This document summarizes a presentation on boundary properties of factorial graph classes. It begins with definitions of hereditary graph classes and factorial growth rates. Classes with index 1 can have constant, polynomial, exponential, or factorial growth of log2|Xn|. Factorial classes have log2|Xn| = Θ(n log n). Minimal superfactorial classes are then discussed, including the class of chordal bipartite graphs and a class defined by forbidden induced subgraphs. Finally, a sequence of superfactorial bipartite graph classes is presented.
The document outlines the aims, objectives, and syllabus for the Mathematics HL (1st exams 2014) course. It includes:
- 10 aims of the course focused on developing mathematical skills, understanding, problem solving, and appreciation of mathematics.
- 6 objectives centered around demonstrating knowledge and understanding of mathematical concepts, problem solving, communication, use of technology, reasoning, and inquiry approaches.
- The syllabus is divided into 8 core topics (Algebra, Functions and equations, Circular functions and trigonometry, Vectors, Statistics and probability, Calculus, and 2 optional topics (Statistics and probability, Sets, relations and groups) that provide 48 hours of instruction each.
This document provides an overview of linear models for classification. It discusses discriminant functions including linear discriminant analysis and the perceptron algorithm. It also covers probabilistic generative models that model class-conditional densities and priors to estimate posterior probabilities. Probabilistic discriminative models like logistic regression directly model posterior probabilities using maximum likelihood. Iterative reweighted least squares is used to optimize logistic regression since there is no closed-form solution.
Exact Matrix Completion via Convex Optimization Slide (PPT)Joonyoung Yi
Slide of the paper "Exact Matrix Completion via Convex Optimization" of Emmanuel J. Candès and Benjamin Recht. We presented this slide in KAIST CS592 Class, April 2018.
- Code: https://github.com/JoonyoungYi/MCCO-numpy
- Abstract of the paper: We consider a problem of considerable practical interest: the recovery of a data matrix from a sampling of its entries. Suppose that we observe m entries selected uniformly at random from a matrix M. Can we complete the matrix and recover the entries that we have not seen? We show that one can perfectly recover most low-rank matrices from what appears to be an incomplete set of entries. We prove that if the number m of sampled entries obeys
𝑚≥𝐶𝑛1.2𝑟log𝑛
for some positive numerical constant C, then with very high probability, most n×n matrices of rank r can be perfectly recovered by solving a simple convex optimization program. This program finds the matrix with minimum nuclear norm that fits the data. The condition above assumes that the rank is not too large. However, if one replaces the 1.2 exponent with 1.25, then the result holds for all values of the rank. Similar results hold for arbitrary rectangular matrices as well. Our results are connected with the recent literature on compressed sensing, and show that objects other than signals and images can be perfectly reconstructed from very limited information.
The document describes sparse matrix reconstruction using a matrix completion algorithm. It begins with an overview of the matrix completion problem and formulation. It then describes the algorithm which uses soft-thresholding to impose a low-rank constraint and iteratively finds the matrix that agrees with the observed entries. The algorithm is proven to converge to the desired solution. Extensions to noisy data and generalized constraints are also discussed.
Aplicaciones de Espacios y Subespacios Vectoriales en la Carrera de MecatrónicaBRYANDAVIDCUBIACEDEO
Se da a conocer un poco sobre los espacios y subespacios vectoriales, además de distintas aplicaciones de los mismos en la mecatrónica y distintos ejercicios aplicando el método Wronskiano para determinar la linealidad de un conjunto de funciones.
Lecture 8 nul col bases dim & rank - section 4-2, 4-3, 4-5 & 4-6njit-ronbrown
The document discusses null spaces, column spaces, and bases of matrices. It begins by defining the null space of a matrix A as the set of all solutions to the homogeneous equation Ax = 0. It then proves that the null space of any matrix is a subspace. Similarly, it defines the column space of A as the set of all linear combinations of A's columns, and proves the column space is always a subspace. The document contrasts the properties of null spaces and column spaces. It also discusses finding bases for null spaces and column spaces. Finally, it covers linear independence, spanning sets, and using pivots to determine bases.
The document provides an introduction to linear algebra concepts for machine learning. It defines vectors as ordered tuples of numbers that express magnitude and direction. Vector spaces are sets that contain all linear combinations of vectors. Linear independence and basis of vector spaces are discussed. Norms measure the magnitude of a vector, with examples given of the 1-norm and 2-norm. Inner products measure the correlation between vectors. Matrices can represent linear operators between vector spaces. Key linear algebra concepts such as trace, determinant, and matrix decompositions are outlined for machine learning applications.
The document outlines the aims, objectives, and syllabus for the Mathematics HL (1st exams 2014) course. It includes:
- 10 aims of the course focused on developing mathematical skills, understanding, problem solving, and appreciation of mathematics.
- 6 objectives centered around demonstrating knowledge and understanding of mathematical concepts, problem solving, communication, use of technology, reasoning, and inquiry approaches.
- The syllabus is divided into 8 core topics (Algebra, Functions and equations, Circular functions and trigonometry, Vectors, Statistics and probability, Calculus, and 2 optional topics (Statistics and probability, Sets, relations and groups) that provide 48 hours of instruction each.
This document provides an overview of linear models for classification. It discusses discriminant functions including linear discriminant analysis and the perceptron algorithm. It also covers probabilistic generative models that model class-conditional densities and priors to estimate posterior probabilities. Probabilistic discriminative models like logistic regression directly model posterior probabilities using maximum likelihood. Iterative reweighted least squares is used to optimize logistic regression since there is no closed-form solution.
Exact Matrix Completion via Convex Optimization Slide (PPT)Joonyoung Yi
Slide of the paper "Exact Matrix Completion via Convex Optimization" of Emmanuel J. Candès and Benjamin Recht. We presented this slide in KAIST CS592 Class, April 2018.
- Code: https://github.com/JoonyoungYi/MCCO-numpy
- Abstract of the paper: We consider a problem of considerable practical interest: the recovery of a data matrix from a sampling of its entries. Suppose that we observe m entries selected uniformly at random from a matrix M. Can we complete the matrix and recover the entries that we have not seen? We show that one can perfectly recover most low-rank matrices from what appears to be an incomplete set of entries. We prove that if the number m of sampled entries obeys
𝑚≥𝐶𝑛1.2𝑟log𝑛
for some positive numerical constant C, then with very high probability, most n×n matrices of rank r can be perfectly recovered by solving a simple convex optimization program. This program finds the matrix with minimum nuclear norm that fits the data. The condition above assumes that the rank is not too large. However, if one replaces the 1.2 exponent with 1.25, then the result holds for all values of the rank. Similar results hold for arbitrary rectangular matrices as well. Our results are connected with the recent literature on compressed sensing, and show that objects other than signals and images can be perfectly reconstructed from very limited information.
The document describes sparse matrix reconstruction using a matrix completion algorithm. It begins with an overview of the matrix completion problem and formulation. It then describes the algorithm which uses soft-thresholding to impose a low-rank constraint and iteratively finds the matrix that agrees with the observed entries. The algorithm is proven to converge to the desired solution. Extensions to noisy data and generalized constraints are also discussed.
Aplicaciones de Espacios y Subespacios Vectoriales en la Carrera de MecatrónicaBRYANDAVIDCUBIACEDEO
Se da a conocer un poco sobre los espacios y subespacios vectoriales, además de distintas aplicaciones de los mismos en la mecatrónica y distintos ejercicios aplicando el método Wronskiano para determinar la linealidad de un conjunto de funciones.
Lecture 8 nul col bases dim & rank - section 4-2, 4-3, 4-5 & 4-6njit-ronbrown
The document discusses null spaces, column spaces, and bases of matrices. It begins by defining the null space of a matrix A as the set of all solutions to the homogeneous equation Ax = 0. It then proves that the null space of any matrix is a subspace. Similarly, it defines the column space of A as the set of all linear combinations of A's columns, and proves the column space is always a subspace. The document contrasts the properties of null spaces and column spaces. It also discusses finding bases for null spaces and column spaces. Finally, it covers linear independence, spanning sets, and using pivots to determine bases.
The document provides an introduction to linear algebra concepts for machine learning. It defines vectors as ordered tuples of numbers that express magnitude and direction. Vector spaces are sets that contain all linear combinations of vectors. Linear independence and basis of vector spaces are discussed. Norms measure the magnitude of a vector, with examples given of the 1-norm and 2-norm. Inner products measure the correlation between vectors. Matrices can represent linear operators between vector spaces. Key linear algebra concepts such as trace, determinant, and matrix decompositions are outlined for machine learning applications.
A discussion on sampling graphs to approximate network classification functionsLARCA UPC
The problem of network classification consists on assigning a finite set of labels to the nodes of the graphs; the underlying assumption is that nodes with the same label tend to be connected via strong paths in the graph. This is similar to the assumptions made by semi-supervised learning algorithms based on graphs, which build an artificial graph from vectorial data. Such semi-supervised algorithms are based on label propagation principles and their accuracy heavily relies on the structure (presence of edges) in the graph.
In this talk I will discuss ideas of how to perform sampling in the network graph, thus sparsifying the structure in order to apply semi-supervised algorithms and compute efficiently the classification function on the network. I will show very preliminary experiments indicating that the sampling technique has an important effect on the final results and discuss open theoretical and practical questions that are to be solved yet.
The document discusses important concepts related to relations and functions. It defines what a relation is and different types of relations such as reflexive, symmetric, transitive, and equivalence relations. It also defines different types of functions including one-to-one, onto, bijective, and inverse functions. It provides examples of binary operations and discusses their properties like commutativity, associativity, and identity elements. It concludes with short answer and very short answer type questions related to these concepts.
This document contains formulas and definitions related to mathematics for Class 12. It covers topics such as relations and functions, inverse trigonometric functions, matrices, determinants, and continuity and differentiability. Some key points include definitions of relations like reflexive, symmetric, and transitive relations. It also provides formulas for inverse trigonometric functions and their properties. Matrices are defined including operations like transpose, addition, and multiplication. Determinants are defined for matrices of various orders.
This document discusses diagonalization of matrices. It defines similarity of matrices and notes that similar matrices have the same characteristic polynomial and eigenvalues. It then discusses diagonalizing matrices by finding the eigenvalues and corresponding eigenvectors, constructing a change of basis matrix P from the eigenvectors, and constructing a diagonal matrix D from the eigenvalues. It provides examples of diagonalizing matrices with real and complex eigenvalues.
Linear vs. quadratic classifier power pointAlaa Tharwat
- The document discusses linear and quadratic discriminant classifiers, which are used to classify patterns into categories.
- Linear discriminant classifiers use linear decision boundaries, while quadratic discriminant classifiers use quadratic decision boundaries defined by quadratic functions.
- The document provides equations to calculate the discriminant functions and decision boundaries for linear and quadratic classifiers. It also gives an example to illustrate the classification process for three classes of data.
The document discusses different types of functions including linear, quadratic, absolute value, and square root functions. It provides the definitions and key properties of each function such as domain, range, intercepts, vertex, and transformations that modify the graph. Examples are worked through demonstrating how to find specific characteristics of each function and graph transformations.
This document discusses eigenvalues and eigenvectors. It defines an eigenvector as a nonzero vector that does not change direction when a matrix is multiplied by it. An eigenvalue is a scalar value such that this equation is satisfied. The document provides examples of finding eigenvalues and eigenvectors. It also discusses eigenspaces, which are subspaces containing eigenvectors. Additionally, it covers the characteristic equation for finding eigenvalues of a matrix and discusses similarity of matrices.
