NIST hash function competition
Spectral Hash is a cryptographic hash function submitted to the NIST hash function competition by Gokay Saldamlı, Cevahir Demirkıran, Megan Maguire, Carl Minden, Jacob Topper, Alex Troesch, Cody Walker, Çetin Kaya Koç. It uses a Merkle–Damgård construction and employs several mathematical structures including finite fields and . The authors claim 512-bit hashes at 51.2 gigabits per second on a 100-MHz Virtex-4 FPGA. Spectral hash is insecure; a method exists to generate arbitrary collisions in the hash state, and therefore in the final hash digest. (Wikipedia).
Cryptographic Hash Functions: Part 1
Cryptographic Hash Functions Applications of Crypto Hash Functions Birthday Problem Secure Hash Algorithm (SHA)
From playlist Network Security
Cryptographic Hash Functions: Part 2
Cryptographic Hash Functions Applications of Crypto Hash Functions Birthday Problem Secure Hash Algorithm (SHA)
From playlist Network Security
Spectral Sequences 02: Spectral Sequence of a Filtered Complex
I like Ivan Mirovic's Course notes. http://people.math.umass.edu/~mirkovic/A.COURSE.notes/3.HomologicalAlgebra/HA/2.Spring06/C.pdf Also, Ravi Vakil's Foundations of Algebraic Geometry and the Stacks Project do this well as well.
From playlist Spectral Sequences
An informal introduction to cryptography. Part of a larger series teaching programming at http://codeschool.org
From playlist Cryptography
Top Hashing Techniques | Popular Hashing Techniques | Network Security | #Shorts | Simplilearn
🔥FREE Cyber Security Course: https://www.simplilearn.com/learn-cyber-security-basics-skillup?utm_campaign=TopHashingTechniquesShorts&utm_medium=ShortsDescription&utm_source=youtube In this video on the top hashing techniques, we are going to take a look at the five most secure hashing alg
From playlist #Shorts | #Simplilearn
From playlist Cryptography Lectures
For the latest information, please visit: http://www.wolfram.com Speaker: Paul Abbott When the eigenvalues of an operator A can be computed and form a discrete set, the spectral zeta function of A reduces to a sum over eigenvalues, when the sum exists. Belloni and Robinett used the “quan
From playlist Wolfram Technology Conference 2014
Live CEOing Ep 22: Image & Audio Import/Export in the Wolfram Language
Watch Stephen Wolfram and teams of developers in a live, working, language design meeting. This episode is about Image & Audio Import/Export in the Wolfram Language.
From playlist Behind the Scenes in Real-Life Software Design
Graph Convolutional Networks (GCN) | GNN Paper Explained
❤️ Become The AI Epiphany Patreon ❤️ ► https://www.patreon.com/theaiepiphany ▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬ In this video I do a deep dive into the graph convolutional networks paper! It's currently the most cited paper in the GNN literature at the time of making this video. You'll learn abou
From playlist Graph Neural Nets
Approximate nearest neighbor search in high dimensions – Piotr Indyk – ICM2018
Mathematical Aspects of Computer Science Invited Lecture 14.7 Approximate nearest neighbor search in high dimensions Piotr Indyk Abstract: The nearest neighbor problem is defined as follows: Given a set P of n points in some metric space (X,𝖣), build a data structure that, given any poin
From playlist Mathematical Aspects of Computer Science
Hacking Livestream #43: Meltdown and Spectre
A much requested live about recent CPU vulnerabilities. A lot of links below. https://googleprojectzero.blogspot.ch/ https://www.youtube.com/watch?v=6O8LTwVfTVs http://pythonsweetness.tumblr.com/post/169166980422/the-mysterious-case-of-the-linux-page-table http://www.openwall.com/lists/os
From playlist Gynvael's [EN] Live
Soledad Villar: "Graph neural networks for combinatorial optimization problems"
Machine Learning for Physics and the Physics of Learning 2019 Workshop IV: Using Physical Insights for Machine Learning "Graph neural networks for combinatorial optimization problems" Soledad Villar - New York University Abstract: Graph neural networks are natural objects to express fu
From playlist Machine Learning for Physics and the Physics of Learning 2019
Theoretical Foundations of Graph Neural Networks
Deriving graph neural networks (GNNs) from first principles, motivating their use, and explaining how they have emerged along several related research lines. Computer Laboratory Wednesday Seminar, 17 February 2021 Slide deck: https://petar-v.com/talks/GNN-Wednesday.pdf Link at Talks.cam: h
From playlist Talks
Index theorems for nodal count and a lateral variation principle - Gregory Berkolaiko
Analysis Seminar Topic: Index theorems for nodal count and a lateral variation principle Speaker: Gregory Berkolaiko Affiliation: Texas A&M University Date: February 01, 2021 For more video please visit http://video.ias.edu
From playlist Mathematics
Lec 16 | MIT 5.74 Introductory Quantum Mechanics II, Spring 2009
Lecture 16: Characterizing Fluctuations View the complete course: http://ocw.mit.edu/5-74S09 License: Creative Commons BY-NC-SA More information at http://ocw.mit.edu/terms More courses at http://ocw.mit.edu
From playlist MIT 5.74 Introductory Quantum Mechanics II, Spring 2009