The signal-to-crosstalk ratio at a specified point in a circuit is the ratio of the power of the wanted signal to the power of the unwanted signal from another channel. The signals are adjusted in each channel so that they are of equal power at the zero transmission level point in their respective channels. The signal-to-crosstalk ratio is usually expressed in dB.
Grammar induction
Grammar induction (or grammatical inference) is the process in machine learning of learning a formal grammar (usually as a collection of re-write rules or productions or alternatively as a finite-state machine or automaton of some kind) from a set of observations, thus constructing a model which accounts for the characteristics of the observed objects. More generally, grammatical inference is that branch of machine learning where the instance space consists of discrete combinatorial objects such as strings, trees and graphs. == Grammar classes == Grammatical inference has often been very focused on the problem of learning finite-state machines of various types (see the article Induction of regular languages for details on these approaches), since there have been efficient algorithms for this problem since the 1980s. Since the beginning of the century, these approaches have been extended to the problem of inference of context-free grammars and richer formalisms, such as multiple context-free grammars and parallel multiple context-free grammars. Other classes of grammars for which grammatical inference has been studied are combinatory categorial grammars, stochastic context-free grammars, contextual grammars and pattern languages. == Learning models == The simplest form of learning is where the learning algorithm merely receives a set of examples drawn from the language in question: the aim is to learn the language from examples of it (and, rarely, from counter-examples, that is, example that do not belong to the language). However, other learning models have been studied. One frequently studied alternative is the case where the learner can ask membership queries as in the exact query learning model or minimally adequate teacher model introduced by Angluin. == Methodologies == There is a wide variety of methods for grammatical inference. Two of the classic sources are Fu (1977) and Fu (1982). Duda, Hart & Stork (2001) also devote a brief section to the problem, and cite a number of references. The basic trial-and-error method they present is discussed below. For approaches to infer subclasses of regular languages in particular, see Induction of regular languages. A more recent textbook is de la Higuera (2010), which covers the theory of grammatical inference of regular languages and finite state automata. D'Ulizia, Ferri and Grifoni provide a survey that explores grammatical inference methods for natural languages. === Induction of probabilistic grammars === There are several methods for induction of probabilistic context-free grammars. === Grammatical inference by trial-and-error === The method proposed in Section 8.7 of Duda, Hart & Stork (2001) suggests successively guessing grammar rules (productions) and testing them against positive and negative observations. The rule set is expanded so as to be able to generate each positive example, but if a given rule set also generates a negative example, it must be discarded. This particular approach can be characterized as "hypothesis testing" and bears some similarity to Mitchel's version space algorithm. The Duda, Hart & Stork (2001) text provide a simple example which nicely illustrates the process, but the feasibility of such an unguided trial-and-error approach for more substantial problems is dubious. === Grammatical inference by genetic algorithms === Grammatical induction using evolutionary algorithms is the process of evolving a representation of the grammar of a target language through some evolutionary process. Formal grammars can easily be represented as tree structures of production rules that can be subjected to evolutionary operators. Algorithms of this sort stem from the genetic programming paradigm pioneered by John Koza. Other early work on simple formal languages used the binary string representation of genetic algorithms, but the inherently hierarchical structure of grammars couched in the EBNF language made trees a more flexible approach. Koza represented Lisp programs as trees. He was able to find analogues to the genetic operators within the standard set of tree operators. For example, swapping sub-trees is equivalent to the corresponding process of genetic crossover, where sub-strings of a genetic code are transplanted into an individual of the next generation. Fitness is measured by scoring the output from the functions of the Lisp code. Similar analogues between the tree structured lisp representation and the representation of grammars as trees, made the application of genetic programming techniques possible for grammar induction. In the case of grammar induction, the transplantation of sub-trees corresponds to the swapping of production rules that enable the parsing of phrases from some language. The fitness operator for the grammar is based upon some measure of how well it performed in parsing some group of sentences from the target language. In a tree representation of a grammar, a terminal symbol of a production rule corresponds to a leaf node of the tree. Its parent nodes corresponds to a non-terminal symbol (e.g. a noun phrase or a verb phrase) in the rule set. Ultimately, the root node might correspond to a sentence non-terminal. === Grammatical inference by greedy algorithms === Like all greedy algorithms, greedy grammar inference algorithms make, in iterative manner, decisions that seem to be the best at that stage. The decisions made usually deal with things like the creation of new rules, the removal of existing rules, the choice of a rule to be applied or the merging of some existing rules. Because there are several ways to define 'the stage' and 'the best', there are also several greedy