The document defines and describes key concepts related to rectangular coordinate systems and functions. It introduces the x-axis, y-axis, and origin that make up the rectangular coordinate system. It then defines various types of functions like linear, quadratic, absolute value and their graphs. Key characteristics of functions like domain, range, and intercepts are also summarized.
4. Linear Algebra for Machine Learning: Eigenvalues, Eigenvectors and Diagona...Ceni Babaoglu, PhD
The seminar series will focus on the mathematical background needed for machine learning. The first set of the seminars will be on "Linear Algebra for Machine Learning". Here are the slides of the fourth part which is discussing eigenvalues, eigenvectors and diagonalization.
Here is the link of the first part which was discussing linear systems: https://www.slideshare.net/CeniBabaogluPhDinMat/linear-algebra-for-machine-learning-linear-systems/1
Here are the slides of the second part which was discussing basis and dimension:
https://www.slideshare.net/CeniBabaogluPhDinMat/2-linear-algebra-for-machine-learning-basis-and-dimension
Here are the slides of the third part which is discussing factorization and linear transformations.
https://www.slideshare.net/CeniBabaogluPhDinMat/3-linear-algebra-for-machine-learning-factorization-and-linear-transformations-130813437
This document discusses using persistent homology to analyze the topological structure of proteins and relate it to protein compressibility. It summarizes that researchers modeled protein molecules as alpha filtrations to obtain multi-scale insight into their tunnel and cavity structures. The persistence diagrams of the alpha filtrations capture the sizes and robustness of these features in a compact way. The researchers found a clear linear correlation between their topological measure and experimentally determined protein compressibility values.
The document discusses properties of determinants, row operations on matrices, and how they affect determinants. It then covers Cramer's rule, vector spaces, subspaces, and the null space and column space of matrices. Specifically, it provides theorems showing that row replacements and scalings of rows do not change the determinant, while row interchanges negate the determinant. Cramer's rule is introduced for solving systems of linear equations. Key concepts for vector spaces and subspaces are defined, and the null space and column space of a matrix are shown to be subspaces.
Density theorems for anisotropic point configurationsVjekoslavKovac1
The document summarizes recent work on density theorems for anisotropic point configurations. It discusses classical results on Euclidean density theorems and their study of "large" measurable sets. It then outlines the general approach taken, involving decomposing counting forms into structured, error, and uniform parts. Finally, it presents some of the author's recent results on anisotropic dilates of simplices, boxes, and trees, proving the existence of such configurations in sets with positive upper Banach density.
Computer Science
Active and Programmable Networks
Active safety systems
Ad Hoc & Sensor Network
Ad hoc networks for pervasive communications
Adaptive, autonomic and context-aware computing
Advance Computing technology and their application
Advanced Computing Architectures and New Programming Models
Advanced control and measurement
Aeronautical Engineering,
Agent-based middleware
Alert applications
Automotive, marine and aero-space control and all other control applications
Autonomic and self-managing middleware
Autonomous vehicle
Biochemistry
Bioinformatics
BioTechnology(Chemistry, Mathematics, Statistics, Geology)
Broadband and intelligent networks
Broadband wireless technologies
CAD/CAM/CAT/CIM
Call admission and flow/congestion control
Capacity planning and dimensioning
Changing Access to Patient Information
Channel capacity modelling and analysis
Civil Engineering,
Cloud Computing and Applications
Collaborative applications
Communication application
Communication architectures for pervasive computing
Communication systems
Computational intelligence
Computer and microprocessor-based control
Computer Architecture and Embedded Systems
Computer Business
Computer Sciences and Applications
Computer Vision
Computer-based information systems in health care
Computing Ethics
Computing Practices & Applications
Congestion and/or Flow Control
Content Distribution
Context-awareness and middleware
Creativity in Internet management and retailing
Cross-layer design and Physical layer based issue
Cryptography
Data Base Management
Data fusion
Data Mining
Data retrieval
Data Storage Management
Decision analysis methods
Decision making
Digital Economy and Digital Divide
Digital signal processing theory
Distributed Sensor Networks
Drives automation
Drug Design,
Drug Development
DSP implementation
E-Business
E-Commerce
E-Government
Electronic transceiver device for Retail Marketing Industries
Electronics Engineering,
Embeded Computer System
Emerging advances in business and its applications
Emerging signal processing areas
Enabling technologies for pervasive systems
Energy-efficient and green pervasive computing
Environmental Engineering,
Estimation and identification techniques
Evaluation techniques for middleware solutions
Event-based, publish/subscribe, and message-oriented middleware
Evolutionary computing and intelligent systems
Expert approaches
Facilities planning and management
Flexible manufacturing systems
Formal methods and tools for designing
Fuzzy algorithms
Fuzzy logics
GPS and location-based app
This document discusses generative and discriminative classifiers. Generative classifiers model the joint distribution of data and labels, while discriminative classifiers directly model the conditional probability of labels given data. Naive Bayes is an example of a generative classifier, while logistic regression is a discriminative classifier that directly models the probability of a label given input features. The document provides mathematical details on naive Bayes, logistic regression, and how logistic regression can be trained to maximize conditional likelihood through gradient descent.
This document contains notes from a math lesson on arithmetic sequences and slope-intercept form. It includes examples of writing equations of sequences, finding terms of sequences, writing equations from slopes and y-intercepts, and graphing linear equations. Students are assigned problems #3-36 from pages 217-218 of their textbook due on November 28.
Analytic Function, C-R equation, Harmonic function, laplace equation, Construction of analytic function, Critical point, Invariant point , Bilinear Transformation
This document outlines the aims and content of the IGCSE Mathematics - Additional Standards syllabus. The aims are to consolidate elementary mathematical skills, develop knowledge of mathematical concepts, foster problem solving abilities, and apply mathematics to real-world situations. The content includes set theory, functions, quadratic functions, indices and surds, polynomials, simultaneous equations, logarithmic and exponential functions, straight line graphs, trigonometry, permutations and combinations, binomial expansions, vectors, matrices, differentiation, and integration. Assessment objectives are to recall and apply techniques, interpret mathematical data, comprehend concepts and relationships, and formulate and solve problems.
The document discusses relational algebra and tuple relational calculus. It defines the basic operators of relational algebra including selection, projection, union, difference, Cartesian product, and rename. It provides examples of how to write queries using each operator. It also discusses tuple relational calculus, defining domains, predicates, quantifiers, and how to write safe queries using this calculus.
The document provides information about changes being made to the Math section of the SAT. It discusses removing quantitative comparisons and focusing more on math reasoning and real-world problems. It outlines the specific math content areas that will be covered, including algebra, functions, geometry, statistics, and probability. It also describes the types of questions that will be asked, such as grid-in questions, and the use of calculators.
5. Linear Algebra for Machine Learning: Singular Value Decomposition and Prin...Ceni Babaoglu, PhD
The seminar series will focus on the mathematical background needed for machine learning. The first set of the seminars will be on "Linear Algebra for Machine Learning". Here are the slides of the fifth part which is discussing singular value decomposition and principal component analysis.
Here are the slides of the first part which was discussing linear systems: https://www.slideshare.net/CeniBabaogluPhDinMat/linear-algebra-for-machine-learning-linear-systems/1
Here are the slides of the second part which was discussing basis and dimension:
https://www.slideshare.net/CeniBabaogluPhDinMat/2-linear-algebra-for-machine-learning-basis-and-dimension
Here are the slides of the third part which is discussing factorization and linear transformations.
https://www.slideshare.net/CeniBabaogluPhDinMat/3-linear-algebra-for-machine-learning-factorization-and-linear-transformations-130813437
Here are the slides of the fourth part which is discussing eigenvalues and eigenvectors.
https://www.slideshare.net/CeniBabaogluPhDinMat/4-linear-algebra-for-machine-learning-eigenvalues-eigenvectors-and-diagonalization
The first report of Machine Learning Seminar organized by Computational Linguistics Laboratory at Kazan Federal University. See http://cll.niimm.ksu.ru/cms/lang/en_US/main/seminars/mlseminar
This document proposes a linear programming (LP) based approach for solving maximum a posteriori (MAP) estimation problems on factor graphs that contain multiple-degree non-indicator functions. It presents an existing LP method for problems with single-degree functions, then introduces a transformation to handle multiple-degree functions by introducing auxiliary variables. This allows applying the existing LP method. As an example, it applies this to maximum likelihood decoding for the Gaussian multiple access channel. Simulation results demonstrate the LP approach decodes correctly with polynomial complexity.
A discussion on sampling graphs to approximate network classification functionsLARCA UPC
The problem of network classification consists on assigning a finite set of labels to the nodes of the graphs; the underlying assumption is that nodes with the same label tend to be connected via strong paths in the graph. This is similar to the assumptions made by semi-supervised learning algorithms based on graphs, which build an artificial graph from vectorial data. Such semi-supervised algorithms are based on label propagation principles and their accuracy heavily relies on the structure (presence of edges) in the graph.
In this talk I will discuss ideas of how to perform sampling in the network graph, thus sparsifying the structure in order to apply semi-supervised algorithms and compute efficiently the classification function on the network. I will show very preliminary experiments indicating that the sampling technique has an important effect on the final results and discuss open theoretical and practical questions that are to be solved yet.
The document discusses important concepts related to relations and functions. It defines what a relation is and different types of relations such as reflexive, symmetric, transitive, and equivalence relations. It also defines different types of functions including one-to-one, onto, bijective, and inverse functions. It provides examples of binary operations and discusses their properties like commutativity, associativity, and identity elements. It concludes with short answer and very short answer type questions related to these concepts.
This document contains formulas and definitions related to mathematics for Class 12. It covers topics such as relations and functions, inverse trigonometric functions, matrices, determinants, and continuity and differentiability. Some key points include definitions of relations like reflexive, symmetric, and transitive relations. It also provides formulas for inverse trigonometric functions and their properties. Matrices are defined including operations like transpose, addition, and multiplication. Determinants are defined for matrices of various orders.
This document discusses diagonalization of matrices. It defines similarity of matrices and notes that similar matrices have the same characteristic polynomial and eigenvalues. It then discusses diagonalizing matrices by finding the eigenvalues and corresponding eigenvectors, constructing a change of basis matrix P from the eigenvectors, and constructing a diagonal matrix D from the eigenvalues. It provides examples of diagonalizing matrices with real and complex eigenvalues.
Linear vs. quadratic classifier power pointAlaa Tharwat
- The document discusses linear and quadratic discriminant classifiers, which are used to classify patterns into categories.
- Linear discriminant classifiers use linear decision boundaries, while quadratic discriminant classifiers use quadratic decision boundaries defined by quadratic functions.
- The document provides equations to calculate the discriminant functions and decision boundaries for linear and quadratic classifiers. It also gives an example to illustrate the classification process for three classes of data.
The document discusses different types of functions including linear, quadratic, absolute value, and square root functions. It provides the definitions and key properties of each function such as domain, range, intercepts, vertex, and transformations that modify the graph. Examples are worked through demonstrating how to find specific characteristics of each function and graph transformations.
This document discusses eigenvalues and eigenvectors. It defines an eigenvector as a nonzero vector that does not change direction when a matrix is multiplied by it. An eigenvalue is a scalar value such that this equation is satisfied. The document provides examples of finding eigenvalues and eigenvectors. It also discusses eigenspaces, which are subspaces containing eigenvectors. Additionally, it covers the characteristic equation for finding eigenvalues of a matrix and discusses similarity of matrices.
