In machine learning, deep learning (DL) focuses on utilizing multilayered neural networks to perform tasks such as classification, regression, and representation learning. The field takes inspiration from biological neuroscience and revolves around stacking artificial neurons into layers and "training" them to process data. The adjective "deep" refers to the use of multiple layers (ranging from three to several hundred or thousands) in the network. Methods used can be supervised, semi-supervised or unsupervised. Some common deep learning network architectures include fully connected networks, deep belief networks, recurrent neural networks, convolutional neural networks, generative adversarial networks, transformers, and neural radiance fields. These architectures have been applied to fields including computer vision, speech recognition, natural language processing, machine translation, bioinformatics, drug design, medical image analysis, climate science, material inspection and board game programs, where they have produced results comparable to and in some cases surpassing human expert performance. Early forms of neural networks were inspired by information processing and distributed communication nodes in biological systems, particularly the human brain. However, current neural networks do not intend to model the brain function of organisms, and are generally seen as low-quality models for that purpose. == Overview == Most modern deep learning models are based on multi-layered neural networks such as convolutional neural networks and transformers, although they can also include propositional formulas or latent variables organized layer-wise in deep generative models such as the nodes in deep belief networks and deep Boltzmann machines. Fundamentally, deep learning refers to a class of machine learning algorithms in which a hierarchy of layers is used to transform input data into a progressively more abstract and composite representation. For example, in an image recognition model, the raw input may be an image (represented as a tensor of pixels). The first representational layer may attempt to identify basic shapes such as lines and circles, the second layer may compose and encode arrangements of edges, the third layer may encode a nose and eyes, and the fourth layer may recognize that the image contains a face. Importantly, a deep learning process can learn which features to optimally place at which level on its own. Prior to deep learning, machine learning techniques often involved hand-crafted feature engineering to transform the data into a more suitable representation for a classification algorithm to operate on. In the deep learning approach, features are not hand-crafted and the model discovers useful feature representations from the data automatically. This does not eliminate the need for hand-tuning; for example, varying numbers of layers and layer sizes can provide different degrees of abstraction. The word "deep" in "deep learning" refers to the number of layers through which the data is transformed. More precisely, deep learning systems have a substantial credit assignment path (CAP) depth. The CAP is the chain of transformations from input to output. CAPs describe potentially causal connections between input and output. For a feedforward neural network, the depth of the CAPs is that of the network and is the number of hidden layers plus one (as the output layer is also parameterized). For recurrent neural networks, in which a signal may propagate through a layer more than once, the CAP depth is potentially unlimited. No universally agreed-upon threshold of depth divides shallow learning from deep learning, but most researchers agree that deep learning involves CAP depth higher than two. CAP of depth two has been shown to be a universal approximator in the sense that it can emulate any function. Beyond that, more layers do not add to the function approximator ability of the network. Deep models (CAP > two) are able to extract better features than shallow models and hence, extra layers help in learning the features effectively. Deep learning architectures can be constructed with a greedy layer-by-layer method. Deep learning helps to disentangle these abstractions and pick out which features improve performance. Deep learning algorithms can be applied to unsupervised learning tasks. This is an important benefit because unlabeled data is more abundant than labeled data. Examples of deep structures that can be trained in an unsupervised manner are deep belief networks. The term deep learning was introduced to the machine learning community by Rina Dechter in 1986, and to artificial neural networks by Igor Aizenberg and colleagues in 2000, in the context of Boolean threshold neurons. The etymology of the term is more complicated. == Interpretations == Deep neural networks are generally interpreted in terms of the universal approximation theorem or probabilistic inference. The classic universal approximation theorem concerns the capacity of feedforward neural networks with a single hidden layer of finite size to approximate continuous functions. In 1989, the first proof was published by George Cybenko for sigmoid activation functions and