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://www2.law.temple.edu/lppp/scholars/2025-scholars/
Maria Consuelo “Cielo” De Dios, LAW ’27, is a Conwell Scholar and a Law and Public Policy Scholar at Temple University Beasley School of Law. After obtaining her bachelor’s degree in psychology and educational studies, she worked as an Educational Researcher under the EF + Math Program.
https://cis.temple.edu/~yu/research/thesis00.pdf
2000 年5月 尽管非特定人的语音识别系统已经达到了令人鼓舞的性能,但是在实际应用时由于说话人和环境的改变通常会使得系统性能显著下降。当遇到特殊口音的说话人,或者环境有一定的噪音时,系统的误识率甚至有可能增加原来的5倍。语音识别要走向实用,就必须克服这个鲁棒性问题,因此语音自 ...
https://cis.temple.edu/~jiewu/research/publications/Publication_files/m37113-chen%20final.pdf
o ef-ficiently execute CNN workloads in resource- and power-constrained environments [50]. In the context of SR, He et al. [51] proposed a highly ptimized FPGA-based hardware accelerator specifically tailored to the FSRCNN [52] network. Additionally, Kim et al. [53] adopted a hardware-software co-
https://www.temple.edu/about/history/temple-traditions
Temple’s more than 130-year history is rich with tradition. From the Owl mascot, to the Temple “T,” to the fight song, Temple pride shines. The Temple "T" Across Temple's campuses and throughout Philadelphia, the Temple “T” is the iconic symbol of the university. Designed by students in a graphic arts and design class in the Tyler School of Art in 1983, the “T” represents ...
https://www.fox.temple.edu/sites/fox/files/SHMM_isre.2018.0831.pdf
Static heterogeneous treatment ef-fects have been widely studied, a typical example being the quantile treatment effect (Chernozhukov and Hansen 2005, Firpo 2007, Qiu and Kumar 2017).
https://cis.temple.edu/~jiewu/research/publications/Publication_files/Paper%206750%20Camera%20Ready%20Version.pdf
One solu-tion involves adjusting model parameters and adding Gaus-sian noise [Xie et al., 2021; Nguyen and et al., 2022], which can counteract backdoor attacks, but may reduce model ef-ficiency.
https://cis.temple.edu/~jiewu/research/publications/Publication_files/FedCPD.pdf
Despite partially mitigating historical information forgetting, this strategy’s ef-ficacy in extracting local features declines when client data exhibits substantial heterogeneity.
https://cis.temple.edu/~latecki/Papers/GIFT-IEEEMM2017.pdf
In summary, we call the feature in (12) Aggregated Contextual Activation (ACA). Next, we will introduce some improvements of (12) concerning its retrieval accuracy and computational ef-ficiency. 1) Improving Accuracy: Similar to diffusion process, the proposed ACA requires an accurate estimation of the context in the data manifold.
https://cis.temple.edu/~jiewu/teaching/Spring%202013/01-savvides-localization-wireless-sensor-networks-fine-grained.pdf
We advocate a distributed algorithm, which we show to be more power ef-ficient than a centralized solution. Furthermore, our approach is based on an iterative process, where increasingly more nodes are able to resolve their location.