the-french-artist/wikipedia_20220301.simple_sentence_embeddings huggingface dataset:
Lakes of Titan 
 As the Huygens-Cassini probe flew by on July 22, 2006, its radar took pictures of the northern part of the planet (which was in its winter), a number of large, smooth areas were found on the surface near the northern pole.
Lakes of Titan 
 Based on this data, scientists announced "definitive evidence of lakes filled with methane on Saturn's moon Titan" in January 2007.
Lakes of Titan 
 The Cassini–Huygens team decided that the smooth areas are the hydrocarbon lakes they had been searching for, the first lakes found off Earth.
Lakes of Titan 
 The heavy moisture and clouds of methane near Titan’s surface could be a sign of evaporation from lakes.
Lakes of Titan 
 The Cassini-Huygens probe flew by in February, 2007, taking radar and camera pictures of some large areas near the north pole that may be large areas of liquid methane and/or ethane, including one sea larger than 100,000 km² (larger than Lake Superior), and another area similar in size to the Caspian Sea.
Lakes of Titan 
 Pictures taken near Titan's southern pole in October 2007 showed similar, but far smaller, areas that look like lakes.
Lakes of Titan 
 [[Image:Huygens shoreline.jpg|thumb|Picture of Titan taken during Huygens''' landing, showing hills, rivers and beaches.]]
Lakes of Titan 
 The Cassini-Huygens probe, flew very close to Titan in December 2007, and took infrared pictures of the chemicals on the surface and found a lake called Ontario Lacus near the south pole.
Lakes of Titan 
 Scientists studying the infrared pictures decided that one or more of the large lakes in the pictures of Saturn's moon Titan does hold liquid and that the liquid is made of hydrocarbons.
Lakes of Titan 
 The pictures taken by the probe as it fell showed no open areas of liquid, but showed rivers that had dried.
Lakes of Titan 
 It was at first thought that dark areas near the center of Titan could be a lake of a liquid or tar-like substance, but the probe landed on the dark area, and it is solid without any liquid.
Lakes of Titan 
 A device known as a penetrometer studied the surface as the probe landed and seemed to find wet clay.
Lakes of Titan 
 Scientists believe that the penetrometer may have landed on a large pebble, and they say that the surface is similar to sand made of ice.
Lakes of Titan 
 On February 13, 2008, scientists said that Titans polar lakes contain "hundreds of times more natural gas and other hydrocarbons than all the known natural gas on Earth."
Lakes of Titan 
 In June 2008, Cassini's Visible and Infrared Mapping Spectrometer confirmed the presence of liquid ethane beyond doubt in a lake in Titan's southern hemisphere.
Lakes of Titan 
 Predictions of Titan's clouds are that during in a Saturnian year, liquid moves from the equator to the poles, in clouds, and falls as rain.
West Coast blues 
 The West Coast blues is a type of blues music related to jazz and jump blues, with lots of piano sounds and jazz guitar solos, which originated from Texas blues musicians that moved to California in the 1940s.
West Coast blues 
 The best known West Coast blues musicians may be guitarist T-Bone Walker, famous for the song "Call It Stormy Monday (But Tuesday Is Just As Bad)", originally from Texas, he had made his first recordings in the late 1920s.
West Coast blues 
 During the early 1940's Walker moved to Los Angeles, where he recorded a lot of music for Capital, Black & White, and Imperial.
West Coast blues 
 Walker an electric guitar sound to the blues, prossibly doing more to make the use of electric guitar popular in blues than anyone else.
West Coast blues 
 Much of his material had a jazzy jump blues feel, which is typical of popular blues from California in the 1940s and 1950s.
West Coast blues 
 Other Texas bluesmen followed: Pianist/songwriter Amos Milburn, singer Percy Mayfield, famous for the song "Hit the Road Jack", and Charles Brown moved to Los Angeles.
West Coast blues 
 Guitarist Pee Wee Crayton divided his time between Los Angeles and San Francisco, while Lowell Fulson, from Texas by way of Oklahoma, moved to Oakland.
West Coast blues 
 After the efforts of Tom Mazzolini, producer of the San Francisco Blues Festival, started in 1974, and the efforts of recording companies like Arhoolie and HighTone, the West Coast is one of the most important blues areas in the country.
