In statistics, Markov chain Monte Carlo (MCMC) is a class of algorithms used to draw samples from a probability distribution. Given a probability distribution, one can construct a Markov chain whose elements' distribution approximates it, i.e. the Markov chain's equilibrium distribution matches the target distribution. The more steps that are included, the more closely the distribution of the sample matches the actual desired distribution. Markov chain Monte Carlo methods are used to study probability distributions that are too complex or too high dimensional to study with analytic techniques alone. Various algorithms exist for constructing such Markov chains, including the Metropolis–Hastings algorithm. == General explanation == Markov chain Monte Carlo methods create samples from a continuous random variable, with probability density proportional to a known function. These samples can be used to evaluate an integral over that variable, as its expected value or variance. Practically, an ensemble of chains is generally developed, starting from a set of points arbitrarily chosen and sufficiently distant from each other. These chains are stochastic processes of "walkers" which move around randomly according to an algorithm that looks for places with a reasonably high contribution to the integral to move into next, assigning them higher probabilities. Random walk Monte Carlo methods are a kind of random simulation or Monte Carlo method. However, whereas the random samples of the integrand used in a conventional Monte Carlo integration are statistically independent, those used in MCMC are autocorrelated. Correlations of samples introduces the need to use the Markov chain central limit theorem when estimating the error of mean values. These algorithms create Markov chains such that they have an equilibrium distribution which is proportional to the function given. == History == The development of MCMC methods is deeply rooted in the early exploration of Monte Carlo (MC) techniques in the mid-20th century, particularly in physics. These developments were marked by the Metropolis algorithm proposed by Nicholas Metropolis, Arianna W. Rosenbluth, Marshall Rosenbluth, Augusta H. Teller, and Edward Teller in 1953, which was designed to tackle high-dimensional integration problems using early computers. Then in 1970, W. K. Hastings generalized this algorithm and inadvertently introduced the component-wise updating idea, later known as Gibbs sampling. Simultaneously, the theoretical foundations for Gibbs sampling were being developed, such as the Hammersley–Clifford theorem from Julian Besag's 1974 paper. Although the seeds of MCMC were sown earlier, including the formal naming of Gibbs sampling in image processing by Stuart Geman and Donald Geman (1984) and the data augmentation method by Martin A. Tanner and Wing Hung Wong (1987), its "revolution" in mainstream statistics largely followed demonstrations of the universality and ease of implementation of sampling methods (especially Gibbs sampling) for complex statistical (particularly Bayesian) problems, spurred by increasing computational power and software like BUGS. This transformation was accompanied by significant theoretical advancements, such as Luke Tierney's (1994) rigorous treatment of MCMC convergence, and Jun S. Liu, Wong, and Augustine Kong's (1994, 1995) analysis of Gibbs sampler structure. Subsequent developments further expanded the MCMC toolkit, including particle filters (Sequential Monte Carlo) for sequential problems, Perfect sampling aiming for exact simulation (Jim Propp and David B. Wilson, 1996), RJMCMC (Peter J. Green, 1995) for handling variable-dimension models, and deeper investigations into convergence diagnostics and the central limit theorem. Overall, the evolution of MCMC represents a paradigm shift in statistical computation, enabling the analysis of numerous previously intractable complex models and continually expanding the scope and impact of statistics. == Mathematical setting == Suppose (Xn) is a Markov Chain in the general state space X {\displaystyle {\mathcal {X}}} with specific properties. We are interested in the limiting behavior of the partial sums: S n ( h ) = 1 n ∑ i = 1 n h ( X i ) {\displaystyle S_{n}(h)={\dfrac {1}{n}}\sum _{i=1}^{n}h(X_{i})} as n goes to infinity. Particularly, we hope to establish the Law of Large Numbers and the Central Limit Theorem for MCMC. In the following, we state some definitions and theorems necessary for the important convergence results. In short, we need the existence of invariant measure and Harris recurrent to establish the Law of Large Numbers of MCMC (Ergodic Theorem). And we need aperiodicity, irreducibility and extra conditions such as reversibility to ensure the Central Limit Theorem holds in