was generalised to feed-forward multi-layer architectures in 1991 by Kurt Hornik. Recent work also showed that universal approximation also holds for non-bounded activation functions such as Kunihiko Fukushima's rectified linear unit. The universal approximation theorem for deep neural networks concerns the capacity of networks with bounded width but the depth is allowed to grow. Lu et al. proved that if the width of a deep neural network with ReLU activation is strictly larger than the input dimension, then the network can approximate any Lebesgue integrable function; if the width is smaller or equal to the input dimension, then a deep neural network is not a universal approximator. The probabilistic interpretation derives from the field of machine learning. It features inference, as well as the optimization concepts of training and testing, related to fitting and generalization, respectively. More specifically, the probabilistic interpretation considers the activation nonlinearity as a cumulative distribution function. The probabilistic interpretation led to the introduction of dropout as regularizer in neural networks. The probabilistic interpretation was introduced by researchers including Hopfield, Widrow and Narendra and popularized in surveys such as the one by Bishop. == History == === Before 1980 === There are two types of artificial neural network (ANN): feedforward neural network (FNN) or multilayer perceptron (MLP) and recurrent neural networks (RNN). RNNs have cycles in their connectivity structure, whereas FNNs do not. In the 1920s, Wilhelm Lenz and Ernst Ising created the Ising model which is essentially a non-learning RNN architecture consisting of neuron-like threshold elements. In 1972, Shun'ichi Amari made this architecture adaptive. His learning RNN was republished by John Hopfield in 1982. Other early recurrent neural networks were published by Kaoru Nakano in 1971. Already in 1948, Alan Turing produced work on "Intelligent Machinery" that was not published in his lifetime, containing "ideas related to artificial evolution and learning RNNs". Frank Rosenblatt (1958) proposed the perceptron, an MLP with 3 layers: an input layer, a hidden layer with randomized weights that did not learn, and an output layer. He later published a 1962 book that also introduced variants and computer experiments, including a version with four-layer perceptrons "with adaptive preterminal networks" where the last two layers have learned weights (here he credits H. D. Block and B. W. Knight). The book cites an earlier network by R. D. Joseph (1960) "functionally equivalent to a variation of" this four-layer system (the book mentions Joseph over 30 times). Should Joseph therefore be considered the originator of proper adaptive multilayer perceptrons with learning hidden units? Unfortunately, the learning algorithm was not a functional one, and fell into oblivion. The first working deep learning algorithm was the Group method of data handling, a method to train arbitrarily deep neural networks, published by Alexey Ivakhnenko and Lapa in 1965. They regarded it as a form of polynomial regression, or a generalization of Rosenblatt's perceptron to handle more complex, nonlinear, and hierarchical relationships. A 1971 paper described a deep network with eight layers trained by this method, which is based on layer by layer training through regression analysis. Superfluous hidden units are pruned using a separate validation set. Since the activation functions of the nodes are Kolmogorov-Gabor polynomials, these were also the first deep networks with multiplicative units or "gates". The first deep learning multilayer perceptron trained by stochastic gradient descent was published in 1967 by Shun'ichi
Multisample anti-aliasing
Multisample anti-aliasing (MSAA) is a type of spatial anti-aliasing, a technique used in computer graphics to remove jaggies. It is an optimization of supersampling, where only the necessary parts are sampled more. Jaggies are only noticed in a small area, so the area is quickly found, and only that is anti-aliased. == Definition == The term generally refers to a special case of supersampling. Initial implementations of full-scene anti-aliasing (FSAA) worked conceptually by simply rendering a scene at a higher resolution, and then downsampling to a lower-resolution output. Most modern GPUs are capable of this form of anti-aliasing, but it greatly taxes resources such as texture, bandwidth, and fillrate. (If a program is highly TCL-bound or CPU-bound, supersampling can be used without much performance hit.) According to the OpenGL GL_ARB_multisample specification, "multisampling" refers to a specific optimization of supersampling. The specification dictates that the renderer evaluate the fragment program once per pixel, and only "truly" supersample the depth and stencil values. (This is not the same as supersampling but, by the OpenGL 1.5 specification, the definition had been updated to include fully supersampling implementations as well.) In graphics literature in general, "multisampling" refers to any special case of supersampling where some components of the final image are not fully supersampled. The lists below refer specifically to the