The document defines and describes key concepts related to rectangular coordinate systems and functions. It introduces the x-axis, y-axis, and origin that make up the rectangular coordinate system. It then defines various types of functions like linear, quadratic, absolute value and their graphs. Key characteristics of functions like domain, range, and intercepts are also summarized.
4. Linear Algebra for Machine Learning: Eigenvalues, Eigenvectors and Diagona...Ceni Babaoglu, PhD
The seminar series will focus on the mathematical background needed for machine learning. The first set of the seminars will be on "Linear Algebra for Machine Learning". Here are the slides of the fourth part which is discussing eigenvalues, eigenvectors and diagonalization.
Here is the link of the first part which was discussing linear systems: https://www.slideshare.net/CeniBabaogluPhDinMat/linear-algebra-for-machine-learning-linear-systems/1
Here are the slides of the second part which was discussing basis and dimension:
https://www.slideshare.net/CeniBabaogluPhDinMat/2-linear-algebra-for-machine-learning-basis-and-dimension
Here are the slides of the third part which is discussing factorization and linear transformations.
https://www.slideshare.net/CeniBabaogluPhDinMat/3-linear-algebra-for-machine-learning-factorization-and-linear-transformations-130813437
This document discusses using persistent homology to analyze the topological structure of proteins and relate it to protein compressibility. It summarizes that researchers modeled protein molecules as alpha filtrations to obtain multi-scale insight into their tunnel and cavity structures. The persistence diagrams of the alpha filtrations capture the sizes and robustness of these features in a compact way. The researchers found a clear linear correlation between their topological measure and experimentally determined protein compressibility values.
The document discusses properties of determinants, row operations on matrices, and how they affect determinants. It then covers Cramer's rule, vector spaces, subspaces, and the null space and column space of matrices. Specifically, it provides theorems showing that row replacements and scalings of rows do not change the determinant, while row interchanges negate the determinant. Cramer's rule is introduced for solving systems of linear equations. Key concepts for vector spaces and subspaces are defined, and the null space and column space of a matrix are shown to be subspaces.
Density theorems for anisotropic point configurationsVjekoslavKovac1
The document summarizes recent work on density theorems for anisotropic point configurations. It discusses classical results on Euclidean density theorems and their study of "large" measurable sets. It then outlines the general approach taken, involving decomposing counting forms into structured, error, and uniform parts. Finally, it presents some of the author's recent results on anisotropic dilates of simplices, boxes, and trees, proving the existence of such configurations in sets with positive upper Banach density.
Computer Science
Active and Programmable Networks
Active safety systems
Ad Hoc & Sensor Network
Ad hoc networks for pervasive communications
Adaptive, autonomic and context-aware computing
Advance Computing technology and their application
Advanced Computing Architectures and New Programming Models
Advanced control and measurement
Aeronautical Engineering,
Agent-based middleware
Alert applications
Automotive, marine and aero-space control and all other control applications
Autonomic and self-managing middleware
Autonomous vehicle
Biochemistry
Bioinformatics
BioTechnology(Chemistry, Mathematics, Statistics, Geology)
Broadband and intelligent networks
Broadband wireless technologies
CAD/CAM/CAT/CIM
Call admission and flow/congestion control
Capacity planning and dimensioning
Changing Access to Patient Information
Channel capacity modelling and analysis
Civil Engineering,
Cloud Computing and Applications
Collaborative applications
Communication application
Communication architectures for pervasive computing
Communication systems
Computational intelligence
Computer and microprocessor-based control
Computer Architecture and Embedded Systems
Computer Business
Computer Sciences and Applications
Computer Vision
Computer-based information systems in health care
Computing Ethics
Computing Practices & Applications
Congestion and/or Flow Control
Content Distribution
Context-awareness and middleware
Creativity in Internet management and retailing
Cross-layer design and Physical layer based issue
Cryptography
Data Base Management
Data fusion
Data Mining
Data retrieval
Data Storage Management
Decision analysis methods
Decision making
Digital Economy and Digital Divide
Digital signal processing theory
Distributed Sensor Networks
Drives automation
Drug Design,
Drug Development
DSP implementation
E-Business
E-Commerce
E-Government
Electronic transceiver device for Retail Marketing Industries
Electronics Engineering,
Embeded Computer System
Emerging advances in business and its applications
Emerging signal processing areas
Enabling technologies for pervasive systems
Energy-efficient and green pervasive computing
Environmental Engineering,
Estimation and identification techniques
Evaluation techniques for middleware solutions
Event-based, publish/subscribe, and message-oriented middleware
Evolutionary computing and intelligent systems
Expert approaches
Facilities planning and management
Flexible manufacturing systems
Formal methods and tools for designing
Fuzzy algorithms
Fuzzy logics
GPS and location-based app
This document discusses generative and discriminative classifiers. Generative classifiers model the joint distribution of data and labels, while discriminative classifiers directly model the conditional probability of labels given data. Naive Bayes is an example of a generative classifier, while logistic regression is a discriminative classifier that directly models the probability of a label given input features. The document provides mathematical details on naive Bayes, logistic regression, and how logistic regression can be trained to maximize conditional likelihood through gradient descent.
This document contains notes from a math lesson on arithmetic sequences and slope-intercept form. It includes examples of writing equations of sequences, finding terms of sequences, writing equations from slopes and y-intercepts, and graphing linear equations. Students are assigned problems #3-36 from pages 217-218 of their textbook due on November 28.
Analytic Function, C-R equation, Harmonic function, laplace equation, Construction of analytic function, Critical point, Invariant point , Bilinear Transformation
This document outlines the aims and content of the IGCSE Mathematics - Additional Standards syllabus. The aims are to consolidate elementary mathematical skills, develop knowledge of mathematical concepts, foster problem solving abilities, and apply mathematics to real-world situations. The content includes set theory, functions, quadratic functions, indices and surds, polynomials, simultaneous equations, logarithmic and exponential functions, straight line graphs, trigonometry, permutations and combinations, binomial expansions, vectors, matrices, differentiation, and integration. Assessment objectives are to recall and apply techniques, interpret mathematical data, comprehend concepts and relationships, and formulate and solve problems.
The document discusses relational algebra and tuple relational calculus. It defines the basic operators of relational algebra including selection, projection, union, difference, Cartesian product, and rename. It provides examples of how to write queries using each operator. It also discusses tuple relational calculus, defining domains, predicates, quantifiers, and how to write safe queries using this calculus.
The document provides information about changes being made to the Math section of the SAT. It discusses removing quantitative comparisons and focusing more on math reasoning and real-world problems. It outlines the specific math content areas that will be covered, including algebra, functions, geometry, statistics, and probability. It also describes the types of questions that will be asked, such as grid-in questions, and the use of calculators.
5. Linear Algebra for Machine Learning: Singular Value Decomposition and Prin...Ceni Babaoglu, PhD
The seminar series will focus on the mathematical background needed for machine learning. The first set of the seminars will be on "Linear Algebra for Machine Learning". Here are the slides of the fifth part which is discussing singular value decomposition and principal component analysis.
Here are the slides of the first part which was discussing linear systems: https://www.slideshare.net/CeniBabaogluPhDinMat/linear-algebra-for-machine-learning-linear-systems/1
Here are the slides of the second part which was discussing basis and dimension:
https://www.slideshare.net/CeniBabaogluPhDinMat/2-linear-algebra-for-machine-learning-basis-and-dimension
Here are the slides of the third part which is discussing factorization and linear transformations.
https://www.slideshare.net/CeniBabaogluPhDinMat/3-linear-algebra-for-machine-learning-factorization-and-linear-transformations-130813437
Here are the slides of the fourth part which is discussing eigenvalues and eigenvectors.
https://www.slideshare.net/CeniBabaogluPhDinMat/4-linear-algebra-for-machine-learning-eigenvalues-eigenvectors-and-diagonalization
The first report of Machine Learning Seminar organized by Computational Linguistics Laboratory at Kazan Federal University. See http://cll.niimm.ksu.ru/cms/lang/en_US/main/seminars/mlseminar
This document proposes a linear programming (LP) based approach for solving maximum a posteriori (MAP) estimation problems on factor graphs that contain multiple-degree non-indicator functions. It presents an existing LP method for problems with single-degree functions, then introduces a transformation to handle multiple-degree functions by introducing auxiliary variables. This allows applying the existing LP method. As an example, it applies this to maximum likelihood decoding for the Gaussian multiple access channel. Simulation results demonstrate the LP approach decodes correctly with polynomial complexity.
Density theorems for Euclidean point configurationsVjekoslavKovac1
1. The document discusses density theorems for point configurations in Euclidean space. Density theorems study when a measurable set A contained in Euclidean space can be considered "large".
2. One classical result is that for any measurable set A contained in R2 with positive upper Banach density, there exist points in A whose distance is any sufficiently large real number. This has been generalized to higher dimensions and other point configurations.
3. Open questions remain about determining all point configurations P for which one can show that a sufficiently large measurable set A contained in high dimensional Euclidean space must contain a scaled copy of P.
Classification and regression based on derivatives: a consistency result for ...tuxette
This document summarizes a presentation on using derivatives for classification and regression of functions. It discusses using smoothing splines to estimate functions and their derivatives from discrete sampled data. A consistency result is presented that finds a classifier or regression function built from the estimated derivative functions that achieves the optimal Bayes risk, as the number of samples and examples increases. The key idea is to use smoothing splines, which consistently estimate functions and derivatives, combined with a consistent classifier or regressor on the estimated values.
This document discusses Taylor series expansions and their application to signal processing and communications. It presents the Taylor series expansion formula in both scalar and matrix form. As an example, it uses Taylor series to approximate the sine function near zero. It also discusses derivatives of linear matrix transforms and quadratic forms, and how the Hessian matrix and its eigenvalues/eigenvectors relate to the shape of quadratic functions.
Density theorems for anisotropic point configurationsVjekoslavKovac1
This document discusses density theorems for anisotropic point configurations. Specifically:
- It summarizes previous results on density theorems for linear configurations in Euclidean spaces.
- It then presents new results on density theorems for anisotropic power-type scalings, where points are scaled by different powers in different coordinates.
- Theorems are proven for anisotropic simplices and boxes in such spaces, showing that any set of positive density must contain scaled copies of these configurations for scales above a certain threshold.
- The proofs use a multiscale approach involving pattern counting forms, smoothed counting forms, and analyzing the structured, uniform, and error parts that arise from decomposing the counting forms. Mult
This document provides an introduction to group theory with applications to quantum mechanics and solid state physics. It begins with definitions of groups and examples of groups that are important in physics. It then discusses several applications of group theory in classical mechanics, quantum mechanics, and solid state physics. Specifically, it explains how group theory can be used to evaluate matrix elements, understand degeneracies of energy eigenvalues, classify electronic states in periodic potentials, and construct models that respect crystal symmetries. It also briefly discusses the use of group theory in nuclear and particle physics.
This document covers key topics in seismic data processing including complex numbers, vectors, matrices, determinants, eigenvalues, singular values, matrix inversion, series, Taylor series, Fourier series, delta functions, and Fourier integrals. It provides examples of using Taylor series to approximate nonlinear systems as linear systems and using Fourier series to approximate periodic functions. The importance of Fourier transforms for spectral analysis and various geophysical applications is also discussed.
Conditional random fields (CRFs) are probabilistic models for segmenting and labeling sequence data. CRFs address limitations of previous models like hidden Markov models (HMMs) and maximum entropy Markov models (MEMMs). CRFs allow incorporation of arbitrary, overlapping features of the observation sequence and label dependencies. Parameters are estimated to maximize the conditional log-likelihood using iterative scaling or tracking partial feature expectations. Experiments show CRFs outperform HMMs and MEMMs on synthetic and real-world tasks by addressing label bias problems and modeling dependencies beyond the previous label.
