Tweak is a graphical user interface (GUI) layer written by Andreas Raab for the Squeak development environment, which in turn is an integrated development environment based on the Smalltalk-80 computer programming language. Tweak is an alternative to an earlier graphic user interface layer called Morphic. Development began in 2001. Applications that use the Tweak software include Sophie (version 1), a multimedia and e-book authoring system, and a family of virtual world systems: Open Cobalt, Teleplace, OpenQwaq, 3d ICC's Immersive Terf and the Croquet Project. == Influences == An experimental version of Etoys, a programming environment for children, used Tweak instead of Morphic. Etoys was a major influence on a similar Squeak-based programming environment known as Scratch.
Outline of brain mapping
The following outline is provided as an overview of and topical guide to brain mapping: Brain mapping – set of neuroscience techniques predicated on the mapping of (biological) quantities or properties onto spatial representations of the (human or non-human) brain resulting in maps. Brain mapping is further defined as the study of the anatomy and function of the brain and spinal cord through the use of imaging (including intra-operative, microscopic, endoscopic and multi-modality imaging), immunohistochemistry, molecular and optogenetics, stem cell and cellular biology, engineering (material, electrical and biomedical), neurophysiology and nanotechnology. == Broad scope == History of neuroscience History of neurology Brain mapping Human brain Neuroscience Nervous system. === The neuron doctrine === Neuron doctrine – A set of carefully constructed elementary set of observations regarding neurons. For more granularity, more current, and more advanced topics, see the cellular level section Asserts that neurons fall under the broader cell theory, which postulates: All living organisms are composed of one or more cells. The cell is the basic unit of structure, function, and organization in all organisms. All cells come from preexisting, living cells. The Neuron doctrine postulates several elementary aspects of neurons: The brain is made up of individual cells (neurons) that contain specialized features such as dendrites, a cell body, and an axon. Neurons are cells differentiable from other tissues in the body. Neurons differ in size, shape, and structure according to their location or functional specialization. Every neuron has a nucleus, which is the trophic center of the cell (The part which must have access to nutrition). If the cell is divided, only the portion containing the nucleus will survive. Nerve fibers are the result of cell processes and the outgrowths of nerve cells. (Several axons are bound together to form one nerve fibril. See also: Neurofilament. Several nerve fibrils then form one large nerve fiber. Myelin, an electrical insulator, forms around selected axons. Neurons are generated by cell division. Neurons are connected by sites of contact and not via cytoplasmic continuity. (A cell membrane isolates the inside of the cell from its environment. Neurons do not communicate via direct cytoplasm to cytoplasm contact.) Law of dynamic polarization. Although the axon can conduct in both directions, in tissue there is a preferred direction of transmission from cell to cell. Elements added later to the initial Neuron doctrine A barrier to transmission exists at the site of contact between two neurons that may permit transmission. (Synapse) Unity of transmission. If a contact is made between two cells, then that contact can be either excitatory or inhibitory, but will always be of the same type. Dale's law, each nerve terminal releases a single type of neurotransmitter. Some of the basic postulates in the Neuron doctrine have been subsequently questioned, refuted, or updated. See the cellular level section topics for additional information. === Map, atlas, and database projects === Brain Activity Map Project – 2013 NIH $3 billion project to map every neuron in the human brain in ten years, based upon the Human Genome Project. NIH Brain Research through Advancing Innovative Neurotechnologies (BRAIN) Initiative [1] Community outreach site for above where the public may comment [2] Human Brain Project (EU) – 1 billion euro, 10-year project to simulate the human brain with supercomputers. BigBrain A high-resolution 3D atlas of the human brain created as part of the HBP. Human Connectome Project – 2009 NIH $30 million project to build a network map of the human brain, including structural (anatomical) and functional elements. Emphasis included research into dyslexia, autism, Alzheimer's disease, and schizophrenia. See also Connectome a, comprehensive map of neural connections in the brain. Allen Brain Atlas – 2003 $100 million project funded by Paul Allen (Microsoft) BrainMaps – National Institute of Health (NIH) database including 60 terabytes of image scans of