Zobrazeno 1 - 10
of 1 360
pro vyhledávání: '"P. McConville"'
Autor:
Leeney, William, McConville, Ryan
Graph Neural Networks (GNNs) can be trained to detect communities within a graph by learning from the duality of feature and connectivity information. Currently, the common approach for optimisation of GNNs is to use comparisons to ground-truth for h
Externí odkaz:
http://arxiv.org/abs/2402.07845
Autor:
White, Grayson W., Yamamoto, Josh K., Elsyad, Dinan H., Schmitt, Julian F., Korsgaard, Niels H., Hu, Jie Kate, Gaines III, George C., Frescino, Tracey S., McConville, Kelly S.
The United States (US) Forest Inventory & Analysis Program (FIA) collects data on and monitors the trends of forests in the US. FIA is increasingly interested in monitoring forest attributes such as biomass at fine geographic and temporal scales, res
Externí odkaz:
http://arxiv.org/abs/2402.03263
Autor:
Leeney, William, McConville, Ryan
Federated Learning is machine learning in the context of a network of clients whilst maintaining data residency and/or privacy constraints. Community detection is the unsupervised discovery of clusters of nodes within graph-structured data. The inter
Externí odkaz:
http://arxiv.org/abs/2312.09023
Autor:
Leeney, William, McConville, Ryan
(1) The enhanced capability of Graph Neural Networks (GNNs) in unsupervised community detection of clustered nodes is attributed to their capacity to encode both the connectivity and feature information spaces of graphs. The identification of latent
Externí odkaz:
http://arxiv.org/abs/2312.09015
Blood glucose simulation allows the effectiveness of type 1 diabetes (T1D) management strategies to be evaluated without patient harm. Deep learning algorithms provide a promising avenue for extending simulator capabilities; however, these algorithms
Externí odkaz:
http://arxiv.org/abs/2310.14743
MLKL deficiency elevates testosterone production in male mice independently of necroptotic functions
Autor:
Shene Chiou, Wayne Cawthorne, Thomas Soerianto, Vinzenz Hofferek, Komal M. Patel, Sarah E. Garnish, Emma C. Tovey Crutchfield, Cathrine Hall, Joanne M. Hildebrand, Malcolm J. McConville, Kate E. Lawlor, Edwin D. Hawkins, Andre L. Samson, James M. Murphy
Publikováno v:
Cell Death and Disease, Vol 15, Iss 11, Pp 1-10 (2024)
Abstract Mixed lineage kinase domain-like (MLKL) is a pseudokinase, best known for its role as the terminal effector of the necroptotic cell death pathway. MLKL-mediated necroptosis has long been linked to various age-related pathologies including ne
Externí odkaz:
https://doaj.org/article/3e983790f4d740f6875bec76ced943af
Autor:
Jovan, Ferdian, Morgan, Catherine, McConville, Ryan, Tonkin, Emma L., Craddock, Ian, Whone, Alan
Publikováno v:
Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2023)
Parkinson's disease (PD) is a slowly progressive, debilitating neurodegenerative disease which causes motor symptoms including gait dysfunction. Motor fluctuations are alterations between periods with a positive response to levodopa therapy ("on") an
Externí odkaz:
http://arxiv.org/abs/2308.02419
Autor:
White, Grayson W., Wieczorek, Jerzy A., Cody, Zachariah W., Tan, Emily X., Chistolini, Jacqueline O., McConville, Kelly S., Frescino, Tracey S., Moisen, Gretchen G.
Comparing and evaluating small area estimation (SAE) models for a given application is inherently difficult. Typically, many areas lack enough data to check unit-level modeling assumptions or to assess unit-level predictions empirically; and no groun
Externí odkaz:
http://arxiv.org/abs/2306.15607
Autor:
Iacob, Alex, Gusmão, Pedro P. B., Lane, Nicholas D., Koupai, Armand K., Bocus, Mohammud J., Santos-Rodríguez, Raúl, Piechocki, Robert J., McConville, Ryan
Human Activity Recognition (HAR) training data is often privacy-sensitive or held by non-cooperative entities. Federated Learning (FL) addresses such concerns by training ML models on edge clients. This work studies the impact of privacy in federated
Externí odkaz:
http://arxiv.org/abs/2305.12134
Autor:
Leeney, William, McConville, Ryan
Graph Neural Networks (GNNs) have improved unsupervised community detection of clustered nodes due to their ability to encode the dual dimensionality of the connectivity and feature information spaces of graphs. Identifying the latent communities has
Externí odkaz:
http://arxiv.org/abs/2305.06026