arXiv research

Develops methods to learn centre groupings from summary statistics in multi-centre studies.

The classifications of holonomy groups in Lorentzian and in Euclidean signature are quite different. A group of interest in Lorentzian signature in n dimensions is the maximal proper subgroup of the Lorentz group, SIM(n-2). Ricci-flat metrics with SIM(2) holonomy were constructed by Kerr and Goldberg, and a single four…

Progress of machine learning in critical care has been difficult to track, in part due to absence of public benchmarks. Other fields of research (such as computer vision and natural language processing) have established various competitions and public benchmarks. Recent availability of large clinical datasets has enabl…

Blood lactate concentration is a strong indicator of mortality risk in critically ill patients. While frequent lactate measurements are necessary to assess patient's health state, the measurement is an invasive procedure that can increase risk of hospital-acquired infections. For this reason we formally define the prob…

Aim: To review how machine learning (ML) is applied to imaging biomarkers in neuro-oncology, in particular for diagnosis, prognosis, and treatment response monitoring. Materials and Methods: The PubMed and MEDLINE databases were searched for articles published before September 2018 using relevant search terms. The sear…

Machine learning analysis of neuroimaging data can accurately predict chronological age in healthy people and deviations from healthy brain ageing have been associated with cognitive impairment and disease. Here we sought to further establish the credentials of "brain-predicted age" as a biomarker of individual differe…

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…

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…

The paper reviews machine learning methods in PET imaging.

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. …

Causal graph aids observational study insights in aSAH patients.

Study on 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.

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.

Method estimates shared and study-specific factors for multi-study data.