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.
This document summarizes the key concepts and components of Gremlin's graph traversal machinery:
- Gremlin uses a traversal language to express graph queries via step composition, with steps mapping traversers between domains.
- Traversals are compiled to bytecode and optimized by traversal strategies before being executed by the Gremlin machine.
- The Gremlin machine consists of steps implementing functions that process traverser streams. Their composition forms the traversal.
- Gremlin is language-agnostic, with language variants translating to a shared bytecode that interacts with the Java-based implementation.
CycleGAN is a framework that uses generative adversarial networks to perform image-to-image translation without paired training examples. It consists of two generators that map images from one domain to another and back, along with two discriminators that classify images as real or fake. The generators are trained to translate images such that they are classified as real by the discriminators, while also remaining consistent when translated back to the original domain. The authors demonstrate it for the task of colorizing grayscale images without paired color-grayscale image samples.
This presentation talks about the five SOLID principles of Object Oriented Design described by Rober C. Martin in his best-seller book "Agile Principles, Patterns, and Practices in C#". The five principle described are:
- Single Responsibility Principle
- Open-Close Principle
- Liskov Substitution Principle
- Interface Segregation Principle
- Dependency Inversion Principle
The presentation is took from the Software Engineering course I run in the bachelor-level informatics curriculum at the University of Padova.
Operator overloading allows operators to work with user-defined types by defining corresponding operator functions. This unit discusses overloading unary and binary operators as member or non-member functions, restrictions on operator overloading, and how inheritance and automatic type conversion relate to operator overloading. The key topics covered include how to overload operators, which operators can and cannot be overloaded, and potential issues with type conversion.
This document discusses implementation of inheritance in Java and C#. It covers key inheritance concepts like simple, multilevel, and hierarchical inheritance. It provides examples of inheritance in Java using keywords like extends, super, this. Interfaces are discussed as a way to achieve multiple inheritance in Java. The document also discusses implementation of inheritance in C# using concepts like calling base class constructors and defining virtual methods.
Inheritance in Object Oriented ProgrammingAshita Agrawal
Object oriented programming uses inheritance, where a derived class inherits properties from a base class. There are four main types of inheritance: single inheritance where a derived class has one base class; multiple inheritance where a derived class has multiple base classes; multilevel inheritance where a class inherits from another derived class; and hierarchical inheritance where one base class is inherited by multiple derived classes. Inheritance enables code reuse and is a fundamental concept of object oriented programming.
This document provides definitions and concepts related to graph theory. It begins with a brief history of graph theory and then defines basic concepts such as graphs, nodes, edges, adjacency, incidence, isomorphism, subgraphs, walks, trails, paths, connectedness, trees, and spanning trees. It also introduces different types of graphs including null graphs, cycle graphs, path graphs, complete graphs, bipartite graphs, and complete bipartite graphs. Finally, it discusses how vector spaces can be associated with graphs and defines the properties of cycle and cutset spaces.
This document summarizes the key concepts and components of Gremlin's graph traversal machinery:
- Gremlin uses a traversal language to express graph queries via step composition, with steps mapping traversers between domains.
- Traversals are compiled to bytecode and optimized by traversal strategies before being executed by the Gremlin machine.
- The Gremlin machine consists of steps implementing functions that process traverser streams. Their composition forms the traversal.
- Gremlin is language-agnostic, with language variants translating to a shared bytecode that interacts with the Java-based implementation.
CycleGAN is a framework that uses generative adversarial networks to perform image-to-image translation without paired training examples. It consists of two generators that map images from one domain to another and back, along with two discriminators that classify images as real or fake. The generators are trained to translate images such that they are classified as real by the discriminators, while also remaining consistent when translated back to the original domain. The authors demonstrate it for the task of colorizing grayscale images without paired color-grayscale image samples.
This presentation talks about the five SOLID principles of Object Oriented Design described by Rober C. Martin in his best-seller book "Agile Principles, Patterns, and Practices in C#". The five principle described are:
- Single Responsibility Principle
- Open-Close Principle
- Liskov Substitution Principle
- Interface Segregation Principle
- Dependency Inversion Principle
The presentation is took from the Software Engineering course I run in the bachelor-level informatics curriculum at the University of Padova.
Operator overloading allows operators to work with user-defined types by defining corresponding operator functions. This unit discusses overloading unary and binary operators as member or non-member functions, restrictions on operator overloading, and how inheritance and automatic type conversion relate to operator overloading. The key topics covered include how to overload operators, which operators can and cannot be overloaded, and potential issues with type conversion.
This document discusses implementation of inheritance in Java and C#. It covers key inheritance concepts like simple, multilevel, and hierarchical inheritance. It provides examples of inheritance in Java using keywords like extends, super, this. Interfaces are discussed as a way to achieve multiple inheritance in Java. The document also discusses implementation of inheritance in C# using concepts like calling base class constructors and defining virtual methods.
Inheritance in Object Oriented ProgrammingAshita Agrawal
Object oriented programming uses inheritance, where a derived class inherits properties from a base class. There are four main types of inheritance: single inheritance where a derived class has one base class; multiple inheritance where a derived class has multiple base classes; multilevel inheritance where a class inherits from another derived class; and hierarchical inheritance where one base class is inherited by multiple derived classes. Inheritance enables code reuse and is a fundamental concept of object oriented programming.