primate and non-primates, integrated with information covering structure and function. NeuroNames – Defines the brain in terms of about 550 primary structures (about 850 unique structures) to which all other structures, names, and synonyms are related. About 15,000 neuroanatomical terms are cross indexed, including many synonyms in seven languages. Coverage includes the brain and spinal cord of the four species most frequently studied by neuroscientists: human, macaque (monkey), rat and mouse. The controlled, standardized vocabulary for each structure is located in an unambiguous, strict physical hierarchy, and these terms are selected based on ease of pronunciation, mnemonic value, and frequency of use in recent neuroscientific publications. Relation of each structure to its superstructures and substructures is included. The controlled vocabulary is suitable for uniquely indexing neuroanatomical information in digital databases. Decade of the Brain 1990–1999 promotion by NIH and the Library of Congress "to enhance public awareness of the benefits to be derived from brain research". Communications targeted Members of Congress, staffs, and the general public to promote funding. Talairach Atlas see Jean Talairach Harvard Whole Brain Atlas see Human brain MNI Template see Medical image computing Blue Brain Project and Artificial brain International Consortium for Brain Mapping see Brain Mapping List of neuroscience databases NIH Toolbox National Institute of Health (USA) toolbox for the assessment of neurological and behavioral function Organization for Human Brain Mapping The Organization for Human Brain Mapping (OHBM) is an international society dedicated to using neuroimaging to discover the organization of the human brain. == Imaging and recording systems == This section covers imaging and recording systems. The general section covers history, neuroimaging, and techniques for mapping specific neural connections. The specific systems section covers the various specific technologies, including experimental and widely deployed imaging and recording systems. === General === Most imaging work to date on individual neurons has been conducted outside the brain, typically on large neurons, and has been most frequently destructive. New techniques are however rapidly emerging. Search on "Single neuron imaging" and see related topics: Biological neuron model, Single-unit recording, Neural oscillation, Computational neuroscience. dMRI (above) is also promising in non-destructive imaging of single neurons inside the brain. History of neuroimaging (redirects from Brain scanner) Neuroimaging (redirects from Brain function map) Connectomics – mapping technique showing neural connections in a nervous system. === Specific systems === Cortical stimulation mapping Diffusion MRI (dMRI) – includes diffusion tensor imaging (DTI) and diffusion functional MRI (DfMRI). dMRI is a recent breakthrough in brain mapping allowing the visualization of cross connections between different anatomical parts of the brain. It allows noninvasive imaging of white matter fiber structure and in addition to mapping can be useful in clinical observations of abnormalities, including damage from stroke. Electroencephalography (EEG) – uses electrodes on the scalp and other techniques to detect the electrical flow of currents. Electrocorticography – intracranial EEG, the practice of using electrodes placed directly on the exposed surface of the brain to record electrical activity from the cerebral cortex. Electrophysiological techniques for clinical diagnosis Functional magnetic resonance imaging (fMRI) Medical image computing (brain research of leads medical and surgical uses of mapping technology) Neurostimulation (in research stimulation is frequently used in conjunction with imaging) Positron emission tomography (PET) – a nuclear medical imaging technique that produces a three-dimensional image or picture of functional processes in the body. The system detects pairs of gamma rays emitted indirectly by a positron-emitting radionuclide (tracer), which is introduced into the body on a biologically active molecule. Three-dimensional images of tracer concentration within the body are then constructed by computer analysis. In modern scanners, three dimensional imaging is often accomplished with the aid of a CT X-ray scan performed on the patient during the same session, in the same machine. === Imaging and recording componentry === ==== Electrochemical ==== Haemodynamic response – the rapid delivery of blood to active neuronal tissues. Blood Oxygenation Level Dependent signal (BOLD), corresponds to the concentration of deoxyhemoglobin. The BOLD effect is based on the fact that when neuronal activity is increased in one part of the brain, there is also an increased amount of cerebral blood flow to that area. Functional m
Multistage interconnection networks
