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Responsible Research and Innovation (RRI) is a term used by the European Union's Framework Programmes to describe scientific research and technological development processes that take into account effects and potential impacts on the environment and society. It gained visibility around the year 2010, arising from predecessors including "ELSA" (Ethical, Legal and Social Aspects) studies prompted by the Human Genome Project. Various slightly different definitions of RRI emerged, but all of them agree that societal challenges should be a primary focus of scientific research, and moreover they agree upon the methods by which that goal should be achieved. RRI involves holding research to high ethical standards, ensuring gender equality in the scientific community, investing policy-makers with the responsibility to avoid harmful effects of innovation, engaging the communities affected by innovation and ensuring that they have the knowledge necessary to understand the implications by furthering science education and Open Access. Organizations that adopted the RRI terminology include the Engineering and Physical Sciences Research Council and the Netherlands Organization for Scientific Research, the latter of which incorporated the language of RRI into their pre-existing program for funding "Societally Responsible Innovating".

"Horizon 2020", the European Commission's program for science funding announced in 2013, made RRI a main focus. In 2014, it was suggested that the "broader impacts" criteria of the National Science Foundation were, despite certain dissimilarities, in effect coming to resemble RRI standards.

One area in which RRI principles are being applied is quantum computing. A research collaboration led by Oxford University within the UK National Quantum Technologies Programme aims to reveal how quantum computing can be socially and economically transformative, and to identify the potential downsides of the "disruption" it might bring about.

Among the criticisms voiced about RRI, prominent concerns include the vagueness of the terminology, the possibility of discouraging blue skies research and the lack of sufficient practical reward for embracing RRI in a research culture based on competition and short-term contracts.

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Red Hat, Inc. is an American IBM subsidiary software company that provides open source software products to enterprises. Founded in 1993, Red Hat has its corporate headquarters in Raleigh, North Carolina, with other offices worldwide.

Red Hat has become associated to a large extent with its enterprise operating system Red Hat Enterprise Linux. With the acquisition of open-source enterprise middleware vendor JBoss, Red Hat also offers Red Hat Virtualization (RHV), an enterprise virtualization product. Red Hat provides storage, operating system platforms, middleware, applications, management products, and support, training, and consulting services.

Red Hat creates, maintains, and contributes to many free software projects. It has acquired several proprietary software product codebases through corporate mergers and acquisitions and has released such software under open source licenses. As of March 2016, Red Hat is the second largest corporate contributor to the Linux kernel version 4.14 after Intel.

On October 28, 2018, IBM announced its intent to acquire Red Hat for $34 billion. The acquisition closed on July 9, 2019. It now operates as an independent subsidiary.

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A recurrent neural network (RNN) is a class of artificial neural networks where connections between nodes form a directed graph along a temporal sequence. This allows it to exhibit temporal dynamic behavior. Derived from feedforward neural networks, RNNs can use their internal state (memory) to process variable length sequences of inputs. This makes them applicable to tasks such as unsegmented, connected handwriting recognition or speech recognition. Recurrent neural networks are theoretically Turing complete and can run arbitrary programs to process arbitrary sequences of inputs.

The term “recurrent neural network” is used indiscriminately to refer to two broad classes of networks with a similar general structure, where one is finite impulse and the other is infinite impulse. Both classes of networks exhibit temporal dynamic behavior. A finite impulse recurrent network is a directed acyclic graph that can be unrolled and replaced with a strictly feedforward neural network, while an infinite impulse recurrent network is a directed cyclic graph that can not be unrolled.

Both finite impulse and infinite impulse recurrent networks can have additional stored states, and the storage can be under direct control by the neural network. The storage can also be replaced by another network or graph if that incorporates time delays or has feedback loops. Such controlled states are referred to as gated state or gated memory, and are part of long short-term memory networks (LSTMs) and gated recurrent units. This is also called Feedback Neural Network (FNN).

In typical libraries like PyTorch Just-in-time compilation plays an important role in efficiently implementing recurrent neural networks.

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Java Astrodynamics Toolkit (JAT), is libraries of components to assist software makers create their own applications to solve problems in Astrodynamics, mission design, spacecraft navigation, guidance and control by means of Java or Matlab. This software is a major collaboration by the University of Texas Computer Science department for use for professional Computer Astronomers and is used by NASA. The software is one of the most advanced Astronomical toolkits available on the globe.