AI Chat Vs Agent

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  Robotics

  Robotics is the interdisciplinary study and practice of the design, construction, operation, and use of robots. A roboticist is someone who specializes in robotics. Robotics usually combines four aspects of design work: a power source (e.g. a battery), mechanical construction, a control system (electrical circuits), and software (run by remote control or artificial intelligence). The goal of most robotics is to design machines that can assist humans in various fields, such as agriculture, construction, domestic work, food processing, inventory management, manufacturing, medicine, military, mining, space exploration, and transportation. Robots impact humans by displacing workers. Some expect this to occur at an increasing rate, leading to proposed solutions such as basic income. Robotics is itself a lucrative business that creates careers, especially for postgraduates. Roboticists often aim to create machines that seem to interface naturally with humans. The field is under active research and development, with areas of interest including robot kinematics and quantum robotics. == Design == Robotics usually combines four aspects of design work to create a robot: Power source: Potential energy sources include wired electricity, a battery, and/or petrol. Mechanical construction: A physical form or combination of forms is designed to functionally achieve tasks within a given range of environments. This can include locomotive elements such as wheels and caterpillar tracks, as well as hydraulic limbs and manipulators (e.g. hands). Control system: Electrical circuits (utilizing components such as diodes and transistors) are used to run software, govern motor movement, and read sensors. Software: A program is how a robot decides when or how to do something. Robotic programs can be run by remote control, artificial intelligence (AI), or a hybrid of the two. AI programming is an important part of robotic navigation and human–robot interaction. === Power source === Many different types of batteries can be used as a power source. Most are lead–acid batteries, which are safe and have relatively long shelf lives but are rather heavy compared to silver–cadmium batteries, which are much smaller in volume and much more expensive. Designing a battery-powered robot needs to take into account factors such as safety, cycle lifetime, and weight. Generators, often some type of internal combustion engine, can also be used, but are often mechanically complex and inefficient. Additionally, a tether could connect the robot to a power supply, saving weight and space, but requiring a cumbersome cable. Potential power sources include: Flywheel energy storage Hydraulics Nuclear Organic garbage (through anaerobic digestion) Pneumatics (compressed gases) Solar power === Mechanical construction === Actuators are the "muscles" of a robot, the parts which convert stored energy into movement. The most popular actuators are electric motors that rotate a wheel or gear and linear actuators that control factory robots. Most robots use electric motors—often brushed and brushless DC motors in portable robots or AC motors in industrial robots and computer numerical control machines—especially in systems with lighter loads and where the predominant form of motion is rotational. Meanwhile, linear actuators move in and out and often have quicker direction changes, particularly when large forces are needed, such as with industrial robotics. They are typically powered by oil or compressed air, but can also be powered by electricity, usually via a motor and a leadscrew. The mechanical rack and pinion is common. Recent alternatives to DC motors are piezoelectric motors, including ultrasonic motors, in which tiny piezoceramic elements vibrate many thousands of times per second, causing linear or rotary motion. One type uses the vibration of the piezo elements to step the motor in a circle or a straight line; another type uses the piezo elements to vibrate a nut or drive a screw. The advantages of these motors are nanometer resolution, speed, and force for their size. Series elastic actuation (SEA) relies on introducing intentional elasticity between the motor actuator and the load for robust force control. Due to the resultant lower reflected inertia, series elastic actuation improves safety during robot interactions or collisions. Further, it provides energy efficiency and shock absorption (mechanical filtering) while reducing excessive wear on the transmission and other components. This approach has successfully been employed in various robots, particularly advanced manufacturing robots and walking humanoid robots. The controller design of a series elastic actuator is most often performed within the passivity framework as it ensures the safety of interaction with unstructured environments. However, this framework suffers from stringent limitations imposed on the controller, which may impact performance. Pneumatic artificial muscles, also known as air muscles, are special tubes that expand (typically up to 42%) when air is forced inside them; they are used in some robot applications. Muscle wire, also known as shape memory alloy, is a material that contracts (under 5%) when electricity is applied; they have been used for some small robots. Electroactive polymers are a plastic material that can contract substantially (up to 380% activation strain) from electricity and have been used in the facial muscles and arms of humanoid robots, as well as to enable new robots to float, fly, swim or walk. Additionally, elastic carbon nanotubes are a promising experimental artificial muscle technology. The absence of defects in carbon nanotubes enables these filaments to deform elastically by several percent, with energy storage levels of perhaps 10 J/cm3 for metal nanotubes. Human biceps could be replaced with wire of this material measuring 8 millimetres (3⁄8 in) in diameter, feasibly allowing future robots to outperform humans. ==== Locomotion ==== Robots with only one or two wheel(s) can have advantages such as greater efficiency, reduced parts, and navigation through confined areas. A one-wheeled robot balances on a round ball; Carnegie Mellon University's Ballbot is the approximate height and width of a person. Several attempts have also been made to build spherical robots (also known as orb bots or ball bots), which move by spinning a weight inside the ball or rotating outer shells. Two-wheeled balancing robots generally use a gyroscope to detect how much a robot is falling and drive the wheels proportionally up to hundreds of times per second to counterbalance the fall, based on inverted pendulum dynamics. NASA's Robonaut has been mounted to a Segway for a similar effect. Most mobile robots have four wheels or continuous tracks. Six wheels can give better traction in outdoor terrain, while tracks provide even more grip. Tracked wheels are common for outdoor off-road robots, but are difficult to use indoors. A small number of skating robots have been developed, one of which is a multimodal walking and skating device with four legs and unpowered wheels. Several robots have been made that can walk on two legs, but not yet as reliably as a human. Many other robots have been built that walk on more than two legs, being significantly easier. Walking robots could be used for uneven terrains, providing a high degree of mobility and efficiency, but two-legged robots can currently only handle flat floors or perhaps stairs. Some approaches have included: The zero moment point (ZMP) is the algorithm used by robots such as Honda's ASIMO. The robot's onboard computer tries to keep the total inertial forces (the combination of Earth's gravity and the acceleration and deceleration of walking) exactly opposed by the floor reaction force (the force of the floor pushing back on the robot's foot). In this way, the two forces cancel out, leaving no moment (force causing the robot to rotate and fall over). Human observers note that this is not exactly how a human walks, with some describing ASIMO's walk as looking like it needs use the bathroom. ASIMO's walking algorithm utilizes some dynamic balancing, but requires a flat surface. Several robots, built in the 1980s by Marc Raibert at the MIT Leg Laboratory, successfully demonstrated very dynamic walking. Initially, a robot with only one leg, and a very small foot could stay upright simply by hopping. The movement is the same as that of a person on a pogo stick. As the robot falls to one side, it would jump slightly in that direction to catch itself. Soon, the algorithm was generalized to two and four legs. A bipedal robot was demonstrated running and even performing somersaults. A quadruped was also demonstrated which could trot, run, pace, and bound. A more advanced approach is a dynamic balancing algorithm, which constantly monitors the robot's motion and places the feet to maintain stability. This technique has been demonstrated by Anybots' Dexter robot (

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  Artificial intelligence industry in Italy