This document provides definitions and concepts related to graph theory. It begins with a brief history of graph theory and then defines basic concepts such as graphs, nodes, edges, adjacency, incidence, isomorphism, subgraphs, walks, trails, paths, connectedness, trees, and spanning trees. It also introduces different types of graphs including null graphs, cycle graphs, path graphs, complete graphs, bipartite graphs, and complete bipartite graphs. Finally, it discusses how vector spaces can be associated with graphs and defines the properties of cycle and cutset spaces.
The document discusses object-oriented programming concepts in C#, including defining classes, constructors, properties, static members, interfaces, inheritance, and polymorphism. It provides examples of defining a simple Cat class with fields, a constructor, properties, and methods. It also demonstrates using the Dog class by creating dog objects, setting their properties, and calling their bark method.
This document discusses abstract classes in C++. It defines an abstract class as a class designed to be used as a base class that cannot be instantiated and must contain at least one pure virtual function. It provides an example of how to declare an abstract class with a pure virtual function and how to derive a class from an abstract class, overriding the pure virtual functions. The importance of abstract classes is that they allow common functionality to be defined for derived classes while leaving implementation details to the derived classes.
Deep Learning A-Z™: Boltzmann Machines - Restricted Boltzmann MachineKirill Eremenko
The document discusses a neural network with visible and hidden nodes. It includes information about movies, genres, directors, actors and awards. The neural network is used to classify 6 movies into genres based on features of the movies like directors, actors, and whether they won specific awards.
What is Python Lambda Function? Python Tutorial | EdurekaEdureka!
YouTube Link: https://youtu.be/RQRCWDK9UkA
** Python Certification Training: https://www.edureka.co/python **
This Edureka PPT on 'Python Lambda' is to educate you about the Lambda functions of Python and help you understand how to use them in various scenarios. Below are the topics covered in this PPT:
What are Python Lambda functions?
Why are they used?
How to write anonymous functions?
Lambda functions within user-defined functions
Using Anonymous functions within
- filter()
- map()
- reduce()
Solving algebric expressions using Lambda
Follow us to never miss an update in the future.
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Inheritance allows new classes called derived classes to be created from existing classes called base classes. Derived classes inherit all features of the base class and can add new features. There are different types of inheritance including single, multilevel, multiple, hierarchical, and hybrid. A derived class can access public and protected members of the base class but not private members. Constructors and destructors of the base class are executed before and after those of the derived class respectively.
Inheritance in java is a mechanism in which one object acquires all the properties and behaviors of parent object. The idea behind inheritance in java is that you can create new classes that are built upon existing classes.
Gremlin is the graph traversal language of Apache TinkerPop, an open source graph computing framework, that is implemented by a great many graph databases, including DSE Graph. Even the most novice Gremlin user will recognize the Gremlin statement of "g.V()", but in this presentation we will stop to take a moment to understand the elements of that ubiquitous statement and the elements of the steps that append to it. With the foundational knowledge of "Gremlin's Anatomy" firmly held, we will perform an autopsy on an advanced Gremlin traversal and thus expose techniques for examining and taming the most complex and confusing Gremlin one might come across.
How to use Map() Filter() and Reduce() functions in Python | EdurekaEdureka!
Youtube Link: https://youtu.be/QxpbE5hDPws
** Python Certification Training: https://www.edureka.co/data-science-python-certification-course**
This Edureka PPT on 'map, filter, and reduce functions in Python' is to educate you about these very important built-in functions in Python. Below are the topics covered in this PPT:
Introduction to map filter reduce
The map() function
The filter() function
The reduce() function
Using map(),filter() and reduce() functions together
filter() within map()
map() within filter()
map() and filter() within reduce()
Follow us to never miss an update in the future.
YouTube: https://www.youtube.com/user/edurekaIN
Instagram: https://www.instagram.com/edureka_learning/
Facebook: https://www.facebook.com/edurekaIN/
Twitter: https://twitter.com/edurekain
LinkedIn: https://www.linkedin.com/company/edureka
Castbox: https://castbox.fm/networks/505?country=in
Part of Lecture series on EE646, Fuzzy Theory & Applications delivered by me during First Semester of M.Tech. Instrumentation & Control, 2012
Z H College of Engg. & Technology, Aligarh Muslim University, Aligarh
Reference Books:
1. T. J. Ross, "Fuzzy Logic with Engineering Applications", 2/e, John Wiley & Sons,England, 2004.
2. Lee, K. H., "First Course on Fuzzy Theory & Applications", Springer-Verlag,Berlin, Heidelberg, 2005.
3. D. Driankov, H. Hellendoorn, M. Reinfrank, "An Introduction to Fuzzy Control", Narosa, 2012.
Please comment and feel free to ask anything related. Thanks!
Chapter 02: Classes Objects and Methods Java by Tushar B KuteTushar B Kute
The lecture was condcuted by Tushar B Kute at YCMOU, Nashik through VLC orgnanized by MSBTE. The contents can be found in book "Core Java Programming - A Practical Approach' by Laxmi Publications.
This document provides an overview of convolutional neural networks (CNNs). It discusses the key steps in a CNN including convolution, ReLU activation, pooling, flattening and fully connected layers. Examples of feature maps being extracted at each convolution step are shown. CNNs have been very successful for tasks like image classification and are widely used by companies such as Google and Facebook.
