This document describes an exact combinatorial algorithm for the graph bisection problem based on branch and bound. The key contributions are new lower bound techniques using max flow and multicommodity packing, as well as novel decomposition and branching rules. The algorithm is able to solve much larger instances than previous approaches, proving optimal solutions for graphs with thousands of nodes in hours by reducing the search space through improved lower bounds and domain pruning.
1) Cables are used in structures like suspension bridges to support and transmit loads. They are considered flexible and inextensible.
2) Cables taking concentrated loads form straight line segments with constant tension. Cables under distributed loads form parabolic shapes with tension varying along the cable length.
3) When a cable's own weight is considered, its deflection curve is defined by hyperbolic functions with tension also varying along the cable.
Here's a toy problem: What is the SMALLEST number of unit balls you can fit in a box such that no more will fit?
In this talk, I will show how just thinking about a naive greedy approach to this problem leads to a simple derivation of several of the most important theoretical results in the field of mesh generation.
We'll prove classic upper and lower bounds on both the number of balls and the complexity of their interrelationships.
Then, we'll relate this problem to a similar one called the Fat Voronoi Problem, in which we try to find point sets such that every Voronoi cell is fat
(the ratio of the radii of the largest contained to smallest containing ball is bounded).
This problem has tremendous promise in the future of mesh generation as it can circumvent the classic lowerbounds presented in the first half of the talk.
Unfortunately the simple approach no longer works.
In the end we will show that the number of neighbors of any cell in a Fat Voronoi Diagram in the plane is bounded by a constant
(if you think that's obvious, spend a minute to try to prove it).
We'll also talk a little about the higher dimensional version of the problem and its wide range of applications.
The document describes an algorithm called PUNCH for partitioning large road networks into smaller parts in parallel. PUNCH first identifies "natural cuts" in the network like sparse areas and rivers that separate dense regions. It contracts the graph between these cuts to create a smaller graph. PUNCH then uses a greedy algorithm and local search on the smaller graph to find an initial partition, improving it through multistart and combination heuristics. Experimental results on a European road network show PUNCH produces high quality partitions an order of magnitude faster than other methods.
Lecture 10b: Classification. k-Nearest Neighbor classifier, Logistic Regression, Support Vector Machines (SVM), Naive Bayes (ppt,pdf)
Chapters 4,5 from the book “Introduction to Data Mining” by Tan, Steinbach, Kumar.
Vector quantization maps high-dimensional vectors to codewords from a finite codebook. Each codeword defines a Voronoi region containing vectors closest to that codeword. The Lloyd and LBG algorithms are commonly used to optimize the codebook for a given dataset by iteratively clustering vectors and recomputing codeword averages. Tree-structured vector quantization improves efficiency by recursively partitioning the codebook into binary groups defined by test vectors. This reduces the number of distance comparisons needed at the cost of potential increases in distortion and storage requirements.
Vector quantization maps high-dimensional vectors to codewords from a finite codebook. Each codeword defines a Voronoi region containing vectors closest to that codeword. The Lloyd and LBG algorithms are commonly used to optimize the codebook for a given dataset by iteratively clustering vectors and recomputing codeword averages. Tree-structured vector quantization reduces comparisons by recursively partitioning the codebook space, at the cost of potential distortion increases. The rate-distortion performance of vector quantization generally exceeds scalar quantization due to its ability to model correlations in vector datasets.
Vector quantization maps high-dimensional vectors to codewords from a finite codebook. Each codeword is the center of a Voronoi region that contains all vectors closest to that codeword. The LBG algorithm trains a vector quantizer by iteratively adjusting codewords to minimize distortion based on a training set. Tree-structured vector quantization further improves efficiency by recursively partitioning the codebook into binary tree structure to reduce distance comparisons at the cost of potential increases in distortion and storage requirements for additional test vectors.
- Dimensionality reduction techniques assign instances to vectors in a lower-dimensional space while approximately preserving similarity relationships. Principal component analysis (PCA) is a common linear dimensionality reduction technique.
- Kernel PCA performs PCA in a higher-dimensional feature space implicitly defined by a kernel function. This allows PCA to find nonlinear structure in data. Kernel PCA computes the principal components by finding the eigenvectors of the normalized kernel matrix.
- For a new data point, its representation in the lower-dimensional space is given by projecting it onto the principal components in feature space using the kernel trick, without explicitly computing features.
1) Cables are used in structures like suspension bridges to support and transmit loads. They are considered flexible and inextensible.
2) Cables taking concentrated loads form straight line segments with constant tension. Cables under distributed loads form parabolic shapes with tension varying along the cable length.
3) When a cable's own weight is considered, its deflection curve is defined by hyperbolic functions with tension also varying along the cable.
Here's a toy problem: What is the SMALLEST number of unit balls you can fit in a box such that no more will fit?
In this talk, I will show how just thinking about a naive greedy approach to this problem leads to a simple derivation of several of the most important theoretical results in the field of mesh generation.
We'll prove classic upper and lower bounds on both the number of balls and the complexity of their interrelationships.
Then, we'll relate this problem to a similar one called the Fat Voronoi Problem, in which we try to find point sets such that every Voronoi cell is fat
(the ratio of the radii of the largest contained to smallest containing ball is bounded).
This problem has tremendous promise in the future of mesh generation as it can circumvent the classic lowerbounds presented in the first half of the talk.
Unfortunately the simple approach no longer works.
