36 skills found · Page 1 of 2
GarrettGunnell / Iterated Function SystemsFractal particles
jherman3 / Ifs FractalIterated Function System fractal visualizer in Rust
two-twelve / FerneryA CLI tool for generating images of ferns 🌿 and other Iterated Function Systems
cedricbonhomme / Iterated Function SystemsIterated Function Systems fractals with OCaml.
leios / Fable.jlGeneral purpose animations via iterated function systems
BlockchainLabs / SpreadCoinSpreadCoin October 5, 2014 Introduction In proof-of-work cryptocurrencies new coins are generated by the network through the process of mining. One of the purposes of mining is to protect network from double spending attacks and history rewriting. Miners generate new blocks and check contents of the blocks generated by other peers for conformation to the network rules. However, many miners now delegate all the checking work crucial to cryptocurrency security to pools. This means that pool operators do not have any large hashing power but have control over generation of new blocks. This brings unnecessary centralization to otherwise decentralized system. Controlling more than 50% of mining power allows to perform double-spending attacks with 100% chance of success but even with less than 50% control it is possible to perform attacks which have chances to succeed1 . The core idea of SpreadCoin is to prevent creation of pools and thus make mining more decentralized and the whole system more secure. Pool Prevention In pooled mining miners perform only the work which is necessary to fulfill the proof-of-work requirements and pools take care of block generation and broadcasting and distribute reward among miners according to the shares they submit. In this scheme miner has two alternatives: 1. Solo mining. In this case miner cannot send shares to the pool because they will not be accepted. 2. Pooled mining. Miner’s shares will be accepted by the pool but in the case miner will actually generate a new block its reward will go to the pool which will redistribute it to all miners. This allows organization of pools because miners has no way to cheat and steal generated money. To prevent creation of pools we must remove this possibility so that if pool will be created than miner can mine in a pool, submit shares as usual and get reward for them but in the case of actually finding a block miner can send it directly to the network instead of the pool and get full reward for it. In SpreadCoin mining is organized in such way that miner must know the following things: 1. Private key corresponding to the coinbase transaction. 2. Whole block, not only its header. This ensures that miner can broadcast mined block and spend coins generated in that block. It may seem that it is necessary to know only the private key to spend coinbase transaction. If two conflicting transactions will appear on the network then the one that was broadcasted first will have much higher probability to be included in a block because each peer remembers and retransmits only the first one of the conflicting transactions. If both miner and pool know private key but only pool knows the content of the block than pool can generate and broadcast spending transaction earlier than miner. If both miner 1 Double-spending. Bitcoin Wiki. https://en.bitcoin.it/wiki/Double-spending and pool know content of the block than miner will be the first one who can broadcast block and spending transaction. To prove knowledge of the private key and whole block there are two new fields in the block header: MinerSignature and hashWholeBlock. MinerSignature is a digital signature of all fields of the block header except for the hashWholeBlock. Changing any information in the block requires regeneration of this signature which means that it is necessary to recalculate it during each iteration of the mining process. This implies that miner must be able to sign any arbitrary data. hashWholeBlock is a SHA-256 hash of the block data arranged as follows: Padding ensures that there is no incentive to mine empty blocks without transactions. Padding values are computed using simple algorithm which initializes last 32 bytes (8 uint32) with hashPrevBlock and