Third person 
 Third-person view, a point-of-view in film, television, and video games that is shown outside any of the actors or characters.
Third person 
 Third-person shooter, a genre of 3D computer and video games in which the onscreen character is seen at a distance from a number of different possible angles
Frederick Pottinger 
 Sir Frederick William Pottinger, 2nd Baronet (1831–1865), police inspector, was born on April 27, 1831, in India.
Frederick Pottinger 
 He became famous for leading the New South Wales police in their hunt for the bushrangers Ben Hall, John Gilbert, Frank Gardiner and John Dunn.
Frederick Pottinger 
 He was the son of Lieutenant-General Sir Henry Pottinger of the British East India Company, and his wife Susanna Maria, née Cooke, of Dublin.
Frederick Pottinger 
 He was an excellent horse rider and spent the next few years working on the gold escort between Gundagai and Goulburn.
Frederick Pottinger 
 Pottinger kept his title secret but in 1860 the inspector-general of police, John McLerie, found out.
Frederick Pottinger 
 In November he became clerk of petty sessions at Dubbo and on October 1, 1861, assistant superintendent of the Southern Mounted Police Patrol.
Frederick Pottinger 
 He wanted to do a good job in the police, but he got into a drunken brawl at Young on 20–21 December 1861.
Frederick Pottinger 
 He was in trouble for not having enough police guards on the gold escort and letting his prisoners escape.
Frederick Pottinger 
 On the night of August 9,  Pottinger and a group of police surrounded the house of Gardiner's mistress, Kate Brown.
Frederick Pottinger 
 On March 5, 1865, at Wascoe's Inn in the Blue Mountains Pottinger accidentally shot himself in the stomach while trying to get onto a moving coach.
Bushfire 
 In south east and south west of Australia, bushfires are most common in the summer and early autumn.
Bushfire 
 In the same period, Australian bushfires caused the death of around 250 people and the destruction of 4,554 homes.
Bushfire 
 But these figures do not include the 173 people who died and the 2000 homes destroyed in the 2009 Victorian bushfires.
Artificial selection 
 Selective breeding is a technique used when breeding domesticated animals, such as dogs, pigeons or cattle.
Artificial selection 
 Some of these animals may have traits that a breeder will want to carry over to the next generation.
Artificial selection 
 The breeder will therefore select those animals to breed which have that quality, rather than those that do not.
Artificial selection 
 Inbreeding is a particular kind of selective breeding, designed to produce a population which is genetically virtually identical.
Artificial selection 
 To control this, most breeds are outcrossed or backcrossed to wild-type individuals, or at least less inbred stock.
Artificial selection 
 Artificial selection may sometimes be unintentional; it is thought that domestication of crops by early humans was largely unintentional.
Selectable marker 
 What this means is, the experimenter can tell the right gene is in the cell because the marker can be seen or detected.
Selectable marker 
 Selectable markers show the success of a transfection or other procedure meant to introduce foreign DNA into a cell.
Selectable marker 
 Selectable markers are often antibiotic resistance genes; bacteria that have been subjected to a procedure to introduce foreign DNA are grown on a medium containing an antibiotic.
Selectable marker 
 Those bacterial colonies that can grow have successfully taken up and expressed the introduced genetic material.
Selectable marker 
 An alternative to a selectable marker is a screenable marker, which allows the researcher to distinguish between wanted and unwanted cells.
Selectable marker 
 Neo gene from Tn5, which confers antibiotic resistance to kanamycin in bacteria and to geneticin in eukaryote cells.
Evolutionary algorithm 
 In Computer Science, especially in artificial intelligence, evolutionary algorithms are a kind of algorithm that simulate evolution to optimise something.
Evolutionary algorithm 
 Each generation of solution is subjected to some kind of fitness function; those that survive are then recombined in some way to make the next generation of solution.
Evolutionary algorithm 
 This is done until a certain level of fitness is reached, or a determined number of generations have been used.
Artificial life 
 Artificial life (commonly Alife or alife) is a field of study which examines systems related to life, its processes, and its evolution.
Artificial life 
 There are three main kinds of alife, named for their approaches:  soft, from software; hard, from hardware; and wet, from biochemistry.
Nils Aall Barricelli 
 Barricelli's early computer-assisted experiments in symbiogenesis and evolution are considered pioneering in artificial life research.
