In machine learning, supervised learning (SL) is a type of machine learning paradigm where an algorithm learns to map input data to a specific output based on example input-output pairs. This process involves training a statistical model using labeled data, meaning each piece of input data is provided with the correct output. The term "supervised" refers to the role of a teacher or supervisor who provides this training data, guiding the algorithm towards correct predictions. For instance, if you want a model to identify cats in images, supervised learning would involve feeding it many images of cats (inputs) that are explicitly labeled "cat" (outputs). The goal of supervised learning is for the trained model to accurately predict the output for new, unseen data. This requires the algorithm to effectively generalize from the training examples, a quality measured by its generalization error. Supervised learning is commonly used for tasks like classification (predicting a category, e.g., spam or not spam) and regression (predicting a continuous value, e.g., house prices). == Steps to follow == To solve a given problem of supervised learning, the following steps must be performed: Determine the type of training samples. Before doing anything else, the user should decide what kind of data is to be used as a training set. In the case of handwriting analysis, for example, this might be a single handwritten character, an entire handwritten word, an entire sentence of handwriting, or a full paragraph of handwriting. Gather a training set. The training set needs to be representative of the real-world use of the function. Thus, a set of input objects is gathered together with corresponding outputs, either from human experts or from measurements. Determine the input feature representation of the learned function. The accuracy of the learned function depends strongly on how the input object is represented. Typically, the input object is transformed into a feature vector, which contains a number of features that are descriptive of the object. The number of features should not be too large, because of the curse of dimensionality; but should contain enough information to accurately predict the output. Determine the structure of the learned function and corresponding learning algorithm. For example, one may choose to use support-vector machines or decision trees. Complete the design. Run the learning algorithm on the gathered training set. Some supervised learning algorithms require the user to determine certain control parameters. These parameters may be adjusted by optimizing performance on a subset (called a validation set) of the training set, or via cross-validation. Evaluate the accuracy of the learned function. After parameter adjustment and learning, the performance of the resulting function should be measured on a test set that is separate from the training set. == Algorithm choice == A wide range of supervised learning algorithms are available, each with its strengths and weaknesses. There is no single learning algorithm that works best on all supervised learning problems (see the No free lunch theorem). There are four major issues to consider in supervised learning: === Bias–variance tradeoff === A first issue is the tradeoff between bias and variance. Imagine that we have available several different, but equally good, training data sets. A learning algorithm is biased for a particular input x {\displaystyle x} if, when trained on each of these data sets, it is systematically incorrect when predicting the correct output for x {\displaystyle x} . A learning algorithm has high variance for a particular input x {\displaystyle x} if it predicts different output values when trained on different training sets. The prediction error of a learned classifier is related to the sum of the bias and the variance of the learning algorithm. Generally, there is a tradeoff between bias and variance. A learning algorithm with low bias must be "flexible" so that it can fit the data well. But if the learning algorithm is too flexible, it will fit each training data set differently, and hence have high variance. A key aspect of many supervised learning methods is that they are able to adjust this tradeoff between bias and variance (either automatically or by providing a bias/variance parameter that the user can adjust). === Function complexity and amount of training data === The second issue is of the amount of training data available relative to the complexity of the "true" function (classifier or regression function). If the true function is simple, then an "inflexible" learning algorithm with high bias and low variance will be able to learn it from a small amount of data. But if the true function is highly complex (e.g., because it involves complex interactions among many different input features and behaves differently in different parts of the input space), then the function will only be able to learn with a large amount of training data paired with a "flexible" learning algorithm with low bias and high variance. === Dimensionality of the input space === A third issue is the dimensionality of the input space. If the input feature vectors have large dimensions, learning the function can be difficult even if the true function only depends on a small