https://www.cla.temple.edu/RePEc/documents/DETU_18_04.pdf
Di erence-in-Di erences (DID) is one of the most important and popular designs for eval-uating causal e ects of policy changes. In its standard format, there are two time periods and two groups: in the rst period no one is treated, and in the second period a \treatment group" becomes treated, whereas a \control group" remains untreated. However, many em-pirical applications of the DID design ...
https://news.temple.edu/news/2023-02-07/temple-professor-discovers-5000-year-old-silver-jewelry-oman
Temple University Professor Kimberly Williams shared her groundbreaking discovery at a conference in Barcelona last summer.
https://www.fox.temple.edu/sites/fox/files/documents/CVs/xueming-luo-cv.pdf
Bio: Xueming Luo is the Charles Gilliland Distinguished Chair Professor of Marketing, Professor of Strategy, and Professor of MIS, and Founder/ Director of the Global Institute for Artificial Intelligence & Business Analytics in the Fox School of Business at Temple University. He is an interdisciplinary thought-leader in leveraging AI/ML algorithms, text/audio/image/video big data ...
https://cis.temple.edu/~tug29203/24spring-4319/lectures/ch1b-1.pdf
free (available) buffers: arriving packets dropped (loss) if no free buffers
https://cis.temple.edu/~he/publications/Conferences/RDMA_MSST06.pdf
Abstract RDMA reduces network latency by eliminating unnec-essary copies from network interface card to application buffers, but how to reduce memory registration cost is a challenge. Previous studies use pin-down cache and batched deregistration to address this issue, but only sim-ple LRU is used as a replacement algorithm to manage the cache space. In this paper, we propose an effective ...
https://cis.temple.edu/~pwang/5603-AI/Project/2021S/Amend_Zach/Amend_Zach_FinalPresentation.pdf
Card Playing AI Bot Jack Amend and Cameron Zach CIS5603 Final Project Spring 2021
https://cis.temple.edu/~jiewu/research/publications/Publication_files/jiang_www_2020.pdf
ABSTRACT Serendipity recommendation has attracted more and more atten-tion in recent years; it is committed to providing recommendations which could not only cater to users’ demands but also broaden their horizons. However, existing approaches usually measure user-item relevance with a scalar instead of a vector, ignoring user preference direction, which increases the risk of unrelated ...
https://cis.temple.edu/~tug29203/21fall-4319/lectures/ch3c-2.ppt
We’re making these slides freely available to all (faculty, students, readers). They’re in PowerPoint form so you see the animations; and can add, modify, and delete slides (including this one) and slide content to suit your needs. They obviously represent a lot of work on our part. In return for use, we only ask the following:
https://ronlevygroup.cst.temple.edu/courses/2016_fall/biost5312/lectures/biostat_lecture_02.pdf
BIO5312 Biostatistics Lecture 02: Probability Dr. Junchao Xia Center of Biophysics and Computational Biology
https://cis.temple.edu/~latecki/Courses/Math3033-Fall09/DekkingBook07/DekkingBook_c2.pdf
Outcomes, events, and probability The world around us is full of phenomena we perceive as random or unpre-dictable. We aim to model these phenomena as outcomes of some experiment, where you should think of experiment in a very general sense. The outcomes are elements of a sample space Ω, and subsets of Ω are called events.The events will be assigned a probability, a number between 0 and 1 ...