In the end we will show that the number of neighbors of any cell in a Fat Voronoi Diagram in the plane is bounded by a constant
(if you think that's obvious, spend a minute to try to prove it).
We'll also talk a little about the higher dimensional version of the problem and its wide range of applications.
The document describes an algorithm called PUNCH for partitioning large road networks into smaller parts in parallel. PUNCH first identifies "natural cuts" in the network like sparse areas and rivers that separate dense regions. It contracts the graph between these cuts to create a smaller graph. PUNCH then uses a greedy algorithm and local search on the smaller graph to find an initial partition, improving it through multistart and combination heuristics. Experimental results on a European road network show PUNCH produces high quality partitions an order of magnitude faster than other methods.
Lecture 10b: Classification. k-Nearest Neighbor classifier, Logistic Regression, Support Vector Machines (SVM), Naive Bayes (ppt,pdf)
Chapters 4,5 from the book “Introduction to Data Mining” by Tan, Steinbach, Kumar.
Vector quantization maps high-dimensional vectors to codewords from a finite codebook. Each codeword defines a Voronoi region containing vectors closest to that codeword. The Lloyd and LBG algorithms are commonly used to optimize the codebook for a given dataset by iteratively clustering vectors and recomputing codeword averages. Tree-structured vector quantization improves efficiency by recursively partitioning the codebook into binary groups defined by test vectors. This reduces the number of distance comparisons needed at the cost of potential increases in distortion and storage requirements.
Vector quantization maps high-dimensional vectors to codewords from a finite codebook. Each codeword defines a Voronoi region containing vectors closest to that codeword. The Lloyd and LBG algorithms are commonly used to optimize the codebook for a given dataset by iteratively clustering vectors and recomputing codeword averages. Tree-structured vector quantization reduces comparisons by recursively partitioning the codebook space, at the cost of potential distortion increases. The rate-distortion performance of vector quantization generally exceeds scalar quantization due to its ability to model correlations in vector datasets.
Vector quantization maps high-dimensional vectors to codewords from a finite codebook. Each codeword is the center of a Voronoi region that contains all vectors closest to that codeword. The LBG algorithm trains a vector quantizer by iteratively adjusting codewords to minimize distortion based on a training set. Tree-structured vector quantization further improves efficiency by recursively partitioning the codebook into binary tree structure to reduce distance comparisons at the cost of potential increases in distortion and storage requirements for additional test vectors.
- Dimensionality reduction techniques assign instances to vectors in a lower-dimensional space while approximately preserving similarity relationships. Principal component analysis (PCA) is a common linear dimensionality reduction technique.
- Kernel PCA performs PCA in a higher-dimensional feature space implicitly defined by a kernel function. This allows PCA to find nonlinear structure in data. Kernel PCA computes the principal components by finding the eigenvectors of the normalized kernel matrix.
- For a new data point, its representation in the lower-dimensional space is given by projecting it onto the principal components in feature space using the kernel trick, without explicitly computing features.
These notes are a basic introduction to SVM, assuming almost no prior exposure. They contain some derivations, details, and explanations that not many SVM tutorials usually delve into. Thus, they're meant to augment primary course material (textbook or lecture notes) on SVMs and to help digest the course material.
System 1 and System 2 were basic early systems for image matching that used color and texture matching. Descriptor-based approaches like SIFT provided more invariance but not perfect invariance. Patch descriptors like SIFT were improved by making them more invariant to lighting changes like color and illumination shifts. The best performance came from combining descriptors with color invariance. Representing images as histograms of visual word occurrences captured patterns in local image patches and allowed measuring similarity between images. Large vocabularies of visual words provided more discriminative power but were costly to compute and store.
This document provides an overview of Linear Discriminant Analysis (LDA) for dimensionality reduction. LDA seeks to perform dimensionality reduction while preserving class discriminatory information as much as possible, unlike PCA which does not consider class labels. LDA finds a linear combination of features that separates classes best by maximizing the between-class variance while minimizing the within-class variance. This is achieved by solving the generalized eigenvalue problem involving the within-class and between-class scatter matrices. The document provides mathematical details and an example to illustrate LDA for a two-class problem.
This document provides an overview of partial derivatives, which are used to analyze functions with multiple variables. Key topics covered include:
- Definitions of limits, continuity, and partial derivatives for multivariable functions.
- Directional derivatives and the gradient, which describe the rate of change in a specified direction.
- The chain rule for partial derivatives, and implicit differentiation.
- Linearization and Taylor series approximations for multivariable functions.
- Finding local extrema and optimizing functions, using techniques like classifying critical points.
Bloom filters provide a space-efficient probabilistic data structure for representing a set in order to support membership queries. They allow false positives but no false negatives. The structure uses k hash functions to map elements to bit positions in a bit array. Querying whether an element is in the set checks if the corresponding bit positions are all set to 1. Modern applications include distributed caching, peer-to-peer networks, routing, and measurement infrastructure where Bloom filters trade off exact representation for speed and space efficiency.
This document provides an overview of support vector machines (SVM). It explains that SVM is a supervised machine learning algorithm used for classification and regression. It works by finding the optimal separating hyperplane that maximizes the margin between different classes of data points. The document discusses key SVM concepts like slack variables, kernels, hyperparameters like C and gamma, and how the kernel trick allows SVMs to fit non-linear decision boundaries.
