The Data Management Association (DAMA), formerly known as the Data Administration Management Association, is a global not-for-profit organization which aims to advance concepts and practices about information management and data management. It describes itself as vendor-independent, all-volunteer organization, and has a membership consisting of technical and business professionals. Its international branch is called DAMA International (or DAMA-I), and DAMA also has various continental and national branches around the world. == History == The Data Management Association International was founded in 1980 in Los Angeles. Other early chapters were: San Francisco, Portland, Seattle, Minneapolis, New York, and Washington D.C. == Data Management Body of Knowledge == DAMA has published the Data Management Body of Knowledge (DMBOK), which contains suggestions on best practices and suggestions of a common vernacular for enterprise data management. The first edition (DAMA-DMBOK) was published on 2009 November 1, the second edition (DAMA-DMBOK2) was published on 2017 July 1., and the Revised second edition (DAMA-DMBOK2 rev.2) was published on 2019 March 19. DMBOK has been described by the authors as being an "equivalent" to the Project Management Body of Knowledge (PMBOK) and Business Analysis Body of Knowledge (BABOK). It encompasses topics such as data architecture, security, quality, modelling, governance, big data, data science, and more. DMBOK also includes the DAMA Data Wheel, an infographic which represents core data management practices. The center of the infographic is data governance, and the surrounding segments each represent a different aspect of data management: Data architecture Data modeling and design Data storage and operations Data security Data integration and interoperability Document management Content management Master data management Reference data and master data Data warehousing Metadata management Data quality Business intelligence Data science == Professional Accreditation == DAMA also provides a professional data management certification for individuals known as a Certified Data Management Professional (CDMP), which is based on the DMBOK as a study reference. There are four levels of certification based on career experience and exam results. The highest level, Fellow, requires 25 years of experience and nomination by DAMA members. It is an example of one of many competing certifications for data management professionals.
LanguageWare
LanguageWare is a natural language processing (NLP) technology developed by IBM, which allows applications to process natural language text. It comprises a set of Java libraries that provide a range of NLP functions: language identification, text segmentation/tokenization, normalization, entity and relationship extraction, and semantic analysis and disambiguation. The analysis engine uses a finite-state machine approach at multiple levels, which aids its performance characteristics while maintaining a reasonably small footprint. The behaviour of the system is driven by a set of configurable lexico-semantic resources which describe the characteristics and domain of the processed language. A default set of resources comes as part of LanguageWare and these describe the native language characteristics, such as morphology, and the basic vocabulary for the language. Supplemental resources have been created that capture additional vocabularies, terminologies, rules and grammars, which may be generic to the language or specific to one or more domains. A set of Eclipse-based customization tooling, LanguageWare Resource Workbench, is available on IBM's alphaWorks site, and allows domain knowledge to be compiled into these resources and thereby incorporated into the analysis process. LanguageWare can be deployed as a set of UIMA-compliant annotators, Eclipse plug-ins or Web Services.