MCMC. === Irreducibility and aperiodicity === Recall that in the discrete setting, a Markov chain is said to be irreducible if it is possible to reach any state from any other state in a finite number of steps with positive probability. However, in the continuous setting, point-to-point transitions have zero probability. In this case, φ-irreducibility generalizes irreducibility by using a reference measure φ on the measurable space ( X , B ( X ) ) {\displaystyle ({\mathcal {X}},{\mathcal {B}}({\mathcal {X}}))} . Definition (φ-irreducibility) Given a measure φ {\displaystyle \varphi } defined on ( X , B ( X ) ) {\displaystyle ({\mathcal {X}},{\mathcal {B}}({\mathcal {X}}))} , the Markov chain ( X n ) {\displaystyle (X_{n})} with transition kernel K ( x , y ) {\displaystyle K(x,y)} is φ-irreducible if, for every A ∈ B ( X ) {\displaystyle A\in {\mathcal {B}}({\mathcal {X}})} with φ ( A ) > 0 {\displaystyle \varphi (A)>0} , there exists n {\displaystyle n} such that K n ( x , A ) > 0 {\displaystyle K^{n}(x,A)>0} for all x ∈ X {\displaystyle x\in {\mathcal {X}}} (Equivalently, P x ( τ A < ∞ ) > 0 {\displaystyle P_{x}(\tau _{A}<\infty )>0} , here τ A = inf { n ≥ 1 ; X n ∈ A } {\displaystyle \tau _{A}=\inf\{n\geq 1;X_{n}\in A\}} is the first n {\displaystyle n} for which the chain enters the set A {\displaystyle A} ). This is a more general definition for irreducibility of a Markov chain in non-discrete state space. In the discrete case, an irreducible Markov chain is said to be aperiodic if it has period 1. Formally, the period of a state ω ∈ X {\displaystyle \omega \in {\mathcal {X}}} is defined as: d ( ω ) := g c d { m ≥ 1 ; K m ( ω , ω ) > 0 } {\displaystyle d(\omega ):=\mathrm {gcd} \{m\geq 1\,;\,K^{m}(\omega ,\omega )>0\}} For the general (non-discrete) case, we define aperiodicity in terms of small sets: Definition (Cycle length and small sets) A φ-irreducible Markov chain ( X n ) {\displaystyle (X_{n})} has a cycle of length d if there exists a small set C {\displaystyle C} , an associated integer M {\displaystyle M} , and a probability distribution ν M {\displaystyle \nu _{M}} such that d is the greatest common divisor of: { m ≥ 1 ; ∃ δ m > 0 such that C is small for ν m ≥ δ m ν M } . {\displaystyle \{m\geq 1\,;\,\exists \,\delta _{m}>0{\text{ such that }}C{\text{ is small for }}\nu _{m}\geq \delta _{m}\nu _{M}\}.} A set C {\displaystyle C} is called small if there exists m ∈ N ∗ {\displaystyle m\in \mathbb {N} ^{}} and a nonzero measure ν m {\displaystyle \nu _{m}} such that: K m ( x , A ) ≥ ν m ( A ) , ∀ x ∈ C , ∀ A ∈ B ( X ) . {\displaystyle K^{m}(x,A)\geq \nu _{m}(A),\quad \forall x\in C,\,\forall A\in {\mathcal {B}}({\mathcal {X}}).} === Harris recurrent === Definition (Harris recurrence) A set A {\displaystyle A} is Harris recurrent if P x ( η A = ∞ ) = 1 {\displaystyle P_{x}(\eta _{A}=\infty )=1} for all x ∈ A {\displaystyle x\in A} , where η A = ∑ n = 1 ∞ I A ( X n ) {\displaystyle \eta _{A}=\sum _{n=1}^{\infty }\mathbb {I} _{A}(X_{n})} is the number of visits of the chain ( X n ) {\displaystyle (X_{n})} to the set A {\displaystyle A} . The chain ( X n ) {\displaystyle (X_{n})} is said to be Harris recurrent if there exists a measure ψ {\displaystyle \psi } such that the chain is ψ {\displaystyle \psi } -irreducible and every measurable set A {\displaystyle A} with ψ ( A ) > 0 {\displaystyle \psi (A)>0} is Harris recurrent. A useful criterion for verifying Harris recurrence is the following: Proposition If for every A ∈ B ( X ) {\displaystyle A\in {\mathcal {B}}({\mathcal {X}})} , we have P x ( τ A < ∞ ) = 1 {\displaystyle P_{x}(\tau _{A}<\infty )=1} for every x ∈ A {\displaystyle x\in A} , then P x ( η A = ∞ ) = 1 {\displaystyle P_{x}(\eta _{A}=\infty )=1} for all x ∈ X {\displaystyle x\in {\mathcal {X}}} , and the chain ( X n ) {\displaystyle (X_{n})} is Harris recurrent. This definition is only needed when the state space X {\displaystyle {\mathcal {X}}} is uncountable. In the countable case, recurrence corresponds to E x [ η x ] = ∞ {\displaystyle \mathbb {E} _{x}[\eta _{x}]=\infty } , which is equivalent to P x ( τ x < ∞ ) = 1 {\displaystyle P_{x}(\tau _{x}<\infty )=1} for all x ∈ X {\displaystyle x\i
Web application