  The artificial intelligence industry in Italy is growing and supports industrial development. In 2024 it reached a new record, reaching 1.2 billion euros with a growth of +58% compared to 2023. While in 2025, the growth of artificial intelligence in the industrial application was even greater than in 2024 both in terms of value and application to industrial sectors. == History == The roots of AI research in Italy extend back to the 1970s, when Italian scholars began exploring automated reasoning, programming language semantics, and pattern recognition. Researchers such as those involved in early projects at the National Research Council and various universities laid the groundwork for subsequent academic and industrial developments in the field. During this period, the focus was predominantly on developing algorithms for automated theorem proving and building systems to reason about complex mathematical problems. This era witnessed the birth of methodologies that would later influence numerous AI subfields, from natural language processing (NLP) to robotics. === Institutional milestones and academic contributions === A turning point in the Italian AI landscape was the formation of the Italian Association for Artificial Intelligence (AIxIA) in 1988. Founded by academics, including Luigia Carlucci Aiello, the association established a platform for collaboration between universities, research centers, and industry. Led by Aiello, AIIA played a role in promoting research, organizing national conferences, and fostering international partnerships that connected Italy's AI community to global networks. At the same time, professors such as Roberto Navigli and numerous practitioners contributed to the advancement of AI in Italy. Navigli has worked in multilingual NLP, including the creation of BabelNet, and led the Minerva project. === Industrial AI === Over recent decades, numerous national and European initiatives supported by funding from programs such as the National Recovery and Resilience Plan (PNRR) have spurred the transition from theoretical research to practical applications. Industrial sectors including manufacturing, banking, and healthcare increasingly embraced AI-driven automation, while research institutions collaborated with industrial partners to deploy cutting-edge solutions. In recent years, Italy has also seen the establishment of specialized research centers and institutes aimed at bridging the gap between academic innovation and industrial application. These initiatives indicate a broader national commitment to integrating AI into the fabric of Italian industry. == Recent developments == === Emergence of generative AI === A landmark in Italy's modern AI evolution is the development of Minerva AI. Developed by the Sapienza NLP research group at Sapienza University of Rome and led by Professor Roberto Navigli, Minerva represents the first family of large language models (LLMs) trained from scratch with a primary focus on the Italian language. ==== Minerva 7B ==== The latest iteration, Minerva 7B, has 7 billion parameters and has been trained on an extensive corpus of over 1.5 trillion words. By using advanced instruction tuning techniques, Minerva 7B is able to produce highly accurate, coherent, and contextually sensitive responses addressing common issues such as hallucinations and inappropriate content generation. This breakthrough sets a benchmark for transparent, open-source AI development in the country. Minerva's development, carried out within the FAIR (Future Artificial Intelligence Research) project in collaboration with CINECA and supported by supercomputing resources like the Leonardo (supercomputer), aligns closely with Italy's cultural and linguistic heritage. === Establishment of AI4I === The recent establishment of the Istituto Italiano per l’Intelligenza Artificiale (AI4I) is part of Italy's strategy to improve its industrial competitiveness in AI. This dedicated institute aims to bridge the gap between research institutions and industrial enterprises; promote training and R&D support to nurture the next generation of Italian AI experts; and enhance national competitiveness. This initiative is expected to serve as a hub for applied AI research, driving innovations that are tailored to the specific needs of Italian industry and public administration. === Benefits of InvestAI === Italy's AI industry stands to benefit from the European InvestAI initiative, a plan unveiled at the recent AI Action Summit in Paris. InvestAI is an effort by the European Commission to mobilize €200 billion for AI investments, with a dedicated €20 billion fund earmarked for building AI gigafactories. These gigafactories are planned as large-scale hubs for training advanced, complex AI models using approximately 100,000 last-generation AI chips. For Italy, this investment presents several major opportunities: Access to State-of-the-Art Infrastructure: Italian companies, research institutions, and start-ups can leverage the gigafactories’ immense computational resources, enabling them to train highly sophisticated language models and other AI systems. Enhanced Competitiveness and Collaboration: With InvestAI's layered funding model where EU funds help de-risk private investments Italian firms can access capital more readily. This will bolster public–private partnerships and create a more dynamic AI ecosystem that spans from academic research to industrial applications. Alignment with National and Regional Initiatives: The Istituto Italiano per l’Intelligenza Artificiale (AI4I), based in Turin, is already recognized as a strategic asset by both Italy and the European Union. As the main recipient of InvestAI funds in Italy, AI4I will play a pivotal role in implementing these investments locally, fostering innovation in sectors like manufacturing, healthcare and aerospace. Commission President Ursula von der Leyen emphasized that InvestAI is designed to democratize AI innovation throughout Europe by ensuring that even smaller companies have access to high-performance computing power. For Italy, this means not only keeping pace with global leaders but also harnessing European-scale investments to transform its AI industry and drive economic growth.

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  Spatial computing

  Spatial computing refers to 3D human–computer interaction techniques that are perceived by users as taking place in the real world, in and around their bodies and physical environments, instead of constrained to and perceptually behind computer screens or in purely virtual worlds. This concept inverts the long-standing practice of teaching people to interact with computers in digital environments, and instead teaches computers to better understand and interact with people more naturally in the human world. This concept overlaps with and encompasses others including extended reality, augmented reality, mixed reality, natural user interface, contextual computing, affective computing, and ubiquitous computing. The usage for labeling and discussing these adjacent technologies is imprecise. Spatial computing devices include sensors—such as RGB cameras, depth cameras, 3D trackers, inertial measurement units, or other tools—to sense and track nearby human bodies (including hands, arms, eyes, legs, mouths) during ordinary interactions with people and computers in a 3D space. They further use computer vision to attempt to understand real world scenes, such as rooms, streets or stores, to read labels, to recognize objects, create 3D maps, and more. Quite often they also use extended reality and mixed reality to superimpose virtual 3D graphics and virtual 3D audio onto the human visual and auditory system as a way of providing information more naturally and contextually than traditional 2D screens. Spatial computing often refers to personal computing devices like headsets and headphones, but other human-computer interactions that leverage real-time spatial positioning for displays, like projection mapping or cave automatic virtual environment displays, can also be considered spatial computing if they leverage human-computer input for the participants. == History == The term "spatial computing" apparently originated in the field of GIS around 1985 or earlier to describe computations on large-scale geospatial information. Early examples of spatial computing in GIS include ArcInfo and its iterations, initially released in 1981, a part of ArcGIS along with ArcEditor, which together provide mapping, analysis, editing, and geoprocessing for geodatabases. This is somewhat related to the modern use, but on the scale of continents, cities, and neighborhoods. Modern spatial computing is more centered on the human scale of interaction, around the size of a living room or smaller. But it is not limited to that scale in the aggregate. In the early 1990s, as field of virtual reality was beginning to be commercialized beyond academic and military labs, a startup called Worldesign in Seattle used the term Spatial Computing to describe the interaction between individual people and 3D spaces, operating more at the human end of the scale than previous GIS examples may have contemplated. The company built a CAVE-like environment it called the Virtual Environment Theater, whose 3D experience was of a virtual flyover of the Giza Plateau, circa 3000 BC. Robert Jacobson, CEO of Worldesign, attributes the origins of the term to experiments at the Human Interface Technology Lab, at the University of Washington, under the direction of Thomas A. Furness III. Jacobson was a co-founder of that lab before spinning off this early VR startup. In 1997, an academic publication by T. Caelli, Peng Lam, and H. Bunke called "Spatial Computing: Issues in Vision, Multimedia and Visualization Technologies" introduced the term more broadly for academic audiences, focusing on a variety of topics such as image processing, dead reckoning navigation, object recognition, and visualizing spatial data. The specific term "spatial computing" was later referenced again in 2003 by Simon Greenwold, as "human interaction with a machine in which the machine retains and manipulates referents to real objects and spaces". MIT Media Lab alumnus John Underkoffler gave a TED talk in 2010 giving a live demo of the multi-screen, multi-user spatial computing systems being developed by Oblong Industries, which sought to bring to life the futuristic interfaces conceptualized by Underkoffler in the films Minority Report and Iron Man. Google Earth, initially released by Keyhole Inc. in 2001 and re-released by Google in 2005 can be considered a capable GIS and includes advanced geospatial tools and capabilities. == Notable instances of the use of spatial computing == In 2019, Microsoft HoloLens released a video outlining Airbus' partnership with Microsoft Azure to utilize the latter's mixed reality services for streamlining and improving the aircraft design process, as well as reducing the error in development. Airbus utilized the HoloLens 2 to this end, and the executive vice president of engineering claimed that their design process' validation phases were "hugely accelerated by 80 percent", as well as "strongly believe[d]" that up to 30% improvements in their industrial tasks could be attained with the HoloLens 2. During the presentational video, Airbus cited the maturity of Microsoft Azure services as "key" for their usage of the HoloLens 2. Also in 2019, the U.S. army partnered with Microsoft to produce a HoloLens based Integrated Visual Augmentation System (IVAS) to enhance infantry members by giving troops various abilities, including but not limited to using holographs to train, projecting 3D maps into their vision, and seeing through smoke and corners. Microsoft received tens of thousands of hours of feedback for their systems by 2021. Sergeant Marc Krugh at the time claimed that Microsoft's partnership has already caused the army to rethink some of its troops' operation strategy. == Products == === Apple Vision Pro === Apple announced Apple Vision Pro, a device it markets as a "spatial computer", on June 5, 2023. It includes several features such as Spatial Audio, two 4K micro-OLED displays, the Apple R1 chip and eye tracking, and released in the United States on February 2, 2024. In announcing the platform, Apple invoked its history of popularizing 2D graphical user interfaces that supplanted prior human-computer interface mechanisms such as the command line. Apple suggests the introduction of spatial computing as a new category of interactive device, on the same level of importance as the introduction of the 2D GUI. Apple Vision Pro runs on a new operating system called visionOS, which combines eye tracking, gesture recognition, and voice input to enable immersive interaction without physical controllers. The platform is aimed at productivity, entertainment, collaboration, and enterprise use cases. === Magic Leap === Magic Leap had also previously used the term “spatial computing” to describe its own devices. Its first headset, the Magic Leap 1, was released on August 8, 2018. Magic Leap’s technology enables the display of content into the real world using an optical see-through head-mounted display, which projects an overlay of a virtual world into the user’s field of view. This allows for an experience where the physical and digital worlds are perceived simultaneously. === Microsoft Hololens === On February 24, 2019, Microsoft released the HoloLens 2, which includes mixed reality tools and can generate interactable, manipulatable holograms in 3D space. The holograms in question can be related to a physical object or completely independent and free-floating. The Azure Spatial Anchors cloud service was released simultaneously, which gives the holograms capability to persist across time and many individuals' devices. === Meta Quest === The Meta Quest 3, a mixed reality gaming headset that includes spatial audio, two color cameras, and grants the ability to interact with virtual characters released on October 9, 2023, at a notably cheaper price than the Apple Vision Pro, but with reduced capabilities. === Snap Spectacles === Spectacles (product) are augmented reality glasses developed by Snap Inc.. The latest generation includes a 46-degree stereoscopic display, adjustable tint, and Snapdragon processors. Spectacles allow users to interact with a collection of augmented reality experiences designed for education, entertainment, and utility. Currently, the device is in the hands of selected developers and creators, as part of an experimental AR ecosystem focused on creativity, use case exploration and expression.

