arXiv research

A new method for joint eQTL mapping and gene network estimation.

Proposes a convex method to estimate GGMs with covariates.

We consider the problem of learning a high-dimensional multi-task regression model, under sparsity constraints induced by presence of grouping structures on the input covariates and on the output predictors. This problem is primarily motivated by expression quantitative trait locus (eQTL) mapping, of which the goal is …

Paper proposes BSP to find stable bimodules of cross-correlated features.

We consider the problem of estimating a sparse multi-response regression function, with an application to expression quantitative trait locus (eQTL) mapping, where the goal is to discover genetic variations that influence gene-expression levels. In particular, we investigate a shrinkage technique capable of capturing a…

Given genetic variations and various phenotypical traits, such as Magnetic Resonance Imaging (MRI) features, we consider two important and related tasks in biomedical research: i)to select genetic and phenotypical markers for disease diagnosis and ii) to identify associations between genetic and phenotypical data. Thes…

We propose a minimum distance estimation method for robust regression in sparse high-dimensional settings. The traditional likelihood-based estimators lack resilience against outliers, a critical issue when dealing with high-dimensional noisy data. Our method, Minimum Distance Lasso (MD-Lasso), combines minimum distanc…

A new method estimates treatment effects across multiple studies considering differences.

Study shows convergence of Fubini-Study currents to equilibrium metrics on Kähler manifolds.

Boosting strategies for merging vs. ensembling studies analyzed.

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.

New method uncovers bias mechanisms in observational studies.

GenAI improves actuarial practices through case studies.

Proves polynomial injectivity of Fubini-Study map for ample line bundles.

Optimal ensemble construction improves prediction accuracy for multi-study tasks, especially in pandemic scenarios.

Study evaluates machine learning for predicting treatment effects in observational studies.

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.

Ablation studies show BCF model's propensity score is not essential for treatment effect estimation.

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.

Study constant mean curvature tubes in homogeneous spaces.

Study on the convergence rate of prescribed scalar curvature flow.

Study on convergence rate of 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.

Study of Fubini-Study forms on surfaces with punctures.

Study derived Lie ∞-groupoids and algebroids in higher differential geometry.

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.

Novel strategy benchmarks observational studies against randomized trials.

Study compares 5 ODE solvers on 3 case studies, finding varying accuracy.

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. …

Study on flat connections with controlled irregularity.

Causal graph aids observational study insights in aSAH patients.

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.

Study evaluates SHAP for credit card default model consistency.

Study invariant spin^r structures on homogeneous spaces.

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.