then goes backward and computes remaining uint32 values using the following recursive formula: 𝐼𝑖 = 𝐼𝑖+3 ∙ 𝐼𝑖+7. This algorithm ensures that there is no efficient way to compute padding values on the fly during hash computation which otherwise could potentially give some advantage to mine empty blocks in certain computing environments. It is important that block is hashed twice. If it was hashed only once then pool could hash the beginning of the block and send resulting hash state to the miners. Each miner would then modify some information in the end of the block and recalculate the hash based on the known state without actual knowledge about what is contained in the beginning of the block. Appending block data to itself make it necessary to know the whole block to recalculate hashWholeBlock. Pool may detect and ban cheating miners. However, many miners may still prefer to cheat so that pool will be completely unusable for honest miners. Miners that have low probability of finding a block will get more profit by stealing reward for accidentally found block even if pool will ban them thereafter. Miners that have enough mining power to find blocks consistently can still connect to a pool and submit shares for some time but steal the first found block. This way they can get both reward for their shares and the actual mined block. Given all this it is expected that no one will create a pool. But even if someone will than it can be countered by releasing stealing miner software which many miners will switch to. Compact Transactions SpreadCoin as well as Bitcoin uses ECDSA signatures. Each address in Bitcoin is a hash of an ECDSA public key. To spend coins sent to an address it is necessary to provide public key matching to that hash and a signature. This results in 139 or 107 bytes for each transaction input script (scriptSig) depending on Block Padding MAX_BLOCK_SIZE Block Padding whether compact public key is used. However, it is possible to recover public key from the signature2 which means that it is not necessary to provide it in transaction input. Together with using compact representation of the signature3 it allows to reduce size of transaction input script from 139 or 107 bytes in Bitcoin to 67 bytes in SpreadCoin. Recovering public key has almost no extra CPU cost compared to the usual signature verification process used in Bitcoin. This is important because the CPU cost of ECDSA signature verification is a bottleneck for Bitcoin transaction processing. Usual output script (scriptPubKey) in Bitcoin looks as follows: OP_DUP OP_HASH160 5bd18804e4bb43a4bb8b6bc88408970bafaf4a38 OP_EQUALVERIFY OP_CHECKSIG In SpreadCoin the semantics of the OP_CHECKSIG instruction was changed to checking signature by hash of the public key (it recovers public key and compares its hash with the provided one). This results in a much simpler script in SpreadCoin: 5bd18804e4bb43a4bb8b6bc88408970bafaf4a38 OP_CHECKSIG This results in additional minor space saving because this script is 3 bytes smaller. Smooth Supply Block reward in Bitcoin is computed using the following formula: 𝑅ℎ = 𝑅0 ∙ 2 −⌊ ℎ 𝑝 ⌋ , where ℎ – block height, 𝑝 – reward halving period, 𝑅0 – initial reward, 𝑅ℎ – reward for block ℎ, ⌊ ⌋ – floor function. This method results in abrupt reward changes near halving points. SpreadCoin uses simple linear interpolation between halving points to make reward decrease much smother. This is achieved by modifying reward using the following formula: 𝑅ℎ ′ = 4 3 (𝑅ℎ − 𝑅ℎ ∙ ℎ mod 𝑝 2𝑝 ). SpreadCoin uses 𝑝 = 2 ∙ 106 as its reward halving period. 