In mathematics a radial basis function (RBF) is a real-valued function φ {\textstyle \varphi } whose value depends only on the distance between the input and some fixed point, either the origin, so that φ ( x ) = φ ^ ( ‖ x ‖ ) {\textstyle \varphi (\mathbf {x} )={\hat {\varphi }}(\left\|\mathbf {x} \right\|)} , or some other fixed point c {\textstyle \mathbf {c} } , called a center, so that φ ( x ) = φ ^ ( ‖ x − c ‖ ) {\textstyle \varphi (\mathbf {x} )={\hat {\varphi }}(\left\|\mathbf {x} -\mathbf {c} \right\|)} . Any function φ {\textstyle \varphi } that satisfies the property φ ( x ) = φ ^ ( ‖ x ‖ ) {\textstyle \varphi (\mathbf {x} )={\hat {\varphi }}(\left\|\mathbf {x} \right\|)} is a radial function. The distance is usually Euclidean distance, although other metrics are sometimes used. They are often used as a collection { φ k } k {\displaystyle \{\varphi _{k}\}_{k}} which forms a basis for some function space of interest, hence the name. Sums of radial basis functions are typically used to approximate given functions. This approximation process can also be interpreted as a simple kind of neural network; this was the context in which they were originally applied to machine learning, in work by David Broomhead and David Lowe in 1988, which stemmed from Michael J. D. Powell's seminal research from 1977. RBFs are also used as a kernel in support vector classification. The technique has proven effective and flexible enough that radial basis functions are now applied in a variety of engineering applications. == Definition == A radial function is a function φ : [ 0 , ∞ ) → R {\textstyle \varphi :[0,\infty )\to \mathbb {R} } . When paired with a norm ‖ ⋅ ‖ : V → [ 0 , ∞ ) {\textstyle \|\cdot \|:V\to [0,\infty )} on a vector space, a function of the form φ c = φ ( ‖ x − c ‖ ) {\textstyle \varphi _{\mathbf {c} }=\varphi (\|\mathbf {x} -\mathbf {c} \|)} is said to be a radial kernel centered at c ∈ V {\textstyle \mathbf {c} \in V} . A radial function and the associated radial kernels are said to be radial basis functions if, for any finite set of nodes { x k } k = 1 n ⊆ V {\displaystyle \{\mathbf {x} _{k}\}_{k=1}^{n}\subseteq V} , all of the following conditions are true: === Examples === Commonly used types of radial basis functions include (writing r = ‖ x − x i ‖ {\textstyle r=\left\|\mathbf {x} -\mathbf {x} _{i}\right\|} and using ε {\textstyle \varepsilon } to indicate a shape parameter that can be used to scale the input of the radial kernel): == Approximation == Radial basis functions are typically used to build up function approximations of the form where the approximating function y ( x ) {\textstyle y(\mathbf {x} )} is represented as a sum of N {\displaystyle N} radial basis functions, each associated with a different center x i {\textstyle \mathbf {x} _{i}} , and weighted by an appropriate coefficient w i . {\textstyle w_{i}.} The weights w i {\textstyle w_{i}} can be estimated using the matrix methods of linear least squares, because the approximating function is linear in the weights w i {\textstyle w_{i}} . Approximation schemes of this kind have been particularly used in time series prediction and control of nonlinear systems exhibiting sufficiently simple chaotic behaviour and 3D reconstruction in computer graphics (for example, hierarchical RBF and Pose Space Deformation). == RBF Network == The sum can also be interpreted as a rather simple single-layer type of artificial neural network called a radial basis function network, with the radial basis functions taking on the role of the activation functions of the network. It can be shown that any continuous function on a compact interval can in principle be interpolated with arbitrary accuracy by a sum of this form, if a sufficiently large number N {\textstyle N} of radial basis functions is used. The approximant y ( x ) {\textstyle y(\mathbf {x} )} is differentiable with respect to the weights w i {\textstyle w_{i}} . The weights could thus be learned using any of the standard iterative methods for neural networks. Using radial basis functions in this manner yields a reasonable interpolation approach provided that the fitting set has been chosen such that it covers the entire range systematically (equidistant data points are ideal). However, without a polynomial term that is orthogonal to the radial basis functions, estimates outside the fitting set tend to perform poorly. == RBFs for PDEs == Radial basis functions are used to approximate functions and so can be used to discretize and numerically solve Partial Differential Equations (PDEs). This was first done in 1990 by E. J. Kansa who developed the first RBF based numerical method. It is called the Kansa method and was used to solve the elliptic Poisson equation and the linear advection-diffusion equation. The function values at points x {\displaystyle \mathbf {x} } in the domain are approximated by the linear combination of RBFs: The derivatives are approximated as such: where N {\displaystyle N} are the number of points in the discretized domain, d {\displaystyle d} the dimension of the domain and λ {\displaystyle \lambda } the scalar coefficients that are unchanged by the differential operator. Different numerical methods based on Radial Basis Functions were developed thereafter. Some methods are the RBF-FD method, the RBF-QR method and the RBF-PUM method.
