Search Keywords

Results Restricted To:

https://www.temple.edu

Total Results: 10

Center for the Advancement of Teaching

https://teaching.temple.edu/

Our Mission Fostering evidence-based teaching so students learn, develop and succeed. Our Vision We envision a culture in higher education where the art and science of teaching is valued and teachers are supported in designing rich, meaningful learning experiences. Our Services The Center for the Advancement of Teaching (CAT) will have both in-person and virtual services available for faculty ...

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 ...

CIS587: The RETE Algorithm - Temple University

https://cis.temple.edu/~ingargio/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 ...

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:

F2 : A Physical Internet Architecture for Fresh Food Distribution Networks

https://cis.temple.edu/~apal/ipic_food.pdf

t‘ ij 8‘2f2;:::;Tg;8t X t Lt‘:Vt C 8‘2f2;:::;Tg (8) where Vtis the volume of the container type t. Constraint(8) simply assumes that multiple containers of different sizes always fit within a truck as far as their cumulative volume is less than the truck’s capacity. This is an over-estimation of the packing ability of the containers.

Privacy-Preserving Federated Neural Architecture Search With Enhanced ...

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

LONG TIME BEHAVIOR OF SOLUTIONS OF AN ELECTROCONVECTION MODEL

https://sites.temple.edu/ignatova/files/2024/08/abig5.pdf

dr (25) (26) for all t ≥ 0. We multiply both sides of the inequality by the integrating factor e2∫ 0 (s)ds er∫ t 1 = = erln

PRESENTATION TITLE - Office of the Vice President for Research

https://research.temple.edu/sites/research/files/media/document/Temple%20University%20Export%20Control%20Introduction%20.pdf

EXPORT To send or take controlled tangible items, software, or information out of the United States in any manner (including hand-carried), to transfer ownership or control of controlled tangible items, software, or information to a foreign person, or to disclose information about controlled items, software, or information to a foreign government or foreign person. The controlled tangible item ...

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.

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.