Content determination is the subtask of natural language generation (NLG) that involves deciding on the information to be communicated in a generated text. It is closely related to the task of document structuring. == Example == Consider an NLG system which summarises information about sick babies. Suppose this system has four pieces of information it can communicate The baby is being given morphine via an IV drop The baby's heart rate shows bradycardia's (temporary drops) The baby's temperature is normal The baby is crying Which of these bits of information should be included in the generated texts? == Issues == There are three general issues which almost always impact the content determination task, and can be illustrated with the above example. Perhaps the most fundamental issue is the communicative goal of the text, i.e. its purpose and reader. In the above example, for instance, a doctor who wants to make a decision about medical treatment would probably be most interested in the heart rate bradycardias, while a parent who wanted to know how her child was doing would probably be more interested in the fact that the baby was being given morphine and was crying. The second issue is the size and level of detail of the generated text. For instance, a short summary which was sent to a doctor as a 160 character SMS text message might only mention the heart rate bradycardias, while a longer summary which was printed out as a multipage document might also mention the fact that the baby is on a morphine IV. The final issue is how unusual and unexpected the information is. For example, neither doctors nor parents would place a high priority on being told that the baby's temperature was normal, if they expected this to be the case. Regardless, content determination is very important to users, indeed in many cases the quality of content determination is the most important factor (from the user's perspective) in determining the overall quality of the generated text. == Techniques == There are three basic approaches to document structuring: schemas (content templates), statistical approaches, and explicit reasoning. Schemas are templates which explicitly specify the content of a generated text (as well as document structuring information). Typically, they are constructed by manually analysing a corpus of human-written texts in the target genre, and extracting a content template from these texts. Schemas work well in practice in domains where content is somewhat standardised, but work less well in domains where content is more fluid (such as the medical example above). Statistical techniques use statistical corpus analysis techniques to automatically determine the content of the generated texts. Such work is in its infancy, and has mostly been applied to contexts where the communicative goal, reader, size, and level of detail are fixed. For example, generation of newswire summaries of sporting events. Explicit reasoning approaches have probably attracted the most attention from researchers. The basic idea is to use AI reasoning techniques (such as knowledge-based rules, planning, pattern detection, case-based reasoning, etc.) to examine the information available to be communicated (including how unusual/unexpected it is), the communicative goal and reader, and the characteristics of the generated text (including target size), and decide on the optimal content for the generated text. A very wide range of techniques has been explored, but there is no consensus as to which is most effective.
Deep Learning Anti-Aliasing
Deep Learning Anti-Aliasing (DLAA) is a form of spatial anti-aliasing developed by Nvidia. DLAA depends on and requires Tensor Cores available in Nvidia RTX cards. DLAA is similar to Deep Learning Super Sampling (DLSS) in its anti-aliasing method, with one important differentiation being that the goal of DLSS is to increase performance at the cost of image quality, whereas the main priority of DLAA is improving image quality at the cost of performance (irrelevant of resolution upscaling or downscaling). DLAA is similar to temporal anti-aliasing (TAA) in that they are both spatial anti-aliasing solutions relying on past frame data. Compared to TAA, DLAA is substantially better when it comes to shimmering, flickering, and handling small meshes like wires. == Technical overview == DLAA collects game rendering data including raw low-resolution input, motion vectors, depth buffers, and exposure information. This information feeds into a convolutional neural network that processes the image to reduce aliasing while preserving fine detail. The neural network architecture employs an auto-encoder design trained on high-quality reference images. The training dataset includes diverse scenarios focusing on challenging cases like sub-pixel details, high-contrast edges, and transparent surfaces. The network then processes frames in real-time. Unlike traditional anti-aliasing solutions that rely on manually written heuristics, such as TAA, DLAA uses its neural network to preserve fine details while eliminating unwanted visual