The document summarizes improvements made in MobileNetV3 models, including using complementary search techniques to find efficient building blocks, modifying nonlinearities like h-swish to be more efficient, and improving expensive layers through techniques like removing unnecessary projections. It also describes experiments that showed MobileNetV3 models achieving better performance versus V1/V2 models on tasks like image classification, object detection, and semantic segmentation while maintaining high efficiency for mobile applications.
Command-line arguments are given after the name of the program in command-line shell of Operating Systems.
To pass command line arguments, we typically define main() with two arguments : first argument is the number of command line arguments and second is list of command-line arguments.
This document discusses encapsulation and methods in C#. It defines encapsulation as enclosing items within a package to prevent access to implementation details. Access specifiers like public, private, protected, internal and protected internal control the scope and visibility of class members. Methods are defined with access specifiers, return types, parameters, and a method body. Parameters can be passed by value, reference, or as output parameters. Examples demonstrate defining and calling methods as well as different ways of passing parameters.
StarGAN is a method for multi-domain image-to-image translation using a single model. It uses an adversarial loss with gradient penalty to train the discriminator. The generator is trained to translate images to different domains based on a target label, reconstruct the original image, and minimize classification and adversarial losses. StarGAN can be trained on multiple datasets by using mask vectors to ignore unknown domain labels. It achieves high quality image translation across different facial attributes and expressions.
The document provides an overview of key concepts in neural networks including neurons, activation functions, how neural networks work and learn. It discusses gradient descent and backpropagation as methods for neural networks to learn by minimizing a cost function comparing predicted and actual outputs. Examples are given of single and multi-layer neural networks for problems like predicting housing prices from input features.
The document discusses various graph algorithms and representations including:
- Adjacency lists and matrices for representing graphs
- Breadth-first search (BFS) which explores edges from a source vertex s level-by-level
- Depth-first search (DFS) which explores "deeper" first, producing a depth-first forest
- Classifying edges as tree, back, forward, or cross based on vertex colors in DFS
- Topological sorting of directed acyclic graphs (DAGs)
- Strongly connected components (SCCs) in directed graphs and using the transpose
Inheritance Introduction, Why and when to use Inheritance?, Modes of Inheritance(public, protected, private), Types of Inheritance- (single, multiple, multilevel, hierarchical, hybrid, multipath)
This document discusses different approaches to identifying clusters or "assemblages" in graph data. It defines assemblages as dense subgraphs with more internal than external connections. Several algorithms are described for finding assemblages, including k-medoids, Newman-Girvan, Louvain, and MCL. Evaluation metrics like modularity and weighted community clustering are also covered. The document aims to explain how to analyze real-world network data to discover meaningful assemblages.
1) The document discusses gossip protocols, which spread information randomly like human gossip or epidemics. Gossip protocols are used for applications like peer sampling, data aggregation, and failure detection.
2) Theoretical aspects of gossip protocols are analyzed, including the probability of partitioning a network, time until partitioning occurs, and bounds on node in-degrees. Simulation results on these metrics are also presented.
3) Several gossip protocols are summarized, including Cyclon, Scamp, and NewsCast. Cyclon incorporates elements like timestamps to improve load balancing and failure detection. Scamp uses partial views and subscription messages to balance loads. NewsCast aggregates information across a dynamic network in a robust manner.
The document discusses object-oriented programming concepts in C#, including defining classes, constructors, properties, static members, interfaces, inheritance, and polymorphism. It provides examples of defining a simple Cat class with fields, a constructor, properties, and methods. It also demonstrates using the Dog class by creating dog objects, setting their properties, and calling their bark method.
This document discusses abstract classes in C++. It defines an abstract class as a class designed to be used as a base class that cannot be instantiated and must contain at least one pure virtual function. It provides an example of how to declare an abstract class with a pure virtual function and how to derive a class from an abstract class, overriding the pure virtual functions. The importance of abstract classes is that they allow common functionality to be defined for derived classes while leaving implementation details to the derived classes.
Deep Learning A-Z™: Boltzmann Machines - Restricted Boltzmann MachineKirill Eremenko
The document discusses a neural network with visible and hidden nodes. It includes information about movies, genres, directors, actors and awards. The neural network is used to classify 6 movies into genres based on features of the movies like directors, actors, and whether they won specific awards.
What is Python Lambda Function? Python Tutorial | EdurekaEdureka!
YouTube Link: https://youtu.be/RQRCWDK9UkA
** Python Certification Training: https://www.edureka.co/python **
This Edureka PPT on 'Python Lambda' is to educate you about the Lambda functions of Python and help you understand how to use them in various scenarios. Below are the topics covered in this PPT:
What are Python Lambda functions?
Why are they used?
How to write anonymous functions?
Lambda functions within user-defined functions
Using Anonymous functions within
- filter()
- map()
- reduce()
Solving algebric expressions using Lambda
Follow us to never miss an update in the future.
YouTube: https://www.youtube.com/user/edurekaIN
Instagram: https://www.instagram.com/edureka_learning/
Facebook: https://www.facebook.com/edurekaIN/
Twitter: https://twitter.com/edurekain
LinkedIn: https://www.linkedin.com/company/edureka
Castbox: https://castbox.fm/networks/505?country=in
Inheritance allows new classes called derived classes to be created from existing classes called base classes. Derived classes inherit all features of the base class and can add new features. There are different types of inheritance including single, multilevel, multiple, hierarchical, and hybrid. A derived class can access public and protected members of the base class but not private members. Constructors and destructors of the base class are executed before and after those of the derived class respectively.
