AI Assistant Job Description

AI Assistant Job Description — independent reviews, comparisons, pricing and step-by-step guides on Aizhi.

  • Color space

    Color space

    A color space is a specific organization of colors. In combination with color profiling supported by various physical devices, it supports reproducible representations of color – whether such representation entails an analog or a digital representation. A color space may be arbitrary, i.e. with physically realized colors assigned to a set of physical color swatches with corresponding assigned color names (including discrete numbers in – for example – the Pantone collection), or structured with mathematical rigor (as with the NCS System, Adobe RGB and sRGB). A "color space" is a useful conceptual tool for understanding the color capabilities of a particular device or digital file. When trying to reproduce color on another device, color spaces can show whether shadow/highlight detail and color saturation can be retained, and by how much either will be compromised. A "color model" is an abstract mathematical model describing the way colors can be represented as tuples of numbers (e.g. triples in RGB or quadruples in CMYK); however, a color model with no associated mapping function to an absolute color space is a more or less arbitrary color system with no connection to any globally understood system of color interpretation. Adding a specific mapping function between a color model and a reference color space establishes within the reference color space a definite "footprint", known as a gamut, and for a given color model, this defines a color space. For example, Adobe RGB and sRGB are two different absolute color spaces, both based on the RGB color model. When defining a color space, the usual reference standard is the CIELAB or CIEXYZ color spaces, which were specifically designed to encompass all colors the average human can see. Since "color space" identifies a particular combination of the color model and the mapping function, the word is often used informally to identify a color model. However, even though identifying a color space automatically identifies the associated color model, this usage is incorrect in a strict sense. For example, although several specific color spaces are based on the RGB color model, there is no such thing as the singular RGB color space. == History == In 1802, Thomas Young postulated the existence of three types of photoreceptors (now known as cone cells) in the eye, each of which was sensitive to a particular range of visible light. Hermann von Helmholtz developed the Young–Helmholtz theory further in 1850: that the three types of cone photoreceptors could be classified as short-preferring (blue), middle-preferring (green), and long-preferring (red), according to their response to the wavelengths of light striking the retina. The relative strengths of the signals detected by the three types of cones are interpreted by the brain as a visible color. But it is not clear that they thought of colors as being points in color space. The color-space concept was likely due to Hermann Grassmann, who developed it in two stages. First, he developed the idea of vector space, which allowed the algebraic representation of geometric concepts in n-dimensional space. Fearnley-Sander (1979) describes Grassmann's foundation of linear algebra as follows: The definition of a linear space (vector space)... became widely known around 1920, when Hermann Weyl and others published formal definitions. In fact, such a definition had been given thirty years previously by Peano, who was thoroughly acquainted with Grassmann's mathematical work. Grassmann did not put down a formal definition—the language was not available—but there is no doubt that he had the concept. With this conceptual background, in 1853, Grassmann published a theory of how colors mix; it and its three color laws are still taught, as Grassmann's law. As noted first by Grassmann... the light set has the structure of a cone in the infinite-dimensional linear space. As a result, a quotient set (with respect to metamerism) of the light cone inherits the conical structure, which allows color to be represented as a convex cone in the 3- D linear space, which is referred to as the color cone. == Examples == Colors can be created in printing with color spaces based on the CMYK color model, using the subtractive primary colors of pigment (cyan, magenta, yellow, and key [black]). To create a three-dimensional representation of a given color space, we can assign the amount of magenta color to the representation's X axis, the amount of cyan to its Y axis, and the amount of yellow to its Z axis. The resulting 3-D space provides a unique position for every possible color that can be created by combining those three pigments. Colors can be created on computer monitors with color spaces based on the RGB color model, using the additive primary colors (red, green, and blue). A three-dimensional representation would assign each of the three colors to the X, Y, and Z axes. Colors generated on a given monitor will be limited by the reproduction medium, such as the phosphor (in a CRT monitor) or filters and backlight (LCD monitor). Another way of creating colors on a monitor is with an HSL or HSV color model, based on hue, saturation, brightness (value/lightness). With such a model, the variables are assigned to cylindrical coordinates. Many color spaces can be represented as three-dimensional values in this manner, but some have more, or fewer dimensions, and some, such as Pantone, cannot be represented in this way at all. == Conversion == Color space conversion is the translation of the representation of a color from one basis to another. This typically occurs in the context of converting an image that is represented in one color space to another color space, the goal being to make the translated image look as similar as possible to the original. == RGB density == The RGB color model is implemented in different ways, depending on the capabilities of the system used. The most common incarnation in general use as of 2021 is the 24-bit implementation, with 8 bits, or 256 discrete levels of color per channel. Any color space based on such a 24-bit RGB model is thus limited to a range of 256×256×256 ≈ 16.7 million colors. Some implementations use 16 bits per component for 48 bits total, resulting in the same gamut with a larger number of distinct colors. This is especially important when working with wide-gamut color spaces (where most of the more common colors are located relatively close together), or when a large number of digital filtering algorithms are used consecutively. The same principle applies for any color space based on the same color model, but implemented at different bit depths. == Lists == CIE 1931 XYZ color space was one of the first attempts to produce a color space based on measurements of human color perception (earlier efforts were by James Clerk Maxwell, König & Dieterici, and Abney at Imperial College) and it is the basis for almost all other color spaces. The CIERGB color space is a linearly-related companion of CIE XYZ. Additional derivatives of CIE XYZ include the CIELUV, CIEUVW, and CIELAB. === Generic === RGB uses additive color mixing, because it describes what kind of light needs to be emitted to produce a given color. RGB stores individual values for red, green and blue. RGBA is RGB with an additional channel, alpha, to indicate transparency. Common color spaces based on the RGB model include sRGB, Adobe RGB, ProPhoto RGB, scRGB, and CIE RGB. CMYK uses subtractive color mixing used in the printing process, because it describes what kind of inks need to be applied so the light reflected from the substrate and through the inks produces a given color. One starts with a white substrate (canvas, page, etc.), and uses ink to subtract color from white to create an image. CMYK stores ink values for cyan, magenta, yellow and black. There are many CMYK color spaces for different sets of inks, substrates, and press characteristics (which change the dot gain or transfer function for each ink and thus change the appearance). YIQ was formerly used in NTSC (North America, Japan and elsewhere) television broadcasts for historical reasons. This system stores a luma value roughly analogous to (and sometimes incorrectly identified as) luminance, along with two chroma values as approximate representations of the relative amounts of blue and red in the color. It is similar to the YUV scheme used in most video capture systems and in PAL (Australia, Europe, except France, which uses SECAM) television, except that the YIQ color space is rotated 33° with respect to the YUV color space and the color axes are swapped. The YDbDr scheme used by SECAM television is rotated in another way. YPbPr is a scaled version of YUV. It is most commonly seen in its digital form, YCbCr, used widely in video and image compression schemes such as MPEG and JPEG. xvYCC is an international digital video color space standard published by the IEC (IEC 61966-2-4). It is based on the ITU BT.601 and BT.709