Nils Aall Barricelli 
 Barricelli, who was independently rich, held an unpaid residency at the Institute for Advanced Study in Princeton, New Jersey in 1953, 1954.
Nils Aall Barricelli 
 He later worked at the University of California, Los Angeles, at Vanderbilt University (until 1964), in the Department of Genetics of the University of Washington, Seattle (until 1968) and then at the Mathematics Institute of the University of Oslo.
Nils Aall Barricelli 
 Barricelli published in a variety of fields including virus genetics, DNA, theoretical biology, space flight, theoretical physics and mathematical language.
Genetic algorithm 
 Genetic algorithms imitate natural biological processes, such as inheritance, mutation, selection and crossover.
Genetic algorithm 
 The concept of genetic algorithms is a search technique often used in computer science to find complex, non-obvious solutions to algorithmic optimisation and search problems.
Genetic algorithm 
 Natural evolution can be modeled as a game, in which the rewards for an organism that plays a good game of life are the passing on of its genetic material to its successors and its continued survival.
Genetic algorithm 
 In natural evolution, how well an individual performs depends on its who it works with and who it competes with, as well as the environment.
Genetic algorithm 
 Genetic algorithms are a simulation of natural selection on a population of candidate solutions to an optimization problem (chromosomes, individuals, creatures, or phenotypes).
Genetic algorithm 
 Some are randomly selected from the current population (based on their fitness) and modified (recombined and possibly randomly mutated) to form a new population.
Genetic algorithm 
 The algorithm ends either after a set number of generations have passed, or a good fitness level has been reached for the population.
Genetic algorithm 
 If the algorithm has ended due to reaching a maximum number of generations, it does not necessarily mean a good fitness level has been obtained.
Genetic algorithm 
 Evaluation: A fitness function scores each solution; the score will be a number that tells how well this solution solves the problem.
Genetic algorithm 
 As a general rule of thumb, genetic algorithms might be useful in problem domains that have a complex fitness landscape as mixing is designed to move the population away from local optima that a traditional hill climbing algorithm might get stuck in.
Genetic algorithm 
 Mutation alone can provide ergodicity of the overall genetic algorithm process (seen as a Markov chain).
Genetic algorithm 
 Examples of problems solved by genetic algorithms include: mirrors designed to funnel sunlight to a solar collector, antennae designed to pick up radio signals in space, walking methods for computer figures, optimal design of aerodynamic bodies in complex flowfields
Genetic algorithm 
 In his Algorithm Design Manual, Skiena advises against genetic algorithms for any task: "It is quite unnatural to model applications in terms of genetic operators like mutation and crossover on bit strings.
Genetic algorithm 
 The analogy with evolution—where significant progress require [sic] millions of years—can be quite appropriate.
Genetic algorithm 
 [...] I have never encountered any problem where genetic algorithms seemed to me the right way to attack it.
Genetic algorithm 
 Further, I have never seen any computational results reported using genetic algorithms that have favorably impressed me.
Genetic algorithm 
 Board games are a very relevant part of the area of genetic algorithms as applied to game theory problems.
Genetic algorithm 
 Much of the early work on computational intelligence and games was directed toward classic board games, such as tic-tac-toe, chess, and checkers.
Genetic algorithm 
 Board games can now, in most cases, be played by a computer at a higher level than the best humans, even with blind exhaustive search techniques.
Genetic algorithm 
 Go strategy is said to rely heavily on pattern recognition, and not just logical analysis as with chess and other more piece-independent games.
Genetic algorithm 
 The huge effective branching factor required for finding high quality solutions heavily restricts the look-ahead that can be used within a move sequence search.
Genetic algorithm 
 The genetic algorithm can be used in computer games to create artificial intelligence (the computer plays against you).
Genetic algorithm 
 This allows for a more realistic game experience; if a human player can find a sequence of steps which always lead to success even when repeated in different games, there can be no challenge left.
Genetic algorithm 
 Conversely if a learning technique such as a genetic algorithm for a strategist can avoid repeating past mistakes, the game will have more playability.
Genetic algorithm 
 A method for representing the challenge in terms of the solution (e.g. routing soldiers in an attack in a strategy game)
Genetic algorithm 
 A fitness or evaluation function in order to determine the quality of an instance (e.g. a measurement of damage done to an opponent in such an attack).