Foreground detection
Foreground detection is one of the major tasks in the field of computer vision and image processing whose aim is to detect changes in image sequences. Background subtraction is any technique which allows an image's foreground to be extracted for further processing (object recognition etc.). Many applications do not need to know everything about the evolution of movement in a video sequence, but only require the information of changes in the scene, because an image's regions of interest are objects (humans, cars, text etc.) in its foreground. After the stage of image preprocessing (which may include image denoising, post processing like morphology etc.) object localisation is required which may make use of this technique. Foreground detection separates foreground from background based on these changes taking place in the foreground. It is a set of techniques that typically analyze video sequences recorded in real time with a stationary camera. == Description == All detection techniques are based on modelling the background of the image, i.e., setting the background and detecting which changes occur. Defining the background can be difficult when it contains shapes, shadows, and moving objects. In defining the background, it is assumed that stationary objects may vary in color and intensity over time. Scenarios in which these techniques apply tend to be very diverse. There can be highly variable sequences, such as images with different lighting, interiors, exteriors, quality, and noise. In addition to real-time processing, systems need to adapt to these changes. A foreground detection system should be able to: Develop a background model (estimate). Be robust to lighting changes, repetitive movements (leaves, waves, shadows), and long-term changes. == Background subtraction == Background subtraction is a widely used approach for detecting moving objects in videos from static cameras. The rationale in the approach is that of detecting the moving objects from the difference between the current frame and a reference frame, often called "background image", or "background model". Background subtraction is mostly done if the image in question is a part of a video stream. Background subtraction provides important cues for numerous applications in computer vision, for example surveillance tracking or human pose estimation. Background subtraction is generally based on a static background hypothesis which is often not applicable in real environments. With indoor scenes, reflections or animated images on screens lead to background changes. Similarly, due to wind, rain or illumination changes brought by weather, static backgrounds methods have difficulties with outdoor scenes. == Temporal average filter == The temporal average filter is a method that was proposed at the Velastin. This system estimates the background model from the median of all pixels of a number of previous images. The system uses a buffer with the pixel values of the last frames to update the median for each image. To model the background, the system examines all images in a given time period called training time. At this time, we only display images and will find the median, pixel by pixel, of all the plots in the background this time. After the training period for each new frame, each pixel value is compared with the input value of funds previously calculated. If the input pixel is within a threshold, the pixel is considered to match the background model and its value is included in the pixbuf. Otherwise, if the value is outside this threshold pixel is classified as foreground, and not included in the buffer. This method cannot be considered very efficient because they do not present a rigorous statistical basis and requires a buffer that has a high computational cost. == Conventional approaches == A robust background subtraction algorithm should be able to handle lighting changes, repetitive motions from clutter and long-term scene changes. The following analyses make use of the function of V(x,y,t) as a video sequence where t is the time dimension, x and y are the pixel location variables. e.g. V(1,2,3) is the pixel intensity at (1,2) pixel location of the image at t = 3 in the video sequence. === Using frame differencing === A motion detection algorithm begins with the segmentation part where foreground or moving objects are segmented from the background. The simplest way to implement this is to take an image as background and take the frames obtained at the time t, denoted by I(t) to compare with the background image denoted by B. Here using simple arithmetic calculations, we can segment out the objects simply by using image subtraction technique of computer vision