A Fire Upon the Deep

A Fire Upon the Deep is a 1992 science fiction novel by American writer Vernor Vinge. It is a space opera involving superhuman intelligences, aliens, variable physics, space battles, love, betrayal, genocide, and a communication medium resembling Usenet. A Fire Upon the Deep won the Hugo Award in 1993, sharing it with Doomsday Book by Connie Willis. Besides the normal print book editions, the novel was also included on a CD-ROM sold by ClariNet Communications along with the other nominees for the 1993 Hugo awards. The CD-ROM edition included numerous annotations by Vinge on his thoughts and intentions about different parts of the book, and was later released as a standalone e-book. It has a loose prequel, A Deepness in the Sky, from 1999, and a direct sequel, The Children of the Sky, from 2012. == Setting == The novel is set in various locations within the Milky Way. The galaxy is divided into four concentric volumes called the "Zones of Thought"; it is not clear to the novel's characters whether this is a natural phenomenon or an artificially created one. Each Zone has fundamental differences in basic physical laws. One of the main consequences of these differences is the effect on intelligence. Artificial intelligence and automation is most directly affected, in that advanced hardware and software from the Beyond or the Transcend will work less and less well as a ship descends towards the Unthinking Depths. Biological intelligence is affected to a lesser degree. The four zones are spoken of in terms of "low" to "high" as follows: The Unthinking Depths are the innermost zone, surrounding the Galactic Center. In it, only minimal forms of intelligence, biological or otherwise, are possible. This means that any ship straying into the Depths will be stranded, effectively permanently. Even if the crew did not die immediately—and some forms of life native to "higher" Zones would likely do so—they would be rendered incapable of even human intelligence, leaving them unable to operate their ship in any meaningful way. Surrounding the Depths is the Slow Zone or Slowness. "Old Earth" is in this Zone, although Earth plays no significant role in the story. Biological intelligence is possible in "the Slowness", but not true, sentient, artificial intelligence. Faster than light travel (FTL) is impossible in the Slow Zone. Faster-than-light communication is impossible into or out of the Slow Zone. As the boundaries of the Zones are subject to change, accidental entry into the Slow Zone is a major hazard at the "Bottom" of the Beyond. Starships which operate near the Beyond/Slow Zone border often have an auxiliary Bussard ramjet drive, so that if they accidentally stray into the Slow Zone, they will at least have a backup (sub-light) drive to try to reach the Beyond. Such ships also tend to include "coldsleep" equipment, as it is likely that any such return will still take many lifetimes for most species. The next layer outward is the Beyond, within which artificial intelligence, FTL travel, and FTL communication are possible. All human civilizations in the Beyond are descended from a single ethnic Norwegian group. The original settlement of this group is known as Nyjora; other human settlements in the Beyond include Straumli Realm and Sjandra Kei. In the Beyond, FTL travel is accomplished by making many small "jumps" across space, with the efficiency of the drive increasing the farther a ship travels from the galactic core. The Beyond is not a homogeneous zone; it includes the "High Beyond", "Middle Beyond", and the "Bottom of the Beyond", depending on distance from the galactic core. The Beyond is populated by a very large number of interstellar and intergalactic civilizations which are linked by an FTL communication network, "the Net", sometimes cynically called the "Net of a Million Lies". The Net is depicted as working much like the Usenet network in the early 1990s, with transcripts of messages containing header and footer information as one would find in such forums. The outermost layer, containing the galactic halo, is the Transcend, within which incomprehensible, superintelligent beings dwell. When a "Beyonder" civilization reaches the point of technological singularity, it can "Transcend", becoming a "Power". Such Powers always seem to relocate to the Transcend, seemingly necessarily, where they become engaged in activities which are entirely mysterious to those in the Beyond. == Plot == An expedition from Straumli