Multistage interconnection networks (MINs) are a class of high-speed computer networks usually composed of processing elements (PEs) on one end of the network and memory elements (MEs) on the other end, connected by switching elements (SEs). The switching elements themselves are usually connected to each other in stages, hence the name. MINs are typically used in high-performance or parallel computing as a low-latency interconnection (as opposed to traditional packet switching networks), though they could be implemented on top of a packet switching network. Though the network is typically used for routing purposes, it could also be used as a co-processor to the actual processors for such uses as sorting; cyclic shifting, as in a perfect shuffle network; and bitonic sorting. == Background == Interconnection network are used to connect nodes, where nodes can be a single processor or group of processors, to other nodes. Interconnection networks can be categorized on the basis of their topology. Topology is the pattern in which one node is connected to other nodes. There are two main types of topology: static and dynamic. Static interconnect networks are hard-wired and cannot change their configurations. A regular static interconnect is mainly used in small networks made up of loosely couple nodes. The regular structure signifies that the nodes are arranged in specific shape and the shape is maintained throughout the networks. Some examples of static regular interconnections are: Completely connected network In a mesh network, multiple nodes are connected with each other. Each node in the network is connected to every other node in the network. This arrangement allows proper communication of the data between the nodes. But, there are a lot of communication overheads due to the increased number of node connections. Shared busThis network topology involves connection of the nodes with each other over a bus. Every node communicates with every other node using the bus. The bus utility ensures that no data is sent to the wrong node. But, the bus traffic is an important parameter which can affect the system. RingThis is one of the simplest ways of connecting nodes with each other. The nodes are connected with each other to form a ring. For a node to communicate with some other node, it has to send the messages to its neighbor. Therefore, the data message passes through a series of other nodes before reaching the destination. This involves increased latency in the system. TreeThis topology involves connection of the nodes to form a tree. The nodes are connected to form clusters and the clusters are in-turn connected to form the tree. This methodology causes increased complexity in the network. Hypercube This topology consists of connections of the nodes to form cubes. The nodes are also connected to the nodes on the other cubes. ButterflyThis is one of the most complex connections of the nodes. As the figure suggests, there are nodes which are connected and arranged in terms of their ranks. They are arranged in the form of a matrix. In dynamic interconnect networks, the nodes are interconnected via an array of simple switching elements. This interconnection can then be changed by use of routing algorithms, such that the path from one node to other nodes can be varied. Dynamic interconnections can be classified as: Single stage Interconnect Network Multistage interconnect Network Crossbar switch connections == Crossbar Switch Connections == In crossbar switch, there is a dedicated path from one processor to other processors. Thus, if there are n inputs and m outputs, we will need nm switches to realize a crossbar. As the number of outputs increases, the number of switches increases by factor of n. For large network this will be a problem. An alternative to this scheme is staged switching. == Single Stage Interconnect Network == In a single stage interconnect network, the input nodes are connected to output via a single stage of switches. The figure shows 88 single stage switch using shuffle exchange. As one can see, from a single shuffle, not all input can reach all output. Multiple shuffles are required for all inputs to be connected to all the outputs. == Multistage Interconnect Network == A multistage interconnect network is formed by cascading multiple single stage switches. The switches can then use their own routing algorithm, or be controlled by a centralized router, to form a completely interconnected network. Multistage Interconnect Network can be classified into three types: Non-blocking: A non-blocking network can connect any idle input to any idle output, regardless of the connections already established across the network. Crossbar is an example of this type of network. Rearrangeable non-blocking: This type of network can establish all possible connections between inputs and outputs by rearranging its existing connections. Blocking: This type of network cannot realize all possible connections between inputs