Clojure is a Lisp dialect designed for the Java Virtual Machine that focuses on functional programming and concurrency. It allows easy interoperability with Java code and provides persistent data structures and software transactional memory for writing concurrent programs that avoid deadlocks. The document provides an overview of Clojure's syntax, data structures, higher-order functions, Java interoperability features, and approach to concurrency through immutable objects and managed references.
The document discusses Java packages and some key concepts around them:
1. A Java package is a mechanism for organizing Java classes into namespaces. Classes in the same package can access each other's package-access members.
2. Programmers use packages to organize classes belonging to the same category. This makes it easy to determine related classes and find files of similar types. Packages also prevent naming conflicts.
3. By convention, package names are written in all lowercase and company names are included in the package name based on the reversed domain name, such as "com.example.mypackage" for a package created by example.com.
The document discusses abstract classes and interfaces in Java. It defines an abstract class as a class that can contain abstract methods without an implementation. An abstract method is defined as a method without an implementation. The document provides an example of an abstract Shape class with an abstract Area() method that is implemented differently in subclasses Rectangle and Circle. It also discusses interfaces as classes that can contain method signatures without implementations, and how classes that implement interfaces must implement all interface methods. The document provides examples of how to create and implement interfaces. Finally, it briefly discusses enums as a way to define constants and assign them to variables.
Threads allow a program to split into multiple threads that can run concurrently. A thread is a lightweight subprocess that shares memory and resources with other threads in a process. Threads allow programs to perform multiple tasks simultaneously or asynchronously. Threads have a life cycle and priority levels that determine their order of execution. Threads can be created by implementing the Runnable interface or extending the Thread class. Synchronization ensures that only one thread can access a shared resource at a time to prevent race conditions. Inter-thread communication allows threads to coordinate using wait(), notify(), and notifyAll() methods. Deadlocks can occur when threads are waiting indefinitely for resources held by each other.
L Fu - Dao: a novel programming language for bioinformaticsJan Aerts
The document introduces Dao, a new programming language for bioinformatics. It discusses Dao's key features like optional typing, native support for concurrent programming, an LLVM-based JIT compiler, simple C interfaces, and the ClangDao tool for wrapping C/C++ libraries. An example demonstrates using thread tasks and futures for concurrent programming. The document outlines future plans to develop BioDao, an open source project providing bioinformatics modules to the Dao language.
This document discusses Java threads and multithreaded programming. It covers defining and creating threads, controlling thread execution, sharing data between threads, and communicating between threads using wait and notify. Key points include extending the Thread class or implementing Runnable to create threads, starting threads with the start() method, and using synchronized to protect shared data from corruption when accessed by multiple threads.
Why we cannot ignore Functional ProgrammingMario Fusco
The document discusses several key concepts in functional programming including:
1) Pure functions that are referentially transparent and have no side effects like modifying variables or data structures.
2) Immutable data and persistent data structures that allow for sharing without synchronization.
3) Separating computational logic from side effects improves modularity and allows for parallelization.
4) A pure functional core can be wrapped with functions that have side effects to push effects to the outer layers.
The document provides an overview of concurrency constructs and models. It discusses threads and locks, and some of the problems with locks like manually locking/unlocking and lock ordering issues. It then covers theoretical models like actors, CSP, and dataflow. Implementation details and problems with different models are discussed. Finally, it highlights some open problems and areas for further work.
Java was developed in 1991 by Sun Microsystems as part of the Green project. A team member named James Gosling grew dissatisfied with C++ and created a new language called Oak, which was later renamed to Java in 1995 by Sun Microsystems. Java is an object-oriented language that is platform independent, robust, reliable, secure, and has features like compilation to bytecode, dynamic binding, and multithreading.
The document discusses Java packages and some key concepts around them:
1. A Java package is a mechanism for organizing Java classes into namespaces. Classes in the same package can access each other's package-access members.
2. Programmers use packages to organize classes belonging to the same category. This makes it easy to determine related classes and find files of similar types. Packages also prevent naming conflicts.
3. By convention, package names are written in all lowercase and company names are included in the package name based on the reversed domain name, such as "com.example.mypackage" for a package created by example.com.
The document discusses abstract classes and interfaces in Java. It defines an abstract class as a class that can contain abstract methods without an implementation. An abstract method is defined as a method without an implementation. The document provides an example of an abstract Shape class with an abstract Area() method that is implemented differently in subclasses Rectangle and Circle. It also discusses interfaces as classes that can contain method signatures without implementations, and how classes that implement interfaces must implement all interface methods. The document provides examples of how to create and implement interfaces. Finally, it briefly discusses enums as a way to define constants and assign them to variables.
