Statistical tests on clustered global earthquake synthetic data sets

Genetic epidemiologists often gather outcome-dependent samples of family data to measure within-family associations of genetic factors with disease outcomes. Generalized linear mixed models provide effective methods to estimate within-family associations but typically require parametric specification of the random effects distribution. Although misspecification of the random effects distribution often leads to little bias in estimated regression coefficients in standard, prospective clustered data settings, some recent studies suggest that such misspecification will impact parameter estimates from outcome-dependent cluster sampling designs. Using analytic results, simulation studies and fits to example data, this study examines the effect of misspecification of random effects distributions on parameter estimates in clustered data settings with outcome-dependent sampling. We show that the effects are consistent with results from prospective cluster sampling settings. In particular, ascertainment corrected mixed model methods that assume normally distributed random intercepts and conditional likelihood approaches provide accurate estimates of within-family covariate effects even under a misspecified random effects distribution.

Improved partitioning technique for density cube-based spatio-temporal clustering method

This paper shows a statistical method of testing the pattern similarity between two sets of regional clusters. First, to measure the similarity the distance between two sets of clusters is defined. Second, a statistical method is formulated to test whether or not the distance between two sets of clusters is close. Third, the concept of spatially well-grouped clusters is defined in conjunction with the above statistical test, and it is shown that this concept is useful for regional analysis. Last, the sets of regional clusters in the Kyushu region is studied by the above statistical test and concept.

There is a strong need for synthetic yet realistic distribution system test data sets that are as diverse, large, and complex to solve as real systems. Such data sets can facilitate the development of advanced algorithms and the assessment of emerging distributed energy resources while avoiding the need to acquire proprietary critical infrastructure or private data. Such synthetic data sets, however, are useful only if they are realistic enough to look and behave similarly to actual systems. This paper presents a comprehensive framework for validating synthetic distribution data sets using a three-pronged statistical, operational, and expert validation approach. It also presents a set of statistical and operational metric targets for achieving realistic data sets based on detailed characterization of more than 10,000 real U.S. utility feeders. The paper demonstrates the use of the proposed validation approach to validate three large-scale synthetic data sets developed by the authors representing Santa Fe, New Mexico; Greensboro, North Carolina; and the San Francisco Bay Area, California.

One of the challenging tasks in visual analytics is to target clustered time-series data sets, since it is important for data analysts to discover patterns changing over time while keeping their focus on particular subsets. In order to leverage the humans ability to quickly visually perceive these patterns, multivariate features should be implemented according to the attributes available. However, a comparative case study has been done using JavaScript libraries to demonstrate the differences in capabilities of using them. A web-based application to monitor the Lustre file system for the systems administrators and the operation teams has been developed using D3 and Highcharts. Lustre file systems are responsible of managing Remote Procedure Calls (RPCs) which include input output (I/O) requests between clients and Object Storage Targets (OSTs). The objective of this application is to provide time-series visuals of these calls and storage patterns of users on Kraken, a University of Tennessee High Performance Computing (HPC) resource in Oak Ridge National Laboratory (ORNL).

Remote sensing geodetic observations (Interferometric Synthetic Aperture Radar [InSAR] and optical correlation [“pixel tracking”]) serve an increasingly diverse and important role in earthquake monitoring and response. This study introduces the Geodetic Centroid (gCent) catalog—an earthquake catalog derived solely from space-based geodetic observations—and analysis of 74 earthquakes (Mw 4.3–7.4) imaged from 1 August 2019 to 01 February 2022. For gCent, we use InSAR and optical correlation observations derived from the Sentinel-1 satellites and various publicly available optical satellites to systematically image all global earthquakes Mw 5.5 or larger and shallower than 25 km, Mw 7.0 or larger at any depth, and other high-impact earthquakes or seismic events of special interest. We invert surface displacements from successfully imaged earthquakes for the location, orientation, and dimensions of a single slipping fault patch that describes the centroid characteristics of the earthquake. These centroid models, in turn, are compiled into a catalog and used in U.S. Geological Survey/Advanced National Seismic System (ANSS) operational earthquake response products such as ShakeMaps and finite-fault models. We provide a comparison of the gCent catalog to the ANSS Comprehensive Catalog and Global Centroid Moment Tensor (Global CMT) catalog to compare reported locations, depths, and magnitudes. We find that global earthquake catalogs not only generally provide reasonably comparable locations (within 10 km on average), but also they systematically overestimate depth that may have implications for earthquake shaking predictions based solely on earthquake origin information. Geodetic magnitudes are comparable to seismically inferred magnitudes, indicating that gCent models are unlikely to be systematically biased by the presence of postseismic deformation. We additionally highlight limitations of the gCent catalog induced by both the limitations of remote sensing imaging of earthquakes and our imposition of a simplified earthquake source description that does not include spatially distributed slip.

