AI Art Krishna

AI Art Krishna — independent reviews, comparisons, pricing and step-by-step guides on Aizhi.

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  Zhura

  Zhura ( ZUR-ə) is a free, web-based screenwriting software application for writing and formatting screenplays to the film industry standard, as well as other formats. Zhura allows users to collaborate on scripts in public or private groups and uses Creative Commons Licensing for all work in the public workspace. On March 29, 2010, Zhura announced its merger with Scripped. Scripped's CEO, Sunil Rajaraman, remains the company's Chief Executive Officer (CEO) as of 2022. The Zhura CEO was Eric MacDonald, a former Cascade Communications engineer. Scripped later closed on April 1, 2015 after a catastrophic, irrecoverable data loss. == Script editor == Screenplay Template – The script editor provides a built-in screenplay template which formats the document to a standard for scripts as recommended by the AMPAS. The screenplay document is composed of seven elements: scene, action, character, dialogue, parenthetical, transition, and shot (see image). Each element has a specific style to which the script editor conforms as you type.Script Formats – Other major script formats for stage play, sitcom, audio drama and comic book are also supported as well as the ability to switch between them.Auto-Complete – Characters, scene headings and custom transitions are “remembered” as they are written and “recalled” with tab-completion when a writer starts a new character, scene heading or transition, respectively.Multiple Editors – With a collaborative editing model comparable to Google Docs, two or more users can edit the same script simultaneously, regardless of having a different operating system or web browser. Import/Export – A screenplay written in another program can be imported into the script editor and automatically conformed to the screenplay template. The closer the original script has adhered to the standard format, the better it will appear when imported. Supported import/export formats include Text (.txt) Word (.doc) Rich Text (.rtf) and OpenDocument (.odt). Scripts can also be exported as a PDF file with additional options.Tracking Changes – Similar to the “tracking” feature in Microsoft Word, a user can review all changes made to a script in the revision history as well as highlight the contributions of each writer. Offline Mode – The Google Gears-based offline functionality is in the process of being updated and is not available for new subscribers, according to the company founders. == Community == Scripped supports typical social networking features such as discussion boards, comments, user profiles, public and private writing groups, internal web mail and instant messaging within the script editor. There is also the option to share scripts with others outside of Scripped by making scripts externally viewable. Scripped is made up entirely of user-generated scripts that other users can share, critique and edit, offering creative support to a community of writers. == Licensing of user-created work == There are three types of work-spaces on Scripped (personal, group and public) with unique copyright and licensing management for the work created in each area. Any work a user originates may be moved from the personal area to a public or group area at any time. Once another user edits a script, however, it cannot be moved into the originator’s personal area. Personal Workspace – Any script created or video uploaded in the user’s personal workspace remains copyrighted to that user. Until the user moves that script or video from their personal area into a group or public area, no other user shares a copyright or license to that work. Private Group Workspace – The copyright to any script created or video uploaded in a private group workspace is allocated by the individual members of the group, however they see fit. Public Workspace – Any script created or video uploaded in the public workspace is assigned a Creative Commons license by the originator of that work. The originator of a script may select one of four Creative Commons licenses before introducing that script to the public. The selection of the license is determined by what the author wants to allow others to do with the work. Below is a list of Creative Commons licenses available for all scripts and videos in the public workspace. Share Alike (BY-SA) This license lets others remix, tweak, and build upon your work even for commercial reasons, as long as they credit the original user and license their new creations under the identical terms. This license is often compared to open source software licenses. All new works based on the original user's will carry the same license, so any derivatives will also allow commercial use. No Derivatives (BY-ND) This license allows for redistribution, commercial and non-commercial, as long as it is passed along unchanged and in whole, with credit to the original user. Non-Commercial, No Derivatives (BY-NC-ND) This license is the most restrictive of the four licenses, allowing redistribution. This license is often called the "free advertising" license because it allows others to download the original user work and share them with others as long as they mention the original user and link back to them, but they can't change them in any way or use them commercially. Non-Commercial, Share Alike (BY-NC-SA) This license lets others remix, tweak, and build upon the original user's work non-commercially, as long as they credit the original user and license their new creations under the identical terms. Others can download and redistribute the original user's work just like the BY-NC-ND license, but they can also translate, make remixes, and produce new stories based on the original user's work. All new work based on the original user's work will carry the same license, so any derivatives will also be non-commercial in nature. == Events == In April 2008, Zhura partnered with Improv Asylum, a comedy troupe in Boston, Massachusetts to produce a live sketch comedy show called "You Wrote It, Live" entirely written by the public on Zhura. Another show was produced in June.

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  Screenless video

  Screenless video is any system for transmitting visual information from a video source without the use of a screen. Screenless computing systems can be divided into three groups: Visual Image, Retinal Direct, and Synaptic Interface. == Visual image == Visual Image screenless display includes any image that the eye can perceive. The most common example of Visual Image screenless display is a hologram. In these cases, light is reflected off some intermediate object (hologram, LCD panel, or cockpit window) before it reaches the retina. In the case of LCD panels the light is refracted from the back of the panel, but is nonetheless a reflected source. Google has proposed a similar system to replace the screens of tablet computers and smartphones. == Retinal display == Virtual retinal display systems are a class of screenless displays in which images are projected directly onto the retina. They are distinguished from visual image systems because light is not reflected from some intermediate object onto the retina, it is instead projected directly onto the retina. Retinal Direct systems, once marketed, hold out the promise of extreme privacy when computing work is done in public places because most snooping relies on viewing the same light as the person who is legitimately viewing the screen, and retinal direct systems send light only into the pupils of their intended viewer. == Synaptic interface == Synaptic Interface screenless video does not use light at all. Visual information completely bypasses the eye and is transmitted directly to the brain. While such systems have only been implemented in humans in rudimentary form - for example, displaying single Braille characters to blind people – success has been achieved in sampling usable video signals from the biological eyes of a living horseshoe crab through their optic nerves, and in sending video signals from electronic cameras into the creatures' brains using the same method.