Realm, a human civilization in the High Beyond, investigates a newly discovered data archive in the Low Transcend. The expedition's facility, High Lab, is gradually compromised by a superintelligence that is accidentally awoken by the researchers. This superintelligence is later known as the Blight. Shortly before the Blight's final "flowering", two self-aware entities, created similarly to the Blight, plot to aid the humans before the Blight can gain its full powers. Finally recognizing their danger, the High Lab researchers attempt to flee in two ships. The Blight destroys one ship; a second ship, carrying many High Lab children in coldsleep boxes, escapes. This ship lands on a distant planet at the Bottom of the Beyond. The planet is occupied by dog-like creatures, dubbed "Tines", who live in packs as group minds. The Tines have a level of technology comparable to the human Middle Ages. Upon landing, however, the two surviving adults, Arne and Sjana Olnsdot, are ambushed and killed by Tine fanatics known as Flenserists, in whose realm they have landed. The Flenserists capture their children, Jefri and Johanna. Johanna is rescued by a Tine named Peregrine and taken to a neighboring kingdom ruled by Woodcarver. A distress signal from the Straumli ship eventually reaches Relay, a major information provider for the Net. A Transcendent being named "Old One" contacts Relay, seeking information about the Blight and the humans who released it. Old One then reconstitutes a human man named Pham Nuwen from the wreckage of a spaceship to act as its agent. Pham remains unsure if he is a construct or if his memories are real. Ravna Bergsndot, the only human Relay employee, traces the Straumli ship's signal to the Tines' world and persuades her employer to investigate. Ravna contracts the merchant vessel Out of Band II to transport her and Pham. The ship is owned by two Skroderiders, Blueshell and Greenstalk. Before the mission is launched, the Blight launches a surprise attack on Relay and kills Old One. As Old One dies, it downloads its anti-Blight information into Pham. Pham, Ravna and the Skroderiders barely escape Relay's destruction in the Out of Band II. During their journey to Tine's World, Ravna communicates with Jefri. Jefri is manipulated to believe that Woodcarver is his enemy. The Flenserist leaders, Steel and Tyrathect, use Ravna's information to develop advanced technology such as cannon and radio communication. Meanwhile, Johanna and the knowledge stored in her dataset device help Woodcarver rapidly develop as well. The Blight expands, taking over several civilizations, brainwashing their populations, and seizing archives in the Beyond. On the Net, some claim that humans are the means by which the Blight is able to spread. Anti-human fanatics destroy the entire civilization of Sjandra Kei, which is Ravna's home world. The Out of Band II is pursued by three fleets: anti-human fanatics, survivors from Sjandra Kei, and a shadow fleet controlled by the Blight. During the pursuit, Ravna and Pham learn that every member of the Skroderider species can be subverted by the Blight; this drives a wedge between the crew members. Ships from Sjandra Kei sacrifice themselves to delay the Blight and the anti-human ships, allowing the Out of Band II to reach Tine's World before the Blight. When the Out of Band II arrives at Tine's World, the humans ally with Woodcarver to defeat the Flenserists and rescue Jefri. Blueshell sacrifices himself to rescue Jefri. Pham then initiates an anti-Blight Countermeasure, which was aboard the humans' ship. The Countermeasure extends the Slow Zone outward by thousands of light years. This envelops and destroys the Blight, but results in the destruction of thousands of civilizations and trillions of deaths. The humans are stranded on the Tines' World, now in the depths of the Slow Zone. Activating the Countermeasure proves fatal to Pham, but before he dies, the remnant of Old One reveals to him that, although his body is a reconstruction, his memories are indeed real. == Related works == Vinge first used the concepts of "Zones of Thought" in a 1988 novella The Blabber, which occurs after Fire. Vinge's novel A Deepness in the Sky (1999) is a prequel to A Fire Upon the Deep set 20,000 years earlier and featuring Pham Nuwen. Vinge's The Children of the Sky, "a near-term sequel to A Fire Upon the Deep", set ten years later, was released in October 2011. Vinge's former wife, Joan D. Vinge, has also written s