A web application (or web app) is application software that is created with web technologies and runs via a web browser. Web applications emerged during the late 1990s and allowed for the server to dynamically build a response to the request, in contrast to static web pages. Web applications are commonly distributed via a web server. There are several different tier systems that web applications use to communicate between the web browsers, the client interface, and server data. Each system has its own uses as they function in different ways. However, there are many security risks that developers must be aware of during development; proper measures to protect user data are vital. Web applications are often constructed with the use of a web application framework. Single-page applications (SPAs) and progressive web apps (PWAs) are two architectural approaches to creating web applications that provide a user experience similar to native apps, including features such as smooth navigation, offline support, and faster interactions. Web applications are often fully hosted on remote cloud services, can require a constant connection to them, and can replace conventional desktop applications for operating systems such as Microsoft Windows, thus facilitating the operation of software as a service as it grants the developer the power to tightly control billing based on use of the remote services as well as vendor lock-in by hosting data remotely. Modern browsers such as Chrome offer sandboxing for every browser tab which improves security and restricts access to local resources. No software installation is required as the app runs within the browser which reduces the need for managing software installations. With the use of remote cloud services, customers do not need to manage servers as that can be left to the developer and the cloud service and can use the software with a relatively low power, low-resource PC such as a thin client. The source code of the application can stay the same across operating systems and devices of users with the use of responsive web design, since it only needs to be compatible with web browsers which adhere to web standards, making the code highly portable and saving on development time. Numerous JavaScript frameworks and CSS frameworks facilitate development. == History == The concept of a "web application" was first introduced in the Java language in the Servlet Specification version 2.2, which was released in 1999. At that time, both JavaScript and XML had already been developed, but the XMLHttpRequest object had only been recently introduced on Internet Explorer 5 as an ActiveX object. Beginning around the early 2000s, applications such as "Myspace (2003), Gmail (2004), Digg (2004), [and] Google Maps (2005)," started to make their client sides more and more interactive. A web page script is able to contact the server for storing/retrieving data without downloading an entire web page. The practice became known as Ajax in 2005. Eventually this was replaced by web APIs using JSON, accessed via JavaScript asynchronously on the client side. In earlier computing models like client-server, the processing load for the application was shared between code on the server and code installed on each client locally. In other words, an application had its own pre-compiled client program which served as its user interface and had to be separately installed on each user's personal computer. An upgrade to the server-side code of the application would typically also require an upgrade to the client-side code installed on each user workstation, adding to the support cost and decreasing productivity. Additionally, both the client and server components of the application were bound tightly to a particular computer architecture and operating system, which made porting them to other systems prohibitively expensive for all but the largest applications. Later, in 1995, Netscape introduced the client-side scripting language called JavaScript, which allowed programmers to add dynamic elements to the user interface that ran on the client side. Essentially, instead of sending data to the server in order to generate an entire web page, the embedded scripts of the downloaded page can perform various tasks such as input validation or showing/hiding parts of the page. "Progressive web apps", the term coined by designer Frances Berriman and Google Chrome engineer Alex Russell in 2015, refers to apps taking advantage of new features supported by modern browsers, which initially run inside a web browser tab but later can run completely offline and can be launched without entering the app URL in the browser. == Structure == Traditional PC applications are typically single-tiered, residing solely on the client machine. In contrast, web applications inherently facilitate a multi-tiered architecture. Though many variations are possible, the most common structure is the three-tiered application. In its most common form, the three tiers are called presentation, application and storage. The first tier, presentation, refers to a web browser itself. The second tier refers to any engine using dynamic web content technology (such as ASP, CGI, ColdFusion, Dart, JSP/Java, Node.js, PHP, Python or Ruby on Rails). The third tier refers to a database that stores data and determines the structure of a user interface. Essentially, when using the three-tiered