Heuristic algorithmsSteady-stateConvergenceUpper boundLaplace equationsAdaptive systemsOptimizationThis technical note considers the dynamic average consensus problem, where a group of networked agents are required to estimate the average of their time-varying reference signals. Almost all existing solutions to...
Fig. 1: A computationally efficient and scalable neuroimaging pipeline is empowered by deep learning algorithms and the workflow manager. a, DeepPrep workflow. DeepPrep accepts both anatomical and fMRI data for preprocessing. White boxes highlight deep learning algorithms for brain tissue segmentation,...
Automated coronary angiography assessment requires precise vessel segmentation, a task complicated by uneven contrast filling and background noise. Our research introduces an ensemble U-Net model, SE-RegUNet, designed to accurately segment coronary vesse
then derives a partition from this activity [29]. One type of dynamic clustering is called label propagation [30], in which limited labeled nodes propagate those labels to connected nodes. Originally, label propagation algorithms required some correctly labeled nodes....
As described below, RDM uses quantitative "Scenario Discovery" algorithms to pursue the same ends. 2 Robust Decision Making (RDM) 29 The resulting scenarios summarize the results of the ABP-style stress tests and can link to the development of adaptive strategies (Groves and Lempert 2007; ...
On the contrary, BLAST and BLAT algorithms erroneously report the coordinate 3377327 as the insertion point. Even more confusing, the best alignment scores reported by either online BLAST or BLAT for this query do not refer to wech but to paralogous heat shock protein genes (3R). Mapping ...
Average fold changes were compared using a Mann-Whitney U test; p = 0.004. Gating strategy is shown in Figure S2. (D) Jurkat cell line expressing the predicted TCR after stimulation with the predicted epitope. Left column: negative control; middle column: TCR4.1 cell line co-cultured with ...
MCKF uses threshold detection to end iterative computations instead of using fixed iteration steps, and hence the number of iteration is dynamic. The actual average iteration steps of MCKF in Case 1 and Case 2 are 3.01 and 3.07, respectively. Plotted in Fig. 6 are the RMSEs of the ...
SpeakEasy is related to earlier label propagation algorithms23,24,25 in the sense that nodes join communities based on exchange of “labels” between connected nodes. These “labels” do not refer to a priori community titles. In this context, labels are unique bits of information that are ...
(16). COMMUNAL was run using consensus-clustering versions of two algorithms, K-means clustering and Partitioning Around Medioids (PAM), due to their robustness in large, noisy datasets. Both methods were run across a range of variables from 100 genes up to 5,000 genes (in ranked order)....