Neurorobotics

Neurorobotics is the combined study of neuroscience, robotics, and artificial intelligence. It is the science and technology of embodied autonomous neural systems. Neural systems include brain-inspired algorithms (e.g. connectionist networks), computational models of biological neural networks (e.g. artificial spiking neural networks, large-scale simulations of neural microcircuits) and actual biological systems (e.g. in vivo and in vitro neural nets). Such neural systems can be embodied in machines with mechanic or any other forms of physical actuation. This includes robots, prosthetic or wearable systems but also, at smaller scale, micro-machines and, at the larger scales, furniture and infrastructures. Neurorobotics is that branch of neuroscience with robotics, which deals with the study and application of science and technology of embodied autonomous neural systems like brain-inspired algorithms. It is based on the idea that the brain is embodied and the body is embedded in the environment. Therefore, most neurorobots are required to function in the real world, as opposed to a simulated environment. Beyond brain-inspired algorithms for robots neurorobotics may also involve the design of brain-controlled robot systems. == Major classes of models == Neurorobots can be divided into various major classes based on the robot's purpose. Each class is designed to implement a specific mechanism of interest for study. Common types of neurorobots are those used to study motor control, memory, action selection, and perception. === Locomotion and motor control === Neurorobots are often used to study motor feedback and control systems, and have proved their merit in developing controllers for robots. Locomotion is modeled by a number of neurologically inspired theories on the action of motor systems. Locomotion control has been mimicked using models or central pattern generators, clumps of neurons capable of driving repetitive behavior, to make four-legged walking robots. Other groups have expanded the idea of combining rudimentary control systems into a hierarchical set of simple autonomous systems. These systems can formulate complex movements from a combination of these rudimentary subsets. This theory of motor action is based on the organization of cortical columns, which progressively integrate from simple sensory input into a complex afferent signals, or from complex motor programs to simple controls for each muscle fiber in efferent signals, forming a similar hierarchical structure. Another method for motor control uses learned error correction and predictive controls to form a sort of simulated muscle memory. In this model, awkward, random, and error-prone movements are corrected for using error feedback to produce smooth and accurate movements over time. The controller learns to create the correct control signal by predicting the error. Using these ideas, robots have been designed which can learn to produce adaptive arm movements or to avoid obstacles in a course. === Learning and memory systems === Robots designed to test theories of animal memory systems. Many studies examine the memory system of rats, particularly the rat hippocampus, dealing with place cells, which fire for a specific location that has been learned. Systems modeled after the rat hippocampus are generally able to learn mental maps of the environment, including recognizing landmarks and associating behaviors with them, allowing them to predict the upcoming obstacles and landmarks. Another study has produced a robot based on the proposed learning paradigm of barn owls for orientation and localization based on primarily auditory, but also visual stimuli. The hypothesized method involves synaptic plasticity and neuromodulation, a mostly chemical effect in which reward neurotransmitters such as dopamine or serotonin affect the firing sensitivity of a neuron to be sharper. The robot used in the study adequately matched the behavior of barn owls. Furthermore, the close interaction between motor output and auditory feedback proved to be vital in the learning process, supporting active sensing theories that are involved in many of the learning models. Neurorobots in these studies are presented with simple mazes or patterns to learn. Some of the problems presented to the neurorobot include recognition of symbols, colors, or other patterns and execute simple