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  • The Business Cloud

    The Business Cloud

    The Business Cloud is an API enabled self-service platform, developed by Domo, that provides an array of services like data connection and data visualization. == History == Domo, Inc. was founded in 2010 by Josh James who also co-founded the web analytics software company Omniture in 1996, which he took public in 2006. Domo launched the Domo Appstore, with 1,000 apps with social and mobile capabilities, in 2016. This appstore creates a network of business apps and an ecosystem of companies into a single, integrated business cloud. This decision came after Domo announced a $131 million round of funding from BlackRock. According to the company, the concept behind The Business Cloud is to connect smaller clouds relating to apps or other functional areas of a business into a single business cloud that allows self-service and other social features to customers. == Services == The Business Cloud is offered as a free service, claimed to be the world's first business cloud with Domo appstore as one of its core services. This free package includes all of the Domo's features and functionality including Domo platform, Domo Apps, visualizations, alerts, company directories, org charts, profiles, tasks and Domo Mobile. The Business Cloud allows customers to leverage their preferred cloud as well as on-premises software and monitor all aspects of their business in routine. The company is supported by a $500 million fund from investors all over the world.

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  • Fatpaint

    Fatpaint

    Fatpaint is a free, online (web-based) graphic design and desktop publishing software product and image editor. It includes integrated tools for creating page layout, painting, coloring and editing pictures and photos, drawing vector images, using dingbat vector clipart, writing rich text, creating ray traced 3D text logos and displaying graphics on products from Zazzle that can be purchased or sold. Fatpaint integrates desktop publishing features with brush painting, vector drawing and custom printed products in a single Flash application. It supports the use of a pressure-sensitive pen tablet and allows the user to add images by searching Wikimedia, Picasa, Flickr, Google, Yahoo, Bing, and Fatpaint's own collection of public domain images. The completed project can be saved on Fatpaint's server or locally. Fatpaint is affiliated with Zazzle, and owned by Mersica (also the developer of MakeWebVideo). == History == Fatpaint was launched in May 2010, after five years of development by Danish-Brazilian software developer, Mario Gomes Cavalcanti. After his departure, he was involved in the development of two of Denmark's most visited websites and is responsible for developing and running Fatpaint. Partner Kenneth Christensen mastered assembler and graphics programming on the Amiga computer. He spent years with Mario on the Amiga demo scene. According to the CEO, Kenneth helped him with the Linux servers while he handled the development, administration, promotion, video production, testing and content. The founder of Fatpaint also created "Make Web Video" (or Video Maker), a web application for creating video presentations for business, families and individuals. Video Maker allows users to give out the videos for personal or business use in a simple and affordable way. == Tools == Fatpaint provides free online logo maker, graphic design, vector drawing, photo editor and paint design in English, Danish and Portuguese. === Photo Editor === Users can change photo colours by manipulating R, G, B and A channels, saturation, contrast, brightness, hue, gamma, sharpness, tint and RGBA matrix. Users can also remove unwanted background and other artifacts by using the paint tools with added effects or by cloning. Multiple photos can be combined into a single image. Users can pick different blend modes and multiple layers. Users can also extract or change parts of the photo by cropping, resizing, skewing, bending, distorting and rotating in 2D and 3D. Hence, users' graphics can be printed on custom products that can be bought and sold for personal and business purposes. === Vector Drawing === Users can choose from 5000 vector images or draw vector graphics and art from scratch, using Fatpaint's vector shape creation tools. It also provides advanced symmetric vector transformation in 2D and 3D, as well as support for colour gradients. Multiple drawings can be combined to form complex vector shapes. Different blend modes and effects are supported. Vector drawings can be cropped, resized, skewed, distorted and rotated in 2D and 3D. Similar to Fatpaint's photo editor, vector graphics can be displayed on custom printed products that can be purchased and sold by the users for personal or business uses. === Paint Design === Fatpaint has full support for Pen Tablets and users can pick pen, brush, airbrush, paint bucket, clone painting, eraser and smudging tools. Fatpaint offers 8 palettes for painting, plus 13 palettes when clone painting. Fatpaint allows users to import or create their own brushes and thousands of free clipart drawings and brush sets that have dynamic brushes, effects and blend modes. Paintings can be combined in different layers and objects. Similarly, paintings can be cropped, resized, skewed, bent, distorted and rotated in 2D and 3D. Moreover, the graphics can be displayed on custom printed products, which users can buy or sell for personal or business uses. == Top Features == 3D Text objects: Create photorealistic, ray-traced 3D text logos and images. Image objects: Paint on multiple layers, import or create your own brushes, clone painting, and painting with effects. Vector drawing objects: Create vector images using multiple paths. Rich text objects with 981 fonts. Effect objects: Blur, Drop Shadow, Glow, Gradient Glow, Bevel, Gradient Bevel, Color manipulations. Page layout: Create multiple pages with a size limit of 64 megapixels, and arrange graphical objects on created pages (each object can be up to 7.8 megapixels in size). Nest graphical objects and transform them into 2D and 3D. Skew, bend and distort images and text. Design, purchase and sell custom-printed products. Fatpaint can send the projects to a printing company. Supports pressure-sensitive pen tablets. Fonts, public domain images, cliparts, and brushes. == Compatibility == Fatpaint supports Firefox, Google Chrome, Opera, and Internet Explorer with cookies and JavaScript enabled. Other browsers may not work correctly due to their support of Java Applets. Fatpaint requires Adobe's Flash 10 or newer and Sun's Java 6 or newer. It is recommended to run on Windows 7 and on Apple and Linux if Java has been disabled. The editor only works on Firefox on Linux. Java and Flash integration do not work on Linux and Apple browsers. WikiMedia search is disabled on those browsers. Fatpaint works best with at least 2 GB RAM and 1 GB video memory, as well as a decent graphics card.

