Results 61 to 70 of about 6,682,316 (219)

Vendi information gain for active learning and its application to ecology

open access: yesMethods in Ecology and Evolution, EarlyView.
Abstract While monitoring biodiversity through camera traps has become an important endeavour for ecological research, identifying species in the captured image data remains a major bottleneck due to limited labelling resources. Active learning—a machine learning paradigm that selects the most informative data to label and train a predictive model ...
Quan Nguyen, Adji Bousso Dieng
wiley   +1 more source

On the asymptotic Bieberbach conjecture

open access: yesInternational Journal of Mathematics and Mathematical Sciences, 1982
The set S consists of complex functions f, univalent in the open unit disk, with f(0)=f'(0)-1=0. We use the asymptotic behavior of the positive semidefinite FitzGerald matrix to show that there is an absolute constant N0 such that, for any f(z)=z+?n ...
Mauriso Alves, Armando J. P. Cavalcante
doaj   +1 more source

Computing Skinning Weights via Convex Duality

open access: yesComputer Graphics Forum, EarlyView.
We present an alternate optimization method to compute bounded biharmonic skinning weights. Our method relies on a dual formulation, which can be optimized with a nonnegative linear least squares setup. Abstract We study the problem of optimising for skinning weights through the lens of convex duality.
J. Solomon, O. Stein
wiley   +1 more source

Symmetric Positive Semi-Definite Fourier Estimator of Spot Covariance Matrix with High Frequency Data

open access: yesRisks
This paper proposes a nonparametric estimator of the spot volatility matrix with high-frequency data. Our newly proposed Positive Definite Fourier (PDF) estimator produces symmetric positive semi-definite estimates and is consistent with a suitable ...
Jiro Akahori   +5 more
doaj   +1 more source

Fast local convergence for flow topology optimization using the lattice Boltzmann method with a modified Newton method

open access: yesNihon Kikai Gakkai ronbunshu, 2015
We propose a Newton-gradient-hybrid optimization method for fluid topology optimization. The method accelerates convergence and reduces computation time. In addition, the fluid-solid boundaries are clearly distinguished.
Kazuo YONEKURA, Yoshihiro KANNO
doaj   +1 more source

The Story(line) So Far: A Survey on Storyline Visualization

open access: yesComputer Graphics Forum, EarlyView.
Abstract Storyline visualizations model narratives as temporal networks, using x‐monotone lines to represent entities and their interactions over time. This technique offers an intuitive way to reveal structural patterns over time, such as character co‐occurrence and narrative flow.
S. Di Bartolomeo   +4 more
wiley   +1 more source

A Splitting Architecture for Exact Reduced Coulomb Friction

open access: yesComputer Graphics Forum, EarlyView.
Abstract Existing approaches to frictional contact dynamics typically either modify the Coulomb law to improve numerical robustness or solve the exact law in a fully coupled monolithic form. However, in its reduced form, exact Coulomb friction can be written as a cone complementarity problem with an augmented velocity, which reveals a natural split ...
Hongcheng Song   +3 more
wiley   +1 more source

Singular Value and Matrix Norm Inequalities between Positive Semidefinite Matrices and Their Blocks

open access: yesJournal of Mathematics
In this paper, we obtain some inequalities involving positive semidefinite 2×2 block matrices and their blocks.
Feng Zhang   +3 more
doaj   +1 more source

A Quantile Model of Firm Investment

open access: yesInternational Economic Review, EarlyView.
ABSTRACT Are firms risk averse? We propose a dynamic model of firm investment under uncertainty that captures firms' risk attitudes through quantile preferences. The firm maximizes its present value, defined as current profits and investment plus the discounted value of the τ$\tau$‐quantile of its value next period.
Heitor Almeida   +3 more
wiley   +1 more source

Structural Results on the HMLasso

open access: yesAxioms
HMLasso (Lasso with High Missing Rate) is a useful technique for sparse regression when a high-dimensional design matrix contains a large number of missing data. To solve HMLasso, an appropriate positive semidefinite symmetric matrix must be obtained. In
Shin-ya Matsushita, Hiromu Sasaki
doaj   +1 more source

Home - About - Disclaimer - Privacy