https://scholarshare.temple.edu/bitstreams/601b9eb7-f797-4055-9ce3-dd557cd76120/download
The effect of group improvisational music therapy on depression in adolescents and
https://cst.temple.edu/sites/cst/files/documents/Conventional%20SEM%20of%20Bacteria.pdf
Miloslav Kaláb, Ann-Fook Yang, Denise Chabot Scanning electron microscopy (SEM) is one of the best suited out of a variety of procedures to visualise the external appearance of bacteria. Bacteria live in various environments and their preparation for SEM thus takes their nature into consideration. The basic principles of isolation, fixation, dehydration, drying, mounting, and photographing ...
https://scholarshare.temple.edu/server/api/core/bitstreams/d2272397-5daf-4c1d-bb7a-6e4d66c13e81/content
Abstract Athlete brands exist within a network of brand relationships. Thus, considering the joint influences of re-lated brands at diferent levels (league, team, and athlete) is essential for understanding how athlete brands are built. We focus on growth factors impacting athletes’ social media followings (Twitter and Instagram) around the critical juncture of team transfer periods. We use ...
https://guides.temple.edu/qda/choosing
This workshop is the second of the two-part workshop on five different Qualitative Data Analysis (QDA) tools – ATLAS.ti, NVivo, Dedoose, Taguette, and QualCoder. The workshop recaps essential and distinctive features demonstrated in the Part 1 recorded workshop and answered questions about the tools. The workshop discusses considerations for choosing among the five tools and provides ...
https://hope.temple.edu/policy-advocacy/closing-college-snap-gap
Congress has created exemptions to restrictive SNAP eligibility that now allow some college students to qualify for benefits. While these exemptions somewhat broadened access to SNAP, they also added significant complexity to a program that already contained a dizzying maze of eligibility rules and application processes. Closing the “ college SNAP gap ” by enrolling students who are ...
https://cis.temple.edu/~pwang/NARS-Intro.html
NARS (Non-Axiomatic Reasoning System) is a project aimed at the building of a general-purpose intelligent system, i.e., a "thinking machine" (also known as " AGI "), that follows the same principles as the human mind, and can solve problems in various domains.
https://cis.temple.edu/~apal/npa.pdf
Abstract Recent advances in radio and embedded systems have enabled the proliferation of wireless sensor networks. Wireless sensor networks are tremendously being used in different environments to perform various monitoring tasks such as search, rescue, disaster relief, target tracking and a number of tasks in smart environments. In many such tasks, node localization is inherently one of the ...
https://cis-linux1.temple.edu/~latecki/Courses/CIS617-04/slides/Ch3DataLink.ppt
The rest (3, 5, 6, 7, 9, …) are data bits. Each check bit forces the parity of some collection of bits, including itself, to be even. To see which check bits the data bit in position k contributes to, rewrite k as a sum of power of 2, e.g.,: 11 = 1 + 2 + 8 and 29 = 1 + 4 + 8 + 16 1001000 is encoded as 00110010000 Check bits are in blue.
https://bulletin.temple.edu/undergraduate/admissions-information/transfer-students/
Transfer Admissions Applicants who wish to be considered for transfer admission should have maintained at least a 2.50 grade point average in 15 or more college-level credits completed after high school at an accredited two- or four-year institution of higher education, although this is no guarantee of admission. The average GPA for entering transfer students is over a 3.00.
https://cis.temple.edu/~jiewu/research/publications/Publication_files/INFOCOM2024_PSFL%20Parallel-Sequential%20Federated%20Learning%20with%20Convergence%20Guarantees.pdf
Abstract—Federated Learning (FL) is a novel distributed learning paradigm which can coordinate multiple clients to jointly train a machine learning model by using their local data samples. Existing FL works can be roughly divided into two categories according to the modes of model training: Parallel FL (PFL) and Sequential FL (SFL). PFL can speed up each round of model training time through ...