https://cis.temple.edu/~jiewu/research/publications/Publication_files/MM_2025_UAV_CR.pdf
HDCFN features two key components: (i) an infrared-guided multiscale feature enhance-ment framework that integrates haze-resistant structural cues from infrared modality with visible features across coarse to fine, im-proving the recovery of small objects, and (ii) a haze distribution-aware cross-modal fusion module that adaptively prioritizes ...
https://cis.temple.edu/~yu/research/PPGSpotter-info24.pdf
Abstract—Free weight training (FWT) is of utmost importance for physical well-being. However, the success of FWT depends heavily on choosing the suitable workload, as improper selections can lead to suboptimal outcomes or injury. Current workload estimation approaches rely on manual recording and special-ized equipment with limited feedback. Therefore, we introduce PPGSpotter, a novel PPG ...
https://global.temple.edu/isss/students/current-students/beyond-immigration-student-information/applying-social-security-number-f-1-and-j-1-students-only
In order to be eligible to apply for a Social Security Number you must: Have an employment/offer of employment: Students in F-1 or J-1 status are not eligible for a Social Security number unless they have an offer of employment. There are no exceptions to this requirement. Be authorized to work in the US. Read about work authorization here. Be in the United States. New students should wait ...
https://sites.temple.edu/immerman/
Professor of History, Emeritus The Edward J. Buthusiem Family Distinguished Faculty Fellow in History, Emeritus Marvin Wachman Director Emeritus of the Center for the Study of Force and Diplomacy (CENFAD). Francis W. DeSerio Chair of Strategic Intelligence, Department of National Security and Strategy, US Army War College, 2013-2016. From September 2007-January 2009 Professor Immerman served ...
https://global.temple.edu/isss/students/current-students/f-1-student/f-1-employment-options/employment-comparisons/optional-practical-training-opt/optional-practical-training-frequently-asked-questions
Optional Practical Training is a type of employment authorization that you can apply for to obtain training that is directly related to an F-1 student’s major area of study. It is intended to provide students with up to 12 months practical experience in their field of study during or upon completion of a degree program. When you apply for OPT, you are applying for an Employment Authorization ...
https://cis.temple.edu/~apal/nfmi_comnet.pdf
Near Field Magnetic Induction (NFMI) based communication is an emerging technology that promises several advantages over the traditional radio frequency (RF) communication including low energy use, ability to work reliably in a variety of difficult propagation media (e.g., water, non-ferromagnetic metals, underground, tissue media of fresh produce & meats, etc.), and low leakage possibility ...
https://cis.temple.edu/~jiewu/research/publications/Publication_files/Reinforcement_Learning-based_Dual-Identity_Double_Auction_in_Personalized_Federated_Learning.pdf
Reinforcement Learning-based Dual-Identity Auction in Personalized Federated Juan Li, Member, IEEE, Zishang Chen, Tianzi Wu, Fellow, IEEE, Yanmin
https://cis.temple.edu/~jiewu/research/publications/Publication_files/Paper%206190%20Camera%20Ready%20Version.pdf
a major challenge for federated learning in diverse settings. Personalized Federated Learning (PFL), [Tan et al., 2022a] addresses these issues by allowing client-specific models that leverage global insights to enhance local outcomes. The main challenge in PFL lies in balancing global knowledge sharing with preserving client-specific information, making the trade- off an important research ...
https://studyabroad.temple.edu/programs/temple-university-japan-campus/semester-academic-year-summer-tokyo
Temple University, Japan Campus (TUJ) offers a semester, academic year, and 10-week summer program. Coursework is available in Asian studies, Japanese language, and a variety of other academic disciplines.
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 ...