2 ECDSA Signatures allow recovery of the public key. Bitcoin Forum. https://bitcointalk.org/?topic=6430.0%29%3F 3 Why the signature is always 65 (1+32+32) bytes long? Bitcoin Stack Exchange. https://bitcoin.stackexchange.com/questions/12554/why-the-signature-is-always-65-13232-bytes-long | NO YEAR 2106 PROBLEM The time stamp field in the block header is now 64 bit instead of 32 bit (Bitcoin) so that much farther date times are possible (>Year 2106) Upcoming features that are in development and will be introduced over the next weeks and months: SERVICENODES A servicenode is a node which runs continuously (24/7) on a server and which provides services within the spreadcoin network. You have to pay a collateral to be able to install a servernode (in return your servicenode will earn a steady income). This collateral is determined by a free market price discovery. (No fix collateral. The price is allowed to fluctuate over time.) COMPETITIVE COLLATERAL Furthermore, to introduce a competitive nature to the servicenodes there will only ever be a limited number of allowed servicenodes worldwide. Since the collateral isn't set in stone, but the amount of servicenodes is fixed, the price of a servicenode will be determined by the participants themselves. It is expected that the price will vary widely over time, which exposes it to the same market forces that hashrate and currency value are exposed to too. SERVICE APPS There are a number of decentralized applications that will run on servicenodes. Most likely those apps will include: 1) "Spread the message" (an in-wallet encrypted messaging system, which allows you to send a message to an SPR address) 2) "Spread the Search" (A decentralized search engine that lets the servicenodes crawl and map the entire internet.) . SPREADX11 SpreadX11 is different from plain X11 by introducing a sophisticated pool prevention mechanism. With SpreadX11 every block header contains additional information (MinerSignature and hashWholeBlock). With the help of this information the protocol ensures that the miner of a new block is always also the first one to know the content of the whole block and the private key to spend the coinbase transaction. (contrary to pool mining where the pool operator is the first one to know those things) So when a miner finds a block, he must himself sign and transmit the block to the network (like solo mining), instead of having a pool handle this for him. This effectively prevents pools by making their rules non-enforceable, since any miner in any assumed pool can always just steal the block reward instead of following the rules set up by the pool. COMPACT TRANSACTIONS SpreadCoin uses a more compact representation for signatures in transactions. SpreadCoin as well as Bitcoin uses ECDSA signatures. While bitcoin keeps a copy of the public key of the corresponding signature around, SpreadCoin ommits this by recovering the public key on the fly directly from the signature. This way it is not necessary to keep the public key of every ECDSA signature in the blockchain, so this leads to *smaller transactions and hence a smaller blockchain (at the cost of a few CPU cycles more). (*reduction in size of transaction from 139 or 107 bytes in Bitcoin to 67 bytes in SpreadCoin.) SMOOTH HALVING Unlike Bitcoin, there are no abrupt reward halvings in SpreadCoin. Block reward is smoothly decreasing over time. UNIQUE DESIGN WITH IN-WALLET VANITYGEN One of the first apps to be built into the wallet is the vanity generator (or vanity gen) which allows anyone to create personalised payment addresses. The easy to use wallet lets you search through trillions of payment addresses allowing you to find one or multiple vanity addresses, which are then stored safely along with the private keys on your own computer - and nowhere else. Searching using the vanity gen is probabilistic, so the amount of time required to find your chosen address patterns depends on how complex the pattern is, the speed of your computer, and a little bit of luck. You can use the vanity gen for a bit of fun, to make your address standout from the crowd or to create a link to a brand, business or other organisation. You can even search for addresses that others might be willing to buy from you. SpreadCoin is a new cryptocurrency which is more decentralized than Bitcoin. It prevents centralization of hashing power in pools, which is one of the main concerns of Bitcoin security. SpreadCoin was fairly launched on 29 July 2014, 9:00 UTC with no premine.
jtauber / PyifsAn Iterated Function System in Python
gapsc-us / EP4IDDThis project includes Matlab functions to simulate MIMO and Equalization systems with Iterative Detection and Decoding
zephyrtronium / Xirhoxirho is a simple generalized iterated function system plotter.
