Formalising progress properties of non-blocking programs

A non-blocking program is one that uses non-blocking primitives, such as load-linked/store-conditional and compare-and-swap, for synchronisation instead of locks so that no process is ever blocked. According to their progress properties, non-blocking programs may be classified as wait-free, lock-free or obstruction-free. However, a precise description of these properties does not exist and it is not unusual to find a definition that is ambiguous or even incorrect. We present a formal definition of the progress properties so that any confusion is removed. The formalisation also allows one to prove the widely believed presumption that wait-freedom is a special case of lock-freedom, which in turn is a special case of obstruction-freedom.

Refining specifications to logic programs

The refinement calculus provides a framework for the stepwise development of imperative programs from specifications. In this paper we study a refinement calculus for deriving logic programs. Dealing with logic programs rather than imperative programs has the dual advantages that, due to the expressive power of logic programs, the final program is closer to the original specification, and each refinement step can achieve more. Together these reduce the overall number of derivation steps. We present a logic programming language extended with specification constructs (including general predicates, assertions, and types and invariants) to form a wide-spectrum language. General predicates allow non-executable properties to be included in specifications. Assertions, types and invariants make assumptions about the intended inputs of a procedure explicit, and can be used during refinement to optimize the constructed logic program. We provide a semantics for the extended logic programming language and derive a set of refinement laws. Finally we apply these to an example derivation.

On learning context-free and context-sensitive languages

The long short-term memory (LSTM) is not the only neural network which learns a context sensitive language. Second-order sequential cascaded networks (SCNs) are able to induce means from a finite fragment of a context-sensitive language for processing strings outside the training set. The dynamical behavior of the SCN is qualitatively distinct from that observed in LSTM networks. Differences in performance and dynamics are discussed.