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  Information schema

  In relational databases, the information schema (information_schema) is an ANSI-standard set of read-only views that provide information about all of the tables, views, columns, and procedures in a database. It can be used as a source of the information that some databases make available through non-standard commands, such as: the SHOW command of MySQL the DESCRIBE command of Oracle's SQLPlus the \d command in psql (PostgreSQL's default command-line program). => SELECT count(table_name) FROM information_schema.tables; count ------- 99 (1 row) => SELECT column_name, data_type, column_default, is_nullable FROM information_schema.columns WHERE table_name='alpha'; column_name | data_type | column_default | is_nullable -------------+-----------+----------------+------------- foo | integer | | YES bar | character | | YES (2 rows) => SELECT FROM information_schema.information_schema_catalog_name; catalog_name -------------- johnd (1 row) == Implementation == As a notable exception among major database systems, Oracle does not as of 2015 implement the information schema. An open-source project exists to address this. RDBMSs that support information_schema include: Amazon Redshift Apache Hive Microsoft SQL Server MonetDB Snowflake MySQL PostgreSQL H2 Database HSQLDB InterSystems Caché MariaDB SingleStore (formerly MemSQL) Mimer SQL Snowflake Trino Presto CrateDB ClickHouse CockroachDB Kinetica DB TiDB RDBMSs that do not support information_schema include: Apache Derby Apache Ignite Firebird Microsoft Access IBM Informix Ingres IBM Db2 Oracle Database SAP HANA SQLite Sybase ASE Sybase SQL Anywhere Teradata Vertica

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  Sprite multiplexing

  Sprite multiplexing is a computer graphics technique where additional sprites (moving images) can be drawn on the screen, beyond the nominal maximum. It is largely historical, applicable principally to older hardware, where limited resources (such as CPU speed and memory) meant only a relatively small number of sprites were supported. On the other hand, it is also true that without multiplexing, the sprite circuitry would be idle much of the time, and limited resources were wasted. == Description == The sprite multiplexing technique is based on the idea that while the hardware may only support a finite number of sprites, it is sometimes possible to re-use the same sprite "slots" more than once per frame or scan line. The program will first use the hardware to draw one or more sprite(s), as normal. Before the next frame (or next scanline) needs to be drawn, the software reprograms the hardware to display additional sprites, in other positions. For example, the Nintendo Entertainment System explicitly supports hardware sprite multiplexing, where it has 64 hardware sprites, but is only capable of rendering 8 of them per scanline. On the older Atari 2600, sprite multiplexing was not intentionally designed in, but programmers discovered they could reset the TIA graphics chip to draw additional sprites on the same scanline. The sprite multiplexing technique relies on the program being able to identify what part of the video screen is being drawn at the moment, or being triggered by the video hardware to run a subroutine at the crucial moment. The programmer must carefully consider the layout of the screen. If the video graphics hardware is not reprogrammed in time for the extra sprites to be displayed, they will not appear, or will be drawn incorrectly. Modern video graphics hardware typically does not use hardware sprites, since modern computer systems do not have the kind of limitations that sprite hardware is designed to circumvent. == Implementations == Systems that allow the programmer to employ the sprite multiplexing technique include: Atari 2600 Atari 8-bit computers Amiga Commodore 64 MSX Nintendo Entertainment System Super Nintendo Entertainment System Master System Sega Genesis/Mega Drive

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  AlphaChip (controversy)