Genetic algorithm 
 In many cases, particularly those involving code optimization, the fitness function may simply be a system timing function.
Genetic algorithm 
 Once a genetic representation and fitness function are defined, a genetic algorithm will instantiate initial candidates as described before, and then improve through repetitive application of mutation, crossover, inversion and selection operators (as defined according to the problem domain).
Genetic algorithm 
 There are limitations of the use of a genetic algorithm compared to alternative optimization algorithms:
Genetic algorithm 
 Repeated fitness function evaluation for complex problems is often the most limiting segment of artificial evolutionary algorithms.
Genetic algorithm 
 Finding the optimal solution to complex problems often requires very expensive fitness function evaluations.
Genetic algorithm 
 In real world problems such as structural optimization problems, a single function evaluation may require several hours to several days of complete simulation.
Genetic algorithm 
 Genetic algorithms sometimes combine different approximation models to solve complex real life problems.
Genetic algorithm 
 That is, where the number of elements which are exposed to mutation is large there is often an exponential increase in search space size.
Genetic algorithm 
 This makes it extremely difficult to use the technique on problems such as designing an engine, a house or a plane.
Genetic algorithm 
 To use genetic algorithms with such problems,  they must be broken down into the simplest representation possible.
Genetic algorithm 
 For this reason, we see evolutionary algorithms encoding designs for fan blades instead of engines, building shapes instead of detailed construction plans, and airfoils instead of whole aircraft designs.
Genetic algorithm 
 The second problem of complexity is the issue of how to protect parts that have evolved to represent good solutions from further destructive mutation, particularly when their fitness assessment requires them to combine well with other parts.
Genetic algorithm 
 In many problems, genetic algorithms have a tendency to converge towards local optima or even arbitrary points rather than the global optimum of the problem.
Genetic algorithm 
 This means that the algorithm does not "know how" to sacrifice short-term fitness to gain longer-term fitness.
Genetic algorithm 
 The likelihood of this occurring depends on the shape of the fitness function: certain problems make it easy to find the global optimum, others may make it easier for the function to find the local optima.
Genetic algorithm 
 This problem may be lessened by using a different fitness function, increasing the rate of mutation, or by using selection techniques that maintain a diverse population of solutions.
Genetic algorithm 
 A common technique to maintain diversity is to use a "niche penalty": any group of individuals of sufficient similarity (niche radius) have a penalty added, which will reduce the representation of that group in the following generations, permitting other (less similar) individuals to be kept in the population.
Genetic algorithm 
 Another possible technique would be to simply replace part of the population with randomly generated individuals, when most of the population is too similar to each other.
Genetic algorithm 
 Diversity is important in genetic algorithms (and genetic programming) because crossing over a homogeneous population does not yield new solutions.
Genetic algorithm 
 In evolution strategies and evolutionary programming, diversity is not essential because of a greater reliance on mutation.
Genetic algorithm 
 Operating on dynamic data sets is difficult, as genomes begin to converge early on towards solutions which may no longer be valid for later data.
Genetic algorithm 
 Several methods have been proposed to fix this by increasing genetic diversity somehow and preventing early convergence, either by increasing the probability of mutation when the solution quality drops (called triggered hypermutation), or by occasionally introducing entirely new, randomly generated elements into the gene pool (called random immigrants).
Genetic algorithm 
 Again, evolution strategies and evolutionary programming can be implemented with a so-called "comma strategy" in which parents are not maintained and new parents are selected only from offspring.
Genetic algorithm 
 Genetic algorithms cannot effectively solve problems in which the only fitness measure is a single right/wrong measure (like decision problems), as there is no way to converge on the solution (no hill to climb).
Genetic algorithm 
 However, if the situation allows the success/failure trial to be repeated giving (possibly) different results, then the ratio of successes to failures provides a suitable fitness measure.
Genetic algorithm 
 For specific optimization problems and problem instances, other optimization algorithms may be more efficient than genetic algorithms in terms of speed of convergence.
Genetic algorithm 
 Alternative and complementary algorithms include evolution strategies, evolutionary programming, simulated annealing, Gaussian adaptation, hill climbing, and swarm intelligence (e.g.: ant colony optimization, particle swarm optimization) and methods based on integer linear programming.