In cluster randomised trials, a measure of intracluster correlation such as the intraclass correlation coefficient (ICC) should be reported for each primary outcome. Providing intracluster correlation estimates may help in calculating sample size of future cluster randomised trials and also in interpreting the results of the trial from which they are derived. For a binary outcome, the ICC is known to be associated with its prevalence, which raises at least two issues. First, it questions the use of ICC estimates obtained on a binary outcome in a trial for sample size calculations in a subsequent trial in which the same binary outcome is expected to have a different prevalence. Second, it challenges the interpretation of ICC estimates because they do not solely depend on clustering level. Other intracluster correlation measures proposed for clustered binary data settings include the variance partition coefficient, the median odds ratio and the tetrachoric correlation coefficient. Under certain assumptions, the theoretical maximum possible value for an ICC associated with a binary outcome can be derived, and we proposed the relative deviation of an ICC estimate to this maximum value as another measure of the intracluster correlation. We conducted a simulation study to explore the dependence of these intracluster correlation measures on outcome prevalence and found that all are associated with prevalence. Even if all depend on prevalence, the tetrachoric correlation coefficient computed with Kirk's approach was less dependent on the outcome prevalence than the other measures when the intracluster correlation was about 0.05. We also observed that for lower values, such as 0.01, the analysis of variance estimator of the ICC is preferred.

In this Article, the targeted adjustment of the relative density of laser additive manufactured components made of AlSi10Mg is considered. The interest in demand-oriented process parameters is steadily increasing. Thus, shorter process times and lower unit costs can be achieved with decreasing component densities. Especially when hot isostatic pressing is considered as a post-processing step. In order to be able to generate process parameters automatically, a model hypothesis is learned via artificial neural networks (ANN) for a density range from 70% to almost 100%, based on a synthetic dataset with equally distributed process parameters and a statistical test series with 256 full factorial combined instances. This allows the achievable relative density to be predicted from given process parameters. Based on the best model, a database approach and supervised training of concatenated ANNs are developed to solve the inverse parameter prediction problem for a target density. In this way, it is possible to generate a parameter prediction model for the high-dimensional result space through constraints that are shown with synthetic test data sets. The presented concatenated ANN model is able to reproduce the origin distribution. The relative density of synthetic data can be predicted with an R2-value of 0.98. The mean build rate can be increased by 12% with the formulation of a hint during the backward model training. The application of the experimental data shows increased fuzziness related to the big data gaps and a small number of instances. For practical use, this algorithm could be trained on increased data sets and can be expanded by properties such as surface quality, residual stress, or mechanical strength. With knowledge of the necessary (mechanical) properties of the components, the model can be used to generate appropriate process parameters. This way, the processing time and the amount of scrap parts can be reduced.

The matrix factorization (MF) technique has been widely adopted for solving the rating prediction problem in recommender systems. The MF technique utilizes the latent factor model to obtain static user preferences (user latent vectors) and item characteristics (item latent vectors) based on historical rating data. However, in the real world, user preferences are not static but full of dynamics. Though there are several previous works that addressed this time-varying issue of user preferences, it seems (to the best of our knowledge) that none of them are specifically designed for tracking concept drift in individual user preferences . Motivated by this, we develop a temporal MF approach for tracking concept drift in each individual user latent vector. There are two key innovative steps in our approach: 1) we develop a modified stochastic gradient descent method to learn an individual user latent vector at each time step and 2) by Lasso regression, we learn a linear model for the transition of the individual user latent vectors. We test our method on a synthetic data set and several real data sets. In comparison with the original MF, our experimental results show that our temporal method is able to achieve lower root mean square errors (RMSEs) for both the synthetic and real data sets. One interesting finding is that the performance gain in RMSE is mostly from those users who indeed have concept drift in their user latent vectors at the time of prediction. In particular, for the synthetic data set and the Ciao data set, there are quite a few users with that property and the performance gains for these two data sets are roughly 20% and 5%, respectively.

