Data compression is concerned with how information is organized in data. The size and importance of these databases will be bigger and bigger in the future; therefore this information must be stored or communicated efficiently though there are many text compression algorithms, they are not well suited for the characteristics of DNA sequences. There are algorithms for DNA compression which takes...

This paper studies a baseball statistic that plays the role of an offensive earned-run average (OERA). The OERA of an individual i s simply the number of earned runs per game that he would score if he batted in all nine positions in the line-up. Evaluation can be performed by hand b y scoring the sequence of times at bat of a given batter. This statistic has the obvious natural interpretation a...

A large class of sequential decision-making problems under uncertainty can be modeled as Markov and Semi-Markov Decision Problems, when their underlying probability structure has a Markov chain. They may be solved by using classical dynamic programming methods. However, dynamic programming methods suffer from the curse of dimensionality and break down rapidly in face of large state spaces. In a...

Average-reward reinforcement learning (ARL) is an undiscounted optimality framework that is generally applicable to a broad range of control tasks. ARL computes gain-optimal control policies that maximize the expected payoff per step. However, gainoptimality has some intrinsic limitations as an optimality criterion, since for example, it cannot distinguish between different policies that all re...

Primary cilia are sensory organelles indispensable for organogenesis and tissue pattern formation. Ciliopathy small GTPase ARLs are proposed as prominent ciliary switches, which when disrupted result in dysfunctional cilia, yet how ARLs are activated remain elusive. Here, we discover a novel small GTPase functional module, which contains ARL-3, ARL-13, and UNC-119, localizes near the poorly und...

Active Reinforcement Learning (ARL) is a twist on RL where the agent observes reward information only if it pays a cost. This subtle change makes exploration substantially more challenging. Powerful principles in RL like optimism, Thompson sampling, and random exploration do not help with ARL. We relate ARL in tabular environments to BayesAdaptive MDPs. We provide an ARL algorithm using Monte-C...

A natural multivariate extension of the two-sided cumulative sum chart is proposed via projection pursuit. A modification is given for improving its performance for the special situation in which the process mean is already shifted at the time the charting begins. Simulation studies show that the new charts have slightly better performance than the competing charts (MC1, MEWMA1 and MEWMA2) in t...