Ricerc@Sapienza

Because of the unavoidable intrinsic noise affecting biochemical processes, astochastic approach is usually preferred whenever a deterministic model givestoo rough information or, worse, may lead to erroneous qualitative behaviorsand/or quantitatively wrong results. In this work we focus on the chemicalmaster equation (CME)-based method which provides an accurate stochasticdescription of complex biochemical reaction networks in terms of the probabilitydistribution of the underlying chemical populations.

We describe Regular Decision Processes (RDPs) a model in between MDPs and POMDPs. Like in POMDPs, the effect of an action may depend on the entire history of actions and observations, but this dependence is restricted to regular functions only. This makes RDP a tractable, yet rich model, that does not hypothesize hidden state, and could possibly be useful for learning dynamic systems.

Software Transactional Memory (STM) stands as powerful concurrent programming paradigm, enabling atomicity and isolation while accessing shared data. On the downside, STM may suffer from performance degradation due to excessive conflicts among concurrent transactions, which cause waste of CPU-cycles and energy because of transaction aborts. An approach to cope with this issue consists of putting in place smart scheduling strategies which temporarily suspend the execution of some transaction in order to reduce the transaction conflict rate.