meaning for each pixels in I(t), take the pixel value denoted by P[I(t)] and subtract it with the corresponding pixels at the same position on the background image denoted as P[B]. In mathematical equation, it is written as: P [ F ( t ) ] = P [ I ( t ) ] − P [ B ] {\displaystyle P[F(t)]=P[I(t)]-P[B]} The background is assumed to be the frame at time t. This difference image would only show some intensity for the pixel locations which have changed in the two frames. Though we have seemingly removed the background, this approach will only work for cases where all foreground pixels are moving, and all background pixels are static. A threshold "Threshold" is put on this difference image to improve the subtraction (see Image thresholding): | P [ F ( t ) ] − P [ F ( t + 1 ) ] | > T h r e s h o l d {\displaystyle |P[F(t)]-P[F(t+1)]|>\mathrm {Threshold} } This means that the difference image's pixels' intensities are 'thresholded' or filtered on the basis of value of Threshold. The accuracy of this approach is dependent on speed of movement in the scene. Faster movements may require higher thresholds. === Mean filter === For calculating the image containing only the background, a series of preceding images are averaged. For calculating the background image at the instant t: B ( x , y , t ) = 1 N ∑ i = 1 N V ( x , y , t − i ) {\displaystyle B(x,y,t)={1 \over N}\sum _{i=1}^{N}V(x,y,t-i)} where N is the number of preceding images taken for averaging. This averaging refers to averaging corresponding pixels in the given images. N would depend on the video speed (number of images per second in the video) and the amount of movement in the video. After calculating the background B(x,y,t) we can then subtract it from the image V(x,y,t) at time t = t and threshold it. Thus the foreground is: | V ( x , y , t ) − B ( x , y , t ) | > T h {\displaystyle |V(x,y,t)-B(x,y,t)|>\mathrm {Th} } where Th is a threshold value. Similarly, we can also use median instead of mean in the above calculation of B(x,y,t). Usage of global and time-independent thresholds (same Th value for all pixels in the image) may limit the accuracy of the above two approaches. === Running Gaussian average === For this method, Wren et al. propose fitting a Gaussian probabilistic density function (pdf) on the most recent n {\displaystyle n} frames. In order to avoid fitting the pdf from scratch at each new frame time t {\displaystyle t} , a running (or on-line cumulative) average is computed. The pdf of every pixel is characterized by mean μ t {\displaystyle \mu _{t}} and variance σ t 2 {\displaystyle \sigma _{t}^{2}} . The following is a possible initial condition (assuming that initially every pixel is background): μ 0 = I 0 {\displaystyle \mu _{0}=I_{0}} σ 0 2 = ⟨ some default value ⟩ {\displaystyle \sigma _{0}^{2}=\langle {\text{some default value}}\rangle } where I t {\displaystyle I_{t}} is the value of the pixel's intensity at time t {\displaystyle t} . In order to initialize variance, we can, for example, use the variance in x and y from a small window around each pixel. Note that background may change over time (e.g. due to illumination changes or non-static background objects). To accommodate for that change, at every frame t {\displaystyle t} , every pixel's mean and variance must be updated, as follows: μ t = ρ I t + ( 1 − ρ ) μ t − 1 {\displaystyle \mu _{t}=\rho I_{t}+(1-\rho )\mu _{t-1}} σ t 2 = d 2 ρ + ( 1 − ρ ) σ t − 1 2 {\displaystyle \sigma _{t}^{2}=d^{2}\rho +(1-\rho )\sigma _{t-1}^{2}} d = | ( I t − μ t ) | {\displaystyle d=|(I_{t}-\mu _{t})|} Where ρ {\displaystyle \rho } determines the size of the temporal window that is used to fit the pdf (usually ρ = 0.01 {\displaystyle \rho =0.01} ) and d {\displaystyle d} is the Euclidean distance between the mean and the value of the pixel. We can now classify a pixel as background if its current intensity lies within some confidence interval of its distribution's mean: | ( I t − μ t ) | σ t > k ⟶ foreground {\displaystyle {\frac {|(I_{t}-\mu _{t})|}{\sigma _{t}}}>k\longrightarrow {\text{foreground}}} | ( I t − μ t ) | σ t ≤ k ⟶ background {\displaystyle {\frac {|(I_{t}-\mu _{t})|}{\sigma _{t}}}\leq k\longrightarrow {\text{background}}} where the parameter k {\displaystyle k} is a free threshold (usuall
DBOS