  The AlphaChip controversy refers to a series of public, scholarly, and legal disputes surrounding a 2021 Nature paper by Google-affiliated researchers. The paper describes an approach to macro placement, a stage of chip floorplanning, based on reinforcement learning (RL), a machine learning method in which a system iteratively improves its decisions by optimizing performance-based reward signals. The primary technical question is whether the new techniques are better than existing (non-AI) techniques. Both internal Google studies and external attempts to replicate the algorithm have failed to show the claimed benefits. No head-to-head comparison is available because the data used in the paper is proprietary, and Google has not released any results from running its algorithm on public benchmarks. This has resulted in considerable skepticism over the paper's claims. In addition, the inability of others (both inside and outside of Google) to replicate the claimed results have sparked concerns about the paper’s methodology, reproducibility, and scientific integrity. The lead researchers of the Nature paper were affiliated with Google Brain, which became part of Google DeepMind, and later spun off into the company Ricursive. == Motivation for research: Macro placement in chip layout == Chip design for modern integrated circuits is a complex, expert-driven process that relies on electronic design automation. It determines the performance of the final chip, and takes weeks or months to complete. Advances that produce better designs, or complete the process faster, are commercially and academically significant. Macro placement is a step during chip design that determines the locations of large circuit components (macros) within a chip. It is followed by detailed placement, which places the far more numerous but much smaller standard cells. Alternatively, mixed-size placement simultaneously places both large macros and millions of small cells, requiring algorithms to handle objects that differ by several orders of magnitude in area and mobility. The number of macros per circuit typically ranges from several to thousands. Wiring must be performed after placement, and the details of this wiring strongly influence the power, performance, and area (PPA) of the completed chip. The full wiring calculation is very resource intensive, so placement tools typically use a proxy cost, a simplified objective function used to guide the placement algorithm during training and evaluation. The faithfulness of the chosen proxy cost to the final objective cost is a critical aspect of placer performance. === State of the art as of 2021 === Chips have been designed since the 1960s, so there were many existing methods as of 2021. Available options included manual design, academic tools, and commercial offerings. Academic methods include combinatorial optimization techniques such as simulated annealing, analytical placement, hierarchical heuristics, and as of 2019 reinforcement learning and broader machine learning techniques.. Existing (non-AI) academic tools for solving the same problem include APlace, NTUplace3, ePlace, RePlace, and DREAMPlace. Commercial EDA vendors also offered automated software tools for floorplanning and mixed-size placement. For instance, as of 2019 Cadence’s Innovus implementation software offered a Concurrent Macro Placer (CMP) feature to automatically place large blocks and standard cells. == The 2021 Nature paper and its claims == In 2021, Nature published a paper under the title “A graph‑placement methodology for fast chip design” co‑authored by 21 Google-affiliated researchers. The paper reported that an RL agent could generate macro placements for integrated circuits "in under six hours" and achieve improvements over human-designed layouts in power, timing performance, and area (PPA), standard chip-quality metrics referring respectively to energy consumption, chip operating speed, and silicon footprint (evaluated after wire routing). It introduced a sequential macro placement algorithm in which macros are placed one at a time instead of optimizing their locations concurrently. At each step, the algorithm selects a location for a single macro on a discretized chip canvas, conditioning its decision on the placements of previously placed macros. This sequential formulation converts macro placement into a long-horizon decision process in which early placement choices constrain later ones. After macro placement, force-directed placement is applied to place standard cells connected to the macros. Deep reinforcement learning is used to train a policy network to place macros by maximizing a reward that reflects final placement quality (for example, wirelength and congestion). Policy learning occurs during self‑play for one or multiple circuit designs. Further placement optimizations refine the overall layout by balancing wirelength, density, and overlap constraints, while treating the macro locations produced by the RL policy as fixed obstacles. The approach relies on pre-training, in which the RL model is first trained on a corpus of prior designs (twenty in the Nature paper) to learn general placement patterns before being fine-tuned on a specific chip. Circuit examples used in the study were parts of proprietary Google TPU designs, called blocks (or floorplan partitions). The paper reported results on five blocks and described the approach as generalizable across chip designs. == Controversy == Soon after the paper's publication, controversy arose over whether the claims were true, whether they were sufficiently proven, and whether academic standards were followed. These controversies arose both within Google and among external academic experts. === Internal dispute at Google and legal proceedings === In 2022, Satrajit Chatterjee, a Google engineer involved in reviewing the AlphaChip work, raised concerns internally and drafted an alternative analysis, (Stronger Baselines) arguing that established methods outperformed the RL approach under fair comparison. In March 2022, Google declined to publish this analysis and terminated Chatterjee's employment. Chatterjee filed a wrongful dismissal lawsuit, alleging that representations related to the AlphaChip research involved fraud and scientific misconduct. According to court documents, Chatterjee's study was conducted "in the context of a large potential Google Cloud deal". He noted that it "would have been unethical to imply that we had revolutionary technology when our tests showed otherwise" and claimed Google was deliberately withholding material information. Furthermore, the committee that reviewed his paper and disapproved its publication was allegedly chaired by subordinates of Jeff Dean, a senior co-author of the Nature paper. Google’s subsequent motion to dismiss was denied, holding that Chatterjee had plausibly alleged retaliation for refusing to engage in conduct he believed would violate state or federal law. === External controversy === The external questions can be summarized in four main points: (a) Are the claims supported by the evidence provided? (b) Did the paper provide enough information to allow the results to be independently reproduced and verified? If so, are the results an improvement over existing academic and commercial tools? (c) Were the comparisons in the paper done fairly and with full disclosure? (d) Were academic standards followed? Each of these is discussed below. ==== Are the claims supported by the evidence provided? ==== The Nature paper described the reduction in design-process time as going from "days or weeks" to "hours", but did not provide per-design time breakdowns or specify the number of engineers, their level of expertise, or the baseline tools and workflow against which this comparison was made. It was also unclear whether the "days or weeks" baseline included time spent on other tasks such as functional design changes. The paper also evaluated the method on fewer benchmarks (five) than is common in the field, and showed mixed results across different evaluation goals While the approach was described as improving circuit area, this claim seems unsupported, as the RL optimization did not alter the overall circuit area, as it adjusted only the locations of fixed-shape non-overlapping circuit components within a fixed rectangular layout boundary. ==== Comparison with existing methods, and replicating the algorithm ==== Because macro placement is largely geometric and its fundamental algorithms are not tied to a specific process node, competing approaches can be evaluated on public benchmarks (tests) across technologies, rather than primarily on proprietary internal designs. This is standard procedure when comparing academic placers, see . In contrast, Google has only reported results only on internal proprietary designs, and as of 2026 has not offered comparisons with prior methods on common benchmarks. Researchers at the University of Califor

