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social networkstrophic interactionsIndividual-level traits mediate interaction outcomes and community structure. It is important, therefore, to identify the minimum number of traits that characterise ecological networks, that is, their 'minimum dimensionality'. Existing methods for estimating minimum ...
Is the current plant breeding community in general adequately equipped to deal with the high dimensionality of these datasets? Acknowledgments R.K.V. thanks the Science and Engineering Research Board (SERB) of the Department of Science and Technology (DST), Government of India, for providing a J...
water, and nutrients13, and abiotic drivers affect biological processes and ecological interactions. Ecosystem-level processes are intrinsically linked to canopy architecture (arrangement of leaves, shoots, etc.)14,15, and are determined by species composition, but are also influenced by disturbance...
1 Autoencoders are a subcategory of unsupervised ANNs, used to minimize the dimensionality of the data when a nonlinear function defines how dependent and independent characteristics relate to one another. 2 The BoT-IoT dataset was developed in the UNSW Canberra Cyber Range Lab by constructing a...
The BERTopic approach follows four main steps: (1) Generating embeddings, (2) dimensionality reduction, (3) clustering, and (4) c-TF-IDF-based topic extraction from the clusters. The “sentence-transformers” library transforms the input text into numerical representations (embeddings) optimized fo...
PCA is a multivariate statistical technique used to reduce the dimensionality of water quality datasets and to maximize the variance by calculating novel uncorrelated variables, called the components, as linear functions of those in the original dataset [5,24]. This reduction occurs when there is ...
(2017) Handling high predictor dimensionality in slope-unit-based landslide susceptibility models through LASSO-penalized Generalized Linear Model. Environmental Modelling & Software 97, 145–156. [14] Can, T., Nefeslioglu, H. A., Gokceoglu, C., Sonmez, H. and Duman, T. Y. (2005) ...
and ΓX ≈ ΩWis a reasonable approximation. Indeed, recent work has shown the theoretical conditions under which the covariance structure ΩWis approximately identifiable from ΓX, where recovery guarantees depend on sparsity, dimensionality, and sample size [23]. ...
While an elegant model, choosing the unit circle as the latent space is problematic as it restricts the dimensionality of the space. For our modelling, we need a description of the properties of the latent variables, \(x_{i}\). We know that geometry is a key consideration of networks, ...