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phylotree.js - a JavaScript library for application development and ...

https://scholarshare.temple.edu/bitstreams/b1ecc345-7a7c-4776-a366-9b5585e714d6/download

Results: phylotree.js is a library that extends the popular data visualization framework d3.js, and is suitable for building JavaScript applications where users can view and interact with phylogenetic trees. The effects of such interactions can be captured and communicated to other package components, making it possible to engineer complex and responsive applications that include phylogenetic ...

MATRICES M - Temple University

https://cis.temple.edu/~latecki/Courses/CIS2166-Fall16/Lectures/MatrixAlg1.pdf

A matrix is a rectangular array of numbers or other mathematical objects, for which operations such as addition and multiplication are defined. Most of this article focuses on real matrices, i.e., matrices whose elements are real numbers. For instance, this is a real matrix: The numbers, symbols or expressions in the matrix are called its entries or its elements. The horizontal and vertical ...

Futexes Are Tricky - Temple University

https://cis.temple.edu/~giorgio/cis307/readings/futex.pdf

This exemplifies that using futexes is really tricky since they provide problems even to their inventors. This docu-ment will hopefully provide correct and detailed instruc-tions on how to use futexes. First an understanding of the kernel interface and its semantic is needed.

Summer Internship Report - cis.temple.edu

https://cis.temple.edu/~pwang/9991-PJ/Reports/OzkanKilic.pdf

AGI SYSTEMS For more than 60 years, AI researchers have been working on creating intelligent machines or programs that can perform intellectual tasks that a human being can. These tasks can vary a lot from playing chess, counting animals in a picture, performing commands given in a natural language to making jokes, having a conversation with a human, producing novel inventions and art. Weak-AI ...

TileSR: Accelerate On-Device Super-Resolution with Parallel Offloading ...

https://cis.temple.edu/~jiewu/research/publications/Publication_files/m37113-chen%20final.pdf

Abstract—Recent years have witnessed the unprecedented performance of convolutional networks in image super-resolution (SR). SR involves upscaling a single low-resolution image to meet application-specific image quality demands, making it vital for mobile devices. However, the excessive computational and memory requirements of SR tasks pose a challenge in mapping SR networks on a single ...