number of those features. This is because the many "extra" dimensions can confuse the learning algorithm and cause it to have high variance. Hence, input data of large dimensions typically requires tuning the classifier to have low variance and high bias. In practice, if the engineer can manually remove irrelevant features from the input data, it will likely improve the accuracy of the learned function. In addition, there are many algorithms for feature selection that seek to identify the relevant features and discard the irrelevant ones. This is an instance of the more general strategy of dimensionality reduction, which seeks to map the input data into a lower-dimensional space prior to running the supervised learning algorithm. === Noise in the output values === A fourth issue is the degree of noise in the desired output values (the supervisory target variables). If the desired output values are often incorrect (because of human error or sensor errors), then the learning algorithm should not attempt to find a function that exactly matches the training examples. Attempting to fit the data too carefully leads to overfitting. You can overfit even when there are no measurement errors (stochastic noise) if the function you are trying to learn is too complex for your learning model. In such a situation, the part of the target function that cannot be modeled "corrupts" your training data – this phenomenon has been called deterministic noise. When either type of noise is present, it is better to go with a higher bias, lower variance estimator. In practice, there are several approaches to alleviate noise in the output values such as early stopping to prevent overfitting as well as detecting and removing the noisy training examples prior to training the supervised learning algorithm. There are several algorithms that identify noisy training examples and removing the suspected noisy training examples prior to training has decreased generalization error with statistical significance. === Other factors to consider === Other factors to consider when choosing and applying a learning algorithm include the following: Heterogeneity of the data. If the feature vectors include features of many different kinds (discrete, discrete ordered, counts, continuous values), some algorithms are easier to apply than others. Many algorithms, including support-vector machines, linear regression, logistic regression, neural networks, and nearest neighbor methods, require that the input features be numerical and scaled to similar ranges (e.g., to the [-1,1] interval). Methods that employ a distance function, such as nearest neighbor methods and support-vector machines with Gaussian kernels, are particularly sensitive to this. An advantage of decision trees is that they easily handle heterogeneous data. Redundancy in the data. If the input features contain redundant information (e.g., highly correlated features), some learning algorithms (e.g., linear regression, logistic regression, and distance-based methods) will perform poorly because of numerical instabilities. These problems can often be solved by imposing some form of regularization. Presence of interactions and non-linearities. If each of the features makes an independent contribution to the output, then algorithms based on linear functions (e.g., linear regression, logistic regression, support-vector machines, naive Bayes) and distance functions (e.g., nearest neighbor methods, support-vector machines with Gaussian kernels) generally perform well. However, if there are complex interactions among features, then algorithms such as decision trees and neural networks work better, becaus
Clip Studio Paint
Clip Studio Paint (previously marketed as Manga Studio in North America), informally known in Japan as Kurisuta (クリスタ), is a family of software applications developed by Japanese graphics software company Celsys. It is used for the digital creation of comics, general illustration, and 2D animation. The software is available in versions for macOS, Windows, iOS, iPadOS, Android, and ChromeOS. The program is widely used by amateur and professional comics creators, and animation studios. The application is sold in editions with varying feature sets. The full-featured edition is a page-based, layered drawing program, with support for bitmap and vector art, text, imported 3D models, and frame-by-frame animation. It is designed for use with a stylus and a graphics tablet or tablet computer. It has drawing tools which emulate natural media such as pencils, ink pens, and brushes, as well as patterns and decorations. It is distinguished from similar programs by features designed for creating comics: tools for creating panel layouts, perspective rulers, sketching, inking, applying tones and textures, coloring, and creating word balloons and captions. == History == The application has it origins in a program for macOS and Windows, released in Japan in 2001 as "Comic Studio". It was sold as "Manga Studio" in the Western market by E Frontier America until 2007, then by Smith Micro Software. Early versions were designed for creating black and white art with only spot color (a typical format for Japanese manga), with version 4 adding support for full-color art. Celsys developed Clip Studio Paint as a replacement for this