grammar inference algorithms. These context-free grammar generating algorithms make the decision after every read symbol: Lempel-Ziv-Welch algorithm creates a context-free grammar in a deterministic way such that it is necessary to store only the start rule of the generated grammar. Sequitur and its modifications. These context-free grammar generating algorithms first read the whole given symbol-sequence and then start to make decisions: Byte pair encoding and its optimizations. === Distributional learning === A more recent approach is based on distributional learning. Algorithms using these approaches have been applied to learning context-free grammars and mildly context-sensitive languages and have been proven to be correct and efficient for large subclasses of these grammars. === Learning of pattern languages === Angluin defines a pattern to be "a string of constant symbols from Σ and variable symbols from a disjoint set". The language of such a pattern is the set of all its nonempty ground instances i.e. all strings resulting from consistent replacement of its variable symbols by nonempty strings of constant symbols. A pattern is called descriptive for a finite input set of strings if its language is minimal (with respect to set inclusion) among all pattern languages subsuming the input set. Angluin gives a polynomial algorithm to compute, for a given input string set, all descriptive patterns in one variable x. To this end, she builds an automaton representing all possibly relevant patterns; using sophisticated arguments about word lengths, which rely on x being the only variable, the state count can be drastically reduced. Erlebach et al. give a more efficient version of Angluin's pattern learning algorithm, as well as a parallelized version. Arimura et al. show that a language class obtained from limited unions of patterns can be learned in polynomial time. === Pattern theory === Pattern theory, formulated by Ulf Grenander, is a mathematical formalism to describe knowledge of the world as patterns. It differs from other approaches to artificial intelligence in that it does not begin by prescribing algorithms and machinery to recognize and classify patterns; rather, it prescribes a vocabulary to articulate and recast the pattern concepts in precise language. In addition to the new algebraic vocabulary, its statistical approach was novel in its aim to: Identify the hidden variables of a data set using real world data rather than artificial stimuli, which was commonplace at the time. Formulate prior distributions for hidden variables and models for the observed variables that form the vertices of a Gibbs-like graph. Study the randomness and variability of these graphs. Create the basic classes of stochastic models applied by listing the deformations of the patterns. Synthesize (sample) from the models, not just analyze signals with it. Broad in its mathematical coverage, pattern theory spans algebra and statistics, as well as local topological and global entropic properties. == Applications == The principle of grammar induction has been applied to other aspects of natural language processing, and has been applied (among many other problems) to semantic parsing, natural language understanding, example-based translation, language acquisition, grammar-based compre
Fling (social network)
Fling was a social media app available for IOS and Android. It was founded in 2014 by Marco Nardone and was taken offline in August 2016. == Overview == In 2012, Marco Nardone founded the startup Unii and launched Unii.com, a social network intended for students in the UK. While working on this service, Nardone had the idea for a messaging service where pictures could be sent to strangers in January 2014. The app Fling was then developed and released between March and July 2014. After a month, it already had 375,000 downloads and 180,000 active users on iOS. Users were able to take pictures inside the app and send them to 50 random people all over the world. The recipient could then choose to answer via chat or reply by sending a picture themselves. The app was used by many users as a medium to exchange sexually explicit pictures and for sexting with strangers. This led to the app being removed from the App Store in June 2015. In the 19 days that followed, flings developers rewrote the App almost completely from scratch, working around the clock. The feature to message random strangers was removed, and the app was readmitted into the App Store as a messenger App resembling Snapchat. But the redesigned Application did not have the success of its predecessor. The funding ran out and the parent company Unii went bankrupt. The company was not able to pay their content moderation team anymore, leading to a new surge of pornographic content on the App. Shortly after that, the Social Network was taken offline in August 2016. It has been inactive since. During the 2 years Fling was online, $21 million was raised from investors while generating no revenue at all. Of this $21 million (£16.5m), £5 million came from Nardone's father. == Allegations against CEO == Former employees made multiple allegations against Marco Nardone, the Founder and CEO of Unii and Fling. According to these claims, he behaved erratic and abusive, throwing "things across the office". He hired his girlfriend as the head of human resources to handle issues between him and his staff. Employees who left the company often had "some part of their pay held back". According to the reports, he also spent the money raised from investors irresponsibly, having no clear concept of a budget. Some of that money was used on expensive restaurants in London, a luxurious office for CEO Nardone and advertisements for Fling on Twitter and Facebook. Nardone also spent time partying in Ibiza with two employees, while the developer team in London frantically tried to get Fling back online after it being removed from the App Store. In December 2017 he pleaded guilty to assaulting his girlfriend at a domestic violence court.