Threads allow a program to split into multiple threads that can run concurrently. A thread is a lightweight subprocess that shares memory and resources with other threads in a process. Threads allow programs to perform multiple tasks simultaneously or asynchronously. Threads have a life cycle and priority levels that determine their order of execution. Threads can be created by implementing the Runnable interface or extending the Thread class. Synchronization ensures that only one thread can access a shared resource at a time to prevent race conditions. Inter-thread communication allows threads to coordinate using wait(), notify(), and notifyAll() methods. Deadlocks can occur when threads are waiting indefinitely for resources held by each other.
L Fu - Dao: a novel programming language for bioinformaticsJan Aerts
The document introduces Dao, a new programming language for bioinformatics. It discusses Dao's key features like optional typing, native support for concurrent programming, an LLVM-based JIT compiler, simple C interfaces, and the ClangDao tool for wrapping C/C++ libraries. An example demonstrates using thread tasks and futures for concurrent programming. The document outlines future plans to develop BioDao, an open source project providing bioinformatics modules to the Dao language.
This document discusses Java threads and multithreaded programming. It covers defining and creating threads, controlling thread execution, sharing data between threads, and communicating between threads using wait and notify. Key points include extending the Thread class or implementing Runnable to create threads, starting threads with the start() method, and using synchronized to protect shared data from corruption when accessed by multiple threads.
Why we cannot ignore Functional ProgrammingMario Fusco
The document discusses several key concepts in functional programming including:
1) Pure functions that are referentially transparent and have no side effects like modifying variables or data structures.
2) Immutable data and persistent data structures that allow for sharing without synchronization.
3) Separating computational logic from side effects improves modularity and allows for parallelization.
4) A pure functional core can be wrapped with functions that have side effects to push effects to the outer layers.
The document provides an overview of concurrency constructs and models. It discusses threads and locks, and some of the problems with locks like manually locking/unlocking and lock ordering issues. It then covers theoretical models like actors, CSP, and dataflow. Implementation details and problems with different models are discussed. Finally, it highlights some open problems and areas for further work.
Java was developed in 1991 by Sun Microsystems as part of the Green project. A team member named James Gosling grew dissatisfied with C++ and created a new language called Oak, which was later renamed to Java in 1995 by Sun Microsystems. Java is an object-oriented language that is platform independent, robust, reliable, secure, and has features like compilation to bytecode, dynamic binding, and multithreading.
Local variables are stored in memory during the execution of a function. When the function exits, the memory for local variables is deallocated. Local variables provide convenient temporary storage but have a short lifetime and cannot persist beyond the function call. Returning a pointer to a local variable from a function results in a dangling pointer as the memory is deallocated when the function exits.
Wrapper induction construct wrappers automatically to extract information f...George Ang
Wrapper induction is a technique to automatically generate wrappers to extract information from web sources. It involves learning extraction rules from labeled examples to construct a wrapper as a finite state machine or set of delimiters. Two main wrapper induction systems are WIEN, which defines wrapper classes including LR, and STALKER, which uses a more expressive model with extraction rules and landmarks to handle structure hierarchically. Remaining challenges include selecting informative examples, generating label pages automatically, and developing more expressive models.
Kotlin is a statically typed programming language that is 100% interoperable with Java. It aims to combine object-oriented and functional programming features and to be more concise than Java. Some key features of Kotlin include type inference, properties that replace getter/setter methods, higher-order functions, lambdas, and coroutines. Using Kotlin can increase developer productivity through less code, fewer errors, and clearer syntax. It also allows existing Java code to remain unchanged while new features are developed in Kotlin. Integrating Kotlin into a project requires adding dependencies and plugins, setting up tooling, and initially targeting a small component for parallel development in both languages.
Generating Assertion Code from OCL: A Transformational Approach Based on Simi...Shinpei Hayashi
This document presents an approach for generating assertion code from the Object Constraint Language (OCL) using model transformations. The approach constructs a hierarchy of programming languages based on their structural similarities. This allows rules for translating OCL to be reused across multiple implementation languages, saving approximately 50% of the effort compared to creating individual translators. An evaluation implemented the approach in Maude and demonstrated its ability to generate code for Java, Python, Haskell and O'Haskell from a single OCL specification.
Chainer is a deep learning framework which is flexible, intuitive, and powerful. This slide introduces some unique features of Chainer and its additional packages such as ChainerMN (distributed learning), ChainerCV (computer vision), ChainerRL (reinforcement learning)
This document provides information about a lab manual for an Object Oriented Programming lab through Java at Guru Nanak Engineering College for the 2007-2008 academic year. It includes the lab objectives, guidelines for students, a list of lab exercises to be completed over 15 weeks, descriptions of OOP concepts, and solutions to sample programs. The document was prepared by D. Rama Rao and approved by G. Prabhakar Raju for use in the Department of Information Technology.