product, based on the company's Illust Studio application, and it was released on May 31, 2012. It was initially distributed in Western markets as "Manga Studio 5", but in 2016, the branding was unified worldwide as "Clip Studio Paint". At this time, version 1.5.4 introduced a new file format (extension .clip) and frame-by-frame animation. In late 2017, Celsys took over direct support for the software worldwide, and ceased its relationship with Smith Micro. In July 2018, Celsys began a partnership with Graphixly for distribution in North America, South America, and Europe. Clip Studio Paint for the Apple iPad was introduced in November 2017, and for the iPhone in December 2019. Clip Studio Paint for Samsung Galaxy tablets and smartphones was released in August 2020 on the Galaxy Store, with versions for other Android devices and Chromebooks released in December. The Windows and macOS versions of the software have been sold and distributed either from the developer's web site or on DVD, and purchased either with a perpetual license or an ongoing subscription. The versions for iPhone, iPad, and Android-based devices are distributed through the corresponding app stores free of charge, but require a subscription – which includes cloud storage – for unrestricted use. Without a subscription, the tablet versions can be used only for a specified number of months, and the phone versions can be used only for 30 hours per month. From 2013 to 2023, regular updates for version 1 were distributed free of additional charge to both perpetual and subscription users. Since the release of version 2 in 2023, feature updates are included only in subscription plans and are available to perpetual licenses at an additional cost. Perpetual licenses can be upgraded permanently or with an annual "update pass". The "update pass" provides early access to features to be included in subsequent perpetual licenses for 12 months, after which the software reverts to the original license if not renewed. In March 2024, version 3 was released, and version 4 introduced additional features in March 2025. == Editions == Clip Studio Paint is available in three editions, with differing feature sets and prices: Debut (bundle-only grade), Pro (adding support for vector-based drawing, custom textures, and comics-focused features), and EX (adding support for multi-page documents, book exporting, and 2D animation). Companion programs include Clip Studio (for managing and sharing digital assets distributed through the Clip Studio web site, managing licenses, and getting updates and support) and Clip Studio Modeler (for setting up 3D materials to use in Clip Studio Paint).
Digital cassettes
Digital audio cassette formats introduced to the professional audio and consumer markets: Digital Audio Tape (or DAT) is the most well-known, and had some success as an audio storage format among professionals and "prosumers" before the prices of hard drive and solid-state flash memory-based digital recording devices dropped in the late 1990s. Hard-drive recording has mostly made DAT obsolete, as hard disk recorders offer more editing versatility than tape, and easier importation into digital audio workstations (DAWs) and non-linear video editing (NLE) systems. Digital Compact Cassette was intended as a digital replacement for the mass-market analog cassette tape, but received very little attention or adaptation. Its failure is generally attributed to higher production costs than audio CDs, durability and indifferent reception by consumers. Digital video cassettes include: Betacam IMX (Sony) D-VHS (JVC) D1 (Sony) D2 (Sony) D3 D5 HD Digital-S D9 (JVC) Digital Betacam (Sony) Digital8 (Sony) DV HDV ProHD (JVC) MiniDV MicroMV == Analog cassettes used as digital data storage == Historically, the compact audio cassette which was originally designed for analog storage of music was used as an alternative to disk drives in the late 1970s and early 1980s to provide data storage for home computers. There is a number of unique and incompatible cassette tape data storage formats that all use the same analog compact audio cassette tape media. The ADAT system uses Super VHS tapes to record 8 synchronized digital audiotracks at once. There have also been several audio recording systems that used VHS video recorders as storage devices and video tape transports, generally by encoding the digital data to be recorded into an analog composite video signal (which resembles static) and then recording this to magnetic tape. These systems were often used as "mixdown" recorders, to record the finished mix from a multi-track recorder in preparation for the manufacture of a vinyl record, cassette tape, or CD. An example was the Dbx Model 700. Another example is the Sony PCM adaptor series. Several companies sold VHS backup solutions in the 1980s and 1990s where data was converted to a video image which was then saved onto a VHS tape. the Corvus "Mirror" ( U.S. patent 4380047A ) the Metrum Model 64 on S-VHS tape, the Danmere Backer tape backup system, the Alpha Microsystems Videotrax the Legacy Storage Systems International VAST (Variable Array Storage) the ArVid the Video Backup System Amiga, The S2 VLBI system at three NASA Deep Space Network complexes and over 20 other radio telescopes stores digital data on SVHS tapes.