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  • ImHex

    ImHex

    ImHex is a free cross-platform hex editor available on Windows, macOS, and Linux. ImHex is used by programmers and reverse engineers to view and analyze binary data. == History == The initial release of the project in November 2020, saw significant interest on GitHub. == Features == Features include: Hex editor Custom pattern matching and analysis scripting language Visual, node based data pre-processor Disassembler Running and visualizing of YARA rules Bookmarks Binary data diffing Additional Tools MSVC, Itanium, D and Rust name demangler ASCII table Calculator Base converter File utilities IEEE 754 floating point decoder Division by invariant multiplication calculator TCP/IP client and server Support for: Data importing and exporting ASCII string, Unicode string, numeric, hexadecimal and regular expressions search Byte manipulation File hashing Plug-ins

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  • Deluxe Paint

    Deluxe Paint

    Deluxe Paint, often referred to as DPaint, is a bitmap graphics editor created by Dan Silva for Electronic Arts and published for the then-new Amiga 1000 in November 1985. A series of updated versions followed, some of which were ported to other platforms. An MS-DOS release with support for the 256 color VGA standard became popular for creating pixel graphics in video games in the 1990s. Author Dan Silva previously worked on the Cut & Paste word processor (1984), also from Electronic Arts. == History == Deluxe Paint began as an in-house art development tool called Prism. As author Dan Silva added features to Prism, it was developed as a showcase product to coincide with the Amiga's debut in 1985. Upon release, it was quickly embraced by the Amiga community and became the de facto graphics (and later animation) editor for the platform. Amiga manufacturer Commodore International later commissioned EA to create version 4.5 AGA to bundle with the new Advanced Graphics Architecture chipset (A1200, A4000) capable Amigas. Version 5 was the last release after Commodore's bankruptcy in 1994. Early versions of Deluxe Paint were available in protected and non copy-protected versions, the latter retailing for a slightly higher price. The copy protection scheme was later dropped. Deluxe Paint was first in a series of products from the Electronic Arts Tools group—then later moved to the ICE (for Interactivity, Creativity, and Education) group—which included such Amiga programs as Deluxe Music Construction Set (preceded by Music Construction Set for the Apple II), Deluxe Video, and the Studio series of paint programs for the Mac. With the development of Deluxe Paint, EA introduced the ILBM and ANIM file format standards for graphics. While widely used on the Amiga, these formats never gained widespread end user acceptance on other platforms, but were heavily used by game development companies. Deluxe Paint was used by LucasArts to make graphics for their adventure games such as The Secret of Monkey Island, and the name of a particular filename used to store the main protagonist Guybrush Threepwood was probably at the origin of his peculiar name. One of the main artist developer of the game, Mark Ferrari, in an interview for The Making of Monkey Island 30th Anniversary Documentary remembers that "there was a pulldown menu in DPaint called brushes, so character sprites were referred to as brushes", and the male protagonist was simply "the guy.brush" until the artist Steve Purcell suggested to take the very name "Guybrush". The author Ron Gilbert remembers that the PC DOS version of the file was named "guybrush.bbm". == Versions == === Amiga === Deluxe Paint I was released in 1985. A major feature was animation by using color cycling. The Amiga natively supports indexed color, where a pixel's color value does not carry any RGB hue information but instead is an index to a color palette (a collection of unique color values). By adjusting the color value in the palette, all pixels with that palette value change simultaneously in the image or animation, creating cyclic movement in the image. In the Christmas demo files on the Deluxe Paint I disk, this kind of animation (which is toggled by pressing the tab key) is used to depict falling snowflakes, a blinking Christmas tree, and a roaring fire in the fireplace. In 1986, Deluxe Paint II was introduced, which added many convenient features such as pattern and gradient fill, which could be selected by right-clicking on a fill tool. An effects menu with e.g. perspective transformation was also added. The screen format could now be changed from a dedicated selection page. Deluxe Paint III appeared in 1989 and added support for Extra Halfbrite. New editing modes allowed one to stencil certain colors to protect them, so it is possible to e.g. paint a landscape from front to back, with the foreground protected by a stencil. A major new feature of Deluxe Paint III was the ability to create cel-like animation, and animbrushes (1MB of RAM is needed for animation). These let the user pick up a section of an animation as an "animbrush", which can then be placed onto the canvas while it animates. Deluxe Paint III was one of the first paint programs to support animbrushes. This is similar to copy and paste, except one can pick up more than one image. Deluxe Paint IV (introduced in 1991), which did not include Silva as the lead programmer, offered significant new features like non-bitplane-indexed Hold-and-Modify support for creating images with up to 4,096 colors. Animation support was improved by adding a light table, i.e. onion skinning, and AnimBrush morphing. The color mixer was now a HAM region at the bottom of the screen (instead of a floating window as before) and allowed mixing adjacent colors similar to a real palette. Deluxe Paint 4.5 AGA appeared the following year, addressing the stability issues and providing support for the new A1200 and A4000 AGA machines and a revamped screen mode interface. It appeared in both standalone and Commodore-bundled versions. The final release, Deluxe Paint V, in 1995, supported true 24-bit RGB images. However, using only the AGA native chipset, the 24-bit RGB color was only held in computer memory, the on-screen image was displayed in HAM8 (18-bit color). === Apple IIGS === DeluxePaint II for the Apple IIGS was developed by Brent Iverson and released in 1987. === MS-DOS === Deluxe Paint II for MS-DOS was released in 1988, It required MS-DOS 2.0 and 640 kB of RAM. It supports CGA, EGA, MCGA, VGA, Hercules and Tandy IBM PC-compatible graphic cards. Deluxe Paint II Enhanced was released in 1989, requiring MS-DOS 2.11 and 640 kB of RAM. It supports resolutions up to 800x600 pixels with 256 colors. Deluxe Paint II Enhanced 2.0, released in 1994, was the most successful MS-DOS version, and was compatible with PC Paintbrush PCX image files. The MS-DOS conversion was done by Brent Iverson with the enhanced features by Steve Shaw. It supports CGA, EGA, MCGA, VGA, Hercules, Tandy, and Amstrad video cards, as well as early Super VGA video cards enabling it to support up to 800 × 600 with 256 (from 262,144) colors and 1024 × 768 with 16 colors. The sister product Deluxe Paint Animation (only for 320×200 pixels and 256 colors) was widely used, especially in video game development. === Atari ST === Deluxe Paint ST was developed by ArtisTech Development, published by Electronic Arts, and was released in 1990. It supports the Atari STE 4096 color palette and animated graphics. Features advertised for the Atari ST version include 3D perspective, design your own fonts, mirror symmetry, multi-color airbrushing & animations, printing up to poster size, split-screen magnification with variable zoom, and working on animations (including multiple animations). == Workflow == "[" and "]" hotkeys step through the indexed palette, turning indexed-pixel-painting into a fast two-handed mouse+keys process, and the right mouse button paints with the background color. For example, transparency is obtained as simply as selecting a background color index (a single right click on the palette GUI to change). colors could be locked from editing by use of a stencil (a list of color indices whose pixels should not be altered in the image data) and simple color-cycling animations could be created using contiguous entries in the palette. This was easy to change the hue and tone of a section of the image by altering the corresponding colors in the palette. (The specific section needed to use a dedicated part of the palette for this technique to work.) Brushes can be cut from the background by using the box, freehand, or polygon selection tools. They can then be used in the same manner as any other brush or pen. This functionality is simpler to use than the "stamp" tool of Photoshop or Alpha Channels as provided in later programs. Brushes can be rotated and scaled, even in 3D. After a brush is selected, it appears attached to the mouse cursor, providing an exact preview of what will be drawn. This allows precise pixel positioning of brushes. Animations stored in IFF ANIM format are delta compressed making animations both smaller and faster to playback. == Reception == Compute! criticized the documentation of the first release of DeluxePaint as inadequate, but stated that "DeluxePaint is a visual arts program of immense scope and flexibility". In later versions the documentation was much improved; for instance DeluxePaint IV came with a 300-page manual. Deluxe Paint was a hit for EA. The main line of the series, particularly installments one to three, has won a total of at least nine awards from independent publications and organizations, including three Amiga-specific awards. Deluxe Paint III also won Commodore International's Enterprise and Vision award in 1990, becoming the first software to win the award, for what the company's judges believed to be best utilizing the Amiga's graphical capabilities. Deluxe Pai

