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Lecture: Debye – Huckel Theory - Temple University

https://ronlevygroup.cst.temple.edu/courses/2016_fall/chem5302/lectures/chem5302_lecture10.pdf

Now, 1 ( ) is the average charge density at due to all the ions when ion number 1 is fixed at the origin. The charge density at can be expressed in terms of the pairs correlation function, which in turn can be expressed in terms of the potential of mean force, which can be expressed in terms of the averaged electrostatic potential.

HearBP: Hear Your Blood Pressure via In-ear ... - Temple University

https://cis.temple.edu/~yu/research/HearBP-info24.pdf

Abstract—Continuous blood pressure (BP) monitoring using wearable devices has received increasing attention due to its importance in diagnosing diseases. However, existing methods mainly measure BP intermittently, involve some form of user effort, and suffer from insuficient accuracy due to sensor properties. In order to overcome these limitations, we study the BP measurement technology ...

tudissv2 - scholarshare.temple.edu

https://scholarshare.temple.edu/bitstreams/48e9e752-7b37-40de-96f0-ed735aa84796/download

Longin Jan Latecki, Advisory Chair, Computer and Information Sciences Slobodan Vucetic, Computer and Information Sciences Haibin Ling, Computer and Information Sciences Jianbo Shi, External Member, University of Pennsylvania

In-Kernel Traffic Sketching for Volumetric DDoS Detection

https://cis.temple.edu/~jiewu/research/publications/Publication_files/ICC25_In-Kernel%20Traffic%20Sketching%20for%20Volumetric%20DDoS%20Detection-final.pdf

Abstract—Emerging network technologies like cloud comput-ing provide flexible services but also introduce vulnerabilities to host servers, such as exposure to Distributed Denial of Service (DDoS) attacks. Traditional host-based detection tools operate in the user space, which can delay detection because network traffic must pass through the kernel space first. Moving detection to the kernel ...

Introduction to Probability, Statistics and Random Processes

https://cis-linux1.temple.edu/~tug29203/25fall-2033/lectures/ch2.pdf

Choose r objects in succession from a population of n distinct objects fa1; a1; ; ang, in such a way that an object once chosen is removed from the population Then we again get an ordered sample, but now there are n - 1 objects left after the rst choice, n - 2 objects left after the second choice, and so on.

Unit #3b - Temple University

https://community.mis.temple.edu/mis5214sec005spring2021/files/2020/03/MIS5214_Unit8_CaseStudy2_Maersk.pdf

Timeline 2016 – Maersk shipping company’s senior system administrators warn company that its network of 80,000+ computers was vulnerable to attack

ArrayPipe: Introducing Job-Array Pipeline Parallelism for High ...

https://cis.temple.edu/~jiewu/research/publications/Publication_files/ArrayPipe-JLU-Infocom-20250520.pdf

i. A novel parallel scheme (JAP) is introduced to enable a batch of sibling jobs to form a concurrent job-array and to execute concurrently, targeting high throughput model exploration. ii. We design ArrayPipe, a framework to support JAP with low-cost job context switching within a job-array and a GPU-Host memory manager for higher training concurrency. iii. We propose a novel scheduling ...

Microsoft Word - IJNPA_submission2-final.doc

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 ...

Images of Galois representations associated to Hida families

https://sites.temple.edu/lang/files/2023/06/BU2015.pdf

Images of Galois representations associated to Hida families Jaclyn Lang University of California, Los Angeles

PowerPoint Presentation

https://cis.temple.edu/~latecki/Courses/AI-Fall12/ColoradoAICourse/Ensemble_Learning.ppt

* Boosting Summary Good points Fast learning Capable of learning any function (given appropriate weak learner) Feature weighting Very little parameter tuning Bad ...