Inheritance in java is a mechanism in which one object acquires all the properties and behaviors of parent object. The idea behind inheritance in java is that you can create new classes that are built upon existing classes.
Gremlin is the graph traversal language of Apache TinkerPop, an open source graph computing framework, that is implemented by a great many graph databases, including DSE Graph. Even the most novice Gremlin user will recognize the Gremlin statement of "g.V()", but in this presentation we will stop to take a moment to understand the elements of that ubiquitous statement and the elements of the steps that append to it. With the foundational knowledge of "Gremlin's Anatomy" firmly held, we will perform an autopsy on an advanced Gremlin traversal and thus expose techniques for examining and taming the most complex and confusing Gremlin one might come across.
How to use Map() Filter() and Reduce() functions in Python | EdurekaEdureka!
Youtube Link: https://youtu.be/QxpbE5hDPws
** Python Certification Training: https://www.edureka.co/data-science-python-certification-course**
This Edureka PPT on 'map, filter, and reduce functions in Python' is to educate you about these very important built-in functions in Python. Below are the topics covered in this PPT:
Introduction to map filter reduce
The map() function
The filter() function
The reduce() function
Using map(),filter() and reduce() functions together
filter() within map()
map() within filter()
map() and filter() within reduce()
Follow us to never miss an update in the future.
YouTube: https://www.youtube.com/user/edurekaIN
Instagram: https://www.instagram.com/edureka_learning/
Facebook: https://www.facebook.com/edurekaIN/
Twitter: https://twitter.com/edurekain
LinkedIn: https://www.linkedin.com/company/edureka
Castbox: https://castbox.fm/networks/505?country=in
Part of Lecture series on EE646, Fuzzy Theory & Applications delivered by me during First Semester of M.Tech. Instrumentation & Control, 2012
Z H College of Engg. & Technology, Aligarh Muslim University, Aligarh
Reference Books:
1. T. J. Ross, "Fuzzy Logic with Engineering Applications", 2/e, John Wiley & Sons,England, 2004.
2. Lee, K. H., "First Course on Fuzzy Theory & Applications", Springer-Verlag,Berlin, Heidelberg, 2005.
3. D. Driankov, H. Hellendoorn, M. Reinfrank, "An Introduction to Fuzzy Control", Narosa, 2012.
Please comment and feel free to ask anything related. Thanks!
Chapter 02: Classes Objects and Methods Java by Tushar B KuteTushar B Kute
The lecture was condcuted by Tushar B Kute at YCMOU, Nashik through VLC orgnanized by MSBTE. The contents can be found in book "Core Java Programming - A Practical Approach' by Laxmi Publications.
This document provides an overview of convolutional neural networks (CNNs). It discusses the key steps in a CNN including convolution, ReLU activation, pooling, flattening and fully connected layers. Examples of feature maps being extracted at each convolution step are shown. CNNs have been very successful for tasks like image classification and are widely used by companies such as Google and Facebook.
The document summarizes improvements made in MobileNetV3 models, including using complementary search techniques to find efficient building blocks, modifying nonlinearities like h-swish to be more efficient, and improving expensive layers through techniques like removing unnecessary projections. It also describes experiments that showed MobileNetV3 models achieving better performance versus V1/V2 models on tasks like image classification, object detection, and semantic segmentation while maintaining high efficiency for mobile applications.
Command-line arguments are given after the name of the program in command-line shell of Operating Systems.
To pass command line arguments, we typically define main() with two arguments : first argument is the number of command line arguments and second is list of command-line arguments.
This document discusses encapsulation and methods in C#. It defines encapsulation as enclosing items within a package to prevent access to implementation details. Access specifiers like public, private, protected, internal and protected internal control the scope and visibility of class members. Methods are defined with access specifiers, return types, parameters, and a method body. Parameters can be passed by value, reference, or as output parameters. Examples demonstrate defining and calling methods as well as different ways of passing parameters.
StarGAN is a method for multi-domain image-to-image translation using a single model. It uses an adversarial loss with gradient penalty to train the discriminator. The generator is trained to translate images to different domains based on a target label, reconstruct the original image, and minimize classification and adversarial losses. StarGAN can be trained on multiple datasets by using mask vectors to ignore unknown domain labels. It achieves high quality image translation across different facial attributes and expressions.
The document provides an overview of key concepts in neural networks including neurons, activation functions, how neural networks work and learn. It discusses gradient descent and backpropagation as methods for neural networks to learn by minimizing a cost function comparing predicted and actual outputs. Examples are given of single and multi-layer neural networks for problems like predicting housing prices from input features.
The document discusses various graph algorithms and representations including:
- Adjacency lists and matrices for representing graphs
- Breadth-first search (BFS) which explores edges from a source vertex s level-by-level
- Depth-first search (DFS) which explores "deeper" first, producing a depth-first forest
- Classifying edges as tree, back, forward, or cross based on vertex colors in DFS
- Topological sorting of directed acyclic graphs (DAGs)
- Strongly connected components (SCCs) in directed graphs and using the transpose
Inheritance Introduction, Why and when to use Inheritance?, Modes of Inheritance(public, protected, private), Types of Inheritance- (single, multiple, multilevel, hierarchical, hybrid, multipath)
This document discusses different approaches to identifying clusters or "assemblages" in graph data. It defines assemblages as dense subgraphs with more internal than external connections. Several algorithms are described for finding assemblages, including k-medoids, Newman-Girvan, Louvain, and MCL. Evaluation metrics like modularity and weighted community clustering are also covered. The document aims to explain how to analyze real-world network data to discover meaningful assemblages.
