Zobrazeno 1 - 10
of 452
pro vyhledávání: '"McDonald, Daniel P."'
Autor:
Bronsard, Lia, Chen, Jinqi, Mazzouza, Léa, McDonald, Daniel, Singh, Nathan, Stantejsky, Dominik, van Brussel, Lee
We give a brief introduction to a divergence penalized Landau-de Gennes functional as a toy model for the study of nematic liquid crystal with colloid inclusion, in the case of unequal elastic constants. We assume that the nematic occupies the exteri
Externí odkaz:
http://arxiv.org/abs/2410.09930
Autor:
McDonald, Daniel Cooper
The first non-obvious case of Hadwiger's Conjecture states that every graph $G$ with chromatic number at least 4 has a $K_4$ minor. We give a new proof that derives the $K_4$ minor from a proper 3-coloring of a subgraph of $G$.
Comment: 2 pages
Comment: 2 pages
Externí odkaz:
http://arxiv.org/abs/2308.05277
Publikováno v:
Practice and Experience in Advanced Research Computing (PEARC '23). Association for Computing Machinery, New York, NY, USA, 332-335. (2023)
GitHub is a popular repository for hosting software projects, both due to ease of use and the seamless integration with its testing environment. Native GitHub Actions make it easy for software developers to validate new commits and have confidence th
Externí odkaz:
http://arxiv.org/abs/2305.10346
Dynamic stochastic general equilibrium (DSGE) models have been an ubiquitous, and controversial, part of macroeconomics for decades. In this paper, we approach DSGEs purely as statstical models. We do this by applying two common model validation chec
Externí odkaz:
http://arxiv.org/abs/2210.16224
Trend filtering is a modern approach to nonparametric regression that is more adaptive to local smoothness than splines or similar basis procedures. Existing analyses of trend filtering focus on estimating a function corrupted by homoskedastic Gaussi
Externí odkaz:
http://arxiv.org/abs/2209.09175
Autor:
Liang, Xiaoxuan, Cohen, Aaron, Heinsfeld, Anibal Solón, Pestilli, Franco, McDonald, Daniel J.
The sparse group lasso is a high-dimensional regression technique that is useful for problems whose predictors have a naturally grouped structure and where sparsity is encouraged at both the group and individual predictor level. In this paper we disc
Externí odkaz:
http://arxiv.org/abs/2208.02942
Autor:
Tuzhilina, Elena, Hastie, Trevor J., McDonald, Daniel J., Tay, J. Kenneth, Tibshirani, Robert
Forecasting methodologies have always attracted a lot of attention and have become an especially hot topic since the beginning of the COVID-19 pandemic. In this paper we consider the problem of multi-period forecasting that aims to predict several ho
Externí odkaz:
http://arxiv.org/abs/2202.09723
Artifacts in functional MRI (fMRI) data cause deviations from common distributional assumptions, introduce spatial and temporal outliers, and reduce the signal-to-noise ratio of the data -- all of which can have negative consequences for downstream s
Externí odkaz:
http://arxiv.org/abs/2108.00319
Publikováno v:
2021 IEEE 17th International Conference on eScience (eScience), 2021, pp. 229-230
Microbiome studies have recently transitioned from experimental designs with a few hundred samples to designs spanning tens of thousands of samples. Modern studies such as the Earth Microbiome Project (EMP) afford the statistics crucial for untanglin
Externí odkaz:
http://arxiv.org/abs/2107.05397
Publikováno v:
Bioinformatics Advances, Volume 2, 2022, 1-8
Methods for global measurement of transcript abundance such as microarrays and RNA-Seq generate datasets in which the number of measured features far exceeds the number of observations. Extracting biologically meaningful and experimentally tractable
Externí odkaz:
http://arxiv.org/abs/2107.02150