Standard test image
A standard test image is a digital image file used across different institutions to test image processing and image compression algorithms. By using the same standard test images, different labs are able to compare results, both visually and quantitatively. The images are in many cases chosen to represent natural or typical images that a class of processing techniques would need to deal with. Other test images are chosen because they present a range of challenges to image reconstruction algorithms, such as the reproduction of fine detail and textures, sharp transitions and edges, and uniform regions. == Historical origins == Test images as transmission system calibration material probably date back to the original Paris to Lyon pantelegraph link. Analogue fax equipment (and photographic equipment for the printing trade) were the largest user groups of the standardized image for calibration technology until the coming of television and digital image transmission systems. == Common test image resolutions == The standard resolution of the images is usually 512×512 or 720×576. Most of these images are available as TIFF files from the University of Southern California's Signal and Image Processing Institute. Kodak has released 768×512 images, available as PNGs, that was originally on Photo CD with higher resolution, that are widely used for comparing image compression techniques.
PhotoLine
PhotoLine is a general purpose bitmap and vector graphics editor developed and published by Computerinsel GmbH for Windows, macOS, and Linux/Wine. It was originally created in 1995 by Gerhard Huber and Martin Huber. The program combines bitmap and vector graphics editing in one seamless working application unlike most graphics software which tend to focus on either bitmap or vector editing and output. PhotoLine is considered as a market competitor to Adobe Photoshop. == Features == PhotoLine edits and composes multi-layer raster and vector images with deep support for masking and alpha compositing and with full color management. Editing and color management in PhotoLine is mostly non-destructive. Image data in layers is preserved without loss of information regardless of the document's image mode or layer transformation. color depth, image resolution, color model, and ICC profile are preserved for each individual layer or group of layers. Layers can be cloned and reused anywhere in the layer stack, including repurposed as layer masks. Layer blending and compositing in PhotoLine supports common blend modes, and features a layer blend range of -200 to +200 percent. It is also possible to control which channels are blended for each layer, adjustment layer, and layer mask or group of layers. Filters, adjustment layers, and brushes have access to Lab and HIS color modes (HIS is a variant of HSL), separately of the color model of the underlying image layer. In Addition to raster and vector editing, PhotoLine can be used for small desktop publishing projects. Multi-page documents with page spreads and text flow between text frames and pages are supported. Character and paragraph styles can be defined. Spot colors, bleed settings, a baseline grid, a table of contents generator, and PDF/X support help with these projects. PhotoLine is however much more limited when compared to dedicated publishing software such as Adobe InDesign or QuarkXPress. PhotoLine incorporates the Open-source software library LibRaw to read raw images from digital cameras for import. Developing these files is non-destructive with a choice of embedding the RAW image data either in the PhotoLine document or link to the external RAW image file. PhotoLine can open raw files as linear unmodified and non color managed source images. Photoshop PSD files can be imported and exported. Core functionality of PhotoLine can be extended through standard Photoshop filter plugins, the G'MIC digital image processing framework, and PSP tubes. External programs can be linked for a seamless round-trip workflow and files can be sent directly for processing in third-party design applications. Custom functionality is further supported through scripting and macro recording. == Early history == Developed by two brothers, Gerhard Huber and Martin Huber, PhotoLine was first released in January 1996 on the Atari ST line of personal computers from Atari Corporation. Previously, Gerhard