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  SIGMOD Edgar F. Codd Innovations Award

  The ACM SIGMOD Edgar F. Codd Innovations Award is a lifetime research achievement award given by the ACM Special Interest Group on Management of Data, at its yearly flagship conference (also called SIGMOD). According to its homepage, it is given "for innovative and highly significant contributions of enduring value to the development, understanding, or use of database systems and databases". The award has been given since 1992. Until 2003, this award was known as the “SIGMOD Innovations Award.” In 2004, SIGMOD, with the unanimous approval of ACM Council, decided to rename the award to honor Dr. E.F. (Ted) Codd (1923 – 2003) who invented the relational data model and was responsible for the significant development of the database field as a scientific discipline. == Recipients ==

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  Document mosaicing

  Document mosaicing is a process that stitches multiple, overlapping snapshot images of a document together to produce one large, high resolution composite. The document is slid under a stationary, over-the-desk camera by hand until all parts of the document are snapshotted by the camera's field of view. As the document slid under the camera, all motion of the document is coarsely tracked by the vision system. The document is periodically snapshotted such that the successive snapshots are overlap by about 50%. The system then finds the overlapped pairs and stitches them together repeatedly until all pairs are stitched together as one piece of document. The document mosaicing can be divided into four main processes. Tracking Feature detecting Correspondences establishing Images mosaicing. == Tracking (simple correlation process) == In this process, the motion of the document slid under the camera is coarsely tracked by the system. Tracking is performed by a process called simple correlation process. In the first frame of snapshots, a small patch is extracted from the center of the image as a correlation template. The correlation process is performed in the four times size of the patch area of the next frame. The motion of the paper is indicated by the peak in the correlation function. The peak in the correlation function indicates the motion of the paper. The template is resampled from this frame and the tracking continues until the template reaches the edge of the document. After the template reaches the edge of the document, another snapshot is taken and the tracking process performs repeatedly until the whole document is imaged. The snapshots are stored in an ordered list to facilitate pairing the overlapped images in later processes. == Feature detecting for efficient matching == Feature detection is the process of finding the transformation that aligns one image with another. There are two main approaches for feature detection. Feature-based approach : Motion parameters are estimated from point correspondences. This approach is suitable for the case that there is plenty supply of stable and detectable features. Featureless approach : When the motion between the two images is small, the motion parameters are estimated using optical flow. On the other hand, when the motion between the two images is large, the motion parameters are estimated using generalised cross-correlation. However, this approach requires a computationally expensive resources. Each image is segmented into a hierarchy of columns, lines, and words to match the organised sets of features across images. Skew angle estimation and columns, lines and words finding are the examples of feature detection operations. === Skew angle estimation === Firstly, the angle that the rows of text make with the image raster lines (skew angle) is estimated. It is assumed to lie in the range of ±20°. A small patch of text in the image is selected randomly and then rotated in the range of ±20° until the variance of the pixel intensities of the patch summed along the raster lines is maximised. To ensure that the found skew angle is accurate, the document mosaic system performs calculation at many image patches and derive the final estimation by finding the average of the individual angles weighted by the variance of the pixel intensities of each patch. === Columns, lines and words finding === In this operation, the de-skewed document is intuitively segmented into a hierarchy of columns, lines and words. The sensitivity to illumination and page coloration of the de-skewed document can be removed by applying a Sobel operator to the de-skewed image and thresholding the output to obtain the binary gradient, de-skewed image. The operation can be roughly separated into 3 steps: column segmentation, line segmentation and word segmentation. Columns are easily segmented from the binary gradient, de-skewed images by summing pixels vertically. Baselines of each row are segmented in the same way as the column segmentation process but horizontally. Finally, individual words are segmented by applying the vertical process at each segmented row. These segmentations are important because the document mosaic is created by matching the lower right corners of words in overlapping images pair. Moreover, the segmentation operation can organize the list of images in the context of a hierarchy of rows and column reliably. The segmentation operation involves a considerable amount of summing in the binary gradient, de-skewed images, which done by construct a matrix of partial sums whose elements are given by p i y = ∑ u = 1 i ∑ v = 1 j b u v {\displaystyle p_{iy}=\sum _{u=1}^{i}\sum _{v=1}^{j}b_{uv}} The matrix of partial sums is calculated in one pass through the binary gradient, de-skewed image. ∑ u = u 1 u 2 ∑ v = v 1 v 2 b u v = p u 2 v 2 + p u 1 v 1 − p u 1 v 2 − p u 2 v 1 {\displaystyle \sum _{u=u_{1}}^{u_{2}}\sum _{v=v_{1}}^{v_{2}}b_{uv}=p_{u_{2}v_{2}}+p_{u_{1}v_{1}}-p_{u_{1}v_{2}}-p_{u_{2}v_{1}}} == Correspondences establishing == The two images are now organized in hierarchy of linked lists in following structure : image=list of columns row=list of words column=list of row word=length (in pixels) At the bottom of the structure, the length of each word is recorded for establishing correspondence between two images to reduce to search only the corresponding structures for the groups of words with the matching lengths. === Seed match finding === A seed match finding is done by comparing each row in image1 with each row in image2. The two rows are then compared to each other by every word. If the length (in pixel) of the two words (one from image1 and one from image2) and their immediate neighbours agree with each other within a predefined tolerance threshold (5 pixels, for example), then they are assumed to match. The row of each image is assumed a match if there are three or more word matches between the two rows. The seed match finding operation is terminated when two pairs of consecutive row match are found. === Match list building === After finishing a seed match finding operation, the next process is to build the match list to generate the correspondences points of the two images. The process is done by searching the matching pairs of rows away from the seed row. == Images mosaicing == Given the list of corresponding points of the two images, finding the transformation of the overlapping portion of the images is the next process. Assuming a pinhole camera model, the transformation between pixels (u,v) of image 1 and pixels (u0, v0) of image 2 is demonstrated by a plane-to-plane projectivity. [ s u ′ s v ′ s ] = [ p 11 p 12 p 13 p 21 p 22 p 23 p 31 p 32 1 ] [ u v 1 ] E q .1 {\displaystyle \left[{\begin{array}{c}su'\\sv'\\s\end{array}}\right]=\left[{\begin{array}{ccc}p_{11}&p_{12}&p_{13}\\p_{21}&p_{22}&p_{23}\\p_{31}&p_{32}&1\end{array}}\right]\left[{\begin{array}{c}u\\v\\1\end{array}}\right]\qquad Eq.1} The parameters of the projectivity is found from four pairs of matching points. RANSAC regression technique is used to reject outlying matches and estimate the projectivity from the remaining good matches. The projectivity is fine-tuned using correlation at the corners of the overlapping portion to obtain four correspondences to sub-pixel accuracy. Therefore, image1 is then transformed into image2's coordinate system using Eq.1. The typical result of the process is shown in Figure 5. === Many images coping === Finally, the whole page composition is built up by mapping all the images into the coordinate system of an "anchor" image, which is normally the one nearest the page center. The transformations to the anchor frame are calculated by concatenating the pair-wise transformations found earlier. The raw document mosaic is shown in Figure 6. However, there might be a problem of non-consecutive images that are overlap. This problem can be solved by performing Hierarchical sub-mosaics. As shown in Figure 7, image1 and image2 are registered, as are image3 and image4, creating two sub-mosaics. These two sub-mosaics are later stitched together in another mosaicing process. == Applied areas == There are various areas that the technique of document mosaicing can be applied to such as : Text segmentation of images of documents Document Recognition Interaction with paper on the digital desk Video mosaics for virtual environments Image registration techniques == Relevant research papers == Huang, T.S.; Netravali, A.N. (1994). "Motion and structure from feature correspondences: A review". Proceedings of the IEEE. 82 (2): 252–268. doi:10.1109/5.265351. D.G. Lowe. [1] Perceptual Organization and Visual Recognition. Kluwer Academic Publishers, Boston, 1985. Irani, M.; Peleg, S. (1991). "Improving resolution by image registration". CVGIP: Graphical Models and Image Processing. 53 (3): 231–239. doi:10.1016/1049-9652(91)90045-L. S2CID 4834546. Shivakumara, P.; Kumar, G. Hemantha; Guru, D. S.; Nagabhushan, P. (2006). "

