There are a total of 16,325 chips. The following 22 physiographic regions are represented: "ADIRONDACK" , "APPALACHIAN PLATEAUS", "BASIN AND RANGE", "BLUE RIDGE", "CASCADE-SIERRA MOUNTAINS", "CENTRAL LOWLAND", "COASTAL PLAIN", "COLORADO PLATEAUS", "COLUMBIA PLATEAU", "GREAT PLAINS", "INTERIOR LOW PLATEAUS", "MIDDLE ROCKY MOUNTAINS", "NEW ENGLAND", "NORTHERN ROCKY MOUNTAINS", "OUACHITA", "OZARK PLATEAUS", "PACIFIC BORDER", and "PIEDMONT", "SOUTHERN ROCKY MOUNTAINS", "SUPERIOR UPLAND", "VALLEY AND RIDGE", "WYOMING BASIN". Input digital terrain models and hillshades are not provided due to the large file size (> 100GB). FilesphysioDL.csv: Table listing all image chips and associated physiographic region (id = unique ID for each chip; region = physiographic region; fnameHS = file name of associated chip in hs folder; fnameTHS = file name of associated chip in ths folder; set = data split (train, test, or validation).chipCounts.csv: Number of chips in each data partition per physiographic province. map.png: Map of data.makeChips.R: R script used to process the data into image chips and create CSV files.inputVectorschipBounds.shp = square extent of each chipchipCenters.shp = center coordinate of each chipprovinces.shp = physiographic provincesprovinces10km.shp = physiographic provinces with a 10 km negative buffer