Results 41 to 50 of about 1,716 (199)

Toral Arrangements and Hyperplane Arrangements

open access: yesRocky Mountain Journal of Mathematics, 1998
The author defines a toral arrangement to be a finite set \({\mathcal A}\) of characters of an algebraic torus \(T\). Such a set corresponds to an integral hyperplane arrangement \(d{\mathcal A}\) in the Lie algebra of the torus given by the kernels of the derivatives of the characters.
openaire   +2 more sources

Automatic Determination of Quasicrystalline Patterns from Microscopy Images

open access: yesAdvanced Intelligent Discovery, EarlyView.
This work introduces a user‐friendly machine learning tool to automatically extract and visualize quasicrystalline tiling patterns from atomically resolved microscopy images. It uses feature clustering, nearest‐neighbor analysis, and support vector machines. The method is broadly applicable to various quasicrystalline systems and is released as part of
Tano Kim Kender   +2 more
wiley   +1 more source

Lattice Sums of Hyperplane Arrangements

open access: yes, 2023
39 pages, 4 ...
Yasushi, Komori   +2 more
openaire   +2 more sources

A blowup algebra for hyperplane arrangements [PDF]

open access: yesAlgebra & Number Theory, 2018
22 ...
Garrousian, Mehdi   +2 more
openaire   +4 more sources

Optimal strong stationary times for random walks on the chambers of a hyperplane arrangement

open access: yes, 2018
This paper studies Markov chains on the chambers of real hyperplane arrangements, a model that generalizes famous examples, such as the Tsetlin library and riffle shuffles.
Nestoridi, Evita
core   +1 more source

Artificial Intelligence‐Driven Insights into Electrospinning: Machine Learning Models to Predict Cotton‐Wool‐Like Structure of Electrospun Fibers

open access: yesAdvanced Intelligent Discovery, EarlyView.
Electrospinning allows the fabrication of fibrous 3D cotton‐wool‐like scaffolds for tissue engineering. Optimizing this process traditionally relies on trial‐and‐error approaches, and artificial intelligence (AI)‐based tools can support it, with the prediction of fiber properties. This work uses machine learning to classify and predict the structure of
Paolo D’Elia   +3 more
wiley   +1 more source

A branch statistic for trees: interpreting coefficients of the characteristic polynomial of braid deformations [PDF]

open access: yesEnumerative Combinatorics and Applications, 2022
Priyavrat Deshpande, Krishna Menon
doaj   +1 more source

Eigenvectors for a random walk on a hyperplane arrangement

open access: yes, 2012
We find explicit eigenvectors for the transition matrix of the Bidigare–Hanlon–Rockmore random walk, from Bidigare et al. (1999) [1]. This is accomplished by using Brown and Diaconisʼ (1998) analysis in [3] of the stationary distribution, together with ...
Graham Denham, Denham, Graham
core   +1 more source

Logarithmic discriminants of hyperplane arrangements

open access: yesLe Matematiche
A recurring task in particle physics and statistics is to compute the complex critical points of a product of powers of affine-linear functions. The logarithmic discriminant characterizes exponents for which such a function has a degenerate critical ...
Kayser, L., Telen, S., Kretschmer, A.
core   +4 more sources

Risk‐aware safe reinforcement learning for control of stochastic linear systems

open access: yesAsian Journal of Control, EarlyView.
Abstract This paper presents a risk‐aware safe reinforcement learning (RL) control design for stochastic discrete‐time linear systems. Rather than using a safety certifier to myopically intervene with the RL controller, a risk‐informed safe controller is also learned besides the RL controller, and the RL and safe controllers are combined together ...
Babak Esmaeili   +2 more
wiley   +1 more source

Home - About - Disclaimer - Privacy