Formal methods | Concurrency control | Process calculi | Model checkers | Formal specification languages
CADP (Construction and Analysis of Distributed Processes) is a toolbox for the design of communication protocols and distributed systems. CADP is developed by the CONVECS team (formerly by the VASY team) at INRIA Rhone-Alpes and connected to various complementary tools. CADP is maintained, regularly improved, and used in many industrial projects. The purpose of the CADP toolkit is to facilitate the design of reliable systems by use of formal description techniques together with software tools for simulation, rapid application development, verification, and test generation. CADP can be applied to any system that comprises asynchronous concurrency, i.e., any system whose behavior can be modeled as a set of parallel processes governed by interleaving semantics. Therefore, CADP can be used to design hardware architecture, distributed algorithms, telecommunications protocols, etc.The enumerative verification (also known as explicit state verification) techniques implemented in CADP, though less general that theorem proving, enable an automatic, cost-efficient detection of design errors in complex systems. CADP includes tools to support use of two approaches in formal methods, both of which are needed for reliable systems design: * Models provide mathematical representations for parallel programs and related verification problems. Examples of models are automata, networks of communicating automata, Petri nets, binary decision diagrams, boolean equation systems, etc. From a theoretical point of view, research on models seeks for general results, independent of any particular description language. * In practice, models are often too elementary to describe complex systems directly (this would be tedious and error-prone). A higher level formalism known as process algebra or process calculus is needed for this task, as well as compilers that translate high-level descriptions into models suitable for verification algorithms. (Wikipedia).
Network Analysis. Lecture 9. Graph partitioning algorithms
Graph density. Graph pertitioning. Min cut, ratio cut, normalized and quotient cuts metrics. Spectral graph partitioning (normalized cut). Direct (spectral) modularity maximization. Multilevel recursive partitioning Lecture slides: http://www.leonidzhukov.net/hse/2015/networks/lectures/le
From playlist Structural Analysis and Visualization of Networks.
Network Analysis. Course introduction.
Introduction to the Social Network Analysis course.
From playlist Structural Analysis and Visualization of Networks.
12 1 Data Structures Overview 5 min
From playlist Algorithms 1
Graph Data Structure 1. Terminology and Representation (algorithms)
This is the first in a series of videos about the graph data structure. It mentions the applications of graphs, defines various terminology associated with graphs, and describes how a graph can be represented programmatically by means of adjacency lists or an adjacency matrix.
From playlist Data Structures
Stack Data Structure - Algorithm
This is an explanation of the dynamic data structure known as a stack. It includes an explanation of how a stack works, along with pseudocode for implementing the push and pop operations with a static array variable.
From playlist Data Structures
Steffen Borgwardt: The role of partition polytopes in data analysis
The field of optimization, and polyhedral theory in particular, provides a powerful point of view on common tasks in data analysis. In this talk, we highlight the role of the so-called partition polytopes and their studies in clustering and classification. The geometric properties of parti
From playlist Workshop: Tropical geometry and the geometry of linear programming
Indexing 17: distributed search
Instead of using MapReduce to construct a single index, we can distribute portions of the index across a cluster of machines. We can then send a query to all the machines, receive partial ranked lists and then combine them into one list that would be returned to the user. This is known as
From playlist IR7 Inverted Indexing
Estimating Transfer Functions and Process Models
Get a Free Trial: https://goo.gl/C2Y9A5 Get Pricing Info: https://goo.gl/kDvGHt Ready to Buy: https://goo.gl/vsIeA5 Estimate continuous-time and discrete-time transfer functions and low-order process models. Use the estimate models for analysis and control design. For more videos, visit