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Passing `"all"` will bind // the callback to all events fired. on: function (name, callback, context) { if (!eventsApi(this, 'on', name, [callback, context]) || !callback) return this; this._events = this._events || {}; var events = this._events[name] || (this._events[name] = []); events.push({callback: callback, context: context, ctx: context || this}); return this; }, // Bind an event to only be triggered a single time. After the first time // the callback is invoked, it will be removed. once: function (name, callback, context) { if (!eventsApi(this, 'once', name, [callback, context]) || !callback) return this; var self = this; var onced = once(function () { self.off(name, onced); callback.apply(this, arguments); }); onced._callback = callback; return this.on(name, onced, context); }, // Remove one or many callbacks. If `context` is null, removes all // callbacks with that function. If `callback` is null, removes all // callbacks for the event. If `name` is null, removes all bound // callbacks for all events. off: function (name, callback, context) { var retain, ev, events, names, i, l, j, k; if (!this._events || !eventsApi(this, 'off', name, [callback, context])) return this; if (!name && !callback && !context) { this._events = {}; return this; } names = name ? [name] : keys(this._events); for (i = 0, l = names.length; i < l; i++) { name = names[i]; if (events = this._events[name]) { this._events[name] = retain = []; if (callback || context) { for (j = 0, k = events.length; j < k; j++) { ev = events[j]; if ((callback && callback !== ev.callback && callback !== ev.callback._callback) || (context && context !== ev.context)) { retain.push(ev); } } } if (!retain.length) delete this._events[name]; } } return this; }, // Trigger one or many events, firing all bound callbacks. Callbacks are // passed the same arguments as `trigger` is, apart from the event name // (unless you're listening on `"all"`, which will cause your callback to // receive the true name of the event as the first argument). trigger: function (name) { if (!this._events) return this; var args = slice.call(arguments, 1); if (!eventsApi(this, 'trigger', name, args)) return this; var events = this._events[name]; var allEvents = this._events.all; if (events) triggerEvents(events, args); if (allEvents) triggerEvents(allEvents, arguments); return this; }, // Tell this object to stop listening to either specific events ... or // to every object it's currently listening to. stopListening: function (obj, name, callback) { var listeners = this._listeners; if (!listeners) return this; var deleteListener = !name && !callback; if (typeof name === 'object') callback = this; if (obj) (listeners = {})[obj._listenerId] = obj; for (var id in listeners) { listeners[id].off(name, callback, this); if (deleteListener) delete this._listeners[id]; } return this; } }; // Regular expression used to split event strings. var eventSplitter = /\s+/; // Implement fancy features of the Events API such as multiple event // names `"change blur"` and jQuery-style event maps `{change: action}` // in terms of the existing API. var eventsApi = function (obj, action, name, rest) { if (!name) return true; // Handle event maps. if (typeof name === 'object') { for (var key in name) { obj[action].apply(obj, [key, name[key]].concat(rest)); } return false; } // Handle space separated event names. if (eventSplitter.test(name)) { var names = name.split(eventSplitter); for (var i = 0, l = names.length; i < l; i++) { obj[action].apply(obj, [names[i]].concat(rest)); } return false; } return true; }; // A difficult-to-believe, but optimized internal dispatch function for // triggering events. 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ChaosKit / ChaosKitLibraries and tools to generate pictures with iterative function systems or Fractal Flames.
CAIMEOX / VoxelGeometryVoxel geometry structure generator
fernandocalenzani / Sed Evolutionary Computing DragonflyThis work proposes a planning methodology of distribution systems formulated as a nonlinear optimization problem, which was solved through the heuristic Dragonfly Optimization Algorithm, which will be developed with the integration between OpenDSS for power flow calculation and Python for collection, modifying the feeder and displaying the results. The Dragonfly Algorithm is responsible for the reconfiguration of the feeder, with the objective function of minimizing the costs of expansion and technical losses. The proposed methodology was tested on the IEEE 123 feeder adapted buses, which is a test network with more than 123 buses, multiple switches, regulators, transformers, etc. Finally, the reconfiguration proposal brought an economy 22% compared to the original expansion plan, simulated with Dragonfly Algorithm, with 30 dragonflies and maximum number of iterations equal to 25, showing the effectiveness of the algorithm when applied to electric power distribution systems.
aws-samples / Amazon Rekognition Custom Labels A2i Automated Continuous Model ImprovementWith Amazon Rekognition Custom Labels, you can easily build and deploy Machine Learning (ML) models to identify custom objects which are specific to your business domain in images without requiring advanced ML knowledge. When combined with Amazon Augmented AI (A2I), you can quickly integrate a ML workflow to capture and label images with a human workforce for model training. As ML lifecycle is an iterative and repetitive process, you need to implement an effective workflow that can provide for continuous model training with new data and automated deployment. Your workflow also needs to be flexible enough to allow for changes without requiring development rework as your business objectives change. Operationalizing an effective and flexible workflow can be resource intensive, especially for customers who have limited machine learning capabilities. In this post, we will use AWS Step Functions, AWS Lambda, and AWS System Manager Parameter Store to automate a configurable ML workflow for Rekognition Custom Labels and A2I. We will provide an overview of the solution and instructions to deploy it with AWS CloudFormation.
