Results 111 to 120 of about 2,509,053 (235)

The Efficacy of Intracanal Medicaments Within the Regenerative Endodontic Procedures on Permanent Necrotic Immature Teeth: Systematic Review and Naïve Indirect‐Comparison Meta‐Analysis

open access: yesInternational Endodontic Journal, EarlyView.
ABSTRACT Objectives The objectives of this study were to estimate and compare the 1‐year clinical success rates of triple antibiotic paste (TAP), double antibiotic paste (DAP), calcium hydroxide (CH) within regenerative endodontic procedures (REPs) on permanent necrotic immature teeth.
Mario Alovisi   +2 more
wiley   +1 more source

On a general matrix-valued unbalanced optimal transport problem

open access: yesEuropean Journal of Applied Mathematics
We introduce a general class of transport distances $\mathrm {WB}_{\Lambda }$ over the space of positive semi-definite matrix-valued Radon measures $\mathcal {M}(\Omega, \mathbb {S}_+^n)$ , called the weighted Wasserstein–Bures distance ...
Bowen Li, Jun Zou
doaj   +1 more source

On Metric Choice in Dimension Reduction for Fréchet Regression

open access: yesInternational Statistical Review, EarlyView.
Summary Fréchet regression is becoming a mainstay in modern data analysis for analysing non‐traditional data types belonging to general metric spaces. This novel regression method is especially useful in the analysis of complex health data such as continuous monitoring and imaging data.
Abdul‐Nasah Soale   +3 more
wiley   +1 more source

Subspace Robust Wasserstein Distances

open access: yes, 2019
Making sense of Wasserstein distances between discrete measures in high-dimensional settings remains a challenge. Recent work has advocated a two-step approach to improve robustness and facilitate the computation of optimal transport, using for instance projections on random real lines, or a preliminary quantization of the measures to reduce the size ...
Paty, François-Pierre, Cuturi, Marco
openaire   +2 more sources

Intrinsic Dimension Estimation Using Wasserstein Distances

open access: yes, 2021
It has long been thought that high-dimensional data encountered in many practical machine learning tasks have low-dimensional structure, i.e., the manifold hypothesis holds. A natural question, thus, is to estimate the intrinsic dimension of a given population distribution from a finite sample.
Block, Adam   +3 more
openaire   +3 more sources

A Comparative Review of Specification Tests for Diffusion Models

open access: yesInternational Statistical Review, EarlyView.
Summary Diffusion models play an essential role in modelling continuous‐time stochastic processes in the financial field. Therefore, several proposals have been developed in the last decades to test the specification of stochastic differential equations.
A. López‐Pérez   +3 more
wiley   +1 more source

Generalized Sliced Wasserstein Distances

open access: yes, 2019
The Wasserstein distance and its variations, e.g., the sliced-Wasserstein (SW) distance, have recently drawn attention from the machine learning community. The SW distance, specifically, was shown to have similar properties to the Wasserstein distance, while being much simpler to compute, and is therefore used in various applications including ...
Kolouri, Soheil   +4 more
openaire   +2 more sources

Critical scaling of the quantum Wasserstein distance

open access: yesPhysical Review Research
Distinguishing quantum states with minimal sampling overhead is of fundamental importance to teach quantum data to an algorithm. Recently, the quantum Wasserstein distance emerged from the theory of quantum optimal transport as a promising tool in this ...
Gonzalo Camacho, Benedikt Fauseweh
doaj   +1 more source

Model-Based Localization and Tracking Using Bluetooth Low-Energy Beacons

open access: yesSensors, 2017
We introduce a high precision localization and tracking method that makes use of cheap Bluetooth low-energy (BLE) beacons only. We track the position of a moving sensor by integrating highly unreliable and noisy BLE observations streaming from multiple ...
F. Serhan Daniş, Ali Taylan Cemgil
doaj   +1 more source

A Non‐Parametric Framework for Correlation Functions on Product Metric Spaces

open access: yesInternational 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

Home - About - Disclaimer - Privacy