The document provides information on Caffe layers and networks for image classification tasks. It describes common layers used in convolutional neural networks (CNNs) like Convolution, Pooling, ReLU and InnerProduct. It also discusses popular CNN architectures for datasets such as MNIST, CIFAR-10 and ImageNet and the steps to prepare the data and train these networks in Caffe. Experiments comparing different CNN configurations on a 4-class image dataset show that removal of layers degrades performance, indicating their importance.
A fast-paced introduction to Deep Learning that starts with a simple yet complete neural network (no frameworks), followed by an overview of activation functions, cost functions, backpropagation, and then a quick dive into CNNs. Next we'll create a neural network using Keras, followed by an introduction to TensorFlow and TensorBoard. For best results, familiarity with basic vectors and matrices, inner (aka "dot") products of vectors, and rudimentary Python is definitely helpful.
Try to imagine the amount of time and effort it would take you to write a bug-free script or application that will accept a URL, port scan it, and for each HTTP service that it finds, it will create a new thread and perform a black box penetration testing while impersonating a Blackberry 9900 smartphone. While you’re thinking, Here’s how you would have done it in Hackersh:
“http://localhost” \
-> url \
-> nmap \
-> browse(ua=”Mozilla/5.0 (BlackBerry; U; BlackBerry 9900; en) AppleWebKit/534.11+ (KHTML, like Gecko) Version/7.1.0.346 Mobile Safari/534.11+”) \
-> w3af
Meet Hackersh (“Hacker Shell”) – A new, free and open source cross-platform shell (command interpreter) with built-in security commands and Pythonect-like syntax.
Aside from being interactive, Hackersh is also scriptable with Pythonect. Pythonect is a new, free, and open source general-purpose dataflow programming language based on Python, written in Python. Hackersh is inspired by Unix pipeline, but takes it a step forward by including built-in features like remote invocation and threads. This 120 minute lab session will introduce Hackersh, the automation gap it fills, and its features. Lots of demonstrations and scripts are included to showcase concepts and ideas.
Обзор последних разработок в области text-to-speech.
+Аудио-файлы с синтетической речью
https://soundcloud.com/user-872135531/sets/samples-of-synthesized-speech-for-modern-text-to-speech-systems-review
Comparing different concurrency models on the JVMMario Fusco
This document compares different concurrency models on the JVM, including the native Java concurrency model based on threads, semaphores, and locks. It discusses the pros and cons of threads and locks, including issues around non-determinism from shared mutable state. Functional programming with immutable data and internal iteration is presented as an alternative that allows for free parallelism. The document also covers actors and their message passing approach, as well as software transactional memory.
The document discusses attention mechanisms and their implementation in TensorFlow. It begins with an overview of attention mechanisms and their use in neural machine translation. It then reviews the code implementation of an attention mechanism for neural machine translation from English to French using TensorFlow. Finally, it briefly discusses pointer networks, an attention mechanism variant, and code implementation of pointer networks for solving sorting problems.
This document discusses closures and functional programming. It begins with an agenda that covers closures as code blocks, their history in languages like Lisp and Scheme, examples of functional programming, and using closures for refactoring. It then discusses a case study on experiences with a polygraph design, including optimizations with closures, packaging, and applying the Demeter principle. Finally, it provides links for further reading on closures.
This document discusses metaprogramming, metaclasses, and the metaobject protocol. It begins with an overview and definitions of key concepts like metaprogramming, metaobjects, and metaclasses. It then covers specific implementations including macros in Lisp, CLOS metaclasses, and how AllegroCache uses a persistent metaclass. Finally, it discusses the metaobject protocol and provides examples of how it allows programs to access and manipulate normally hidden language elements like classes and methods.
- The document is a lab manual for an introductory Java programming course that provides instructions on installing Java, using an IDE, and writing simple Java programs.
- It explains how to create a "Hello World" Java program using BlueJ or JCreator IDEs, compile and run the program, and addresses common errors students may encounter.
- The document provides an example "Hello World" Java program and explains the key components of a basic Java class including the class definition, main method, and use of System.out.println to display output.
The document contains the lab manual for the Java lab subject CS2309 for the V semester B.E CSE program. It includes 11 exercises to be completed as part of the lab. The exercises cover topics like implementing rational numbers, date class, lisp-like lists, stacks, object serialization, scientific calculator, multi-threading and a mini-project on a programmer's editor. It also provides the syllabus, list of experiments, requirements and a sample program for implementing rational numbers.