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  Clipmap

  In computer graphics, clipmapping is a method of clipping a mipmap to a subset of data pertinent to the geometry being displayed. This is useful for loading as little data as possible when memory is limited, such as on a graphics processing unit. The technique is used for LODing in NVIDIA’s implementation of voxel cone tracing. The high-resolution levels of the mipmapped scene representation are clipped to a region near the camera, while lower resolution levels are clipped further away. == MegaTexture == MegaTexture is a clipmap implementation developed by id Software. It was introduced in their id Tech 4 engine and also appeared in id Tech 5 and id Tech 6 before being removed in id Tech 7. MegaTexture is a texture allocation technique that uses a single, extremely large texture rather than repeating multiple smaller textures. It is also featured in Splash Damage's game Enemy Territory: Quake Wars, and was developed by id Software former technical director John Carmack. MegaTexture employs a single large texture space for static terrain. The texture is stored on removable media or a computer's hard drive and streamed as needed, allowing large amounts of detail and variation over a large area with comparatively little RAM usage. Depending on the pixel resolution per square meter, covering a large area could require several gigabytes of memory. However, RAM is also filled by the rest of the game and the underlying operating system, limiting the amount available for texturing. As the player moves around the game, different sections of the MegaTexture are loaded into memory. They are then scaled to the correct size and applied to the 3D models of the terrain. Id has presented a more advanced technique that builds upon the MegaTexture idea and virtualizes both the geometry and the textures to obtain unique geometry down to the equivalent of the texel: the sparse voxel octree (SVO). It works by raycasting the geometry represented by voxels (instead of triangles) stored in an octree. The goal is to stream parts of the octree into video memory, going further down along the tree for nearby objects to give them more details, and to use higher level, larger voxels for farther objects, which give an automatic level of detail (LOD) system for both geometry and textures at the same time. The geometric detail that can be obtained using this method is nearly infinite, which removes the need for faking 3-dimensional details with techniques such as normal mapping. Despite that most voxel rendering tests use very large amounts of memory (up to several GB), Jon Olick of id Software claimed the technology is able to compress such SVO to 1.15 bits per voxel of position data. == Virtual texturing == Unlike clipmaps, which clip each mip level around a viewpoint-dependent clipcenter and therefore work best for terrain, virtual texturing preprocesses texture data into equally sized tiles that can be streamed for arbitrary textured geometry. Rage, powered by the id Tech 5 engine, uses a more advanced technique called virtual texturing. Textures can measure up to 128000×128000 pixels and are also used for in-game models and sprites, etc. and not just the terrain. Wolfenstein: The New Order and the 2016 version of Doom also use these. Carmageddon: Reincarnation also uses virtual texturing, though unlike id's virtual texturing system, which is designed for unique texture-mapping everywhere, their system is designed to use storage space sparingly while still offering good blend of texture variation and resolution.

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  Joox

  Joox (stylised in all caps) is a music streaming service owned by Tencent, launched in January 2015. Joox is the biggest music streaming app in Asian markets such as Hong Kong, Macau, Indonesia, Malaysia, Myanmar, Thailand and also in South Africa before it was shut down in early 2022. Joox is a freemium service, providing most of its songs free, while some songs are only available for premium users, offered via paid subscriptions or by doing different tasks offered. In 2017, Joox launched their service in their first non-Asian market, South Africa, which for an unknown reason shut down five years later. The service now accounts for more than 50% of all music streaming app downloads in their Asian markets. The number of music-streaming users in Hong Kong, Macau, Malaysia, Thailand, Myanmar and Indonesia was expected to reach 87 million by 2020. == Background == Before the emergence of Joox, Tencent owned QQ Music, one of the largest music streaming and download service in China. In 2015, they introduced Joox as their expansion of music services to overseas market instead of mainland China, starting first in Hong Kong. Instead of providing free services by playing audio ads to users like Spotify, another major music service, Joox focused on banner ads, splash ads and other advertising methods such as category playlists and in-app skins. They claimed it as a success. Joox offered their premium VIP access to DStv subscribers free of charge. DStv is the sister company to Tencent and is the primary pay-TV provider in South Africa. In November 2021, it was announced that Joox will stop streaming in South Africa in March 2022.

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  Database virtualization

  Database virtualization is the decoupling of the database layer, which lies between the storage and application layers within the application stack. Virtualization of the database layer enables a shift away from the physical, toward the logical or virtual. Virtualization enables compute and storage resources to be pooled and allocated on demand. This enables both the sharing of single server resources for multi-tenancy, as well as the pooling of server resources into a single logical database or cluster. In both cases, database virtualization provides increased flexibility, more granular and efficient allocation of pooled resources, and more scalable computing. == Virtual data partitioning == The act of partitioning data stores as a database grows has been in use for several decades. There are two primary ways that data has been partitioned inside legacy data management systems: Shared-data databases: an architecture that assumes all database cluster nodes share a single partition. Inter-node communications are used to synchronize update activities performed by different nodes on the cluster. Shared-data data management systems are limited to single-digit node clusters. Shared-nothing databases: an architecture in which all data is segregated to internally managed partitions with clear, well-defined data location boundaries. Shared-nothing databases require manual partition management. In virtual partitioning, logical data is abstracted from physical data by autonomously creating and managing large numbers of data partitions (100s to 1000s). Because they are autonomously maintained, the resources required to manage the partitions are minimal. This kind of massive partitioning results in: Partitions that are small, efficiently managed, and load-balanced. Systems that do not require re-partitioning events to define additional partitions, even when the hardware is changed. “Shared-data” and “shared-nothing” architectures allow scalability through multiple data partitions and cross-partition querying and transaction processing without full partition scanning. == Horizontal data partitioning == Partitioning database sources from consumers is a fundamental concept. With greater numbers of database sources, inserting a horizontal data virtualization layer between the sources and consumers helps address this complexity. Rick van der Lans, the author of multiple books on SQL and relational databases, has defined data virtualization as "the process of offering data consumers a data access interface that hides the technical aspects of stored data, such as location, storage structure, API, access language, and storage technology." == Advantages == Added flexibility and agility for existing computing infrastructure. Enhanced database performance. Pooling and sharing computing resources, either splitting them (multi-tenancy) or combining them (clustering). Simplification of administration and management. Increased fault tolerance.