From playlist Control System Design and Analysis
Quantitative Risk Analysis | What Is Quantitative Risk Analysis? | PMI-RMP Course | Simplilearn
This video on Quantitative Risk Analysis will help you understand how to perform Quantitative RIsk Analysis, List the tools and Techniques required for the analysis. This Video will also help you in EMV analysis and Probability Distribution. 🔥Free Project Management Course: https://www.sim
From playlist PMI-RMP® Training Videos [2022 Updated]
Quantitative Risk Analysis | What Is Quantitative Risk Analysis? | PMI-RMP Course | Simplilearn
This video on Quantitative Risk Analysis will help you understand how to perform Quantitative RIsk Analysis, List the tools and Techniques required for the analysis.This Video will also help you in EMV analysis and Probability Distribution. #QuantitativeRiskAnalysis #WhatIsQuantitativeRis
From playlist PMI-RMP® Training Videos [2022 Updated]
Stanford CS229: Machine Learning | Summer 2019 | Lecture 17 - Factor Analysis & ELBO
For more information about Stanford’s Artificial Intelligence professional and graduate programs, visit: https://stanford.io/3E4MouM Anand Avati Computer Science, PhD To follow along with the course schedule and syllabus, visit: http://cs229.stanford.edu/syllabus-summer2019.html
From playlist Stanford CS229: Machine Learning Course | Summer 2019 (Anand Avati)
Jonathan Gair - Rapid and robust parameter estimation for gravitational wave observations
Recorded 03 December 2021. Jonathan Gair of the Max Planck Institute for Gravitational Physics, Albert Einstein Institute, presents "Rapid and robust parameter estimation for gravitational wave observations" at IPAM's Workshop IV: Big Data in Multi-Messenger Astrophysics. Abstract: Over th
From playlist Workshop: Big Data in Multi-Messenger Astrophysics
Clustering (2): Hierarchical Agglomerative Clustering
Hierarchical agglomerative clustering, or linkage clustering. Procedure, complexity analysis, and cluster dissimilarity measures including single linkage, complete linkage, and others.
From playlist cs273a
Twenty third SIAM Activity Group on FME Virtual Talk Series
Date: Thursday, December 2, 2021, 1PM-2PM ET Speaker 1: Renyuan Xu, University of Southern California Speaker 2: Philippe Casgrain, ETH Zurich and Princeton University Moderator: Ronnie Sircar, Princeton Universit Join us for a series of online talks on topics related to mathematical fina
From playlist SIAM Activity Group on FME Virtual Talk Series
This is the first lecture on "A Markovian perspective on some singular SPDEs" taught by Professor Nicolas Perkowski. For more materials and slides visit: https://sites.google.com/view/oneworld-pderandom/home
From playlist Summer School on PDE & Randomness
Aretha Teckentrup: Numerical analysis of Gaussian process regression
LMS Computer Science Colloquium 2021
From playlist LMS Computer Science Colloquium Nov 2021
Matrix liberation process - Y. Ueda - Workshop 2 - CEB T3 2017
Yoshimichi Ueda / 24.10.17 Matrix liberation process We introduce a natural random matrix counterpart of the so-called liberation process introduced by Voiculescu in the framework of free probability, and consider its possible LDP in the large N limit. ---------------------------------
From playlist 2017 - T3 - Analysis in Quantum Information Theory - CEB Trimester
Statistical Rethinking Fall 2017 - week10 lecture19
Week 10, lecture 19 for Statistical Rethinking: A Bayesian Course with Examples in R and Stan, taught at MPI-EVA in Fall 2017. This lecture covers Chapters 14 and 15. Slides are available here: https://speakerdeck.com/rmcelreath/statistical-rethinking-fall-2017-lecture-19 Additional in
From playlist Statistical Rethinking Fall 2017
Qualitative And Quantitative Risk Analysis Explained | Risk Analysis Techniques | Simplilearn
In this video on Qualitative and Quantitative Risk Analysis, we'll go into detail about how each of them work, how it's performed and the tools and techniques required to document it. 🔥Explore Our Free Courses With Completion Certificate by SkillUp: https://www.simplilearn.com/skillup-free
From playlist PMI-RMP® Training Videos [2022 Updated]
10b Data Analytics: Spatial Continuity
Lecture on the impact of spatial continuity to motivate characterization and modeling of spatial continuity.
From playlist Data Analytics and Geostatistics