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  Car–Parrinello molecular dynamics

  Car–Parrinello molecular dynamics (CPMD) refers to either a method used in molecular dynamics (also known as the Car–Parrinello method) or the computational chemistry software package used to implement this method. The CPMD method is one of the major methods for calculating ab initio molecular dynamics (ab initio MD or AIMD). Ab initio molecular dynamics (AIMD) is a computational method that uses first principles through quantum mechanics to simulate the motion of atoms in a system. It is a type of molecular dynamics (MD) simulation that does not rely on empirical potentials or force fields to describe the interactions between atoms, but rather calculates these interactions entirely from the electronic structure of the system using quantum mechanics. In an ab initio MD simulation, the total energy of the system is calculated at each time step using density functional theory (DFT), Hartree-Fock (HF), or other electronic structure calculation methods. The forces acting on each atom are then determined from the gradient of the energy with respect to the atomic coordinates, and the equations of motion are solved to predict the trajectory of the atoms. AIMD permits chemical bond breaking and forming events to occur and accounts for electronic polarization effect. Therefore, Ab initio MD simulations can be used to study a wide range of phenomena, including the structural, thermodynamic, and dynamic properties of materials and chemical reactions. They are particularly useful for systems that are not well described by empirical potentials or force fields, such as systems with strong electronic correlation or systems with many degrees of freedom. However, ab initio MD simulations are computationally demanding and require significant computational resources. The CPMD method is related to the more common Born–Oppenheimer molecular dynamics (BOMD) method in that the quantum mechanical effect of the electrons is included in the calculation of energy and forces for the classical motion of the nuclei. CPMD and BOMD are different types of AIMD. However, whereas BOMD treats the electronic structure problem within the time-independent Schrödinger equation, CPMD explicitly includes the electrons as active degrees of freedom, via (fictitious) dynamical variables. The software is a parallelized plane wave / pseudopotential implementation of density functional theory, particularly designed for ab initio molecular dynamics. == Car–Parrinello method == The Car–Parrinello method is a type of molecular dynamics, usually employing periodic boundary conditions, planewave basis sets, and density functional theory, proposed by Roberto Car and Michele Parrinello in 1985 while working at SISSA, who were subsequently awarded the Dirac Medal by ICTP in 2009. In contrast to Born–Oppenheimer molecular dynamics wherein the nuclear (ions) degree of freedom are propagated using ionic forces which are calculated at each iteration by approximately solving the electronic problem with conventional matrix diagonalization methods, the Car–Parrinello method explicitly introduces the electronic degrees of freedom as (fictitious) dynamical variables, writing an extended Lagrangian for the system which leads to a system of coupled equations of motion for both ions and electrons. In this way, an explicit electronic minimization at each time step, as done in Born–Oppenheimer MD, is not needed: after an initial standard electronic minimization, the fictitious dynamics of the electrons keeps them on the electronic ground state corresponding to each new ionic configuration visited along the dynamics, thus yielding accurate ionic forces. In order to maintain this adiabaticity condition, it is necessary that the fictitious mass of the electrons is chosen small enough to avoid a significant energy transfer from the ionic to the electronic degrees of freedom. This small fictitious mass in turn requires that the equations of motion are integrated using a smaller time step than the one (1–10 fs) commonly used in Born–Oppenheimer molecular dynamics. Currently, the CPMD method can be applied to systems that consist of a few tens or hundreds of atoms and access timescales on the order of tens of picoseconds. == General approach == In CPMD the core electrons are usually described by a pseudopotential and the wavefunction of the valence electrons are approximated by a plane wave basis set. The ground state electronic density (for fixed nuclei) is calculated self-consistently, usually using the density functional theory method. Kohn-Sham equations are often used to calculate the electronic structure, where electronic orbitals are expanded in a plane-wave basis set. Then, using that density, forces on the nuclei can be computed, to update the trajectories (using, e.g. the Verlet integration algorithm). In addition, however, the coefficients used to obtain the electronic orbital functions can be treated as a set of extra spatial dimensions, and trajectories for the orbitals can be calculated in this context. == Fictitious dynamics == CPMD is an approximation of the Born–Oppenheimer MD (BOMD) method. In BOMD, the electrons' wave function must be minimized via matrix diagonalization at every step in the trajectory. CPMD uses fictitious dynamics to keep the electrons close to the ground state, preventing the need for a costly self-consistent iterative minimization at each time step. The fictitious dynamics relies on the use of a fictitious electron mass (usually in the range of 400 – 800 a.u.) to ensure that there is very little energy transfer from nuclei to electrons, i.e. to ensure adiabaticity. Any increase in the fictitious electron mass resulting in energy transfer would cause the system to leave the ground-state BOMD surface. === Lagrangian === L = 1 2 ( ∑ I n u c l e i M I R ˙ I 2 + μ ∑ i o r b i t a l s ∫ d r | ψ ˙ i ( r , t ) | 2 ) − E [ { ψ i } , { R I } ] + ∑ i j Λ i j ( ∫ d r ψ i ψ j − δ i j ) , {\displaystyle {\mathcal {L}}={\frac {1}{2}}\left(\sum _{I}^{\mathrm {nuclei} }\ M_{I}{\dot {\mathbf {R} }}_{I}^{2}+\mu \sum _{i}^{\mathrm {orbitals} }\int d\mathbf {r} \ |{\dot {\psi }}_{i}(\mathbf {r} ,t)|^{2}\right)-E\left[\{\psi _{i}\},\{\mathbf {R} _{I}\}\right]+\sum _{ij}\Lambda _{ij}\left(\int d\mathbf {r} \ \psi _{i}\psi _{j}-\delta _{ij}\right),} where μ {\displaystyle \mu } is the fictitious mass parameter; E[{ψi},{RI}] is the Kohn–Sham energy density functional, which outputs energy values when given Kohn–Sham orbitals and nuclear positions. === Orthogonality constraint === ∫ d r ψ i ∗ ( r , t ) ψ j ( r , t ) = δ i j , {\displaystyle \int d\mathbf {r} \ \psi _{i}^{}(\mathbf {r} ,t)\psi _{j}(\mathbf {r} ,t)=\delta _{ij},} where δij is the Kronecker delta. === Equations of motion === The equations of motion are obtained by finding the stationary point of the Lagrangian under variations of ψi and RI, with the orthogonality constraint. M I R ¨ I = − ∇ I E [ { ψ i } , { R I } ] {\displaystyle M_{I}{\ddot {\mathbf {R} }}_{I}=-\nabla _{I}\,E\left[\{\psi _{i}\},\{\mathbf {R} _{I}\}\right]} μ ψ ¨ i ( r , t ) = − δ E δ ψ i ∗ ( r , t ) + ∑ j Λ i j ψ j ( r , t ) , {\displaystyle \mu {\ddot {\psi }}_{i}(\mathbf {r} ,t)=-{\frac {\delta E}{\delta \psi _{i}^{}(\mathbf {r} ,t)}}+\sum _{j}\Lambda _{ij}\psi _{j}(\mathbf {r} ,t),} where Λij is a Lagrangian multiplier matrix to comply with the orthonormality constraint. === Born–Oppenheimer limit === In the formal limit where μ → 0, the equations of motion approach Born–Oppenheimer molecular dynamics. == Software packages == There are a number of software packages available for performing AIMD simulations. Some of the most widely used packages include: CP2K: an open-source software package for AIMD. Quantum Espresso: an open-source package for performing DFT calculations. It includes a module for AIMD. VASP: a commercial software package for performing DFT calculations. It includes a module for AIMD. Gaussian: a commercial software package that can perform AIMD. NWChem: an open-source software package for AIMD. LAMMPS: an open-source software package for performing classical and ab initio MD simulations. SIESTA: an open-source software package for AIMD. ORCA: a general-purpose quantum chemistry package. == Applications == Studying the behavior of water across different environments, such as near a hydrophobic graphene sheet. Investigating the structure and dynamics of liquid water at ambient temperature. Solving the heat transfer problems (heat conduction and thermal radiation), such as in Si/Ge superlattices. Probing the proton transfer along hydrogen-bonds in different environments, such as in 1D water chains inside carbon nanotubes. Evaluating the critical point of crystals, composites, and solid-state materials, such as aluminum. Predicting and modelling different phases and phase transitions, such as in the amorphous phase of the phase-change memory material GeSbTe. Studying the combustion of combustibles, such as lignite-water systems. Measuring th

