arXiv research

Profile entropy measures learnability and compressibility of discrete distributions.

Paper describes profiles of multivariate normal distributions and novel estimators for mutual information.

Estimates lower bounds for isoperimetric profiles and improves on previous estimates for specific manifolds.

Study lampshuffler groups' isoperimetric profiles, refining previous estimates.

Study proposes new OPE estimators for two-player zero-sum games.

Unified plug-in approach for estimating symmetric properties of distributions efficiently.

New efficient algorithm for approximate PML distribution.

We consider the problem of estimating the curvature profile along the boundaries of digital objects in segmented black-and-white images. We start with the curvature estimator proposed by Roussillon et al., which is based on the calculation of \emph{maximal digital circular arcs} (MDCA). We extend this estimator to the …

Non-Negative Matrix Factorization, NMF, attempts to find a number of archetypal response profiles, or parts, such that any sample profile in the dataset can be approximated by a close profile among these archetypes or a linear combination of these profiles. The non-negativity constraint is imposed while estimating arch…

Estimates isoperimetric profiles in product manifolds with varying volumes.

Study ridge regression for non-identically distributed data with varying variances.

Enhanced estimates for ancient ovals and translators in 3D and 4D.

Sharp Gaussian isoperimetry proven along Ricci flow.

The study characterizes convex bodies with equal isoperimetric profiles to half-spaces and estimates their volume behavior.

Estimates population profile from small random samples.

Study computes isoperimetric profiles in low-dimensional Riemannian products.

The paper improves stability estimates for soap bubble theorem in curved domains.

Paper offers a framework for estimating symmetric properties efficiently.

We study the local Szegö-Weinberger profile in a geodesic ball $B_g(y_0,r_0)$ centered at a point $y_0$ in a Riemannian manifold $(\M,g)$. This profile is obtained by maximizing the first nontrivial Neumann eigenvalue $μ_2$ of the Laplace-Beltrami Operator $Δ_g$ on $\M$ among subdomains of $B_g(y_0,r_0)$ with fixed vol…

We prove a comparison theorem for the isoperimetric profiles of simple closed curves evolving by the normalized curve shortening flow: If the isoperimetric profile of the region enclosed by the initial curve is greater than that of some `model' convex region with exactly four vertices and with reflection symmetry in bo…

We introduce a fractional Yamabe flow involving nonlocal conformally invariant operators on the conformal infinity of asymptotically hyperbolic manifolds, and show that on the conformal spheres $(\Sn, [g_{\Sn}])$, it converges to the standard sphere up to a Möbius diffeomorphism. This result allows us to obtain extinct…

We estimate from below the isoperimetric profile of $S^2 \times \re^2$ and use this information to obtain lower bounds for the Yamabe constant of $S^2 \times \re^2$. This provides a lower bound for the Yamabe invariants of products $S^2 \times M^2$ for any closed Riemann surface $M$. Explicitly we show that $Y(S^2 \tim…

Model trains agents to optimize saving and investment strategies for diverse retirement needs.

PRoFILE accurately estimates feature importance under distribution shifts.

We prove a comparison theorem for the isoperimetric profiles of solutions of the normalized Ricci flow on the two-sphere: If the isoperimetric profile of the initial metric is greater than that of some positively curved axisymmetric metric, then the inequality remains true for the isoperimetric profiles of the evolved …

Method detects lithium-ion battery knee onset for early warning.

We present a detailed methodological study of the application of the modified profile likelihood method for the calibration of nonlinear financial models characterised by a large number of parameters. We apply the general approach to the Log-Periodic Power Law Singularity (LPPLS) model of financial bubbles. This model …

We prove that the isoperimetric profile of a convex domain $Ω$ with compact closure in a Riemannian manifold $(M^{n+1},g)$ satisfies a second order differential inequality which only depends on the dimension of the manifold and on a lower bound on the Ricci curvature of $Ω$. Regularity properties of the profile and top…

A method for profiling systematic uncertainties in SBI using Factorizable Normalizing Flows.

Novel method diagnoses large language models' reasoning abilities.

Paper improves likelihood estimation for discrete distributions.

A method to produce personalized classification models to automatically review online dating profiles on Tinder is proposed, based on the user's historical preference. The method takes advantage of a FaceNet facial classification model to extract features which may be related to facial attractiveness. The embeddings fr…

Graph neural network improves SOH estimation of lithium-ion batteries.

A method to describe Riemann surfaces using graph profiles is proposed.

In cheminformatics, compound-target binding profiles has been a main source of data for research. For data repositories that only provide positive profiles, a popular assumption is that unreported profiles are all negative. In this paper, we caution audience not to take this assumption for granted, and present empirica…

Dimensionality-reduction methods are a fundamental tool in the analysis of large data sets. These algorithms work on the assumption that the "intrinsic dimension" of the data is generally much smaller than the ambient dimension in which it is collected. Alongside their usual purpose of mapping data into a smaller dimen…

Method controls extrapolation in prediction profiles for statistical and machine learning models.

Background: While machine learning (ML) models are rapidly emerging as promising screening tools in critical care medicine, the identification of homogeneous subphenotypes within populations with heterogeneous conditions such as pediatric sepsis may facilitate attainment of high-predictive performance of these prognost…

This paper focuses on the problem of estimating historical traffic volumes between sparsely-located traffic sensors, which transportation agencies need to accurately compute statewide performance measures. To this end, the paper examines applications of vehicle probe data, automatic traffic recorder counts, and neural …

We equip many non compact non simply connected surfaces with smooth Riemannian metrics whose isoperimetric profile is smooth, a highly non generic property. The computation of the profile is based on a calibration argument, a rearrangement argument, the Bol-Fiala curvature dependent inequality, together with new result…

We introduce a spectrum of monotone coarse invariants for metric measure spaces called Poincaré profiles. The two extremes of this spectrum determine the growth of the space, and the separation profile as defined by Benjamini--Schramm--Timár. In this paper we focus on properties of the Poincaré profiles of groups with …

Bayesian approach clusters survival data for better risk prediction.

Logarithmic separation profile in hyperbolic groups shows hierarchical structure.

Develops a hybrid MtFA approach for high-dimensional data clustering.

Framework detects shape shifts in functional profiles using Fréchet mean and shape invariant model.

Study compares isoperimetric profiles on manifolds with integral Ricci curvature bounds.

Tissue heterogeneity is a major confounding factor in studying individual populations that cannot be resolved directly by global profiling. Experimental solutions to mitigate tissue heterogeneity are expensive, time consuming, inapplicable to existing data, and may alter the original gene expression patterns. Here we a…

Big spatio-temporal datasets, available through both open and administrative data sources, offer significant potential for social science research. The magnitude of the data allows for increased resolution and analysis at individual level. While there are recent advances in forecasting techniques for highly granular te…