Transduction (machine learning)
In logic, statistical inference, and supervised learning, transduction or transductive inference is reasoning from observed, specific (training) cases to specific (test) cases. In contrast, induction is reasoning from observed training cases to general rules, which are then applied to the test cases. The distinction is most interesting in cases where the predictions of the transductive model are not achievable by any inductive model. Note that this is caused by transductive inference on different test sets producing mutually inconsistent predictions. Transduction was introduced in a computer science context by Vladimir Vapnik in the 1990s, motivated by his view that transduction is preferable to induction since, according to him, induction requires solving a more general problem (inferring a function) before solving a more specific problem (computing outputs for new cases): "When solving a problem of interest, do not solve a more general problem as an intermediate step. Try to get the answer that you really need but not a more general one.". An example of learning which is not inductive would be in the case of binary classification, where the inputs tend to cluster in two groups. A large set of test inputs may help in finding the clusters, thus providing useful information about the classification labels. The same predictions would not be obtainable from a model which induces a function based only on the training cases. Some people may call this an example of the closely related semi-supervised learning, since Vapnik's motivation is quite different. The most well-known example of a case-bases learning algorithm is the k-nearest neighbor algorithm, which is related to transductive learning algorithms. Another example of an algorithm in this category is the Transductive Support Vector Machine (TSVM). A third possible motivation of transduction arises through the need to approximate. If exact inference is computationally prohibitive, one may at least try to make sure that the approximations are good at the test inputs. In this case, the test inputs could come from an arbitrary distribution (not necessarily related to the distribution of the training inputs), which wouldn't be allowed in semi-supervised learning. An example of an algorithm falling in this category is the Bayesian Committee Machine (BCM). == Historical context == The mode of inference from particulars to particulars, which Vapnik came to call transduction, was already distinguished from the mode of inference from particulars to generalizations in part III of the Cambridge philosopher and logician W.E. Johnson's 1924 textbook, Logic. In Johnson's work, the former mode was called 'eduction' and the latter was called 'induction'. Bruno de Finetti developed a purely subjective form of Bayesianism in which claims about objective chances could be translated into empirically respectable claims about subjective credences with respect to observables through exchangeability properties. An early statement of this view can be found in his 1937 La Prévision: ses Lois Logiques, ses Sources Subjectives and a mature statement in his 1970 Theory of Probability. Within de Finetti's subjective Bayesian framework, all inductive inference is ultimately inference from particulars to particulars. == Example problem == The following example problem contrasts some of the unique properties of transduction against induction. A collection of points is given, such that some of the points are labeled (A, B, or C), but most of the points are unlabeled (?). The goal is to predict appropriate labels for all of the unlabeled points. The inductive approach to solving this problem is to use the labeled points to train a supervised learning algorithm, and then have it predict labels for all of the unlabeled points. With this problem, however, the supervised learning algorithm will only have five labeled points to use as a basis for building a predictive model. It will certainly struggle to build a model that captures the structure of this data. For example, if a nearest-neighbor algorithm is used, then the points near the middle will be labeled "A" or "C", even though it is apparent that they belong to the same cluster as the point labeled "B", compared to semi-supervised learning. Transduction has the advantage of being able to consider all of the points, not just the labeled points, while performing the labeling task. In this case, transductive algorithms would label the unlabeled points according to the clusters to which they naturally belong. The points in the middle, therefore, would most likely be labeled "B", because they are packed very close to that cluster. An advantage of transduction is that it may be able to make better predictions with fewer labeled points, because it uses the natural breaks found in the unlabeled points. One disadvantage of transduction is that it builds no predictive model. If a previously unknown point is added to the set, the entire transductive algorithm would need to be repeated with all of the points in order to predict a label. This can be computationally expensive if the data is made available incrementally in a stream. Further, this might cause the predictions of some of the old points to change (which may be good or bad, depending on the application). A supervised learning algorithm, on the other