1) The document discusses gossip protocols, which spread information randomly like human gossip or epidemics. Gossip protocols are used for applications like peer sampling, data aggregation, and failure detection.
2) Theoretical aspects of gossip protocols are analyzed, including the probability of partitioning a network, time until partitioning occurs, and bounds on node in-degrees. Simulation results on these metrics are also presented.
3) Several gossip protocols are summarized, including Cyclon, Scamp, and NewsCast. Cyclon incorporates elements like timestamps to improve load balancing and failure detection. Scamp uses partial views and subscription messages to balance loads. NewsCast aggregates information across a dynamic network in a robust manner.
Randomness conductors are a general framework that unifies various combinatorial objects like expanders, extractors, condensers, and universal hash functions. They can transform a probability distribution X with a certain amount of "entropy" into another distribution X' with a specified amount of entropy. The document discusses how expanders, extractors, and other objects are special cases of randomness conductors. It also describes how zigzag graph products can be used to construct explicit constant-degree randomness conductors and discusses some open problems in further studying and constructing these objects.
Slides of a talk at CMU Theory lunch (http://www.cs.cmu.edu/~theorylunch/20111116.html) and Capital Area Theory seminar (http://www.cs.umd.edu/areas/Theory/CATS/#Grigory).
This document discusses techniques for reducing crosstalk and delay in VLSI interconnects. Crosstalk occurs when signals on neighboring wires capacitively couple, increasing delay and introducing noise. Wider wires and increased spacing reduce capacitance but increase area. Shielding critical wires and layer assignment can prevent coupling. Repeater insertion breaks long wires into shorter segments to reduce delay. Optimal repeater placement is equally spaced. Staggering wire layout and using differential signaling also mitigate crosstalk.
Vlsi physical design automation on partitioningSushil Kundu
This document provides an introduction to VLSI physical design automation and partitioning. It discusses the importance of partitioning large circuits into smaller subcircuits for manageable design. The objectives of partitioning are to minimize the number of partitions and interconnections between partitions. Common partitioning algorithms discussed include min-cut bipartitioning, Kernighan-Lin iterative improvement algorithm, and other methods like ratio cut, genetic algorithms, and simulated annealing. Partitioning is an essential step in the physical design flow and impacts circuit performance and layout costs.
This document provides information about calculating volumes of solids of revolution using integral calculus. It discusses the disk method and washer method for setting up integrals to solve for volumes. Examples are provided for finding the volume of a solid rotated about the x-axis using each method. Students are reminded of the definition of a definite integral and the process for calculating areas. Helpful links and sources are listed at the end.
Mesh Generation and Topological Data AnalysisDon Sheehy
The document discusses mesh generation as a preprocessing step for topological data analysis (TDA). It describes how mesh generation can be used to decompose a domain into simple elements to approximate functions and compute persistence diagrams. Specifically, generating a quality Voronoi mesh allows the Voronoi filtration to approximate the sublevel filtration of the function and provide a good approximation of the persistence diagram. While meshing may not seem like an obvious approach for TDA, the document argues it can provide the necessary geometric and topological guarantees to make it a valid preprocessing step.
This document introduces the concept of average sensitivity of algorithms and summarizes results for several graph algorithms. It defines average sensitivity as the average change in an algorithm's output when a single input element is changed. The document presents algorithms for minimum spanning tree, minimum cuts, and matching problems that have low average sensitivity. It argues that average sensitivity is an important dimension for understanding the stability of algorithms and their practical use with noisy real-world data.
This document proposes a fast single-pass k-means clustering algorithm. It begins by discussing the rationale and theory behind k-means clustering, focusing on using it to enable fast search through large datasets. It then describes the ball k-means and surrogate methods algorithms, explaining how they provide provably better clustering for highly clusterable data. Implementation details are covered regarding search techniques, vector representations, and parallelization. Evaluation results show the approach works well on synthetic and real-world datasets, providing an order of magnitude speed improvement over traditional k-means while maintaining clustering quality. The document concludes by discussing applications for nearest neighbor search through large customer datasets.
This document discusses diagnosing issues with NMR hardware, including spectrometers, probes, and test equipment. It provides tips for checking if problems are with the transmitter, receiver, or probe. Simple ways to test components include using an oscilloscope to check for RF pulses from the transmitter and injecting a test signal to check the receiver. The document also provides guidance on building a simple probe, including how to calculate inductance and choose capacitors for resonance tuning and impedance matching. Attenuators, signal generators, and oscilloscopes that can be suitable for troubleshooting are also discussed.
An automated and user-friendly optical tweezers for biomolecular investigat...Dr. Pranav Rathi
An automated optical tweezers system was designed and constructed for biomolecular investigations. Key aspects included automation and control of the tweezers, calibration of stiffness and sensitivity, and DNA sample preparation and experiments. Results showed DNA overstretching and unzipping experiments in both water and heavy water. Future work will focus on further automation and investigating DNA-protein interactions.