artifacts. == History == DLAA was initially called and marketed by Nvidia as DLSS 2x. The first game that added support for DLAA was The Elder Scrolls Online, which implemented the feature in 2021. By June 2022, DLAA was only available in six games. This number rose to 17 by February 2023. In June 2023, TechPowerUp reported that "DLAA is seeing sluggish adoption among game developers", and that Nvidia was working on adding DLAA to the quality presets of DLSS to boost adoption. By December 2023, DLAA was supported in 41 games. In early 2025, an update for the Nvidia App added a driver-based DLSS override feature that enables users to activate DLAA even in games that do not support it natively. == Differences between TAA and DLAA == TAA is used in many modern video games and game engines; however, all previous implementations have used some form of manually written heuristics to prevent temporal artifacts such as ghosting and flickering. One example of this is neighborhood clamping which forcefully prevents samples collected in previous frames from deviating too much compared to nearby pixels in newer frames. This helps to identify and fix many temporal artifacts, but deliberately removing fine details in this way is analogous to applying a blur filter, and thus the final image can appear blurry when using this method. DLAA uses an auto-encoder convolutional neural network trained to identify and fix temporal artifacts, instead of manually programmed heuristics as mentioned above. Because of this, DLAA can generally resolve detail better than other TAA and TAAU implementations, while also removing most temporal artifacts. == Differences between DLSS and DLAA == While DLSS handles upscaling with a focus on performance, DLAA handles anti-aliasing with a focus on visual quality. DLAA runs at the given screen resolution with no upscaling or downscaling functionality provided by DLAA. DLSS and DLAA share the same AI-driven anti-aliasing method. As such, DLAA functions like DLSS without the upscaling part. Both are made by Nvidia and require Tensor Cores. However, DLSS and DLAA cannot be enabled at the same time, only one can be selected depending on whether performance or image quality is prioritized. == Reception == TechPowerUp found that "[c]ompared to TAA and DLSS, DLAA is clearly producing the best image quality, especially at lower resolutions", arguing that, while "DLSS was already doing a better job than TAA at reconstructing small objects", "DLAA does an even better job". In a Cyberpunk 2077 performance test, IGN stated that "DLAA provided somewhat similar results [FPS wise] to the normal raster mode in most cases but got significant performance boost with the help of frame generation", a feature not available when using native resolution. Rock Paper Shotgun noted that, while DLAA is "not a completely perfect form of anti-aliasing, as the occasional jaggies are present", it "looks a lot sharper overall [than TAA], and especially in motion." According to PC World, "DLAA offers very good anti-aliasing without losing visual information — alternatives like TAA tend to struggle during motion-filled scenes, where DLAA doesn’t. Furthermore, DLAA’s loss of performance is lower than with conventional anti-aliasing methods."
Downloadable content
Downloadable content (DLC) is additional content created for an already released video game, distributed through the Internet by the game's publisher. It can be added for no extra cost or as a form of video game monetization, enabling the publisher to gain additional revenue from a title after it has been purchased, often using a microtransaction system. DLC can range from cosmetic content, such as skins, to new in-game content, like characters, levels, modes, and larger expansions that may contain a mix of such content as a continuation of the base game. In some games, multiple DLCs (including future DLC not yet released) may be bundled as part of a "season pass"—typically at a discount rather than purchasing each DLC individually. While the Dreamcast was the first home console to support DLC (albeit in a limited form due to hardware and internet connection limitations), Microsoft's Xbox helped popularize the concept. Since the seventh generation of video game consoles, DLC has been a prevalent feature of major video game platforms with internet connectivity. == Etymology == Since the popularization of microtransactions in online distribution platforms such as Steam, the term DLC has become a synonymous for any form of paid content in video games, regardless of whether they constitute the download of new content. Furthermore, this led to the creation of the oxymoronic term "on-disc DLC" for content included on the game's original files but locked behind a paywall. == History == === Precursors to DLC === The earliest form of downloadable content were offerings of full games, such as on the Atari 2600's GameLine service, which allowed users to download games using a telephone line. A similar service, Sega Channel, allowed for the downloading of games to the Sega Genesis over a cable