A physical data model (or database design) is a representation of a data design as implemented, or intended to be implemented, in a database management system. In the lifecycle of a project it typically derives from a logical data model, though it may be reverse-engineered from a given database implementation. A complete physical data model will include all the database artifacts required to create relationships between tables or to achieve performance goals, such as indexes, constraint definitions, linking tables, partitioned tables or clusters. Analysts can usually use a physical data model to calculate storage estimates; it may include specific storage allocation details for a given database system. As of 2012 seven main databases dominate the commercial marketplace: Informix, Oracle, Postgres, SQL Server, Sybase, IBM Db2 and MySQL. Other RDBMS systems tend either to be legacy databases or used within academia such as universities or further education colleges. Physical data models for each implementation would differ significantly, not least due to underlying operating-system requirements that may sit underneath them. For example: SQL Server runs only on Microsoft Windows operating-systems (Starting with SQL Server 2017, SQL Server runs on Linux. It's the same SQL Server database engine, with many similar features and services regardless of your operating system), while Oracle and MySQL can run on Solaris, Linux and other UNIX-based operating-systems as well as on Windows. This means that the disk requirements, security requirements and many other aspects of a physical data model will be influenced by the RDBMS that a database administrator (or an organization) chooses to use. == Physical schema == Physical schema is a term used in data management to describe how data is to be represented and stored (files, indices, etc.) in secondary storage using a particular database management system (DBMS) (e.g., Oracle RDBMS, Sybase SQL Server, etc.). In the ANSI/SPARC Architecture three schema approach, the internal schema is the view of data that involved data management technology. This is as opposed to an external schema that reflects an individual's view of the data, or the conceptual schema that is the integration of a set of external schemas. The logical schema was the way data were represented to conform to the constraints of a particular approach to database management. At that time the choices were hierarchical and network. Describing the logical schema, however, still did not describe how physically data would be stored on disk drives. That is the domain of the physical schema. Now logical schemas describe data in terms of relational tables and columns, object-oriented classes, and XML tags. A single set of tables, for example, can be implemented in numerous ways, up to and including an architecture where table rows are maintained on computers in different countries.
Semantic interpretation
Semantic interpretation is an important component in dialog systems. It is related to natural language understanding, but mostly it refers to the last stage of understanding. The goal of interpretation is binding the user utterance to concept, or something the system can understand. Typically it is creating a database query based on user utterance.
Packed pixel
In packed pixel or chunky framebuffer organization, the bits defining each pixel are clustered and stored consecutively. For example, if there are 16 bits per pixel, each pixel is represented in two consecutive (contiguous) 8-bit bytes in the framebuffer. If there are 4 bits per pixel, each framebuffer byte defines two pixels, one in each nibble. The latter example is as opposed to storing a single 4-bit pixel in a byte, leaving 4 bits of the byte unused. If a pixel has more than one channel, the channels are interleaved when using packed pixel organization. Packed pixel displays were common on early microcomputer system that shared a single main memory for both the central processing unit (CPU) and display driver. In such systems, memory was normally accessed a byte at a time, so by packing the pixels, the display system could read out several pixels worth of data in a single read operation. Packed pixel is one of two major ways to organize graphics data in memory, the other being planar organization, where each pixel is made of individual bits stored in their own plane. For a 4-bit color value, memory would be organized as four screen-sized planes of one bit each and a single pixel's value built up by selecting the appropriate bit from each plane. Planar organization has the advantage that the data can be accessed in parallel, and is used when memory bandwidth is an issue.
Shader lamps
Shader lamps is a computer graphic technique used to change the appearance of physical objects. The still or moving objects are illuminated, using one or more video projectors, by static or animated texture or video stream. The method was invented at University of North Carolina at Chapel Hill by Ramesh Raskar, Greg Welch, Kok-lim Low and Deepak Bandyopadhyay in 1999 [1] as a follow on to Spatial Augmented Reality [2] also invented at University of North Carolina at Chapel Hill in 1998 by Ramesh Raskar, Greg Welch and Henry Fuchs. A 3D graphic rendering software is typically used to compute the deformation caused by the non perpendicular, non-planar or even complex projection surface. Complex objects (or aggregation of multiple simple objects) create self shadows that must be compensated by using several projectors. The objects are typically replaced by neutral color ones, the projection giving all its visual properties, thus the name shader lamps. The technique can be used to create a sense of invisibility, by rendering transparency. The object is illuminated not by a replacement of its own visual properties, but by the corresponding visual surface placed behind the object as seen from an arbitrary viewing point.