ARB_multisample definition. == Description == In supersample anti-aliasing, multiple locations are sampled within every pixel, and each of those samples is fully rendered and combined with the others to produce the pixel that is ultimately displayed. This is computationally expensive, because the entire rendering process must be repeated for each sample location. It is also inefficient, as aliasing is typically only noticed in some parts of the image, such as the edges, whereas supersampling is performed for every single pixel. In multisample anti-aliasing, if any of the multi sample locations in a pixel is covered by the triangle being rendered, a shading computation must be performed for that triangle. However this calculation only needs to be performed once for the whole pixel regardless of how many sample positions are covered; the result of the shading calculation is simply applied to all of the relevant multi sample locations. In the case where only one triangle covers every multi sample location within the pixel, only one shading computation is performed, and these pixels are little more expensive than (and the result is no different from) the non-anti-aliased image. This is true of the middle of triangles, where aliasing is not an issue. (Edge detection can reduce this further by explicitly limiting the MSAA calculation to pixels whose samples involve multiple triangles, or triangles at multiple depths.) In the extreme case where each of the multi sample locations is covered by a different triangle, a different shading computation will be performed for each location and the results then combined to give the final pixel, and the result and computational expense are the same as in the equivalent supersampled image. The shading calculation is not the only operation that must be performed on a given pixel; multisampling implementations may variously sample other operations such as visibility at different sampling levels. == Advantages == The pixel shader usually only needs to be evaluated once per pixel for every triangle covering at least one sample point. The edges of polygons (the most obvious source of aliasing in 3D graphics) are anti-aliased. Since multiple subpixels per pixel are sampled, polygonal details smaller than one pixel that might have been missed without MSAA can be captured and made a part of the final rendered image if enough samples are taken. == Disadvantages == === Alpha testing === Alpha testing is a technique common to older video games used to render translucent objects by rejecting pixels from being written to the framebuffer. If the alpha value of a translucent fragment (pixel) is below a specified threshold, it will be discarded. Because this is performed on a pixel by pixel basis, the image does not receive the benefits of multi-sampling (all of the multisamples in a pixel are discarded based on the alpha test) for these pixels. The resulting image may contain aliasing along the edges of transparent objects or edges within textures, although the image quality will be no worse than it would be without any anti-aliasing. Translucent objects that are modelled using alpha-test textures will also be aliased due to alpha testing. This effect can be minimized by rendering objects with transparent textures multiple times, although this would result in a high performance reduction for scenes containing many transparent objects. === Aliasing === Because multi-sampling calculates interior polygon fragments only once per pixel, aliasing and other artifacts will still be visible inside rendered polygons where fragment shader output contains high frequency components. === Performance === While less performance-intensive than SSAA (supersampling), it is possible in certain scenarios (scenes heavy in complex fragments) for MSAA to be multiple times more intensive for a given frame than post processing anti-aliasing techniques such as FXAA, SMAA and MLAA. Early techniques in this category tend towards a lower performance impact, but suffer from accuracy problems. More recent post-processing based anti-aliasing techniques such as temporal anti-aliasing (TAA), which reduces aliasing by combining data from previously rendered frames, have seen the reversal of this trend, as post-processing AA becomes both more versatile and more expensive than MSAA, which cannot antialias an entire frame alone. == Sampling methods == === Point sampling === In a point-sampled mask, the coverage bit for each multisample is only set if the multisample is located inside the rendered primitive. Samples are never taken from outside a rendered primitive, so images produced using point-sampling will be geometrically correct, but filtering quality may be low because the proportion of bits set in the pixel's coverage mask may not be equal to the proportion of the pixel that is actually covered by the fragment in question. === Area sampling === Filtering quality can be improved by using area sampled masks. In this method, the number of bits set in a coverage mask for a pixel should be proportionate to the actual area coverage of the fragment. This will result in some coverage bits being set for multisamples that are not actually located within the rendered