Separation of Macromolecules by Their Size: The Mean Span Dimensioncypztm
Size Exclusion Chromatograpphy (SEC, also called GPC) separates polymer molecules by their size in dilute solution, but which size parameter to use has been a matter of debate. This presentation contains a brief summary of our work on this problem.
This document discusses adiabatic gate teleportation and its applications. It begins with an overview of joint work done by Dave Bacon of the University of Washington along with Steve Flammia, Alice Neels, and Andrew Landahl on this topic. The rest of the document discusses the history of classical computing using unreliable components, ideas from Kitaev and Freedman on topological quantum computing using anyons, and an open controversy around whether topological quantum computing is truly fault-tolerant.
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.
The document discusses connected dominating sets and short cycles. It begins by explaining that excluding longer cycles makes related problems easier to solve. Specifically, it shows that on graphs with girth at least five, high degree vertices must be in any minimum dominating set. However, this does not hold for connected dominating sets, since connectivity must also be maintained. It then describes how to obtain fixed-parameter tractable algorithms for connected dominating set problems by guessing the minimum dominating set and extending it. It also shows that these problems do not admit polynomial kernels by providing a reduction from Fair Connected Colors, which is W-hard.
This document describes a fast single-pass k-means clustering algorithm. It begins with an overview and rationale for using k-means clustering to enable fast search through large datasets. It then covers the theory behind clusterable data and k-means failure modes. The document outlines ball k-means and surrogate clustering algorithms. It discusses how to implement fast vector search methods like locality sensitive hashing. The document presents results on synthetic datasets and discusses applications like customer segmentation for a company with 100 million customers.
The document summarizes a presentation on using lattice quantum chromodynamics (LQCD) to study hadron structure. LQCD allows for first principles calculations of properties of hadrons like baryon masses and the quark content of nucleons by performing numerical simulations on discrete spacetime lattices. Results shown include baryon masses calculated with various lattice spacings and quark masses. Nucleon structure quantities like spin require more complex three-point correlation functions on the lattice.
This document provides an overview of VLSI physical design automation. It begins with introducing the intended audience for VLSI CAD, which includes VLSI students, circuit designers, process engineers, and those interested in solving hard computational problems. The objectives of VLSI layout design are then outlined, which are to review fabrication materials and processes, understand the basic algorithm concepts used in layout design, and learn about state-of-the-art academic and commercial physical design automation techniques. The document then describes the basic steps in the physical design cycle, including partitioning, floorplanning, placement, routing, and compaction. Circuit partitioning is discussed in more detail, including definitions, formulations, representation, iterative algorithms like Kernighan-Lin, and other
AI 101: An Introduction to the Basics and Impact of Artificial IntelligenceIndexBug
Imagine a world where machines not only perform tasks but also learn, adapt, and make decisions. This is the promise of Artificial Intelligence (AI), a technology that's not just enhancing our lives but revolutionizing entire industries.
GraphSummit Singapore | The Future of Agility: Supercharging Digital Transfor...Neo4j
Leonard Jayamohan, Partner & Generative AI Lead, Deloitte
This keynote will reveal how Deloitte leverages Neo4j’s graph power for groundbreaking digital twin solutions, achieving a staggering 100x performance boost. Discover the essential role knowledge graphs play in successful generative AI implementations. Plus, get an exclusive look at an innovative Neo4j + Generative AI solution Deloitte is developing in-house.
UiPath Test Automation using UiPath Test Suite series, part 5DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 5. In this session, we will cover CI/CD with devops.
Topics covered:
CI/CD with in UiPath
End-to-end overview of CI/CD pipeline with Azure devops
Speaker:
Lyndsey Byblow, Test Suite Sales Engineer @ UiPath, Inc.
Best 20 SEO Techniques To Improve Website Visibility In SERPPixlogix Infotech
Boost your website's visibility with proven SEO techniques! Our latest blog dives into essential strategies to enhance your online presence, increase traffic, and rank higher on search engines. From keyword optimization to quality content creation, learn how to make your site stand out in the crowded digital landscape. Discover actionable tips and expert insights to elevate your SEO game.
Unlocking Productivity: Leveraging the Potential of Copilot in Microsoft 365, a presentation by Christoforos Vlachos, Senior Solutions Manager – Modern Workplace, Uni Systems
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.
Essentials of Automations: The Art of Triggers and Actions in FMESafe Software
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We’ll kick things off by showcasing the most commonly used event-based triggers, introducing you to various automation workflows like manual triggers, schedules, directory watchers, and more. Plus, see how these elements play out in real scenarios.
Whether you’re tweaking your current setup or building from the ground up, this session will arm you with the tools and insights needed to transform your FME usage into a powerhouse of productivity. Join us to discover effective strategies that simplify complex processes, enhancing your productivity and transforming your data management practices with FME. Let’s turn complexity into clarity and make your workspaces work wonders!
How to Get CNIC Information System with Paksim Ga.pptxdanishmna97
Pakdata Cf is a groundbreaking system designed to streamline and facilitate access to CNIC information. This innovative platform leverages advanced technology to provide users with efficient and secure access to their CNIC details.
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
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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.
HCL Notes and Domino License Cost Reduction in the World of DLAUpanagenda
Webinar Recording: https://www.panagenda.com/webinars/hcl-notes-and-domino-license-cost-reduction-in-the-world-of-dlau/
The introduction of DLAU and the CCB & CCX licensing model caused quite a stir in the HCL community. As a Notes and Domino customer, you may have faced challenges with unexpected user counts and license costs. You probably have questions on how this new licensing approach works and how to benefit from it. Most importantly, you likely have budget constraints and want to save money where possible. Don’t worry, we can help with all of this!