actions based on the pattern. In the case of the barn owl simulation, the robot had to determine its location and direction to navigate in its environment. === Action selection and value systems === Action selection studies deal with negative or positive weighting to an action and its outcome. Neurorobots can and have been used to study simple ethical interactions, such as the classical thought experiment where there are more people than a life raft can hold, and someone must leave the boat to save the rest. However, more neurorobots used in the study of action selection contend with much simpler persuasions such as self-preservation or perpetuation of the population of robots in the study. These neurorobots are modeled after the neuromodulation of synapses to encourage circuits with positive results. In biological systems, neurotransmitters such as dopamine or acetylcholine positively reinforce neural signals that are beneficial. One study of such interaction involved the robot Darwin VII, which used visual, auditory, and a simulated taste input to "eat" conductive metal blocks. The arbitrarily chosen good blocks had a striped pattern on them while the bad blocks had a circular shape on them. The taste sense was simulated by conductivity of the blocks. The robot had positive and negative feedbacks to the taste based on its level of conductivity. The researchers observed the robot to see how it learned its action selection behaviors based on the inputs it had. Other studies have used herds of small robots which feed on batteries strewn about the room, and communicate its findings to other robots. === Sensory perception === Neurorobots have also been used to study sensory perception, particularly vision. These are primarily systems that result from embedding neural models of sensory pathways in automatas. This approach gives exposure to the sensory signals that occur during behavior and also enables a more realistic assessment of the degree of robustness of the neural model. It is well known that changes in the sensory signals produced by motor activity provide useful perceptual cues that are used extensively by organisms. For example, researchers have used the depth information that emerges during replication of human head and eye movements to establish robust representations of the visual scene. == Biological robots == Biological robots are not officially neurorobots in that they are not neurologically inspired AI systems, but actual neuron tissue wired to a robot. This employs the use of cultured neural networks to study brain development or neural interactions. These typically consist of a neural culture raised on a multielectrode array (MEA), which is capable of both recording the neural activity and stimulating the tissue. In some cases, the MEA is connected to a computer which presents a simulated environment to the brain tissue and translates brain activity into actions in the simulation, as well as providing sensory feedback The ability to record neural activity gives researchers a window into a brain, which they can use to learn about a number of the same issues neurorobots are used for. An area of concern with the biological robots is ethics. Many questions are raised about how to treat such experiments. The central question concerns consciousness and whether or not the rat brain experiences it. There are many theories about how to define consciousness. == Implications for neuroscience == Neuroscientists benefit from neurorobotics because it provides a blank slate to test various possible methods of brain function in a controlled and testable environment. While robots are more simplified versions of the systems they emulate, they are more specific, allowing more direct testing of the issue at hand. They also have the benefit of being accessible at all times, while it is more difficult to monitor large portions of a brain while the human or animal is active, especially individual neurons. The development of neuroscience has produced neural treatments. These include pharmaceuticals and neural rehabilitation. Progress is dependent on an intricate understanding of the brain and how exactly it functions. It is difficult to study the brain, especially in humans, due to the danger associated with cranial surgeries. Neurorobots can improved the range of tests and experiments that can be performed in the study of neural processes.

