Spectral layout is a class of algorithm for drawing graphs. The layout uses the eigenvectors of a matrix, such as the Laplace matrix of the graph, as Cartesian coordinates of the graph's vertices. The idea of the layout is to compute the two largest (or smallest) eigenvalues and corresponding eigenvectors of the Laplacian matrix of the graph and then use those for actually placing the nodes.Usually nodes are placed in the 2 dimensional plane. An embedding into more dimensions can be found by using more eigenvectors.In the 2-dimensional case, for a given node which corresponds to the row/column in the (symmetric) Laplacian matrix of the graph, the and -coordinates are the -th entries of the first and second eigenvectors of , respectively. (Wikipedia).
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
Spectral Sequences 01: How to read them.
How to read a spectral sequence.
From playlist Spectral Sequences
Here we show a quick way to set up a face in desmos using domain and range restrictions along with sliders. @shaunteaches
From playlist desmos
Elba Garcia-Failde - Quantisation of Spectral Curves of Arbitrary Rank and Genus via (...)
The topological recursion is a ubiquitous procedure that associates to some initial data called spectral curve, consisting of a Riemann surface and some extra data, a doubly indexed family of differentials on the curve, which often encode some enumerative geometric information, such as vol
From playlist Workshop on Quantum Geometry
Using the properties of parallelograms to solve for the missing diagonals
👉 Learn how to solve problems with parallelograms. A parallelogram is a four-sided shape (quadrilateral) such that each pair of opposite sides are parallel and are equal. Some of the properties of parallelograms are: each pair of opposite sides are equal, each pair of opposite sides are pa
From playlist Properties of Parallelograms
Ana Romero: Effective computation of spectral systems and relation with multi-parameter persistence
Title: Effective computation of spectral systems and their relation with multi-parameter persistence Abstract: Spectral systems are a useful tool in Computational Algebraic Topology that provide topological information on spaces with generalized filtrations over a poset and generalize the
From playlist AATRN 2022
From playlist Geometry TikToks
New Image Analysis Tools for Manuscripts
15 minute presentation: new approaches to developing tools for supporting research in digital manuscript collections.
From playlist New Directions for Digital Scholarship
What are the properties that make up a parallelogram
👉 Learn how to solve problems with parallelograms. A parallelogram is a four-sided shape (quadrilateral) such that each pair of opposite sides are parallel and are equal. Some of the properties of parallelograms are: each pair of opposite sides are equal, each pair of opposite sides are pa
From playlist Properties of Parallelograms
CERIAS Security: Hazard Spaces Knowing Where, When, What Hazards Occur 2/6
Clip 2/6 Speaker: Peter Bajcsy · University of Illinois at Urbana-Champaign/ National Center for Supercomputing Applications (NCSA) While considering all existing hazards for humans due to (a) natural disastrous events, (b) failures of human hazard attention or (c) intentional harmful
From playlist The CERIAS Security Seminars 2005 (2)
MIMO wireless system design for 5G, LTE, and WLAN in MATLAB:
Learn how to model, simulate and test 5G, WLAN, LTE massive MIMO, hybrid beamforming design in MATLAB and Simulink - Free resource to learn about large-scale antenna systems for 5G wireless systems: http://bit.ly/2H3sKa4 Get a free product Trial: https://goo.gl/ZHFb5u Learn more about MAT
From playlist Hybrid beamforming and MIMO designs for 5G, LTE, and WLAN
Fourier transform for Class D-modules - David Ben Zvi
Locally Symmetric Spaces Seminar Topic: Fourier transform for Class D-modules Speaker: David Ben Zvi Affiliation: University of Texas at Austin; Member, School of Mathematics Date: Febuary 13, 2018 For more videos, please visit http://video.ias.edu
From playlist Mathematics
CERIAS Security: Hazard Spaces Knowing Where, When, What Hazards Occur 5/6
Clip 5/6 Speaker: Peter Bajcsy · University of Illinois at Urbana-Champaign/ National Center for Supercomputing Applications (NCSA) While considering all existing hazards for humans due to (a) natural disastrous events, (b) failures of human hazard attention or (c) intentional harmful
From playlist The CERIAS Security Seminars 2005 (2)
EEVblog #891 - Siglent SSA3021X vs Rigol DSA815 Spectrum Analyser
Dave compares the new Siglent SSA3021A 2.1GHz spectrum analyser with similar priced Rigol DSA815. Noise floor, clock and PLL phase noise and other performance aspects are measured and compared between the two models. Bugs?, yup, got those too! Forum: http://www.eevblog.com/forum/blog/eevbl
From playlist Spectrum Analyser
CMB-Bharat: A Comprehensive next-generation CMB mission proposal by Tarun Souradeep
Program Cosmology - The Next Decade ORGANIZERS : Rishi Khatri, Subha Majumdar and Aseem Paranjape DATE : 03 January 2019 to 25 January 2019 VENUE : Ramanujan Lecture Hall, ICTS Bangalore The great observational progress in cosmology has revealed some very intriguing puzzles, the most i
From playlist Cosmology - The Next Decade
Positive Lyapunov exponents and mixing in stochastic fluid flow. Part III - Samuel Punshon-Smith
Topic: Positive Lyapunov exponents and mixing in stochastic fluid flow. Part III Speaker: Samuel Punshon-Smith Affiliation: IAS Date: May 5, 2022 In this three part lecture series, we will present a series of works by Bedrossian, Blumenthal and Punshon-Smith on the chaotic mixing and enha
From playlist Seminar in Analysis and Geometry
Iain Johnstone: Eigenvalues and variance components
Abstract: Motivated by questions from quantitative genetics, we consider high dimensional versions of some common variance component models. We focus on quadratic estimators of 'genetic covariance' and study the behavior of both the bulk of the estimated eigenvalues and the largest estimat
From playlist Probability and Statistics