This document provides an introduction to spectral graph theory. It discusses how spectral graph theory connects combinatorics and algebra through studying graphs using eigenvalues and eigenvectors of adjacency matrices. It covers applications of spectral graph theory such as spectral clustering, which uses eigenvectors of the graph Laplacian as features for clustering nodes, and graph convolutional networks, which apply graph filtering and node-wise transformations to classify nodes in a graph.
A new implementation of k-MLE for mixture modelling of Wishart distributionsFrank Nielsen
This document discusses a new implementation of k-MLE for mixture modelling of Wishart distributions. It begins with an overview of the Wishart distribution and its properties as an exponential family. It then describes the original k-MLE algorithm and how it can be adapted for Wishart distributions by using Hartigan and Wang's strategy instead of Lloyd's strategy to avoid empty clusters. The document also discusses approaches for initializing the clusters, such as k-means++, and proposes a heuristic to determine the number of clusters on-the-fly rather than fixing k.
This document provides an introduction to matrix algebra and random vectors. It defines key concepts such as vectors, matrices, matrix operations, and properties of positive definite matrices. Vectors are defined as arrays of real numbers that can be added or multiplied by scalars. Matrices are rectangular arrays of numbers that can be added or multiplied. Positive definite matrices are matrices where the quadratic form is always nonnegative. The eigenvalues and eigenvectors of a symmetric positive definite matrix allow geometric interpretation of distances defined by the matrix.
This document summarizes the key points of a thesis oral presentation on wavelet and frame theory. It discusses systems and frames, and how frames overcome limitations of orthonormal systems by providing stable reconstructions. It then outlines contributions made in the thesis to analyzing Gabor and wavelet systems using dual Gramian analysis. This allows constructing frames with desired properties like compact support and symmetry. It presents two papers published from the work and provides background on notions like Bessel systems, frames, Riesz sequences, and how the dual Gramian analysis connects frame properties to the adjoint system through a duality principle.
Let f(x) =
and g(x) =
1 x
x
that fog is not defined.
19.
If f : A B is bijective, then write n(A) in terms of n(B).
20.
If f : A B is onto and n(A) = 5, n(B) = 3. Then write n(A).
SHORT ANSWER TYPE QUESTIONS (2 MARKS)
1.
Define reflexive, symmetric
A T(1)-type theorem for entangled multilinear Calderon-Zygmund operatorsVjekoslavKovac1
This document summarizes a talk given by Vjekoslav Kovač at a joint mathematics conference. The talk concerned establishing T(1)-type theorems for entangled multilinear Calderón-Zygmund operators. Specifically, Kovač discussed studying multilinear singular integral forms where the functions partially share variables, known as an "entangled structure." He outlined establishing generalized modulation invariance and Lp estimates for such operators. The talk motivated further studying related problems involving bilinear ergodic averages and forms with more complex graph structures. Kovač specialized his techniques to bipartite graphs, multilinear Calderón-Zygmund kernels, and "perfect" dyadic models.
This document discusses the computation of definite integrals involving certain polynomials expressed as hypergeometric functions. It defines several types of polynomials including Lucas polynomials, generalized harmonic numbers, Bernoulli polynomials, Gegenbauer polynomials, Laguerre polynomials, Hermite polynomials, Legendre polynomials, Chebyshev polynomials, Euler polynomials, and the generalized Riemann zeta function. It provides the explicit formulas and generating functions for each polynomial. The document contains new results for definite integrals expressed in terms of these polynomials and the hypergeometric function.
1) The document discusses connections between Dialectica categories, Kolmogorov problems, and Veloso problems. Objects in the Dialectica category PV represent problems, with their elements representing instances and possible solutions.
2) A Kolmogorov problem is a triple representing an instance, possible solutions, and a condition relating them. Kolmogorov problems can model intuitionistic logic and provide semantics.
3) Morphisms in the Dialectica category represent reductions between problems. The document gives examples of geometric problems and their reductions using this framework.
Similar to Victor Zamaraev – Boundary properties of factorial classes of graphs (20)
Предсказание оттока игроков из World of TanksYandex
Одна из наиболее часто возникающих задач в бизнес-аналитике для компаний — это предсказание оттока клиентов. Ведь если заранее знать, что клиент собирается уйти к конкуренту, его можно попытаться остановить. Задача будет рассмотрена на примере прогнозирования оттока игроков из World of Tanks.
Как принять/организовать работу по поисковой оптимизации сайта, Сергей Царик,...Yandex
Лекция Сергея Царика в Школе вебмастеров: «Как принять/организовать работу по поисковой оптимизации сайта».
https://academy.yandex.ru/events/webmasters_school/yawebm2015/
Основные этапы и методы поисковой оптимизации
Рассмотрим проработку стратегии продвижения, планирование ресурсов на проект, поймем как нужно прорабатывать семантическое ядро для продвижения, разберемся с очередностью всех работ.
Разложим по полочкам основные приемы оптимизации в связке с внутренними и внешними факторами ранжирования поисковых систем, а также в связке с поведенческими факторами и характеристиками. Разберемся с тем, что же должен делать оптимизатор для достижения топа.
Что должно включать в себя ТЗ на поисковую оптимизацию
Разберемся с основными блоками технического задания от оптимизатора, с тем, каким оно должно быть с точки зрения подачи информации и ее глубины.
Сравнение in-house подхода и агентства
Рассмотрим все «за» и «против» оптимизатора в штате компании и вне её.
На основе каких метрик нужно оценивать эффективность оптимизаторской работы
Выделим ключевые показатели эффективности работы оптимизатора, рассмотрим процесс их измерения, динамику, разберемся с возможными «миксами» и их связкой с мотивацией подрядчика.
Структурированные данные, Юлия Тихоход, лекция в Школе вебмастеров ЯндексаYandex
Лекция Юлия Тихоход в Школе вебмастеров: «Структурированные данные на поиске»
https://academy.yandex.ru/events/webmasters_school/yawebm2015/
Что такое микроразметка и в чём её польза
Что такое микроразметка (семантическая разметка, семантическая микроразметка) и кому она нужна. Очень кратко — всё, что я знаю о применении семантической разметки поисковыми системами и другими веб-сервисами.
Передача данных в машиночитаемом виде
Какие ещё есть способы передать данные о сайте поисковым системам кроме микроразметки, особенности разных способов. Что бывает с плохими вебмастерами, которые пытаются обмануть поисковые системы и передать неверные данные.
Типы разметки
Из чего состоит микроразметка, какие бывают словари и синтаксисы. Популярные сочетания словарей и синтаксисов, как правильно выбирать нужную комбинацию для своего сайта.
Передача данных об интернет-магазине
Разбор семантической разметки: что в принципе доступно для разметки в интернет-магазине, что это даёт, а что можно не размечать вовсе.
Проверка правильности микроразаметки
Ошибки в микроразметке, способы их обнаружения и исправления. Популярные валидаторы микроразметки. Какие ошибки непременно нужно исправлять, а что можно игнорировать.
Представление сайта в поиске, Сергей Лысенко, лекция в Школе вебмастеров ЯндексаYandex
Лекция Сергея Лысенко в Школе вебмастеров: «Представление сайта в поиске»
https://academy.yandex.ru/events/webmasters_school/yawebm2015/
Основные элементы сниппетов: как влиять на их формирование
Как по внешнему виду и содержанию визитки судят, стоит ли «связываться», так и по представлению сайта на странице выдачи пользователи решают, стоит ли переходить на сайт. Как изменить представление сайта в выдаче поисковых систем? Что может повлиять на CTR и что для этого нужно сделать? Рассмотрим фавиконки, навигационные цепочки, быстрые ссылки и многое, многое другое.
Зачем нам заголовок: как им управлять
Что должно быть в заголовке, а чего уж точно не стоит делать. Как избавиться от мусора и расставить акценты. И как это скажется на представлении сайта в поиске.
Основной контент аннотации и мета-описания: что нам они дают
Сниппет — зачем он нужен? Как обрабатываются данные для аннотаций? Что в сниппете помогает, а что мешает пользователю сориентироваться? Как подсказать поисковой системе, что выводить в сниппете? От Open Graf до schema.org. Инструменты, возможности, рекомендации.
Плохие методы продвижения сайта, Екатерины Гладких, лекция в Школе вебмастеро...Yandex
Лекция Екатерины Гладких в Школе вебмастеров: «Плохие методы продвижения сайта»
https://academy.yandex.ru/events/webmasters_school/yawebm2015/
Как завязывают с портянками
Как использовать wordstat, чтобы превратить текст в SEO-портянку. Как Яндекс определяет текстовый спам и какие ограничения могут быть применены к сайтам, злоупотребляющим ключевыми словами.
Эффектное размещение SEO-ссылок
Какие бывают SEO-ссылки и как они классифицируются в базе Яндекса. В чём отличие SEO-ссылок от рекламы. Как размещать SEO-ссылки наиболее эффектно. Методы борьбы против ссылочного спама – АГС и Минусинск. Снятие ссылок.
Поведенческие факторы, медитативные практики
Популярные сервисы накрутки: как это работает и как это не работает. Методы накрутки и методы борьбы с мошенничеством. Примеры пользовательских сессий и кто на самом деле посещает ваш сайт. Как выйти из-под санкций за накрутку поведенческих факторов.
Основные принципы ранжирования, Сергей Царик и Антон Роменский, лекция в Школ...Yandex
Лекция Сергея Царика и Антона Роменского в Школе вебмастеров: «Основные принципы ранжирования»
https://academy.yandex.ru/events/webmasters_school/yawebm2015/
Как работает поиск
При запросе пользователя к поисковой системе происходит множество процессов, которые позволяют дать наиболее релевантный ответ. Рассмотрим основные механизмы формирования выдачи: формулы, Матрикснет, персонализацию и обновления.
Что учитывается при ранжировании сайтов
Так как сайты разные и по-разному решают пользовательские задачи, при ранжировании поисковой системе нужно учитывать множество факторов. Поговорим о том, что обязательно должно быть на сайте для правильной индексации.
Ещё о факторах ранжирования
Какой контент действительно важен и как его правильно представить. Для правильного ранжирования сайта важно разобраться с его региональной привязкой. Разберёмся, какой регион присваивать сайту и как сделать это правильно.
Реальный кейс долгосрочной работы над позициями
Посмотрим на реальном примере, как изменялись основные жизненные характеристики (трафик, конверсии) сайта на пути в топ выдачи поисковых систем.
Основные принципы индексирования сайта, Александр Смирнов, лекция в Школе веб...Yandex
Лекция Александра Смирнова в Школе вебмастеров: «Основные принципы индексирования сайта».
https://academy.yandex.ru/events/webmasters_school/yawebm2015/
Как поиск находит страницу, её путь до появления в поиске
Поисковые системы постоянно собирают информацию о страницах в интернете. Как же это происходит и как добавить страницы своего сайта в поиск? Проверка индексирования сайта.
Как управлять роботом (зеркала, sitemap, robots.txt)
Множество сайтов в интернете доступны сразу по нескольким адресам. Как указать поисковому роботу на основной и как скорректировать индексирование?
Особенности индексирования
Современные сайты используют различные технологии в своей работе. Рассмотрим, как настроить их правильно и сделать контент доступным для робота.
Как улучшить индексирование (дубли, HTTP-ответ, удаление из поиска)
В поиск попадают различные страницы, которые известны роботу. Какие нужны, а какие нет? Как повлиять на их индексирование?
