Hideitsu Hino
YOU?
Author Swipe
View article: Information‐based probabilistic verification scores and predictability measures: Seasonal prediction examples
Information‐based probabilistic verification scores and predictability measures: Seasonal prediction examples Open
Assessing prediction quality through a process known as verification that compares past predictions and corresponding observations is fundamental for advancing prediction models and systems to enhance their utility. For the verification of…
View article: Misspecifying non-compensatory as compensatory IRT: analysis of estimated skills and variance
Misspecifying non-compensatory as compensatory IRT: analysis of estimated skills and variance Open
Multidimensional item response theory is a statistical test theory used to estimate the latent skills of learners and the difficulty levels of problems based on test results. Both compensatory and non-compensatory models have been proposed…
View article: Sparse coding-based multiframe superresolution for efficient synchrotron radiation microspectroscopy
Sparse coding-based multiframe superresolution for efficient synchrotron radiation microspectroscopy Open
In nanostructure extraction, advanced techniques like synchrotron radiation and electron microscopy are often hindered by radiation damage and charging artifacts from long exposure times. This study presents a multiframe superresolution me…
View article: Complex non-backtracking matrix for directed graphs
Complex non-backtracking matrix for directed graphs Open
Graph representation matrices are essential tools in graph data analysis. Recently, Hermitian adjacency matrices have been proposed to investigate directed graph structures. Previous studies have demonstrated that these matrices can extrac…
View article: Optimal spectroscopic measurement design: Bayesian framework for rational data acquisition
Optimal spectroscopic measurement design: Bayesian framework for rational data acquisition Open
We propose an optimal experimental design method for spectroscopic measurements that can determine the appropriate number and placement of measurement points in a rational manner. Spectroscopic measurements are fundamental for material cha…
View article: Data-driven proactive prediction of pumice drifting patterns using similarity search of the Kuroshio current axis
Data-driven proactive prediction of pumice drifting patterns using similarity search of the Kuroshio current axis Open
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 f…
View article: An $(ε,δ)$-accurate level set estimation with a stopping criterion
An $(ε,δ)$-accurate level set estimation with a stopping criterion Open
The level set estimation problem seeks to identify regions within a set of candidate points where an unknown and costly to evaluate function's value exceeds a specified threshold, providing an efficient alternative to exhaustive evaluation…
View article: An Efficient Orlicz-Sobolev Approach for Transporting Unbalanced Measures on a Graph
An Efficient Orlicz-Sobolev Approach for Transporting Unbalanced Measures on a Graph Open
We investigate optimal transport (OT) for measures on graph metric spaces with different total masses. To mitigate the limitations of traditional $L^p$ geometry, Orlicz-Wasserstein (OW) and generalized Sobolev transport (GST) employ Orlicz…
View article: Scalable Sobolev IPM for Probability Measures on a Graph
Scalable Sobolev IPM for Probability Measures on a Graph Open
We investigate the Sobolev IPM problem for probability measures supported on a graph metric space. Sobolev IPM is an important instance of integral probability metrics (IPM), and is obtained by constraining a critic function within a unit …
View article: Fast symplectic integrator for Nesterov-type acceleration method
Fast symplectic integrator for Nesterov-type acceleration method Open
In this paper, explicit stable integrators based on symplectic and contact geometries are proposed for a family of non-autonomous ordinarily differential equations (ODEs) found in improving convergence rate of Nesterov’s accelerated gradie…
View article: An embedding structure of determinantal point process
An embedding structure of determinantal point process Open
This paper investigates the information geometrical structure of a determinantal point process (DPP). It demonstrates that a DPP is embedded in the exponential family of log-linear models. The extent of deviation from an exponential family…
View article: Separating urban heat island circulation and convective cells through dynamic mode decomposition
Separating urban heat island circulation and convective cells through dynamic mode decomposition Open
This study applies dynamic mode decomposition (DMD) to three‐dimensional simulation results of urban heat island circulation (UHIC, which is horizontal circulation) and thermals (vertical convections). The aim of this study is to revisit h…
View article: A Family of Distributions of Random Subsets for Controlling Positive and Negative Dependence
A Family of Distributions of Random Subsets for Controlling Positive and Negative Dependence Open
Positive and negative dependence are fundamental concepts that characterize the attractive and repulsive behavior of random subsets. Although some probabilistic models are known to exhibit positive or negative dependence, it is challenging…
View article: Duality induced by an embedding structure of determinantal point process
Duality induced by an embedding structure of determinantal point process Open