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  Scale space implementation

  In the areas of computer vision, image analysis and signal processing, the notion of scale-space representation is used for processing measurement data at multiple scales, and specifically enhance or suppress image features over different ranges of scale (see the article on scale space). A special type of scale-space representation is provided by the Gaussian scale space, where the image data in N dimensions is subjected to smoothing by Gaussian convolution. Most of the theory for Gaussian scale space deals with continuous images, whereas one when implementing this theory will have to face the fact that most measurement data are discrete. Hence, the theoretical problem arises concerning how to discretize the continuous theory while either preserving or well approximating the desirable theoretical properties that lead to the choice of the Gaussian kernel (see the article on scale-space axioms). This article describes basic approaches for this that have been developed in the literature, see also for an in-depth treatment regarding the topic of approximating the Gaussian smoothing operation and the Gaussian derivative computations in scale-space theory, and for a complementary treatment regarding hybrid discretization methods. == Statement of the problem == The Gaussian scale-space representation of an N-dimensional continuous signal, f C ( x 1 , ⋯ , x N , t ) , {\displaystyle f_{C}\left(x_{1},\cdots ,x_{N},t\right),} is obtained by convolving fC with an N-dimensional Gaussian kernel: g N ( x 1 , ⋯ , x N , t ) . {\displaystyle g_{N}\left(x_{1},\cdots ,x_{N},t\right).} In other words: L ( x 1 , ⋯ , x N , t ) = ∫ u 1 = − ∞ ∞ ⋯ ∫ u N = − ∞ ∞ f C ( x 1 − u 1 , ⋯ , x N − u N , t ) ⋅ g N ( u 1 , ⋯ , u N , t ) d u 1 ⋯ d u N . {\displaystyle L\left(x_{1},\cdots ,x_{N},t\right)=\int _{u_{1}=-\infty }^{\infty }\cdots \int _{u_{N}=-\infty }^{\infty }f_{C}\left(x_{1}-u_{1},\cdots ,x_{N}-u_{N},t\right)\cdot g_{N}\left(u_{1},\cdots ,u_{N},t\right)\,du_{1}\cdots du_{N}.} However, for implementation, this definition is impractical, since it is continuous. When applying the scale space concept to a discrete signal fD, different approaches can be taken. This article is a brief summary of some of the most frequently used methods. == Separability == Using the separability property of the Gaussian kernel g N ( x 1 , … , x N , t ) = G ( x 1 , t ) ⋯ G ( x N , t ) {\displaystyle g_{N}\left(x_{1},\dots ,x_{N},t\right)=G\left(x_{1},t\right)\cdots G\left(x_{N},t\right)} the N-dimensional convolution operation can be decomposed into a set of separable smoothing steps with a one-dimensional Gaussian kernel G along each dimension L ( x 1 , ⋯ , x N , t ) = ∫ u 1 = − ∞ ∞ ⋯ ∫ u N = − ∞ ∞ f C ( x 1 − u 1 , ⋯ , x N − u N , t ) G ( u 1 , t ) d u 1 ⋯ G ( u N , t ) d u N , {\displaystyle L(x_{1},\cdots ,x_{N},t)=\int _{u_{1}=-\infty }^{\infty }\cdots \int _{u_{N}=-\infty }^{\infty }f_{C}(x_{1}-u_{1},\cdots ,x_{N}-u_{N},t)G(u_{1},t)\,du_{1}\cdots G(u_{N},t)\,du_{N},} where G ( x , t ) = 1 2 π t e − x 2 2 t {\displaystyle G(x,t)={\frac {1}{\sqrt {2\pi t}}}e^{-{\frac {x^{2}}{2t}}}} and the standard deviation of the Gaussian σ is related to the scale parameter t according to t = σ2. Separability will be assumed in all that follows, even when the kernel is not exactly Gaussian, since separation of the dimensions is the most practical way to implement multidimensional smoothing, especially at larger scales. Therefore, the rest of the article focuses on the one-dimensional case. == The sampled Gaussian kernel == When implementing the one-dimensional smoothing step in practice, the presumably simplest approach is to convolve the discrete signal fD with a sampled Gaussian kernel: L ( x , t ) = ∑ n = − ∞ ∞ f ( x − n ) G ( n , t ) {\displaystyle L(x,t)=\sum _{n=-\infty }^{\infty }f(x-n)\,G(n,t)} where G ( n , t ) = 1 2 π t e − n 2 2 t {\displaystyle G(n,t)={\frac {1}{\sqrt {2\pi t}}}e^{-{\frac {n^{2}}{2t}}}} (with t = σ2) which in turn is truncated at the ends to give a filter with finite impulse response L ( x , t ) = ∑ n = − M M f ( x − n ) G ( n , t ) {\displaystyle L(x,t)=\sum _{n=-M}^{M}f(x-n)\,G(n,t)} for M chosen sufficiently large (see error function) such that 2 ∫ M ∞ G ( u , t ) d u = 2 ∫ M t ∞ G ( v , 1 ) d v < ε . {\displaystyle 2\int _{M}^{\infty }G(u,t)\,du=2\int _{\frac {M}{\sqrt {t}}}^{\infty }G(v,1)\,dv<\varepsilon .