DBOS (Formerly Database-Oriented Operating System, now just DBOS) is an open source durable workflow execution software library written for the Python, TypeScript, Java, and Go programming languages. DBOS arose from a joint open source project from MIT and Stanford, after a discussion between Michael Stonebraker and Matei Zaharia on how to scale and improve scheduling and performance of millions of Apache Spark tasks. Today it is a commercial company that offers an open source system to add durable computing to any software, built on concepts derived from the joint research project. == History == === 2020: Academic R&D Project === DBOS originated in 2020 as a joint open source project between MIT, Stanford, and Carnegie Mellon. The project explored the idea of operating system services built atop a distributed database - a database-oriented operating system meant to simplify and improve the scalability, security and resilience of large-scale distributed applications. The basic concept was to run a multi-node multi-core, transactional, highly-available distributed database, such as VoltDB, as the only application for a microkernel, and then to implement scheduling, messaging, file systems and other operating system services on top of the database. The architectural philosophy is described by this quote from the abstract of their initial preprint: All operating system state should be represented uniformly as database tables, and operations on this state should be made via queries from otherwise stateless tasks. This design makes it easy to scale and evolve the OS without whole-system refactoring, inspect and debug system state, upgrade components without downtime, manage decisions using machine learning, and implement sophisticated security features. A prototype was built with competitive performance to existing systems. ==
ISO/IEC 11801
International standard ISO/IEC 11801 Information technology — Generic cabling for customer premises specifies general-purpose telecommunication cabling systems (structured cabling) that are suitable for a wide range of applications (analog and ISDN telephony, various data communication standards, building control systems, factory automation). It is published by ISO/IEC JTC 1/SC 25/WG 3 of the International Organization for Standardization (ISO) and the International Electrotechnical Commission (IEC). It covers both balanced copper cabling and optical fibre cabling. The standard was designed for use within commercial premises that may consist of either a single building or of multiple buildings on a campus. It was optimized for premises that span up to 3 km, up to 1 km2 office space, with between 50 and 50,000 persons, but can also be applied for installations outside this range. A major revision was released in November 2017, unifying requirements for commercial, home and industrial networks. == Classes and categories == The standard defines several link/channel classes and cabling categories of twisted-pair copper interconnects, which differ in the maximum frequency for which a certain channel performance is required: Class A: Up to 100 kHz using Category 1 cable and connectors Class B: Up to 1 MHz using Category 2 cable and connectors Class C: Up to 16 MHz using Category 3 cable and connectors Class D: Up to 100 MHz using Category 5e cable and connectors Class E: Up to 250 MHz using Category 6 cable and connectors Class EA: Up to 500 MHz using category 6A cable and connectors (Amendments 1 and 2 to ISO/IEC 11801, 2nd Ed.) Class F: Up to 600 MHz using Category 7 cable and connectors Class FA: Up to 1 GHz (1000 MHz) using Category 7A cable and connectors (Amendments 1 and 2 to ISO/IEC 11801, 2nd Ed.) Class BCT-B: Up to 1 GHz (1000 MHz) using with coaxial cabling for BCT applications. (ISO/IEC 11801-1, Edition 1.0 2017-11) Class I: Up to 2 GHz (2000 MHz) using Category 8.1 cable and connectors (ISO/IEC 11801-1, Edition 1.0 2017-11) Class II: Up to 2 GHz (2000 MHz) using Category 8.2 cable and connectors (ISO/IEC 11801-1, Edition 1.0 2017-11) The standard link impedance is 100 Ω. (The older 1995 version of the standard also permitted 120 Ω and 150 Ω in Classes A−C, but this was removed from the 2002 edition.) The standard defines several classes of optical fiber interconnect: OM1: Multimode, 62.5 μm core; minimum modal bandwidth of 200 MHz·km at 850 nm OM2: Multimode, 50 μm core; minimum modal bandwidth of 500 MHz·km at 850 nm OM3: Multimode, 50 μm core; minimum modal bandwidth of 2000 MHz·km at 850 nm OM4: Multimode, 50 μm core; minimum modal bandwidth of 4700 MHz·km at 850 nm OM5: Multimode, 50 μm core; minimum modal bandwidth of 4700 MHz·km at 850 nm and 2470 MHz·km at 953 nm OS1: Single-mode, maximum attenuation 1 dB/km at 1310 and 1550 nm OS1a: Single-mode, maximum attenuation 1 dB/km at 1310, 1383, and 1550 nm OS2: Single-mode, maximum attenuation 0.4 dB/km at 1310, 1383, and 1550 nm Grandfathered === OM5 === OM5 fiber is designed for wideband applications using SWDM multiplexing of 4–16 carriers (40G=4λ×10G, 100G=4λ×25G, 400G=4×4λ×25G) in the 850–953 nm range. === Category 7 === Class F channel and Category 7 cable are backward compatible