Alternatives to animal testingAlternatives to animal testing are the development and implementation of test methods that avoid the use of live animals. There is widespread agreement that a reduction in the number of animals used and the refinement of testing to reduce suffering should be important goals for the industries involved. Two major alternatives to in vivo animal testing are in vitro cell culture techniques and in silico computer simulation.
Hybrid vehicle drivetrainHybrid vehicle drivetrains transmit power to the driving wheels for hybrid vehicles. A hybrid vehicle has multiple forms of motive power. Hybrids come in many configurations. For example, a hybrid may receive its energy by burning gasoline, but switch between an electric motor and a combustion engine. Electrical vehicles have a long history combining internal combustion and electrical transmission - as in a diesel–electric power-train - although they have mostly been used for rail locomotives.
Animal testingAnimal testing, also known as animal experimentation, animal research, and in vivo testing, is the use of non-human animals in experiments that seek to control the variables that affect the behavior or biological system under study. This approach can be contrasted with field studies in which animals are observed in their natural environments or habitats. Experimental research with animals is usually conducted in universities, medical schools, pharmaceutical companies, defense establishments, and commercial facilities that provide animal-testing services to the industry.
Learning to rankLearning to rank or machine-learned ranking (MLR) is the application of machine learning, typically supervised, semi-supervised or reinforcement learning, in the construction of ranking models for information retrieval systems. Training data consists of lists of items with some partial order specified between items in each list. This order is typically induced by giving a numerical or ordinal score or a binary judgment (e.g. "relevant" or "not relevant") for each item.
Unsupervised learningUnsupervised learning, is paradigm in machine learning where, in contrast to supervised learning and semi-supervised learning, algorithms learn patterns exclusively from unlabeled data. Neural network tasks are often categorized as discriminative (recognition) or generative (imagination). Often but not always, discriminative tasks use supervised methods and generative tasks use unsupervised (see Venn diagram); however, the separation is very hazy. For example, object recognition favors supervised learning but unsupervised learning can also cluster objects into groups.
Hyperparameter (machine learning)In machine learning, a hyperparameter is a parameter whose value is used to control the learning process. By contrast, the values of other parameters (typically node weights) are derived via training. Hyperparameters can be classified as model hyperparameters, that cannot be inferred while fitting the machine to the training set because they refer to the model selection task, or algorithm hyperparameters, that in principle have no influence on the performance of the model but affect the speed and quality of the learning process.
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.
Kalahari DesertThe Kalahari Desert is a large semi-arid sandy savanna in Southern Africa extending for , covering much of Botswana, as well as parts of Namibia and South Africa. It is not to be confused with the Angolan, Namibian, and South African Namib coastal desert, whose name is of Khoekhoegowab origin and means "vast place". Kalahari is derived from the Tswana word Kgala, meaning "the great thirst", or Kgalagadi, meaning "a waterless place"; the Kalahari has vast areas covered by red sand without any permanent surface water.
Spring FrameworkThe Spring Framework is an application framework and inversion of control container for the Java platform. The framework's core features can be used by any Java application, but there are extensions for building web applications on top of the Java EE (Enterprise Edition) platform. The framework does not impose any specific programming model.. The framework has become popular in the Java community as an addition to the Enterprise JavaBeans (EJB) model. The Spring Framework is open source.
Enterprise architecture frameworkAn enterprise architecture framework (EA framework) defines how to create and use an enterprise architecture. An architecture framework provides principles and practices for creating and using the architecture description of a system. It structures architects' thinking by dividing the architecture description into domains, layers, or views, and offers models - typically matrices and diagrams - for documenting each view. This allows for making systemic design decisions on all the components of the system and making long-term decisions around new design requirements, sustainability, and support.