line. While the GameLine and Sega Channel services allowed for the distribution of entire titles, they did not provide downloadable content for existing titles. Expansion packs were sold at retail for some PC games, which featured content such as additional levels, characters, or maps for a base game. They often required an installation of the original game in order to function, but some games (such as Half-Life) had "standalone" expansions, which were essentially spin-off games that reused engine code and assets from the original game. === On consoles === The Dreamcast was the first console to feature online support as a standard; DLC was available, though limited in size due to the narrowband connection and the 200 block limit of the Visual Memory Unit memory card. These online features were still considered a breakthrough in video games. With the release of the Xbox, Microsoft was the second company to implement downloadable content. Many Xbox titles, including Splinter Cell, Halo 2, and Ninja Gaiden, offered varying amounts of extra content, available for download through the Xbox Live service. Most of this content was available free. With the advent of the GameCube, Nintendo was the third company to implement downloadable content. Many GameCube titles offered varying amounts of extra content from Game Boy Advance titles with the GameCube – Game Boy Advance link cable. All of this content was available free. The Xbox 360 (2005) included more robust support for digital distribution, including DLC downloads and purchases, via its Xbox Live Marketplace service. Microsoft believed that publishers would benefit by offering small pieces of content at a small cost ($1 to $5), rather than full expansion packs (~$20), as this would allow players to pick and chose what content they desired, providing revenue to the publishers. Microsoft also utilized a digital currency known as "Microsoft Points" for transactions, which could also be purchased through physical gift cards to avoid the banking fees associated with the small price points. The PlayStation 3 (2006) adopted the same approach with their downloadable hub, the PlayStation Store. Sony planned on having the bulk of its content be purchased separately via many separate online microtransactions for PlayStation Network titles, including Gran Turismo HD Concept and Gran Turismo 5 Prologue. The Wii (2006) featured a sparser amount of downloadable content on their Wii Shop Channel, the bulk of which is accounted for by digital distribution of emulated Nintendo titles from previous generations. Music video games, such as titles from the Guitar Hero and Rock Band franchises, took significant advantage of downloadable content as a means of offering new songs to be played in-game. Harmonix claimed that Guitar Hero II would feature "more online content than anyone has ever seen in a game to this date." Rock Band features the largest number of downloadable items of any console video game, with a steady number of new songs that were added weekly between 2007 and 2013. Acquiring all the downloadable content for Rock Band would, as of July 12, 2012, cost $5,880.10. === On personal computers === As the popularity and speed of internet connections rose, so did the popularity of using the internet for digital distribution of media. User-created game mods and maps were distributed exclusively online, as they were mainly created by people without the infrastructure capable of distributing the content through physical media. In 1997, Cavedog offered a new unit every month as free downloadable content for their real-time strategy computer game Total Annihilation. Later PC digital distribution platforms, such as Games for Windows Marketplace and Steam, would add support for DLC in a similar manner to consoles. === On handhelds === Nokia phones of the late 1990s and early 2000s shipped with side-scrolling shooter Space Impact, available on various models. With the introduction of WAP in 2000, additional downloadable content for the game, with extra levels, became available. The Nintendo Wi-Fi Connection service on the Nintendo DS could be used to obtain a form of DLC for certain games, such as Picross DS—where players could download puzzle "packs" of classic puzzles from previous Picross series games (such as Mario's Picross). as well as downloadable user generated content. Due to the Nintendo DS's use of cartridges and lack of dedicated storage, most "DLC" for DS games was limited in scope, or in some cases (such as Professor Layton and the Curious Village and Moero! Nekketsu Rhythm Damashii Osu! Tatakae! Ouendan 2), was already part of the game's data on the cartridge, and merely unlocked. Its successor, the Nintendo 3DS, natively supported the purchase of DLC for supported titles via Nintendo eShop. Starting with iPhone OS 3, downloadable content became available for the platform via applications bought from the App Store. While this ability was initially only available to developers for paid applications, Apple eventually allowed for developers to offer this in free applications as