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  • Qstack

    Qstack

    Qstack is a cloud management platform developed by GreenQloud, a cloud computing software company founded in Reykjavik, Iceland in February 2010. Qstack enables its users to manage multiple clouds and hybrid deployments through a single self-service portal. Qstack is in continuous development, incorporating developments within infrastructure, cloud, and application management solutions. The next release of Qstack is slated for June 2017. == History == In 2014 when Jonsi Stefansson joined as CEO, Greenqloud pivoted its operational focus to development of Qstack with beta launch in the fall of 2015, and began offering support, technical services and certifications for the software. == Features == Qstack is hypervisor agnostic (KVM, VMware, Hyper-V) and can manage private clouds in multiple locations as well as AWS, Azure, and EC2-compatible public clouds from its user interface. Qstack combines proprietary software with open-source components, and the company claims to harden them to meet the strict security standards often required by enterprise deployments. Qstack features VM templates for Windows, Linux, and other operating systems. It also features full SSH/RDP access to instances, virtual routers, firewalls, and load balancers built into the interface. == Reception == In a 2015 review, IDG columnist J. Peter Bruzzese praised Qstack’s user interface for its ease-of-use and clean look.

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  • Programming tool

    Programming tool

    A programming tool or software development tool is a computer program that is used to develop another computer program, usually by helping the developer manage computer files. For example, a programmer may use a tool called a source code editor to edit source code files, and then a compiler to convert the source code into machine code files. They may also use build tools that automatically package executable program and data files into shareable packages or install kits. A set of tools that are run one after another, with each tool feeding its output to the next one, is called a toolchain. An integrated development environment (IDE) integrates the function of several tools into a single program. Usually, an IDE provides a source code editor as well as other built-in or plug-in tools that help with compiling, debugging, and testing. Whether a program is considered a development tool can be subjective. Some programs, such as the GNU compiler collection, are used exclusively for software development while others, such as Notepad, are not meant specifically for development but are nevertheless often used for programming. == Categories == Notable categories of development tools: Assembler – Converts assembly language into machine code Bug tracking system – Software application that records software bugs Build automation – Building software via an unattended fashion Code review software – Activity where one or more people check a program's code Compiler – Software that translates code from one programming language to another Compiler-compiler – Program that generates parsers or compilers, a.k.a. parser generator Debugger – Software for debugging a computer program Decompiler – Program translating executable to source code Disassembler – Computer program to translate machine language into assembly language Documentation generator – Automation technology for creating software documentation Graphical user interface builder – Software development tool Linker – Program that combines intermediate build files into an executable file Loader – Loads executable files into memory and prepares them for execution by the CPU. Memory debugger – Software memory problem finder Minifier – Removal of unnecessary characters in code without changing its functionality Pretty-printer – Formatting to make code or markup easier to readPages displaying short descriptions of redirect targets Performance profiler – Measuring the time or resources used by a section of a computer program Static code analyzer – Analysis of computer programs without executing themPages displaying short descriptions of redirect targets Source code editor – Text editor specializing in software codePages displaying short descriptions of redirect targets Source code generation – Type of computer programmingPages displaying short descriptions of redirect targets Version control system – Stores and tracks versions of files

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  • Distributed Common Ground System