and outputs. This is because a connection between one free input to another free output is blocked by an existing connection in the network. The number of switching elements required to realize a non-blocking network in highest, followed by rearrangeable non-blocking. Blocking network uses least switching elements. == Examples == Multiple types of multistage interconnection networks exist. === Omega network === An Omega network consists of multiple stages of 22 switching elements. Each input has a dedicated connection to an output. An NN omega network has log2(N) stages and N/2 switching elements in each stage for a perfect shuffle between stages. Thus the network has complexity of 0(N log(N)). Each switching element can employ its own switching algorithm. Consider an 88 omega network. There are 8! = 40320 1-to-1 mappings from input to output. There are 12 switching element for a total permutation of 2^12 = 4096. Thus, it is a blocking network. === Clos network === A Clos network uses 3 stages to switch from N inputs to N outputs. In the first stage, there are r= N/n crossbar switches and each switch is of size nm. In the second stage there are m switches of size rr and finally the last stage is a mirror of the first stage with r switches of size mn. A clos network will be completely non-blocking if m >= 2n-1. The number of connections, though more than omega network is much less than that of a crossbar network. === Beneš network === A Beneš network is a rearrangeably non-blocking network derived from the clos network by initializing n = m = 2. There are (2log2(N) - 1) stages, with each stage containing N/2 22 crossbar switches. An 88 Beneš network has 5 stages of switching elements, and each stage has 4 switching elements. The center three stages has two 44 benes network. The 44 Beneš network, can connect any input to any output recursively.
Critical security parameter
In cryptography, a critical security parameter (CSP) is information that is either user or system defined and is used to operate a cryptography module in processing encryption functions including cryptographic keys and authentication data, such as passwords, the disclosure or modification of which can compromise the security of a cryptographic module or the security of the information protected by the module.
SIGINT Activity Designator
A SIGINT Activity Designator (or SIGAD) identifies a signals intelligence (SIGINT) line of collection activity associated with a signals collection station, such as a base or a ship. For example, the SIGAD for Menwith Hill in the UK is USD1000. SIGADs are used by the signals intelligence agencies of Australia, Canada, New Zealand, the United Kingdom, and the United States (the Five Eyes). There are several thousand SIGADs including the substation SIGADs denoted with a trailing alpha character. Several dozen of these are significant. The leaked Boundless Informant reporting screenshot showed that it summarized 504 active SIGADs during a 30-day period in March 2013. == General format == A SIGAD consists of five to eight case insensitive alphanumeric characters. It takes the general form of an alphanumeric designator normally composed of a two- or three-letter prefix followed by one to three numbers. Often a dash is used to separate the alphabetic and numeric characters in the primary part of the designator, but less frequently a space is used as a separator or the alphabetic and numeric characters are concatenated together. An additional alphabetic character can be added to denote a sub-designator for a subset of the primary unit, such as a detachment. Lastly, a numeric character can be added after the aforementioned alphabetic to provide for a sub-sub-designator. In the examples below an X represents an alphabetic character and an N represents a numeric character that are part of the primary designator. Likewise, an x represents an alphabetic character and an n represents a numeric character that are part of a sub-designator. Here are valid generalized examples of SIGADs: The first two characters show which country operates the particular SIGINT facility, which can be US for the United States, UK for the United Kingdom, CA for Canada, AU for Australia and NZ for New Zealand. A third letter shows what sort of staff runs the station. SIGADs beginning with US without a third letter are used for intercept facilities run by the NSA. == PRISM SIGAD == One prominent SIGAD as of April 2013 is US-984XN, with an unclassified codename of PRISM. It is "the number one source of raw intelligence used for NSA analytic reports" according to National Security Agency sources in a document leaked by Edward Snowden. The President's Daily Brief, an all-source intelligence product, cited SIGAD US-984XN as a source in 1,477 items in 2012. The U.S. government operates the PRISM electronic surveillance collection program through NSA's Special Source Operations, an alliance with