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  SQL programming tool

  In the field of software, SQL programming tools provide platforms for database administrators (DBAs) and application developers to perform daily tasks efficiently and accurately. Database administrators and application developers often face constantly changing environments which they rarely completely control. Many changes result from new development projects or from modifications to existing code, which, when deployed to production, do not always produce the expected result. For organizations to better manage development projects and the teams that develop code, suppliers of SQL programming tools normally provide more than facility to the database administrator or application developer to aid in database management and in quality code-deployment practices. == Features == SQL programming tools may include the following features: === SQL editing === SQL editors allow users to edit and execute SQL statements. They may support the following features: cut, copy, paste, undo, redo, find (and replace), bookmarks block indent, print, save file, uppercase/lowercase keyword highlighting auto-completion access to frequently used files output of query result editing query-results committing and rolling-back transactions inside cut paper === Object browsing === Tools may display information about database objects relevant to developers or to database administrators. Users may: view object descriptions view object definitions (DDL) create database objects enable and disable triggers and constraints recompile valid or invalid objects query or edit tables and views Some tools also provide features to display dependencies among objects, and allow users to expand these dependent objects recursively (for example: packages may reference views, views generally reference tables, super/subtypes, and so on). === Session browsing === Database administrators and application developers can use session browsing tools to view the current activities of each user in the database. They can check the resource-usage of individual users, statistics information, locked objects and the current running SQL of each individual session. === User-security management === DBAs can create, edit, delete, disable or enable user-accounts in the database using security-management tools. DBAs can also assign roles, system privileges, object privileges, and storage-quotas to users. === Debugging === Some tools offer features for the debugging of stored procedures: step in, step over, step out, run until exception, breakpoints, view & set variables, view call stack, and so on. Users can debug any program-unit without making any modification to it, including triggers and object types. === Performance monitoring === Monitoring tools may show the database resources — usage summary, service time summary, recent activities, top sessions, session history or top SQL — in easy-to-read graphs. Database administrators can easily monitor the health of various components in the monitoring instance. Application developers may also make use of such tools to diagnose and correct application-performance problems as well as improve SQL server performance. === Test data === Test data generation tools can populate the database by realistic test data for server or client side testing purposes. Also, this kind of software can upload sample blob files to database.

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  Reservoir sampling

  Reservoir sampling is a family of randomized algorithms for choosing a simple random sample, without replacement, of k items from a population of unknown size n in a single pass over the items. The size of the population n is not known to the algorithm and is typically too large for all n items to fit into main memory. The population is revealed to the algorithm over time, and the algorithm cannot look back at previous items. At any point, the current state of the algorithm must permit extraction of a simple random sample without replacement of size k over the part of the population seen so far. == Motivation == Suppose we see a sequence of items, one at a time. We want to keep 10 items in memory, and we want them to be selected at random from the sequence. If we know the total number of items n and can access the items arbitrarily, then the solution is easy: select 10 distinct indices i between 1 and n with equal probability, and keep the i-th elements. The problem is that we do not always know the exact n in advance. == Simple: Algorithm R == A simple and popular but slow algorithm, Algorithm R, was created by Jeffrey Vitter. Initialize an array R {\displaystyle R} indexed from 1 {\displaystyle 1} to k {\displaystyle k} , containing the first k items of the input x 1 , . . . , x k {\displaystyle x_{1},...,x_{k}} . This is the reservoir. For each new input x i {\displaystyle x_{i}} , generate a random number j uniformly in { 1 , . . . , i } {\displaystyle \{1,...,i\}} . If j ∈ { 1 , . . . , k } {\displaystyle j\in \{1,...,k\}} , then set R [ j ] := x i . {\displaystyle R[j]:=x_{i}.} Otherwise, discard x i {\displaystyle x_{i}} . Return R {\displaystyle R} after all inputs are processed. This algorithm works by induction on i ≥ k {\displaystyle i\geq k} . While conceptually simple and easy to understand, this algorithm needs to generate a random number for each item of the input, including the items that are discarded. The algorithm's asymptotic running time is thus O ( n ) {\displaystyle O(n)} . Generating this amount of randomness and the linear run time causes the algorithm to be unnecessarily slow if the input population is large. This is Algorithm R, implemented as follows: == Optimal: Algorithm L == If we generate n {\displaystyle n} random numbers u 1 , . . . , u n ∼ U [ 0 , 1 ] {\displaystyle u_{1},...,u_{n}\sim U[0,1]} independently, then the indices of the smallest k {\displaystyle k} of them is a uniform sample of the k {\displaystyle k} -subsets of { 1 , . . . , n } {\displaystyle \{1,...,n\}} . The process can be done without knowing n {\displaystyle n} : Keep the smallest k {\displaystyle k} of u 1 , . . . , u i {\displaystyle u_{1},...,u_{i}} that has been seen so far, as well as w i {\displaystyle w_{i}} , the index of the largest among them. For each new u i + 1 {\displaystyle u_{i+1}} , compare it with u w i {\displaystyle u_{w_{i}}} . If u i + 1 < u w i {\displaystyle u_{i+1} Read more →

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  Belief–desire–intention model

  For popular psychology, the belief–desire–intention (BDI) model of human practical reasoning was developed by Michael Bratman as a way of explaining future-directed intention. BDI is fundamentally reliant on folk psychology (the 'theory theory'), which is the notion that our mental models of the world are theories. It was used as a basis for developing the belief–desire–intention software model. == Applications == BDI was part of the inspiration behind the BDI software architecture, which Bratman was also involved in developing. Here, the notion of intention was seen as a way of limiting time spent on deliberating about what to do, by eliminating choices inconsistent with current intentions. BDI has also aroused some interest in psychology. BDI formed the basis for a computational model of childlike reasoning CRIBB.