Genetic algorithm 
 The suitability of genetic algorithms is dependent on the amount of knowledge of the problem; well known problems often have better, more specialized approaches.
Genetic algorithm 
 In 1950, Alan Turing proposed a "learning machine" which would parallel the principles of evolution.
Genetic algorithm 
 Computer simulation of evolution started as early as in 1954 with the work of Nils Aall Barricelli, who was using the computer at the Institute for Advanced Study in Princeton, New Jersey.
Genetic algorithm 
 Starting in 1957,  the Australian quantitative geneticist Alex Fraser published a series of papers on simulation of artificial selection of organisms with multiple loci controlling a measurable trait.
Genetic algorithm 
 From these beginnings, computer simulation of evolution by biologists became more common in the early 1960s, and the methods were described in books by Fraser and Burnell (1970) and Crosby (1973).
Genetic algorithm 
 In addition, Hans-Joachim Bremermann published a series of papers in the 1960s that also adopted a population of solution to optimization problems, undergoing recombination, mutation, and selection.
Genetic algorithm 
 Although Barricelli, in work he reported in 1963, had simulated the evolution of ability to play a simple game, artificial evolution became a widely recognized optimization method as a result of the work of Ingo Rechenberg and Hans-Paul Schwefel in the 1960s and early 1970s – Rechenberg's group was able to solve complex engineering problems through evolution strategies.
Genetic algorithm 
 Another approach was the evolutionary programming technique of Lawrence J. Fogel, which was proposed for generating artificial intelligence.
Genetic algorithm 
 Evolutionary programming originally used finite state machines for predicting environments, and used variation and selection to optimize the predictive logics.
Genetic algorithm 
 Genetic algorithms in particular became popular through the work of John Holland in the early 1970s, and particularly his book Adaptation in Natural and Artificial Systems (1975).
Genetic algorithm 
 His work originated with studies of cellular automata, conducted by Holland and his students at the University of Michigan.
Genetic algorithm 
 Holland introduced a formalized framework for predicting the quality of the next generation, known as Holland's schema theorem.
Genetic algorithm 
 Research in GAs remained largely theoretical until the mid-1980s, when The First International Conference on Genetic Algorithms was held in Pittsburgh, Pennsylvania.
Ingo Rechenberg 
 In the 1960s and 1970s he invented a highly influential set of optimization methods known as evolution strategies (from German Evolutionsstrategie'').
Ingo Rechenberg 
 His group successfully applied the new algorithms to challenging problems such as aerodynamic wing design.
Ingo Rechenberg 
 These were the first serious technical applications of artificial evolution, an important subset of the still growing field of bionics.
Ingo Rechenberg 
 Since 1972 he has been a full professor at the  Technical University of Berlin, where he is heading the Department of Bionics and Evolution Techniques.
Ingo Rechenberg 
 His awards include the Lifetime Achievement Award of the Evolutionary Programming Society (US, 1995) and the Evolutionary Computation Pioneer Award of the IEEE Neural Networks Society (US, 2002).
Ingo Rechenberg 
 Ingo Rechenberg (1971): Evolutionsstrategie - Optimierung technischer Systeme nach Prinzipien der biologischen Evolution (PhD thesis).
John Henry Holland 
 He was Professor of Psychology and Professor of Electrical Engineering and Computer Science at the University of Michigan, Ann Arbor.
Ontario Lacus 
 Ontario Lacus is about 20,000 km2 in area, and is named after Lake Ontario in North America, which is a similar size and shape.
Penetrometer 
 The penetrometer is dropped on soil or it is pressed against soil to measure how deep a hole it makes.
Penetrometer 
 Penetrometers are used on space probes such as the Cassini-Huygens probe, to measure the amount of moisture in soil on other planets.
Crème brûlée 
 The custard is baked, and then sprinkled with sugar Casa  that is caramelized under a broiler or with a kitchen blowtorch.
Crème brûlée 
 The first printed recipe for a dessert called crème brûlée is from the 1691 edition of the French cookbook Le Cuisinier Royal et Bourgeois by Francois Massialot, a cook at the Palace of Versailles.
Crème brûlée 
 It may predate Massialot's cookbook with one authority claiming it was first served at Trinity in 1630.
Crème brûlée 
 It is a rich custard often flavoured with cinnamon and lemon or orange zest topped with carmelized sugar.