Мобильное приложение: как и зачем, Александр Лукин, лекция в Школе вебмастеро...Yandex
Лекция Александра Лукина в Школе вебмастеров: «Мобильное приложение: как и зачем»
https://academy.yandex.ru/events/webmasters_school/yawebm2015/
Проектирование. Быть или не быть
Обсудим обоснование для разработки мобильного приложения — какую ценность оно может принести для проекта и бизнеса. Определим основные типы приложений и сценарии использования. Рассмотрим основные технологии и выбор оптимальных для конкретных задач. ТЗ — как оценить и какие особенности необходимо учесть.
Разработка. Важные детали
На что обратить внимание на этапе разработки и тестирования, заметки по специфике мобильных экосистем. Выбираем арсенал SDK для всестороннего анализа проекта в полёте.
Публикация и продвижение
Кратко рассмотрим специфику Google Play и AppStore. Проведём экскурс в мир мобильного маркетинга, подчеркнём сходства с вебом и отличия от него. Рассмотрим ключевые метрики для анализа продукта и процесса продвижения, а также способы их повышения.
Сайты на мобильных устройствах, Олег Ножичкин, лекция в Школе вебмастеров Янд...Yandex
Лекция Олега Ножичкина в Школе вебмастеров: «Сайты на мобильных устройствах»
https://academy.yandex.ru/events/webmasters_school/yawebm2015/
Статистика и тренды по мобильному интернету
Основные показатели мобильного интернет-рынка. Тенденции роста мобильной аудитории.
Новые алгоритмы ранжирования поисковых систем
Адаптация сайта к мобильным пользователям и её влияние на позиции в поисковой выдаче.
Возможности для бизнеса в мобильном вебе
Мобильный сайт позволяет воспользоваться дополнительными возможностями взаимодействия с пользователем. Рассмотрим конкретные примеры.
Мобильный сайт и приложение — в чём разница
Чем отличается мобильное приложение от мобильно сайта. Какие преимущества и недостатки у каждого варианта.
Представление сайтов на мобильных устройствах
Адаптивные сайты. Мобильные сайты. Сайты для десктопа. Чем они отличаются, какие преимущества у каждого типа и нужно ли переключаться между мобильной и десктоп-версиями?
Удобный мобильный сайт для пользователя
Поведение пользователей на мобильном сайте. Отличия от десктопа, достижение целей и простые правила увеличения конверсии.
Специфика разработки мобильного сайта
Особенности проектирования, разработки и тестирования сайтов.
Инструменты для разработки мобильных сайтов
Готовые инструменты для проектирования и тестирования. Примеры фреймворков.
Качественная аналитика сайта, Юрий Батиевский, лекция в Школе вебмастеров Янд...Yandex
Лекция Юрия Батиевского в Школе вебмастеров: «Качественная аналитика сайта»
https://academy.yandex.ru/events/webmasters_school/yawebm2015/
Что мы хотим от аналитики сайта
На какие вопросы должна отвечать аналитика сайта. Как аналитика сайта связана с аналитикой бизнеса. На какие блоки можно поделить аналитику онлайн-процессов. Какой должна быть идеальная аналитическая система.
Анализ общих показателей бизнеса
Как построить систему аналитики бизнеса в интернете. Ключевые показатели эффективности (KPI). Построение воронки продаж. Business Intelligence — сквозная аналитика всех процессов.
Обзор инструментов для анализа сайта и аудитории
Яндекс.Метрика и Google Analytics как основа веб-аналитики. Инструменты для веб-мастеров. Инструменты для анализа действий пользователей (Kiss-metrics, Woopra, Mixpanel). Системы для подсчета целевых действий, CPA и ROMI.
Анализ каналов привлечения клиентов
Как анализировать источники трафика. Популярные инструменты для анализа.
Пройти тест по теме
Процесс развертывания системы аналитики сайта
Подготовка к установке систем веб-аналитики. Тонкости установки и настройки трекеров. Подключение коллтрекинга и дополнительных инструментов фиксации целевых действий. Настройка пользовательских сценариев. Пример по анализу пользовательского сценария.
Что можно и что нужно измерять на сайте, Петр Аброськин, лекция в Школе вебма...Yandex
Лекция Петра Аброськина в Школе вебмастеров: «Что можно и что нужно измерять на сайте».
https://academy.yandex.ru/events/webmasters_school/yawebm2015/
Базовые принципы веб-аналитики
Как работает веб-аналитика и какие подводные камни есть в учёте и анализе данных. Как правильно работать с данными.
Основные метрики и термины
Посетители, визиты, глубина просмотра, время на сайте — какие метрики важны и чем они отличаются.
Как выбрать правильный KPI
Самый важный этап в веб-аналитике и продвижении сайта. Какие цели выбрать интернет-магазину, сайту услуг, контентному проекту и т.д.
Ключевые группы отчетов и применение знаний на практике
Семь главных типов отчётов для бизнеса. Анализ контекстной рекламы, SEO и контента сайта — на конкретных примерах.
Как правильно поставить ТЗ на создание сайта, Алексей Бородкин, лекция в Школ...Yandex
Лекция Алексея Бородкина в Школе вебмастеров: «Как правильно поставить ТЗ на создание сайта».
https://academy.yandex.ru/events/webmasters_school/yawebm2015/
ТЗ: две буквы с большим потенциалом
Что такое техническое задание. Какое место оно занимает в веб-разработке. Какие цели преследует. И каким требованиям оно должно отвечать.
Что нужно сделать, прежде чем садиться за ТЗ
Зачем нужна подготовка к написанию ТЗ. Какую информацию нужно собрать и как выстроить этот процесс. На каком этапе веб-разработки нужно писать ТЗ — и что будет, если этот момент упустить. Какое отношение имеют к ТЗ прототипы, пользовательские истории и прочие инструменты проектирования.
Хорошее ТЗ
Как соединить в один документ описание интерфейсов, структуру данных и много чего ещё. Структура правильного, хорошего ТЗ с подробным разбором каждого пункта. С какой стороны приступать и как эффективнее всего выстроить работу.
Кто должен писать ТЗ
Кто может написать хорошее ТЗ. Где найти такого человека и как встроить его в общие процессы. Что делать, если ТЗ пишет сам заказчик.
Плохое ТЗ
Популярные ошибки. Чем они ужасны и как их избежать.
Жизнь с ТЗ
По какой схеме нужно согласовывать ТЗ. Как применять его в дальнейшей работе. Кому не нужно показывать ТЗ ни при каких обстоятельствах. Что делать, если ТЗ никому не нравится.
ТЗ по ГОСТ: ад на Земле
Краткая история развития ТЗ со времён Брежнева и до наших дней. Почему я старательно избегаю слова «ТЗ». Почему вы должны нервно вздрагивать при слове «ГОСТ». Что делать, если вы работаете с госзаказчиком.
Как защитить свой сайт, Пётр Волков, лекция в Школе вебмастеровYandex
Лекция Петра Волкова в Школе вебмастеров: «Как защитить свой сайт».
https://academy.yandex.ru/events/webmasters_school/yawebm2015/
Актуальные типы угроз и динамика их развития
Компрометация сервера и её последствия. Распределённые атаки типа «отказ в обслуживании». Подмена или добавление рекламы на стороне клиента. Атаки, направленные на пользователей. Проблемы, связанные со внешним содержимым.
Управление рисками безопасности веб-сайтов
Разные типы сайтов подвержены разным типам рисков информационной безопасности. Понимание целей и подходов злоумылшенников как ключ к эффективному снижению рисков. Методы монетизации атак на сайты.
Доступный инструментарий и методики для обеспечения безопасности
Открытые инструменты форензики для типовых и сложных проектов. Системы обнаружения вторжений, подходы к проектированию безопасности в архитектуре и процессах.
Как правильно составить структуру сайта, Дмитрий Сатин, лекция в Школе вебмас...Yandex
Лекция Дмитрия Сатина в Школе вебмастеров: «Как правильно составить структуру сайта».
https://academy.yandex.ru/events/webmasters_school/yawebm2015/
Структура сайта, ориентированная на человека; построение структуры, карточная сортировка
Содержимое сайтов часто организовано так, как кажется удобным разработчику или контент-менеджеру компании. Чаще всего такие структуры неудобны для реальных посетителей, потому что не совпадают с их знаниями, не поясняют, как устроен материал, и не помогают найти желаемое. Структура, ориентированная на пользователя, повышает вероятность того, что посетители найдут нужную информацию или товар и сделают это быстро.
Стройте структуру, исходя из пользовательских сценариев. Выделение на сайте разделов, соответствующих структуре компании или схеме процесса закупки, как правило, усложняет навигацию для пользователя. Правильная структура учитывает уровень знаний покупателя и использует понятные ему термины и способы группировки.
Разные типы структур, средства навигации, дальнейший поиск информации на странице
Структуры сайтов, на которых ищут что-то определённое, отличаются от тех, что используются на сайтах, посетители которых ещё не уверены, что именно они хотят или как называется нужная вещь. Строгие структуры — например, организация по наименованию товара, производителю, — предполагают один способ группировки. При нестрогой организации данные можно группировать по теме, по жизненной ситуации и так далее. Используйте средства навигации, которые помогают понять, как организован материал. Решая, какой будет визуальная реализация навигации на сайте, необходимо учитывать количество разделов и связи �
Технические особенности создания сайта, Дмитрий Васильева, лекция в Школе веб...Yandex
Лекция Дмитрия Васильева в Школе вебмастеров: «Технические особенности создания сайта».
https://academy.yandex.ru/events/webmasters_school/yawebm2015/
Сайт — расплывчатое понятие
Раньше под словом «сайт» понимался набор HTML-страниц, расположенных в домене второго или третьего уровня. Появление социальных сетей размыло это понятие.
Как выбрать домен
Различные варианты, и какой из них подойдёт именно вашему сайту: доменные зоны, читаемые и нечитаемые домены, кириллица и латиница.
Подходы к созданию сайтов
Первые сайты делались на чистом HTML. Сейчас такой способ ещё встречается, но подавляющее большинство веб-страниц создаются при помощи CMS, фреймворков, конструкторов.
Составные сущности: структура, макеты дизайна, интерактивные элементы, контент, система прав. Размещение сайта на хостинге. Российские и зарубежные, дорогие и дешевые, облачные и традиционные провайдеры. Кратко о тонкостях взаимодействия с ними.
Что такое HTTPS
Всё более популярный безопасный протокол доступа к сайту. Нужен ли он вам и в каких случаях. Как выбрать платформу для сайта, основные системы управления сайтом (CMS) и конструкторы.
Сайт после запуска
Сайты создаются с конкретной целью, обычно связанной с получением дохода. Как контент сайта и его технические характеристики напрямую могут влиять на бизнес-эффективность.
Конструкторы для отдельных элементов сайта, Елена Першина, лекция в Школе веб...Yandex
Лекция Елены Першиной в Школе вебмастеров: «Конструкторы для отдельных элементов сайта».
https://academy.yandex.ru/events/webmasters_school/yawebm2015/
О пользе тех или иных технологий
Взгляд в будущее, короткий обзор других полезных технологий и «опасностей», которые подстерегают на пути к правильному их выбору.
Как выбрать поиск для сайта
Поиск для сайта — важный инструмент навигации. Чтобы оценить качество поиска по своему сайту, посмотрите на количество уходов со страницы результатов. Полнота, скорость индексирования, обработка запросов (исправление ошибок, опечаток, неправильной раскладки) — без этого невозможно представить качественный поиск.