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  Scriptella

  Scriptella is an open source extract transform load (ETL) and script execution tool written in Java. It allows the use of SQL or another scripting language suitable for the data source to perform required transformations. Scriptella does not offer any graphical user interface. == Typical use == Database migration. Database creation/update scripts. Cross-database ETL operations, import/export. Alternative for Ant task. Automated database schema upgrade. == Features == Simple XML syntax for scripts. Add dynamics to your existing SQL scripts by creating a thin wrapper XML file: Support for multiple datasources (or multiple connections to a single database) in an ETL file. Support for many useful JDBC features, e.g. parameters in SQL including file blobs and JDBC escaping. Performance and low memory usage are one of the primary goals. Support for evaluated expressions and properties (JEXL syntax) Support for cross-database ETL scripts by using elements Transactional execution Error handling via elements Conditional scripts/queries execution (similar to Ant if/unless attributes but more powerful) Easy-to-Use as a standalone tool or Ant task, without deployment or installation. Easy-To-Run ETL files directly from Java code. Built-in adapters for popular databases for a tight integration. Support for any database with JDBC/ODBC compliant driver. Service Provider Interface (SPI) for interoperability with non-JDBC DataSources and integration with scripting languages. Out of the box support for JSR 223 (Scripting for the Java Platform) compatible languages. Built-in CSV, TEXT, XML, LDAP, Lucene, Velocity, JEXL and Janino providers. Integration with Java EE, Spring Framework, JMX and JNDI for enterprise ready scripts.

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  World Congress of Universal Documentation

  The World Congress of Universal Documentation was held from 16 to 21 August 1937 in Paris, France. Delegates from 45 countries met to discuss means by which all of the world's information, in print, in manuscript, and in other forms, could be efficiently organized and made accessible. == The Congress in the history of information science == The Congress, held at the Trocadéro under "the auspices" of the Institut International de Bibliographie, was "the apotheosis" of a general movement in the 1930s towards the classification of the growing mass of information and the improvement of access to that information. For the first time in the history of information science, technological means were beginning to catch up with theoretical ends, and the discussions at the conference reflected that fact. Its participation in the Congress was one of the first projects of the American Documentation Institute (ADI). Participants in the conference discussed what has been more recently called "a continuously updated hypertext encyclopedia." Joseph Reagle sees many of the ideas considered at the conference as forerunners of some of the key goals and norms of Wikipedia. == Microfilm == The main resolution adopted by the congress proposed that microfilm be used to make information universally available. Watson Davis, chairman of the American delegation and president of the ADI, stated that the volume of information being produced created difficult problems of access and preservation, but that these could be solved by the use of microfilm. In his address to the Congress, Davis said: Most immediate and practical to put into operation is the microfilming of material in libraries upon demand. It will become fashionable and economical to send a potential book borrower a little strip of microfilm for his permanent possession instead of the book and then badgering him to return it before he has had a chance to use it effectively. I believe that reading machines for microfilm will become as common as typewriters in studies and laboratories. If the principal libraries and information centers of the world will cooperate in such "bibliofilm services," as they are called, if they exchange orders and have essentially uniform methods, forms for ordering, standard microfilm format and production methods and comparable if not uniform prices, the resources of any library will be placed at the disposal of any scholar or scientist anywhere in the world. All the libraries cooperating will merge into one world library without loss of identity or individuality. The world's documentation will become available to even the most isolated and individualistic scholar. The Congress included two separate exhibits on microfilm. One was of the equipment used at the Bibliothèque nationale de France and the other, coordinated by Herman H. Fussler of the University of Chicago, consisting of "an entire microfilm laboratory," complete with cameras, a darkroom, and various kinds of reading machines. Emanuel Goldberg presented a paper on an early copying camera he had invented. Other resolutions passed by the Congress concerned uniform standards for the preparation of articles, for classifying books and other documents, for indexing newspapers and periodicals, and for cooperation between libraries. == H. G. Wells == In his address to the Congress, H. G. Wells said that he thought that his idea of the "world brain" was a precursor to the ideas other delegates were proposing, and explicitly linked the projects being discussed to the work of the encyclopédistes: I am speaking of a process of mental organization throughout the world which I believe to be as inevitable as anything can be in human affairs. All the distresses and horrors of the present time are fundamentally intellectual. The world has to pull its mind together, and this [Congress] is the beginning of its efforts. Civilization is a Phoenix. It perishes in flames and even as it dies it is born again. This synthesis of knowledge upon which you are working is the necessary beginning of a new world. It is good to be meeting here in Paris where the first encyclopedia of power was made. It would be impossible to overrate our debt to Diderot and his associates. == Other participants == Participants in the Congress included authors, librarians, scholars, archivists, scientists, and editors. Some of the notable people in attendance not mentioned above were:

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  External memory algorithm

  In computing, external memory algorithms or out-of-core algorithms are algorithms that are designed to process data that are too large to fit into a computer's main memory at once. Such algorithms must be optimized to efficiently fetch and access data stored in slow bulk memory (auxiliary memory) such as hard drives or tape drives, or when memory is on a computer network. External memory algorithms are analyzed in the external memory model. == Model == External memory algorithms are analyzed in an idealized model of computation called the external memory model (or I/O model, or disk access model). The external memory model is an abstract machine similar to the RAM machine model, but with a cache in addition to main memory. The model captures the fact that read and write operations are much faster in a cache than in main memory, and that reading long contiguous blocks is faster than reading randomly using a disk read-and-write head. The running time of an algorithm in the external memory model is defined by the number of reads and writes to memory required. The model was introduced by Alok Aggarwal and Jeffrey Vitter in 1988. The external memory model is related to the cache-oblivious model, but algorithms in the external memory model may know both the block size and the cache size. For this reason, the model is sometimes referred to as the cache-aware model. The model consists of a processor with an internal memory or cache of size M, connected to an unbounded external memory. Both the internal and external memory are divided into blocks of size B. One input/output or memory transfer operation consists of moving a block of B contiguous elements from external to internal memory, and the running time of an algorithm is determined by the number of these input/output operations. == Algorithms == Algorithms in the external memory model take advantage of the fact that retrieving one object from external memory retrieves an entire block of size B. This property is sometimes referred to as locality. Searching for an element among N objects is possible in the external memory model using a B-tree with branching factor B. Using a B-tree, searching, insertion, and deletion can be achieved in O ( log B ⁡ N ) {\displaystyle O(\log _{B}N)} time (in Big O notation). Information theoretically, this is the minimum running time possible for these operations, so using a B-tree is asymptotically optimal. External sorting is sorting in an external memory setting. External sorting can be done via distribution sort, which is similar to quicksort, or via a M B {\displaystyle {\tfrac {M}{B}}} -way merge sort. Both variants achieve the asymptotically optimal runtime of O ( N B log M B ⁡ N B ) {\displaystyle O\left({\frac {N}{B}}\log _{\frac {M}{B}}{\frac {N}{B}}\right)} to sort N objects. This bound also applies to the fast Fourier transform in the external memory model. The permutation problem is to rearrange N elements into a specific permutation. This can either be done either by sorting, which requires the above sorting runtime, or inserting each element in order and ignoring the benefit of locality. Thus, permutation can be done in O ( min ( N , N B log M B ⁡ N B ) ) {\displaystyle O\left(\min \left(N,{\frac {N}{B}}\log _{\frac {M}{B}}{\frac {N}{B}}\right)\right)} time. == Applications == The external memory model captures the memory hierarchy, which is not modeled in other common models used in analyzing data structures, such as the random-access machine, and is useful for proving lower bounds for data structures. The model is also useful for analyzing algorithms that work on datasets too big to fit in internal memory. A typical example is geographic information systems, especially digital elevation models, where the full data set easily exceeds several gigabytes or even terabytes of data. This methodology extends beyond general purpose CPUs and also includes GPU computing as well as classical digital signal processing. In general-purpose computing on graphics processing units (GPGPU), powerful graphics cards (GPUs) with little memory (compared with the more familiar system memory, which is most often referred to simply as RAM) are utilized with relatively slow CPU-to-GPU memory transfer (when compared with computation bandwidth). == History == An early use of the term "out-of-core" as an adjective is in 1962 in reference to devices that are other than the core memory of an IBM 360. An early use of the term "out-of-core" with respect to algorithms appears in 1971.

