Audio multicore cableAn audio multicore cable (often colloquially referred to as a multicore, snake cable or snake) is a thick cable which usually contains 4–64 individual audio cables inside a common, sturdy outer jacket. Audio multicore cables are used to convey many audio signals between two locations, such as in audio recording, sound reinforcement, PA systems and broadcasting. Multicores often route many signals from microphones or musical instruments to a mixing console, and can also carry signals from a mixing console back to speakers.
Cone dystrophyA cone dystrophy is an inherited ocular disorder characterized by the loss of cone cells, the photoreceptors responsible for both central and color vision. The most common symptoms of cone dystrophy are vision loss (age of onset ranging from the late teens to the sixties), sensitivity to bright lights, and poor color vision. Therefore, patients see better at dusk. Visual acuity usually deteriorates gradually, but it can deteriorate rapidly to 20/200; later, in more severe cases, it drops to "counting fingers" vision.
Feature (machine learning)In machine learning and pattern recognition, a feature is an individual measurable property or characteristic of a phenomenon. Choosing informative, discriminating and independent features is a crucial element of effective algorithms in pattern recognition, classification and regression. Features are usually numeric, but structural features such as strings and graphs are used in syntactic pattern recognition. The concept of "feature" is related to that of explanatory variable used in statistical techniques such as linear regression.
EvaluationIn common usage, evaluation is a systematic determination and assessment of a subject's merit, worth and significance, using criteria governed by a set of standards. It can assist an organization, program, design, project or any other intervention or initiative to assess any aim, realisable concept/proposal, or any alternative, to help in decision-making; or to ascertain the degree of achievement or value in regard to the aim and objectives and results of any such action that has been completed.
Feature (computer vision)In computer vision and , a feature is a piece of information about the content of an image; typically about whether a certain region of the image has certain properties. Features may be specific structures in the image such as points, edges or objects. Features may also be the result of a general neighborhood operation or feature detection applied to the image. Other examples of features are related to motion in image sequences, or to shapes defined in terms of curves or boundaries between different image regions.
Retinitis pigmentosaRetinitis pigmentosa (RP) is a genetic disorder of the eyes that causes loss of vision. Symptoms include trouble seeing at night and decreasing peripheral vision (side and upper or lower visual field). As peripheral vision worsens, people may experience "tunnel vision". Complete blindness is uncommon. Onset of symptoms is generally gradual and often begins in childhood. Retinitis pigmentosa is generally inherited from one or both parents. It is caused by genetic variants in nearly 100 genes.
Program evaluationProgram evaluation is a systematic method for collecting, analyzing, and using information to answer questions about projects, policies and programs, particularly about their effectiveness and efficiency. In both the public sector and private sector, as well as the voluntary sector, stakeholders might be required to assess—under law or charter—or want to know whether the programs they are funding, implementing, voting for, receiving or opposing are producing the promised effect.
Feature learningIn machine learning, feature learning or representation learning is a set of techniques that allows a system to automatically discover the representations needed for feature detection or classification from raw data. This replaces manual feature engineering and allows a machine to both learn the features and use them to perform a specific task. Feature learning is motivated by the fact that machine learning tasks such as classification often require input that is mathematically and computationally convenient to process.
