Symbolic analysis has many applications in the design of analog circuits. Existing approaches rely on two forms of symbolic-expression representation: expanded sum-of-product form and arbitrarily nested form. Expanded form suffers the problem that the number of product terms grows exponentially with the size of a circuit. Nested form is neither canonical nor amenable to symbolic manipulation. I...

Distances on symbolic musical sequences are needed for a variety of applications, from music retrieval to automatic music generation. These musical sequences belong to a given corpus (or style) and it is obvious that a good distance on musical sequences should take this information into account; being able to define a distance ex nihilo which could be applicable to all music styles seems implau...

In this paper, we extend a symbolic association framework to being able to handle missing elements in multimodal sequences. The general scope of the work is the symbolic associations of object-word mappings as it happens in language development on infants. This scenario has been long interested by Artificial Intelligence, Psychology and Neuroscience. In this work, we extend a recent approach fo...

Sequence processing involves several tasks such as clustering, classification, prediction, and transduction of sequential data which can be symbolic, non-symbolic or mixed. Examples of symbolic data patterns occur in modelling natural (human) language, while the prediction of water level of River Thames is an example of processing non-symbolic data. If the content of a sequence will be varying ...

We introduce a temporal pattern model called Temporal Interval Tree Association Rules (Tita rules or Titar). This pattern model can express both uncertainty and temporal inaccuracy of temporal events. Among other things, Tita rules can express the usual time point operators, synchronicity, order, and chaining, as well as temporal negation and disjunctive temporal constraints. Using this represe...

We seek a high level abstraction not directly on states level, but as a function over states. By constructing the function we identify substructures in the topology of HMM that correspond to “macro” status. In order to construct the function, additional information – sparse manually labeled or automatically generated sources – is needed and task dependent. Different functions can be constructed...

A version of PatMinr [2] has been submitted to the MIREX task on Discovery of Repeated Themes & Sections [1]. PatMinr can find repetitions of sequential patterns from monophonic sequences represented in a symbolic format. This document presents the model in more details, and specifies the particular parameters used in the version submitted to the MIREX competition. 1. VERSION SUBMITTED TO MIREX...

We propose a novel approach for building finite memory predictive models similar in spirit to variable memory length Markov models (VLMMs). The models are constructed by first transforming the n-block structure of the training sequence into a spatial structure of points in a unit hypercube, such that the longer is the common suffix shared by any two n-blocks, the closer lie their point represen...

Acknowledgments I would like to particularly thank my advisor, Yorick Wilks. Over the years I have known him, Yorick has always been insightful, cheerful and generous. His ability to understand and constructively critique research over an extraordinarily wide range of topics has been extremely helpful, both professionally and academically. Moreover, his willingness to consider and even nurture ...

We introduce persistent contextual neural networks (PCNNs) as a probabilistic model for learning symbolic data sequences, aimed at discovering complex algorithmic dependencies in the sequence. PCNNs are similar to recurrent neural networks but feature an architecture inspired by finite automata and a modified time evolution to better model memory effects. An effective training procedure using a...