Sprite multiplexing
Sprite multiplexing is a computer graphics technique where additional sprites (moving images) can be drawn on the screen, beyond the nominal maximum. It is largely historical, applicable principally to older hardware, where limited resources (such as CPU speed and memory) meant only a relatively small number of sprites were supported. On the other hand, it is also true that without multiplexing, the sprite circuitry would be idle much of the time, and limited resources were wasted. == Description == The sprite multiplexing technique is based on the idea that while the hardware may only support a finite number of sprites, it is sometimes possible to re-use the same sprite "slots" more than once per frame or scan line. The program will first use the hardware to draw one or more sprite(s), as normal. Before the next frame (or next scanline) needs to be drawn, the software reprograms the hardware to display additional sprites, in other positions. For example, the Nintendo Entertainment System explicitly supports hardware sprite multiplexing, where it has 64 hardware sprites, but is only capable of rendering 8 of them per scanline. On the older Atari 2600, sprite multiplexing was not intentionally designed in, but programmers discovered they could reset the TIA graphics chip to draw additional sprites on the same scanline. The sprite multiplexing technique relies on the program being able to identify what part of the video screen is being drawn at the moment, or being triggered by the video hardware to run a subroutine at the crucial moment. The programmer must carefully consider the layout of the screen. If the video graphics hardware is not reprogrammed in time for the extra sprites to be displayed, they will not appear, or will be drawn incorrectly. Modern video graphics hardware typically does not use hardware sprites, since modern computer systems do not have the kind of limitations that sprite hardware is designed to circumvent. == Implementations == Systems that allow the programmer to employ the sprite multiplexing technique include: Atari 2600 Atari 8-bit computers Amiga Commodore 64 MSX Nintendo Entertainment System Super Nintendo Entertainment System Master System Sega Genesis/Mega Drive
Autonomous agent
An autonomous agent is an artificial intelligence (AI) system that can perform complex tasks independently. == Definitions == There are various definitions of autonomous agent. According to Brustoloni (1991): "Autonomous agents are systems capable of autonomous, purposeful action in the real world." According to Maes (1995): "Autonomous agents are computational systems that inhabit some complex dynamic environment, sense and act autonomously in this environment, and by doing so realize a set of goals or tasks for which they are designed." Franklin and Graesser (1997) review different definitions and propose their definition: "An autonomous agent is a system situated within and a part of an environment that senses that environment and acts on it, over time, in pursuit of its own agenda and so as to effect what it senses in the future." They explain that: "Humans and some animals are at the high end of being an agent, with multiple, conflicting drives, multiples senses, multiple possible actions, and complex sophisticated control structures. At the low end, with one or two senses, a single action, and an absurdly simple control structure we find a thermostat." == Agent appearance == Lee et al. (2015) post safety issue from how the combination of external appearance and internal autonomous agent have impact on human reaction about autonomous vehicles. Their study explores the human-like appearance agent and high level of autonomy are strongly correlated with social presence, intelligence, safety and trustworthiness. In specific, appearance impacts most on affective trust while autonomy impacts most on both affective and cognitive domain of trust where cognitive trust is characterized by knowledge-based factors and affective trust is largely emotion driven. == Applications == Agentic AI systems: Advanced AI agents that can scope out projects and complete them with necessary tools, representing a significant evolution from simple task-oriented systems. Internet of things (IoT) Integration: Autonomous agents increasingly interact with IoT devices, enabling smart home systems, industrial monitoring, and urban infrastructure management. Collaborative software development: Tools like Cognition AI's Devin aim to create autonomous software engineers capable of complex reasoning, planning, and completing engineering tasks requiring thousands of decisions. Enterprise automation: Business process automation platforms like Salesforce's Agentforce provide autonomous bots for various service functions. == Challenges and considerations == Uncertainty and incomplete information: Autonomous agents must make decisions with limited or uncertain information about their environment and future states. Integration complexity: Incorporating autonomous agents into existing systems and workflows can be technically challenging and resource-intensive. Scalability: As systems become more complex and more agents are used, maintaining coordination and avoiding conflicts becomes increasingly difficult. Trust: Research has shown the combination of external appearance and internal autonomous capabilities significantly impacts human reactions and trust. Lee et al. (2015) found that human-like appearance and high levels of autonomy are strongly correlated with social presence, intelligence, safety, and trustworthiness perceptions. Specifically, appearance impacts affective trust most significantly, while autonomy affects both affective and cognitive trust domains, where affective trust is emotionally driven, and cognitive trust is characterized by knowledge-based factors. Vulnerability to manipulation: Researchers from Harvard, MIT and other educational institutions found that AI agents could become vulnerable to manipulation and could perform detrimental actions in the process of being helpful. == Ethical and regulatory concerns == Accountability: Determining responsibility when autonomous agents make incorrect or harmful decisions remains a complex issue. Privacy and security: autonomous agents often require access to sensitive data, raising concerns about data protection and system security.