Spectral clustering algorithms represent data as a weighted graph and cluster points based on the eigenvectors of matrices derived from the graph such as the Laplacian matrix. The algorithms involve constructing a matrix representation of the dataset, computing the eigenvalues and eigenvectors of the matrix to map points to a lower dimensional space, and then grouping points based on their mapping. Specifically, the algorithm maps points to components of the second eigenvector (Fiedler vector) of the Laplacian matrix to partition the graph into two clusters that minimize the cut between them.
The document provides an overview of concepts from a course on automata, computability, and complexity. It discusses expectations for prerequisites, collaboration policies, and examples of finite automata and the languages they recognize. The key points covered are:
- Finite automata are defined formally as 5-tuples representing states, alphabets, transitions, start states, and accepting states.
- Regular operations on languages, such as union, concatenation, and star are introduced.
- It is shown that the class of languages recognizable by finite automata (regular languages) is closed under all basic set operations on languages, including complement, intersection, union, and the regular operations. Proofs of closure properties use constructions to
This document discusses spatial indexing techniques for multidimensional point data. It describes grid files which partition space into grid cells, each associated with a disk page. It also covers tree-based methods like the kd-tree which partitions space recursively based on dimension values. Z-ordering and space-filling curves like the Hilbert curve are presented as mapping multidimensional points to a linear ordering to enable range queries on a B-tree. The document compares techniques and analyzes properties like the number of disk accesses for range queries.
Support vector machine in data mining.pdfRubhithaA
1. Support vector machines (SVMs) are a type of machine learning algorithm that learn nonlinear decision boundaries using kernel functions to transform data into higher dimensions.
2. SVMs find the optimal separating hyperplane that maximizes the margin between positive and negative examples. This hyperplane is determined by the support vectors, which are the data points closest to the decision boundary.
3. The SVM optimization problem involves minimizing a loss function subject to constraints. This can be solved using Lagrangian duality, which transforms the problem into an equivalent maximization problem over dual variables instead of the original weights and biases.
The document discusses various techniques for clustering data, including hierarchical clustering, k-means algorithms, and distance measures. It provides examples of how different types of data like documents, customer purchases, DNA sequences can be represented as vectors and clustered. Key clustering approaches described are hierarchical agglomerative clustering using different linkage criteria, k-means clustering and its variant BFR for large datasets.
This document provides an introduction to analog filters. It discusses the classification of filters as either digital or analog, and passive or active analog filters. It describes the basic types of filters - lowpass, highpass, bandpass and bandstop. Circuit examples are provided for passive lowpass, highpass, bandpass and bandstop filters using resistors, capacitors and inductors. An example is shown of designing a bandpass filter with a specified center frequency and bandwidth. Basic active filter circuits are also illustrated for lowpass, highpass, bandpass and bandstop filters using op-amps.
Support vector machines (SVMs) find the optimal separating hyperplane between two classes of data points that maximizes the margin between the classes. SVMs address nonlinear classification problems by using kernel functions to implicitly map inputs into high-dimensional feature spaces. The three key ideas of SVMs are: 1) Allowing for misclassified points using slack variables. 2) Seeking a large margin hyperplane for better generalization. 3) Using the "kernel trick" to efficiently perform computations in high-dimensional feature spaces without explicitly computing the mappings.
This document discusses self-similar sets and fractals, and provides examples of the von Koch curve and Peano's space-filling curve. It introduces the concept of Hausdorff dimension and measure as a way to quantify the dimension of fractal objects that fall between integer dimensions. Specifically, it shows that the von Koch curve has Hausdorff dimension of log(4)/log(3), explaining its properties of having infinite length but Lebesgue measure of zero.
- The document provides an introduction to linear algebra and MATLAB. It discusses various linear algebra concepts like vectors, matrices, tensors, and operations on them.
- It then covers key MATLAB topics - basic data types, vector and matrix operations, control flow, plotting, and writing efficient code.
- The document emphasizes how linear algebra and MATLAB are closely related and commonly used together in applications like image and signal processing.
CVPR2009 tutorial: Kernel Methods in Computer Vision: part I: Introduction to...zukun
The document summarizes an introduction to kernel classifiers presentation. It discusses how linear techniques like classification, regression, and dimensionality reduction are often successful due to smoothness and intuitiveness. However, linear classifiers may fail when data is not linearly separable. Kernel methods address this by projecting data into a higher-dimensional feature space where it may be linearly separable through the use of kernels.
I am Duncan V. I am a Single Variable Calculus Assignment Solver at mathhomeworksolver.com. I hold a Master's in Mathematics, from Manchester, United Kingdom. I have been helping students with their assignments for the past 12 years. I solved assignments related to Single Variable Calculus.
Visit mathhomeworksolver.com or email support@mathhomeworksolver.com. You can also call on +1 678 648 4277 for any assistance with Single Variable Calculus Assignment.
This document discusses vector spaces and subspaces. It begins by defining a vector space as a set V with two operations, vector addition and scalar multiplication, that satisfy certain properties. Examples of vector spaces include R2 and the space of real polynomials of degree n or less.
It then defines a subspace as a subset of a vector space that is itself a vector space under the inherited operations. For a subset to be a subspace, it must be closed under vector addition and scalar multiplication, and contain the zero vector. Examples given include lines and planes through the origin in R3.
The span of a set S of vectors is defined as the set of all linear combinations of the vectors in S, and it
TrustArc Webinar - 2024 Global Privacy SurveyTrustArc
How does your privacy program stack up against your peers? What challenges are privacy teams tackling and prioritizing in 2024?