The document discusses topics related to variables and identifiers in imperative programming languages. It covers variable concepts like name, value, type, and scope. It also discusses data types, expressions, control statements, subprograms, abstract data types, exception handling, and object-oriented languages. Specifically, it describes different types of variables based on their lifetime and storage, such as static, stack-dynamic, explicit dynamic, and implicit dynamic variables. It also explains scoping rules and how the visibility of variables is determined based on where they are declared.
This document provides an overview of network simulation using the Network Simulator 2 (NS2). It discusses the motivation for network simulation, what simulation is, and the advantages and drawbacks of simulation. It then describes the structure and programming of a simulation, including maintaining event lists and processing events. The document outlines NS2, including how to get it, create topologies, add traffic, observe behavior using NAM, and provides examples of simple NS2 scripts. It also briefly discusses adding new protocols to NS2 and finding documentation. The key points covered are the fundamentals and use of discrete event network simulation using the NS2 tool.
Configuring Mahout Clustering Jobs - Frank Scholtenlucenerevolution
See conference video - http://www.lucidimagination.com/devzone/events/conferences/ApacheLuceneEurocon2011
For more than a decade internet search engines have helped users find documents they are looking for. However, what if users aren't looking for anything specific but want a summary of a large document collection and want to be surprised? One solution to this problem is document clustering. Clustering algorithms group documents that have similar content. Real-life examples of clustering are clustered search results of Google news, or tag clouds which group documents under a shared label. Apache Mahout is a framework for scalable machine learning on top of Apache Hadoop and can be used for large scale document clustering. This talk introduces clustering in general and shows you step-by-step how to configure Mahout clustering jobs to create a tag cloud from a document collection. This talk is suitable for people who have some experience with Hadoop and perhaps Mahout. Knowledge of clustering is not required.
Introduction to Deep Learning with Pythonindico data
A presentation by Alec Radford, Head of Research at indico Data Solutions, on deep learning with Python's Theano library.
The emphasis of the presentation is high performance computing, natural language processing (using recurrent neural nets), and large scale learning with GPUs.
Video of the talk available here: https://www.youtube.com/watch?v=S75EdAcXHKk
This document provides an overview of Clojure and why one may want to try it. Some key points include:
- Clojure is a functional programming language that runs on the JVM and allows easy interoperability with Java.
- It has a very small and elegant syntax based on Lisp with sensible macro names and prefix notation.
- Clojure encourages pure functional programming and the use of immutable data structures, while providing tools like Software Transactional Memory to allow safe mutable state changes.
- Its focus on functions as first-class citizens and referential transparency can provide benefits for writing parallel and concurrent code more easily compared to other languages.
Local variables are stored in memory during the execution of a function. When the function exits, the memory for local variables is deallocated. Local variables provide convenient temporary storage but have a short lifetime and cannot persist beyond the function call. Returning a pointer to a local variable from a function results in a dangling pointer as the memory is deallocated when the function exits.
Wrapper induction construct wrappers automatically to extract information f...George Ang
Wrapper induction is a technique to automatically generate wrappers to extract information from web sources. It involves learning extraction rules from labeled examples to construct a wrapper as a finite state machine or set of delimiters. Two main wrapper induction systems are WIEN, which defines wrapper classes including LR, and STALKER, which uses a more expressive model with extraction rules and landmarks to handle structure hierarchically. Remaining challenges include selecting informative examples, generating label pages automatically, and developing more expressive models.
Kotlin is a statically typed programming language that is 100% interoperable with Java. It aims to combine object-oriented and functional programming features and to be more concise than Java. Some key features of Kotlin include type inference, properties that replace getter/setter methods, higher-order functions, lambdas, and coroutines. Using Kotlin can increase developer productivity through less code, fewer errors, and clearer syntax. It also allows existing Java code to remain unchanged while new features are developed in Kotlin. Integrating Kotlin into a project requires adding dependencies and plugins, setting up tooling, and initially targeting a small component for parallel development in both languages.
Generating Assertion Code from OCL: A Transformational Approach Based on Simi...Shinpei Hayashi
This document presents an approach for generating assertion code from the Object Constraint Language (OCL) using model transformations. The approach constructs a hierarchy of programming languages based on their structural similarities. This allows rules for translating OCL to be reused across multiple implementation languages, saving approximately 50% of the effort compared to creating individual translators. An evaluation implemented the approach in Maude and demonstrated its ability to generate code for Java, Python, Haskell and O'Haskell from a single OCL specification.
Chainer is a deep learning framework which is flexible, intuitive, and powerful. This slide introduces some unique features of Chainer and its additional packages such as ChainerMN (distributed learning), ChainerCV (computer vision), ChainerRL (reinforcement learning)
This document provides information about a lab manual for an Object Oriented Programming lab through Java at Guru Nanak Engineering College for the 2007-2008 academic year. It includes the lab objectives, guidelines for students, a list of lab exercises to be completed over 15 weeks, descriptions of OOP concepts, and solutions to sample programs. The document was prepared by D. Rama Rao and approved by G. Prabhakar Raju for use in the Department of Information Technology.