SENATOROVAI / Conjugate Gradient Sparse Cg Solver CourseThe Conjugate Gradient (CG) method is an efficient iterative algorithm for solving large, sparse systems of linear equations where the matrix is symmetric and positive-definite. It finds the minimum of a quadratic function by generating conjugate search directions, ensuring convergence in at most steps for an matrix.Solver
kach / Chaos Game Fractal FoliageCode for "Learning to Play the Chaos Game: Dreaming of fractal foliage by differentiating iterated function systems"
grz0zrg / TwigsTwigs – 512 bytes procedural graphics in C code (IFS Fractal, Linux, fbdev)
mekhontsev / IfstileAn algebraic framework for the discovery and analysis of self-affine tiles and fractal structures.
anujkumarthakur / Rust TutorialIntroduction Note: This edition of the book is the same as The Rust Programming Language available in print and ebook format from No Starch Press. Welcome to The Rust Programming Language, an introductory book about Rust. The Rust programming language helps you write faster, more reliable software. High-level ergonomics and low-level control are often at odds in programming language design; Rust challenges that conflict. Through balancing powerful technical capacity and a great developer experience, Rust gives you the option to control low-level details (such as memory usage) without all the hassle traditionally associated with such control. Who Rust Is For Rust is ideal for many people for a variety of reasons. Let’s look at a few of the most important groups. Teams of Developers Rust is proving to be a productive tool for collaborating among large teams of developers with varying levels of systems programming knowledge. Low-level code is prone to a variety of subtle bugs, which in most other languages can be caught only through extensive testing and careful code review by experienced developers. In Rust, the compiler plays a gatekeeper role by refusing to compile code with these elusive bugs, including concurrency bugs. By working alongside the compiler, the team can spend their time focusing on the program’s logic rather than chasing down bugs. Rust also brings contemporary developer tools to the systems programming world: Cargo, the included dependency manager and build tool, makes adding, compiling, and managing dependencies painless and consistent across the Rust ecosystem. Rustfmt ensures a consistent coding style across developers. The Rust Language Server powers Integrated Development Environment (IDE) integration for code completion and inline error messages. By using these and other tools in the Rust ecosystem, developers can be productive while writing systems-level code. Students Rust is for students and those who are interested in learning about systems concepts. Using Rust, many people have learned about topics like operating systems development. The community is very welcoming and happy to answer student questions. Through efforts such as this book, the Rust teams want to make systems concepts more accessible to more people, especially those new to programming. Companies Hundreds of companies, large and small, use Rust in production for a variety of tasks. Those tasks include command line tools, web services, DevOps tooling, embedded devices, audio and video analysis and transcoding, cryptocurrencies, bioinformatics, search engines, Internet of Things applications, machine learning, and even major parts of the Firefox web browser. Open Source Developers Rust is for people who want to build the Rust programming language, community, developer tools, and libraries. We’d love to have you contribute to the Rust language. People Who Value Speed and Stability Rust is for people who crave speed and stability in a language. By speed, we mean the speed of the programs that you can create with Rust and the speed at which Rust lets you write them. The Rust compiler’s checks ensure stability through feature additions and refactoring. This is in contrast to the brittle legacy code in languages without these checks, which developers are often afraid to modify. By striving for zero-cost abstractions, higher-level features that compile to lower-level code as fast as code written manually, Rust endeavors to make safe code be fast code as well. The Rust language hopes to support many other users as well; those mentioned here are merely some of the biggest stakeholders. Overall, Rust’s greatest ambition is to eliminate the trade-offs that programmers have accepted for decades by providing safety and productivity, speed and ergonomics. Give Rust a try and see if its choices work for you. Who This Book Is For This book assumes that you’ve written code in another programming language but doesn’t make any assumptions about which one. We’ve tried to make the material broadly accessible to those from a wide variety