In the fifth annual Global Privacy Benchmarks Survey, we asked over 1,800 global privacy professionals and business executives to share their perspectives on the current state of privacy inside and outside of their organizations. This year’s report focused on emerging areas of importance for privacy and compliance professionals, including considerations and implications of Artificial Intelligence (AI) technologies, building brand trust, and different approaches for achieving higher privacy competence scores.
See how organizational priorities and strategic approaches to data security and privacy are evolving around the globe.
This webinar will review:
- The top 10 privacy insights from the fifth annual Global Privacy Benchmarks Survey
- The top challenges for privacy leaders, practitioners, and organizations in 2024
- Key themes to consider in developing and maintaining your privacy program
These notes are a basic introduction to SVM, assuming almost no prior exposure. They contain some derivations, details, and explanations that not many SVM tutorials usually delve into. Thus, they're meant to augment primary course material (textbook or lecture notes) on SVMs and to help digest the course material.
System 1 and System 2 were basic early systems for image matching that used color and texture matching. Descriptor-based approaches like SIFT provided more invariance but not perfect invariance. Patch descriptors like SIFT were improved by making them more invariant to lighting changes like color and illumination shifts. The best performance came from combining descriptors with color invariance. Representing images as histograms of visual word occurrences captured patterns in local image patches and allowed measuring similarity between images. Large vocabularies of visual words provided more discriminative power but were costly to compute and store.
This document provides an overview of Linear Discriminant Analysis (LDA) for dimensionality reduction. LDA seeks to perform dimensionality reduction while preserving class discriminatory information as much as possible, unlike PCA which does not consider class labels. LDA finds a linear combination of features that separates classes best by maximizing the between-class variance while minimizing the within-class variance. This is achieved by solving the generalized eigenvalue problem involving the within-class and between-class scatter matrices. The document provides mathematical details and an example to illustrate LDA for a two-class problem.
This document provides an overview of partial derivatives, which are used to analyze functions with multiple variables. Key topics covered include:
- Definitions of limits, continuity, and partial derivatives for multivariable functions.
- Directional derivatives and the gradient, which describe the rate of change in a specified direction.
- The chain rule for partial derivatives, and implicit differentiation.
- Linearization and Taylor series approximations for multivariable functions.
- Finding local extrema and optimizing functions, using techniques like classifying critical points.
Bloom filters provide a space-efficient probabilistic data structure for representing a set in order to support membership queries. They allow false positives but no false negatives. The structure uses k hash functions to map elements to bit positions in a bit array. Querying whether an element is in the set checks if the corresponding bit positions are all set to 1. Modern applications include distributed caching, peer-to-peer networks, routing, and measurement infrastructure where Bloom filters trade off exact representation for speed and space efficiency.
This document provides an overview of support vector machines (SVM). It explains that SVM is a supervised machine learning algorithm used for classification and regression. It works by finding the optimal separating hyperplane that maximizes the margin between different classes of data points. The document discusses key SVM concepts like slack variables, kernels, hyperparameters like C and gamma, and how the kernel trick allows SVMs to fit non-linear decision boundaries.
Spectral clustering algorithms represent data as a weighted graph and cluster points based on the eigenvectors of matrices derived from the graph such as the Laplacian matrix. The algorithms involve constructing a matrix representation of the dataset, computing the eigenvalues and eigenvectors of the matrix to map points to a lower dimensional space, and then grouping points based on their mapping. Specifically, the algorithm maps points to components of the second eigenvector (Fiedler vector) of the Laplacian matrix to partition the graph into two clusters that minimize the cut between them.
The document provides an overview of concepts from a course on automata, computability, and complexity. It discusses expectations for prerequisites, collaboration policies, and examples of finite automata and the languages they recognize. The key points covered are:
- Finite automata are defined formally as 5-tuples representing states, alphabets, transitions, start states, and accepting states.
- Regular operations on languages, such as union, concatenation, and star are introduced.
- It is shown that the class of languages recognizable by finite automata (regular languages) is closed under all basic set operations on languages, including complement, intersection, union, and the regular operations. Proofs of closure properties use constructions to
This document discusses spatial indexing techniques for multidimensional point data. It describes grid files which partition space into grid cells, each associated with a disk page. It also covers tree-based methods like the kd-tree which partitions space recursively based on dimension values. Z-ordering and space-filling curves like the Hilbert curve are presented as mapping multidimensional points to a linear ordering to enable range queries on a B-tree. The document compares techniques and analyzes properties like the number of disk accesses for range queries.
Support vector machine in data mining.pdfRubhithaA
1. Support vector machines (SVMs) are a type of machine learning algorithm that learn nonlinear decision boundaries using kernel functions to transform data into higher dimensions.
2. SVMs find the optimal separating hyperplane that maximizes the margin between positive and negative examples. This hyperplane is determined by the support vectors, which are the data points closest to the decision boundary.
3. The SVM optimization problem involves minimizing a loss function subject to constraints. This can be solved using Lagrangian duality, which transforms the problem into an equivalent maximization problem over dual variables instead of the original weights and biases.
The document discusses various techniques for clustering data, including hierarchical clustering, k-means algorithms, and distance measures. It provides examples of how different types of data like documents, customer purchases, DNA sequences can be represented as vectors and clustered. Key clustering approaches described are hierarchical agglomerative clustering using different linkage criteria, k-means clustering and its variant BFR for large datasets.