primitive, and can cause aliasing and other artifacts. == Sample patterns == === Regular grid === A regular grid sample pattern, where multisample locations form an evenly spaced grid throughout the pixel, is easy to implement and simplifies attribute evaluation (i.e. setting subpixel masks, sampling color and depth). This method is computationally expensive due to the large number of samples. Edge optimization is poor for screen-aligned edges, but image quality is good when the number of multisamples is large. === Sparse regular grid === A sparse regular grid sample pattern is a subset of samples that are chosen from the regular grid sample pattern. As with the regular grid, attribute evaluation is simplified due to regular spacing. The method is less computationally expensive due to having a fewer samples. Edge optimization is good for screen aligned edges, and image quality is good for a moderate number of multisamples. === Stochastic sample patterns === A stochastic sample pattern is a random distribution of multisamples throughout the pixel. The irregular spacing of samples makes attribute evaluation complicated. The method is cost efficient due to low sample count (compared to regular grid patterns). Edge optimization with this method, although sub-optimal for screen aligned edges. Image quality is excellent for a moderate number of samples. == Quality == Compared to supersampling, multisample anti-aliasing can provide similar quality at higher performance, or better quality for the same performance. Further improved results can be achieved by using rotated grid subpixel masks. The additional bandwidth required by multi-sampling is reasonably low if Z and colour compression are available. Most modern GPUs support 2×, 4×, and 8× MSAA samples. Higher values result in better quality, but are slower.
Social media stock bubble
The social media bubble is a hypothesis stating that there was a speculative boom and bust phenomenon in the field of social media in the 2010s, particularly in the United States. The Wall Street Journal defined a bubble as stocks "priced above a level that can be justified by economic fundamentals," but this bubble includes social media. Social networking services (SNS) have seen huge growth since 2006, but some investors believed around 2014-2015, that the "bubble" was similar to the dot-com bubble of the late 1990s and early 2000s. In 2015, Mark Cuban, owner of the Dallas Mavericks NBA team and star of the TV show, Shark Tank, sounded an alarm on his personal blog over the social media bubble, calling it worse than the tech bubble in 2000 due to the lack of liquidity in social media stocks. A year prior, however, Cuban told CNBC that he did not believe social media stocks were on the verge of a bubble. In a letter to investors in 2014, David Einhorn, who runs the hedge-fund Greenlight Capital, wrote that "we are witnessing our second tech bubble in 15 years." He went on to write, "What is uncertain is how much further the bubble can expand, and what might pop it." Einhorn cited several factors supporting the existence an over-exuberance including "rejection of conventional valuation methods" and "huge first day IPO pops for companies that have done little more than use the right buzzwords and attract the right venture capital." Since those claims, services like Facebook, Twitter, Instagram, and Snapchat have grown to become multi-billion-dollar corporations generating enormous revenues, though some continue to lose money. == History of social networking services == Social networking services have grown and evolved with time since the launch of SixDegrees.com in 1997. Cutting edge at its time, SixDegrees.com allowed users to create a profile, invite friends, and connect within its platform. At its peak, SixDegrees.com had more than 3.5 million users. Between 1997 and 2001 more social sites aimed at allowing users to connect with others for personal, professional, or dating reasons. Friendster and MySpace were next to enter the social SNS arena, followed by Facebook in 2004. Even though MySpace had a following of more than 300 million users, it could not compete with Facebook, which now has overtaken the social networking world. However, as development of SNS started to emerge, a market saturation began to take effect. Some classrooms have begun to incorporate technology in daily learning as well as social channels specific to student's course work. Traditional social media sites are used, as are educational oriented sites such as ShowMe and Educreations Interactive Whiteboard. == Controversies == While SNS continue to play an influential role in helping people form real-world connections via the Internet, renewed concerns over the social media bubble have surfaced due to recent controversies. These threats include growing concerns about breaches in data, the rise of bot accounts, and the sharing of fake news on SNS platforms. There are also concerns that big data figures associated with these SNS are inflated or fake, as well as worries about the role the platforms played in national elections (see Russian interference in the 2016 United States elections). These issues have resulted in a lack of trust among the sites' users.