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Join HCL Ambassador Marc Thomas in this webinar with a special guest appearance from Franz Walder. It will give you the tools and know-how to stay on top of what is going on with Domino licensing. You will be able lower your cost through an optimized configuration and keep it low going forward.
These topics will be covered
- Reducing license cost by finding and fixing misconfigurations and superfluous accounts
- How do CCB and CCX licenses really work?
- Understanding the DLAU tool and how to best utilize it
- Tips for common problem areas, like team mailboxes, functional/test users, etc
- Practical examples and best practices to implement right away
TrustArc Webinar - 2024 Global Privacy SurveyTrustArc
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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.
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UiPath Test Automation using UiPath Test Suite series, part 6DianaGray10
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
Climate Impact of Software Testing at Nordic Testing DaysKari Kakkonen
My slides at Nordic Testing Days 6.6.2024
Climate impact / sustainability of software testing discussed on the talk. ICT and testing must carry their part of global responsibility to help with the climat warming. We can minimize the carbon footprint but we can also have a carbon handprint, a positive impact on the climate. Quality characteristics can be added with sustainability, and then measured continuously. Test environments can be used less, and in smaller scale and on demand. Test techniques can be used in optimizing or minimizing number of tests. Test automation can be used to speed up testing.
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ABSTRACT: A prima vista, un mattoncino Lego e la backdoor XZ potrebbero avere in comune il fatto di essere entrambi blocchi di costruzione, o dipendenze di progetti creativi e software. La realtà è che un mattoncino Lego e il caso della backdoor XZ hanno molto di più di tutto ciò in comune.
Partecipate alla presentazione per immergervi in una storia di interoperabilità, standard e formati aperti, per poi discutere del ruolo importante che i contributori hanno in una comunità open source sostenibile.
BIO: Sostenitrice del software libero e dei formati standard e aperti. È stata un membro attivo dei progetti Fedora e openSUSE e ha co-fondato l'Associazione LibreItalia dove è stata coinvolta in diversi eventi, migrazioni e formazione relativi a LibreOffice. In precedenza ha lavorato a migrazioni e corsi di formazione su LibreOffice per diverse amministrazioni pubbliche e privati. Da gennaio 2020 lavora in SUSE come Software Release Engineer per Uyuni e SUSE Manager e quando non segue la sua passione per i computer e per Geeko coltiva la sua curiosità per l'astronomia (da cui deriva il suo nickname deneb_alpha).
Sudheer Mechineni, Head of Application Frameworks, Standard Chartered Bank
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Goodbye Windows 11: Make Way for Nitrux Linux 3.5.0!SOFTTECHHUB
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Observability Concepts EVERY Developer Should Know -- DeveloperWeek Europe.pdfPaige Cruz
Monitoring and observability aren’t traditionally found in software curriculums and many of us cobble this knowledge together from whatever vendor or ecosystem we were first introduced to and whatever is a part of your current company’s observability stack.
While the dev and ops silo continues to crumble….many organizations still relegate monitoring & observability as the purview of ops, infra and SRE teams. This is a mistake - achieving a highly observable system requires collaboration up and down the stack.
I, a former op, would like to extend an invitation to all application developers to join the observability party will share these foundational concepts to build on:
Observability Concepts EVERY Developer Should Know -- DeveloperWeek Europe.pdf
Graph Partition with Natural Cuts
1. PUNCH
Partitioning Using Natural-Cut Heuristics
Daniel Delling (Microsoft Research)
Andrew V. Goldberg (Microsoft Research)
Ilya Razenshteyn (Moscow University)
Renato F. Werneck (Microsoft Research)
May 19, 2010
2. Motivation
Goal: process a continental-sized road network in parallel
(Europe: 18M nodes and 43M arcs).
The first natural step: divide it into “small” parts with few
arcs between them.
Partition problems are NP-hard, but routinely solved using
different heuristics.
4. Applications: routing on road networks
Idea: Precompute distances between boundary nodes of each cell.
Overlay Graph:
Nodes — boundary nodes
Edges between boundary nodes,
Search Graph:
t
Source and target cell,
s
Overlay graph,
Use bidirectional Dijkstra
Number of cut edges affects the performance heavily.
More applications: arc-flags and reach.
5. Existing solvers
METIS [KK’99], SCOTCH [PR’96], KAPPA [HSS’10],
KASPAR [OS’10], KAFFPA [SS’10]. General purpose, some
are fast, some produce very good solution.
There are many more . . .
Our goal: partitioner tailored to road networks,
emphasize quality, still fast enough in practice.
6. Formal definition
Input: undirected graph G = (V , E ).
Result: partition (V = V1 ∪ V2 ∪ . . . ∪ Vk , with Vi ∩ Vj = ∅).
Goal: minimize number of edges between Vi .
Two common variants:
given U, require |Vi | ≤ U for every i,
given k and , require |Vi | ≤ (1 + ) n/k .
We focus on the first one.
Rebalancing is possible.
7. Intuition
Road networks: dense regions (grids, cities) interleaved with
natural cuts (mountains, parks, rivers, deserts, sparse areas,
freeways).
8. Summary of the algorithm
Filtering:
Contracts dense regions,
Reduces graph size,
Preserves natural cuts structure.