with Class D/Category 5e and Class E/Category 6. Class F features even stricter specifications for crosstalk and system noise than Class E. To achieve this, shielding was added for individual wire pairs and the cable as a whole. Unshielded cables rely on the quality of the twists to protect from EMI. This involves a tight twist and carefully controlled design. Cables with individual shielding per pair such as Category 7 rely mostly on the shield and therefore have pairs with longer twists. The Category 7 cable standard was ratified in 2002, and primarily introduced to support 10 gigabit Ethernet over 100 m of copper cabling. Like the earlier standards, it contains four twisted copper wire pairs rated for transmission frequencies of up to 600 MHz. However, in 2006, Category 6A was ratified for Ethernet to allow 10 Gbit/s while still using the conventional 8P8C connector. Care is required to avoid signal degradation by mixing cable and connectors not designed for that use, however similar. Most manufacturers of active equipment and network cards have chosen to support the 8P8C for their 10 gigabit Ethernet products on copper and not GG45, ARJ45, or TERA connectors as Class F would have originally called for. Therefore, the Category 6 specification was revised to Category 6A to permit this use; products therefore require a Class EA channel (ie, Cat 6A). As of 2019, some equipment has been introduced which has connectors supporting the Class F (Category 7) channel. Note, however, that Category 7 is not recognized by the TIA/EIA. === Category 7A === Class FA (Class F Augmented) channels and Category 7A cables, introduced by ISO 11801 Edition 2 Amendment 2 (2010), are defined at frequencies up to 1000 MHz. The intent of the Class FA was to possibly support the future 40 gigabit Ethernet: 40GBASE-T. Simulation results have shown that 40 gigabit Ethernet may be possible at 50 meters and 100 gigabit Ethernet at 15 meters. In 2007, researchers at Pennsylvania State University predicted that either 32 nm or 22 nm circuits would allow for 100 gigabit Ethernet at 100 meters. However, in 2016, the IEEE 802.3bq working group ratified the amendment 3 which defines 25GBASE-T and 40GBASE-T on Category 8 cabling specified to 2000 MHz. The Class FA therefore does not support 40G Ethernet. As of 2025, there is no equipment that has connectors supporting the Class FA (Category 7A) channel. Category 7A is not recognized in TIA/EIA. === Category 8 === Category 8 was ratified by the TR43 working group under ANSI/TIA 568-C.2-1. It is defined up to 2000 MHz and only for distances up to 30 m or 36 m, depending on the patch cords used. ISO/IEC JTC 1/SC 25/WG 3 developed the equivalent standard ISO/IEC 11801-1:2017/COR 1:2018, with two options: Class I channel (Category 8.1 cable): minimum cable design U/FTP or F/UTP, fully backward compatible and interoperable with Class EA (Category 6A) using 8P8C connectors; Class II channel (Category 8.2 cable): F/FTP or S/FTP minimum, interoperable with Class FA (Category 7A) using TERA or GG45. == Abbreviations for twisted pairs == Annex E, Acronyms for balanced cables, provides a system to specify the exact construction for both unshielded and shielded balanced twisted pair cables. It uses three letters—U for unshielded, S for braided shielding, and F for foil shielding—to form a two-part abbreviation in the form of xx/xTP, where the first part specifies the type of overall cable shielding, and the second part specifies shielding for individual cable elements. Common cable types include U/UTP (unshielded cable); U/FTP (individual pair shielding without the overall screen); F/UTP, S/UTP, or SF/UTP (overall screen without individual shielding); and F/FTP, S/FTP, or SF/FTP (overall screen with individual foil shielding). == 2017 edition == In November 2017, a new edition was released by ISO/IEC JTC 1/SC 25 "Interconnection of information technology equipment" subcommittee. It is a major revision of the standard which has unified several prior standards for commercial, home, and industrial networks, as well as data centers, and defines requirements for generic cabling and distributed building networks. The new series of standards replaces the former 11801 standard and includes six parts: == Versions == ISO/IEC 11801:1995 (Ed. 1) ISO/IEC 11801:2000 (Ed. 1.1) – Edition 1, Amendment 1 ISO/IEC 11801:2002 (Ed. 2) ISO/IEC 11801:2008 (Ed. 2.1) – Edition 2, Amendment 1 ISO/IEC 11801:2010 (Ed. 2.2) – Edition 2, Amendment 2 ISO/IEC 11801-1:2017, -1:2017/Cor 1:2018, -2:2017, -3:2017, -3:2017/Amd 1:2021, -3:2017/Cor 1:2018, -4:2017, -4:2017/Cor 1:2018, -5:2017, -5:2017/Cor 1:2018, -6:2017, -6:2017/Cor 1:2018 (As of September 2023, this set is current.)