    Distributed Common Ground System

    The Distributed Common Ground System (DCGS) is a system which produces military intelligence for multiple branches of the American military. == DCGS Programs == DCGS-N - DCGS for the United States Navy DCGS-A - DCGS for the United States Army AF DCGS - DCGS for the United States Air Force DCGS-MC - DCGS for the United States Marine Corps DCGS-SOF - DCGS for the United States Special Operations Forces IS&A Support Center - DCGS-A Help Desk for the United States Army - https://dcgsahelp.max.gov/ - Max.gov sunset 15 December 2023 == Description == While in U.S. Air Force use, the system produces intelligence collected by the U-2 Dragonlady, RQ-4 Global Hawk, MQ-9 Reaper and MQ-1 Predator. The previous system of similar use was the Deployable Ground Station (DGS), which was first deployed in July 1994. Subsequent version of DGS were developed from 1995 through 2009. Although officially designated a "weapons system", it consists of computer hardware and software connected together in a computer network, devoted to processing and dissemination of information such as images. The 480th Intelligence, Surveillance and Reconnaissance Wing of the Air Combat Command operates and maintains the USAF system. A plan envisioned in 1998 was to develop interoperable systems for the Army and Navy, in addition to the Air Force. By 2006, version 10.6 was deployed by the Air Force, and a version known as DCGS-A was developed for the Army. After a 2010 report by General Michael T. Flynn, the program was intended to use cloud computing and be as easy to use as an iPad, which soldiers over a few years were commonly using. By April 2011, project manager Colonel Charles Wells announced version 3 of the Army system (code named "Griffin") was being deployed in the US war in Afghanistan. In January 2012, the United States Army Communications-Electronics Research, Development and Engineering Center hosted a meeting based on the DCGS-A early experience. It brought together technology providers in the hope of developing more integrated systems using cloud computing with open architectures, compared to previously specialized custom-built systems. A major contractor was Lockheed Martin, with computers supplied by Silicon Graphics International out of its Chippewa Falls, Wisconsin office. Software known as the Analyst's Notebook, originally developed by i2 Limited, was included in DCGS-A. IBM acquired i2 in 2011. Some US Army personnel reported using a Palantir Technologies product to improve their ability to predict locations of improvised explosive devices. An April 2012 report recommending further study after initial success. Palantir software was rated easy to use, but did not have the flexibility and wide number of data sources of DCGS-A. In July 2012, Congressman Duncan D. Hunter (from California, the state where Palantir is based) complained of US DoD obstacles to its wider use. Although a limited test in August 2011 by the Test and Evaluation Command had recommended deployment, operation problems of DCGS-A included the baseline system was "not operationally effective" with reboots on average about every 8 hours. A set of improvements was identified in November 2012. The press reported some of the shortcomings uncovered by General Genaro Dellarocco in the tests. The ambitious goal of integrating 473 data sources for 75 million reports proved to be challenging, after spending an estimated $2.3 billion on the Army system alone. In May 2013 Politico reported that Palantir lobbyists and some anonymous returning veterans continued to advocate the use of its software, despite its interoperability limits. In particular, members of special forces and US Marines were not required to use the official Army system. Similar stories appeared in other publications, with Army representatives (such as Major General Mary A. Legere) citing the limitations of various systems. Congressman Hunter was a member of the House Armed Services Committee which required a review of the program, after two other members of congress sent an open letter to Secretary of Defense Leon Panetta. The Senate Defense Appropriations Subcommittee included testimony from Army Chief of Staff General Ray Odierno. The 130th Engineer Brigade (United States) has found the system to be "unstable, slow, not friendly and a major hindrance to operations". The equivalent system for the United States Navy was planned for initial deployment by 2015, and within a shipboard network called Consolidated Afloat Networks and Enterprise Services (CANES) by 2016. Some early testing was announced in 2009 aboard the aircraft carrier USS Harry Truman. A portion of the software, a distributed data framework for the DCGS integration backbone (DIB) version 4, was submitted to an open-source software repository of the Codice Foundation on GitHub. The framework was new for DIB version 4, replacing the legacy DIB portal with an Ozone Widget Framework interface. It was written in the Java programming language. == DCGS-A == Distributed Common Ground System-Army (DCGS-A) is the United States Army's primary system to post data, process information, and disseminate Intelligence, Surveillance and Reconnaissance (ISR) information about the threat, weather, and terrain to echelons. DCGS-A provides commanders the ability to task battle-space sensors and receive intelligence information from multiple sources. === Promotion === An August 17, 2011, UPI article quoted i2 Chief Executive Officer Robert Griffin who commented on DCGS-A's best-of-breed approach to development. The article detailed the Army contracting with i2 for Analyst's Notebook software. "With its open architecture, Analyst's Notebook supports the Army's strategy to employ and integrate best-of-breed solutions from across the industry to meet the dynamic needs users face in the field on a daily basis." A February 1, 2012, article in the Army web page quoted Mark Kitz, DCGS-A technical director. DCGS-A "uses the latest in cloud technology to rapidly gather, collaborate and share intelligence data from multiple sources to deliver a common operating picture. DCGS-A is able to rapidly adapt to changing operational environments by leveraging an iterative development model and open architecture allowing for collaboration with multiple government, industry and academic partners." A July 2012 article in SIGNAL Magazine, monthly publication of the Armed Forces Communications and Electronics Association, promoted DCGS-A as taking advantage of technological environments with which young soldiers are familiar. The article quoted the DCGS-A program manager, Col. Charles Wells on the systems benefits. The article also included Lockheed Martin's DCGS-A program manager. The Milwaukee Journal Sentinel published an article May 4, 2012, about Wisconsin-located companies helping DCGS-A with cloud computing technology. The article promoted the speed when cloud computing processes intelligence and cost savings by analyzing data in the field. === The U.S. Army's 2011 Posture Statement === The U.S. Army released its 2011 Army Posture Statement March 2. It included a statement on DCGS-A: “The Distributed Common Ground System-Army (DCGS-A) is the Army's premier intelligence, surveillance, and reconnaissance (ISR) enterprise for the tasking of sensors, analysis and processing of data, exploitation of data, and dissemination of intelligence (TPED) across all echelons. It is the Army component of the larger Defense Intelligence Information Enterprise (DI2E) and interoperable with other Service DCGS programs. Under the DI2E framework, USD (I) hopes to provide COCOM Joint Intelligence Operations Centers (JIOCs) capabilities interoperable with DCGS-A through a Cloud/widget approach. DCGS-A connects tactical, operational, and theater-level commanders to hundreds of intelligence and intelligence-related data sources at all classification levels and allows them to focus efforts of the entire ISR community on their information requirements. === Comparisons === Some Ground Commanders who describe DCGS-A as "unwieldy and unreliable, hard to learn and difficult to use," supporting alternative software from Palantir Technologies. Palantir software supports small unit situational awareness, but is not sufficiently funded to support the broader role that DCGS-A fulfills. == Operators == 480th Intelligence, Surveillance and Reconnaissance Wing 9th Intelligence Squadron 13th Intelligence Squadron 548th Intelligence, Surveillance and Reconnaissance Group 548 Operational Support Squadron 48th Intelligence Squadron 101st Intelligence Squadron 113th Air Support Operations Squadron 127th Command and Control Squadron 161st Intelligence Squadron

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  • Halite AI Programming Competition

    Halite AI Programming Competition

    Halite is an open-source computer programming contest developed by the hedge fund/tech firm Two Sigma in partnership with a team at Cornell Tech. Programmers can see the game environment and learn everything they need to know about the game. Participants are asked to build bots in whichever language they choose to compete on a two-dimensional virtual battle field. == History == Benjamin Spector and Michael Truell created the first Halite competition in 2016, before partnering with Two Sigma later that year. === Halite I === Halite I asked participants to conquer territory on a grid. It launched in November 2016 and ended in February 2017. Halite I attracted about 1,500 players. === Halite II === Halite II was similar to Halite I, but with a space-war theme. It ran from October 2017 until January 2018. The second installment of the competition attracted about 6,000 individual players from more than 100 countries. Among the participants were professors, physicists and NASA engineers, as well as high school and university students. === Halite III === Halite III launched in mid-October 2018. It ran from October 2018 to January 2019, with an ocean themed playing field. Players were asked to collect and manage Halite, an energy resource. By the end of the competition, Halite III included more than 4000 players and 460 organizations. === Halite IV === Halite IV was hosted by Kaggle, and launched in mid-June 2020.