Assembly phase:
Works with much smaller graph,
Finds actual partition.
9. Outline of the talk
Introduction
Natural cuts
Assembly phase
Experiments
Conclusion
10. Outline of the talk
Introduction
Natural cuts
Assembly phase
Experiments
Conclusion
11. Natural cuts
Sparse sets that
separate dense areas.
Minimum s–t cuts are
trivial (average degree
< 3).
Sparsest cuts would be
OK, but they are
intractable.
Our notion of natural
cut is both tractable and
useful.
12. Natural cuts
Pick centers in a
randomized manner.
v Compute minimum cut
between the core and the
ring.
Repeat until every node
is inside of at least two
cores.
13. Natural cuts
Pick centers in a
randomized manner.
v Compute minimum cut
between the core and the
U/10 nodes ring.
Repeat until every node
is inside of at least two
cores.
14. Natural cuts
Pick centers in a
randomized manner.
v Compute minimum cut
between the core and the
U/10 nodes ring.
Repeat until every node
is inside of at least two
cores.
U nodes
15. Natural cuts
Pick centers in a
randomized manner.
v Compute minimum cut
between the core and the
U/10 nodes ring.
Repeat until every node
is inside of at least two
cores.
U nodes
16. Natural cuts
Take a union of all natural cuts found and contract everything
between them.
The resulting graph is much smaller than the original one.
U = 106 — 18M nodes to 10K nodes
U = 103 — 18M nodes to 1.3M nodes
17. Natural cuts
Take a union of all natural cuts found and contract everything
between them.
The resulting graph is much smaller than the original one.
U = 106 — 18M nodes to 10K nodes
U = 103 — 18M nodes to 1.3M nodes
18. Natural cuts
Take a union of all natural cuts found and contract everything
between them.
The resulting graph is much smaller than the original one.
U = 106 — 18M nodes to 10K nodes
U = 103 — 18M nodes to 1.3M nodes
19. Tiny cuts
The most obvious natural cuts — 1-cuts and 2-cuts.
We handle them explicitly before processing natural cuts.
Greatly decreases graph size (by half) and overall running
time,
20. Outline of the talk
Introduction
Natural cuts
Assembly phase
Experiments
Conclusion
21. Assembly phase
Three ingredients:
Greedy algorithm,
Local search,
Multistart and combination heuristics (optional).
22. Greedy algorithm
We combine well-connected small fragments in a randomized
fashion.
Repeat until maximal.
Finds initial partition.
23. Greedy algorithm
We combine well-connected small fragments in a randomized
fashion.
Repeat until maximal.
Finds initial partition.
24. Greedy algorithm
We combine well-connected small fragments in a randomized
fashion.
Repeat until maximal.
Finds initial partition.
25. Greedy algorithm
We combine well-connected small fragments in a randomized
fashion.
Repeat until maximal.
Finds initial partition.
26. Greedy algorithm
We combine well-connected small fragments in a randomized
fashion.
Repeat until maximal.
Finds initial partition.
27. Greedy algorithm
We combine well-connected small fragments in a randomized
fashion.
Repeat until maximal.
Finds initial partition.
28. The local search
Pick two neighboring cells, disassemble them, apply greedy
algorithm to the subproblem.
Repeat several times for every pair of neighboring cells.
29. The local search
Pick two neighboring cells, disassemble them, apply greedy
algorithm to the subproblem.
Repeat several times for every pair of neighboring cells.
30. The local search
Pick two neighboring cells, disassemble them, apply greedy
algorithm to the subproblem.
Repeat several times for every pair of neighboring cells.
31. Multistart and combination heuristics
Since the local search is typically much faster than the natural cuts
detection, we can use the following two heuristics:
Multistart: since the local search is randomized, we can
repeat it several times.
Combination: keep track of several solutions, and combine
them from time to time.
32. Outline of the talk
Introduction
Natural cuts
Assembly phase
Experiments
Conclusion
33. Experimental evaluation
C++/OpenMP
Tested on Western Europe map (18M nodes, 43M arcs).
Machine: Intel Xeon X5680 (two six-core 3.33GHz CPUs)
with DDR3-1333MHz RAM.
34. A typical use-case
Europe, U = 64K .
Tiny cuts contraction: 25 seconds (18M nodes to 9M nodes).
Natural cuts identification: 50 seconds (12 cores, 9M nodes to
100K nodes).
Greedy + local search: only 5 seconds (12 cores).
35. Running times on Europe
Tiny cuts
200
Natural cuts
Greedy + Local search
180
q
160
140
120
q
Time (s)
100
80
q
60
q
40
q
q
q q q q q q q
20
q
q
q
q
q
q q q
0
210 212 214 216 218 220 222
maximum cell size
36. Influence of ϕ
The local search tries every edge ϕ times.
15000
Dependence on phi
q
14000
13000
cut size
12000
q
q
q
q
q
q
q
q
11000
q q
10000
0.1 1 10 100 1000 10000
time (s)
38. Balanced partitions
Recall that there are two variants of requirements on |Vi |:
given U, require |Vi | ≤ U for every i,
given k and , require |Vi | ≤ (1 + ) n/k .
PUNCH solves the first, but most existing solvers find
-balanced partitions.
Rebalancing:
Run PUNCH with U = (1 + ) n/k ,
If there are too many regions, redistribute them.