This paper investigates the information geometrical structure of a determinantal point process (DPP). It demonstrates that a DPP is embedded in the exponential family of log-linear models. The extent of deviation from an exponential family…
View article: A Short Survey on Importance Weighting for Machine Learning
A Short Survey on Importance Weighting for Machine Learning Open
Importance weighting is a fundamental procedure in statistics and machine learning that weights the objective function or probability distribution based on the importance of the instance in some sense. The simplicity and usefulness of the …
View article: An Introduction to SGTPPR: Sparse Geochemical Tectono‐Magmatic Setting Probabilistic MembershiP DiscriminatoR
An Introduction to SGTPPR: Sparse Geochemical Tectono‐Magmatic Setting Probabilistic MembershiP DiscriminatoR Open
We present a new and easy‐to‐use geochemical tectono‐magmatic setting discriminator to calculate the probability of membership (the Sparse Geochemical Tectono‐magmatic setting Probabilistic membershiP discriminatoR, SGTPPR) that runs in Ex…
View article: Scalable Counterfactual Distribution Estimation in Multivariate Causal Models
Scalable Counterfactual Distribution Estimation in Multivariate Causal Models Open
We consider the problem of estimating the counterfactual joint distribution of multiple quantities of interests (e.g., outcomes) in a multivariate causal model extended from the classical difference-in-difference design. Existing methods f…
View article: Synthesizing differentially private location traces including co-locations
Synthesizing differentially private location traces including co-locations Open
Privacy-preserving location synthesizers have been widely studied to perform private geo-data analysis. They have also been used for generating datasets for research or competitions. However, existing location synthesizers do not take into…
View article: Hawkes process modeling quantifies complicated firing behaviors of cortical neurons during sleep and wakefulness
Hawkes process modeling quantifies complicated firing behaviors of cortical neurons during sleep and wakefulness Open
Despite the importance of sleep to the cerebral cortex, how much sleep changes cortical neuronal firing remains unclear due to complicated firing behaviors. Here we quantified firing of cortical neurons using Hawkes process modeling that c…
View article: ATNAS: Automatic Termination for Neural Architecture Search
ATNAS: Automatic Termination for Neural Architecture Search Open
Neural architecture search (NAS) is a framework for automating the design process of a neural network structure. While the recent one-shot approaches have reduced the search cost, there still exists an inherent trade-off between cost and p…
View article: Regression analysis and variable selection to determine the key subduction-zone parameters that determine the maximum earthquake magnitude
Regression analysis and variable selection to determine the key subduction-zone parameters that determine the maximum earthquake magnitude Open
Large variations in the maximum earthquake magnitude ( $$M_{{\text{max}}}$$ ) have been observed among the world’s subduction zones. There is still no universal relationship between $$M_{{\text{max}}}$$ and a given subduction-zone …
View article: Cost-effective framework for gradual domain adaptation with multifidelity
Cost-effective framework for gradual domain adaptation with multifidelity Open
In domain adaptation, when there is a large distance between the source and target domains, the prediction performance will degrade. Gradual domain adaptation is one of the solutions to such an issue, assuming that we have access to interm…
View article: Active Learning by Query by Committee with Robust Divergences
Active Learning by Query by Committee with Robust Divergences Open
Active learning is a widely used methodology for various problems with high measurement costs. In active learning, the next object to be measured is selected by an acquisition function, and measurements are performed sequentially. The quer…
View article: End-condition for solution small angle X-ray scattering measurements by kernel density estimation
End-condition for solution small angle X-ray scattering measurements by kernel density estimation Open
The set of python scripts and some datasets for estimating the minimum X-ray exposure time for X-ray solution scattering experiments using statistical and mathematical approaches. We apply a statistical inequality to estimate the kernel de…
View article: End-condition for solution small angle X-ray scattering measurements by kernel density estimation
End-condition for solution small angle X-ray scattering measurements by kernel density estimation Open
The set of python scripts and some datasets for estimating the minimum X-ray exposure time for X-ray solution scattering experiments using statistical and mathematical approaches. We apply a statistical inequality to estimate the kernel de…
View article: End-condition for solution small angle X-ray scattering measurements by kernel density estimation
End-condition for solution small angle X-ray scattering measurements by kernel density estimation Open
We develop a method for calculating the minimum X-ray exposure time for X-ray solution scattering experiments using statistical and mathematical approaches to enhance the measurement efficiency while maintaining data quality. Experts can d…