Score bug
A score bug is a digital on-screen graphic which is displayed in a broadcast of a sporting event, displaying the current score and other statistics. It is similar in function to a scoreboard, and is usually placed at either the top or lower third of the television screen. == History == The concept of a persistent score bug was devised by Sky Sports head David Hill, who was dissatisfied over having to wait to see what the score was after tuning into a football match in-progress. The score bug was introduced when Sky launched its coverage of the then newly-formed English Premier League in August 1992. Hill's boss repeatedly demanded that the graphic be removed, describing it as the "stupidest thing [he] had ever seen". Hill defied the boss's demands and kept the graphic in place. ITV introduced a score bug at the start of the 1993–94 football season, and the BBC introduced a score bug towards the end of 1993. The concept was introduced to the United States by ABC Sports and ESPN during coverage of the 1994 FIFA World Cup. Their justification for the graphic was to provide a location for a rotating series of sponsor logos, in order to allow matches to air without commercial interruption. With the acquisition of rights to the National Football League (NFL) by BSkyB's American sibling Fox (a fellow venture of Rupert Murdoch), Hill became the first president of Fox Sports. Under Hill's leadership, Fox introduced a version of the score bug branded as the "Fox Box", which was part of its inaugural season of NFL coverage in 1994. Variety criticized it as an "annoying see-through clock and score graphic" and expressed concern for people "who actually watched the beginning of the game and would rather have their screen clear of graphics". Hill even received a death threat from an irate viewer, with a specific emphasis on him being a "foreigner", but the score bug soon became a ubiquitous feature for American football broadcasts, along with almost all American sports broadcasts in the years that followed. Dick Ebersol of NBC Sports initially opposed the idea of a score bug, as he thought that fans would dislike seeing more graphics on the screen and would change the channel from blowout games if the score was constantly being displayed. Since the 2010s, the on-air design and positioning of some score bugs have been influenced by the needs of Internet video (especially when viewing an event on devices with smaller screens), including bugs noticeably larger than prior iterations designed with television viewing in mind, or designs primarily kept towards the bottom-center of the screen (easing the ability for the bug to remain visible when highlights are cropped for square videos posted on social media). == Details == Score bugs used in team sports typically include the names of both teams, an abbreviation of the team's name, and/or the team's logo; for individual sports, they include the names of individual competitors. In sports where a game clock or playing periods are used, those are generally also displayed as part of the score bug. Some broadcasts also include teams' win-loss records. In 2024, ESPN experimented with adding a persistent win probability meter to its bug in Major League Baseball, which was based on input from its statisticians. === Variations === In addition to the above information, score bugs in some sports include additional information: In baseball, score bugs display the current inning, number of outs, the pitch clock if applicable, and a graphic displaying which bases are occupied; and usually include names of the current pitcher and batter, the pitcher's pitch count, and the number of balls and strikes accrued by the batter. In basketball, score bugs generally include the shot clock, the number of fouls accrued by each team, and whether a team is in the bonus. In cricket, score bugs often take the form of larger dashboards across the bottom of the screen, displaying the current team up and their number of runs, wickets, and overs, a display showing the runs scored and number of balls faced by the current batting partnership, and statistics for the opposing team's bowler (including the number of wickets scored and runs given up). In American football, score bugs usually include the play clock and the down and distance of the current play; they also incorporate graphics indicating when a penalty flag has been thrown. In ice hockey, score bugs display when a penalty or power play is in effect, and often include the number of shots on goal accrued by each team. In golf, Fox popularized the display of a persistent leaderboard graphic in the bottom-right of the screen, usually displaying the top 5. ==== Racing ==== Telecasts of automobile races often include a score bug with the current positions of participants, statistics such as distance behind the leader, and the remaining distance or number of laps. In the mid-2010s, NASCAR broadcasters such as Fox began to transition from horizontal tickers to vertical leaderboards (also referred to as "pylons", in reference to the physical scoring pylons at). The CW differentiated itself by using a horizontal display that divides the field into multiple columns along the bottom of the screen.