Social media as a news source
Social media as a news source is defined as the use of online social media platforms such as Instagram, TikTok, and Facebook rather than the use of traditional media platforms like the newspaper or live TV to obtain news. Television had just begun to turn a nation of people who once listened to media content into watchers of media content between the 1950s and the 1980s when the popularity of social media had also begun creating a nation of media content creators. Almost half of Americans use social media as a news source, according to the Pew Research Center. As social media's role in news consumption grows, questions have emerged about its impact on knowledge, the formation of echo chambers, and the effectiveness of fact-checking efforts in combating misinformation. Social media platforms allow user-generated content and sharing content within one's own virtual network. Using social media as a news source allows users to engage with news in a variety of ways including: Consuming and discovering news Sharing or reposting news Posting one's own photos, videos, or reports of news (i.e., engage in citizen or participatory journalism) Commenting on news posts Using social media as a news source has become an increasingly popular way for people of all age groups to obtain current and important information. Just like many other new forms of technology there are going to be pros and cons. There are ways that social media positively affects the world of news and journalism but it is important to acknowledge that there are also ways in which social media has a negative effect on the news. With this accessibility, people now have more ways to consume false news, biased news, and even disturbing content. In 2019, the Pew Research Center created a poll that reported Americans are wary about the ways that social media sites share news and certain content. This wariness of accuracy grew as awareness that social media sites could be exploited by bad actors who concoct false narratives and fake news. == Relationship to traditional news sources == Unlike traditional news platforms such as newspapers and news shows, social media platforms allow people without professional journalistic backgrounds to create news and cover events that news agencies might not cover. Social media users may read a set of news that differs slightly from what newspaper editors prioritize in the print press. A 2019 study found that Facebook and Twitter users are more likely to share politics, public affairs, and visual media news. Typically social media users circulate more towards posting about negative news. A study of tweets found that while optimistic-sounding and neutral-sounding tweets were equally likely to express certainty or uncertainty, the pessimistic tweets were nearly twice as likely to appear certain of an outcome than uncertain. These results could imply that posts of a more pessimistic nature that are also written with an air of certainty are more likely to be shared or otherwise permeate groups on Twitter. A similar bias towards negativity has developed on Facebook, where internal memos revealed that an algorithm built to promote "meaningful social interaction" actually incentivized publishers to promote negative and sensational news. Biases towards negativity need to be considered when the utility of new media is addressed, as the potential for human opinion to overemphasize any particular news story is greater despite general improvement. In order to compete in this rapidly changing technological environment, there has been an upheaval of traditional news sources onto online spaces. The production and circulation of newspaper prints have continued to globally decline in accordance with the increasing presence of news outlets on social media. Prominent platforms such as Twitter and Facebook have been key in engaging users through the integration of journalistic news into their newsfeeds. This feature has now become a foundational part of these apps' interfaces. Social media incentivizes both legacy news brands and individual professional journalists to share their reporting and interact with audiences on social platforms to boost engagement. However, most people who consume news on social media report that accessing news is not their main motivation for being on social media, but rather, they see and consume news incidentally. Nonetheless, informational interviews reveal that these consumers rely on being informed through social media. Some news consumers attest that a news brand's participation in social media does not improve their trust in the brand and that more in-depth reporting and more transparency about biases would improve trust instead. == Use as a news source == Globally, data from 2020 shows that over 70% of adult participants from Kenya, South Africa, Chile, Bulgaria, Greece, and Argentina utilized social media for news while those from France, the UK, the Netherlands, Germany, and Japan were reportedly less than 40 percent. According to the Pew Research Center, 20% of