The document provides information on Caffe layers and networks for image classification tasks. It describes common layers used in convolutional neural networks (CNNs) like Convolution, Pooling, ReLU and InnerProduct. It also discusses popular CNN architectures for datasets such as MNIST, CIFAR-10 and ImageNet and the steps to prepare the data and train these networks in Caffe. Experiments comparing different CNN configurations on a 4-class image dataset show that removal of layers degrades performance, indicating their importance.
A fast-paced introduction to Deep Learning that starts with a simple yet complete neural network (no frameworks), followed by an overview of activation functions, cost functions, backpropagation, and then a quick dive into CNNs. Next we'll create a neural network using Keras, followed by an introduction to TensorFlow and TensorBoard. For best results, familiarity with basic vectors and matrices, inner (aka "dot") products of vectors, and rudimentary Python is definitely helpful.
Try to imagine the amount of time and effort it would take you to write a bug-free script or application that will accept a URL, port scan it, and for each HTTP service that it finds, it will create a new thread and perform a black box penetration testing while impersonating a Blackberry 9900 smartphone. While you’re thinking, Here’s how you would have done it in Hackersh:
“http://localhost” \
-> url \
-> nmap \
-> browse(ua=”Mozilla/5.0 (BlackBerry; U; BlackBerry 9900; en) AppleWebKit/534.11+ (KHTML, like Gecko) Version/7.1.0.346 Mobile Safari/534.11+”) \
-> w3af
Meet Hackersh (“Hacker Shell”) – A new, free and open source cross-platform shell (command interpreter) with built-in security commands and Pythonect-like syntax.
Aside from being interactive, Hackersh is also scriptable with Pythonect. Pythonect is a new, free, and open source general-purpose dataflow programming language based on Python, written in Python. Hackersh is inspired by Unix pipeline, but takes it a step forward by including built-in features like remote invocation and threads. This 120 minute lab session will introduce Hackersh, the automation gap it fills, and its features. Lots of demonstrations and scripts are included to showcase concepts and ideas.
Обзор последних разработок в области text-to-speech.
+Аудио-файлы с синтетической речью
https://soundcloud.com/user-872135531/sets/samples-of-synthesized-speech-for-modern-text-to-speech-systems-review
Comparing different concurrency models on the JVMMario Fusco
This document compares different concurrency models on the JVM, including the native Java concurrency model based on threads, semaphores, and locks. It discusses the pros and cons of threads and locks, including issues around non-determinism from shared mutable state. Functional programming with immutable data and internal iteration is presented as an alternative that allows for free parallelism. The document also covers actors and their message passing approach, as well as software transactional memory.
The document discusses attention mechanisms and their implementation in TensorFlow. It begins with an overview of attention mechanisms and their use in neural machine translation. It then reviews the code implementation of an attention mechanism for neural machine translation from English to French using TensorFlow. Finally, it briefly discusses pointer networks, an attention mechanism variant, and code implementation of pointer networks for solving sorting problems.
This document discusses closures and functional programming. It begins with an agenda that covers closures as code blocks, their history in languages like Lisp and Scheme, examples of functional programming, and using closures for refactoring. It then discusses a case study on experiences with a polygraph design, including optimizations with closures, packaging, and applying the Demeter principle. Finally, it provides links for further reading on closures.
This document discusses metaprogramming, metaclasses, and the metaobject protocol. It begins with an overview and definitions of key concepts like metaprogramming, metaobjects, and metaclasses. It then covers specific implementations including macros in Lisp, CLOS metaclasses, and how AllegroCache uses a persistent metaclass. Finally, it discusses the metaobject protocol and provides examples of how it allows programs to access and manipulate normally hidden language elements like classes and methods.
- The document is a lab manual for an introductory Java programming course that provides instructions on installing Java, using an IDE, and writing simple Java programs.
- It explains how to create a "Hello World" Java program using BlueJ or JCreator IDEs, compile and run the program, and addresses common errors students may encounter.
- The document provides an example "Hello World" Java program and explains the key components of a basic Java class including the class definition, main method, and use of System.out.println to display output.
The document contains the lab manual for the Java lab subject CS2309 for the V semester B.E CSE program. It includes 11 exercises to be completed as part of the lab. The exercises cover topics like implementing rational numbers, date class, lisp-like lists, stacks, object serialization, scientific calculator, multi-threading and a mini-project on a programmer's editor. It also provides the syllabus, list of experiments, requirements and a sample program for implementing rational numbers.