Display list
A display list, also called a command list in Direct3D 12 and a command buffer in Vulkan, is a series of graphics commands or instructions that are run when the list is executed. Systems that make use of display list functionality are called retained mode systems, while systems that do not are as opposed to immediate mode systems. In OpenGL, display lists are useful to redraw the same geometry or apply a set of state changes multiple times. This benefit is also used with Direct3D 12's bundle command lists. In Direct3D 12 and Vulkan, display lists are regularly used for per-frame recording and execution. == Origins in vector displays == The vector monitors or calligraphic displays of the 1960s and 1970s used electron beam deflection to draw line segments, points, and sometimes curves directly on a CRT screen. Because the image would immediately fade, it needed to be redrawn many times a second (storage tube CRTs retained the image until blanked, but they were unsuitable for interactive graphics). To refresh the display, a dedicated CPU called a Display Processor or Display Processing Unit (DPU) was used, which had a memory buffer for a "display list", "display file", or "display program" containing line segment coordinates and other information. Advanced Display Processors also supported control flow instructions, which were useful for drawing repetitive graphics such as text, and some could perform coordinate transformations such as 3D projection. == Home computer display list functionality == One of the earliest systems with a true display list was the Atari 8-bit computers. The display list (actually called so in Atari terminology) is a series of instructions for ANTIC, the video co-processor used in these machines. This program, stored in the computer's memory and executed by ANTIC in real-time, can specify blank lines, any of six text modes and eight graphics modes, which sections of the screen can be horizontally or vertically fine-scrolled, and trigger Display List Interrupts (called raster interrupts or HBI on other systems). The Amstrad PCW family contains a Display List function called the 'Roller RAM'. This is a 512-byte RAM area consisting of 256 16-bit pointers in RAM, one for each line of the 720 × 256 pixel display. Each pointer identifies the location of 90 bytes of monochrome pixels that hold the line's 720 pixel states. The 90 bytes of 8 pixel states are spaced at 8-byte intervals, so there are 7 unused bytes between each byte of pixel data. This suits how the text-orientated PCW constructs a typical screen buffer in RAM, where the first character's 8 rows are stored in the first 8 bytes, the second character's rows in the next 8 bytes, and so on. The Roller RAM was implemented to speed up display scrolling as it would have been unacceptably slow for its 3.4 MHz Z80 to move up the 23 KB display buffer 'by hand' i.e. in software. The Roller RAM starting entry used at the beginning of a screen refresh is controlled by a Z80-writable I/O register. Therefore, the screen can be scrolled simply by changing this I/O register. Another system using a Display List-like feature in hardware is the Amiga, which, not coincidentally, was also designed by some of the same people who developed the custom hardware for the Atari 8-bit computers. Once directed to produce a display mode, it would continue to do so automatically for every following scan line. The computer also included a dedicated co-processor, called "Copper", which ran a simple program or 'Copper List' intended for modifying hardware registers in sync with the display. The Copper List instructions could direct the Copper to wait for the display to reach a specific position on the screen, and then change the contents of hardware registers. In effect, it was a processor dedicated to servicing raster interrupts. The Copper was used by Workbench to mix multiple display modes (multiple resolutions and color palettes on the monitor at the same time), and by numerous programs to create rainbow and gradient effects on the screen. The Amiga Copper was also capable of reconfiguring the sprite engine mid-frame, with only one scanline of delay. This allowed the Amiga to draw more than its 8 hardware sprites, so long as the additional sprites did not share scanlines (or the one scanline gap) with more than 7 other sprites. i.e., so long as at least one sprite had finished drawing, another sprite could be added below it on the screen. Additionally, the later 32-bit AGA chipset allowed the drawing of bigger sprites (more pixels per row) while retaining the same multiplexing. The Amiga also had dedicated block-shifter ("blitter") hardware, which could draw larger objects into a framebuffer. This was often used in place of, or in addition to, sprites. In more primitive systems, the results of a display list can be simulated, though at the cost of CPU-intensive writes to certain display modes, color control, or other visual effect registers in the video device, rather than a series of rendering commands executed by the device. Thus, one must create the displayed image using some other rendering process, either before or while the CPU-driven display generation executes. In many cases, the image is also modified or re-rendered between frames. The image is then displayed in various ways, depending on the exact way in which the CPU-driven display code is implemented. Examples of the results possible on these older machines requiring CPU-driven video include effects such as Commodore 64/128's FLI mode, or Rainbow Processing on the ZX Spectrum. == Usage