well in October 2009. == On-disc DLC == In some cases, a purchased DLC may not actually download new content to the device, but merely consists of data used to enable associated content that is already present within the game's data. DLC of this nature revealed via data mining is typically referred to as "on-disc DLC" or PULC (premium unlockable content). This practice has sometimes been considered controversial, with publishers being accused of using what is effectively a microtransaction to lock access to content that was already contained within the game as sold at retail. Data relating to future DLC may be included on-disc or downloaded during updates for technical reasons as well, either to ensure online multiplayer compatibility for existing content between players who have not yet purchased the new DLC, or as dormant support code for planned content that is still in development at the time of the release. == Monetization == Downloadable content is often offered for a price. Since Facebook games popularized the business model of microtransactions, some have criticized downloadable content as being overpriced and an incentive for developers to leave items out of the initial release, with The Elder Scrolls IV: Oblivion's horse armor DLC having faced a mixed reception upon its release for that reason. However, by 2009, the Horse Armor DLC was one of the top ten content packs that Bethesda had sold, which justified the DLC model for future games. Where a normal software disc may allow its license sold or traded, DLC is generally locked to a specific user's account and does not come with the ability to transfer that license to another user. In addition to individual content downloads, video game publishers sometimes offer a "season pass", which allows users to pre-order a selection of upcoming content over a specific time period, and ensuring the customer's ability to immediately obtain the content upon release. As users do not have the ability to fully preview the content before their purchase, there is a chance that the content of a season
Mixvoip
Mixvoip S.A. is a Luxembourg-based telecommunications service provider founded in 2008. The company offers IP telephony, high-speed Internet connectivity, and IT solutions to businesses and individuals. == Company history == In November 2017, Mixvoip expanded its operations to Belgium and Germany. At the beginning of 2019, the company acquired the telecommunications provider Voipgate. In December 2019, Mixvoip was named Telecom Company of the Year at the Luxembourg ICT Awards 2019 organized by Farvest and IT One. A 2024 article in Duke described the company's transition during the 2010s from traditional telephony services to cloud-based communication platforms. In the end of 2024, the ILR published the statistics about electronic communications in Luxembourg, including Mixvoip in the fix telephony section. In July 2025, Mixvoip acquired Crossing Telecom. In 2026, Mixvoip acquired Nomado's portfolio.
Modulation error ratio
The modulation error ratio (MER) is a measure used to quantify the performance of a digital radio (or digital TV) transmitter or receiver in a communications system using digital modulation (such as QAM). A signal sent by an ideal transmitter or received by a receiver would have all constellation points precisely at the ideal locations, however various imperfections in the implementation (such as noise, low image rejection ratio, phase noise, carrier suppression, distortion, etc.) or signal path cause the actual constellation points to deviate from the ideal locations. Transmitter MER can be measured by specialized equipment, which demodulates the received signal in a similar way to how a real radio demodulator does it. Demodulated and detected signal can be used as a reasonably reliable estimate for the ideal transmitted signal in MER calculation. == Definition == An error vector is a vector in the I-Q plane between the ideal constellation point and the point received by the receiver. The Euclidean distance between the two points is its magnitude. The modulation error ratio is equal to the ratio of the root mean square (RMS) power (in Watts) of the reference vector to the power (in Watts) of the error. It is defined in dB as: M E R ( d B ) = 10 log 10 ( P s i g n a l P e r r o r ) {\displaystyle \mathrm {MER(dB)} =10\log _{10}\left({P_{\mathrm {signal} } \over P_{\mathrm {error} }}\right)} where Perror is the RMS power of the error vector, and Psignal is the RMS power of ideal transmitted signal. MER is defined as a percentage in a compatible (but reciprocal) way: M E R ( % ) = P e r r o r P s i g n a l × 100 % {\displaystyle \mathrm {MER(\%)} ={\sqrt {P_{\mathrm {error} } \over P_{\mathrm {signal} }}}\times 100\%} with the same definitions. MER is closely related to error vector magnitude (EVM), but MER is calculated from the average power of the signal. MER is also closely related to signal-to-noise ratio. MER includes all imperfections including deterministic amplitude imbalance, quadrature error and distortion, while noise is random by nature.