Here, we propose a binding site prediction method based on the high frequency end of the spectrum in the native state of the protein structural dynamics. The spectrum is obtained using an elastic network model (GNM). High frequency vibrating (HFV) residues are determined from the fastest modes dynamics. HFV residue clusters and the associated surface patch residues are tested for their likelihood to locate at the binding interfaces using two different data sets, the Benchmark Set of mainly enzymes and antigen/antibodies and the Cluster Set of more diverse structures. The binding interface is identified to be within 7.5 A of the HFV residue clusters in the Benchmark Set and Cluster Set, for 77% and 70% of the structures, respectively. The success rate increases to 88% and 84%, respectively, by using the surface patches. The results suggest that concave binding interfaces, typically those of enzyme-binding sites, are enriched by the HFV residues. Thus, we expect that the association of HFV residues with the interfaces is mostly for enzymes. If, however, a binding region has invaginations and cavities, as in some of the antigen/antibodies and in cases in the Cluster data set, we expect it would be detected there too. This implies that binding sites possess several (inter-related) properties such as cavities, high packing density, conservation, and disposition for hotspots at binding surfaces. It further suggests that the high frequency vibrating residue-based approach is a potential tool for identification of regions likely to serve as protein-binding sites. The software is available at http://www.prc.boun.edu.tr/PRC/software.html.

Clustering categorical data sets using tabu search techniques

Seismology has continuously recorded ground‐motion spanning up to decades. Blind, uninformed search for similar‐signal waveforms within this continuous data can detect small earthquakes missing from earthquake catalogs, yet doing so with naive approaches is computationally infeasible. We present results from an improved version of the Fingerprint And Similarity Thresholding (FAST) algorithm, an unsupervised data‐mining approach to earthquake detection, now available as open‐source software. We use FAST to search for small earthquakes in 6–11 yr of continuous data from 27 channels over an 11‐station local seismic network near the Diablo Canyon nuclear power plant in central California. FAST detected 4554 earthquakes in this data set, with a 7.5% false detection rate: 4134 of the detected events were previously cataloged earthquakes located across California, and 420 were new local earthquake detections with magnitudes −0.3≤ML≤2.4, of which 224 events were located near the seismic network. Although seismicity rates are low, this study confirms that nearby faults are active. This example shows how seismology can leverage recent advances in data‐mining algorithms, along with improved computing power, to extract useful additional earthquake information from long‐duration continuous data sets.

Seismic hazard assessment in low-to-moderate seismicity regions can benefit from the knowledge of surface deformation rates to better constrain earthquake recurrence models. This, however, amounts to assuming that the known seismicity rate, generally observed over historical times (i.e., up to a few centuries in Europe), provides a representative sample of the underlying long-term activity. We here investigate how this limited sampling can affect the estimated seismic hazard and whether it can explain the disagreement between the seismic moment loading rate as seen by nowadays Global Navigation Satellite Systems (GNSS) measurements and the seismic moment release rate by past earthquakes, as is sometimes observed in regions with limited activity. We approach this issue by running simulations of earthquake time series over very long timescales that account for temporal clustering and the known magnitude–frequency distribution in such regions, and that those are constrained to a seismic moment rate balance between geodetic and seismicity estimates at very long timescales. We show that, in the example of southeastern Switzerland, taken here as a case study, this sampling issue can indeed explain this disagreement, although it is likely that other phenomena, including aseismic deformation and changes in strain rate due to erosional and/or glacial rebound, may also play a significant role in this mismatch.

Most of the well established clustering algorithms assume that the underlying clustering structure of dataset does not change over the time. Hence, those algorithms fail to identify underlying cluster structures in currently available large scale dynamic data sources in an efficient manner. This paper presents a Multi Agent based approach to identify partitional clusters in a dynamic data source. Set of partitional clusters in a dynamic data source is identified by interactions and negotiations among the agents who represent data records in the data source. After identification of potential clusters for data records that are assigned to what are called cluster agents. By interactions and negotiations between cluster agents and data record agents, the identified cluster configuration is continuously improved according to the internal cluster evaluation measures. The proposed method is evaluated by synthetic data sets with different number of clusters in 2D and 3D spaces. Results indicate that the proposed method successfully identifies the clusters in those datasets with minimal human intervention.

In view of an optimal exploitation of the Swarm data set, we have prepared and tested software dedicated to the determination of accurate core magnetic field models and of the Euler angles between the magnetic sensors and the satellite reference frame. The dedicated core field model estimation is derived directly from the GFZ Reference Internal Magnetic Model (GRIMM) inversion and modeling family. The data selection techniques and the model parameterizations are similar to what were used for the derivation of the second (Lesur et al., 2010) and third versions of GRIMM, although the usage of observatory data is not planned in the framework of the application to Swarm. The regularization technique applied during the inversion process smoothes the magnetic field model in time. The algorithm to estimate the Euler angles is also derived from the CHAMP studies. The inversion scheme includes Euler angle determination with a quaternion representation for describing the rotations. It has been built to handle possible weak time variations of these angles. The modeling approach and software have been initially validated on a simple, noise-free, synthetic data set and on CHAMP vector magnetic field measurements. We present results of test runs applied to the synthetic Swarm test data set.