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  • Stevens Award

    Stevens Award

    The Stevens Award is a software engineering lecture award given by the Reengineering Forum, an industry association. The international Stevens Award was created to recognize outstanding contributions to the literature or practice of methods for software and systems development. The first award was given in 1995. The presentations focus on the current state of software methods and their direction for the future. This award lecture is named in memory of Wayne Stevens (1944-1993), a consultant, author, pioneer, and advocate of the practical application of software methods and tools. The Stevens Award and lecture is managed by the Reengineering Forum. The award was founded by International Workshop on Computer Aided Software Engineering (IWCASE), an international workshop association of users and developers of computer-aided software engineering (CASE) technology, which merged into The Reengineering Forum. Wayne Stevens was a charter member of the IWCASE executive board. == Recipients == 1995: Tony Wasserman 1996: David Harel 1997: Michael Jackson 1998: Thomas McCabe 1999: Tom DeMarco 2000: Gerald Weinberg 2001: Peter Chen 2002: Cordell Green 2003: Manny Lehman 2004: François Bodart 2005: Mary Shaw, Jim Highsmith 2006: Grady Booch 2007: Nicholas Zvegintzov 2008: Harry Sneed 2009: Larry Constantine 2010: Peter Aiken 2011: Jared Spool, Barry Boehm 2012: Philip Newcomb 2013: Jean-Luc Hainaut 2014: François Coallier 2015: Pierre Bourque

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  • Tail latency

    Tail latency

    Tail latency is a term used to describe the high-percentile response times seen in a system. This is usually measured at the 95th, 99th, or 99.9th percentile, not the average latency. In distributed systems, cloud computing, and large-scale web services, even a small number of slow requests can make the user experience and system performance much worse. Tail latency often happens because of things like resource contention, network variability, garbage collection pauses, and hardware heterogeneity. A major problem in system design is managing tail latency, because lowering average latency doesn't always make the worst-case performance better. To lessen its effects, people often use techniques like request hedging, replication, load balancing, and adaptive timeouts. In latency-sensitive applications like search engines, financial systems, and real-time services, where service-level objectives (SLOs) are often based on high-percentile latencies, it is especially important to understand and improve tail latency.

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  • Linux Trace Toolkit

    Linux Trace Toolkit

    The Linux Trace Toolkit (LTT) is a set of tools that is designed to log program execution details from a patched Linux kernel and then perform various analyses on them, using console-based and graphical tools. LTT has been mostly superseded by its successor LTTng (Linux Trace Toolkit Next Generation). LTT allows the user to see in-depth information about the processes that were running during the trace period, including when context switches occurred, how long the processes were blocked for, and how much time the processes spent executing vs. how much time the processes were blocked. The data is logged to a text file and various console-based and graphical (GTK+) tools are provided for interpreting that data. In order to do data collection, LTT requires a patched Linux kernel. The authors of LTT claim that the performance hit for a patched kernel compared to a regular kernel is minimal; Their testing has reportedly shown that this is less than 2.5% on a "normal use" system (measured using batches of kernel makes) and less than 5% on a file I/O intensive system (measured using batches of tar). == Usage == === Collecting trace data === Data collection is Started by: trace 15 foo This command will cause the LTT tracedaemon to do a trace that lasts for 15 seconds, writing trace data to foo.trace and process information from the /proc filesystem to foo.proc. The trace command is actually a script which runs the program tracedaemon with some common options. It is possible to run tracedaemon directly and in that case, the user can use a number of command-line options to control the data which is collected. For the complete list of options supported by tracedaemon, see the online manual page for tracedaemon. === Viewing the results === Viewing the results of a trace can be accomplished with: traceview foo This command will launch a graphical (GTK+) traceview tool that will read from foo.trace and foo.proc. This tool can show information in various interesting ways, including Event Graph, Process Analysis, and Raw Trace. The Event Graph is perhaps the most interesting view, showing the exact timing of events like page faults, interrupts, and context switches, in a simple graphical way. The traceview command is a wrapper for a program called tracevisualizer. For the complete list of options supported by tracevisualizer, see the online manual page for tracevisualizer.

