It is well known that the type system of ML is overly re strictive in its handling of recursion certain intuitively sound terms do not pass ML s type check We formalise this intuition and show that the restriction is semantical there are computable semantical functions which cannot be expressed by well typed syntactical terms

Dependent ML is a functional programming language that extends ML with a restricted form of dependent types. In this paper, we study a call-by-value continuation-passing style (CPS) transform for ML 0 , a core of DML that excludes effects. In particular, we demonstrate how the type derivation of an expression in ML 0 can be transformed into the type derivation of the CPS transform of the expres...

The Standard ML of New Jersey compiler has been under development for five years now. We have developed a robust and complete environment for Standard ML that supports the implementation of large software systems and generates efficient code. The compiler has also served as a laboratory for developing novel implementation techniques for a sophisticated type and module system, continuation based...

An increase in cloud-based computing leads to an increased worry in the security of user data. Typically, data is sent to a third-party server which performs analytics or machine learning on the data. However, in most of these scenarios, the data involved is sensitive and should remain private. Homomorphic encryption, a form of encryption that allows functions to be performed on encrypted ciphe...

We study the complexity of type reconstruction for a core fragment of ML with lambda abstraction, function application, and the polymorphic let declaration. We derive exponential upper and lower bounds on recognizing the typable core ML expressions. Our primary technical tool is uniication of succinctly represented type expressions. After observing that core ML expressions, of size n, can be ty...

We describe a variant of the Standard ML programming language which incorporates a facility for the replacement of modular components during program execution. This useful extension of the language builds upon existing compiler technology which permits the separate compilation of modular units of a Standard ML program. Defined naively, this extension would incur performance overheads due to the...

Standard ML is an excellent language for many kinds of programming. It is safe, efcient, suitably abstract, and concise. There are many aspects of the language that work well. However, nothing is perfect: Standard ML has a few shortcomings. In some cases there are obvious solutions, and in other cases further research is required. The Meta-Language of the Edinburgh LCF theorem-proving system [1...

The following notes give an overview of Standard ML Mod ules system Part gives an introduction to ML Modules aimed at the reader who is familiar with a functional programming language but has little or no experience with ML programming Part is a half day practical intended to give the reader an opportu nity to modify a small but non trivial piece of software using functors signatures and struct...