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Analysis of Muskingum Equation Based Flood Routing Schemes - Sites

https://sites.temple.edu/sserrano/files/2020/08/18-Analysis-of-Muskingum-Equation-Based-Flood-Routing-Schemes.pdf

By John J. Gelegenis1 and Sergio E. Serrano2 ABSTRACT: The linear Muskingum method continues to be a simple and popular procedure for river flood routing. An alternative algorithm for the numerical estimation of the Muskingum routing parameters is presented. Fully implicit and semi-implicit finite-difference schemes are compared for accuracy with respect to the tradi-tional graphical procedure ...

Chapter 1 Theories of Artificial Intelligence — Meta-theoretical considerat

https://cis.temple.edu/~pwang/Publication/TheoriesOfAI.pdf

This chapter addresses several central meta-theoretical issues of AI and AGI. After ana-lyzing the nature of the field, three criteria for desired theories are proposed: correctness, concreteness, and compactness. The criteria are clarified in the AI context, and using them, the current situation in the field is evaluated.

PHLR - Center for Public Health Law Research

https://phlr.temple.edu/

Law is a powerful tool that can advance health and well-being. By developing and promoting the measurement and evaluation of the law’s impact, through the field of legal epidemiology, we strive to support evidence-based, proactive, and creative policies that advance public health and well-being for all.

Outlier Detection with Kernel Density Functions

https://cis.temple.edu/~latecki/Papers/mldm07.pdf

Abstract. Outlier detection has recently become an important prob-lem in many industrial and ̄nancial applications. In this paper, a novel unsupervised algorithm for outlier detection with a solid statistical foun-dation is proposed. First we modify a nonparametric density estimate with a variable kernel to yield a robust local density estimation. Out-liers are then detected by comparing the ...

Section 7 Closures of Relations R - Temple University

https://cis.temple.edu/~latecki//Courses/CIS166-Spring07/Lectures/ch7.4.pdf

Section 7.4 Closures of Relations Definition: The closure of a R relation with respect property P is the relation number of ordered R to obtain pairs property to

OPENING STATEMENT V. ARGUMENT – WHERE IS THE LINE?

https://law.temple.edu/aer/2019/03/23/opening-statement-v-argument-where-is-the-line/

When does an opening statement, meant to be persuasive, veer off into impermissible argument? The line is in some sense indefinable – and it feels like the only formula is like that of obscenity – “I shall not today attempt further to define the kinds of material I understand to be embraced within that shorthand

Microsoft Word - DissertationHandbook Feb 1 Revision

https://education.temple.edu/sites/education/files/uploads/grad/DissertationHandbook%20Feb%201%20Revision.pdf

Even though this in a personal presentation of the dissertation process, I have tried to present as clear a presentation as I could of the relevant rules and regulations regarding not only the dissertation but the end game of your doctoral program. Your dissertation chairperson will try and help you at all times, but faculty are not always correct in their remembrance of the rules, so ...

Why are women still earning less than men? | Temple Now

https://news.temple.edu/news/2021-03-17/why-are-women-still-earning-less-men

Judith A. Levine, poverty scholar at Temple University, discusses the feminization of poverty, the gender wage gap and why women make less money than men.

The OpenNARS implementation of the Non-Axiomatic Reasoning System

https://cis.temple.edu/~pwang/Publication/OpenNARS.pdf

(c (f 1 2) + 1 2) (with f being the frequency, c the con dence) of the highest ranked desire is determined and if it exceeds a certain threshold, the system executes this operation. After the execution, an event, stating that this operation was executed, is input into the system.

2 Outcomes, events, and probability - Temple University

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 ...