DreamLab

DreamLab was a volunteer computing Android and iOS app launched in 2015 by Imperial College London and the Vodafone Foundation. It was discontinued on 2nd April 2025. == Description == The app helped to research cancer, COVID-19, new drugs and tropical cyclones. To do this, DreamLab accessed part of the device's processing power, with the user's consent, while the owner charged their smartphone, to speed up the calculations of the algorithms from Imperial College London. The aim of the tropical cyclone project was to prepare for climate change risks. Other projects aimed to find existing drugs and food molecules that could help people with COVID-19 and other diseases. The performance of 100,000 smartphones would reach the annual output of all research computers at Imperial College in just three months, with a nightly runtime of six hours. The app was developed in 2015 by the Garvan Institute of Medical Research in Sydney and the Vodafone Foundation. In May 2020, the project had over 490,000 registered users.

Nanosemantics

Nanosemantics Lab is a Russian IT company specializing in natural language processing (NLP), computer vision (CV), speech technologies (ASR/TTS) and creation of interactive dialog interfaces, particularly chatbots and virtual assistants, based on artificial intelligence (AI). The company uses neural network platforms, including its own-made platform PuzzleLib which works on Russian-made microprocessor architecture Elbrus and Russia-based Astra Linux operating system. The company was founded in 2005 by Igor Ashmanov and Natalya Kaspersky. == Profile == The company was one of the first on Russian market to develop dialog interfaces for different branches of businesses, as well as to support community of AI developers. The company's most demanded product, as for beginning of the 2020s, is the automated "online advisers", functioning as chat bots, made for helping customers with usage of commercial products. In 2009 the company released an online service called iii.ru, where visitors were able to create their own AI-based virtual personalities entitles "infs" (for free). A visitor was able to train its own "inf" and let them chat to other "live" visitors as well with other "infs". More than 2.3 million of "infs" were created and trained by visitors over several years. Nanosemantics Lab maintains its own linguistic programming language for AI development called Dialog Language (DL). Popular social networks and instant messaging services may be used as base platforms. Nanosemantics' AI bots support different types of businesses: banks and financial services, telecommunications, retail, travel and automobile industry, home appliances production, etc. Among its solutions, Nanosemantics lists projects for various companies and institutions, among them VTB, Beeline, MTS, Sberbank, Higher School of Economics, Webmoney, Gazpromneft, Rostelecom, Ford Motors, Ministry of Health of the Russian Federation and others. The company uses the term "inf" for naming its numerous types of chat bots. The term was coined by co-founder Igor Ashmanov, head of Ashmanov & Partners. A 2014 scholarly research at Higher School of Economics, called "Basics of Business Informatics", states that such "infs", when used at business, may lower load on employees, collect statistics useful for understanding market demand and also may increase customer loyalty by providing fast and informative answers due to usage of large databases. The same research describes Nanosemantics' project for Russian branch of Ford Motors company, when AI capabilities were used for promoting the car model Ford Kuga. The research pointed out that within 2 months since beginning, the promo-website conducted 47774 talks of visitors with the specialized "inf", which indicated several hundred thousand of questions and the longest chat lasted for 3 hours 10 minutes. One-year promo campaign showed that 28.6% of people who made pre-orders talked to an "inf". In 2016 Nanosemantics launched a SaaS platform aimed at creating customized virtual assistants by users. The company's flagship product is considered to be Dialog Operating System (DialogOS), a professional corporate platform for creating intellectual voice and textual bots. It has its own linguistic programming language for creation of flexible scenarios and ready-studied neural natural language processing modules that are able to understand human interlocutors. In 2021 the company presented technology called NLab Speech ASR which contains a set of neural-networking algorithms for processing audio signals and analysis of texts that were trained and calibrated using speech-based big data marked up manually. The technology allows speed of processing of data up to "6 real-time factor" and precision values in noisy audio