sJIVE combines structure and prediction in multi-source data.
problem Analyzing multi-source data with shared and unique structures.
method Supervised Joint and Individual Variation Explained (sJIVE) method.
result sJIVE outperforms existing methods in noisy data.
Predicting registration error can be useful for evaluation of registration procedures, which is important for the adoption of registration techniques in the clinic. In addition, quantitative error prediction can be helpful in improving the registration quality. The task of predicting registration error is demanding due…
A new method estimates treatment effects across multiple studies considering differences.
problem Estimating treatment effects across multiple studies with varying conditions.
method The multi-study R-learner framework that accounts for between-study heterogeneity.
result The multi-study R-learner is more efficient and normal than existing methods in the presence of heterogeneity.
Study shows convergence of Fubini-Study currents to equilibrium metrics on Kähler manifolds.
problem Convergence of Fubini-Study currents to equilibrium metrics in Kähler geometry.
method Analysis of continuous Hermitian metrics and their Fubini-Study currents on line bundles.
result The scaled difference between Fubini-Study currents and equilibrium metrics converges to zero in the sense of currents.
Boosting strategies for merging vs. ensembling studies analyzed.
problem Deciding between merging and ensembling studies for boosting.
method Analytical transition point and bias-variance decomposition for boosting with linear learners.
result Theoretical guidelines for merging vs. ensembling studies.
A critical decision point when training predictors using multiple studies is whether studies should be combined or treated separately. We compare two multi-study prediction approaches in the presence of potential heterogeneity in predictor-outcome relationships across datasets: 1) merging all of the datasets and traini…
Treatment recommendations within Clinical Practice Guidelines (CPGs) are largely based on findings from clinical trials and case studies, referred to here as research studies, that are often based on highly selective clinical populations, referred to here as study cohorts. When medical practitioners apply CPG recommend…
This article examines five common misunderstandings about case-study research: (1) Theoretical knowledge is more valuable than practical knowledge; (2) One cannot generalize from a single case, therefore the single case study cannot contribute to scientific development; (3) The case study is most useful for generating …
Acute respiratory infections have epidemic and pandemic potential and thus are being studied worldwide, albeit in many different contexts and study formats. Predicting infection from symptom data is critical, though using symptom data from varied studies in aggregate is challenging because the data is collected in diff…
This paper reviewed the machine learning-based studies for quantitative positron emission tomography (PET). Specifically, we summarized the recent developments of machine learning-based methods in PET attenuation correction and low-count PET reconstruction by listing and comparing the proposed methods, study designs an…
Ricci flow simulations show unstable Fubini-Study metrics develop singularities.
problem Understanding the behavior of unstable perturbations in Ricci flow.
method Numerical simulations of Ricci flow starting from unstable Fubini-Study metrics.
result Ricci flow solutions from unstable Fubini-Study metrics develop local singularities.
New method uncovers bias mechanisms in observational studies.
problem Understanding the sources of bias in observational studies.
method Analyzing the relationship between bias magnitude and nuisance function estimators' performance.
result Method can distinguish between common sources of causal bias.
GenAI improves actuarial practices through case studies.
problem Improving actuarial practices using AI.
method Four case studies using LLMs, Retrieval-Augmented Generation, and vision-enabled LLMs.
result GenAI enhances claim cost prediction, market comparisons, and car damage classification.
Proves polynomial injectivity of Fubini-Study map for ample line bundles.
problem Injectivity of Fubini-Study map for ample line bundles.
method Polynomial injectivity proof with polynomial dependence on ample line bundle exponent.
result Quantitative version of injectivity proved, polynomial in ample line bundle exponent.
Optimal ensemble construction improves prediction accuracy for multi-study tasks, especially in pandemic scenarios.
problem Poor out-of-study prediction performance due to heterogeneous datasets.
method Optimal ensemble construction using a two-stage stacking strategy that jointly estimates ensemble weights and study-specific model parameters.
result Our method outperforms multi-study stacking and other standard methods in predicting excess mortality during the pandemic.
Study evaluates machine learning for predicting treatment effects in observational studies.
problem Challenges in measuring treatment effects due to confounding bias in observational studies.
method Simulated two scenarios with and without confounding, using linear and non-linear relationships. Used machine learning models (linear regression, lasso regression, random forest) to predict counterfactuals and treatment effects.
result Machine learning models perform well under linearity but poorly under non-linearity, even in the presence of confounding.