adults in the United States in 2018 said they get their news from social media "often," compared to 16% who said they often get news from print newspapers, 26% who often get it from the radio, 33% who often get it from news websites, and 49% who often get it from TV. The same survey found that social media was the most popular way for American adults age 18–29 to get news, the second-to-last most popular way for Americans age 20–49 to get news, and the least popular way for American adults age 50-64 and 65+ to get the news. In 2019, the Pew Research Center found that over half of Americans (54%) either got their news "sometimes" or "often" from social media, and Facebook was the most popular social media site where American adults got their news. However, at least 50% off all respondents reported that the following were either a "very big problem" or a "moderately big problem" for getting news on social media: One-sided news (83%) Inaccurate news (81%) Censorship of the news (69%) Uncivil discussions about the news (69%) Harassment of journalists (57%) News organizations or personalities being banned (53%) Violent or disturbing news images or videos (51%) In a later survey from the same year, the Pew Research Center reported that 18% of American adults reported that the most common way they get news about politics and the election was from social media. Additional source information shows that from politics and the United States presidential election in 2016, the popularity of fake news had grown to global attention. With this information, the study explains that more than 60 percent of adults receive their news from social media, the most popular being Facebook. With the increase of fake news, and the large amount of adult participation on these social media sites, it made it much harder for those who were searching for news to find a source that they could find credible. Another study found that adult participants found their own friends on Facebook to be a more reliable source of information online compared to a professional news organization. Although, when news was posted by a news organization online, they were then found more reliable compared to when they are shared by their online friends. Showing that adult participants found that the news that was only posted on Facebook and social media was much more credible to them than compared to other forms of information spreading. The study further states that these outcomes have the potential explanation that the topic of the news article played a part in the ways they were affected. This could have affected the way adult participants interacted with the different news sources, such as their online friends compared to a news organization, prominently because depending on the story, they want to have the correct information about the news from the most credible source. === By young people === Social media platforms are some of the most easily accessible forms of news and with the growing generations, the technology is only going to grow. With that, the use of social media in younger generations is also going to grow alongside it. Technology in the hands of young kids can be a concern moving into the future. Globally, there is evidence that through social media, youth have become more directly involved in protests, social campaigns and generally, in the sharing of news across multiple platforms. The number of people who use social media platforms such as Twitter, Facebook, Instagram, or Snapchat as ways to seek information has increased significantly in recent years especially for people who are part of the younger generation.TikTok is a rapidly expanding platform that young adults can use to find news content on social media. TikTok is one of the sites that young adults and teens utilize to get news about trending themes and controversial topics. The younger generation accepts without hesitation the information that thei
Data profiling
Data profiling is the process of examining the data available from an existing information source (e.g. a database or a file) and collecting statistics or informative summaries about that data. The purpose of these statistics may be to: Find out whether existing data can be easily used for other purposes Improve the ability to search data by tagging it with keywords, descriptions, or assigning it to a category Assess data quality, including whether the data conforms to particular standards or patterns Assess the risk involved in integrating data in new applications, including the challenges of joins Discover metadata of the source database, including value patterns and distributions, key candidates, foreign-key candidates, and functional dependencies Assess whether known metadata accurately describes the actual values in the source database Understanding data challenges early in any data intensive project, so that late project surprises are avoided. Finding data problems late in the project can lead to delays and cost overruns. Have an enterprise view of all data, for uses such as master data management, where