of programming backgrounds. We don’t spend a lot of time talking about what programming is or how to think about it. If you’re entirely new to programming, you would be better served by reading a book that specifically provides an introduction to programming. How to Use This Book In general, this book assumes that you’re reading it in sequence from front to back. Later chapters build on concepts in earlier chapters, and earlier chapters might not delve into details on a topic; we typically revisit the topic in a later chapter. You’ll find two kinds of chapters in this book: concept chapters and project chapters. In concept chapters, you’ll learn about an aspect of Rust. In project chapters, we’ll build small programs together, applying what you’ve learned so far. Chapters 2, 12, and 20 are project chapters; the rest are concept chapters. Chapter 1 explains how to install Rust, how to write a Hello, world! program, and how to use Cargo, Rust’s package manager and build tool. Chapter 2 is a hands-on introduction to the Rust language. Here we cover concepts at a high level, and later chapters will provide additional detail. If you want to get your hands dirty right away, Chapter 2 is the place for that. At first, you might even want to skip Chapter 3, which covers Rust features similar to those of other programming languages, and head straight to Chapter 4 to learn about Rust’s ownership system. However, if you’re a particularly meticulous learner who prefers to learn every detail before moving on to the next, you might want to skip Chapter 2 and go straight to Chapter 3, returning to Chapter 2 when you’d like to work on a project applying the details you’ve learned. Chapter 5 discusses structs and methods, and Chapter 6 covers enums, match expressions, and the if let control flow construct. You’ll use structs and enums to make custom types in Rust. In Chapter 7, you’ll learn about Rust’s module system and about privacy rules for organizing your code and its public Application Programming Interface (API). Chapter 8 discusses some common collection data structures that the standard library provides, such as vectors, strings, and hash maps. Chapter 9 explores Rust’s error-handling philosophy and techniques. Chapter 10 digs into generics, traits, and lifetimes, which give you the power to define code that applies to multiple types. Chapter 11 is all about testing, which even with Rust’s safety guarantees is necessary to ensure your program’s logic is correct. In Chapter 12, we’ll build our own implementation of a subset of functionality from the grep command line tool that searches for text within files. For this, we’ll use many of the concepts we discussed in the previous chapters. Chapter 13 explores closures and iterators: features of Rust that come from functional programming languages. In Chapter 14, we’ll examine Cargo in more depth and talk about best practices for sharing your libraries with others. Chapter 15 discusses smart pointers that the standard library provides and the traits that enable their functionality. In Chapter 16, we’ll walk through different models of concurrent programming and talk about how Rust helps you to program in multiple threads fearlessly. Chapter 17 looks at how Rust idioms compare to object-oriented programming principles you might be familiar with. Chapter 18 is a reference on patterns and pattern matching, which are powerful ways of expressing ideas throughout Rust programs. Chapter 19 contains a smorgasbord of advanced topics of interest, including unsafe Rust, macros, and more about lifetimes, traits, types, functions, and closures. In Chapter 20, we’ll complete a project in which we’ll implement a low-level multithreaded web server! Finally, some appendixes contain useful information about the language in a more reference-like format. Appendix A covers Rust’s keywords, Appendix B covers Rust’s operators and symbols, Appendix C covers derivable traits provided by the standard library, Appendix D covers some useful development tools, and Appendix E explains Rust editions. There is no wrong way to read this book: if you want to skip ahead, go for it! You might have to jump back to earlier chapters if you experience any confusion. But do whatever works for you. An important part of the process of learning Rust is learning how to read the error messages the compiler displays: these will guide you toward working code. As such, we’ll provide many examples that don’t compile along with the error message the compiler will show you in each situation. Know that if you enter and run a random example, it may not compile! Make sure you read the surrounding text to see whether the example you’re trying to run is meant to error. Ferris will also help you distinguish code that isn’t meant to work:
rodrigosetti / IfsIterating Function Systems
