3,206 research outputs found
On the Power of Insertion P Systems of Small Size
In this article we investigate insertion systems of small size in the framework
of P systems. We consider P systems with insertion rules having one symbol context and
we show that they have the computational power of matrix grammars. If contexts of
length two are permitted, then any recursively enumerable language can be generated.
In both cases an inverse morphism and a weak coding were applied to the output of the
corresponding P systems
Substructure Discovery Using Minimum Description Length and Background Knowledge
The ability to identify interesting and repetitive substructures is an
essential component to discovering knowledge in structural data. We describe a
new version of our SUBDUE substructure discovery system based on the minimum
description length principle. The SUBDUE system discovers substructures that
compress the original data and represent structural concepts in the data. By
replacing previously-discovered substructures in the data, multiple passes of
SUBDUE produce a hierarchical description of the structural regularities in the
data. SUBDUE uses a computationally-bounded inexact graph match that identifies
similar, but not identical, instances of a substructure and finds an
approximate measure of closeness of two substructures when under computational
constraints. In addition to the minimum description length principle, other
background knowledge can be used by SUBDUE to guide the search towards more
appropriate substructures. Experiments in a variety of domains demonstrate
SUBDUE's ability to find substructures capable of compressing the original data
and to discover structural concepts important to the domain. Description of
Online Appendix: This is a compressed tar file containing the SUBDUE discovery
system, written in C. The program accepts as input databases represented in
graph form, and will output discovered substructures with their corresponding
value.Comment: See http://www.jair.org/ for an online appendix and other files
accompanying this articl
Complexity and modeling power of insertion-deletion systems
SISTEMAS DE INSERCIÓN Y BORRADO: COMPLEJIDAD Y
CAPACIDAD DE MODELADO
El objetivo central de la tesis es el estudio de los sistemas de inserción y borrado y su
capacidad computacional. Más concretamente, estudiamos algunos modelos de
generación de lenguaje que usan operaciones de reescritura de dos cadenas. También
consideramos una variante distribuida de los sistemas de inserción y borrado en el
sentido de que las reglas se separan entre un número finito de nodos de un grafo.
Estos sistemas se denominan sistemas controlados mediante grafo, y aparecen en
muchas áreas de la Informática, jugando un papel muy importante en los lenguajes
formales, la lingüística y la bio-informática. Estudiamos la decidibilidad/
universalidad de nuestros modelos mediante la variación de los parámetros de tamaño
del vector. Concretamente, damos respuesta a la cuestión más importante
concerniente a la expresividad de la capacidad computacional: si nuestro modelo es
equivalente a una máquina de Turing o no. Abordamos sistemáticamente las
cuestiones sobre los tamaños mínimos de los sistemas con y sin control de grafo.COMPLEXITY AND MODELING POWER OF
INSERTION-DELETION SYSTEMS
The central object of the thesis are insertion-deletion systems and their computational
power. More specifically, we study language generating models that use two string
rewriting operations: contextual insertion and contextual deletion, and their
extensions. We also consider a distributed variant of insertion-deletion systems in the
sense that rules are separated among a finite number of nodes of a graph. Such
systems are refereed as graph-controlled systems. These systems appear in many
areas of Computer Science and they play an important role in formal languages,
linguistics, and bio-informatics. We vary the parameters of the vector of size of
insertion-deletion systems and we study decidability/universality of obtained models.
More precisely, we answer the most important questions regarding the expressiveness
of the computational model: whether our model is Turing equivalent or not. We
systematically approach the questions about the minimal sizes of the insertiondeletion
systems with and without the graph-control
Deepr: A Convolutional Net for Medical Records
Feature engineering remains a major bottleneck when creating predictive
systems from electronic medical records. At present, an important missing
element is detecting predictive regular clinical motifs from irregular episodic
records. We present Deepr (short for Deep record), a new end-to-end deep
learning system that learns to extract features from medical records and
predicts future risk automatically. Deepr transforms a record into a sequence
of discrete elements separated by coded time gaps and hospital transfers. On
top of the sequence is a convolutional neural net that detects and combines
predictive local clinical motifs to stratify the risk. Deepr permits
transparent inspection and visualization of its inner working. We validate
Deepr on hospital data to predict unplanned readmission after discharge. Deepr
achieves superior accuracy compared to traditional techniques, detects
meaningful clinical motifs, and uncovers the underlying structure of the
disease and intervention space
On the Computational Complexity and Formal Hierarchy of Second Order Recurrent Neural Networks
Artificial neural networks (ANNs) with recurrence and self-attention have
been shown to be Turing-complete (TC). However, existing work has shown that
these ANNs require multiple turns or unbounded computation time, even with
unbounded precision in weights, in order to recognize TC grammars. However,
under constraints such as fixed or bounded precision neurons and time, ANNs
without memory are shown to struggle to recognize even context-free languages.
In this work, we extend the theoretical foundation for the -order
recurrent network ( RNN) and prove there exists a class of a
RNN that is Turing-complete with bounded time. This model is capable of
directly encoding a transition table into its recurrent weights, enabling
bounded time computation and is interpretable by design. We also demonstrate
that nd order RNNs, without memory, under bounded weights and time
constraints, outperform modern-day models such as vanilla RNNs and gated
recurrent units in recognizing regular grammars. We provide an upper bound and
a stability analysis on the maximum number of neurons required by nd order
RNNs to recognize any class of regular grammar. Extensive experiments on the
Tomita grammars support our findings, demonstrating the importance of tensor
connections in crafting computationally efficient RNNs. Finally, we show
order RNNs are also interpretable by extraction and can extract state
machines with higher success rates as compared to first-order RNNs. Our results
extend the theoretical foundations of RNNs and offer promising avenues for
future explainable AI research.Comment: 12 pages, 5 tables, 1 figur
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