Как выбрать карты для сайта
Уход посетителя с сайта на «большие» Яндекс.Карты за точной информацией об организации может обернуться потерей клиента, который уже был готов к покупке. Чтобы этого не допустить, лучше сделать интерактивную карту прямо на сайте.
Автоматизация оплаты на сайте
Люди привыкают платить картой, сегодня даже уличные киоски принимают их. Поэтому многим посетителям кажется «подозрительным» интернет-магазин, в котором недоступны электронные платежи. Начать приём банковских карт в онлайне очень просто, главное выбрать для этого подходящую технологию.
Перевод важных страниц
На каких языках говорит ваша аудитория, много ли у вас посетителей из-за рубежа? Ответы на эти вопросы даст Яндекс.Метрика. Именно она поможет оценить, нужно ли тратиться на профессионального переводчика и готовить отдельные описания товаров или новости на других языках. Во многих случаях для совершения покупки достаточно и простого машинного перевода. Узнайте, как его настроить, чтобы ключевые разделы сайта автоматически переводились для иностранных посетителей.
Социальная интеграция
Как заставить пользователей говорить о себе в социальных сетях? В первую очередь нужно сделать хороший продукт или услугу, но и без удобного инструмента для «шаринга» в соцсетях — никуда. Рекомендации о том, как выбрать и установить такой инструмент к себе на сайт.
Контент для интернет-магазинов, Катерина Ерошина, лекция в Школе вебмастеров ...Yandex
Лекция Катерины Ерошиной в Школе вебмастеров: «Контент для интернет-магазинов».
https://academy.yandex.ru/events/webmasters_school/yawebm2015/
Виды контента для интернет-магазинов
Основные страницы, карточки товаров, каталог в целом. Письма покупателям. Статьи для интернет-магазина.
Основные сервисные страницы: что нужно знать покупателю
О страницах доставки, оплаты, контактов, условий работы.
Страница товара интернет-магазина: какой нужен текст, чтобы товар нашли
Признаки товаров. Сниппеты товарных позиций. Когда текст не нужен вообще. Постоянная и техническая информация на карточке.
Блог и внешние публикации интернет-магазина
О чем писать, чтобы подогреть интерес к магазину. Сторителлинг. UGC: методы вовлечения (кратко).
Персонализация интернет-магазина: стать ближе к покупателю
Красивый пример личного бренда директора магазина.
Копирайтинг для интернет-магазина: на чём можно и нельзя экономить
Что делать, если у вас 100 000 товарных позиций и они постоянно меняются.
Хорошее ТЗ копирайтеру для наполнения интернет-магазина
Что должен знать копирайтер, чтобы не писать ерунду.
Как оценить работу копирайтера
Стандартные проверки. Контроль качества текста средствами аналитики.
Как написать хороший текст для сайта, Катерина Ерошина, лекция в Школе вебмас...Yandex
Лекция Катерины Ерошиной в Школе вебмастеров: «Как написать хороший текст для сайта».
https://academy.yandex.ru/events/webmasters_school/yawebm2015/
Назначение и типы текстов на сайте и вне его
Цель текста — влиять на поведение пользователя. Самое простое — информировать, самое сложное — привести к покупке. Виды текстов для внешних публикаций. Белые книги и другие способы подтвердить экспертизу.
Контент-план для наполнения, развития сайта и внешних публикаций
Как проектировать контент для нового сайта. Как наращивать информационную массу сайта. Внешние контакты с потребителем.
Разные уровни вовлечения: информируем, продаём, помогаем
Пройти по пути покупателя, выдавать информацию, необходимую для совершения следующего шага. Ловушки на этом пути.
Информационный стиль: применение с пониманием
Чистить текст без фанатизма. Эмоциональное вовлечение. Рациональное обоснование.
Структура и вёрстка
Заголовки и подзаголовки, списки, абзацы, иерархия подачи информации.
SEO-аспекты и LSI-копирайтинг
Понимание ценности ключей. Зачем копирайтеру нужно семантическое ядро.
Оценка качества текста (чеклист)
Уникальность, фактическая достоверность, соответствие целям, информационная плотность, грамотность.
Usability и дизайн - как не помешать пользователю, Алексей Иванов, лекция в Ш...Yandex
Лекция Алексея Иванова в Школе вебмастеров: «Usability и дизайн: как не помешать пользователю».
https://academy.yandex.ru/events/webmasters_school/yawebm2015/
Что такое юзабилити и почему оно важно
Поведение пользователей на сайте и достижение ими запланированных целей зависит не только от контента, но и от удобства сайта.
Информационное и функциональное наполнение сайта
Перед созданием сайта нужно правильно определить, какая информация и какой функционал должны быть на сайте. При этом нужно исходить не из того, что у вас есть, а из того, что будет нужно будущим посетителям вашего сайта.
Проектирование входных страниц
В зависимости от целей сайта и источников посетителей нужно сформулировать требования к входным страницам сайта и их содержанию.
Сценарии поведения пользователя
Для правильного распределения информации нужно описать сценарии взаимодействия с сайтом для разных групп посетителей. Рассмотрим методы совмещения разных сценариев на одном сайте.
Пройти тест по теме
Управление конверсией
В большинстве случаев мы ждем от посетителя сайта какого-то целевого действия. Это может быть регистрация, отправка заявки, звонок или что-то ещё. Вы увидите способы мотивации посетителей к совершению целевого действия для различных типов сайтов.
Пройти тест по теме
Основные принципы распределения информации
В рамках этого блока вы увидите, как нужно распределять информацию на странице, чтобы посетители увидели всё, что вы хотите им показать.
Мобильная версия сайта и принципы юзабилити
Всё больше посетителей приходят на сайт с мобильных устройств. Рассмотрим основные особенности взаимодействия с информацией с мобильного устройства и подходы к адаптации сайта под них.
Cайт. Зачем он и каким должен быть, Алексей Иванов, лекция в Школе вебмастеро...Yandex
Лекция Алексея Иванова в Школе вебмастеров Яндекса: «Сайт. Зачем он и каким должен быть».
https://academy.yandex.ru/events/webmasters_school/yawebm2015/
Типы сайтов и потребности аудитории
В зависимости от решаемых задач, сайты можно разделить на несколько характерных типов с разными функциями и контентом. Перед созданием сайта важно понять, чего ждут посетители и какими хотят видеть веб-страницы. При этом на один и тот же сайт может попадать разная аудитория, которая ведёт себя по-разному и каждая имеет свои потребности. Для каждого сегмента нужно разработать отдельные сценарии взаимодействия с информацией на вашей площадке.
Сайт с точки зрения бизнеса
Чаще всего сайт создается для решения конкретных бизнес-задач. Рассмотрим различные типы монетизации сайтов и особенности каждого из них.
Основные показатели и методы измерения
Одно из главных преимуществ цифровых каналов — детальная аналитика взаимодействия посетителей с сайтом. В данном блоке рассмотрим основные инструменты измерения, ключевые показатели сайта, на которые нужно обращать внимание, и подходы к интерпретации полученных данных для принятия решений.
Authoring a personal GPT for your research and practice: How we created the Q...Leonel Morgado
Thematic analysis in qualitative research is a time-consuming and systematic task, typically done using teams. Team members must ground their activities on common understandings of the major concepts underlying the thematic analysis, and define criteria for its development. However, conceptual misunderstandings, equivocations, and lack of adherence to criteria are challenges to the quality and speed of this process. Given the distributed and uncertain nature of this process, we wondered if the tasks in thematic analysis could be supported by readily available artificial intelligence chatbots. Our early efforts point to potential benefits: not just saving time in the coding process but better adherence to criteria and grounding, by increasing triangulation between humans and artificial intelligence. This tutorial will provide a description and demonstration of the process we followed, as two academic researchers, to develop a custom ChatGPT to assist with qualitative coding in the thematic data analysis process of immersive learning accounts in a survey of the academic literature: QUAL-E Immersive Learning Thematic Analysis Helper. In the hands-on time, participants will try out QUAL-E and develop their ideas for their own qualitative coding ChatGPT. Participants that have the paid ChatGPT Plus subscription can create a draft of their assistants. The organizers will provide course materials and slide deck that participants will be able to utilize to continue development of their custom GPT. The paid subscription to ChatGPT Plus is not required to participate in this workshop, just for trying out personal GPTs during it.
The binding of cosmological structures by massless topological defectsSérgio Sacani
Assuming spherical symmetry and weak field, it is shown that if one solves the Poisson equation or the Einstein field
equations sourced by a topological defect, i.e. a singularity of a very specific form, the result is a localized gravitational
field capable of driving flat rotation (i.e. Keplerian circular orbits at a constant speed for all radii) of test masses on a thin
spherical shell without any underlying mass. Moreover, a large-scale structure which exploits this solution by assembling
concentrically a number of such topological defects can establish a flat stellar or galactic rotation curve, and can also deflect
light in the same manner as an equipotential (isothermal) sphere. Thus, the need for dark matter or modified gravity theory is
mitigated, at least in part.
The use of Nauplii and metanauplii artemia in aquaculture (brine shrimp).pptxMAGOTI ERNEST
Although Artemia has been known to man for centuries, its use as a food for the culture of larval organisms apparently began only in the 1930s, when several investigators found that it made an excellent food for newly hatched fish larvae (Litvinenko et al., 2023). As aquaculture developed in the 1960s and ‘70s, the use of Artemia also became more widespread, due both to its convenience and to its nutritional value for larval organisms (Arenas-Pardo et al., 2024). The fact that Artemia dormant cysts can be stored for long periods in cans, and then used as an off-the-shelf food requiring only 24 h of incubation makes them the most convenient, least labor-intensive, live food available for aquaculture (Sorgeloos & Roubach, 2021). The nutritional value of Artemia, especially for marine organisms, is not constant, but varies both geographically and temporally. During the last decade, however, both the causes of Artemia nutritional variability and methods to improve poorquality Artemia have been identified (Loufi et al., 2024).
Brine shrimp (Artemia spp.) are used in marine aquaculture worldwide. Annually, more than 2,000 metric tons of dry cysts are used for cultivation of fish, crustacean, and shellfish larva. Brine shrimp are important to aquaculture because newly hatched brine shrimp nauplii (larvae) provide a food source for many fish fry (Mozanzadeh et al., 2021). Culture and harvesting of brine shrimp eggs represents another aspect of the aquaculture industry. Nauplii and metanauplii of Artemia, commonly known as brine shrimp, play a crucial role in aquaculture due to their nutritional value and suitability as live feed for many aquatic species, particularly in larval stages (Sorgeloos & Roubach, 2021).
Or: Beyond linear.
Abstract: Equivariant neural networks are neural networks that incorporate symmetries. The nonlinear activation functions in these networks result in interesting nonlinear equivariant maps between simple representations, and motivate the key player of this talk: piecewise linear representation theory.
Disclaimer: No one is perfect, so please mind that there might be mistakes and typos.
dtubbenhauer@gmail.com
Corrected slides: dtubbenhauer.com/talks.html
The cost of acquiring information by natural selectionCarl Bergstrom
This is a short talk that I gave at the Banff International Research Station workshop on Modeling and Theory in Population Biology. The idea is to try to understand how the burden of natural selection relates to the amount of information that selection puts into the genome.
