There is a widespread need for techniques that can discover structure from time series data. Recently introduced techniques such as Automatic Bayesian Covariance Discovery (ABCD) provide a way to find structure within a single time series by searching through a space of covariance kernels that is generated using a simp…
On-device research index
arXiv research
A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
168,742 papers · 148 categories
Trend · papers per month
48 results for “ABCD study”
The paper studies the face angles of tetrahedra with a fixed base.
problem Determine the closure and boundary of the set of face angles of tetrahedra with a given base.
method Analyzes the set of tetrahedra with a given base and calculates the cosine of the angles between the faces.
result The closure and boundary of the set of face angles are determined.
Curvature tensors can always be matched to a metric tensor under certain conditions.
problem Sectionally positive curvature tensors and their relationship to metric tensors.
method Existence and uniqueness of a metric tensor such that .
result A metric tensor can be found for sectionally positive curvature tensors, and it is unique up to a constant factor.
ABCD model generates synthetic graphs for community detection with improved scalability and interpretability.
problem Synthetic graphs for community detection are limited in scalability and interpretability.
method Developed a new random graph model (ABCD) with community structure and power-law distribution.
result ABCD model solves scalability and interpretability issues of LFR model.
We propose a four-layer fully-connected neural network (FNN) for predicting fluid intelligence scores from T1-weighted MR images for the ABCD-challenge. In addition to the volumes of brain structures, the FNN uses cortical WM/GM contrast and cortical thickness at 78 cortical regions. These last two measurements were de…
We prove the stability of the Gieseker point of an irreducible homogeneous bundle over a rational homogeneous space. As an application we get a sharp upper estimate for the first eigenvalue of the Laplacian of an arbitrary Kaehler metric on a compact Hermitian symmetric spaces of ABCD--type.
Auto-encoding brain networks with applications to analyzing large-scale brain imaging datasetsstat.ML
Develops a new method to analyze brain networks for cognitive traits.
problem Challenges in summarizing and relating brain connectomes to human traits.
method Graph Auto-Encoding (GATE) model using deep learning.
result GATE improves prediction accuracy and efficiency over existing methods.
Bayesian Deep Noise Neural Network (B-DeepNoise) estimates predictive densities and uncertainty.
problem Estimating predictive densities and uncertainty in deep neural networks.
method Extends random noise to all hidden layers, using Gibbs sampling for posterior computation.
result Superior performance in prediction accuracy and uncertainty quantification.
The paper introduces invariants to describe period-doubling routes to chaos in dynamical systems.
problem Understanding the dynamics of period-doubling routes to chaos in complex systems.
method Introducing three topological invariants to describe the topology of period-doubling routes to chaos.
result Ascribed symbolic dynamics to perturbations of the Shilnikov homoclinic scenario and dynamics of the Henon map.
We establish sufficient conditions for existence of curves minimizing length as measured with respect to a degenerate metric on the plane while enclosing a specified amount of Euclidean area. Non-existence of minimizers can occur and examples are provided. This continues the investigation begun in [ABCDS] where the met…
Gaussian Processes (GPs) provide a general and analytically tractable way of modeling complex time-varying, nonparametric functions. The Automatic Bayesian Covariance Discovery (ABCD) system constructs natural-language description of time-series data by treating unknown time-series data nonparametrically using GP with …
We propose regularizing the empirical loss for semi-supervised learning by acting on both the input (data) space, and the weight (parameter) space. We show that the two are not equivalent, and in fact are complementary, one affecting the minimality of the resulting representation, the other insensitivity to nuisance va…
Selective inference improves multi-task neuroimaging analysis.
problem Improving predictive performance and modeling accuracy in neuroimaging studies.
method Proposes a framework for selective inference to jointly identify relevant covariates and conduct valid inference in a sparsity-inducing model.
result Selective inference yields tighter confidence intervals and more accurate signal recovery than single-task methods.
PIML model improves hydrological predictions by blending physics and ML.
problem Hydrological models either lack predictive accuracy or fail to maintain physical consistency.
method Physics Informed Machine Learning (PIML) that integrates physics-based models and ML algorithms.
result PIML model outperforms both physics-based and ML models in predicting streamflow and evapotranspiration.
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…
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.
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…
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 Theorymath.GT
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.
Investigation of the Estimation Accuracy of 5 Different Numerical ODE Solvers on 3 Case Studiesmath.NA
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 Aided Causal Discovery in an Observational Aneurysmal Subarachnoid Hemorrhage Studystat.ME
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.