https://cis.temple.edu/~jiewu/research/publications/Publication_files/FedCPD.pdf
0 2 2 2 L1 + E 0G2 + E2 0G2 2 + 2 L2E2 0G2 2 + 2 Theorem 2. (Non-convex FedCPD convergence). 0 < e < 0, e 2 f1 1; 2; : : : ; Eg, where represents the de- 2; cay factor for the learning rate. If the learning rate for each epoch satisfies the following condition, the loss function de-creases monotonically, leading to convergence:
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London: J. Murray; 1859. Vaughan TG. IcyTree: rapid browser-based visualization for phylogenetic trees and networks. Bioinformatics. 2017;33:btx155. Kreft Ł, Botzki A, Coppens F, Vandepoele K, Van Bel M. PhyD3: a phylogenetic tree viewer with extended phyloXML support for functional genomics data visualization. Bioinformatics. 2017;33(18):2946 ...
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https://cis.temple.edu/~jiewu/research/publications/Publication_files/ICDE2024_Online_Federated_Learning_on_Distributed_Unknown_Data_Using_UAVs.pdf
For the energy consumption during the learning phase, we set e1 = 0.01J and e2 = 80J [18]. To better align with real-world data collection scenarios, we design fine-grained PoI data models from three perspectives: data distribution, data generation patterns, and data quality.
https://ira.temple.edu/sites/ira/files/Temple%20University%20CDS-2024-2025-v2.pdf
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https://dabi.temple.edu/external/zoran/papers/Rafaa_Complex_Net_2025.pdf
Let G denote a spatiotemporal multiplex network defined as G(V,E,L,T), where: V = v1, v2, ..., v E ..., e { n} represents the set of vertices (counties), = { e1, e2, m} represents the set of edges, L = { l1, l2, l3, l4, l5, l6} represents the set of lay-ers, and T = t1, t2, ..., t { k} represents a set of time steps.