It's based on the first part of this research paper:
The cost of information acquisition by natural selection
Ryan Seamus McGee, Olivia Kosterlitz, Artem Kaznatcheev, Benjamin Kerr, Carl T. Bergstrom
bioRxiv 2022.07.02.498577; doi: https://doi.org/10.1101/2022.07.02.498577
Current Ms word generated power point presentation covers major details about the micronuclei test. It's significance and assays to conduct it. It is used to detect the micronuclei formation inside the cells of nearly every multicellular organism. It's formation takes place during chromosomal sepration at metaphase.
When I was asked to give a companion lecture in support of ‘The Philosophy of Science’ (https://shorturl.at/4pUXz) I decided not to walk through the detail of the many methodologies in order of use. Instead, I chose to employ a long standing, and ongoing, scientific development as an exemplar. And so, I chose the ever evolving story of Thermodynamics as a scientific investigation at its best.
Conducted over a period of >200 years, Thermodynamics R&D, and application, benefitted from the highest levels of professionalism, collaboration, and technical thoroughness. New layers of application, methodology, and practice were made possible by the progressive advance of technology. In turn, this has seen measurement and modelling accuracy continually improved at a micro and macro level.
Perhaps most importantly, Thermodynamics rapidly became a primary tool in the advance of applied science/engineering/technology, spanning micro-tech, to aerospace and cosmology. I can think of no better a story to illustrate the breadth of scientific methodologies and applications at their best.
PPT on Direct Seeded Rice presented at the three-day 'Training and Validation Workshop on Modules of Climate Smart Agriculture (CSA) Technologies in South Asia' workshop on April 22, 2024.
Phenomics assisted breeding in crop improvementIshaGoswami9
As the population is increasing and will reach about 9 billion upto 2050. Also due to climate change, it is difficult to meet the food requirement of such a large population. Facing the challenges presented by resource shortages, climate
change, and increasing global population, crop yield and quality need to be improved in a sustainable way over the coming decades. Genetic improvement by breeding is the best way to increase crop productivity. With the rapid progression of functional
genomics, an increasing number of crop genomes have been sequenced and dozens of genes influencing key agronomic traits have been identified. However, current genome sequence information has not been adequately exploited for understanding
the complex characteristics of multiple gene, owing to a lack of crop phenotypic data. Efficient, automatic, and accurate technologies and platforms that can capture phenotypic data that can
be linked to genomics information for crop improvement at all growth stages have become as important as genotyping. Thus,
high-throughput phenotyping has become the major bottleneck restricting crop breeding. Plant phenomics has been defined as the high-throughput, accurate acquisition and analysis of multi-dimensional phenotypes
during crop growing stages at the organism level, including the cell, tissue, organ, individual plant, plot, and field levels. With the rapid development of novel sensors, imaging technology,
and analysis methods, numerous infrastructure platforms have been developed for phenotyping.
Immersive Learning That Works: Research Grounding and Paths ForwardLeonel Morgado
We will metaverse into the essence of immersive learning, into its three dimensions and conceptual models. This approach encompasses elements from teaching methodologies to social involvement, through organizational concerns and technologies. Challenging the perception of learning as knowledge transfer, we introduce a 'Uses, Practices & Strategies' model operationalized by the 'Immersive Learning Brain' and ‘Immersion Cube’ frameworks. This approach offers a comprehensive guide through the intricacies of immersive educational experiences and spotlighting research frontiers, along the immersion dimensions of system, narrative, and agency. Our discourse extends to stakeholders beyond the academic sphere, addressing the interests of technologists, instructional designers, and policymakers. We span various contexts, from formal education to organizational transformation to the new horizon of an AI-pervasive society. This keynote aims to unite the iLRN community in a collaborative journey towards a future where immersive learning research and practice coalesce, paving the way for innovative educational research and practice landscapes.
hematic appreciation test is a psychological assessment tool used to measure an individual's appreciation and understanding of specific themes or topics. This test helps to evaluate an individual's ability to connect different ideas and concepts within a given theme, as well as their overall comprehension and interpretation skills. The results of the test can provide valuable insights into an individual's cognitive abilities, creativity, and critical thinking skills
Victor Zamaraev – Boundary properties of factorial classes of graphs
1. Boundary properties of factorial classes of graphs
Victor Zamaraev
Laboratory of Algorithms and Technologies for Networks Analysis (LATNA),
Higher School of Economics
Joint work with
Vadim Lozin, University of Warwick
Workshop on Extremal Graph Theory
6 June 2014
3. Boundary properties of factorial classes of graphs
Introduction
All considered graphs are simple (undirected, without loops and
without multiple edges).
3 / 28
4. Boundary properties of factorial classes of graphs
Introduction
All considered graphs are simple (undirected, without loops and
without multiple edges).
Graphs are labeled by natural numbers 1, . . . , n
6
4 5
1
2
3
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5. Boundary properties of factorial classes of graphs
Introduction
Definition
A class is a set of graphs closed under isomorphism.
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6. Boundary properties of factorial classes of graphs
Introduction
Definition
A class is a set of graphs closed under isomorphism.
Definition
A class of graphs is hereditary if it is closed under taking induced
subgraphs.
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7. Boundary properties of factorial classes of graphs
Introduction
Definition
A class is a set of graphs closed under isomorphism.
Definition
A class of graphs is hereditary if it is closed under taking induced
subgraphs.
Exapmle
Let X be a hereditary class and W4 ∈ X. Then C4 ∈ X.
1
2
3 4
5 1
2 3
4
W4 C4 4 / 28
8. Boundary properties of factorial classes of graphs
Introduction
Every hereditary graph class X can be defined by a set of
forbidden induced subgraphs.
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9. Boundary properties of factorial classes of graphs
Introduction
Every hereditary graph class X can be defined by a set of
forbidden induced subgraphs.
Let M be a set of graphs. Then Free(M) denotes the set of all
graphs not containing induced subgraphs isomorphic to graphs from
M.
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10. Boundary properties of factorial classes of graphs
Introduction
Every hereditary graph class X can be defined by a set of
forbidden induced subgraphs.
Let M be a set of graphs. Then Free(M) denotes the set of all
graphs not containing induced subgraphs isomorphic to graphs from
M.
Statement
Class X is hereditary if and only if there exists M such that
X = Free(M).
We say that graphs in X are M-free.
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11. Boundary properties of factorial classes of graphs
Introduction
Every hereditary graph class X can be defined by a set of
forbidden induced subgraphs.
Let M be a set of graphs. Then Free(M) denotes the set of all
graphs not containing induced subgraphs isomorphic to graphs from
M.
Statement
Class X is hereditary if and only if there exists M such that
X = Free(M).
We say that graphs in X are M-free.
Example
For the class of bipartite graphs M is {C3, C5, C7, . . . }, i.e.
B = Free(C3, C5, C7, . . . ). 5 / 28
12. Boundary properties of factorial classes of graphs
Introduction
For a class X denote by Xn the set of n-vertex graphs from X.
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13. Boundary properties of factorial classes of graphs
Introduction
For a class X denote by Xn the set of n-vertex graphs from X.
Example
Let P be the class of all graph.
|Pn| = 2(n
2) = 2n(n−1)/2
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14. Boundary properties of factorial classes of graphs
Introduction
For a class X denote by Xn the set of n-vertex graphs from X.
Example
Let P be the class of all graph.
|Pn| = 2(n
2) = 2n(n−1)/2
log2 |Pn| = Θ(n2)
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15. Boundary properties of factorial classes of graphs
Introduction
Theorem (Alekseev V. E., 1992; Bollob´as B. and Thomason A., 1994)
For every infinite hereditary class X, which is not the class of all
graphs:
log2 |Xn| = 1 −
1
c(X)
n2
2
+ o(n2
), (1)
where c(X) ∈ N is the index of class X.
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16. Boundary properties of factorial classes of graphs
Introduction
Theorem (Alekseev V. E., 1992; Bollob´as B. and Thomason A., 1994)
For every infinite hereditary class X, which is not the class of all
graphs:
log2 |Xn| = 1 −
1
c(X)
n2
2
+ o(n2
), (1)
where c(X) ∈ N is the index of class X.
(i) For c(X) > 1, log2 |Xn| = Θ(n2)
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17. Boundary properties of factorial classes of graphs
Introduction
Theorem (Alekseev V. E., 1992; Bollob´as B. and Thomason A., 1994)
For every infinite hereditary class X, which is not the class of all
graphs:
log2 |Xn| = 1 −
1
c(X)
n2
2
+ o(n2
), (1)
where c(X) ∈ N is the index of class X.
(i) For c(X) > 1, log2 |Xn| = Θ(n2)
(ii) For c(X) = 1, log2 |Xn| = o(n2)
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18. Boundary properties of factorial classes of graphs
Introduction
Let c(X) = 1
Question
What are possible rates of growth of a function log2 |Xn|?
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19. Boundary properties of factorial classes of graphs
Introduction
Let c(X) = 1
Question
What are possible rates of growth of a function log2 |Xn|?
Scheinerman E.R., Zito J. (1994)
Constant classes: log2 |Xn| = Θ(1).
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20. Boundary properties of factorial classes of graphs
Introduction
Let c(X) = 1
Question
What are possible rates of growth of a function log2 |Xn|?
Scheinerman E.R., Zito J. (1994)
Constant classes: log2 |Xn| = Θ(1).
Polynomial classes: log2 |Xn| = Θ(log n).
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21. Boundary properties of factorial classes of graphs
Introduction
Let c(X) = 1
Question
What are possible rates of growth of a function log2 |Xn|?
Scheinerman E.R., Zito J. (1994)
Constant classes: log2 |Xn| = Θ(1).
Polynomial classes: log2 |Xn| = Θ(log n).
Exponential classes: log2 |Xn| = Θ(n).
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22. Boundary properties of factorial classes of graphs
Introduction
Let c(X) = 1
Question
What are possible rates of growth of a function log2 |Xn|?
Scheinerman E.R., Zito J. (1994)
Constant classes: log2 |Xn| = Θ(1).
Polynomial classes: log2 |Xn| = Θ(log n).
Exponential classes: log2 |Xn| = Θ(n).
Factorial classes: log2 |Xn| = Θ(n log n).
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23. Boundary properties of factorial classes of graphs
Introduction
Let c(X) = 1
Question
What are possible rates of growth of a function log2 |Xn|?
Scheinerman E.R., Zito J. (1994)
Constant classes: log2 |Xn| = Θ(1).
Polynomial classes: log2 |Xn| = Θ(log n).
Exponential classes: log2 |Xn| = Θ(n).
Factorial classes: log2 |Xn| = Θ(n log n).
All other classes are superfactorial.
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24. Boundary properties of factorial classes of graphs
Introduction
Let c(X) = 1
Question
What are possible rates of growth of a function log2 |Xn|?
Scheinerman E.R., Zito J. (1994)
Constant classes: log2 |Xn| = Θ(1).
Polynomial classes: log2 |Xn| = Θ(log n).
Exponential classes: log2 |Xn| = Θ(n).
Factorial classes: log2 |Xn| = Θ(n log n).
All other classes are superfactorial.
There are no intermediate growth rates between first four ranges.
For exmaple, there is no hereditary class X with
log2 |Xn| = Θ(
√
n).
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25. Boundary properties of factorial classes of graphs
Introduction
Constant
Polynomial
Exponential
Factorial layer
Classes with index 1
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26. Boundary properties of factorial classes of graphs
Introduction
Example
Constant class: Co – complete graphs (1).
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27. Boundary properties of factorial classes of graphs
Introduction
Example
Constant class: Co – complete graphs (1).
Polynomial class: E1 – graphs with at most one edge
( n
2 + 1).
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28. Boundary properties of factorial classes of graphs
Introduction
Example
Constant class: Co – complete graphs (1).
