We provide several machine-independent characterizations of deterministic complexity classes in the model of computation proposed by L. Blum, M. Shub and S. Smale. We provide a characterization of partial recursive functions over any arbitrary structure. We show that polynomial time computable functions over any arbitrary structure can be characterized in term of safe recursive functions. We sh...

Uncertainty is critical to questions about climate change policy. Recently developed recursive integrated assessment models have become the primary tools for studying and quantifying the policy implications of uncertainty. We decompose the channels through which uncertainty affects policy and quantify them in a recursive extension of a benchmark integrated assessment model. The first wave of re...

Recursive neural networks are a new connec-tionist model recently introduced for processing graphs. Linear recursive networks are a special sub-class where the neurons have linear activation functions. The approximation properties of recursive networks are tightly connected to the possibility of distinguishing the patterns by generating a different internal encoding for each input of the domain...

The relational model base system represents a significant departure from the data-base dependent decision models that currently exist when discussing real-time data analysis and decision making. This research involves detailing cases of prototypical relational model-base-centered integrative systems and the incorporated relational model-base structures for each system as proposed by Sutherland ...

The classical non-recursive methods to estimate unknown parameters of the model, such as the maximum likelihood method, the method of least squares etc. eventually require maximization procedures. These methods are often difficult to implement, especially for non i.i.d. models. If for every sample size n, when new data are acquired, an estimator has to be computed afresh, and if a numerical met...

We revisit the SL synchronous programming model introduced by Boussinot andDe Simone (IEEE, Trans. on Soft. Eng., 1996). We discuss an alternative designof the model including thread spawning and recursive definitions and we exploresome basic properties of the revised model: determinism, reactivity, CPS translationto a tail recursive form, computational expressivity, and a compo...

We define the concept of a logic frame, which extends the concept of an abstract logic by adding the concept of a syntax and an axiom system. In a recursive logic frame the syntax and the set of axioms are recursively coded. A recursive logic frame is called recursively (countably) compact, if every recursive (respectively, countable) finitely consistent theory has a model. We show that for log...

We propose the first implementation of an infinite-order generative dependency model. The model is based on a new recursive neural network architecture, the Inside-Outside Recursive Neural Network. This architecture allows information to flow not only bottom-up, as in traditional recursive neural networks, but also topdown. This is achieved by computing content as well as context representation...

A multivariate normal statistical model defined by the Markov properties determined by an acyclic digraph admits a recursive factorization of its likelihood function (LF) into the product of conditional LFs, each factor having the form of a classical multivariate linear regression model (≡ MANOVA model). Here these models are extended in a natural way to normal linear regression models whose LF...