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  Symbolic regression

  Symbolic regression (SR) is a type of regression analysis that searches the space of mathematical expressions to find the model that best fits a given dataset, both in terms of accuracy and simplicity. No particular model is provided as a starting point for symbolic regression. Instead, initial expressions are formed by randomly combining mathematical building blocks such as mathematical operators, analytic functions, constants, and state variables. Usually, a subset of these primitives will be specified by the person operating it, but that's not a requirement of the technique. The symbolic regression problem for mathematical functions has been tackled with a variety of methods, including recombining equations most commonly using genetic programming, as well as more recent methods utilizing Bayesian methods and neural networks. Another non-classical alternative method to SR is called Universal Functions Originator (UFO), which has a different mechanism, search-space, and building strategy. Further methods such as Exact Learning attempt to transform the fitting problem into a moments problem in a natural function space, usually built around generalizations of the Meijer-G function. By not requiring a priori specification of a model, symbolic regression isn't affected by human bias, or unknown gaps in domain knowledge. It attempts to uncover the intrinsic relationships of the dataset, by letting the patterns in the data itself reveal the appropriate models, rather than imposing a model structure that is deemed mathematically tractable from a human perspective. The fitness function that drives the evolution of the models takes into account not only error metrics (to ensure the models accurately predict the data), but also special complexity measures, thus ensuring that the resulting models reveal the data's underlying structure in a way that's understandable from a human perspective. This facilitates reasoning and favors the odds of getting insights about the data-generating system, as well as improving generalisability and extrapolation behaviour by preventing overfitting. Accuracy and simplicity may be left as two separate objectives of the regression—in which case the optimum solutions form a Pareto front—or they may be combined into a single objective by means of a model selection principle such as minimum description length. It has been proven that symbolic regression is an NP-hard problem. Nevertheless, if the sought-for equation is not too complex it is possible to solve the symbolic regression problem exactly by generating every possible function (built from some predefined set of operators) and evaluating them on the dataset in question. == Difference from classical regression == While conventional regression techniques seek to optimize the parameters for a pre-specified model structure, symbolic regression avoids imposing prior assumptions, and instead infers the model from the data. In other words, it attempts to discover both model structures and model parameters. This approach has the disadvantage of having a much larger space to search, because not only the search space in symbolic regression is infinite, but there are an infinite number of models which will perfectly fit a finite data set (provided that the model complexity isn't artificially limited). This means that it will possibly take a symbolic regression algorithm longer to find an appropriate model and parametrization, than traditional regression techniques. This can be attenuated by limiting the set of building blocks provided to the algorithm, based on existing knowledge of the system that produced the data; but in the end, using symbolic regression is a decision that has to be balanced with how much is known about the underlying system. Nevertheless, this characteristic of symbolic regression also has advantages: because the evolutionary algorithm requires diversity in order to effectively explore the search space, the result is likely to be a selection of high-scoring models (and their corresponding set of parameters). Examining this collection could provide better insight into the underlying process, and allows the user to identify an approximation that better fits their needs in terms of accuracy and simplicity. == Benchmarking == === SRBench === In 2021, SRBench was proposed as a large benchmark for symbolic regression. In its inception, SRBench featured 14 symbolic regression methods, 7 other ML methods, and 252 datasets from PMLB. The benchmark intends to be a living project: it encourages the submission of improvements, new datasets, and new methods, to keep track of the state of the art in SR. === SRBench Competition 2022 === In 2022, SRBench announced the competition Interpretable Symbolic Regression for Data Science, which was held at the GECCO conference in Boston, MA. The competition pitted nine leading symbolic regression algorithms against each other on a novel set of data problems and considered different evaluation criteria. The competition was organized in two tracks, a synthetic track and a real-world data track. ==== Synthetic Track ==== In the synthetic track, methods were compared according to five properties: re-discovery of exact expressions; feature selection; resistance to local optima; extrapolation; and sensitivity to noise. Rankings of the methods were: QLattice PySR (Python Symbolic Regression) uDSR (Deep Symbolic Optimization) ==== Real-world Track ==== In the real-world track, methods were trained to build interpretable predictive models for 14-day forecast counts of COVID-19 cases, hospitalizations, and deaths in New York State. These models were reviewed by a subject expert and assigned trust ratings and evaluated for accuracy and simplicity. The ranking of the methods was: uDSR (Deep Symbolic Optimization) QLattice geneticengine (Genetic Engine) == Non-standard methods == Most symbolic regression algorithms prevent combinatorial explosion by implementing evolutionary algorithms that iteratively improve the best-fit expression over many generations. Recently, researchers have proposed algorithms utilizing other tactics in AI. Silviu-Marian Udrescu and Max Tegmark developed the "AI Feynman" algorithm, which attempts symbolic regression by training a neural network to represent the mystery function, then runs tests against the neural network to attempt to break up the problem into smaller parts. For example, if f ( x 1 , . . . , x i , x i + 1 , . . . , x n ) = g ( x 1 , . . . , x i ) + h ( x i + 1 , . . . , x n ) {\displaystyle f(x_{1},...,x_{i},x_{i+1},...,x_{n})=g(x_{1},...,x_{i})+h(x_{i+1},...,x_{n})} , tests against the neural network can recognize the separation and proceed to solve for g {\displaystyle g} and h {\displaystyle h} separately and with different variables as inputs. This is an example of divide and conquer, which reduces the size of the problem to be more manageable. AI Feynman also transforms the inputs and outputs of the mystery function in order to produce a new function which can be solved with other techniques, and performs dimensional analysis to reduce the number of independent variables involved. The algorithm was able to "discover" 100 equations from The Feynman Lectures on Physics, while a leading software using evolutionary algorithms, Eureqa, solved only 71. AI Feynman, in contrast to classic symbolic regression methods, requires a very large dataset in order to first train the neural network and is naturally biased towards equations that are common in elementary physics.