trusted telecommunications providers. == SIGADs for spy ships == The declassified SIGAD for the USS Liberty (AGTR-5) was USN-855. The USS Liberty incident occurred on 8 June 1967, during the Six-Day War, when Israeli Air Force jet fighter aircraft and Israeli Navy motor torpedo boats attacked the USS Liberty in international waters. The USS Pueblo (AGER-2) was a technical research ship, which was boarded and captured by North Korean forces on 23 January 1968, in what is known as the Pueblo incident. The declassified SIGAD for the NSA Direct Support Unit (DSU) from the Naval Security Group (NSG) on the USS Pueblo patrol involved in the incident was USN-467Y. The USS Pueblo, which officially remains a commissioned vessel of the United States Navy, is the only ship of the U.S. Navy currently being held captive. == Vietnam War SIGADs == The following are the Vietnam War-era declassified SIGADs from inside South Vietnam during the period of 1969 to 1975: Some locations have multiple SIGADs due to different types of collection activities and/or collection at different times during the period. The SIGADs beginning with USA were operated by the United States Air Force's United States Air Force Security Service (USAFSS). The SIGADs beginning with USM were operated by the United States Army's Army Security Agency (ASA). Lastly, the SIGADs beginning with USN were operated by the United States Navy's Naval Security Group (NAVSECGRU). All three of these units have been merged into other units or inactivated. The above list consists of the higher-echelon SIGADs. It does not include the numerous miscellaneous and temporary detachments, or direction finding stations belonging to major units or sites unless that detachment or site was the only one stationed in South Vietnam. Many of the "dets" were short-lived, often formed to support ongoing MACV operations or forward deployments of combat operational or maneuver units. These detachments usually were designated by a letter suffix attached to the higher-echelon SIGAD such as "USM-633J," which was a detachment of the 372d Radio Research Company, USM-633, supporting the United States Army's 25th Infantry Division. === Supporting Southeast Asia SIGADs === The following declassified SIGADs were highly relevant to the Vietnam Campaign, but were located in areas outside of South Vietnam in Southeast Asia. Again, detachments are not listed separately. In the case of the USS Maddox, naval Direct Support Units (DSUs) used the SIGAD USN-467 as a generic designator for their missions. Each specific patrol received a letter suffix for its duration. The subsequent mission would receive the next letter in an alphabetic sequence. Thus, SIGAD USN-467N specifically designates the USS Maddox patrol involved with the Gulf of Tonkin incident. == Joint Base SIGADs == In November 2005, the US Congress performed a fifth round of Base Realignment and Closure. This 2005 law also created twelve joint bases by merging adjacent installations belonging to different services in an effort to reduce costs and improve efficiencies. Joint bases with a primarily SIGINT mission have SIGADs that begin with USJ. A joint base would have a primary SIGAD in the general form of USJ-NNN, where NNN are numeric characters. An actual example is not given, since these units are currently active.
Web intelligence
Web intelligence is the area of scientific research and development that explores the roles and makes use of artificial intelligence and information technology for new products, services and frameworks that are empowered by the World Wide Web. The term was coined in a paper written by Ning Zhong, Jiming Liu Yao and Y.Y. Ohsuga in the Computer Software and Applications Conference in 2000. == Research == The research about the web intelligence covers many fields – including data mining (in particular web mining), information retrieval, pattern recognition, predictive analytics, the semantic web, web data warehousing – typically with a focus on web personalization and adaptive websites.
Strong secrecy
Strong secrecy is a term used in formal proof-based cryptography for making propositions about the security of cryptographic protocols. It is a stronger notion of security than syntactic (or weak) secrecy. Strong secrecy is related with the concept of semantic security or indistinguishability used in the computational proof-based approach. Bruno Blanchet provides the following definition for strong secrecy: Strong secrecy means that an adversary cannot see any difference when the value of the secret changes For example, if a process encrypts a message m an attacker can differentiate between different messages, since their ciphertexts will be different. Thus m is not a strong secret. If however, probabilistic encryption were used, m would be a strong secret. The randomness incorporated into the encryption algorithm will yield different ciphertexts for the same value of m.