https://sites.temple.edu/pdames/files/2016/07/DamesTokekarKumarISRR2015.pdf
t = fzr 1;t;zr 2;t;:::;zr m;tg to targets that it detects within the field of view (FoV) of its sensor. The number of measurements, mt, varies over time due to false negative and false positive detections and the mo-tion of the robots and the targets.
https://cis.temple.edu/~giorgio/cis587/readings/rete.html
(R1 (has-goal ?x simplify) (expression ?x 0 + ?y) ==>....) (R2 (has-goal ?x simplify) (expression ?x 0 * ?y) ==>....) and the following facts: (has-goal e1 simplicity) (expression e1 0 + 3) (has-goal e2 simplicity) (expression e2 0 + 5) (has-goal e3 simplicity) (expression e3 0 * 2) Then the Rete is +----------+ | ENTRANCE | +----------+ x ...
https://faculty.cst.temple.edu/~szyld/reports/randCholQR_rev2_report.pdf
Only the trian-gular factor ˆR is needed, so some (exactly) orthogonal Qtmp exists such that Qtmp ˆR = ˆW + E2 = S2S1V + E1 + E2. (17) Analysis of E2 is provided in Section 5.2.4. In step 3, solving the triangular system Q ˆR = V also creates errors. These are analyzed in a row-wise fashion in Section 5.2.5, taking the form
https://scholarshare.temple.edu/bitstreams/b1ecc345-7a7c-4776-a366-9b5585e714d6/download
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 ...
https://cst.temple.edu/sites/cst/files/AlgebraFall1999.pdf
2. Let R be a commutative ring with 1, and let e ∈ R be an idempotent (e2 = e). Prove:
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:
https://medicine.temple.edu/departments-centers/clinical-departments/surgery/departmental-research/faculty-research/abdominal-organ-transplant
Taylor GA, Fagenson AM, Kuo LE, Pitt HA, Lau KN. J Am Coll Surg. 2021 Apr;232 (4):470-480.e2. doi: 10.1016/j.jamcollsurg.2020.11.020. Epub 2020 Dec 18. PMID: 33346079 Gunder, M., Lakhter, V., Lau, K., Karhadkar, S. S., Di Carlo, A., & Bashir, R. (2019). Endovascular intervention for iliac vein thrombosis after simultaneous kidney-pancreas ...
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://cis.temple.edu/~jiewu/research/publications/Publication_files/Privacy-Preserving_Federated_Neural_Architecture_Search_With_Enhanced_Robustness_for_Edge_Computing.pdf
It enables a group of users to collaboratively train a shared global model [3] or multiple personalized models [12], [13], while keeping their local data private. Supposed that there are clients K = {e1, e2, . . . , e , and } each client k possesses a dataset k Nk {(xj, yj)} . In hori- e D := j=1
https://cis.temple.edu/~jiewu/research/publications/Publication_files/Paper%206190%20Camera%20Ready%20Version.pdf
For notation, tindicates the communication round and e2 1=2;1;2;:::;Erefers to the local iterations, where Eis the total number of local updates. Thus, tE+ erepresents the e-th local iteration in the (t+ 1)-th round.