system, the web browser sends requests to the engine, which then services them by making queries and updates against the database and generates a user interface. The 3-tier solution may fall short when dealing with more complex applications, and may need to be replaced with the n-tiered approach; the greatest benefit of which is how business logic (which resides on the application tier) is broken down into a more fine-grained model. Another benefit would be to add an integration tier, which separates the data tier and provides an easy-to-use interface to access the data. For example, the client data would be accessed by calling a "list_clients()" function instead of making an SQL query directly against the client table on the database. This allows the underlying database to be replaced without making any change to the other tiers. There are some who view a web application as a two-tier architecture. This can be a "smart" client that performs all the work and queries a "dumb" server, or a "dumb" client that relies on a "smart" server. The client would handle the presentation tier, the server would have the database (storage tier), and the business logic (application tier) would be on one of them or on both. While this increases the scalability of the applications and separates the display and the database, it still does not allow for true specialization of layers, so most applications will outgrow this model. == Security == Security breaches on these kinds of applications are a major concern because it can involve both enterprise information and private customer data. Protecting these assets is an important part of any web application, and there are some key operational areas that must be included in the development process. This includes processes for authentication, authorization, asset handling, input, and logging and auditing. Building security into the applications from the beginning is sometimes more effective and less disruptive in the long run. == Development == Writing web applications is simplified with the use of web application frameworks. These frameworks facilitate rapid application development by allowing a development team to focus on the parts of their application which are unique to their goals without having to resolve common development issues such as user management. In addition, there is potential for the development of applications on Internet operating systems, although currently there are not many viable platforms that fit this model.
Peter Gerstoft
Peter Gerstoft is a Danish-American scientist and engineer specializing in ocean acoustics, seismology, and signal processing. He is currently a professor in the Department of Electrical and Photonics Engineering at the Technical University of Denmark. He was previously a Distinguished Data Scientist at the Scripps Institution of Oceanography at the University of California, San Diego and an adjunct professor in the Department of Electrical and Computer Engineering at UC San Diego. == Education == Gerstoft received his MSc in engineering from the Technical University of Denmark in 1983 and another MSc from the University of Western Ontario in 1984. He completed his PhD in engineering at the Technical University of Denmark in 1986. == Career == Gerstoft began his career in acoustics and vibrations at Odegaard & Danneskiold-Samsøe (1987–1992). He then served as a Senior Scientist at the NATO SACLANT Undersea Research Centre in La Spezia, Italy, from 1992 to 1997. Between 1999 and 2000, Gerstoft worked as a Senior Seismic Acoustic Officer with the Comprehensive Nuclear-Test-Ban Treaty Organization. He has been a Data Scientist at the Scripps Institution of Oceanography since 1997. From 2013, he held an adjunct faculty position in Electrical and Computer Engineering at UC San Diego, where he taught courses on seismology, data assimilation, and machine learning for physical systems. Gerstoft retired from UC San Diego in 2025 and accepted an appointment as Professor of Electrical and Photonics Engineering at the Technical University of Denmark in 2026 . == Research and contributions == Gerstoft's research focuses on environmental signal processing, with a particular emphasis on inversion methods, including their theoretical development, algorithmic implementation, and practical applications. In the 1990s, he investigated the use of nonlinear optimization and Bayesian approaches in acoustic inverse problems related to source localization and environmental parameter estimation. His work integrated physical propagation models with Bayesian sampling methods and a range of likelihood functions. These techniques have been applied to various data types, including vertical sensor arrays, single-sensor broadband data, and transmission loss measurements, and contributed to a