Results 71 to 80 of about 60,914 (171)
On Spatial Point Processes With Composition‐Valued Marks
International Statistical Review, EarlyView.Summary Methods for marked spatial point processes with scalar marks have seen extensive development in recent years. While the impressive progress in data collection and storage capacities has yielded an immense increase in spatial point process data with highly challenging non‐scalar marks, methods for their analysis are not equally well developed ...
Matthias Eckardt +2 more
wiley +1 more source
A Non‐Parametric Framework for Correlation Functions on Product Metric Spaces
International Statistical Review, EarlyView.Summary We propose a non‐parametric framework for analysing data defined over products of metric spaces, a versatile class encountered in various fields. This framework accommodates non‐stationarity and seasonality and is applicable to both local and global domains, such as the Earth's surface, as well as domains evolving over linear time or time ...
Pier Giovanni Bissiri +3 more
wiley +1 more source
Tensor Changepoint Detection and Eigenbootstrap
Journal of Time Series Analysis, EarlyView.ABSTRACT Tensor data consisting of multivariate outcomes over the items and across the subjects with longitudinal and cross‐sectional dependence are considered. A completely distribution‐free and tweaking‐parameter‐free detection procedure for changepoints at different locations is designed, which does not require training data.
Michal Pešta +2 more
wiley +1 more source
Philosophical Issues, EarlyView.ABSTRACT This essay asks a new question: When someone with a firm understanding of basic operations nevertheless remains ignorant of a complex logical or mathematical truth, precisely what kind of information are they missing? I introduce “catenary truths,” a significant component of this non‐omniscient shortfall.
Michael G. Titelbaum
wiley +1 more source
Spatial depth for data in metric spaces
Scandinavian Journal of Statistics, EarlyView.Abstract We propose a novel measure of statistical depth, the metric spatial depth, for data residing in an arbitrary metric space. The measure assigns high (low) values for points located near (far away from) the bulk of the data distribution, allowing quantifying their centrality/outlyingness.
Joni Virta
wiley +1 more source
Machine learning the dimension of a Fano variety. [PDF]
Nat Commun, 2023 Coates T, Kasprzyk AM, Veneziale S.
europepmc +1 more source
Image of mathematics: A case study of two women's early mathematics experiences
School Science and Mathematics, EarlyView.Abstract People often view mathematics as abstract, cold, and irrelevant to real life, and their school experiences likely influence such views. In this case study, we investigated the mathematics experiences of two women who participated in an afterschool girls‐only STEM club 30 years ago when they were in fifth and sixth grades.
Lili Zhou +4 more
wiley +1 more source
Exploring 2D Geometric Shape Classification Using AI‐Driven Feature Tables in Mathematics
School Science and Mathematics, EarlyView.ABSTRACT This study explored the effectiveness of an AI‐integrated instructional task designed to enhance preservice teachers' understanding of the features and hierarchical relationships of 2D geometric shapes. Originally developed and tested in online K‐12 professional development settings, this intervention was adapted for in‐person preservice ...
Yasemin Gunpinar, Woonhee Sung
wiley +1 more source
The Journal of Physiology, EarlyView.Abstract figure legend Schematic overview of the experimental and computational framework for investigating hiPSC‐CM electrophysiology with MEA systems. The MEA‐based model integrates experimental data with phenotype‐specific ionic models and tissue‐level heterogeneity.
Sofia Botti +2 more
wiley +1 more source
On computing local monodromy and the numerical local irreducible decomposition
Transactions of the London Mathematical Society, Volume 13, Issue 1, December 2026.Abstract Similarly to the global case, the local structure of a holomorphic subvariety at a given point is described by its local irreducible decomposition. Geometrically, the key requirement for obtaining a local irreducible decomposition is to compute the local monodromy action of a generic linear projection at the given point, which is always well ...
Parker B. Edwards +1 more
wiley +1 more source