in OpenGL == To delimit a display list, the glNewList and glEndList functions are used, and to execute the list, the glCallList function is used. Almost all rendering commands that occur between the function calls are stored in the display list. Commands that affect the client state are not stored in display lists. Display lists are named with an integer value, and creating a display list with the same name as one already created overrides the first. The glNewList function expects two arguments: an integer representing the name of the list, and an enumeration for the compilation mode. The two modes include GL_COMPILE_AND_EXECUTE, which compiles and immediately executes, and GL_COMPILE, which only compiles the list. Display lists enable the use of the retained mode rendering pattern, which is a system in which graphics commands are recorded (retained) to execute in succession at a later time. This is contrary to immediate mode, where graphics commands are immediately executed on client calls. == Usage in Direct3D 12 == Command lists are created using the ID3D12Device::CreateCommandList function. Command lists may be created in several types: direct, bundle, compute, copy, video decode, video process, and video encoding. Direct command lists specify that a command list the GPU can execute, and doesn't inherit any GPU state. Bundles, are best used for storing and executing small sets of commands any number of times. This is used differently than regular command lists, where commands stored in a command list are typically executed only once. Compute command lists are used for general computations, with a common use being calculating mipmaps. A copy command list is strictly for copying and the video decode and video process command lists are for video decoding and processing respectively. Upon creation, command lists are in the recording state. Command lists may be re-used by calling the ID3D12GraphicsCommandList::Reset function. After recording commands, the command list must be transitioned out of the recording state by calling ID3D12GraphicsCommandList::Close. The command list is then executed by calling ID3D12CommandQueue::ExecuteCommandLists.
Terrorism and social media
Terrorism, fear, and media are interconnected. Terrorists use the media to advertise their attacks and or messages, and the media uses terrorism events to further aid their ratings. Both promote unwarranted propaganda that instills mass amounts of public fear. The leader of al-Qaeda, Osama bin Laden, discussed the weaponization of media in a letter written after his organization committed the terrorist attacks on September 11, 2001. In that letter, bin Laden stated that fear was the deadliest weapon. He noted that the Western civilization has become obsessed with mass media, quickly consuming what will bring them fear. He further stated that societies are bringing this problem on their own people by giving media coverage an inherent power. In relation to one's need for media coverage, al-Qaeda and other militant Jihadi terrorist organizations can be classified as a far-right radical offshoot of mainstream mass media. The Jihad needs to conceptualize their martyrdom by leaving behind manifestos and live videos of their attacks; it is crucially important to them that their ill deeds are being covered by news media. The components the media looks for to deem the news "worthy" enough to publicize are categorized into ten qualities; terrorists usually exceed half in their attacks. These include: Immediacy, Conflict, Negativity, Human Interest, Photographability, Simple Story Lines, Topicality, Exclusivity, Reliability, and Local Interest. Historically, morality and profitability are two motivations which are not easily weighed when delivering news; recent news coverage has become far more motivated in making money for their parent corporation than serving as a defender of truth, doing true journalistic fact-finding, and shielding the public from news which is sensational, outright untrue, or politically-motivated propaganda. A study concerning the disparity in coverage of terrorist events took attacks from the ten‑year span of 2005–2015 and found that 136 episodes of terrorism occurred in the United States. LexisNexis Academic and CNN were the platforms used to measure the media coverage. It was found that out of other terrorist attacks showed on the news, one's with Muslim perpetrators received more than 357% coverage. In addition to this disparity, attacks also received more coverage when they were targeted at the government, had high fatality rates, and showed arrests being made. These findings were aligned with America's tendency to categorize Muslim people as a threat to national security. Thus, mass media coverage on terrorism is creating fake narratives and an absence of related coverage. For instance, the American public believes that crime rates have been on the rise which in fact they have been on an all-time low. Given that the media often covers crime almost immediately and frequently, suggests that people infer it happening all the time. In reference to the disparity in terror attacks, three attacks were seen to have the least media coverage of all the 136. The Sikh Temple massacre in Wisconsin which had 2.6% coverage, the Kansas synagogue killings which had 2.2%, and the Charleston Church deaths which only resulted in 5.1% coverage. The three events had commonalities worth mentioning in that they all had white perpetrators and were not directed at government intuitions (in fact all targeted minorities). The media's obsession