general framework for inversion based on Gaussian assumptions. He has also conducted research in machine learning and sparse signal processing, particularly in the context of sensor array data. This includes applications such as direction of arrival estimation and source localization, including for seismic events such as the 2011 Tōhoku earthquake and for ship tracking in ocean environments. His work on sparse Bayesian sequential methods and techniques for estimating Lagrange multipliers in constrained optimization problems has contributed to the development of adaptive and high-resolution signal processing techniques. Gerstoft has applied supervised learning and deep neural networks to problems in physical acoustics, including source localization in ocean waveguides. He has also co-authored several review articles on the use of machine learning in acoustics and seismology. == Honors == Fulbright Scholar, Massachusetts Institute of Technology (1989–1990) Fellow, Acoustical Society of America (2003) Member, American Geophysical Union (since 2004) Senior Member, Institute of Electrical and Electronics Engineers (2018) Fellow, Institute of Electrical and Electronics Engineers (2023) == Selected publications == === Book === Diachok, O., Caiti, A., Gerstoft, P., & Schmidt, H. (Eds.). Full Field Inversion Methods in Ocean and Seismo-Acoustics. Kluwer Academic Publishers, 1995. === Selected articles === Gerstoft, P. (1994). "Inversion of seismo-acoustic data using genetic algorithms and a posteriori probability distributions". Journal of the Acoustical Society of America. 95 (2): 770–782. doi:10.1121/1.408467. Gerstoft, P., & Mecklenbrauker, C. F. (1998). "Ocean acoustic inversion with estimation of a posteriori probability distributions". Journal of the Acoustical Society of America. 104 (2): 808–819. doi:10.1121/1.423287. Sabra, K. G., Gerstoft, P., Roux, P., Kuperman, W. A., & Fehler, M. (2005). "Extracting time-domain Green's function estimates from ambient seismic noise". Geophysical Research Letters. 32, L03310. Xenaki, A., Gerstoft, P., & Mosegaard, K. (2014). "Compressive beamforming". Journal of the Acoustical Society of America. 136, 260–271. Niu, H., Reeves, D., & Gerstoft, P. (2017). "Source localization in an ocean waveguide using supervised machine learning". Journal of the Acoustical Society of America. 142, 1176–1188.
Global Language Monitor
The Global Language Monitor (GLM) is a company based in Austin, Texas, that analyzes trends in the English language. == History == Founded in Silicon Valley in 2003 by Paul J.J. Payack, the GLM describes its role as "a media analytics company that documents, analyzes and tracks cultural trends in language the world over, with a particular emphasis upon International and Global English". In April 2008, GLM moved its headquarters from San Diego to Austin. In July 2020, GLM announced that the word covid was its Top Word of 2020 for English. The company has been repeatedly criticized by linguists for promoting misinformation about language. Writing on Language Log, the linguist Ben Zimmer accused it of "hoodwink[ing] unsuspecting journalists on a range of pseudoscientific claims".
Sudip Roy (computer scientist)
Sudip Roy is a computer scientist and technology executive. He is the co-founder and chief technology officer of Adaption. He has worked on large-scale machine learning systems at organizations including Google DeepMind and Cohere. == Education == Roy earned a PhD in Computer Science from Cornell University. He holds a B.Tech in Computer Science and Engineering from the Indian Institute of Technology (IIT), Kharagpur. == Career == Sudip worked at Google Brain (now part of Google DeepMind) on systems research and large-scale data management. During his tenure, he contributed to infrastructure projects including Pathways and TensorFlow Extended, which support training and inference workflows for production machine learning models. He later served as Senior Director of Engineering at Cohere, leading work on inference infrastructure and fine-tuning systems. In late 2025, he co-founded the company Adaption Labs with Sara Hooker. The company focuses on developing AI systems designed for continuous learning and adaptation. Roy’s research spans systems for AI and AI for systems, including work on optimizing system performance and compilers. His publications have appeared in conferences such as MLSys, NeurIPS, SIGMOD, and KDD. He has been a program committee member or reviewer for the conferences SIGMOD, VLDB, ICDE, and MLSys. == Awards == He is the recipient of the MLSys Outstanding Paper Award (2022) and the SIGMOD Best Paper Award (2011). He holds multiple patents in machine learning systems, including methods for learned graph optimizations and neural network-based device placement.