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  Regulation of algorithms

  Regulation of algorithms, or algorithmic regulation, is the creation of laws, rules and public sector policies for promotion and regulation of algorithms, particularly in artificial intelligence and machine learning. For the subset of AI algorithms, the term regulation of artificial intelligence is used. The regulatory and policy landscape for artificial intelligence (AI) is an emerging issue in jurisdictions globally, including in the European Union. Regulation of AI is considered necessary to both encourage AI and manage associated risks, but challenging. Another emerging topic is the regulation of blockchain algorithms (Use of the smart contracts must be regulated) and is mentioned along with regulation of AI algorithms. Many countries have enacted regulations of high frequency trades, which is shifting due to technological progress into the realm of AI algorithms. The motivation for regulation of algorithms is the apprehension of losing control over the algorithms, whose impact on human life increases. Multiple countries have already introduced regulations in case of automated credit score calculation—right to explanation is mandatory for those algorithms. For example, The IEEE has begun developing a new standard to explicitly address ethical issues and the values of potential future users. Bias, transparency, and ethics concerns have emerged with respect to the use of algorithms in diverse domains ranging from criminal justice to healthcare—many fear that artificial intelligence could replicate existing social inequalities along race, class, gender, and sexuality lines. == Regulation of artificial intelligence == === Public discussion === In 2016, Joy Buolamwini founded Algorithmic Justice League after a personal experience with biased facial detection software in order to raise awareness of the social implications of artificial intelligence through art and research. In 2017 Elon Musk advocated regulation of algorithms in the context of the existential risk from artificial general intelligence. According to NPR, the Tesla CEO was "clearly not thrilled" to be advocating for government scrutiny that could impact his own industry, but believed the risks of going completely without oversight are too high: "Normally the way regulations are set up is when a bunch of bad things happen, there's a public outcry, and after many years a regulatory agency is set up to regulate that industry. It takes forever. That, in the past, has been bad but not something which represented a fundamental risk to the existence of civilisation." In response, some politicians expressed skepticism about the wisdom of regulating a technology that is still in development. Responding both to Musk and to February 2017 proposals by European Union lawmakers to regulate AI and robotics, Intel CEO Brian Krzanich has argued that artificial intelligence is in its infancy and that it is too early to regulate the technology. Instead of trying to regulate the technology itself, some scholars suggest to rather develop common norms including requirements for the testing and transparency of algorithms, possibly in combination with some form of warranty. One suggestion has been for the development of a global governance board to regulate AI development. In 2020, the European Union published its draft strategy paper for promoting and regulating AI. Algorithmic tacit collusion is a legally dubious antitrust practise committed by means of algorithms, which the courts are not able to prosecute. This danger concerns scientists and regulators in EU, US and beyond. European Commissioner Margrethe Vestager mentioned an early example of algorithmic tacit collusion in her speech on "Algorithms and Collusion" on March 16, 2017, described as follows: "A few years ago, two companies were selling a textbook called The Making of a Fly. One of those sellers used an algorithm which essentially matched its rival’s price. That rival had an algorithm which always set a price 27% higher than the first. The result was that prices kept spiralling upwards, until finally someone noticed what was going on, and adjusted the price manually. By that time, the book was selling – or rather, not selling – for 23 million dollars a copy." In 2018, the Netherlands employed an algorithmic system SyRI (Systeem Risico Indicatie) to detect citizens perceived being high risk for committing welfare fraud, which quietly flagged thousands of people to investigators. This caused a public protest. The district court of Hague shut down SyRI referencing Article 8 of the European Convention on Human Rights (ECHR). In 2020, algorithms assigning exam grades to students in the UK sparked open protest under the banner "Fuck the algorithm." This protest was successful and the grades were taken back. In 2024, the Munich Convention on AI, Data and Human Rights was introduced as part of growing international efforts to regulate artificial intelligence through a human rights lens. Developed through a collaborative drafting process involving scholars from the Technical University of Munich, Stellenbosch University, Ulster University, and KNUST, the initiative calls for an international conversation on a binding treaty to safeguard human rights and the principles enshrined in the UN Charter in the age of AI. === Implementation === AI law and regulations can be divided into three main topics, namely governance of autonomous intelligence systems, responsibility and accountability for the systems, and privacy and safety issues. The development of public sector strategies for management and regulation of AI has been increasingly deemed necessary at the local, national, and international levels and in fields from public service management to law enforcement, the financial sector, robotics, the military, and international law. There are many concerns that there is not enough visibility and monitoring of AI in these sectors. In the United States financial sector, for example, there have been calls for the Consumer Financial Protection Bureau to more closely examine source code and algorithms when conducting audits of financial institutions' non-public data. In the United States, on January 7, 2019, following an Executive Order on 'Maintaining American Leadership in Artificial Intelligence', the White House's Office of Science and Technology Policy released a draft Guidance for Regulation of Artificial Intelligence Applications, which includes ten principles for United States agencies when deciding whether and how to regulate AI. In response, the National Institute of Standards and Technology has released a position paper, the National Security Commission on Artificial Intelligence has published an interim report, and the Defense Innovation Board has issued recommendations on the ethical use of AI. In April 2016, for the first time in more than two decades, the European Parliament adopted a set of comprehensive regulations for the collection, storage, and use of personal information, the General Data Protection Regulation (GDPR)1 (European Union, Parliament and Council 2016). The GDPR's policy on the right of citizens to receive an explanation for algorithmic decisions highlights the pressing importance of human interpretability in algorithm design. In 2016, China published a position paper questioning the adequacy of existing international law to address the eventuality of fully autonomous weapons, becoming the first permanent member of the U.N. Security Council to broach the issue, and leading to proposals for global regulation. In the United States, steering on regulating security-related AI is provided by the National Security Commission on Artificial Intelligence. In 2017, the U.K. Vehicle Technology and Aviation Bill imposes liability on the owner of an uninsured automated vehicle when driving itself and makes provisions for cases where the owner has made "unauthorized alterations" to the vehicle or failed to update its software. Further ethical issues arise when, e.g., a self-driving car swerves to avoid a pedestrian and causes a fatal accident. In 2021, the European Commission proposed the Artificial Intelligence Act. == Algorithm certification == There is a concept of algorithm certification emerging as a method of regulating algorithms. Algorithm certification involves auditing whether the algorithm used during the life cycle 1) conforms to the protocoled requirements (e.g., for correctness, completeness, consistency, and accuracy); 2) satisfies the standards, practices, and conventions; and 3) solves the right problem (e.g., correctly model physical laws), and satisfies the intended use and user needs in the operational environment. == Regulation of blockchain algorithms == Blockchain systems provide transparent and fixed records of transactions and hereby contradict the goal of the European GDPR, which is to give individuals full control of their private data. By implementing the Decree on Development of Digital Economy, Bel

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  Xulvi-Brunet–Sokolov algorithm

  Xulvi-Brunet and Sokolov's algorithm generates networks with chosen degree correlations. This method is based on link rewiring, in which the desired degree is governed by parameter ρ. By varying this single parameter it is possible to generate networks from random (when ρ = 0) to perfectly assortative or disassortative (when ρ = 1). This algorithm allows to keep network's degree distribution unchanged when changing the value of ρ. == Assortative model == In assortative networks, well-connected nodes are likely to be connected to other highly connected nodes. Social networks are examples of assortative networks. This means that an assortative network has the property that almost all nodes with the same degree are linked only between themselves. The Xulvi-Brunet–Sokolov algorithm for this type of networks is the following. In a given network, two links connecting four different nodes are chosen randomly. These nodes are ordered by their degrees. Then, with probability ρ, the links are randomly rewired in such a way that one link connects the two nodes with the smaller degrees and the other connects the two nodes with the larger degrees. If one or both of these links already existed in the network, the step is discarded and is repeated again. Thus, there will be no self-connected nodes or multiple links connecting the same two nodes. Different degrees of assortativity of a network can be achieved by changing the parameter ρ. Assortative networks are characterized by highly connected groups of nodes with similar degree. As assortativity grows, the average path length and clustering coefficient increase. == Disassortative model == In disassortative networks, highly connected nodes tend to connect to less-well-connected nodes with larger probability than in uncorrelated networks. Examples of such networks include biological networks. The Xulvi-Brunet and Sokolov's algorithm for this type of networks is similar to the one for assortative networks with one minor change. As before, two links of four nodes are randomly chosen and the nodes are ordered with respect to their degrees. However, in this case, the links are rewired (with probability p) such that one link connects the highest connected node with the node with the lowest degree and the other link connects the two remaining nodes randomly with probability 1 − ρ. Similarly, if the new links already existed, the previous step is repeated. This algorithm does not change the degree of nodes and thus the degree distribution of the network.

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