MicroTCA
MicroTCA (short for Micro Telecommunications Computing Architecture, also: μTCA) is a modular, open standard, created and maintained by the PCI Industrial Computer Manufacturers Group (PICMG). It provides the electrical, mechanical, thermal and management specifications to create a switched fabric computer system, using Advanced Mezzanine Cards (AMC), connected directly to a backplane. MicroTCA is a descendant of the AdvancedTCA standard. == History == The rapid expansion of mobile telecommunications and their associated services (such as text messages) at the beginning of the millennium increased the demand of processing power in telecommunication systems. The existing "carrier grade" (see RAS) computing architectures were not fit to house the high performance processors of the time. In order to answer those demands, about 100 companies worked together in PICMG, resulting in the Advanced Telecommunications Architecture (AdvancedTCA, ATCA), published in 2002. After the introduction of AdvancedTCA, a standard was developed, to cater towards smaller telecommunications systems at the edge of the network. This standard was geared towards a more compact, less expensive systems, without cutting back on reliability or data throughput. This standard, called MicroTCA, was ratified 2006. MicroTCA systems migrated after its release into non-telecommunication sectors, like defence, avionics and science. This resulted in extensions to the base-standard, called modules. == Modules == === MicroTCA.0 === The base-specification for properties common to all other modules, ratified July 6, 2006. This includes: Mechanical specifications, like possible dimensions of card cages, backplanes and supported AMC-modules Electrical specifications, like power distribution and interface layout Thermal specifications, like possible cooling layouts or available cooling power Management specifications A second revision of the base-specifications was ratified January 16, 2020, containing some corrections, as well as alterations, necessary to implement higher speed Ethernet fabrics, like 10GBASE-KR and 40GBASE-KR4. === MicroTCA.1 === This module adds specifications for ruggedized systems, using forced air for cooling. Possible scenarios for MicroTCA.1-based systems include outside plant telecom, industrial and aerospace environments === MicroTCA.2 === This module adds specifications for more stringent requirements with regards to temperature, shock, vibration and other environmental conditions. These specifications are geared towards use in outside plant telecom, machine and transport industry, as well as military airborne, shipboard and ground mobile equipment. MicroTCA.2 allows the use of air- and conduction-cooled AMC-modules. === MicroTCA.3 === This module adds specifications for even more stringent requirements with regards to temperature, shock, vibration and other environmental conditions. These specifications are geared towards use in outside plant telecom, machine and transport industry, as well as military airborne, shipboard and ground mobile equipment. MicroTCA.3 requires the use of conduction-cooled AMC-modules. === MicroTCA.4 === This module extends the AMC with a Rear Transition Module (RTM), increasing PCB-space and modularity. AMC and RTM are connected with a connector, located in zone 3, defined in MicroTCA.0. These specifications are geared towards use in large-scale scientific devices, like particle accelerators or telescopes. == Components of MicroTCA == === Card Cage === The card cage (also: shelf, crate) houses all the other components and as such has two primary functions: Provide mechanical stability to the other components Ensure sufficient cooling There exist a wide array of card cages. They usually differ in: the type of modules they support (MTCA.0, MTCA.1, ...) the number of slots they provide (typically between 2 and 12) the architecture of the installed backplane (see below) the cooling scheme they use (i.e. airflow front-to-back, bottom-to-top, side-to-side, conductive,...) === Backplane === The backplane is a printed circuit board, mounted directly into the card cage. It connects all other components of a MicroTCA system to each other and provides power, data access and management access to them. Two types of power are distributed over the backplane, Management Power (+3.3 V) and Payload Power (+12 V). Unlike typical backplanes, where power is distributed to all components via a common "powerplane" in the PCB, on a MicroTCA backplane, Management and Payload Power are distributed to each component individually. While Management Power is provided to each module connected to a powered backplane, Payload Power has to be granted by the MicroTCA Carrier Hub (MCH), after ensuring that the module is MicroTCA-compatible. The standard defines various communication buses, which the backplane can/should provide: Gigabit Ethernet IPMI SATA Fat pipe (can be used for PCIe, SRIO or 10G/40G Ethernet) Point to Point Links Clocks JTAG === Cooling Unit === The Cooling Unit (CU) provides controlled air flow in air-flow-cooled card cages. It usually consists of an array of fans and a controller, which