Search Keywords

Results Restricted To:

https://www.temple.edu

Total Results: 13

Mission & Vision | Lewis Katz School of Medicine | Lewis Katz School of ...

https://medicine.temple.edu/about/mission-vision

Mission Align with our diverse communities to advance medicine and improve health through education, research, training, and development of the next generation of clinicians, educators, and scientists. Vision The Lewis Katz School of Medicine will inspire a new standard of excellence in education and research and lead social change in medicine. In 2024, the school launched Inspiring Excellence ...

phylotree.js - a JavaScript library for application development and ...

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 ...

MS in Health Informatics | College of Public Health | College of Public ...

https://cph.temple.edu/marketing/lp/MSHI

Career-Focused Curriculum – Gain expertise in health data analytics, AI in healthcare, IT strategy, and more. Designed for All Backgrounds – Ideal for professionals with or without prior health informatics experience.

CIS587: The RETE Algorithm - Temple University

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 ...

C. Anthony Di Benedetto - Fox School of Business

https://www.fox.temple.edu/directory/c-anthony-di-benedetto-tonyd

Biography Anthony Di Benedetto is Professor of Marketing and Senior Washburn Research Fellow at the Fox School of Business, Temple University, Philadelphia, PA, USA. He is the Academic Director of the Executive MBA program, and has taught in the DBA, Ph. D., Executive MBA, Online MBA, International MBA, and undergraduate programs. He has held visiting professorships at Bocconi University ...

FedCPD: Personalized Federated Learning with Prototype-Enhanced ...

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:

MyPDESuite - College of Education and Human Development

https://education.temple.edu/certification/tims-mypdesuite

The Pennsylvania Department of Education (PDE) requires that all applications for credentials be completed and submitted online via PDE's Teacher Information Management System (TIMS) - MyPDESuite. Paper applications are no longer being accepted and can no longer be submitted directly to Temple University for processing. How To Apply for Your Certification Please go to MyPDESuite - https://www ...

Analysis of Randomized Householder-Cholesky QR Factorization with ...

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

FedCPD: Personalized Federated Learning with Prototype-Enhanced ...

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.

Online Federated Learning on Distributed Unknown Data Using UAVs

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.