The document discusses topics related to variables and identifiers in imperative programming languages. It covers variable concepts like name, value, type, and scope. It also discusses data types, expressions, control statements, subprograms, abstract data types, exception handling, and object-oriented languages. Specifically, it describes different types of variables based on their lifetime and storage, such as static, stack-dynamic, explicit dynamic, and implicit dynamic variables. It also explains scoping rules and how the visibility of variables is determined based on where they are declared.
This document provides an overview of network simulation using the Network Simulator 2 (NS2). It discusses the motivation for network simulation, what simulation is, and the advantages and drawbacks of simulation. It then describes the structure and programming of a simulation, including maintaining event lists and processing events. The document outlines NS2, including how to get it, create topologies, add traffic, observe behavior using NAM, and provides examples of simple NS2 scripts. It also briefly discusses adding new protocols to NS2 and finding documentation. The key points covered are the fundamentals and use of discrete event network simulation using the NS2 tool.
Configuring Mahout Clustering Jobs - Frank Scholtenlucenerevolution
See conference video - http://www.lucidimagination.com/devzone/events/conferences/ApacheLuceneEurocon2011
For more than a decade internet search engines have helped users find documents they are looking for. However, what if users aren't looking for anything specific but want a summary of a large document collection and want to be surprised? One solution to this problem is document clustering. Clustering algorithms group documents that have similar content. Real-life examples of clustering are clustered search results of Google news, or tag clouds which group documents under a shared label. Apache Mahout is a framework for scalable machine learning on top of Apache Hadoop and can be used for large scale document clustering. This talk introduces clustering in general and shows you step-by-step how to configure Mahout clustering jobs to create a tag cloud from a document collection. This talk is suitable for people who have some experience with Hadoop and perhaps Mahout. Knowledge of clustering is not required.
Introduction to Deep Learning with Pythonindico data
A presentation by Alec Radford, Head of Research at indico Data Solutions, on deep learning with Python's Theano library.
The emphasis of the presentation is high performance computing, natural language processing (using recurrent neural nets), and large scale learning with GPUs.
Video of the talk available here: https://www.youtube.com/watch?v=S75EdAcXHKk
This document provides an overview of Clojure and why one may want to try it. Some key points include:
- Clojure is a functional programming language that runs on the JVM and allows easy interoperability with Java.
- It has a very small and elegant syntax based on Lisp with sensible macro names and prefix notation.
- Clojure encourages pure functional programming and the use of immutable data structures, while providing tools like Software Transactional Memory to allow safe mutable state changes.
- Its focus on functions as first-class citizens and referential transparency can provide benefits for writing parallel and concurrent code more easily compared to other languages.
This document introduces Clojure for Java developers with little Clojure experience. It discusses why Clojure is a good option, what Clojure is, its core concepts like immutability and functional programming, and how to interact with Java from Clojure. It also provides an overview of managing Clojure projects and deploying Clojure applications to the cloud.
This document provides an introduction to the Clojure programming language. It discusses Clojure's four main aspects: functional programming, its basis in Lisp, running on the Java Virtual Machine, and support for concurrency. It provides examples and explanations of Clojure's functional style, homoiconic Lisp syntax, interoperability with Java, and approaches for managing concurrent state through vars, refs, atoms and agents. It also recommends tools for getting started with Clojure and links to additional learning resources.
Clojure is a Lisp dialect that runs on the Java Virtual Machine (JVM) and provides excellent concurrency support and tight integration with Java. It retains the advantages of Lisp such as being simple, expressive, and flexible while also allowing access to existing Java code and libraries. Clojure makes concurrency easy through features like transactional memory and agents. It can be used to create both Java and Clojure libraries and extends the Java platform.
This document discusses interoperability between Clojure and Python. It outlines Jython, which allows Python code to run on the Java Virtual Machine (JVM), and Clojure, a Lisp dialect that also runs on the JVM. It then covers how Clojure and Jython code can interact and call each other due to both running on the JVM. It provides examples of implementing Python objects in Clojure and calling Jython functions from Clojure.
DevNexus 2018: Learn Java 8, lambdas and functional programmingHenri Tremblay
This session walks through tons of examples in live coding. You will learn everything you need to know about lambdas and functional programming in Java 8. I’m the supplier. You’re the consumer (and you will get the joke after the session).
Reactive Programming in the Browser feat. Scala.js and PureScriptLuka Jacobowitz
This document provides an introduction to reactive programming in the browser using Scala.js and PureScript. It discusses the similarities and differences between Scala.js and PureScript, including support for functional programming concepts like higher-kinded types, monadic comprehensions, and ADTs with pattern matching. It then introduces reactive programming as programming with asynchronous data streams and how this is useful for developing user interfaces with pure functional programming. Finally, it demonstrates an OutWatch library that aims to update the DOM efficiently, handle subscriptions automatically, and reduce the need for higher-order observables.