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  • Algorithmic inference

    Algorithmic inference

    Algorithmic inference gathers new developments in the statistical inference methods made feasible by the powerful computing devices widely available to any data analyst. Cornerstones in this field are computational learning theory, granular computing, bioinformatics, and, long ago, structural probability (Fraser 1966). The main focus is on the algorithms which compute statistics rooting the study of a random phenomenon, along with the amount of data they must feed on to produce reliable results. This shifts the interest of mathematicians from the study of the distribution laws to the functional properties of the statistics, and the interest of computer scientists from the algorithms for processing data to the information they process. == The Fisher parametric inference problem == Concerning the identification of the parameters of a distribution law, the mature reader may recall lengthy disputes in the mid 20th century about the interpretation of their variability in terms of fiducial distribution (Fisher 1956), structural probabilities (Fraser 1966), priors/posteriors (Ramsey 1925), and so on. From an epistemology viewpoint, this entailed a companion dispute as to the nature of probability: is it a physical feature of phenomena to be described through random variables or a way of synthesizing data about a phenomenon? Opting for the latter, Fisher defines a fiducial distribution law of parameters of a given random variable that he deduces from a sample of its specifications. With this law he computes, for instance "the probability that μ (mean of a Gaussian variable – omeur note) is less than any assigned value, or the probability that it lies between any assigned values, or, in short, its probability distribution, in the light of the sample observed". == The classic solution == Fisher fought hard to defend the difference and superiority of his notion of parameter distribution in comparison to analogous notions, such as Bayes' posterior distribution, Fraser's constructive probability and Neyman's confidence intervals. For half a century, Neyman's confidence intervals won out for all practical purposes, crediting the phenomenological nature of probability. With this perspective, when you deal with a Gaussian variable, its mean μ is fixed by the physical features of the phenomenon you are observing, where the observations are random operators, hence the observed values are specifications of a random sample. Because of their randomness, you may compute from the sample specific intervals containing the fixed μ with a given probability that you denote confidence. === Example === Let X be a Gaussian variable with parameters μ {\displaystyle \mu } and σ 2 {\displaystyle \sigma ^{2}} and { X 1 , … , X m } {\displaystyle \{X_{1},\ldots ,X_{m}\}} a sample drawn from it. Working with statistics S μ = ∑ i = 1 m X i {\displaystyle S_{\mu }=\sum _{i=1}^{m}X_{i}} and S σ 2 = ∑ i = 1 m ( X i − X ¯ ) 2 , where X ¯ = S μ m {\displaystyle S_{\sigma ^{2}}=\sum _{i=1}^{m}(X_{i}-{\overline {X}})^{2},{\text{ where }}{\overline {X}}={\frac {S_{\mu }}{m}}} is the sample mean, we recognize that T = S μ − m μ S σ 2 m − 1 m = X ¯ − μ S σ 2 / ( m ( m − 1 ) ) {\displaystyle T={\frac {S_{\mu }-m\mu }{\sqrt {S_{\sigma ^{2}}}}}{\sqrt {\frac {m-1}{m}}}={\frac {{\overline {X}}-\mu }{\sqrt {S_{\sigma ^{2}}/(m(m-1))}}}} follows a Student's t distribution (Wilks 1962) with parameter (degrees of freedom) m − 1, so that f T ( t ) = Γ ( m / 2 ) Γ ( ( m − 1 ) / 2 ) 1 π ( m − 1 ) ( 1 + t 2 m − 1 ) m / 2 . {\displaystyle f_{T}(t)={\frac {\Gamma (m/2)}{\Gamma ((m-1)/2)}}{\frac {1}{\sqrt {\pi (m-1)}}}\left(1+{\frac {t^{2}}{m-1}}\right)^{m/2}.} Gauging T between two quantiles and inverting its expression as a function of μ {\displaystyle \mu } you obtain confidence intervals for μ {\displaystyle \mu } . With the sample specification: x = { 7.14 , 6.3 , 3.9 , 6.46 , 0.2 , 2.94 , 4.14 , 4.69 , 6.02 , 1.58 } {\displaystyle \mathbf {x} =\{7.14,6.3,3.9,6.46,0.2,2.94,4.14,4.69,6.02,1.58\}} having size m = 10, you compute the statistics s μ = 43.37 {\displaystyle s_{\mu }=43.37} and s σ 2 = 46.07 {\displaystyle s_{\sigma ^{2}}=46.07} , and obtain a 0.90 confidence interval for μ {\displaystyle \mu } with extremes (3.03, 5.65). == Inferring functions with the help of a computer == From a modeling perspective the entire dispute looks like a chicken-egg dilemma: either fixed data by first and probability distribution of their properties as a consequence, or fixed properties by first and probability distribution of the observed data as a corollary. The classic solution has one benefit and one drawback. The former was appreciated particularly back when people still did computations with sheet and pencil. Per se, the task of computing a Neyman confidence interval for the fixed parameter θ is hard: you do not know θ, but you look for disposing around it an interval with a possibly very low probability of failing. The analytical solution is allowed for a very limited number of theoretical cases. Vice versa a large variety of instances may be quickly solved in an approximate way via the central limit theorem in terms of confidence interval around a Gaussian distribution – that's the benefit. The drawback is that the central limit theorem is applicable when the sample size is sufficiently large. Therefore, it is less and less applicable with the sample involved in modern inference instances. The fault is not in the sample size on its own part. Rather, this size is not sufficiently large because of the complexity of the inference problem. With the availability of large computing facilities, scientists refocused from isolated parameters inference to complex functions inference, i.e. re sets of highly nested parameters identifying functions. In these cases we speak about learning of functions (in terms for instance of regression, neuro-fuzzy system or computational learning) on the basis of highly informative samples. A first effect of having a complex structure linking data is the reduction of the number of sample degrees of freedom, i.e. the burning of a part of sample points, so that the effective sample size to be considered in the central limit theorem is too small. Focusing on the sample size ensuring a limited learning error with a given confidence level, the consequence is that the lower bound on this size grows with complexity indices such as VC dimension or detail of a class to which the function we want to learn belongs. === Example === A sample of 1,000 independent bits is enough to ensure an absolute error of at most 0.081 on the estimation of the parameter p of the underlying Bernoulli variable with a confidence of at least 0.99. The same size cannot guarantee a threshold less than 0.088 with the same confidence 0.99 when the error is identified with the probability that a 20-year-old man living in New York does not fit the ranges of height, weight and waistline observed on 1,000 Big Apple inhabitants. The accuracy shortage occurs because both the VC dimension and the detail of the class of parallelepipeds, among which the one observed from the 1,000 inhabitants' ranges falls, are equal to 6. == The general inversion problem solving the Fisher question == With insufficiently large samples, the approach: fixed sample – random properties suggests inference procedures in three steps: === Definition === For a random variable and a sample drawn from it a compatible distribution is a distribution having the same sampling mechanism M X = ( Z , g θ ) {\displaystyle {\mathcal {M}}_{X}=(Z,g_{\boldsymbol {\theta }})} of X with a value θ {\displaystyle {\boldsymbol {\theta }}} of the random parameter Θ {\displaystyle \mathbf {\Theta } } derived from a master equation rooted on a well-behaved statistic s. === Example === You may find the distribution law of the Pareto parameters A and K as an implementation example of the population bootstrap method as in the figure on the left. Implementing the twisting argument method, you get the distribution law F M ( μ ) {\displaystyle F_{M}(\mu )} of the mean M of a Gaussian variable X on the basis of the statistic s M = ∑ i = 1 m x i {\textstyle s_{M}=\sum _{i=1}^{m}x_{i}} when Σ 2 {\displaystyle \Sigma ^{2}} is known to be equal to σ 2 {\displaystyle \sigma ^{2}} (Apolloni, Malchiodi & Gaito 2006). Its expression is: F M ( μ ) = Φ ( m μ − s M σ m ) , {\displaystyle F_{M}(\mu )=\Phi {\left({\frac {m\mu -s_{M}}{\sigma {\sqrt {m}}}}\right)},} shown in the figure on the right, where Φ {\displaystyle \Phi } is the cumulative distribution function of a standard normal distribution. Computing a confidence interval for M given its distribution function is straightforward: we need only find two quantiles (for instance δ / 2 {\displaystyle \delta /2} and 1 − δ / 2 {\displaystyle 1-\delta /2} quantiles in case we are interested in a confidence interval of level δ symmetric in the tail's probabilities) as indicated on the left in the diagram showing the behavior of

