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The existence of missing geomagnetic reversals has been proposed, with potential for new magnetostratigraphic age controls. We estimate geomagnetic reversal frequency from 0 to 155 Ma using adaptive‐bandwidth kernel density estimation (AKDE) to evaluate data sparseness and to assess how reversal frequency changes when recently identified geomagnetic reversals are incorporated into the geomagnetic polarity time scale (GPTS) data set. AKDE is a two‐stage procedure that uses an initial density estimator based on an initial (pilot) bandwidth. We found that the pilot bandwidth determined using cross‐validation is stable with respect to data set length. The AKDE results obtained based on the cross‐validated pilot bandwidth reveal four troughs after the Cretaceous Normal Superchron, spaced 13.5–15.0 Myr apart and corresponding to relatively long chrons (>0.8 Myr). One trough near 32 Ma becomes less distinct after the four recently identified reversals are added to the data set. This sensitivity suggests that troughs in the frequency curve may indicate missing geomagnetic reversals.

In a product market or stock market, different products or stocks compete for the same consumers or purchasers. We propose a method to estimate the time-varying transition matrix of the product share using a multivariate time series of the product share. The method is based on the assumption that each of the observed time series of shares is a stationary distribution of the underlying Markov processes characterized by transition probability matrices. We estimate transition probability matrices for every observation under natural assumptions. We demonstrate, on a real-world dataset of the share of automobiles, that the proposed method can find intrinsic transition of shares. The resulting transition matrices reveal interesting phenomena, for example, the change in flows between TOYOTA group and GM group for the fiscal year where TOYOTA group's sales beat GM's sales, which is a reasonable scenario.

Pumice drifting poses substantial risks to maritime navigation and coastal communities. While traditional ocean-current-based simulations effectively predict drifting patterns, they are resource-intensive and unsuitable for real-time use following abrupt eruptions. This study proposes a data-driven framework that enables rapid, low-cost pumice drift prediction, leveraging daily-reported Kuroshio Current axis (KCA) patterns and conducting similarity searches on pre-existing simulation datasets. Focusing on eruptions at Bayonnaise Rocks in the Izu Islands, Japan, we demonstrate that the dynamic time warping distance, a measure of similarity between the current KCA pattern and historical KCA patterns, accurately predicts drifting scenarios within the critical first 10 days post-eruption. This method reliably identifies high-risk cases, including those affecting coastal regions, without requiring new simulations. By refining simulation datasets and enhancing prediction accuracy, this framework can become a practical tool for hazard assessments, offering a scalable solution for proactive disaster-risk management in response to unpredictable pumice eruptions.

Large variations in the maximum earthquake magnitude (Mmax) have been observed among the world’s subduction zones. There is still no universal relationship between Mmax and a given subduction-zone parameter, such as plate age, plate dip angle, or plate velocity, which suggests that multiple parameters control Mmax. Here, we conduct exhaustive variable selections that are based on three evaluation criteria; leave-one-out cross-validation errors (LOOCVE), Akaike information criterion (AIC), and Bayesian information criterion (BIC) to determine the combination of subduction-zone parameters that best explains Mmax. Multiple linear regression analyses are applied using 18 subduction-zone parameters as potential candidates for the explanatory variables of Mmax. The minimum BIC is obtained when five variables (trench sediment thickness, existence of an accretionary prism, upper-plate crustal thickness, bending radius of the subducting oceanic plate, and trench depth) are selected as explanatory variables; each variable contributes positively to Mmax. Minimum LOOCVE and AIC values are obtained when eight variables (the five parameters for BIC, plus the along-strike plate convergence rate, age of the subducting plate, and maximum depth of the subducting plate) are selected. Our selection of the trench sediment thickness and plate bending radius contributing to Mmax is consistent with previous studies. The results show that increasing upper-plate crustal thickness results in a large Mmax. In addition to smoothing the subducting-plate interface via subducted sediments, along-dip extension of the crustal area along the convergent plate boundary would be important for generating a large earthquake.Graphic Abstract

We introduce a spectrum-adapted expectation-maximization (EM) algorithm for high-throughput analysis of a large number of spectral datasets by considering the weight of the intensity corresponding to the measurement energy steps. Proposed method was applied to synthetic data in order to evaluate the performance of the analysis accuracy and calculation time. Moreover, the proposed method was performed to the spectral data collected from graphene and MoS 2 field-effect transistors devices. The calculation completed in less than 13.4 s per set and successfully detected systematic peak shifts of the C 1s in graphene and S 2p in MoS 2 peaks. This result suggests that the proposed method can support the investigation of peak shift with two advantages: (1) a large amount of data can be processed at high speed; and (2) stable and automatic calculation can be easily performed.