ISSCO Graphics
Integrated Software Systems Corporation (ISSCO), doing business as ISSCO Graphics, was an American software developer and publisher based in San Diego, California, and active from 1970 to 1986. They were best known for their enterprise graphics software packages, including Tellagraf, CueChart and Disspla. == History == ISSCO Graphics had considered acquiring Breakthrough Software, whose software focus involved PC DOS, as a means of getting into the PC arena, but backed off when Computer Associates made an offer to acquire ISSCO. By early 1987 it was reported that "Issco users breathe sigh of relief" that all was well. The ISSCO User's Group was founded in 1976. ISSCO, which was founded in 1970 by Peter Preuss, was acquired by Computer Associates in 1986. == Notable products == === Tellagraf === ISSCO's Tellagraf is an early software package designed to allow end-users to "turn out full color, professional quality charts" with initial results displayed on a screen, modified as needed, and then "a final 'hard-copy' can be made .. or made into 35mm color transparencies for projection onto a screen." Users of Tellagraf often had access to CueChart and Disspla software. Often computer sites having one had all three. Terminals with varying degrees of graphics, such as the DEC's VT100 and Tektronix's Tektronix 4xxx family of text and graphics terminals. were supported, and the software ran on popular computing platforms. Four years are important to Tellagraf's early history: 1978: ease of use 1980: graphic-artist quality 1982: introduction of CueChart, and recognition by IEEE. 1983: "quality graphics enters the mainstream of data processing with ..." Tellegraf was eventually acquired by Computer Associates and renamed CA-Tellegraf. SAS users found it helpful. Universities, research institutes and financial services firms were among early users. === Disspla === Disspla is a package of data plotting subroutines that can be used from high level languages. It was also acquired by Computer Associates. === Tellaplan === In 1983 ISSCO introduced Tellaplan, "a project planning, report and schedule charting system for Tell-A- Graf users in IBM MVS or CMS or Digital Equipment Corp. VAX computers" atop which they built "two visual project management software packages" three years later.
Ernst Dickmanns
Ernst Dieter Dickmanns is a German pioneer of dynamic computer vision and of driverless cars. Dickmanns has been a professor at the University of the Bundeswehr Munich (1975–2001), and visiting professor to Caltech and to MIT, teaching courses on "dynamic vision". == Biography == Dickmanns was born in 1936. He studied aerospace and aeronautics at RWTH Aachen (1956–1961), and control engineering at Princeton University (1964/65); from 1961 to 1975 he was associated with the German Aero-Space Research Establishment (now DLR) Oberpfaffenhofen, working in the fields of flight dynamics and trajectory optimization. In 1971/72 he spent a Post-Doc Research Associateship with the NASA-Marshall Space Flight Center, Huntsville (orbiter re-entry). From 1975 to 2001 he was with UniBw Munich, where he initiated the 'Institut fuer Flugmechanik und Systemdynamik' (IFS), the Institut fuer die 'Technik Autonomer Systeme' (TAS), and the research activities in machine vision for vehicle guidance. == Pioneering work in autonomous driving == In the early 1980s his team equipped a Mercedes-Benz van with cameras and other sensors. The 5-ton van was re-engineered that it was possible to control steering wheel, throttle, and brakes through computer commands based on real-time evaluation of image sequences. Software was written that translated the sensory data into appropriate driving commands. For safety reasons, initial experiments in Bavaria took place on streets without traffic. In 1986 the Robot Car "VaMoRs" managed to drive all by itself and by 1987 was capable of driving itself at speeds up to 96 kilometres per hour (60 mph). One of the greatest challenges in high-speed autonomous driving arises through the rapidly changing visual street scenes. Back then, computers were much slower than they are today (~1% of 1%); therefore, sophisticated computer vision strategies were necessary to react in real time. The team of Dickmanns solved the problem through an innovative approach to dynamic vision. Spatiotemporal models were used right from the beginning, dubbed '4-D approach', which did not need storing previous images but nonetheless was able to yield estimates of all 3-D position and velocity components. Attention control including artificial saccadic movements of the platform carrying the cameras allowed the system to focus its attention on the most relevant details of the visual input. Kalman filters have