data may exceed 82%. In March 2022 the technology was included in Russia's Joint Registry for Russian Programs for Computers and Databases. As well, another technology was included: NLab Speech TTS, which is text-to-speech system that produces synthesized speech from printed text. == Joint projects == Nanosemantics participates in Ashmanov & Partners' projects related to AI. Since 2014, it helps in development of hardware "personal assistant" called Lexy, a solution similar to Amazon Alexa and the analogues. In August 2019 it was announced that Nanosemantics is going to participate in creation of open operating system for creating automated voice assistants. The project was called SOVA (Smart Open Virtual Assistant) and received investment of 300 million roubles (~$4,6 million) from Russian state-maintained National Technological Initiative. The company maintains long-term partnerships with Skolkovo Innovation Center (resident of IT cluster), branch association "Neuronet" and Yandex. Together with USA-based startup Remedy Logic, Nanosemantics has developed a medical diagnostic system for finding, using AI, spinal pathologies in tomography images of human bodies. Among them: central, foraminal and lateral lumbar stenosis, hernias, arthrosis. The system offers options of treatment. Since August 2021 the company is the resident of Technology Valley of Moscow State University. Also in 2021, Nanosemantics became a member of Committee on Artificial Intelligence within the Russian Association of Software Developers "Native Soft". The company states as one of its missions support of initiatives aimed at preservation and development of the Russian language. In May 2021, together with Pushkin Institute, the company created a chat bot called Phil, that explains to Russian people meaning of different Russian neologisms, and offers synonyms for them. Bot's vocabulary contains more than 500 neologisms, as well the bot can give advice on jargonisms and other types of specific words. Also in 2021, Nanosemanics Lab has signed the first-ever Russian "Codex of ethics of artificial intelligence". It establishes guidelines for ethical behavior of businesses that implement AI-based solutions. === IT contests === The company regularly organizes All-Russian Turing Test competitions for IT developers. Some of these events are co-organized with Microsoft. During the competitions, judges randomly choose virtual interlocutor and have a short conversation with them. They have to determine if a human or a machine is talking to them. An interlocutor may be either a bot or its human creator or operator. The results are measured in per cent of judges that were successfully convinced by a machine that it was a human. In 2021 Nanosemantics took part in federal project "Artificial Intelligence" by National Technological Initiative. In December 2021 the company together with state enterprise "Resource Center of Universal Design and Rehabilitation Technologies" (RCUD-RT) held an all-Russian hackathon aimed at development of AI solutions for medicine. During 3 days, participants created several training programs for patients with speech disorders. In April 2022, another hackathon by Nanosemantics was held together with MIREA – Russian Technological University. Students were participating and trying to generate algorithms for voice deepfakes. 17 teams contested in creation of software that generated artificial voice of a certain person. == Recognition == Since its foundation, Nanosemantics Lab has received a number of recognitions and awards. Among them are several professional ROTOR awards for the website iii.ru (created in 2009). The website gives the general public the means to create and train virtual assistants, which can then be used on a website or integrated into social networks. In 2013, a virtual assistant called Dana, created for Beeline Kazakhstan, was awarded with professional prize "Crystal Headset" in nomination "the best applying of technology". In 2015, the RBTH international media service included Nanosemantics in its list of "Top 50 Startups" in Russia. In 2016, the company received Russian state-maintained award called Runet Prize in two nominations: "State and Society" and "Technology and Innovation". In 2021, in Velikiy Novgorod, Nanosemantics team has won a hackathon aimed at finding means of discovering corruption schemes in Russian laws. In February 2022 the company won another contest by National Technological Initiative, called "Prochtenie", aimed at creation of AI systems for checking schoolchildren's school essays. The Nanosemantics team was awarded 20 million rubles for "overcoming technological barrier" in contest dedicated to English language, and 12 million for 1st place in special nomination "Structure" in Russian-language essay contest.