This document introduces Clojure and ClojureScript as functional programming languages that target the JVM and JavaScript respectively. It notes that Clojure is a Lisp dialect that is succinct, enables Java interoperability, is multi-threaded, lazy, and fun to use. ClojureScript allows building modular and immutable web applications using the Google Closure compiler while interacting with React through libraries like Reagent and styling with Garden. The document advocates considering Clojure/ClojureScript for building the next web application.
This document summarizes several programming languages that are currently in development or may be enhanced in the future, including Python 3000, Ruby 2, Groovy, and Scala. Python 3000 is an upcoming version of Python that will include changes like multi-line imports and reverse iteration. Ruby 2 promises improvements like bytecode compilation and thread safety. Groovy is designed for the Java platform and includes features like closures. Scala combines object-oriented and functional programming with a static type system and interoperability with Java.
This document provides an introduction to the Clojure programming language. It discusses Clojure's roots in functional programming concepts like lambda calculus. It also covers Clojure's features like being dynamically typed, Java interoperability, immutability, and support for concurrency through mechanisms like atoms, refs, agents, and vars. The document is presented by Renzo Borgatti and provides an overview of Clojure along with examples to demonstrate its capabilities.
고급 자바 8 교육 (6일 중 6일차)
티맥스소프트 연구소에 연구소장으로 재직 중이던 2013년 10월에 진행한 자바 언어 강의 내용입니다.
JVM에 대한 이해와 Java 8에 대한 소개를 포함하려고 노력하였습니다.
아래 강의 동영상이 있습니다.
http://javadom.blogspot.com/2017/07/8-6.html
Can two JVM languages, none of them Java, work harmoniously, side by side? Should they? In this lecture we’ll see how code written in Groovy and Scala work with Java and how they can be made to work with each other. The lecture is based on the experience of introducing Scala to an actual Groovy project.
These are the outline slides that I used for the Pune Clojure Course.
The slides may not be much useful standalone, but I have uploaded them for reference.
The code example shows 10 threads concurrently manipulating shared data of 100 vectors with 10 numbers each, by randomly swapping two positions in two random vectors using Clojure's Software Transactional Memory (STM) to ensure consistency across 100,000 iterations per thread, without any numbers getting lost due to the transactions. It initializes the vectors, defines a swap and report function, runs the threads concurrently swapping values, and reports the results.
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Clojure - A practical LISP for the JVM
1. Clojure
Clojure
A practical LISP for the JVM
¨
Matthias Nußler
m.nuessler@web.de
February 1, 2011
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2. Clojure
Outline
Introduction
LISP
LISP Quotes
The Clojure Language
Java Interoperability
Calling Java from Clojure
Calling Clojure from Java
Concurrency
IDE Support
Questions & Answers
Bibliography
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3. Clojure
Introduction
What is Clojure?
Clojure is
A fresh approach to LISP
A functional programming language
Created by Rich Hickey
Dynamically typed
Hosted on the Java Virtual Machine
Java interoperability is a design goal, rather than an
implementation detail
Strong concurrency semantics
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4. Clojure
LISP
LISP
LISt Processing (lists are a major data structure)
Originally designed in 1958 by John McCarthy
One of the oldest programming languages in existence
(only Fortran is older)
Once the favored programming language for AI research
Based on lambda calculus
Numerous LIPS dialects exist such as
Scheme
Common LISP
“Lots of Irritating Superfluous Parentheses”?
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5. Clojure
LISP
LISP Quotes
LISP Quotes (1)
“Lisp is worth learning for the profound enlightenment
experience you will have when you finally get it; that experience
will make you a better programmer for the rest of your days,
even if you never actually use Lisp itself a lot.”
–Eric Raymond: How to Become a Hacker
“Lisp is a programmable programming language.”
–John Foderaro: CACM, September 1991
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6. Clojure
LISP
LISP Quotes
LISP Quotes (2)
“Greenspun’s Tenth Rule of Programming: any sufficiently
complicated C or Fortran program contains an ad hoc
informally-specified bug-ridden slow implementation of half of
Common Lisp.”