The goal of fairness-aware classification is to categorize data while taking into account potential issues of fairness, discrimination, neutrality, and/or independence. For example, when applying data mining technologies to university admissions, admission criteria must be non-discriminatory and fair with regard to sensitive features, such as gender or race. In this context, such fairness can be formalized as statistical independence between classification results and sensitive features. The main purpose of this paper is to analyze this formal fairness in order to achieve better trade-offs between fairness and prediction accuracy, which is important for applying fairness-aware classifiers in practical use. We focus on a fairness-aware classifier, Calders and Verwer’s two-naive-Bayes (CV2NB) method, which has been shown to be superior to other classifiers in terms of fairness. We hypothesize that this superiority is due to the difference in types of independence. That is, because CV2NB achieves actual independence, rather than satisfying model-based independence like the other classifiers, it can account for model bias and a deterministic decision rule. We empirically validate this hypothesis by modifying two fairness-aware classifiers, a prejudice remover method and a reject option-based classification (ROC) method, so as to satisfy actual independence. The fairness of these two modified methods was drastically improved, showing the importance of maintaining actual independence, rather than model-based independence. We additionally extend an approach adopted in the ROC method so as to make it applicable to classifiers other than those with generative models, such as SVMs.

We propose a method to extract connectivity between neurons for extracellularly recorded multiple spike trains. The method removes pseudo-correlation caused by propagation of information along an indirect pathway, and is also robust against the influence from unobserved neurons. The estimation algorithm consists of iterations of a simple matrix inversion, which is scalable to large data sets. The performance is examined by synthetic spike data.

In spatio-temporal data analysis, dimension reduction is necessary to extract intrinsic structures and to avoid over-parametrization problems. The spatial dynamic factor model (SDFM) reduces dimension of the data by decomposing them into spatial and temporal variations. The spatial variation is represented by a few spatially structured vectors, called factor loading vectors. The SDFM cannot be directly applied when the data contain missing values and their observation sites vary with time. We extend the factor loading vector to a smooth continuous function obtained by basis expansion, where we call the extended model the spatially continuous dynamic factor model (SCDFM), and estimate the SCDFM using the maximum L 2 penalized likelihood method. We derive model selection criteria to select a regularization parameter and the number of factors. Applications to synthetic and real data show the effectiveness of our modeling strategy in terms of estimation accuracy and stability.

In spatio-temporal data analysis, dimension reduction is necessary to extract intrinsic structures and to avoid over-parametrization problems. The spatial dynamic factor model (SDFM) reduces dimension of the data by decomposing them into spatial and temporal variations. The spatial variation is represented by a few spatially structured vectors, called factor loading vectors. The SDFM cannot be directly applied when the data contain missing values and their observation sites vary with time. We extend the factor loading vector to a smooth continuous function obtained by basis expansion, where we call the extended model the spatially continuous dynamic factor model (SCDFM), and estimate the SCDFM using the maximum L2 penalized likelihood method. We derive model selection criteria to select a regularization parameter and the number of factors. Applications to synthetic and real data show the effectiveness of our modeling strategy in terms of estimation accuracy and stability.

This paper proposes an extension of principal component analysis for Gaussian process (GP) posteriors, denoted by GP-PCA. Since GP-PCA estimates a low-dimensional space of GP posteriors, it can be used for meta-learning, which is a framework for improving the performance of target tasks by estimating a structure of a set of tasks. The issue is how to define a structure of a set of GPs with an infinite-dimensional parameter, such as coordinate system and a divergence. In this study, we reduce the infiniteness of GP to the finite-dimensional case under the information geometrical framework by considering a space of GP posteriors that have the same prior. In addition, we propose an approximation method of GP-PCA based on variational inference and demonstrate the effectiveness of GP-PCA as meta-learning through experiments.