Words of fixed size q are commonly referred to as q-grams. We consider the problem of q-gram filtration, a method commonly used to speed up sequence comparison. We are interested in the statistics of the number of q-grams common to two random texts (where multiplicities are not counted) in the non uniform Bernoulli model. In the exact and dependent model, when omitting border effects, a q-gram ...

A quantitative model for the damping of oscillations of the semiquinone absorption after successive light flashes is presented. It is based on the equilibrium between the states Q(A)-Q(B) and Q(A) Q(-B). A fit of the model to the experimental results obtained for reaction centers from Rhodopseudomonas sphaeroides gave a value of α = [Q(A)-Q(B)I/(IQ(A)-Q(Bl)+ [Q(A)Q(-B)I) = 0.065 +/- 0.005 (T= 2...

The study was conducted to investigate the fatty acid profile of intramuscular fat in longissimus lumborum (LL) of three genotypic groups: Qazvinian native (Q, n=10), crossbred Qazvinian native × Saanen breed (QS, n=10) and backcrossed Qazvinian native × Saanen breed (QSS, n=9) male kids.All of kids were weaned at 75-days-old and then fed with a diet consisted of concentrate (70%) and alfalfa h...

We investigate the persistence probability in the Voter model for dimensions d ≥ 2. This is achieved by mapping the Voter model onto a continuum reaction–diffusion system. Using path integral methods , we compute the persistence probability r(q, t), where q is the number of " opinions " in the original Voter model. We find r(q, t) ∼ exp[−f 2 (q)(ln t) 2 ] in d = 2; r(q, t) ∼ exp[−f d (q)t (d−2)...

Model-free reinforcement learning (RL) is a powerful, general tool for learning complex behaviors. However, its sample efficiency is often impractically large for solving challenging real-world problems, even with off-policy algorithms such as Q-learning. A limiting factor in classic model-free RL is that the learning signal consists only of scalar rewards, ignoring much of the rich information...

Existing multi-agent reinforcement learning methods are limited typically to a small number of agents. When the agent number increases largely, the learning becomes intractable due to the curse of the dimensionality and the exponential growth of user interactions. In this paper, we present Mean Field Reinforcement Learning where the interactions within the population of agents are approximated ...

An episodic unsupervised learning simulation using the Q-Learning method is developed to learn the optimal shape and shape change policy for a problem with four state variables. Optimality is addressed by reward functions based on airfoil properties such as lift coefficient, drag coefficient, and moment coefficient about the leading edge representing optimal shapes for specified flight conditio...

Reinforcement learning can provide a robust and natural means for agents to learn how to coordinate their action choices in multiagent systems. We examine some of the factors that can influence the dynamics of the learning process in such a setting. We first distinguish reinforcement learners that are unaware of (or ignore) the presence of other agents from those that explicitly attempt to lear...