is connected to the backplane. The MicroTCA Carrier Hub (MCH) can read-out temperature sensors (if present) and fan speed, as well as change fan speed via IPMI. The Cooling Unit is usually fitted to a specific card cage. Some CUs are easily detachable (i.e. for cleaning or replacement), while other card cages come with integrated, non-detachable CUs. === Power Module === The Power Module (PM, also: Power Supply) converts the AC power from the power line to the +3.3 V Management Power (MP) and +12 V Payload Power (PP), both of which are DC. There exist a variety of power modules, which differ in: form factor (i.e. double width, single width) input voltage (110 V, 220 V, both) output power (i.e. 600 W, 1000 W) The power module senses the presence of a module in a slot via a specified pin in the module connector, and immediately provides that module with management power. Payload power is managed by the MicroTCA Carrier Hub (MCH), which communicates with the power module via IPMI. The power module uses its own type of connector, and can thus only be installed into designated slots, which in turn can't carry any other type of module. Some card cages provide an additional power module slot for redundancy. In such a case, one slot is the primary, which will provide power by default, and the other one is secondary, providing power only, if the primary does not. === MicroTCA Carrier Hub === The MicroTCA Carrier Hub (MCH) is the central managing device of a MicroTCA card cage. It manages power distribution and cooling. It usually also provides Gigabit Ethernet and/or PCIe/Serial RapidIO switching. Some MCHs additionally provide clocking. As the name indicates, they are the hub of various star topologies (i.e. for Ethernet, PCIe) on the backplane and thus require dedicated slot(s). Some backplanes support two MCHs for redundancy. In this case there are two MCH slots, with one being designated primary, and one secondary. === Advanced Mezzanine Card === Advanced Mezzanine Card (AMC) is a standard for hot-pluggable PCBs. It was originally developed to be used in AdvancedTCA systems. The standard specifies: the dimensions of the PCB with two width variants (single, double) and three height variants (Compact, Mid-size, Full) type, location and orientation of connectors (i.e. Zone 1, 2, 3) There is a huge variation of functionalities, an AMC can fulfill: Computing (i.e. a module with CPU, RAM, SSD and on-board graphics) Storage (i.e. SSD carrier) Graphics card FPGA card (i.e. for signal processing) FMC carrier Digitizer card (Analog-Digital and Digital-Analog Conversion) Clocking and Triggering and others === Rear Transition Module (MTCA.4 only) === The Rear Transition Module (RTM) was added in the MicroTCA.4 standard. It is connected directly to an AMC via a connector, located in zone 3, requiring a double width AMC and RTM. An RTM has about the same dimensions, as an AMC, basically doubling the available PCB-space per slot in an MTCA.4 card cage. Its power is provided by the AMC. Thus an RTM can not operate on its own, but requires a paired AMC. The zone 3 connector is electrically free configurable, making it possible, that a mechanically fitting AMC-RTM pair is electrically incompatible. To avoid damage due to that incompatibility, a mechanical code-pin was added to MTCA.4-compatible AMCs and RTMs, mechanically preventing the installation of an electrically incompatible RTM to an AMC. The functionality of RTMs includes, but is not limited to: RF-signal pre-/post-processing (i.e. filtering, Up-/Down-conversion, Vector De-/Modulation) Digital signal pre-/post-processing Clock-generation/-distribution Device interfaces Date storage CPU (only MCH-RTM)
Gaumina
Gaumina is the largest interactive agency in the Baltics, providing services of web design, web development, online advertising, video, multimedia, mobile and viral. The company works on projects for Procter & Gamble, Nokia, Nissan, Unilever, YX Energi, 7 Up, Vodafone, MTV, Dunnes Stores, Philip Morris, FIBA Europe as well as Irish public sector. == History == Founded in 1998, Gaumina accounts for 39 percent of the Lithuanian interactive market and has completed more than 2,000 online projects. Since 2004 the company has been operating in the UK and Ireland as Gaumina.co.uk. In 2007 Gaumina gained wide media coverage for winning three awards in three days. A website developed by Gaumina won the Best Social Networking website award at the same the Irish Golden Spiders awards. A website developed by Gaumina was named among the 21 best European multimedia projects of 2007 in the final of Europrix Top Talent Award in Austria. The company was also named one of the winners of the national Innovation Prize 2007, awarding the Lithuania's most innovative companies, in the category of Innovative Enterprise. The agency was named "Digital Agency of the Year" by International advertising festival Golden Hammer in September 2008. The agency also won the main prize at the best at Best Use of Film, Digital Animation or Motion Graphics category by the Irish Golden Spider awards in November 2008. Gaumina is currently managed by CEO Darius Bagdžiūnas.