with terror is making people fearful of the wrong things and not attentive enough to the issues that are radically unseen. Not only are minorities usually not the perpetrators of domestic terrorism, but they are common victims in mass casualties or proximal witnesses to the attacks. In an early 2000s study, 72 Israeli adults were measured pre and posttest for increased anxiety after being exposed to news broadcasts of terrorism attacks. The study found that the group exposed to the broadcasts without any treatment (preparation intervention) had heightened levels of anxiety compared to the group that received the treatment along with viewing the broadcast. Since preparatory intervention is not yet normalized, people in proximity to ongoing coverage of terror events are suffering from the lasting impacts of fear and anxiety. Preparatory Intervention, in this case, was conducted by a group facilitator who introduced a topic concerning terrorism in which participants were instructed to write down feelings to share with the group and later learn to cope with. A discourse of fear created by mass media presence, but false information is leading people to prepare for the wrong situations. In the early 2000s, police units circulated public schools flooding the idea of Stranger Danger into the minds of adolescents. Children and their parents cautiously separated from strangers while perpetrators in those families' social circles continued to offend under the radar. For myths are becoming common, precedent and real danger is buried beneath the surface. It is these implementations of fear that are falsifying the true narrative which for terrorism is a huge social problem but one that is not resolved through entertainment and mass media production. Mass media like news outlets and even social media platforms are contributing to the growing discourse of fear surrounding terrorism. Terrorism and social media refers to the use of social media platforms to radicalize and recruit violent and non-violent extremists. According to some researchers the convenience, affordability, and broad reach of social media platforms such as YouTube, Facebook and Twitter, terrorist groups and individuals have increasingly used social media to further their goals, recruit members, and spread their message. Attempts have been made by various governments and agencies to thwart the use of social media by terrorist organizations.Terror groups take to social media because it's cheap, accessible, and facilitates quick access to a lot of people. Social media allow them to engage with their networks. In the past, it wasn't so easy for these groups to engage with the people they wanted to whereas social media allows terrorists to release their messages right to their intended audience and interact with them in real time. "Spend some time following the account, and you realize that you're dealing with a real human being with real ideas- albeit boastful, hypocritical, violent ideas". Al- Qaeda has been noted as being as being one of the terror groups that uses social media the most extensively. "While almost all terrorist groups have websites, al qaeda [sic] is the first to fully exploit the internet. This reflects al-Qaeda's unique characteristics." Despite the risks of making statements, such as enabling governments to locate terror group leaders, terror leaders communicate regularly with video and audio messages which are posted on the website and disseminated on the internet. ISIS uses social media to their advantage when releasing threatening videos of beheadings. ISIS uses this tactic to scare normal people on social media. Similarly, Western domestic terrorists also use social media and technology to spread their ideas. == Traditional media == Many authors have proposed that media attention increases perceptions of risk of fear of terrorism and crime and relates to how much attention the person pays to the news. The relationship between terrorism and the media has long been noted. Terrorist organizations depend on the open media systems of democratic countries to further their goals and spread their messages. To garner publicity for their cause, terrorist organizations resort to acts of violence and aggression that deliberately target civilians. This method has proven to be effective in gathering attention: It cannot be denied that although terrorism has proved remarkably ineffective as the major weapon for taking down governments and capturing political power, it has been a remarkably successful means of publicizing a political cause and relaying the terrorist threat to a wider audience, particularly in the open and pluralistic countries of the West. When one says 'terrorism' in a democratic society, one also says 'media'. While a media organization may not support the goals of terrorist organizations, it is their job to report current events and issues. In the fiercely competitive media environment, when a terrorist attack occurs, media outlets scramble to cover the event. In doing so, the media help to further the message of terrorist organizations: To summarise briefly on the symbiotic nature of the relationship between terrorists and the media, the recent history of terrorism in many democratic countries vividly demonstrates that terrorists do thrive on the oxygen of publicity, and it is foolish to deny this. This does not mean that the established democratic media share the values of the terrorists. It does demonstrate, however, that the free media in an open society are particularly vulnerable to exploitation and manipulation by ru