Polynomial class: E1 – graphs with at most one edge
( n
2 + 1).
Exponential class: Co + Co (2n−1).
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29. Boundary properties of factorial classes of graphs
Introduction
Example
Constant class: Co – complete graphs (1).
Polynomial class: E1 – graphs with at most one edge
( n
2 + 1).
Exponential class: Co + Co (2n−1).
Factorial class: F – forests (nn−2 < |Fn| < n2n).
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30. Boundary properties of factorial classes of graphs
Introduction
Alekseev V.E. (1997)
Constant classes: log2 |Xn| = Θ(1).
Polynomial classes: log2 |Xn| = Θ(log n).
Exponential classes: log2 |Xn| = Θ(n).
Factorial classes: log2 |Xn| = Θ(n log n).
All other classes are superfactorial.
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31. Boundary properties of factorial classes of graphs
Introduction
Alekseev V.E. (1997)
Constant classes: log2 |Xn| = Θ(1).
Polynomial classes: log2 |Xn| = Θ(log n).
Exponential classes: log2 |Xn| = Θ(n).
Factorial classes: log2 |Xn| = Θ(n log n).
All other classes are superfactorial.
1 Structural characterizations were obtained for the first three
layers.
2 In every of the four layers all minimal classes were found.
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32. Boundary properties of factorial classes of graphs
Introduction
Constant
Polynomial
Exponential
Factorial layer
Classes with index 1
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33. Boundary properties of factorial classes of graphs
Introduction
Balogh J., Bollob´as B., Weinreich D. (2000)
Constant classes: log2 |Xn| = Θ(1).
Polynomial classes: log2 |Xn| = Θ(log n).
Exponential classes: log2 |Xn| = Θ(n).
Factorial classes: log2 |Xn| = Θ(n log n).
All other classes are superfactorial.
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34. Boundary properties of factorial classes of graphs
Introduction
Balogh J., Bollob´as B., Weinreich D. (2000)
Constant classes: log2 |Xn| = Θ(1).
Polynomial classes: log2 |Xn| = Θ(log n).
Exponential classes: log2 |Xn| = Θ(n).
Factorial classes: log2 |Xn| = Θ(n log n).
All other classes are superfactorial.
In addition
1 Characterized lower part of the factorial layer, i.e. classes with
|Xn| < n(1+o(1))n.
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35. Boundary properties of factorial classes of graphs
Introduction
Examples of factorial classes:
forests
planar graphs
line graphs
cographs
permutation graphs
threshold graphs
graphs of bounded vertex degree
graphs of bounded clique-width
et al.
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36. Boundary properties of factorial classes of graphs
Introduction
Problem
Characterize factorial layer.
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37. Boundary properties of factorial classes of graphs
Minimal superfactorial classes
Minimal superfactorial classes
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38. Boundary properties of factorial classes of graphs
Minimal superfactorial classes
Minimal superfactorial classes
Constant
Polynomial
Exponential
Factorial
Classes with index 1
39. Boundary properties of factorial classes of graphs
Minimal superfactorial classes
Minimal superfactorial classes
Constant
Polynomial
Exponential
Factorial
Classes with index 1
? ? ?
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43. Boundary properties of factorial classes of graphs
Minimal superfactorial classes
Minimal superfactorial classes
log2 |Xn| = Θ(n log2
n)
CB = Free(C3, C5, C6, C7, . . .)
Theorem (Spinrad J. P., 1995)
log2 |CBn| = Θ(n log2
n)
Question
Is the class of chordal bipartite graphs a minimal superfactorial?
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44. Boundary properties of factorial classes of graphs
Minimal superfactorial classes
Minimal superfactorial classes
Theorem (Dabrowski K., Lozin V.V., Zamaraev V., 2012)
Let X = Free(2C4, 2C4 + e) ∩ CB. Then log2 |Xn| = Θ(n log2
n).
2C4 2C4 + e
19 / 28
45. Boundary properties of factorial classes of graphs
Minimal superfactorial classes
Minimal superfactorial classes
Theorem (Dabrowski K., Lozin V.V., Zamaraev V., 2012)
Let X = Free(2C4, 2C4 + e) ∩ CB. Then log2 |Xn| = Θ(n log2
n).
2C4 2C4 + e
Open question
Is the class Free(2C4, 2C4 + e) ∩ CB a minimal superfactorial?
19 / 28
46. Boundary properties of factorial classes of graphs
Minimal superfactorial classes
Minimal superfactorial classes
Denote by Bk the class of (C4, C6, ..., C2k)-free bipartite graphs.
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47. Boundary properties of factorial classes of graphs
Minimal superfactorial classes
Minimal superfactorial classes
Denote by Bk the class of (C4, C6, ..., C2k)-free bipartite graphs.
Statement (follows from the results of Lazebnik F., et al., 1995)
For each integer k ≥ 2, the class Bk is superfactorial.
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48. Boundary properties of factorial classes of graphs
Minimal superfactorial classes
Minimal superfactorial classes
Denote by Bk the class of (C4, C6, ..., C2k)-free bipartite graphs.
Statement (follows from the results of Lazebnik F., et al., 1995)
For each integer k ≥ 2, the class Bk is superfactorial.
Infinite sequence of superfactorial classes
B2 ⊃ B3 ⊃ B4 . . . .
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49. Boundary properties of factorial classes of graphs
Minimal superfactorial classes
Minimal superfactorial classes
Denote by Bk the class of (C4, C6, ..., C2k)-free bipartite graphs.
Statement (follows from the results of Lazebnik F., et al., 1995)
For each integer k ≥ 2, the class Bk is superfactorial.
Infinite sequence of superfactorial classes
B2 ⊃ B3 ⊃ B4 . . . .
In this sequence there is no minimal superfactorial class.
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50. Boundary properties of factorial classes of graphs
Minimal superfactorial classes
Limit classes
Definition
Given a sequence X1 ⊇ X2 ⊇ X3 ⊇ . . . of graph classes, we will
say that the sequence converges to a class X if
i≥1
Xi = X.
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51. Boundary properties of factorial classes of graphs
Minimal superfactorial classes
Limit classes
Definition
Given a sequence X1 ⊇ X2 ⊇ X3 ⊇ . . . of graph classes, we will
say that the sequence converges to a class X if
i≥1
Xi = X.
Example
The sequence B2 ⊃ B3 ⊃ B4 . . . converges to the factorial class F
of forests, i.e.
i≥1
Bi = F.
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52. Boundary properties of factorial classes of graphs
Minimal superfactorial classes
Limit classes
Definition
Given a sequence X1 ⊇ X2 ⊇ X3 ⊇ . . . of graph classes, we will
say that the sequence converges to a class X if
i≥1
Xi = X.
Example
The sequence B2 ⊃ B3 ⊃ B4 . . . converges to the factorial class F
of forests, i.e.
i≥1
Bi = F.
Definition
A class X of graphs is a limit class (for the factorial layer) if there
is a sequence of superfactorial classes converging to X.
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53. Boundary properties of factorial classes of graphs
Minimal superfactorial classes
Boundary classes
Definition
A limit class is called boundary (or minimal) if it does not properly
contain any other limit class.
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54. Boundary properties of factorial classes of graphs
Minimal superfactorial classes
Boundary classes
Definition
A limit class is called boundary (or minimal) if it does not properly
contain any other limit class.
Theorem
A finitely defined class is superfactorial if and only if it contains a
boundary class.
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55. Boundary properties of factorial classes of graphs
Minimal superfactorial classes
Boundary classes
Definition
A limit class is called boundary (or minimal) if it does not properly
contain any other limit class.
Theorem
A finitely defined class is superfactorial if and only if it contains a
boundary class.
Theorem
The class of forests is a boundary class.
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56. Boundary properties of factorial classes of graphs
Minimal superfactorial classes
Are there more boundary classes?
There are five more boundary classes, which can be easly obtained
from the class of forests.
23 / 28
57. Boundary properties of factorial classes of graphs
Minimal superfactorial classes
Are there more boundary classes?
There are five more boundary classes, which can be easly obtained
from the class of forests.
Two of them are:
1 complements of forests;
2 bipartite complements of forests;
23 / 28
58. Boundary properties of factorial classes of graphs
Minimal superfactorial classes
Are there more boundary classes?
There are five more boundary classes, which can be easly obtained
from the class of forests.
Two of them are:
1 complements of forests;
2 bipartite complements of forests;
1
5
2
6
3
7
4
8
F
1
5
2
6
3
7
4
8
Bipartite complement of F
23 / 28
59. Boundary properties of factorial classes of graphs
Minimal superfactorial classes
Are there more boundary classes?
There are five more boundary classes, which can be easly obtained
from the class of forests.
Two of them are:
1 complements of forests;
2 bipartite complements of forests;
1
5
2
6
3
7
4
8
F
1
5
2
6
3
7
4
8
Bipartite complement of F
Question
Are there other boundary classes?
23 / 28
60. Boundary properties of factorial classes of graphs
Minimal superfactorial classes
Lozin’s conjecture
Conjecture (Lozin’s conjecture, [Lozin V.V., Mayhill C., Zamaraev V., 2011])
A hereditary graph class X is factorial if and only if at least one of
the following three classes: X ∩ B, X ∩ B и X ∩ S is factorial and
each of these classes is at most factorial.
B – bipartite graphs
B – complements of bipartite graphs
S – split graphs
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61. Boundary properties of factorial classes of graphs
Minimal superfactorial classes
Proper boundary subclasses
B2 = Free(C4) ∩ B CB = Free(C3, C5, C6, . . .)
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63. Boundary properties of factorial classes of graphs
Minimal superfactorial classes
Proper boundary subclasses
B2 = Free(C4) ∩ B CB = Free(C3, C5, C6, . . .)
superfactorial superfactorial
i≥1
Bi = F ⊂ B2
i≥1
Bi = F ⊂ CB
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64. Boundary properties of factorial classes of graphs
Minimal superfactorial classes
Proper boundary subclasses
B2 = Free(C4) ∩ B CB = Free(C3, C5, C6, . . .)
superfactorial superfactorial
i≥1
Bi = F ⊂ B2
i≥1
Bi = F ⊂ CB
Bi ⊆ B2, i ≥ 1 Bi CB, i ≥ 1
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65. Boundary properties of factorial classes of graphs
Minimal superfactorial classes
Proper boundary subclasses
B2 = Free(C4) ∩ B CB = Free(C3, C5, C6, . . .)
superfactorial superfactorial
i≥1
Bi = F ⊂ B2
i≥1
Bi = F ⊂ CB
Bi ⊆ B2, i ≥ 1 Bi CB, i ≥ 1
Definition
Let X be a superfactorial class and S a boundary subclass
contained in X. We say that S is a proper boundary subclass of X
if there is a sequence of superfactorial subclasses of X converging
to S.
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66. Boundary properties of factorial classes of graphs
Minimal superfactorial classes
Proper boundary subclasses
Theorem
There are no proper boundary subclasses of chordal bipartite
graphs.
26 / 28
67. Boundary properties of factorial classes of graphs
Minimal superfactorial classes
Proper boundary subclasses
Theorem
There are no proper boundary subclasses of chordal bipartite
graphs.
Theorem
The class of forests is the only proper boundary subclass of B2.
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68. Boundary properties of factorial classes of graphs
Minimal superfactorial classes
Open problems
Open question
Find a minimal superfactorial class.
27 / 28
69. Boundary properties of factorial classes of graphs
Minimal superfactorial classes
Open problems
Open question
Find a minimal superfactorial class.
Open question
Is the list of boundary classes we found complete?
27 / 28