hand, can label new points instantly, with very little computational cost. == Transduction algorithms == Transduction algorithms can be broadly divided into two categories: those that seek to assign discrete labels to unlabeled points, and those that seek to regress continuous labels for unlabeled points. Algorithms that seek to predict discrete labels tend to be derived by adding partial supervision to a clustering algorithm. Two classes of algorithms can be used: flat clustering and hierarchical clustering. The latter can be further subdivided into two categories: those that cluster by partitioning, and those that cluster by agglomerating. Algorithms that seek to predict continuous labels tend to be derived by adding partial supervision to a manifold learning algorithm. === Partitioning transduction === Partitioning transduction can be thought of as top-down transduction. It is a semi-supervised extension of partition-based clustering. It is typically performed as follows: Consider the set of all points to be one large partition. While any partition P contains two points with conflicting labels: Partition P into smaller partitions. For each partition P: Assign the same label to all of the points in P. Of course, any reasonable partitioning technique could be used with this algorithm. Max flow min cut partitioning schemes are very popular for this purpose. === Agglomerative transduction === Agglomerative transduction can be thought of as bottom-up transduction. It is a semi-supervised extension of agglomerative clustering. It is typically performed as follows: Compute the pair-wise distances, D, between all the points. Sort D in ascending order. Consider each point to be a cluster of size 1. For each pair of points {a,b} in D: If (a is unlabeled) or (b is unlabeled) or (a and b have the same label) Merge the two clusters that contain a and b. Label all points in the merged cluster with the same label. === Continuous Label Transduction === These methods seek to regress continuous labels, often via manifold learning techniques. The idea is to learn a low-dimensional representation of the data and infer values smoothly across the manifold. == Applications and related concepts == Transduction is closely related to: Semi-supervised learning – uses both labeled and unlabeled data but typically induces a model. Case-based reasoning – such as the k-nearest neighbor (k-NN) algorithm, often considered a transductive method. Transductive Support Vector Machines (TSVM) – extend standard SVMs to incorporate unlabeled test data during training. Bayesian Committee Machine (BCM) – an approximation method that makes transductive predictions when exact inference is too costly.
Digital cinematography
Digital cinematography is the process of capturing (recording) a motion picture using digital image sensors rather than through film stock. As digital technology has improved in recent years, this practice has become dominant. Since the 2000s, most movies across the world have been captured as well as distributed digitally. Many vendors have brought products to market, including traditional film camera vendors like Arri and Panavision, as well as new vendors like Red, Blackmagic, Silicon Imaging, Vision Research and companies which have traditionally focused on consumer and broadcast video equipment, like Sony, GoPro, and Panasonic. As of 2023, professional 4K digital cameras were approximately equal to 35mm film in their resolution and dynamic range capacity. Some filmmakers still prefer to use film picture formats to achieve the desired results. == History == The basis for digital cameras are metal–oxide–semiconductor (MOS) image sensors. The first practical semiconductor image sensor was the charge-coupled device (CCD), based on MOS capacitor technology. Following the commercialization of CCD sensors during the late 1970s to early 1980s, the entertainment industry slowly began transitioning to digital imaging and digital video over the next two decades. The CCD was followed by the CMOS active-pixel sensor (CMOS sensor), developed in the 1990s. Beginning in the late 1980s, Sony began marketing the concept of "electronic cinematography," utilizing its analog Sony HDVS professional video cameras. The effort met with very little success. However, this led to one of the earliest high definition video shot feature movies, Julia and Julia (1987). Rainbow (1996) was the world's first film to utilize extensive digital post production techniques. Shot entirely with Sony's first Solid State Electronic Cinematography cameras and featuring over 35 minutes of digital image processing and visual effects, all post production, sound effects, editing and scoring were completed digitally. The Digital High Definition image was transferred to a 35mm negative via an electron beam recorder for theatrical release. The first digitally videoed and post produced feature was Windhorse, shot in Tibet and Nepal in 1996 on the Sony DVW-700WS Digital Betacam and the prosumer Sony DCR-VX1000. The offline editing (Avid) and the online post and color work (Roland House / da Vinci) were also all digital. The film, transferred to 35mm negative for theatrical release, won Best U.S. Feature at the Santa Barbara Film Festival in 1998. In 1997, with the introduction of HDCAM recorders and 1920 × 1080 pixel digital professional video cameras based on