15,173 research outputs found
Formal Modeling of Connectionism using Concurrency Theory, an Approach Based on Automata and Model Checking
This paper illustrates a framework for applying formal methods techniques, which are symbolic in nature, to specifying and verifying neural networks, which are sub-symbolic in nature. The paper describes a communicating automata [Bowman & Gomez, 2006] model of neural networks. We also implement the model using timed automata [Alur & Dill, 1994] and then undertake a verification of these models using the model checker Uppaal [Pettersson, 2000] in order to evaluate the performance of learning algorithms. This paper also presents discussion of a number of broad issues concerning cognitive neuroscience and the debate as to whether symbolic processing or connectionism is a suitable representation of cognitive systems. Additionally, the issue of integrating symbolic techniques, such as formal methods, with complex neural networks is discussed. We then argue that symbolic verifications may give theoretically well-founded ways to evaluate and justify neural learning systems in the field of both theoretical research and real world applications
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Attentional capture by meaning: A multi-level modelling study
We present a computational study of attentional capture by meaning, based on Barnard et al's key-distractor attentional blink task. We highlight a sequence of models, from an abstract black-box to a structurally detailed white-box model. Each of these models reproduces the major findings from the key-distractor blink task. We argue that such multi-level modelling gives greater confidence in the theoretical position encapsulated by these models
Concepts of quantum non-Markovianity: a hierarchy
Markovian approximation is a widely-employed idea in descriptions of the
dynamics of open quantum systems (OQSs). Although it is usually claimed to be a
concept inspired by classical Markovianity, the term quantum Markovianity is
used inconsistently and often unrigorously in the literature. In this report we
compare the descriptions of classical stochastic processes and quantum
stochastic processes (as arising in OQSs), and show that there are inherent
differences that lead to the non-trivial problem of characterizing quantum
non-Markovianity. Rather than proposing a single definition of quantum
Markovianity, we study a host of Markov-related concepts in the quantum regime.
Some of these concepts have long been used in quantum theory, such as quantum
white noise, factorization approximation, divisibility, Lindblad master
equation, etc.. Others are first proposed in this report, including those we
call past-future independence, no (quantum) information backflow, and
composability. All of these concepts are defined under a unified framework,
which allows us to rigorously build hierarchy relations among them. With
various examples, we argue that the current most often used definitions of
quantum Markovianity in the literature do not fully capture the memoryless
property of OQSs. In fact, quantum non-Markovianity is highly
context-dependent. The results in this report, summarized as a hierarchy
figure, bring clarity to the nature of quantum non-Markovianity.Comment: Clarifications and references added; discussion of the related
classical hierarchy significantly improved. To appear in Physics Report
Blockout: Dynamic Model Selection for Hierarchical Deep Networks
Most deep architectures for image classification--even those that are trained
to classify a large number of diverse categories--learn shared image
representations with a single model. Intuitively, however, categories that are
more similar should share more information than those that are very different.
While hierarchical deep networks address this problem by learning separate
features for subsets of related categories, current implementations require
simplified models using fixed architectures specified via heuristic clustering
methods. Instead, we propose Blockout, a method for regularization and model
selection that simultaneously learns both the model architecture and
parameters. A generalization of Dropout, our approach gives a novel
parametrization of hierarchical architectures that allows for structure
learning via back-propagation. To demonstrate its utility, we evaluate Blockout
on the CIFAR and ImageNet datasets, demonstrating improved classification
accuracy, better regularization performance, faster training, and the clear
emergence of hierarchical network structures
Skeletal Rigidity of Phylogenetic Trees
Motivated by geometric origami and the straight skeleton construction, we
outline a map between spaces of phylogenetic trees and spaces of planar
polygons. The limitations of this map is studied through explicit examples,
culminating in proving a structural rigidity result.Comment: 17 pages, 12 figure
GliomaPredict: A Clinically Useful Tool for Assigning Glioma Patients to Specific Molecular Subtypes
Background: Advances in generating genome-wide gene expression data have accelerated the development of molecular-based tumor classification systems. Tools that allow the translation of such molecular classification schemas from research into clinical applications are still missing in the emerging era of personalized medicine.
Results: We developed GliomaPredict as a computational tool that allows the fast and reliable classification of glioma patients into one of six previously published stratified subtypes based on sets of extensively validated classifiers derived from hundreds of glioma transcriptomic profiles. Our tool utilizes a principle component analysis (PCA)-based approach to generate a visual representation of the analyses, quantifies the confidence of the underlying subtype assessment and presents results as a printable PDF file. GliomaPredict tool is implemented as a plugin application for the widely-used GenePattern framework.
Conclusions: GliomaPredict provides a user-friendly, clinically applicable novel platform for instantly assigning gene expression-based subtype in patients with gliomas thereby aiding in clinical trial design and therapeutic decisionmaking. Implemented as a user-friendly diagnostic tool, we expect that in time GliomaPredict, and tools like it, will become routinely used in translational/clinical research and in the clinical care of patients with gliomas
Master Regulators, Regulatory Networks, and Pathways of Glioblastoma Subtypes
Glioblastoma multiforme (GBM) is the most common malignant brain tumor. GBM samples are classified into subtypes based on their transcriptomic and epigenetic profiles. Despite numerous studies to better characterize GBM biology, a comprehensive study to identify GBM subtype-specific master regulators, gene regulatory networks, and pathways is missing. Here, we used FastMEDUSA to compute master regulators and gene regulatory networks for each GBM subtype. We also ran Gene Set Enrichment Analysis and Ingenuity Pathway Analysis on GBM expression dataset from The Cancer Genome Atlas Project to compute GBM- and GBM subtype-specific pathways. Our analysis was able to recover some of the known master regulators and pathways in GBM as well as some putative novel regulators and pathways, which will aide in our understanding of the unique biology of GBM subtypes
A Gapless, Unambiguous Genome Sequence of the Enterohemorrhagic Escherichia coli O157:H7 Strain EDL933.
Escherichia coli EDL933 is the prototypic strain for enterohemorrhagic E. coli serotype O157:H7, associated with deadly food-borne outbreaks. Because the publicly available sequence of the EDL933 genome has gaps and >6,000 ambiguous base calls, we here present an updated high-quality, unambiguous genome sequence with no assembly gaps
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