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Explore the people, places, events, achievements, struggles and stories that shaped our journey.
Movements, leaders, victories and the continuing fight for equality.
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Meet the people whose lives, choices and achievements shaped the journey.
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Moments that changed communities, movements, institutions and the nation.
Katherine Johnson’s mathematical calculations helped guide some of America’s most important early space missions while she confronted the racial and gender barriers faced by Black women in twentieth-century America.
MORE →Reflects the personal views, recollections, and perspective of the author, Mike Davis.
This is a personal recollection on the Move fire on May 13, 1985
A liquid state machine (LSM) is a type of reservoir computer that uses a spiking neural network. An LSM consists of a large collection of units (called nodes, or neurons). Each node receives time varying input from external sources (the inputs) as well as from other nodes. Nodes are randomly connected to each other. The recurrent nature of the connections turns the time varying input into a spatio-temporal pattern of activations in the network nodes. The spatio-temporal patterns of activation are read out by linear discriminant units.
The soup of recurrently connected nodes will end up computing a large variety of nonlinear functions on the input. Given a large enough variety of such nonlinear functions, it is theoretically possible to obtain linear combinations (using the read out units) to perform whatever mathematical operation is needed to perform a certain task, such as speech recognition or computer vision.
The word liquid in the name comes from the analogy drawn to dropping a stone into a still body of water or other liquid. The falling stone will generate ripples in the liquid. The input (motion of the falling stone) has been converted into a spatio-temporal pattern of liquid displacement (ripples).
LSMs have been put forward as a way to explain the operation of brains. LSMs are argued to be an improvement over the theory of artificial neural networks because:
Criticisms of LSMs as used in computational neuroscience are that
If a reservoir has fading memory and input separability, with help of a readout, it can be proven the liquid state machine is a universal function approximator using Stone–Weierstrass theorem.[1]
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A liquid state machine (LSM) is a type of reservoir computer that uses a spiking neural network. An LSM consists of a large collection of units (called nodes, or neurons). Each node receives time varying input from external sources (the inputs) as well as from other nodes. Nodes are randomly connected to each other. The recurrent nature of the connections turns the time varying input into a spatio-temporal pattern of activations in the network nodes. The spatio-temporal patterns of activation are read out by linear discriminant units. The soup of recurrently connected nodes will end up computing a large variety of nonlinear functions on the input. Given a large enough variety of such nonlinear functions, it is theoretically possible to obtain linear combinations (using the read out units) to perform whatever mathematical operation is needed to perform a certain task, such as speech recognition or computer vision. The word liquid in the name comes from the analogy drawn to dropping a stone into a still body of water or other liquid. The falling stone will generate ripples in the liquid. The input (motion of the falling stone) has been converted into a spatio-temporal pattern of liquid displacement (ripples). LSMs have been put forward as a way to explain the operation of brains. LSMs are argued to be an improvement over the theory of artificial neural networks because: Circuits are not hard coded to perform a specific task. Continuous time inputs are handled "naturally". Computations on various time scales can be done using the same network. The same network can perform multiple computations. Criticisms of LSMs as used in computational neuroscience are that LSMs don't actually explain how the brain functions. At best they can replicate some parts of brain functionality. There is no guaranteed way to dissect a working network and figure out how or what computations are being performed. There is very little control over the process.
Liquid crystal (LC) is a state of matter whose properties are between those of conventional liquids and those of solid crystals. There are many types of LC phases, which can be classified by the molecular shape of their subunits, the arrangement and symmetry of the phase, as well as the method by which the liquid crystalline phase is accessed. A molecule which can access LC mesophases is called a mesogen. Just as in crystalline solids, the ordering present in liquid crystals offers anisotropic optical, electrical, and magnetic properties. Their crystalline-like anisotropy alongside their liquid-like stimuli responsiveness and flow have made LCs useful in applications in displays (notably Liquid-crystal displays), sensors, and soft robotics. Liquid crystals can be divided into three main types: thermotropic, lyotropic, and metallotropic. Thermotropic and lyotropic liquid crystals consist mostly of organic molecules, although a few minerals are also known. Thermotropic LCs exhibit a phase transition into the LC phase as temperature changes. Lyotropic LCs exhibit phase transitions as a function of both temperature and concentration of molecules in a solvent (typically water). Metallotropic LCs are composed of both organic and inorganic molecules; their LC transition additionally depends on the inorganic-organic composition ratio. Examples of LCs exist both in the natural world and in technological applications. Lyotropic LCs abound in living systems; many proteins and cell membranes are LCs, as well as the tobacco mosaic virus. LCs in the mineral world include solutions of soap and various related detergents, and some clays. Widespread liquid-crystal displays (LCD) use liquid crystals.
