Zobrazeno 1 - 10
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pro vyhledávání: '"Simanek, A."'
Autor:
Reinhart, Graeme, Simanek, Brian
Two interesting phenomena for the construction of quantum states are that of mutually unbiased bases and that of balanced states. We explore a constructive approach to each phenomenon that involves orthogonal polynomials on the unit circle. In the ca
Externí odkaz:
http://arxiv.org/abs/2408.06472
Autor:
Simanek, Brian, Wellman, Richard
We show that under very mild conditions on a measure $\mu$ on the real line, the span of $\{x^n\}_{n=j}^{\infty}$ is dense in $L^2(\mu)$ for any $j\in\mathbb{N}$. We also present a slightly weaker result with an interesting proof that uses Sobolev or
Externí odkaz:
http://arxiv.org/abs/2406.18353
Autor:
Martinů, Jan, Šimánek, Petr
This study investigates the application of deep-learning diffusion models for the super-resolution of weather data, a novel approach aimed at enhancing the spatial resolution and detail of meteorological variables. Leveraging the capabilities of diff
Externí odkaz:
http://arxiv.org/abs/2406.04099
Autor:
Simanek, Brian
We resolve some questions posed by Handelman in 1996 concerning log convex integrable functions. In particular, we give a negative answer to a question he posed concerning the integrability of $h^2(x)/h(2x)$ when $h$ is integrable and log convex and
Externí odkaz:
http://arxiv.org/abs/2402.10970
Autor:
Kraus, Adam, Simanek, Brian
Publikováno v:
Analysis and Mathematical Physics 14 (2024), no. 5
We consider families of polynomial lemniscates in the complex plane and determine if they bound a Jordan domain. This allows us to find examples of regions for which we can calculate the projection of $\bar{z}$ to the Bergman space of the bounded reg
Externí odkaz:
http://arxiv.org/abs/2312.12603
Optimization is an integral part of modern deep learning. Recently, the concept of learned optimizers has emerged as a way to accelerate this optimization process by replacing traditional, hand-crafted algorithms with meta-learned functions. Despite
Externí odkaz:
http://arxiv.org/abs/2312.07174
Autor:
Kraus, Adam, Simanek, Brian
Publikováno v:
Journal of Elasticity 156 (2024), no. 4-5, 1085-1100
We consider polynomial approximations of z-bar to better understand the torsional rigidity of polygons. Our main focus is on low degree approximations and associated extremal problems that are analogous to Polya's conjecture for torsional rigidity of
Externí odkaz:
http://arxiv.org/abs/2309.16450
Deep learning methods dominate short-term high-resolution precipitation nowcasting in terms of prediction error. However, their operational usability is limited by difficulties explaining dynamics behind the predictions, which are smoothed out and mi
Externí odkaz:
http://arxiv.org/abs/2301.11707
The short-term prediction of precipitation is critical in many areas of life. Recently, a large body of work was devoted to forecasting radar reflectivity images. The radar images are available only in areas with ground weather radars. Thus, we aim t
Externí odkaz:
http://arxiv.org/abs/2211.16824
Designing faster optimization algorithms is of ever-growing interest. In recent years, learning to learn methods that learn how to optimize demonstrated very encouraging results. Current approaches usually do not effectively include the dynamics of t
Externí odkaz:
http://arxiv.org/abs/2211.16268