Condensation algorithm

The condensation algorithm (Conditional Density Propagation) is a computer vision algorithm. The principal application is to detect and track the contour of objects moving in a cluttered environment. Object tracking is one of the more basic and difficult aspects of computer vision and is generally a prerequisite to object recognition. Being able to identify which pixels in an image make up the contour of an object is a non-trivial problem. Condensation is a probabilistic algorithm that attempts to solve this problem. The algorithm itself is described in detail by Isard and Blake in a publication in the International Journal of Computer Vision in 1998. One of the most interesting facets of the algorithm is that it does not compute on every pixel of the image. Rather, pixels to process are chosen at random, and only a subset of the pixels end up being processed. Multiple hypotheses about what is moving are supported naturally by the probabilistic nature of the approach. The evaluation functions come largely from previous work in the area and include many standard statistical approaches. The original part of this work is the application of particle filter estimation techniques. The algorithm's creation was inspired by the inability of Kalman filtering to perform object tracking well in the presence of significant background clutter. The presence of clutter tends to produce probability distributions for the object state which are multi-modal and therefore poorly modeled by the Kalman filter. The condensation algorithm in its most general form requires no assumptions about the probability distributions of the object or measurements. == Algorithm overview == The condensation algorithm seeks to solve the problem of estimating the conformation of an object described by a vector x t {\displaystyle \mathbf {x_{t}} } at time t {\displaystyle t} , given observations z 1 , . . . , z t {\displaystyle \mathbf {z_{1},...,z_{t}} } of the detected features in the images up to and including the current time. The algorithm outputs an estimate to the state conditional probability density p ( x t | z 1 , . . . , z t ) {\displaystyle p(\mathbf {x_{t}} |\mathbf {z_{1},...,z_{t}} )} by applying a nonlinear filter based on factored sampling and can be thought of as a development of a Monte-Carlo method. p ( x t | z 1 , . . . , z t ) {\displaystyle p(\mathbf {x_{t}} |\mathbf {z_{1},...,z_{t}} )} is a representation of the probability of possible conformations for the objects based on previous conformations and measurements. The condensation algorithm is a generative model since it models the joint distribution of the object and the observer. The conditional density of the object at the current time p ( x t | z 1 , . . . , z t ) {\displaystyle p(\mathbf {x_{t}} |\mathbf {z_{1},...,z_{t}} )} is estimated as a weighted, time-indexed sample set { s t ( n ) , n = 1 , . . . , N } {\displaystyle \{s_{t}^{(n)},n=1,...,N\}} with weights π t ( n ) {\displaystyle \pi _{t}^{(n)}} . N is a parameter determining the number of sample sets chosen. A realization of p ( x t | z 1 , . . . , z t ) {\displaystyle p(\mathbf {x_{t}} |\mathbf {z_{1},...,z_{t}} )} is obtained by sampling with replacement from the set s t {\displaystyle s_{t}} with probability equal to the corresponding element of π t {\displaystyle \pi _{t}} . The assumptions that object dynamics form a temporal Markov chain and that observations are independent of each other and the dynamics facilitate the implementation of the condensation algorithm. The first assumption allows the dynamics of the object to be entirely determined by the conditional density p ( x t | x t − 1 ) {\displaystyle p(\mathbf {x_{t}} |\mathbf {x_{t-1}} )} . The model of the system dynamics determined by p ( x t | x t − 1 ) {\displaystyle p(\mathbf {x_{t}} |\mathbf {x_{t-1}} )} must also be selected for the algorithm, and generally includes both deterministic and stochastic dynamics. The algorithm can be summarized by initialization at time t = 0 {\displaystyle t=0} and three steps at each time t: === Initialization === Form the initial sample set and weights by sampling according to the prior distribution. For example, specify as Gaussian and set the weights equal to each other. === Iterative procedure === Sample with replacement N {\displaystyle N} times from the set { s 0 ( n ) , n = 1 , . . . , N } {\displaystyle \{s_{0}^{(n)},n=1,...,N\}} with probability { π 0 ( n ) , n = 1 , . . . , N } {\displaystyle \{\pi _{0}^{(n)},n=1,...,N\}} to generate a realization of p ( x t | z 1 , . . . , z t ) {\displaystyle p(\mathbf {x_{t}} |\mathbf {z_{1},...,z_{t}} )} . Apply the learned dynamics p ( x t | x t − 1 ) {\displaystyle p(\mathbf {x_{t}} |\mathbf {x_{t-1}} )} to each element of this new set, to generate a new set { s t ( n ) } {\displaystyle \{s_{t}^{(n)}\}} . To take into account the current observation z t {\displaystyle \mathbf {z_{t}} } , set π t ( n ) = p ( z t | s ( n ) ) ∑ j = 1 N p ( z t | s ( j ) ) {\displaystyle \pi _{t}^{(n)}={\frac {p(\mathbf {z_{t}} |s^{(n)})}{\sum _{j=1}^{N}p(\mathbf {z_{t}} |s^{(j)})}}} for each element { s t ( n ) } {\displaystyle \{s_{t}^{(n)}\}} . This algorithm outputs the probability distribution p ( x t | z 1 , . . . , z t ) {\displaystyle