Qstack
Qstack is a cloud management platform developed by GreenQloud, a cloud computing software company founded in Reykjavik, Iceland in February 2010. Qstack enables its users to manage multiple clouds and hybrid deployments through a single self-service portal. Qstack is in continuous development, incorporating developments within infrastructure, cloud, and application management solutions. The next release of Qstack is slated for June 2017. == History == In 2014 when Jonsi Stefansson joined as CEO, Greenqloud pivoted its operational focus to development of Qstack with beta launch in the fall of 2015, and began offering support, technical services and certifications for the software. == Features == Qstack is hypervisor agnostic (KVM, VMware, Hyper-V) and can manage private clouds in multiple locations as well as AWS, Azure, and EC2-compatible public clouds from its user interface. Qstack combines proprietary software with open-source components, and the company claims to harden them to meet the strict security standards often required by enterprise deployments. Qstack features VM templates for Windows, Linux, and other operating systems. It also features full SSH/RDP access to instances, virtual routers, firewalls, and load balancers built into the interface. == Reception == In a 2015 review, IDG columnist J. Peter Bruzzese praised Qstack’s user interface for its ease-of-use and clean look.
Web worker
A web worker, as defined by the World Wide Web Consortium (W3C) and the Web Hypertext Application Technology Working Group (WHATWG), is a JavaScript script executed from an HTML page that runs in the background, independently of scripts that may also have been executed from the same HTML page. Web workers are often able to utilize multi-core CPUs more effectively. The W3C and WHATWG envision web workers as long-running scripts that are not interrupted by scripts that respond to clicks or other user interactions. Keeping such workers from being interrupted by user activities should allow Web pages to remain responsive at the same time as they are running long tasks in the background. The web worker specification is part of the HTML Living Standard. == Overview == As envisioned by WHATWG, web workers are relatively heavy-weight and are not intended to be used in large numbers. They are expected to be long-lived, with a high start-up performance cost, and a high per-instance memory cost. Web workers run outside the context of an HTML document's scripts. Consequently, while they do not have access to the DOM, they can facilitate concurrent execution of JavaScript programs. == Features == Web workers interact with the main document via message passing. The following code creates a Worker that will execute the JavaScript in the given file. To send a message to the worker, the postMessage method of the worker object is used as shown below. The onmessage property uses an event handler to retrieve information from a worker. Once a worker is terminated, it goes out of scope and the variable referencing it becomes undefined; at this point a new worker has to be created if needed. == Example == The simplest use of web workers is for performing a computationally expensive task without interrupting the user interface. In this example, the main document spawns a web worker to compute prime numbers, and progressively displays the most recently found prime number. The main page is as follows: The Worker() constructor call creates a web worker and returns a worker object representing that web worker, which is used to communicate with the web worker. That object's onmessage event handler allows the code to receive messages from the web worker. The Web Worker itself is as follows: To send a message back to the page, the postMessage() method is used to post a message when a prime is found. == Support == If the browser supports web workers, a Worker property will be available on the global window object. The Worker property will be undefined if the browser does not support it. The following example code checks for web worker support on a browser Web workers are currently supported by Chrome, Opera, Edge, Internet Explorer (version 10), Mozilla Firefox, and Safari. Mobile Safari for iOS has supported web workers since iOS 5. The Android browser first supported web workers in Android 2.1, but support was removed in Android versions 2.2–4.3 before being restored in Android 4.4.