CCD technology, the idea, now re-branded as "digital cinematography," began to gain traction in the market. Shot and released in 1998, The Last Broadcast is believed by some to be the first feature-length video shot and edited entirely on consumer-level digital equipment. In May 1999, George Lucas challenged the supremacy of the movie-making medium of film for the first time by including footage filmed with high-definition digital cameras in Star Wars: Episode I – The Phantom Menace. The digital footage blended seamlessly with the footage shot on film and he announced later that year he would film its sequels entirely on hi-def digital video. Also in 1999, digital projectors were installed in four theaters for the showing of The Phantom Menace. In May 2000, Vidocq, which was directed by Pitof, began principal photography shot entirely using a Sony HDW-F900 camera, with the video being released in September the next year. According to the Guinness World Records, Vidocq is the first full length feature filmed in digital high resolution. In June 2000, Star Wars: Episode II – Attack of the Clones began principal photography shot entirely using a Sony HDW-F900 camera as Lucas had previously stated. The film was released in May 2002. In May 2001 Once Upon a Time in Mexico was also shot in 24 frame-per-second high-definition digital video, partially developed by George Lucas using a Sony HDW-F900 camera, following Robert Rodriguez's introduction to the camera at Lucas' Skywalker Ranch facility whilst editing the sound for Spy Kids. A lesser-known movie, Russian Ark (2002), was also shot with the same camera and was the first tapeless digital movie, recorded on HDD instead of tape. In 2009, Slumdog Millionaire became the first movie shot mainly in digital to be awarded the Academy Award for Best Cinematography. The highest-grossing movie in the history of cinema, Avatar (2009), not only was shot on digital cameras as well, but also made the main revenues at the box office no longer by film, but digital projection. Major movies shot on digital video overtook those shot on film in 2013. Since 2016 over 90% of major films were shot on digital video. As of 2017, 92% of films are shot on digital. Only 24 major films released in 2018 were shot on 35mm. Since the 2000s, most movies across the world have been captured as well as distributed digitally. Today, cameras from companies like Sony, Panasonic, JVC and Canon offer a variety of choices for shooting high-definition video. At the high-end of the market, there has been an emergence of cameras aimed specifically at the digital cinema market. These cameras from Sony, Vision Research, Arri, Blackmagic Design, Panavision, Grass Valley and Red offer resolution and dynamic range that exceeds that of traditional video cameras, which are designed for the limited needs of broadcast television. == Technology == Digital cinematography captures motion pictures digitally in a process analogous to digital photography. While there is a clear technical distinction that separates the images captured in digital cinematography from video, the term "digital cinematography" is usually applied only in cases where digital acquisition is substituted for film acquisition, such as when shooting a feature film. The term is seldom applied when digital acquisition is substituted for video acquisition, as with live broadcast television programs. === Recording === ==== Cameras ==== Professional cameras include the Sony CineAlta (F) Series, Blackmagic Cinema Camera, Red One, Arri D-20, D-21 and Alexa, Panavision Genesis, Silicon Imaging SI-2K, Thomson Viper, Vision Research Phantom, IMAX 3D camera based on two Vision Research Phantom cores, Weisscam HS-1 and HS-2, GS Vitec noX, and the Fusion Camera System. Independent micro-budget filmmakers have also pressed low-cost consumer and prosumer cameras into service for digital filmmaking. Flagship smartphones like the Apple iPhone have been used to shoot movies like Unsane (shot on the iPhone 7 Plus) and Tangerine (shot on three iPhone 5S phones) and in January 2018, Unsane's director and Oscar winner Steven Soderbergh expressed an interest in filming other productions solely with iPhones going forward. ==== Sensors ==== Digital cinematography cameras capture digital images using image sensors, either charge-coupled device (CCD) sensors or CMOS active-pixel sensors, usually in one of two arrangements. Single chip cameras designed specifically for the digital cinematography market often use a single sensor (much like digital photo cameras), with dimensions similar in size to a 16 or 35 mm film frame or even (as with the Vision 65) a 65 mm film frame. An image can be projected onto a single large sensor exactly the same way it can be projected onto a film frame, so cameras with this design can be made with PL, PV and similar mounts, in order to use the wide range of existing high-end cinematography lenses available. Their large sensors also let these cameras achieve the same shallow depth of field as 35 or 65 mm motion picture film cameras, which many cinematographers consider an essential visual tool. Codecs Professional raw video recording codecs include Blackmagic Raw, Red Raw, Arri Raw and Canon Raw. ==== Video formats ==== Unlike other video formats, which are specified in terms of vertical resolution (for example, 1080p, which is 1920×1080 