} A common choice is to set M to a constant C times the standard deviation of the Gaussian kernel M = C σ + 1 = C t + 1 {\displaystyle M=C\sigma +1=C{\sqrt {t}}+1} where C is often chosen somewhere between 3 and 6. Using the sampled Gaussian kernel can, however, lead to implementation problems, in particular when computing higher-order derivatives at finer scales by applying sampled derivatives of Gaussian kernels. When accuracy and robustness are primary design criteria, alternative implementation approaches should therefore be considered. For small values of ε (10−6 to 10−8) the errors introduced by truncating the Gaussian are usually negligible. For larger values of ε, however, there are many better alternatives to a rectangular window function. For example, for a given number of points, a Hamming window, Blackman window, or Kaiser window will do less damage to the spectral and other properties of the Gaussian than a simple truncation will. Notwithstanding this, since the Gaussian kernel decreases rapidly at the tails, the main recommendation is still to use a sufficiently small value of ε such that the truncation effects are no longer important. == The discrete Gaussian kernel == A more refined approach is to convolve the original signal with the discrete Gaussian kernel T(n, t) L ( x , t ) = ∑ n = − ∞ ∞ f ( x − n ) T ( n , t ) {\displaystyle L(x,t)=\sum _{n=-\infty }^{\infty }f(x-n)\,T(n,t)} where T ( n , t ) = e − t I n ( t ) {\displaystyle T(n,t)=e^{-t}I_{n}(t)} and I n ( t ) {\displaystyle I_{n}(t)} denotes the modified Bessel functions of integer order, n. This is the discrete counterpart of the continuous Gaussian in that it is the solution to the discrete diffusion equation (discrete space, continuous time), just as the continuous Gaussian is the solution to the continuous diffusion equation. This filter can be truncated in the spatial domain as for the sampled Gaussian L ( x , t ) = ∑ n = − M M f ( x − n ) T ( n , t ) {\displaystyle L(x,t)=\sum _{n=-M}^{M}f(x-n)\,T(n,t)} or can be implemented in the Fourier domain using a closed-form expression for its discrete-time Fourier transform: T ^ ( θ , t ) = ∑ n = − ∞ ∞ T ( n , t ) e − i θ n = e t ( cos ⁡ θ − 1 ) . {\displaystyle {\widehat {T}}(\theta ,t)=\sum _{n=-\infty }^{\infty }T(n,t)\,e^{-i\theta n}=e^{t(\cos \theta -1)}.} With this frequency-domain approach, the scale-space properties transfer exactly to the discrete domain, or with excellent approximation using periodic extension and a suitably long discrete Fourier transform to approximate the discrete-time Fourier transform of the signal being smoothed. Moreover, higher-order derivative approximations can be computed in a straightforward manner (and preserving scale-space properties) by applying small support central difference operators to the discrete scale space representation. As with the sampled Gaussian, a plain truncation of the infinite impulse response will in most cases be a sufficient approximation for small values of ε, while for larger values of ε it is better to use either a decomposition of the discrete Gaussian into a cascade of generalized binomial filters or alternatively to construct a finite approximate kernel by multiplying by a window function. If ε has been chosen too large such that effects of the truncation error begin to appear (for example as spurious extrema or spurious responses to higher-order derivative operators), then the options are to decrease the value of ε such that a larger finite kernel is used, with cutoff where the support is very small, or to use a tapered window. == Recursive filters == Since computational efficiency is often important, low-order recursive filters are often used for scale-space smoothing. For example, Young and van Vliet use a third-order recursive filter with one real pole and a pair of complex poles, applied forward and backward to make a sixth-order symmetric approximation to the Gaussian with low computational complexity for any smoothing scale. By relaxing a few of the axioms, Lindeberg concluded that good smoothing filters would be "normalized Pólya frequency sequences", a family of discrete kernels that includes all filters with real poles at 0 < Z < 1 and/or Z > 1, as well as with real zeros at Z < 0. For symmetry, which leads to approximate directional homogeneity, these filters must be further restricted to pairs of poles and zeros that lead to zero-phase filters. To match the transfer function curvature at zero frequency of the discrete Gaussian, which ensures an approximate semi-group property of additive t, two poles at Z = 1 + 2 t − ( 1 + 2 t ) 2 − 1 {\displaystyle