This document provides an introduction to analog filters. It discusses the classification of filters as either digital or analog, and passive or active analog filters. It describes the basic types of filters - lowpass, highpass, bandpass and bandstop. Circuit examples are provided for passive lowpass, highpass, bandpass and bandstop filters using resistors, capacitors and inductors. An example is shown of designing a bandpass filter with a specified center frequency and bandwidth. Basic active filter circuits are also illustrated for lowpass, highpass, bandpass and bandstop filters using op-amps.
Support vector machines (SVMs) find the optimal separating hyperplane between two classes of data points that maximizes the margin between the classes. SVMs address nonlinear classification problems by using kernel functions to implicitly map inputs into high-dimensional feature spaces. The three key ideas of SVMs are: 1) Allowing for misclassified points using slack variables. 2) Seeking a large margin hyperplane for better generalization. 3) Using the "kernel trick" to efficiently perform computations in high-dimensional feature spaces without explicitly computing the mappings.
This document discusses self-similar sets and fractals, and provides examples of the von Koch curve and Peano's space-filling curve. It introduces the concept of Hausdorff dimension and measure as a way to quantify the dimension of fractal objects that fall between integer dimensions. Specifically, it shows that the von Koch curve has Hausdorff dimension of log(4)/log(3), explaining its properties of having infinite length but Lebesgue measure of zero.
- The document provides an introduction to linear algebra and MATLAB. It discusses various linear algebra concepts like vectors, matrices, tensors, and operations on them.
- It then covers key MATLAB topics - basic data types, vector and matrix operations, control flow, plotting, and writing efficient code.
- The document emphasizes how linear algebra and MATLAB are closely related and commonly used together in applications like image and signal processing.
CVPR2009 tutorial: Kernel Methods in Computer Vision: part I: Introduction to...zukun
The document summarizes an introduction to kernel classifiers presentation. It discusses how linear techniques like classification, regression, and dimensionality reduction are often successful due to smoothness and intuitiveness. However, linear classifiers may fail when data is not linearly separable. Kernel methods address this by projecting data into a higher-dimensional feature space where it may be linearly separable through the use of kernels.
I am Duncan V. I am a Single Variable Calculus Assignment Solver at mathhomeworksolver.com. I hold a Master's in Mathematics, from Manchester, United Kingdom. I have been helping students with their assignments for the past 12 years. I solved assignments related to Single Variable Calculus.
Visit mathhomeworksolver.com or email support@mathhomeworksolver.com. You can also call on +1 678 648 4277 for any assistance with Single Variable Calculus Assignment.
This document discusses vector spaces and subspaces. It begins by defining a vector space as a set V with two operations, vector addition and scalar multiplication, that satisfy certain properties. Examples of vector spaces include R2 and the space of real polynomials of degree n or less.
It then defines a subspace as a subset of a vector space that is itself a vector space under the inherited operations. For a subset to be a subspace, it must be closed under vector addition and scalar multiplication, and contain the zero vector. Examples given include lines and planes through the origin in R3.
The span of a set S of vectors is defined as the set of all linear combinations of the vectors in S, and it
TrustArc Webinar - 2024 Global Privacy SurveyTrustArc
How does your privacy program stack up against your peers? What challenges are privacy teams tackling and prioritizing in 2024?
In the fifth annual Global Privacy Benchmarks Survey, we asked over 1,800 global privacy professionals and business executives to share their perspectives on the current state of privacy inside and outside of their organizations. This year’s report focused on emerging areas of importance for privacy and compliance professionals, including considerations and implications of Artificial Intelligence (AI) technologies, building brand trust, and different approaches for achieving higher privacy competence scores.
See how organizational priorities and strategic approaches to data security and privacy are evolving around the globe.
This webinar will review:
- The top 10 privacy insights from the fifth annual Global Privacy Benchmarks Survey
- The top challenges for privacy leaders, practitioners, and organizations in 2024
- Key themes to consider in developing and maintaining your privacy program
HCL Notes und Domino Lizenzkostenreduzierung in der Welt von DLAUpanagenda
Webinar Recording: https://www.panagenda.com/webinars/hcl-notes-und-domino-lizenzkostenreduzierung-in-der-welt-von-dlau/
DLAU und die Lizenzen nach dem CCB- und CCX-Modell sind für viele in der HCL-Community seit letztem Jahr ein heißes Thema. Als Notes- oder Domino-Kunde haben Sie vielleicht mit unerwartet hohen Benutzerzahlen und Lizenzgebühren zu kämpfen. Sie fragen sich vielleicht, wie diese neue Art der Lizenzierung funktioniert und welchen Nutzen sie Ihnen bringt. Vor allem wollen Sie sicherlich Ihr Budget einhalten und Kosten sparen, wo immer möglich. Das verstehen wir und wir möchten Ihnen dabei helfen!
Wir erklären Ihnen, wie Sie häufige Konfigurationsprobleme lösen können, die dazu führen können, dass mehr Benutzer gezählt werden als nötig, und wie Sie überflüssige oder ungenutzte Konten identifizieren und entfernen können, um Geld zu sparen. Es gibt auch einige Ansätze, die zu unnötigen Ausgaben führen können, z. B. wenn ein Personendokument anstelle eines Mail-Ins für geteilte Mailboxen verwendet wird. Wir zeigen Ihnen solche Fälle und deren Lösungen. Und natürlich erklären wir Ihnen das neue Lizenzmodell.
Nehmen Sie an diesem Webinar teil, bei dem HCL-Ambassador Marc Thomas und Gastredner Franz Walder Ihnen diese neue Welt näherbringen. Es vermittelt Ihnen die Tools und das Know-how, um den Überblick zu bewahren. Sie werden in der Lage sein, Ihre Kosten durch eine optimierte Domino-Konfiguration zu reduzieren und auch in Zukunft gering zu halten.