–Philip Greenspun
“the greatest single programming language ever designed”
–Alan Kay
More quotes: http://www.paulgraham.com/quotes.html
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7. Clojure
The Clojure Language
Clojure Language Basics
“No syntax” :-)
Prefix notation
Lists are a core data structure in Clojure/LISP
Code is written in the language’s own data-structures:
(function param1 param2)
Code as data philosophy
Constants: Evaluate to themselves
Symbols: Evaluate to the value bound to them (unless
quoted)
Bind a value to a symbol with def:
(def a-symbol 42) ; => #’user/a-symbol
a-symbol ; => 42
’a-symbol ; => a-symbol
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8. Clojure
The Clojure Language
Function definition
fn creates an (anonymous) function object
Similar to lambda in Scheme (and Ruby)
(fn [x] (* x x))
Or even shorter using a macro: #(* %1 %1)
Function object can be bound to a symbol
(def square (fn square [x] (* x x)))
(square 3) ; => 9
Clojure provides a convenient shortcut
def plus fn → defn
(defn square [x] (* x x))
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9. Clojure
The Clojure Language
Clojure Data Structures
Data type Example
Numbers 1, 3.14, 3/4, ...
Characters a b c
Strings "a string" → Clojure strings are Java strings
Regexps #""
Symbols a b c
Keywords :default :key
Lists (1 2 3)
Vectors [1 2 3]
Maps {:a 1, :b 2, :c 3}
Sets #{"red" "green" "blue"}
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10. Clojure
The Clojure Language
Higher Order Functions
Higher order functions functions use functions as parameters or
return values. Examples:
every?
filter
map
reduce
Factorial using reduce
1 (defn factorial [n]
2 (let [ numbers ( range 1 (+ 1 n))]
3 ( reduce * numbers )))
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11. Clojure
Java Interoperability
Interoperability Overview
With Clojure it is possible to
Create instances of Java classes
Invoke instance methods
Invoke static methods on Java classes
Implement Java interfaces, extend classes
Generate Java classes and interfaces
Compile Clojure code to Java byte code
Call Clojure from Java
→ Access to thousands of Java libraries and frameworks
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12. Clojure
Java Interoperability
Calling Java from Clojure
Creating Instances
Using the new special form
1 ( import ’(java.text SimpleDateFormat ))
2 (def sdf (new SimpleDateFormat " yyyy-MM-dd "))
Shorter with dot notation
3 (def sdf ( SimpleDateFormat . " yyyy-MM-dd "))
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18. Clojure
Java Interoperability
Calling Clojure from Java
Calling Clojure from Java
1 import clojure .lang.RT;
2 import clojure .lang.Var;
3
4 public class Driver {
5 public static void main( String [] args) throws Exceptio
6 RT. loadResourceScript (" clojure_script .clj");
7 Var report = RT.var("clj. script . examples ", " print-re
8 Integer result = ( Integer ) report . invoke ("Siva");
9 System .out. println ("[Java] Result : " + result );
10 }
11 }
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19. Clojure
Concurrency
Clojure’s Approach to Concurrency
Immutable objects
Persistent data-structures (preserve the previous version
of themselves when modfied)
Managed references (4 different kinds)
Clojure programs are guaranteed not to deadlock
Software Transactional Memory (STM) system
has ACI properties: atomicity, consistency, isolation (no
durability)
implements multiversion concurrency control (MVCC)
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20. Clojure
Concurrency
Managed References
Ref. type Useful for
ref shared changes, synchronous, coordinated changes
agent shared changes, asynchronous, independent changes
atom shared changes, synchronous, independent changes
var isolated changes
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21. Clojure
Concurrency
Refs, Agents, Atoms, Vars (1)
Refs
A ref holds a value that can be changed in a synchronous
and coordinated manner
Creating a ref: (def all-users (ref {}))
Dereferencing a ref: (deref all-users)
Mutating a ref using the ref-set, alter or commute
functions
(ref-set all-users {})
STM transaction required!
Correct way: (dosync (ref-set all-users {}))
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22. Clojure
Concurrency
Refs, Agents, Atoms, Vars (2)
Agents
Allow for asynchronous and independent changes to
shared mutable data
Hold values that can be changed using special functions
(asynchronously, at some point in the future)
Atoms
Allow for synchronous and independent changes to
mutable data
Atoms are updated synchronously (immediately).
22 / 26
23. Clojure
Concurrency
Refs, Agents, Atoms, Vars (2)
Agents
Allow for asynchronous and independent changes to
shared mutable data
Hold values that can be changed using special functions
(asynchronously, at some point in the future)
Atoms
Allow for synchronous and independent changes to
mutable data
Atoms are updated synchronously (immediately).
22 / 26
24. Clojure
Concurrency
Refs, Agents, Atoms, Vars (3)
Vars
Can be thought of as pointers to mutable storage locations,
which can be updated on a per- thread basis
Similar to java.lang.ThreadLocal
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25. Clojure
IDE Support
IDE Support
Eclipse: Counterclockwise
http://code.google.com/p/counterclockwise/
IntelliJ IDEA: La Clojure
http://plugins.intellij.net/plugin/?id=4050
Netbeans: Enclojure
http://www.enclojure.org/
Emacs
Vim
Textmate
...
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