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  • StatCrunch

    StatCrunch

    StatCrunch is a web-based statistical software application from Pearson Education. StatCrunch was originally created for use in college statistics courses. As a full-featured statistics package, it is now also used for research and for other statistical analysis purposes. == History == American statistics professor Webster West created StatCrunch in 1997. Over the next 19 years West assisted by others added many more statistical procedures and graphing capabilities, and made user interface improvements. In 2005, West received two awards for StatCrunch: the CAUSEweb Resource of the Year Award and the MERLOT Classics Award. In 2013, the StatCrunch Java code was rewritten in JavaScript in order to avoid Java browser security problems, and so that it would run on iOS and Android. In 2015, new ways of importing data were added, including importing multi-page data directly from Wikipedia tables and other Web sources, and also importing with drag-and-drop for various data formats. In 2016, StatCrunch was acquired by Pearson Education, which had already been serving as the primary distributor of StatCrunch for several years. == Software == A StatCrunch license is included with many of Pearson's statistical textbooks. Because StatCrunch is a web application, it works on multiple platforms, including Windows, macOS, iOS, and Android. Data in StatCrunch is represented in a "data table" view, which is similar to a spreadsheet view, but unlike spreadsheets, the cells in a data table can only contain numbers or text. Formulas cannot be stored in these cells. There are many ways to import data into StatCrunch. Data can be typed directly into cells in the data table. Entire blocks of data may be cut-and-pasted into the data table. Text files (.csv, .txt, etc.) and Microsoft Excel files (.xls and .xlsx) can be drag-and-dropped into the data table. Data can be pulled into StatCrunch directly from Wikipedia tables or other Web tables, including multi-page tables. Data can be loaded directly from Google Drive and Dropbox. Shared data sets saved by other StatCrunch community users can be searched for by title or keyword and opened in a data table. Graphs, results, and reports created by StatCrunch can be shared with other users, in addition to the sharing of data sets. StatCrunch has a library of data transformation functions. StatCrunch can also recode and reorganize data. All data is stored in memory, and all processing happens on the client, so response is fast, even with large data sets. StatCrunch can interact with multiple graphs simultaneously. If a user selects a data point on one graph, then that same data point is highlighted on all other displayed graphs. In addition to standard statistical and graphing procedures, StatCrunch has a collection of about forty "applets" which illustrate statistical concepts interactively.

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  • Fabric Connect

    Fabric Connect

    Fabric Connect, in computer networking usage, is the name used by Extreme Networks to market an extended implementation of the IEEE 802.1aq and IEEE 802.1ah-2008 standards. The Fabric Connect technology was originally developed by the Enterprise Solutions R&D department within Nortel Networks. In 2009, Avaya, Inc acquired Nortel Networks Enterprise Business Solutions; this transaction included the Fabric Connect intellectual property together with all of the Ethernet Switching platforms that supported it. Subsequently, the Fabric Connect technology became part of the Extreme Networks portfolio by virtue of their 2017 purchase of the Avaya Networking business and assets. It was during the Avaya era that this technology was promoted as the lead element of the Virtual Enterprise Network Architecture (VENA). == Technologies == === Fabric Connect === Fabric Connect's provides network-wide, end-to-end, multi-layer virtualization. A network virtualization capability, based on an enhanced implementation of the IEEE 802.1aq Shortest Path Bridging (SPB) standard, Fabric Connect offers the ability to create a simplified network that can dynamically virtualize elements to efficiently provision and utilize resources, thus reducing the strain on the network and personnel. Extreme Networks base the Fabric Connect technology on the SPB standard, including support for RFC 6329, and have integrated IP Routing and IP Multicast support; this unified technology allows for the replacement of multiple conventional protocols such as Spanning Tree, RIP and/or OSPF, ECMP, and PIM. === Fabric Attach === An adjunct to the Fabric Connect technology, Fabric Attach allows network operators to extend network virtualization directly into conventional wiring closets (using existing non-Fabric Ethernet switches) and automate the provisioning of devices to their appropriate virtual network. This is particularly relevant for the mass of unattended network end-point that are now appearing, such as IP Phones, Wireless Access Points, and IP Cameras. Fabric Attach standardized protocols such as 802.1AB LLDP to exchange credentials and obtain provisioning information that allows "Client" Switches to be automatically re-configured on the fly with parameters that let Traffic Flows Map through to Fabric Connect Edge Switches (aka "Backbone Edge Bridge" in SPB definition) functioning as a Fabric Attach "Server" Switch. This method is described by an IETF "Internet Draft", pending further standardization activity. Fabric Attach is typically used to automate Wiring Closet connectivity, but has the potential to be extensible for use in the Data Center, with Virtual Machines being able to dynamically request VLAN/VSN (Virtual Service Network) assignment based upon application requirements. == Hardware products == === Virtual Services Platform 9000 Series === A range of modular chassis-based products, featuring a carrier-grade Linux operation system, and designed for high-performance deployment scenarios that need to scale to multiple terabits of switching capacity and support 10 and 40 gigabit Ethernet connections, and is designed eventually to support 100 gigabit Ethernet. === Virtual Services Platform 8000 Series === A compact form-factor platform delivering high-density 10/40 gigabit Ethernet connectivity, and targeted at mid-market through to mid-size enterprise core switch applications. === Virtual Services Platform 7000 Series === A range of high-end 10 gigabit Ethernet stackable switches that extend fabric-based networking to the data center top-of-rack. They support 40 gigabit Ethernet via the MDA Slot. === Virtual Services Platform 4000 Series === A range of high-end gigabit Ethernet stackable switches that extend Fabric-based networking to branch and metro locations. === Ethernet Routing Switch 5000 Series === A range of high-end gigabit Ethernet stackable switches that provides enterprise-class desktop features, including PoE, and offers 10 Gbit/s uplink connections. Each Switch supports up to 144 Gbit/s of virtual backplane capacity, delivering up to 1.152 Tbit/s for a system of eight, creating a virtual backplane through a stacking configuration. === Ethernet Routing Switch 4000 Series === A range of gigabit Ethernet stackable switches that provide enterprise-class desktop features, including PoE/PoE+, and offer 1/10 Gbit/s uplink connections. Each switch supports up to 48 Gbit/s of virtual backplane capacity, delivering up to 384 Gbit/s for a system of 8, creating a virtual backplane through a stacking configuration. === Ethernet Routing Switch 3500 Series === These entry-level gigabit Ethernet stackable switches provide enterprise-class desktop features, including PoE/PoE+, and 1 Gbit/s uplink connections.

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