pixels), digital cinema formats are usually specified in terms of horizontal resolution. As a shorthand, these resolutions are often given in "nK" notation, where n is the multiplier of 1024 such that the horizontal resolution of a corresponding full-aperture, digitized film frame is exactly 1024 n {\displaystyle 1024n} pixels. Here the "K" has a customary meaning corresponding to the binary prefix "kibi" (ki). For instance, a 2K image is 2048 pixels wide, and a 4K image is 4096 pixels wide. Vertical resolutions vary with aspect ratios though; so a 2K image with an HDTV (16:9) aspect ratio is 2048×1152 pixels, while a 2K image with a SDTV or Academy ratio (4:3) is 2048×1536 pixels, and one with a Panavision ratio (2.39:1) would be 2048×856 pixels, and so on. Due to the "nK" notation not corresponding to specific horizontal resolutions per format a 2K image lacking, for example, the typical 35mm film soundtrack space, is only 182
Visopsys
Visopsys (Visual Operating System), is an operating system, written by Andy McLaughlin. Development of the operating system began in 1997. The operating system is licensed under the GNU GPL, with the headers and libraries under the less restrictive LGPL license. It runs on the 32-bit IA-32 architecture. It features a multitasking kernel, supports asynchronous I/O and the FAT line of file systems. It requires a Pentium processor. == History == The development of Visopsys began in 1997, being written by Andy McLaughlin. The first public release of the Operating System was on 2 March 2001, with version 0.1. In this release, Visopsys was a 32 bit operating system, supporting preemptive multitasking and virtual memory. == System overview == Visopsys uses a monolithic kernel, written in the C programming language, with elements of assembly language for certain interactions with the hardware. The operating system supports a graphical user interface, with a small C library.
Democratization of technology
Democratization of technology is the process by which access to technology rapidly extends to an ever-broader audience, especially from a select group of people to the average public. New technologies and improved user experiences have empowered those outside of the technical industry to access and use technological products and services. At an increasing scale, consumers have greater access to use and purchase technologically sophisticated products, as well as to participate meaningfully in the development of these products. Industry innovation and user demand have been associated with more affordable, user-friendly products. This is an ongoing process, beginning with the development of mass production and increasing dramatically as digitization became commonplace. Thomas Friedman argued that the era of globalization has been characterized by the democratization of technology, democratization of finance, and democratization of information. Technology has been critical in the latter two processes, facilitating the rapid expansion of access to specialized knowledge and tools, as well as changing the way that people view and demand such access. A counter argument is that this is just a process of 'massification' - more people can use banks, technology, have access to information, but it does not mean there is any more democratic influence over its production, or that this massification promotes Democracy. == History == Scholars and social critics often cite the invention of the printing press as a major invention that changed the course of history. The force of the printing press rested not in its impact on the printing industry or inventors, but on its ability to transmit information to a broader public by way of mass production. This event is so widely recognized because of its social impact – as a democratizing force. The printing press is often seen as the historical counterpart to the Internet. After the development of the Internet in 1969, its use remained limited to communications between scientists and within government, although use of email and boards gained popularity among those with access. It did not become a popular means of communication until the 1990s. In 1993 the US federal government opened the Internet to commerce and the creation of HTML formed the basis for universal accessibility. === Major innovations === The Internet has played a critical role in modern life as a typical feature of most Western households, and has been key in the democratization of knowledge. It not only constitutes arguably the most critical innovation in this trend thus far; it has also allowed users to gain knowledge of and access to other technologies. Users can learn of new developments more quickly, and purchase high-tech products otherwise only actively marketed to recognized experts. Social media has also empowered and emboldened users to become contributors and critics of technological developments. Some have argued that cloud computing is having a major effect by allowing users greater access through mobility and pay-as-you-use capacity. The open-source model allows users to participate directly in development of software, rather than indirect participation, through contributing opinions. By being shaped by the user, development is directly responsive to user demand and can be obtained for free or at a low cost. In a comparable trend, arduino and littleBits have made