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  Viewport

  A viewport is a polygon viewing region in computer graphics. In computer graphics theory, there are two region-like notions of relevance when rendering some objects to an image. In textbook terminology, the world coordinate window is the area of interest (meaning what the user wants to visualize) in some application-specific coordinates, e.g. miles, centimeters etc. The word window as used here should not be confused with the GUI window, i.e. the notion used in window managers. Rather it is an analogy with how a window limits what one can see outside a room. In contrast, the viewport is an area (typically rectangular) expressed in rendering-device-specific coordinates, e.g. pixels for screen coordinates, in which the objects of interest are going to be rendered. Clipping to the world-coordinates window is usually applied to the objects before they are passed through the window-to-viewport transformation. For a 2D object, the latter transformation is simply a combination of translation and scaling, the latter not necessarily uniform. An analogy of this transformation process based on traditional photography notions is to equate the world-clipping window with the camera settings and the variously sized prints that can be obtained from the resulting film image as possible viewports. Because the physical-device-based coordinates may not be portable from one device to another, a software abstraction layer known as normalized device coordinates is typically introduced for expressing viewports; it appears for example in the Graphical Kernel System (GKS) and later systems inspired from it. In 3D computer graphics, the viewport refers to the 2D rectangle used to project the 3D scene to the position of a virtual camera. A viewport is a region of the screen used to display a portion of the total image to be shown. In virtual desktops, the viewport is the visible portion of a 2D area which is larger than the visualization device. When viewing a document in a web browser, the viewport is the region of the browser window which contains the visible portion of the document. If the size of the viewport changes, for example as a result of the user resizing the browser window, then the browser may reflow the document (recalculate the locations and sizes of elements of the document). If the document is larger than the viewport, the user can control the portion of the document which is visible by scrolling in the viewport.

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  Capture the flag (cybersecurity)

  In computer security, Capture the Flag (CTF) is an exercise in which participants attempt to find text strings, called "flags", which are secretly hidden in purposefully vulnerable programs or websites. They can be used for both competitive or educational purposes. In two main variations of CTFs, participants either steal flags from other participants (attack/defense-style CTFs) or from organizers (jeopardy-style challenges). A mixed competition combines these two styles. Competitions can include hiding flags in hardware devices, they can be both online or in-person, and can be advanced or entry-level. The game is inspired by the traditional outdoor sport with the same name. CTFs are used as a tool for developing and refining cybersecurity skills, making them popular in both professional and academic settings. == Overview == Capture the Flag (CTF) is a cybersecurity competition that is used to test and develop computer security skills. It was first developed in 1996 at DEF CON, the largest cybersecurity conference in the United States which is hosted annually in Las Vegas, Nevada. The conference hosts a weekend of cybersecurity competitions, including their flagship CTF. Two popular CTF formats are jeopardy and attack-defense. Both formats test participant’s knowledge in cybersecurity, but differ in objective. In the Jeopardy format, participating teams must complete as many challenges of varying point values from a various categories such as cryptography, web exploitation, and reverse engineering. In the attack-defense format, competing teams must defend their vulnerable computer systems while attacking their opponent's systems. The exercise involves a diverse array of tasks, including exploitation and cracking passwords, but there is little evidence showing how these tasks translate into cybersecurity knowledge held by security experts. Recent research has shown that the Capture the Flag tasks mainly covered technical knowledge but lacked social topics like social engineering and awareness on cybersecurity. == Educational applications == CTFs have been shown to be an effective way to improve cybersecurity education through gamification. There are many examples of CTFs designed to teach cybersecurity skills to a wide variety of audiences, including PicoCTF, organized by the Carnegie Mellon CyLab, which is oriented towards high school students, and Arizona State University supported pwn.college. Beyond educational CTF events and resources, CTFs has been shown to be a highly effective way to instill cybersecurity concepts in the classroom. CTFs have been included in undergraduate computer science classes such as Introduction to Information Security at the National University of Singapore. CTFs are also popular in military academies. They are often included as part of the curriculum for cybersecurity courses, with the NSA organized Cyber Exercise culminating in a CTF competition between the US service academies and military colleges. == Competitions == Many CTF organizers register their competition with the CTFtime platform. This allows the tracking of the position of teams over time and across competitions. These include "Plaid Parliament of Pwning", "More Smoked Leet Chicken", "Dragon Sector", "dcua", "Eat, Sleep, Pwn, Repeat", "perfect blue", "organizers" and "Blue Water". Overall the "Plaid Parliament of Pwning" and "Dragon Sector" have both placed first worldwide the most with three times each. === Community competitions === Every year there are dozens of CTFs organized in a variety of formats. Many CTFs are associated with cybersecurity conferences such as DEF CON, various editions of SANS Institute's NetWars, HITCON, and BSides. The DEF CON CTF, an attack-defence CTF, is notable for being one of the oldest CTF competitions to exist, and has been variously referred to as the "World Series", "Superbowl", and "Olympics", of hacking by media outlets. The NYU Tandon hosted Cybersecurity Awareness Worldwide (CSAW) CTF is one of the largest open-entry competitions for students learning cybersecurity from around the world. In 2021, it hosted over 1200 teams during the qualification round. In addition to conference organized CTFs, many CTF clubs and teams organize CTF competitions. Many CTF clubs and teams are associated with universities, such as the CMU associated Plaid Parliament of Pwning, which hosts PlaidCTF, and the ASU associated Shellphish. Some community CTFs are online and open to all participants. The SANS Institute Holiday Hack Challenge and TryHackMe Advent of Cyber. === Government-supported competitions === Governmentally supported CTF competitions include the DARPA Cyber Grand Challenge and ENISA European Cybersecurity Challenge. In 2023, the US Space Force-sponsored Hack-a-Sat CTF competition included, for the first time, a live orbital satellite for participants to exploit. === Corporate-supported competitions === Corporations and other organizations sometimes use CTFs as a training or evaluation exercise, with benefits similar to those in educational settings. In addition to internal CTF exercises, some corporations such as Google and Tencent host publicly accessible CTF competitions. == In popular culture == In Mr. Robot, a qualification round for the DEF CON CTF competition is depicted in the season 3 opener "eps3.0_power-saver-mode.h". The logo for DEF CON can be seen in the background. In The Undeclared War, a CTF is depicted in the opening scene of the series as a recruitment exercise used by GCHQ. Go Go Squid!, a Chinese television series, is based around training for and competing in highly stylized CTF competitions .