p(\mathbf {x_{t}} |\mathbf {z_{1},...,z_{t}} )} which can be directly used to calculate the mean position of the tracked object, as well as the other moments of the tracked object. Cumulative weights can instead be used to achieve a more efficient sampling. == Implementation considerations == Since object-tracking can be a real-time objective, consideration of algorithm efficiency becomes important. The condensation algorithm is relatively simple when compared to the computational intensity of the Ricatti equation required for Kalman filtering. The parameter N {\displaystyle N} , which determines the number of samples in the sample set, will clearly hold a trade-off in efficiency versus performance. One way to increase efficiency of the algorithm is by selecting a low degree of freedom model for representing the shape of the object. The model used by Isard 1998 is a linear parameterization of B-splines in which the splines are limited to certain configurations. Suitable configurations were found by analytically determining combinations of contours from multiple views, of the object in different poses, and through principal component analysis (PCA) on the deforming object. Isard and Blake model the object dynamics p ( x t | x t − 1 ) {\displaystyle p(\mathbf {x_{t}} |\mathbf {x_{t-1}} )} as a second order difference equation with deterministic and stochastic components: p ( x t | x t − 1 ) ∝ e − 1 2 | | B − 1 ( ( x t − x ¯ ) − A ( x t − 1 − x ¯ ) ) | | 2 ) {\displaystyle p(\mathbf {x_{t}} |\mathbf {x_{t-1}} )\propto e^{-{\frac {1}{2}}||B^{-1}((\mathbf {x_{t}} -\mathbf {\bar {x}} )-A(\mathbf {x_{t-1}} -\mathbf {\bar {x}} ))||^{2})}} where x ¯ {\displaystyle \mathbf {\bar {x}} } is the mean value of the state, and A {\displaystyle A} , B {\displaystyle B} are matrices representing the deterministic and stochastic components of the dynamical model respectively. A {\displaystyle A} , B {\displaystyle B} , and x ¯ {\displaystyle \mathbf {\bar {x}} } are estimated via Maximum Likelihood Estimation while the object performs typical movements. The observation model p ( z | x ) {\displaystyle p(\mathbf {z} |\mathbf {x} )} cannot be directly estimated from the data, requiring assumptions to be made in order to estimate it. Isard 1998 assumes that the clutter which may make the object not visible is a Poisson random process with spatial density λ {\displaystyle \lambda } and that any true target measurement is unbiased and normally distributed with standard deviation σ {\displaystyle \sigma } . The basic condensation algorithm is used to track a single object in time. It is possible to extend the condensation algorithm using a single probability distribution to describe the likely states of multiple objects to track multiple objects in a scene at the same time. Since clutter can cause the object probability distribution to split into multiple peaks, each peak represents a hypothesis about the object configuration. Smoothing is a statistical technique of conditioning the distribution based on both past and future measurements once the tracking is complete in order to reduce the effects of multiple peaks. Smoothing cannot be directly done in real-time since it requires information of future measurements. == Applications == The algorithm can be used for vision-based robot localization of mobile robots. Instead of tracking the position of an object in the scene, however, the position of the camera platform is tracked. This allows the camera platform to be globally localized given a visual map of the environment. Extensions of the condensation algorithm have also been used to recognize human gestures in image sequences. This application of the condensation algorithm impacts the ran

Comparison of video editing software

This is a comparison of non-linear video editing software applications. See also a more complete list of video editing software. == General information == This table gives basic general information about the different editors: === Active === === Discontinued / Inactive === ==== Definition ==== professional: used for full length Hollywood movies; professional (small): mainly used for paid commercials, short films or podcasts/YouTube channels; prosumer: Mainly targeting private use, anything that can do more than just trimming a film; basic: trimming a film; == System requirements == This table lists the operating systems that different editors can run on without emulation, as well as other system requirements. Note that minimum system requirements are listed; some features (like High Definition support) may be unavailable with these specifications. "Unix" includes the similar Linux, BSD and Unix-like operating systems. == High definition/High resolution import == The table below indicates the ability of each program to import various High Definition video or High resolution video formats for editing. == Feature set == == Output options == Please note that recording to Blu-ray does not imply 1080@50p/60p . Most only support up to 1080i 25/30 frames per second recording. Also not all formats can be output.

Neurorobotics

DreamLab

Nanosemantics

Condensation algorithm

Comparison of video editing software

Condensation algorithm

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