electronics more accessible to users of all backgrounds and ages. The development of 3D printers has the potential to increasingly democratize production. Generative artificial intelligence tools have the potential to democratize the process of innovation by improving the ability of individuals to specify and visualize ideas. The democratization of artificial intelligence refers to the transition from AI as a high-cost, specialized field to one accessible to non-experts and smaller organizations. This process is driven by the release of open-weights models, the availability of cloud computing for model training, and the emergence of no-code development platforms. While early AI development was concentrated within Big Tech firms and elite research universities, the 2020s saw a proliferation of public tools like ChatGPT and repositories such as Hugging Face, which lowered the technical barriers to entry. However, the trend has faced criticism as the "illusion of democratization," as the underlying GPU hardware remains concentrated among a few global providers. == Cultural impact == This trend is linked to the spread of knowledge of and ability to perform high-tech tasks, challenging previous conceptions of expertise. Widespread access to technology, including lower costs, was critical to the transition to the new economy. Similarly, democratization of technology was also fuelled by this economic transition, which produced demands for technological innovation and optimism in technology-driven progress. Since the 1980s, a spreading constructivist conception of technology has emphasized that the social and technical domains are critically intertwined. Scholars have argued that technology is non-neutral, defined contextually and locally by a certain relationship with society. Andrew Feenberg, a central thinker in the philosophy of technology, argued that democratizing technology means expanding technological design to include alternative interests and values. When successful in doing so, this can be a tool for increasing inclusiveness. This also suggests an important participatory role for consumers if technology is to be truly democratic. Feenberg asserts that this must be achieved by consumer intervention in a liberated design process. Improved access to specialized knowledge and tools has been associated with an increase in the "do it yourself" (DIY) trend. This has also been associated with consumerization, whereby personal or privately owned devices and software are also used for business purposes. Some have argued that this is linked to reduced dependence on traditional information technology departments. Astra Taylor, the author of the book The People's Platform: Taking Back Power and Culture in the Digital Age, argues, "The promotion of Internet-enabled amateurism is a lazy substitute for real equality of opportunity." === Industry impact === In some ways, democratization of technology has strengthened this industry. Markets have broadened and diversified. Consumer feedback and input is available at a very low or no cost. However, related industries are experiencing decreased demand for qualified professionals as consumers are able to fill more of their demands themselves. Users of a range of types and status have access to increasingly similar technology. Because of the decreased costs and expertise necessary to use products and software, professionals (e.g. in the audio industry) may experience loss of work. In some cases, technology is accessible but sufficiently complex that most users without specialized training are able to operate it without necessarily understanding how it works. Additionally, the process of consumerization has led to an influx in the number of devices in businesses and accessing private networks that IT departments cannot control or access. While this can lead to lowered operating costs and increased innovation, it is also associated with security concerns that most businesses are unable to address at the pace of the spread of technology. === Political impact === Some scholars have argued that technological change will bring about a third wave of democracy. The Internet has been recognized for its role in promoting increased citizen advocacy and government transparency. Jesse Chen, a leading thinker in democratic engagement technologies, distinguishes the democratizing effects of technology from democracy itself. Chen has argued that, while the Internet may have democratizing effects, the Internet alone cannot deliver democracy at all levels of society unless technologies are purposely designed for the nuances of democracy, specifically the engagement of large groups of people in between elections in and beyond government. The spread of the Internet and other forms of technology has led to increased global connectivity. Many scholars believe that it has been associated in the developing world not only with increased Western influence, but also with the spread of democracy through increased communication, efficiency, and access to information. Scholars have drawn associations between the level of technological connectedness and democracy in many nations. Technology can enhance democracy in the developed world as well. In addition to increased communication and transparency, some electorates have implemented online voting to accommodate an increased number of citizens.