key data is needed, or data governance for improving data quality. == Introduction == Data profiling refers to the analysis of information for use in a data warehouse in order to clarify the structure, content, relationships, and derivation rules of the data. Profiling helps to not only understand anomalies and assess data quality, but also to discover, register, and assess enterprise metadata. The result of the analysis is used to determine the suitability of the candidate source systems, usually giving the basis for an early go/no-go decision, and also to identify problems for later solution design. == How data profiling is conducted == Data profiling utilizes methods of descriptive statistics such as minimum, maximum, mean, mode, percentile, standard deviation, frequency, variation, aggregates such as count and sum, and additional metadata information obtained during data profiling such as data type, length, discrete values, uniqueness, occurrence of null values, typical string patterns, and abstract type recognition. The metadata can then be used to discover problems such as illegal values, misspellings, missing values, varying value representation, and duplicates. Different analyses are performed for different structural levels. E.g. single columns could be profiled individually to get an understanding of frequency distribution of different values, type, and use of each column. Embedded value dependencies can be exposed in a cross-columns analysis. Finally, overlapping value sets possibly representing foreign key relationships between entities can be explored in an inter-table analysis. Normally, purpose-built tools are used for data profiling to ease the process. The computational complexity increases when going from single column, to single table, to cross-table structural profiling. Therefore, performance is an evaluation criterion for profiling tools. == When is data profiling conducted? == According to Kimball, data profiling is performed several times and with varying intensity throughout the data warehouse developing process. A light profiling assessment should be undertaken immediately after candidate source systems have been identified and DW/BI business requirements have been satisfied. The purpose of this initial analysis is to clarify at an early stage if the correct data is available at the appropriate detail level and that anomalies can be handled subsequently. If this is not the case the project may be terminated. Additionally, more in-depth profiling is done prior to the dimensional modeling process in order assess what is required to convert data into a dimensional model. Detailed profiling extends into the ETL system design process in order to determine the appropriate data to extract and which filters to apply to the data set. Additionally, data profiling may be conducted in the data warehouse development process after data has been loaded into staging, the data marts, etc. Conducting data at these stages helps ensure that data cleaning and transformations have been done correctly and in compliance of requirements. == Benefits and examples == Data profiling can improve data quality, shorten the implementation cycle of major projects, and improve users' understanding of data. Discovering business knowledge embedded in data itself is one of the significant benefits derived from data profiling. It can improve data accuracy in corporate databases.
Color image pipeline
An image pipeline or video pipeline is the set of components commonly used between an image source (such as a camera, a scanner, or the rendering engine in a computer game), and an image renderer (such as a television set, a computer screen, a computer printer or cinema screen), or for performing any intermediate digital image processing consisting of two or more separate processing blocks. An image/video pipeline may be implemented as computer software, in a digital signal processor, on an FPGA, or as fixed-function ASIC. In addition, analog circuits can be used to do many of the same functions. Typical components include image sensor corrections (including debayering or applying a Bayer filter), noise reduction, image scaling, gamma correction, image enhancement, colorspace conversion (between formats such as RGB, YUV or YCbCr), chroma subsampling, framerate conversion, image compression/video compression (such as JPEG), and computer data storage/data transmission. Typical goals of an imaging pipeline may be perceptually pleasing end-results, colorimetric precision, a high degree of flexibility, low cost/low CPU utilization/long battery life, or reduction in bandwidth/file size. Some functions may be algorithmically linear. Mathematically, those elements can be connected in any order without changing the end-result. As digital computers use a finite approximation to numerical computing, this is in practice not true. Other elements may be non-linear or time-variant. For both cases, there is often one or a few sequences of components that makes sense for optimum precision and minimum hardware-cost/CPU-load.
