The hierarchical fair-service curve (HFSC) is a network scheduling algorithm for a network scheduler proposed by Ion Stoica, Hui Zhang and T. S. Eugene from Carnegie Mellon University at SIGCOMM 1997 In this paper, we propose a scheduling algorithm that to the best of our knowledge is the first that can support simultaneously (a) hierarchical link-sharing service, (b) guaranteed real-time service with provable tight delay bounds, and (c) decoupled delay and bandwidth allocation (which subsumes priority scheduling). This is achieved by defining and incorporating fairness property, which is essential for link-sharing, into Service-Curve based schedulers, which can decouple the allocation of bandwidth and delay. We call the hierarchical version of the resulted algorithm a Hierarchical Fair Service Curve (H-FSC) Algorithm. We analyze the performance of H-FSC and present simulation results to demonstrate the advantages of H-FSC over previously proposed algorithms such as H-PFQ and CBQ. Preliminary experimental results based on a prototype implementation in NetBSD are also presented. It is based on a QoS and CBQ. An implementation of HFSC is available in all operating systems based on the Linux kernel, such as e.g. OpenWrt, and also in DD-WRT, NetBSD 5.0, FreeBSD 8.0 and OpenBSD 4.6. (Wikipedia).
This video introduced fair division. Site: http://mathispower4u.com
From playlist Fair Division
Unit 7 - practice problem 4 question
From playlist Courses and Series
Unit 5 - practice problem 2 solution
From playlist Courses and Series
http://mathispower4u.wordpress.com/
From playlist Applications of Definite Integration
Unit 7 - no price discrimination part 1
From playlist Courses and Series
Unit 5 - practice problem 1 question
From playlist Courses and Series
03.7 - ISE2021 - Knowledge Graphs
Information Service Engineering 2021 Prof. Dr. Harald Sack Karlsruhe Institute of Technology Summer semester 2021 Lecture 7: Knowledge Graphs 2 3.7 Knowledge Graphs - Knowledge Graphs - Gartner Hype Cycle - Knowledge Graph Applications - Public Knowledge Graphs - Proprietary Knowledge Gr
From playlist ISE 2021 - Lecture 07, 02.06.2021
Unit 4 - social surplus part 1
From playlist Courses and Series
M.K. Lords interviews Andreas Antonopoulos
I'll be speaking with security expert Andreas Antonopoulos about bitcoin and other topics. There were some connection issues that we worked through, but other than that it was very informative. Follow him on Twitter at @aantonop.
From playlist Interviews and Shows
What REST APIs Are and How to Design One | Coding Workshop Part I | CareerCon 2019 | Kaggle
Watch Rachael Tatman, a data scientist at Kaggle give a workshop on coding. In this three-day workshop, youโll learn how to go from a model trained in a notebook to a custom API with full documentation. Weโll even deploy our models so that you can share them or add them to your portfolio!
From playlist Kaggle CareerCon 2019 | Full Sessions
Deep Learning of Hierarchical Multiscale Differential Equation Time Steppers
This video by Yuying Liu introduces a new deep learning architecture to accurately and efficiently integrate multiscale differential equations forward in time. This approach is benchmarked on several illustrative dynamical systems. Check out the paper on arXiv: https://arxiv.org/abs/20
From playlist Data-Driven Science and Engineering
6.2.9 An Introduction to Clustering - Video 5: Hierarchical Clustering
MIT 15.071 The Analytics Edge, Spring 2017 View the complete course: https://ocw.mit.edu/15-071S17 Instructor: Allison O'Hair The method of hierarchical clustering, combining, dendrogram, predictive model License: Creative Commons BY-NC-SA More information at https://ocw.mit.edu/terms Mo
From playlist MIT 15.071 The Analytics Edge, Spring 2017
Velocity 2012: Albert Wenger, "Threats & Opportunities for a Faster and Stronger Web"
Albert Wenger Managing Partner Union Square Ventures
From playlist Velocity US 2012
Joshua Agar: "Automatic Feature Extraction from Hyperspectral Imagery using Deep Recurrent Neura..."
Machine Learning for Physics and the Physics of Learning 2019 Workshop I: From Passive to Active: Generative and Reinforcement Learning with Physics "Automatic Feature Extraction from Hyperspectral Imagery using Deep Recurrent Neural Networks" Joshua Agar, Lehigh University Abstract: Cha
From playlist Machine Learning for Physics and the Physics of Learning 2019
29C3: An Overview of Secure Name Resolution (EN)
Speaker: Matthรคus Wander DNSSEC, DNSCurve and Namecoin There's about 100 top-level domains signed with DNSSEC and .nl recently hit 1M second-level domains. At this occasion, we take a look at the goods and the bads of DNSSEC deployment, including amplification attacks, Zensursula-like DN
From playlist 29C3: Not my department
Machine Learning Full Course - 12 Hours | Machine Learning Roadmap [2023] | Edureka
๐ฅ ๐๐๐ฎ๐ซ๐๐ค๐ ๐๐๐๐ก๐ข๐ง๐ ๐๐๐๐ซ๐ง๐ข๐ง๐ ๐๐จ๐ฎ๐ซ๐ฌ๐ ๐๐๐ฌ๐ญ๐๐ซ ๐๐ซ๐จ๐ ๐ซ๐๐ฆ : https://www.edureka.co/masters-program/machine-learning-engineer-training (๐๐ฌ๐ ๐๐จ๐๐: ๐๐๐๐๐๐๐๐๐) This Edureka Machine Learning Full Course video will help you understand and learn Machine Learning Algorithms in detail. This Machine Learning
From playlist Machine Learning Algorithms in Python (With Demo) | Edureka
Hierarchical Clustering 3: single-link vs. complete-link
[http://bit.ly/s-link] Agglomerative clustering needs a mechanism for measuring the distance between two clusters, and we have many different ways of measuring such a distance. We explain the similarities and differences between single-link, complete-link, average-link, centroid method and
From playlist Hierarchical Clustering