Reflection (computer graphics)
Reflection in computer graphics is used to render reflective objects like mirrors and shiny surfaces. Accurate reflections are commonly computed using ray tracing whereas approximate reflections can usually be computed faster by using simpler methods such as environment mapping. Reflections on shiny surfaces like wood or tile can add to the photorealistic effects of a 3D rendering. == Approaches to reflection rendering == For rendering environment reflections there exist many techniques that differ in precision, computational and implementation complexity. Combination of these techniques are also possible. Image order rendering algorithms based on tracing rays of light, such as ray tracing or path tracing, typically compute accurate reflections on general surfaces, including multiple reflections and self reflections. However these algorithms are generally still too computationally expensive for real time rendering (even though specialized HW exists, such as Nvidia RTX) and require a different rendering approach from typically used rasterization. Reflections on planar surfaces, such as planar mirrors or water surfaces, can be computed simply and accurately in real time with two pass rendering — one for the viewer, one for the view in the mirror, usually with the help of stencil buffer. Some older video games used a trick to achieve this effect with one pass rendering by putting the whole mirrored scene behind a transparent plane representing the mirror. Reflections on non-planar (curved) surfaces are more challenging for real time rendering. Main approaches that are used include: Environment mapping (e.g. cube mapping): a technique that has been widely used e.g. in video games, offering reflection approximation that's mostly sufficient to the eye, but lacking self-reflections and requiring pre-rendering of the environment map. The precision can be increased by using a spatial array of environment maps instead of just one. It is also possible to generate cube map reflections in real time, at the cost of memory and computational requirements. Screen space reflections (SSR): a more expensive technique that traces rays come from pixel data.This requires the data of surface normal and either depth buffer (local space) or position buffer (world space).The disadvantage is that objects not captured in the rendered frame cannot appear in the reflections, which results in unresolved and or false intersections causing artefacts such as reflection vanishment and virtual image. SSR was originally introduced as Real Time Local Reflections in CryENGINE 3. == Types of reflection == Polished - A polished reflection is an undisturbed reflection, like a mirror or chrome surface. Blurry - A blurry reflection means that tiny random bumps, or microfacets, on the surface of the material causes the reflection to be blurry. Metallic - A reflection is metallic if the highlights and reflections retain the color of the reflective object. Glossy - This term can be misused: sometimes, it is a setting which is the opposite of blurry (e.g. when "glossiness" has a low value, the reflection is blurry). Sometimes the term is used as a synonym for "blurred reflection". Glossy used in this context means that the reflection is actually blurred. === Polished or mirror reflection === Mirrors are usually almost 100% reflective. === Metallic reflection === Normal (nonmetallic) objects reflect light and colors in the original color of the object being reflected. Metallic objects reflect lights and colors altered by the color of the metallic object itself. === Blurry reflection === Many materials are imperfect reflectors, where the reflections are blurred to various degrees due to surface roughness that scatters the rays of the reflections. === Glossy reflection === Fully glossy reflection, shows highlights from light sources, but does not show a clear reflection from objects. == Examples of reflections == === Wet floor reflections === The wet floor effect is a graphic effects technique popular in conjunction with Web 2.0 style pages, particularly in logos. The effect can be done manually or created with an auxiliary tool which can be installed to create the effect automatically. Unlike a standard computer reflection (and the Java water effect popular in first-generation web graphics), the wet floor effect involves a gradient and often a slant in the reflection, so that the mirrored image appears to be hovering over or resting on a wet floor.
Influencer speak
Influencer speak is a speech pattern commonly associated with English-speaking digital content creators, particularly on platforms such as TikTok. This style is characterized by linguistic features such as uptalk, where intonation rises at the end of declarative sentences, and vocal fry, a low, creaky vibration in speech. These features are often used to engage audiences. == Characteristics == Influencer speak is commonly associated with: Uptalk – a rising intonation at the end of statements Vocal fry – a creaky sound often occurring at the end of sentences Use of filler words and slang – contributes to a conversational tone that resonates with audiences == Origins == The origins of "influencer speak" are linked to the "Valley Girl" accent, which became prominent in the 1980s. This earlier style included features such as uptalk and vocal fry, which have been adapted for digital platforms. Linguists have noted that these patterns are often led by young women, who are recognized as linguistic innovators in sociolinguistic research. == Sociolinguistic significance == "Influencer speak" is used to maintain audience engagement. Features such as uptalk help speakers retain the "conversational floor," ensuring continuous attention from listeners. A study conducted by UCLA researchers has shown that creators adjust their speech styles based on the platform and audience. For example, a comedic tone may be emphasized on TikTok, while a more professional tone may be used on platforms such as LinkedIn or YouTube.