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  List of security-focused operating systems

  This is a list of operating systems specifically focused on security. Similar concepts include security-evaluated operating systems that have achieved certification from an auditing organization, and trusted operating systems that provide sufficient support for multilevel security and evidence of correctness to meet a particular set of requirements. == Linux == === Android-based === GrapheneOS is a security-focused, Android-based mobile OS that uses a hardened kernel, C library, custom memory allocator (hardened_malloc), and a hardened Chromium-based browser named Vanadium. It also offers privacy/security features, such as Duress PIN/Password or disabling the USB-C port at a driver/hardware level to avoid exploitation. It deploys exploit mitigations such as hardware-based memory tagging, secure app spawning, restricted dynamic code loading, and more. === Debian-based === Linux Kodachi is a security-focused operating system. Tails is aimed at preserving privacy and anonymity. KickSecure is a security-focused Linux distribution that aims to be "hardened by default". It uses network hardening, kernel hardening, Strong Linux User Account Isolation, better randomness, root access restrictions, and app-specific hardening. Whonix is an anonymity focused operating system based on KickSecure. It consists of two virtual machines, And all communications are routed through Tor. === Other Linux distributions === Alpine Linux is designed to be small, simple, and secure. It uses musl, BusyBox, and OpenRC instead of the more commonly used glibc, GNU Core Utilities, and systemd. Owl - Openwall GNU/Linux, a security-enhanced Linux distribution for servers. Secureblue, a Fedora Silverblue based distro that uses a hardened kernel, custom memory allocator (hardened_malloc), Trivalent, a security-focused, Chromium-based browser inspired by Vanadium, and many other exploit mitigations. == BSD == OpenBSD is a Unix-like operating system that emphasizes portability, standardization, correctness, proactive security, and integrated cryptography. == Xen == Qubes OS aims to provide security through isolation. Isolation is provided through the use of virtualization technology. This allows the segmentation of applications into secure virtual machines.

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  Computer Dreams

  Computer Dreams is a 1988 film created by Digital Vision Entertainment and released by MPI Home Video. Written, produced and directed by Geoffrey de Valois and hosted by Amanda Pays, it consists primarily of clips and behind-the-scenes work of early computer graphics animation. Notably included are Luxo Jr. and Red's Dream, the first two short films from Pixar. The film is an hour long and features an electronic score by Music Fantastic. It was revised and re-released on DVD as The History of Computer Animation, Volume 2. It won the Winner Gold Special Jury Award at the 1989 Houston International Film Festival, and the 1989 Golden Decade Award from the US Film & Video Festival. Music used includes: Gail Lennon - Desire, Gail Lennon - Like A Dream, Shandi Sinnamon - Making It,

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  DigitaltMuseum

  DigitaltMuseum (lit. 'The Digital Museum') is a website database in Norwegian and Swedish for art, images and cultural history museums. The service was established in 2009 after a trial period. The database is developed and operated by KulturIT. KulturIT ANS was established by the Norwegian Museum of Cultural History and Maihaugen in consultation with the Norwegian Archive, Library and Museum Authority (ABM) in 2007. In 2015, the company underwent a corporate transformation and KulturIT AS was established on 12 February. The website has per 2025 around 2,548,022 images. Many of the images are in the public domain or under Creative Commons licenses and are being imported into Wikimedia Commons. The website's API was developed in 2012. == Institutions == As of 2025, there are 223 collaborating museums. == Mission == DigitaltMuseum aims to make the museums' collections accessible to all interested parties, regardless of time and place. The website aims to facilitate easy use of the collections through various methods including image searches, research, teaching and joint knowledge development. DigitaltMuseum contains collections from several hundred Norwegian and Swedish museums, totalling around five million objects. The website contains both historical images from the areas and themes covered by the museums, as well as images of artefacts from the collections. Parts of the collection have previously only been shown in the museums' exhibitions and books and have therefore rarely or never been shown to the public.

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  MY F.C.

  MY F.C. is a freemium app designed to organise and administer football teams. It is developed by MY F.C. Limited, a private company headquartered in Auckland, New Zealand. The app allows users to build a team by adding players and from there they can create trainings and matches, keep up with relevant news in the curated newsfeed, record statistics both individually and team based, follow the games live in the match-centre. The app also features integrated lineup builder with custom team kits. == History == Founders Sam Jenkins, Mike Simpson and Sam Jasper started MY F.C. in 2015 to help them "run their football lives". The app was launched on Android and iOS on 14 February 2017. == Accolades == MY F.C. won the first place prize at Bank of New Zealand Start-up Alley 2017 competition that aims to discover New Zealand start-ups who are doing innovative work and ready to establish themselves as long-term, sustainable businesses. The prize package included $15,000 and a trip to San Francisco.

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