Solar energySolar energy is radiant light and heat from the Sun that is harnessed using a range of technologies such as solar power to generate electricity, solar thermal energy (including solar water heating), and solar architecture. It is an essential source of renewable energy, and its technologies are broadly characterized as either passive solar or active solar depending on how they capture and distribute solar energy or convert it into solar power.
Numerical weather predictionNumerical weather prediction (NWP) uses mathematical models of the atmosphere and oceans to predict the weather based on current weather conditions. Though first attempted in the 1920s, it was not until the advent of computer simulation in the 1950s that numerical weather predictions produced realistic results. A number of global and regional forecast models are run in different countries worldwide, using current weather observations relayed from radiosondes, weather satellites and other observing systems as inputs.
Learning organizationIn business management, a learning organization is a company that facilitates the learning of its members and continuously transforms itself. The concept was coined through the work and research of Peter Senge and his colleagues. Learning organizations may develop as a result of the pressures facing modern organizations; this enables them to remain competitive in the business environment. There are many definitions of a learning organization as well as typologies of kinds of learning organizations.
Reinforcement learning from human feedbackIn machine learning, reinforcement learning from human feedback (RLHF) or reinforcement learning from human preferences is a technique that trains a "reward model" directly from human feedback and uses the model as a reward function to optimize an agent's policy using reinforcement learning (RL) through an optimization algorithm like Proximal Policy Optimization. The reward model is trained in advance to the policy being optimized to predict if a given output is good (high reward) or bad (low reward).
Multi-agent reinforcement learningMulti-agent reinforcement learning (MARL) is a sub-field of reinforcement learning. It focuses on studying the behavior of multiple learning agents that coexist in a shared environment. Each agent is motivated by its own rewards, and does actions to advance its own interests; in some environments these interests are opposed to the interests of other agents, resulting in complex group dynamics. Multi-agent reinforcement learning is closely related to game theory and especially repeated games, as well as multi-agent systems.
Lattice-based access controlIn computer security, lattice-based access control (LBAC) is a complex access control model based on the interaction between any combination of objects (such as resources, computers, and applications) and subjects (such as individuals, groups or organizations). In this type of label-based mandatory access control model, a lattice is used to define the levels of security that an object may have and that a subject may have access to. The subject is only allowed to access an object if the security level of the subject is greater than or equal to that of the object.
Quadratic variationIn mathematics, quadratic variation is used in the analysis of stochastic processes such as Brownian motion and other martingales. Quadratic variation is just one kind of variation of a process. Suppose that is a real-valued stochastic process defined on a probability space and with time index ranging over the non-negative real numbers. Its quadratic variation is the process, written as , defined as where ranges over partitions of the interval and the norm of the partition is the mesh.
RSBACRule-set-based access control (RSBAC) is an open source access control framework for current Linux kernels, which has been in stable production use since January 2000 (version 1.0.9a). Free open source GNU General Public License (GPL) Linux kernel security extension Independent of governments and big companies Several well-known and new security models, e.g.
OccupancyWithin the context of building construction and building codes, occupancy is the use (actual or intended) of a building (or its portion) for the shelter or support of persons, animals or property. A closely related meaning is the number of units in such a building that are rented, leased, or otherwise in use. Lack of occupancy, in this sense, is known as vacancy. It is possible to have multiple occupancies (or building uses) within one building. For example, a high-rise building can have retail stores occupying the lower levels, while the upper levels are residential.
Self-playSelf-play is a technique for improving the performance of reinforcement learning agents. Intuitively, agents learn to improve their performance by playing "against themselves". In multi-agent reinforcement learning experiments, researchers try to optimize the performance of a learning agent on a given task, in cooperation or competition with one or more agents. These agents learn by trial-and-error, and researchers may choose to have the learning algorithm play the role of two or more of the different agents.