Social trading
Social trading is a form of investing that allows investors to observe the trading behavior of their peers and expert traders. The primary objective is to follow their investment strategies using copy trading or mirror trading. Social trading requires little or no knowledge about financial markets. == History == One of the first social trading platforms was Collective2] which began offering a social trading functionality to retail traders as early as 2003 (preceding ZuluTrade by four years). In 2010, social trading started to achieve a greater degree of mainstream appeal with eToro, followed by Wikifolio in 2012. Europe-based NAGA, listed on Frankfurt Stock Exchange since 2017, claims more than EUR 27 billion was traded on its platform in the second half of 2019. Some of the other contemporary social trading platforms and tech providers are Trading Motion, Brokeree Solutions, iSystems, and FX Junction, among others. === Research === MIT Computer Scientist and researcher Yaniv Altshuler described social trading networks as complex adaptive systems, and in his 2014 research on eToro's OpenBook, wrote that "Having the inherent ability to share ideas and information between each others, OpenBook's users are given a new source of information they can use in order to enhance their trading performance. As the users are not playing against each other but rather – against the market, this situation becomes a non zero-sum game, hence incentivizing the users to share as much information as possible." His paper concludes that "social trading provides much better opportunities for profiting compared with individual trading," but that users make "excellent but sometimes not optimal decisions in selecting experts when they can see others' choices." A 2015 World Economic Forum report described social trading networks as disruptors, which "have emerged to provide low-cost, sophisticated alternatives to traditional wealth managers. These solutions cater to a broader customer base and empower customers to have more control of their wealth management," and "pose a tangible threat to the traditional practices of the wealth management industry". Economist Nouriel Roubini's thinktank predicted in 2016 that "newer forms of investment, such as socially responsible investments and social trading will bring some of the largest industry growth in the coming years." A 2017 St. John's University study found that 'leader' traders, or those with followers, are more susceptible to the disposition effect than investors that are not being followed by any other traders, with the authors suggesting the observation may be explained by "leaders feeling responsible towards their followers and an urge to not let them down, by fear of losing followers when admitting a bad investment decision and signaling confidence in their initial investment choice, or by an attempt of newly appointed leaders to manage their self-image." Social trading may potentially also change how much risk investors take. A recent experimental study argues that merely providing information on the success of others may lead to a significant increase in risk taking. This increase in risk taking may even be larger when subjects are provided with the option to directly copy others. == Characteristics == Social trading is an alternative way of analyzing financial data by looking at what other traders are doing and comparing and copying their techniques and strategies. Prior to the advent of social trading, investors and traders were relying on fundamental or technical analysis to form their investment decisions. Using social trading investors and traders could integrate into their investment decision-process social indicators from trading data-feeds of other traders. Social trading platforms or networks can be considered a subcategory of social networking services. Social trading allows traders to trade online with the help of others and some have claimed shortens the learning curve from novice to experienced trader. Traders can interact with others, watch others take trades, then duplicate their trades and learn what prompted the top performer to take a trade in the first place. By copying trades, traders can learn which strategies work and which do not work. Social trading is used to do speculation; in the moral context speculative practices are considered negatively and to be avoided by each individual. who conversely should maintain a long-term horizon avoiding any types of short term speculation. Social Media has permeated the trading world such that two main types of trading has evolved: Traditional Trades Single (or non-social) trade: Trader A places a normal trade by himself or herself; This can by manual or automated Social Trading There are two main types of social trading: Copy trade: Trader A places exactly the same trade as trader B's one single trade; (iii) Mirror trade: Trader A automatically executes trader B's every single trade, i.e., trader A follows exactly trader B's trading activities. Other variations offered on some platforms allow users to copy another trader's portfolio (copy portfolio), and follow a trader's dividends (copy dividends), where whenever a followed trader withdraws money from his or her account, a proportional amount of money will be withdrawn from the balance of their follower, in real time. === Key features === Information flow: Unencumbered access to information is important in financial markets and that makes the free exchange of information of interest to small scale as well as individual investors. Cooperative trading: Social trading offers traders the opportunity to work together in trading teams which can trade the markets collaboratively, whether by pooling funds, dividing research or through sharing information. Monetization: As with social networks in the broader sense, monetization strategies are not always clear. As with social networks in general, it is possible, however, that the long-term worth of such websites may come from the variety and depth of data about their users which their active communities are likely to generate. Transparency: Social trading platforms reveal traders' performance stats, open and past positions, and market sentiment, giving members complete information to assess the credibility of the contributors they follow on the platform.