In this paper, as a fundamental study on the theory of Morse functions and their higher dimensional versions or fold maps and applications to geometric theory of manifolds, which were started in 1950s by differential topologists such as Thom and Whitney and have been studied actively, we study algebraic and differentia…
Study evaluates and compares numerical differentiation methods on three case studies.
problem Evaluating and comparing numerical differentiation methods for efficiency.
method Forward, Backward, and Centered Finite-Difference methods applied at two levels of precision.
result Different methods perform differently across case studies, with varying levels of computational cost and accuracy.
Ablation studies show BCF model's propensity score is not essential for treatment effect estimation.
problem Understanding the necessity of propensity score in nonparametric treatment effect estimation.
method Partial ablation studies of Bayesian Causal Forest (BCF) model.
result Excluding estimated propensity score does not affect treatment effect estimation or uncertainty quantification.
Cognitive brain imaging is accumulating datasets about the neural substrate of many different mental processes. Yet, most studies are based on few subjects and have low statistical power. Analyzing data across studies could bring more statistical power; yet the current brain-imaging analytic framework cannot be used at…
Study finds dividend policy has no significant effect on IPO stock prices.
problem Impact of dividend policy on IPO price performance.
method Long-run performance statistics and GARCH model, dummy variable used.
result Dividend policy has no significant effect on IPO stock prices.
Study constant mean curvature tubes in homogeneous spaces.
problem Global geometry of constant mean curvature tubes.
method Screw-motion invariants, foliation, numerical isoperimetric profile.
result Foliation result and embeddedness proof.
Study on the convergence rate of prescribed scalar curvature flow.
problem Prescribing scalar curvature on manifolds.
method Inspired by Yamabe flow convergence rate study, analyze the prescribed scalar curvature flow convergence rate.
result Determine the convergence rate of the prescribed scalar curvature flow.
Study on convergence rate of weighted Yamabe flow.
problem Weighted Yamabe problem on smooth metric measure spaces.
method Weighted Yamabe flow and its convergence rate analysis.
result Study and analysis of convergence rate of the weighted Yamabe flow.
The causal assumptions, the study design and the data are the elements required for scientific inference in empirical research. The research is adequately communicated only if all of these elements and their relations are described precisely. Causal models with design describe the study design and the missing data mech…
Virtual knot theory is a generalization (discovered by the author in 1996) of knot theory to the study of all oriented Gauss codes. (Classical knot theory is a study of planar Gauss codes.) Graph theory studies non-planar graphs via graphical diagrams with virtual crossings. Virtual knot theory studies non-planar Gauss…
Rigidity of Fubini-Study metric on odd complex Grassmannians.
problem Characterize infinitesimal deformations of the Fubini-Study metric on complex Grassmannians.
method Explicit description of infinitesimal Einstein deformations, integration analysis.
result Fubini-Study metric on odd complex Grassmannians is rigid.
Study of Fubini-Study forms on surfaces with punctures.
problem Analyzing Fubini-Study forms on surfaces with punctures.
method Using Hermitian metrics, holomorphic line bundles, and Kodaira maps.
result Fubini-Study forms grow polynomially near punctures.
Study derived Lie ∞-groupoids and algebroids in higher differential geometry.
problem Addressing problems in higher differential geometry using derived Lie ∞-groupoids and algebroids.
method Construct CFO structures, study L∞-algebroids, homotopical algebras, and homotopy-coherent representations.
result Construct Atiyah classes for L∞-algebroids pairs and study singular foliations and their holonomies.
Medical imaging machine learning algorithms are usually evaluated on a single dataset. Although training and testing are performed on different subsets of the dataset, models built on one study show limited capability to generalize to other studies. While database bias has been recognized as a serious problem in the co…
In category theory, monads, which are monoid objects on endofunctors, play a central role closely related to adjunctions. Monads have been studied mostly in algebraic situations. In this dissertation, we study this concept in some categories of smooth manifolds. Namely, the tangent functor in the category of smooth man…
Financial event studies often misestimate causal effects due to misspecified factor models.
problem Misspecification of factor models in financial event studies leads to inconsistent estimates of causal effects.
method Proposed synthetic control methods to construct replicating portfolios from control securities.
result Synthetic control methods provide more accurate estimates of causal effects in event studies.
