NePTuNe combines neural and tensor methods for efficient link prediction in knowledge graphs.
problem Incomplete knowledge graphs, especially in link prediction.
method Hybrid model combining neural and tensor factorization methods.
result NePTuNe achieves state-of-the-art performance on FB15K-237 and near state-of-the-art on WN18RR datasets.
CNN predicts kidney function from biopsies.
problem Predict future kidney function from biopsies.
method Used Convolutional Neural Networks (CNNs).
result CNNs accurately predict kidney function.
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.
Analyzes merging vs. ensembling for multi-study prediction, showing transition point for better performance.
problem Choosing between merging or ensembling multiple studies for prediction.
method Analyzes ridge regression approaches, comparing merging and ensembling methods.
result There is a transition point where ensembling outperforms merging as cross-study heterogeneity increases.
System exposes study population descriptions in clinical guidelines.
problem Challenges in understanding applicability of clinical guidelines.
method Developed an ontology-enabled prototype system using SIO.
result Allows medical practitioners to better understand study populations.
This research improves infection prediction from symptoms across diverse study designs.
problem Predicting infection from symptom data is challenging due to varied study formats and contexts.
method Assesses transfer learning methods for improving prediction across different study types.
result It is possible to use data from one study to predict infection in another, with performance close to or better than using a single dataset.
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 …
New method combines brain imaging data from multiple studies to improve cognitive decoding.
problem Low statistical power in individual neuroimaging studies.
method A new methodology to analyze brain responses across tasks without a unified theoretical framework.
result Improves decoding performance for 80% of 35 functional-imaging studies.
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.
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.
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.
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.
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.
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.
Paper tackles dataset bias in medical imaging studies, proposing a method to unlearn study membership.
problem Dataset bias limits model generalization across different medical imaging studies.
method Proposes a framework to unlearn study membership by transforming data to an intermediate space indistinguishable by study membership.
result Models learn general properties of etiology under study, not dataset-specific peculiarities.
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.
The paper reviews machine learning methods in PET imaging.
problem Improving accuracy in PET attenuation correction and reconstruction.
method Summarizes recent machine learning-based studies in PET imaging.
result Discusses the performance and challenges of current methods.
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.
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…
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.
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…
Meta-analysis of USDA forecasts and market reactions studies.
problem Evaluating the accuracy and market reactions to USDA forecasts.
method Conducted a meta-analysis on two groups of studies focusing on USDA forecasts and market reactions.
result There is heterogeneity in the results of studies evaluating USDA forecasts and market reactions.
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.
The paper studies curvature equations and their solvability.
problem Solving curvature type equations and their Dirichlet problems.
method General class of fully nonlinear curvature equations, Christoffel-Minkowski problem, degenerate equations.
result Solvability of curvature type equations and Dirichlet problems.
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.
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.
Study on cuspidal edges and cross-caps in 3D geometry.
problem Understanding the geometry of folded cuspidal edges and cross-caps.
method Analyzing geometrical invariants and submersions preserving flat geometry.
result Geometrical invariants uniquely determine cuspidal cross-caps up to order 5.
Study several weak forms of a lemma on compact complex manifolds.
problem Understanding weak forms of a lemma on compact complex manifolds.
method Complete unified study of weak forms of the $\ddb-$Lemma.
result Unified understanding of various weak forms of the $\ddb-$Lemma.
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.
The study examines almost cosymplectic statistical manifolds and their properties.
problem Characterizing and understanding almost cosymplectic statistical manifolds.
method Analyzing basic properties, proving a characterization theorem, studying curvature, and constructing examples.
result Characterization theorem and corollary for almost cosymplectic statistical manifolds with Kaehler leaves.
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 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 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.
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.
BB-FDR boosts power and controls FDR in multi-experiment studies.
problem Analyzing large-scale, multi-experiment studies for statistical significance.
method Empirical-Bayes method using deep neural networks and black box models.
result BB-FDR outperforms competing methods in discovering significant outcomes and selecting key variables.