Stixel
In computer vision, a stixel (portmanteau of "stick" and "pixel") is a superpixel representation of depth information in an image, in the form of a vertical stick that approximates the closest obstacles within a certain vertical slice of the scene. Introduced in 2009, stixels have applications in robotic navigation and advanced driver-assistance systems, where they can be used to define a representation of robotic environments and traffic scenes with a medium level of abstraction. == Definition == One of the problems of scene understanding in computer vision is to determine horizontal freespace around the camera, where the agent can move, and the vertical obstacles delimiting it. An image can be paired with depth information (produced e.g. from stereo disparity, lidar, or monocular depth estimation), allowing a dense tridimensional reconstruction of the observed scene. One drawback of dense reconstruction is the large amount of data involved, since each pixel in the image is mapped to an element of a point cloud. Vision problems characterised by planar freespace delimited by mostly vertical obstacles, such as traffic scenes or robotic navigation, can benefit from a condensed representation that allows to save memory and processing time. Stixels are thin vertical rectangles representing a slice of a vertical surface belonging to the closest obstacle in the observed scene. They allow to dramatically reduce the amount of information needed to represent a scene in such problems. A stixel is characterised by three parameters: vertical coordinate of the bottom, height of the stick, and depth. Stixels have fixed width, with each stixel spanning over a certain number of image columns, allowing downsampling of the horizontal image resolution. In the original formulation, each column of the image would contain at most one stixel, and later extensions were developed to allow multiple stixels on each column, allowing to represent multiple objects at different distances. == Stixel estimation == The input to stixel estimation is a dense depth map, that can be computed from stereo disparity or other means. The original approach computes an occupancy grid that can be segmented to estimate the freespace, with dynamic programming providing an efficient method to find an optimal segmentation. Alternative approaches can be used instead of occupancy grid mapping, such as manifold-based methods. The freespace boundary provides the base points of the obstacles at closest longitudinal distance, however multiple objects at different distances might appear in each column of the image. To fully define the obstacles, their height should be estimated, and this is accomplished by segmenting the depth of the object from the depth of the background. A membership function over the pixels can be defined based on the depth value, where the membership represents the confidence of a pixel belonging to the closest vertical obstacle or to the background, and a cut separating the obstacles from the background can again be computed effectively with dynamic programming. Once both the freespace and the obstacle height are known, the stixels can be estimated by fusing the information over the columns spanned by each stixel, and finally a refined depth of the stixel can be estimated via model fitting over the depth of the pixels covered by the stixel, possibly paired with confidence information (e.g. disparity confidence produced by methods such as semi-global matching).
Creative work
A creative work is a manifestation of creative effort in the world through a creative process involving one or more individuals. The term includes fine artwork (sculpture, paintings, drawing, sketching, performance art), dance, writing (literature), filmmaking, and musical composition. The term is frequently used in the context of copyright. It is an important concept in both philosophy and law. Creative works require a creative mindset and are not typically rendered in an arbitrary fashion, although works may demonstrate (i.e., have in common) a degree of arbitrariness, such that it is improbable that two people would independently create the same work. At its base, creative work involves two main steps – having an idea, and then turning that idea into a substantive form or process. Typically, the creative process results in work that has some aesthetic value, identified as a creative expression. Naturally, this expression generally invokes external stimuli (e.g., influences and experiences) which a person draws on because they view the source as creative or inspirational; the degree to which this is reflected may be used in determinations of the derivativeness of the created work. Alternatively, the creator may draw on imagination, and their references may be clouded even to them, for the nature of imagination is as yet not fully understood philosophically, and the level of necessary self-examination of an artist's internal processing is a challenge for even those most self-aware of their minds and mental processes. == Legal definition == === United Kingdom === For the purpose of section 221(2)(c) of the Income Tax (Trading and Other Income) Act 2005, the expression "creative works" means: (a) literary, dramatic, musical or artistic works, or (b) designs,created by the taxpayer personally or, if the qualifying trade, profession or vocation is carried on in partnership, by one or more of the partners personally.