and Martin had worked on making graphics cards for Atari computers and writing drivers for image scanners. Atari's market share was declining, and the brothers considered developing a video game to expand the business. This led them to search for image editing software that would run on Atari computers and fit their game project. Only an image editor called tms Cranach came close to what Gerhard and Martin had in mind. tms Cranach was a Raster graphics editor running on Atari's MegaST/STe, TT030, and Falcon030 systems. However, Cranach turned out to be expensive software and complicated to use. The brothers contacted tms (Cranach's developers) and this resulted in an offer from tms to purchase Cranach and its source code, as tms intended to exit the Atari software market. After the purchase of Cranach and its source code Gerhard and Martin initially continued to sell Cranach, but sales were low. In 1995 the two decided to start developing a new graphics editor called "PhotoLine". PhotoLine was developed from scratch and written in C++. It nevertheless contained a lot of know-how from Cranach (which was written in C). PhotoLine first release was launched one year later in 1996. With the growing popularity of Microsoft Windows, the release of Windows 95, and the limiting graphics hardware on the Atari platforms, the developers switched development platforms and continued development of PhotoLine for Windows only. The first Windows version (PhotoLine 2.2) was released in the middle of 1997. Shortly after, the Atari version was discontinued and saw its final release as PhotoLine 2.30. The Huber brothers released this final Atari version into the public domain in 2012. The first Classic Mac OS version of PhotoLine 6 appeared in 1999 after many ex-Atari users who had switched to Mac OS pressured the PhotoLine developers to release an Apple port. == Linux Support == PhotoLine runs natively under Windows and MacOS. While a native Linux version of PhotoLine is not available, running PhotoLine under Wine is actively supported and maintained by the developers. Running PhotoLine under Linux/Wine PhotoLine enables the user to allow Little CMS to fully support color management under Linux instead of the native OS CMS. == File format == Native PhotoLine files have the extension .PLD, which is an abbreviation of "PhotoLine Document". It can contain embedded JPEG, PNG, or camera raw images. It contains a preview image in JPEG or PNG format, which is used by the operating system or third-party applications to display a thumbnail of its contents. Thumbnails are natively supported on MacOS X. During installation on Windows the user is presented with an option to install a PLD thumbnail preview driver which enables thumbnails of PLD content in Windows Explorer. Alternatively, the FastPictureViewer Standalone Codec Pack provides the ability to display PLD thumbnails in Windows Explorer. == Version History == PhotoLine was first developed for the Atari ST computer. Version 2 was the first version for Windows, and since version 6 PhotoLine is also available for MacOS.
Belief–desire–intention model
For popular psychology, the belief–desire–intention (BDI) model of human practical reasoning was developed by Michael Bratman as a way of explaining future-directed intention. BDI is fundamentally reliant on folk psychology (the 'theory theory'), which is the notion that our mental models of the world are theories. It was used as a basis for developing the belief–desire–intention software model. == Applications == BDI was part of the inspiration behind the BDI software architecture, which Bratman was also involved in developing. Here, the notion of intention was seen as a way of limiting time spent on deliberating about what to do, by eliminating choices inconsistent with current intentions. BDI has also aroused some interest in psychology. BDI formed the basis for a computational model of childlike reasoning CRIBB.
Oculus Medium
Oculus Medium is a digital sculpting software that works with virtual reality headsets and 6DoF motion controllers. It is used to create and paint digital sculptures. Medium works only on Oculus Rift. It was released on December 5, 2016, following with a major update in 2018 introducing new features and a revamped UI. On December 9, 2019, Oculus Medium was acquired by Adobe and re-named to "Medium by Adobe".