Diese Themen werden behandelt
- Reduzierung der Lizenzkosten durch Auffinden und Beheben von Fehlkonfigurationen und überflüssigen Konten
- Wie funktionieren CCB- und CCX-Lizenzen wirklich?
- Verstehen des DLAU-Tools und wie man es am besten nutzt
- Tipps für häufige Problembereiche, wie z. B. Team-Postfächer, Funktions-/Testbenutzer usw.
- Praxisbeispiele und Best Practices zum sofortigen Umsetzen
Building Production Ready Search Pipelines with Spark and MilvusZilliz
Spark is the widely used ETL tool for processing, indexing and ingesting data to serving stack for search. Milvus is the production-ready open-source vector database. In this talk we will show how to use Spark to process unstructured data to extract vector representations, and push the vectors to Milvus vector database for search serving.
Driving Business Innovation: Latest Generative AI Advancements & Success StorySafe Software
Are you ready to revolutionize how you handle data? Join us for a webinar where we’ll bring you up to speed with the latest advancements in Generative AI technology and discover how leveraging FME with tools from giants like Google Gemini, Amazon, and Microsoft OpenAI can supercharge your workflow efficiency.
During the hour, we’ll take you through:
Guest Speaker Segment with Hannah Barrington: Dive into the world of dynamic real estate marketing with Hannah, the Marketing Manager at Workspace Group. Hear firsthand how their team generates engaging descriptions for thousands of office units by integrating diverse data sources—from PDF floorplans to web pages—using FME transformers, like OpenAIVisionConnector and AnthropicVisionConnector. This use case will show you how GenAI can streamline content creation for marketing across the board.
Ollama Use Case: Learn how Scenario Specialist Dmitri Bagh has utilized Ollama within FME to input data, create custom models, and enhance security protocols. This segment will include demos to illustrate the full capabilities of FME in AI-driven processes.
Custom AI Models: Discover how to leverage FME to build personalized AI models using your data. Whether it’s populating a model with local data for added security or integrating public AI tools, find out how FME facilitates a versatile and secure approach to AI.
We’ll wrap up with a live Q&A session where you can engage with our experts on your specific use cases, and learn more about optimizing your data workflows with AI.
This webinar is ideal for professionals seeking to harness the power of AI within their data management systems while ensuring high levels of customization and security. Whether you're a novice or an expert, gain actionable insights and strategies to elevate your data processes. Join us to see how FME and AI can revolutionize how you work with data!
Unlock the Future of Search with MongoDB Atlas_ Vector Search Unleashed.pdfMalak Abu Hammad
Discover how MongoDB Atlas and vector search technology can revolutionize your application's search capabilities. This comprehensive presentation covers:
* What is Vector Search?
* Importance and benefits of vector search
* Practical use cases across various industries
* Step-by-step implementation guide
* Live demos with code snippets
* Enhancing LLM capabilities with vector search
* Best practices and optimization strategies
Perfect for developers, AI enthusiasts, and tech leaders. Learn how to leverage MongoDB Atlas to deliver highly relevant, context-aware search results, transforming your data retrieval process. Stay ahead in tech innovation and maximize the potential of your applications.
#MongoDB #VectorSearch #AI #SemanticSearch #TechInnovation #DataScience #LLM #MachineLearning #SearchTechnology
Programming Foundation Models with DSPy - Meetup SlidesZilliz
Prompting language models is hard, while programming language models is easy. In this talk, I will discuss the state-of-the-art framework DSPy for programming foundation models with its powerful optimizers and runtime constraint system.
“An Outlook of the Ongoing and Future Relationship between Blockchain Technologies and Process-aware Information Systems.” Invited talk at the joint workshop on Blockchain for Information Systems (BC4IS) and Blockchain for Trusted Data Sharing (B4TDS), co-located with with the 36th International Conference on Advanced Information Systems Engineering (CAiSE), 3 June 2024, Limassol, Cyprus.
Goodbye Windows 11: Make Way for Nitrux Linux 3.5.0!SOFTTECHHUB
As the digital landscape continually evolves, operating systems play a critical role in shaping user experiences and productivity. The launch of Nitrux Linux 3.5.0 marks a significant milestone, offering a robust alternative to traditional systems such as Windows 11. This article delves into the essence of Nitrux Linux 3.5.0, exploring its unique features, advantages, and how it stands as a compelling choice for both casual users and tech enthusiasts.
Sudheer Mechineni, Head of Application Frameworks, Standard Chartered Bank
Discover how Standard Chartered Bank harnessed the power of Neo4j to transform complex data access challenges into a dynamic, scalable graph database solution. This keynote will cover their journey from initial adoption to deploying a fully automated, enterprise-grade causal cluster, highlighting key strategies for modelling organisational changes and ensuring robust disaster recovery. Learn how these innovations have not only enhanced Standard Chartered Bank’s data infrastructure but also positioned them as pioneers in the banking sector’s adoption of graph technology.
How to Get CNIC Information System with Paksim Ga.pptxdanishmna97
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GraphSummit Singapore | The Future of Agility: Supercharging Digital Transfor...Neo4j
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In this work, we equipped AFL, a popular fuzzer, with DIAR and examined two critical Linux libraries -- Libxml's xmllint, a tool for parsing xml documents, and Binutil's readelf, an essential debugging and security analysis command-line tool used to display detailed information about ELF (Executable and Linkable Format). Our preliminary results show that AFL+DIAR does not only discover new paths more quickly but also achieves higher coverage overall. This work thus showcases how starting with lean and optimized seeds can lead to faster, more comprehensive fuzzing campaigns -- and DIAR helps you find such seeds.