Novel strategy benchmarks observational studies against randomized trials.
problem Benchmarking observational studies for treatment effect bias.
method Statistical test for null hypothesis of treatment effect difference.
result Valid lower bound on maximum bias strength for any subgroup.
Study compares 5 ODE solvers on 3 case studies, finding varying accuracy.
problem Comparing estimation accuracy of 5 ODE solvers on 3 case studies.
method Used 5 different numerical ODE solvers (Euler's, Heun's, Midpoint, Runge-Kutta 4th order, ODE45) on 3 case studies and compared their results.
result Different solvers have varying accuracy depending on the case study.
The primary goal of this study is doing a meta-analysis research on two groups of published studies. First, the ones that focus on the evaluation of the United States Department of Agriculture (USDA) forecasts and second, the ones that evaluate the market reactions to the USDA forecasts. We investigate four questions. …
Causal graph aids observational study insights in aSAH patients.
problem Lack of clear objectives and tools for identifying necessary adjustments in observational studies.
method Uses causal directed acyclic graphs (DAGs) to provide insights mid-study and identify necessary data enhancements.
result Midway insights and necessary data enhancements identified for meaningful causal questions.
Study on flat connections with controlled irregularity.
problem Boundedness of algebraic flat connections with limited irregularity.
method Analysis of families of algebraic flat connections and holonomic D-modules.
result Established boundedness of families of algebraic flat connections with controlled irregularity.
We study a generalization of the familiar Poincaré map, first implicitely introduced by N.N. Nekhoroshev in his study of persistence of invariant tori in hamiltonian systems, and discuss some of its properties and applications. In particular, we apply it to study persistence and bifurcation of invariant tori.
Study examines factors influencing lending to SMEs by Kenyan banks.
problem Lack of creditworthiness makes SMEs difficult to finance by banks.
method Descriptive research design, census of 43 banks, secondary data analysis.
result Bank size and liquidity significantly influence lending to SMEs, while credit risk and interest rates do not.
Study evaluates SHAP for credit card default model consistency.
problem Model transparency and fairness in credit card default prediction models.
method Evaluates SHAP stability in credit card default prediction models via a case study.
result SHAP consistency is related to variable importance level.
Study invariant spin^r structures on homogeneous spaces.
problem Classify invariant spin^r structures on homogeneous spaces.
method Introduce and study G-invariance of spin^r structures on homogeneous spaces. result Classification of invariant spin^r structures in terms of isotropy representation.
The purpose of this paper is to study *-Ricci tensor on Sasakian manifold. Here, φ-confomally flat and confomally flat *-η-Einstein Sasakian manifold are studied. Next, we consider *-Ricci symmetric conditon on Sasakian manifold. Finally, we study a special type of metric called *-Ricci soliton on Sasakian manifold.
Study on encoding neural architectures for NAS, showing impact on performance.
problem Impact of different encodings on NAS performance.
method Formal definition and characterization of encodings, empirical study of encodings' effectiveness.
result Different encodings can significantly affect NAS performance.
Method estimates shared and study-specific factors for multi-study data.
problem Covariance estimation for multi-study data with shared and study-specific components.
method Spectral decomposition for latent factors, surrogate Bayesian regressions for loadings and variances.
result Strong frequentist guarantees and superior performance in simulations and real data.
Study solves equations on tori for Calabi-Yau problems.
problem Solving equations on tori for Calabi-Yau problems.
method Study of fully nonlinear equations on flat tori.
result Solvability of equations on tori established.
Study of digital topology concepts like hyperspaces and function graphs.
problem Adapting classical topology concepts to digital topology.
method Define digital hyperspaces and function graphs, study their properties.
result Some relationships and graphical properties of digital hyperspaces and function graphs.
Study evaluates different mathematical models for three case studies using statistical fitting.
problem Estimating outcomes in population dynamics, temperature variations, and market equilibrium.
method Applied various statistical equations (e.g., fractional exponential, sinusoidal) to three case studies.
result Optimal models differ by case study (fractional exponential for population dynamics, sinusoidal for temperature and market equilibrium).
We define and study the secondary Chern-Euler class for a general submanifold of a Riemannian manifold. Using this class, we define and study index for a vector field with non-isolated singularities on a submanifold. As an application, our studies give conceptual proofs of a classical result of Chern.