been extended to perspective imaging and were used to achieve robust autonomous driving even in presence of noise and uncertainty. Feedback of prediction errors allowed bypassing the (ill-conditioned) inversion of perspective projection by least-squares parameter fits. When in 1986/83 the EUREKA-project 'PROgraMme for a European Traffic of Highest Efficiency and Unprecedented Safety' (PROMETHEUS) was initiated by the European car manufacturing industry (funding in the range of several hundred million Euros), the initially planned autonomous lateral guidance by buried cables was dropped and substituted by the much more flexible machine vision approach proposed by Dickmanns, and partially encouraged by his successes. Most of the major car companies participated; so did Dickmanns and his team in cooperation with the Daimler-Benz AG. Substantial progress was made in the following 7 years. In particular, Dickmanns' robot cars learned to drive in traffic under various conditions. An accompanying human driver with a "red button" made sure the robot vehicle could not get out of control and become a danger to the public. Since 1992, driving in public traffic was standard as final step in real-world testing. Several dozen Transputers, a special breed of parallel computers, were used to deal with the (by 1990s standards) enormous computational demands. Two culmination points were achieved in 1994/95, when Dickmanns´ re-engineered autonomous S-Class Mercedes-Benz performed international demonstrations. The first was the final presentation of the PROMETHEUS project in October 1994 on Autoroute 1 near the airport Charles-de-Gaulle in Paris. With guests on board, the twin vehicles of Daimler-Benz (VITA-2) and UniBwM (VaMP) drove more than 1,000 kilometres (620 mi) on the three-lane highway in standard heavy traffic at speeds up to 130 kilometres per hour (81 mph). Driving in free lanes, convoy driving with distance keeping depending on speed, and lane changes left and right with autonomous passing have been demonstrated; the latter required interpreting the road scene also in the rear hemisphere. Two cameras with different focal lengths for each hemisphere have been used in parallel for this purpose. The second culmination point was a 1,758 kilometres (1,092 mi) trip in the fall of 1995 from Munich in Bavaria to Odense in Denmark to a project meeting and back. Both longitudinal and lateral guidance were performed autonomously by vision. On highways, the robot achieved speeds exceeding 175 kilometres per hour (109 mph) (there is no general speed limit on the Autobahn). Publications from Dickmann's research group indicate a mean autonomously driven distance without resets of ~9 kilometres (5.6 mi); the longest autonomously driven stretch reached 158 kilometres (98 mi). More than half of the resets required were achieved autonomously (no human intervention). This is particularly impressive considering that the system used black-and-white video-cameras and did not model situations like road construction sites with yellow lane markings; lane-changes at over 140 kilometres per hour (87 mph), and other traffic with more than 40 kilometres per hour (25 mph) relative speed have been handled. In total, 95% autonomous driving (by distance) was achieved. In the years 1994 to 2004 the elder 5-ton van 'VaMoRs' was used to develop the capabilities needed for driving on networks of minor (also unsealed) roads and for cross-country driving including avoidance of negative obstacles like ditches. Turning off onto crossroads of unknown width and intersection angles required a big effort, but has been achieved with "Expectation-based, Multi-focal, Saccadic vision" (EMS-vision). This vertebrate-type vision uses animation capabilities based on knowledge about subject classes (including the autonomous vehicle itself) and their potential behaviour in certain situations. This rich background is used for control of gaze and attention as well as for locomotion. Beside ground vehicle guidance, also applications of the 4-D approach to dynamic vision for unmanned air vehicles (conventional aircraft and helicopters) have been investigated. Autonomous visual landing approaches and landings have been demonstrated in hardware-in-the-loop simulations with visual/inertial data fusion. Real-world autonomous visual landing approaches till shortly before touchdown have been performed in 1992 with the twin-propeller aircraft Dornier 128 of the University of Brunswick at the airport there. Another success of this machine vision technology was the first ever visually controlled grasping experiment of a free-floating object in weightlessness on board the Space Shuttle Columbia D2-mission in 1993 as part of the 'Rotex'-experiment of DLR.