- These are slides of the talk given at IEEE International Conference on Software Testing Verification and Validation Workshop, ICSTW 2022.
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Welcome to UiPath Test Automation using UiPath Test Suite series part 6. In this session, we will cover Test Automation with generative AI and Open AI.
UiPath Test Automation with generative AI and Open AI webinar offers an in-depth exploration of leveraging cutting-edge technologies for test automation within the UiPath platform. Attendees will delve into the integration of generative AI, a test automation solution, with Open AI advanced natural language processing capabilities.
Throughout the session, participants will discover how this synergy empowers testers to automate repetitive tasks, enhance testing accuracy, and expedite the software testing life cycle. Topics covered include the seamless integration process, practical use cases, and the benefits of harnessing AI-driven automation for UiPath testing initiatives. By attending this webinar, testers, and automation professionals can gain valuable insights into harnessing the power of AI to optimize their test automation workflows within the UiPath ecosystem, ultimately driving efficiency and quality in software development processes.
What will you get from this session?
1. Insights into integrating generative AI.
2. Understanding how this integration enhances test automation within the UiPath platform
3. Practical demonstrations
4. Exploration of real-world use cases illustrating the benefits of AI-driven test automation for UiPath
Topics covered:
What is generative AI
Test Automation with generative AI and Open AI.
UiPath integration with generative AI
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
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5. Motivation
Applications:
Load balancing for parallel computing,
Preprocessing step for some road network algorithms,
Divide-and-conquer (e.g. VLSI design).
Possible approaches:
NP-hard, O(log n) best approximation [R¨cke ’08].
a
In practice: approximate solutions or high(er) running time.
Heuristics:
numerous very fast, good solvers.
often tailored to certain graph classes (e.g. road networks),
no guarantees on the quality of solution (let alone optimality).
We want exact algorithms!
6. Branch and Bound
Standard technique for solving NP-hard problems exactly.
We keep track of A, B ⊆ V . Want to find the best bisection
that is consistent with (A, B) (i.e. A ⊆ V1 , B ⊆ V2 ).
7. Branch and Bound
Standard technique for solving NP-hard problems exactly.
We keep track of A, B ⊆ V . Want to find the best bisection
that is consistent with (A, B) (i.e. A ⊆ V1 , B ⊆ V2 ).
Branch: pick v ∈ V (A ∪ B), split (A, B) into (A ∪ {v }, B)
and (A, B ∪ {v }).
8. Branch and Bound
Standard technique for solving NP-hard problems exactly.
We keep track of A, B ⊆ V . Want to find the best bisection
that is consistent with (A, B) (i.e. A ⊆ V1 , B ⊆ V2 ).
Branch: pick v ∈ V (A ∪ B), split (A, B) into (A ∪ {v }, B)
and (A, B ∪ {v }).
Bound: if L ≥ U, then we can throw (A, B) away, where
U: best known bisection (updated on-line),
L: lower bound on all bisections consistent with (A, B).
9. Branch and Bound
Standard technique for solving NP-hard problems exactly.
We keep track of A, B ⊆ V . Want to find the best bisection
that is consistent with (A, B) (i.e. A ⊆ V1 , B ⊆ V2 ).
Branch: pick v ∈ V (A ∪ B), split (A, B) into (A ∪ {v }, B)
and (A, B ∪ {v }).
Bound: if L ≥ U, then we can throw (A, B) away, where
U: best known bisection (updated on-line),
L: lower bound on all bisections consistent with (A, B).
Crucial ingredient: computing the lower bound L.
10. Known lower bounds
Linear programming [FMdSWW ’98] [S ’01]
hundreds of nodes, several hours,
Semidefinite programming [AFHM ’08]
up to 6000 nodes, several hours,
Combinatorial multicommodity flow solver [SST ’03]
hundreds of nodes, several hours,
Degree-based simple combinatorial bound [F ’05]
random graphs up to 50 nodes, several minutes.
11. Summary
Our result:
Exact combinatorial algorithm for graph bisection
works well for graphs with a small minimum bisection
road networks, VLSI instances, meshes...
solves much larger instances than previous approaches.
Main contributions:
new lower bound techniques
branching rules
novel decomposition technique
12. Flow-based lower bound
We have A, B ⊆ V and want to lower-bound all bisections
that are consistent with (A, B),
13. Flow-based lower bound
We have A, B ⊆ V and want to lower-bound all bisections
that are consistent with (A, B),
Obvious bound: min-cut (max-flow) between A and B,
14. Flow-based lower bound
We have A, B ⊆ V and want to lower-bound all bisections
that are consistent with (A, B),
Obvious bound: min-cut (max-flow) between A and B,
Pros:
easy to compute,
if cut is balanced, then we can update the upper bound,
15. Flow-based lower bound
We have A, B ⊆ V and want to lower-bound all bisections
that are consistent with (A, B),
Obvious bound: min-cut (max-flow) between A and B,
Pros:
easy to compute,
if cut is balanced, then we can update the upper bound,
Cons:
if |A| |B|, then max-flow is too small,
in sparse graphs minimum cuts are typically very unbalanced.