In physics, a state of matter or phase of matter is one of the distinct forms in which matter can exist. Four states of matter are observable in everyday life: solid, liquid, gas, and plasma. Different states are distinguished by the ways the component particles (atoms, molecules, ions and electrons) are arranged, and how they behave collectively. In a solid, the particles are tightly packed and held in fixed positions, giving the material a definite shape and volume. In a liquid, the particles remain close together but can move past one another, allowing the substance to maintain a fixed volume while adapting to the shape of its container. In a gas, the particles are far apart and move freely, allowing the substance to expand and fill both the shape and volume of its container. Plasma is similar to a gas, but it also contains charged particles (ions and free electrons) that move independently and respond to electric and magnetic fields. Beyond the classical states of matter, a wide variety of additional states are known to exist (see list of states of matter). Some of these lie between the traditional categories; for example, liquid crystals exhibit properties of both solids and liquids. Others represent entirely different kinds of ordering. Magnetic states, for instance, do not depend on the spatial arrangement of atoms, but rather on the alignment of their intrinsic magnetic moments (spins). Even in a solid where atoms are fixed in position, the spins can organize in distinct ways, giving rise to magnetic states such as ferromagnetism or antiferromagnetism. Some states occur only under extreme conditions, such as Bose–Einstein condensates and Fermionic condensates (both in extreme cold), neutron-degenerate matter (in extreme density), and quark–gluon plasma (at extremely high energy). The term phase is sometimes used as a synonym for state of matter, but it is possible for a single compound to form different phases that are in the same state of matter. For example, ice is the solid state of water, but there are multiple phases of ice with different crystal structures, which are formed at different pressures and temperatures.
Liquid hydrogen (H2(l)) is the liquid state of the element hydrogen. Hydrogen is found naturally in the molecular H2 form. To exist as a liquid, H2 must be cooled below its critical point of 33 K. However, for it to be in a fully liquid state at atmospheric pressure, H2 needs to be cooled to 20.28 K (−252.87 °C; −423.17 °F). A common method of obtaining liquid hydrogen involves a compressor resembling a jet engine in both appearance and principle. Liquid hydrogen is typically used as a concentrated form of hydrogen storage. Storing it as liquid takes less space than storing it as a gas at normal temperature and pressure. However, the liquid density is very low compared to other common fuels. Once liquefied, it can be maintained as a liquid for some time in thermally insulated containers. There are two spin isomers of hydrogen. Room temperature hydrogen is 75% orthohydrogen. At cryogenic temperature it converts exothermically to parahydrogen. The thermodynamic lowest energy state for liquid hydrogen consists of 99.79% parahydrogen and 0.21% orthohydrogen. To avoid the exothermic heat release and resulting excessive boil-off in storage, catalytic conversion to parahydrogen during liquefaction is employed. Hydrogen requires a theoretical minimum of 3.3 kWh/kg (12 MJ/kg) to liquefy, and 3.9 kWh/kg (14 MJ/kg) including converting the hydrogen to the para isomer. Existing liquefaction facilities use 10–13 kWh/kg (36–47 MJ/kg) compared to a 33 kWh/kg (119 MJ/kg) heating value of hydrogen. More recent work shows future facilities are expected to cut the specific energy demand by half to 6.5 kWh/kg (23 MJ/kg).
Before the 1921 destruction of Tulsa’s Greenwood District, Black residents had created a remarkable center of business and community life. The district included stores, professional offices, entertainment venues and homes owned by Black citizens. Understanding Greenwood means learning what was built—not only what was burned.
MORE →Brown v. Board of Education in 1954.