Learning rateIn machine learning and statistics, the learning rate is a tuning parameter in an optimization algorithm that determines the step size at each iteration while moving toward a minimum of a loss function. Since it influences to what extent newly acquired information overrides old information, it metaphorically represents the speed at which a machine learning model "learns". In the adaptive control literature, the learning rate is commonly referred to as gain. In setting a learning rate, there is a trade-off between the rate of convergence and overshooting.
Respiratory tract infectionRespiratory tract infections (RTIs) are infectious diseases involving the respiratory tract. An infection of this type usually is further classified as an upper respiratory tract infection (URI or URTI) or a lower respiratory tract infection (LRI or LRTI). Lower respiratory infections, such as pneumonia, tend to be far more severe than upper respiratory infections, such as the common cold. Upper respiratory tract infection The upper respiratory tract is considered the airway above the glottis or vocal cords; sometimes, it is taken as the tract above the cricoid cartilage.
Polyclonal B cell responsePolyclonal B cell response is a natural mode of immune response exhibited by the adaptive immune system of mammals. It ensures that a single antigen is recognized and attacked through its overlapping parts, called epitopes, by multiple clones of B cell. In the course of normal immune response, parts of pathogens (e.g. bacteria) are recognized by the immune system as foreign (non-self), and eliminated or effectively neutralized to reduce their potential damage. Such a recognizable substance is called an antigen.
Poisson distributionIn probability theory and statistics, the Poisson distribution is a discrete probability distribution that expresses the probability of a given number of events occurring in a fixed interval of time or space if these events occur with a known constant mean rate and independently of the time since the last event. It is named after French mathematician Siméon Denis Poisson ('pwɑːsɒn; pwasɔ̃). The Poisson distribution can also be used for the number of events in other specified interval types such as distance, area, or volume.
Markov decision processIn mathematics, a Markov decision process (MDP) is a discrete-time stochastic control process. It provides a mathematical framework for modeling decision making in situations where outcomes are partly random and partly under the control of a decision maker. MDPs are useful for studying optimization problems solved via dynamic programming. MDPs were known at least as early as the 1950s; a core body of research on Markov decision processes resulted from Ronald Howard's 1960 book, Dynamic Programming and Markov Processes.
HelminthiasisHelminthiasis, also known as worm infection, is any macroparasitic disease of humans and other animals in which a part of the body is infected with parasitic worms, known as helminths. There are numerous species of these parasites, which are broadly classified into tapeworms, flukes, and roundworms. They often live in the gastrointestinal tract of their hosts, but they may also burrow into other organs, where they induce physiological damage.
Opportunistic infectionAn opportunistic infection is an infection caused by pathogens (bacteria, fungi, parasites or viruses) that take advantage of an opportunity not normally available. These opportunities can stem from a variety of sources, such as a weakened immune system (as can occur in acquired immunodeficiency syndrome or when being treated with immunosuppressive drugs, as in cancer treatment), an altered microbiome (such as a disruption in gut microbiota), or breached integumentary barriers (as in penetrating trauma).
Q-learningQ-learning is a model-free reinforcement learning algorithm to learn the value of an action in a particular state. It does not require a model of the environment (hence "model-free"), and it can handle problems with stochastic transitions and rewards without requiring adaptations. For any finite Markov decision process (FMDP), Q-learning finds an optimal policy in the sense of maximizing the expected value of the total reward over any and all successive steps, starting from the current state.
Adaptive immune systemThe adaptive immune system, also known as the acquired immune system, or specific immune system is a subsystem of the immune system that is composed of specialized, systemic cells and processes that eliminate pathogens or prevent their growth. The acquired immune system is one of the two main immunity strategies found in vertebrates (the other being the innate immune system). Like the innate system, the adaptive immune system includes both humoral immunity components and cell-mediated immunity components and destroys invading pathogens.
Non-equilibrium thermodynamicsNon-equilibrium thermodynamics is a branch of thermodynamics that deals with physical systems that are not in thermodynamic equilibrium but can be described in terms of macroscopic quantities (non-equilibrium state variables) that represent an extrapolation of the variables used to specify the system in thermodynamic equilibrium. Non-equilibrium thermodynamics is concerned with transport processes and with the rates of chemical reactions.