17. Packing lower bound
Let A, B ⊆ V be a current partial assignment,
˜
Let A be an extension of A of size |V |/2,
18. Packing lower bound
Let A, B ⊆ V be a current partial assignment,
˜
Let A be an extension of A of size |V |/2,
˜
Λ = minA cut(A, B) is a valid lower bound
˜
˜ ˜
cut(A, B): min-cut between A and B.
19. Packing lower bound
Let A, B ⊆ V be a current partial assignment,
˜
Let A be an extension of A of size |V |/2,
˜
Λ = minA cut(A, B) is a valid lower bound
˜
˜ ˜
cut(A, B): min-cut between A and B.
We want a lower bound for Λ.
21. Packing lower bound
We lower bound Λ as follows.
We partition free nodes into cells connected to B.
22. Packing lower bound
We lower bound Λ as follows.
We partition free nodes into cells connected to B.
If a subset of V of size n/2 hits k cells...
23. Packing lower bound
We lower bound Λ as follows.
We partition free nodes into cells connected to B.
If a subset of V of size n/2 hits k cells...
...we have a flow between the set and B of value k.
24. Packing lower bound
We lower bound Λ as follows.
We partition free nodes into cells connected to B.
If a subset of V of size n/2 hits k cells...
...we have a flow between the set and B of value k.
The worst case (lower bound) is when fewest cells are hit:
pick entire cells, from biggest to smallest.
⇒ balanced cells are better
25. Flow + Packing
We can combine flow and packing lower bounds by removing flow
edges before computing cells.
26. Flow + Packing
We can combine flow and packing lower bounds by removing flow
edges before computing cells.
28. Local search
For lower bound we pick cells from biggest to smallest,
Balanced cells are better,
Before local search
140
120
100
80
size
60
40
20
0
0 5 10 15 20 25 30 35
cell
29. Local search
For lower bound we pick cells from biggest to smallest,
Balanced cells are better,
We do very fast local search that swaps nodes between cells.
Before local search
140
After local search
120
100
80
size
60
40
20
0
0 5 10 15 20 25 30 35
cell
32. Branching rule and forced assignments
Branch on vertex likely to increase L the most:
1. far from A and B (to produce better cells)
2. well connected to other vertices (to increase flow)
Forced assignments:
use logical implications to fix some vertices to A or B
works if upper and lower bounds are close
eliminate many potential branching nodes
35. Branching on regions
Algorithm is very sensitive to the degrees of assigned nodes.
Idea: branch on larger (precomputed) regions.
Problem: a region can cross the minimum bisection.
36. Decomposition
Suppose we want to prove lower bound L.
Divide edges into L + 1 disjoint sets E1 , E2 , . . . , EL+1 .
37. Decomposition
Suppose we want to prove lower bound L.
Divide edges into L + 1 disjoint sets E1 , E2 , . . . , EL+1 .
38. Decomposition
Suppose we want to prove lower bound L.
Divide edges into L + 1 disjoint sets E1 , E2 , . . . , EL+1 .
39. Decomposition
Suppose we want to prove lower bound L.
Divide edges into L + 1 disjoint sets E1 , E2 , . . . , EL+1 .
40. Decomposition
Suppose we want to prove lower bound L.
Divide edges into L + 1 disjoint sets E1 , E2 , . . . , EL+1 .
41. Decomposition
Suppose we want to prove lower bound L.
Divide edges into L + 1 disjoint sets E1 , E2 , . . . , EL+1 .
42. Decomposition
Suppose we want to prove lower bound L.
Divide edges into L + 1 disjoint sets E1 , E2 , . . . , EL+1 .
For every i, contract Ei and solve a smaller problem.
43. Decomposition
Suppose we want to prove lower bound L.
Divide edges into L + 1 disjoint sets E1 , E2 , . . . , EL+1 .
For every i, contract Ei and solve a smaller problem.
The minimum bisection (of size ≤ L) can not intersect all Ei .
44. Decomposition
Suppose we want to prove lower bound L.
Divide edges into L + 1 disjoint sets E1 , E2 , . . . , EL+1 .
For every i, contract Ei and solve a smaller problem.
The minimum bisection (of size ≤ L) can not intersect all Ei .
Ei should be a set of clumps (high degree, well spread).
54. Walshaw instances
Standard benchmark for graph partitioning.
instance n m opt BB nodes time (s)
add32 4 960 9 462 11 225 3
uk 4 824 6 837 19 1 624 3
3elt 4 720 13 722 90 12 707 82
whitaker3 9 800 28 989 127 7 044 133
fe 4elt2 11 143 32 818 130 10 391 224
4elt 15 606 45 878 139 25 912 769
data 2 851 15 093 189 495 569 759 5 750 388
Optimum bisections were known before, but without proofs.
55. Instances from “exact” literature
State-of-the-art approaches:
[Arm07]: semidefinite programming
[HPZ11]: quadratic programming
instance n m opt time [Arm07] [HPZ11]
KKT putt01 m2 115 433 28 0.81 1.67 1.51
mesh.274.469 274 469 37 0.03 8.52 24.62
gap2669.24859 2669 29037 55 0.15 348.95 —
taq170.424 170 4317 55 3.00 28.68 —
gap2669.6182 2669 12280 74 34.90 651.03 —
taq1021.2253 1021 4510 118 134.61 169.65 —
Our algorithm is much worse when the answer is large.
56. Conclusions
Combinatorial branch-and-bound for graph bisections.
Answer size matters much more than problem size.
Challenge: solve Europe (18M vertices, 22.5M edges).