130 research outputs found
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Netzsprech - Another Case for Distributed 'Rule' Systems
This paper compares conventional symbolic rule systems with distributed network models, considerably arguing for the latter. NETZSPRECH - a network that transcribes German texts similar to NetTalk is first introduced for this purpose and serves as an example for the arguments
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Taxonomies and Part-Whole Hierarchies in the Acquisition of Word Meaning - A Connectionist Model
The aim of this paper is to introduce a simple connectionist model for the acquisition of word meaning, and to demonstrate how this model can be enhanced based on empirical observations about language learning in children. The main sources are observations by Markman (1989, 1990) about constraints children place on word meaning, and Nelson (1988), as well as Benelli (1988), about the role of language in the acquisition of concept taxonomies. The model enhancements based on these observations, and those authors' conclusions, are mainly built on well-known neural mechanisms such as resonance, reset and recruitment, as first introduced in the adaptive resonance theory (ART) models by Grossberg (1976). This way the strength of connectionist models in plausibly modeling detailed aspects of natural language is underlined
06231 Abstracts Collection -- Towards Affordance-Based Robot Control
From June 5 to June 9, 2006, the Dagstuhl Seminar 06231 ``Towards Affordance-Based Robot Control\u27\u27 was held in the International Conference and Research Center (IBFI), Schloss Dagstuhl.
During the seminar, several participants presented their current
research, and ongoing work and open problems were discussed. Abstracts of
the presentations given during the seminar as well as abstracts of
seminar results and ideas are put together in this paper.
%The first section describes the seminar topics and goals in general.
Links to extended abstracts or full papers are provided, if available.
Additionally, papers related to a selection of the above-mentioned presentations willbe published in a proceedings volume (Springer LNAI) early in 2007
CryoNuSeg: A Dataset for Nuclei Instance Segmentation of Cryosectioned H&E-Stained Histological Images
Nuclei instance segmentation plays an important role in the analysis of
Hematoxylin and Eosin (H&E)-stained images. While supervised deep learning
(DL)-based approaches represent the state-of-the-art in automatic nuclei
instance segmentation, annotated datasets are required to train these models.
There are two main types of tissue processing protocols, namely formalin-fixed
paraffin-embedded samples (FFPE) and frozen tissue samples (FS). Although
FFPE-derived H&E stained tissue sections are the most widely used samples, H&E
staining on frozen sections derived from FS samples is a relevant method in
intra-operative surgical sessions as it can be performed fast. Due to
differences in the protocols of these two types of samples, the derived images
and in particular the nuclei appearance may be different in the acquired whole
slide images. Analysis of FS-derived H&E stained images can be more challenging
as rapid preparation, staining, and scanning of FS sections may lead to
deterioration in image quality.
In this paper, we introduce CryoNuSeg, the first fully annotated FS-derived
cryosectioned and H&E-stained nuclei instance segmentation dataset. The dataset
contains images from 10 human organs that were not exploited in other publicly
available datasets, and is provided with three manual mark-ups to allow
measuring intra-observer and inter-observer variability. Moreover, we
investigate the effects of tissue fixation/embedding protocol (i.e., FS or
FFPE) on the automatic nuclei instance segmentation performance of one of the
state-of-the-art DL approaches. We also create a baseline segmentation
benchmark for the dataset that can be used in future research.
A step-by-step guide to generate the dataset as well as the full dataset and
other detailed information are made available to fellow researchers at
https://github.com/masih4/CryoNuSeg
Non-linear versus non-gaussian volatility models
One of the most challenging topics in financial time series analysis is the modeling of conditional variances of asset returns. Although conditional variances are not directly observable there are numerous approaches in the literature to overcome this problem and to predict volatilities on the basis of historical asset returns. The most prominent approach is the class of GARCH models where conditional variances are governed by a linear autoregressive process of past squared returns and variances. Recent research in this field, however, has focused on modeling asymmetries of conditional variances by means of non-linear models. While there is evidence that such an approach improves the fit to empirical asset returns, most non-linear specifications assume conditional normal distributions and ignore the importance of alternative models. Concentrating on the distributional assumptions is, however, essential since asset returns are characterized by excess kurtosis and hence fat tails that cannot be explained by models with suffcient heteroskedasticity. In this paper we take up the issue of returns' distributions and contrast it with the specification of non-linear GARCH models. We use daily returns for the Dow Jones Industrial Average over a large period of time and evaluate the predictive power of different linear and non-linear volatility specifications under alternative distributional assumptions. Our empirical analysis suggests that while non-linearities do play a role in explaining the dynamics of conditional variances, the predictive power of the models does also depend on the distributional assumptions. (author's abstract)Series: Report Series SFB "Adaptive Information Systems and Modelling in Economics and Management Science
Controlling oscillatory behaviour of a two neuron recurrent neural network using inputs
Haschke R, Steil JJ, Ritter H. Controlling oscillatory behaviour of a two neuron recurrent neural network using inputs. In: Dorffner G, Bischof H, Hornik K, eds. Artificial Neural Networks - ICANN 2001. Lecture notes in computer science. Vol 2130. Springer; 2001: 1109-1114.We derive analytical expressions of codim-1-bifurcations for a fully connected, additive two-neuron network with sigmoidal activations, where the two external inputs are regarded as bifurcation parameters. The obtained Neimark-Sacker bifurcation curve encloses a region in input space with stable oscillatory behaviour, in which it is possible to control the oscillation frequency by adjusting the inputs
Riemannian tangent space mapping and elastic net regularization for cost-effective EEG markers of brain atrophy in Alzheimer's disease
The diagnosis of Alzheimer's disease (AD) in routine clinical practice is
most commonly based on subjective clinical interpretations. Quantitative
electroencephalography (QEEG) measures have been shown to reflect
neurodegenerative processes in AD and might qualify as affordable and thereby
widely available markers to facilitate the objectivization of AD assessment.
Here, we present a novel framework combining Riemannian tangent space mapping
and elastic net regression for the development of brain atrophy markers. While
most AD QEEG studies are based on small sample sizes and psychological test
scores as outcome measures, here we train and test our models using data of one
of the largest prospective EEG AD trials ever conducted, including MRI
biomarkers of brain atrophy.Comment: Presented at NIPS 2017 Workshop on Machine Learning for Healt
Editorial: Sleep, vigilance & disruptive behaviors
The Frontiers in Psychiatry Research Theme of Sleep, vigilance, and disruptive behaviors has two aims: first, to promote the understanding of the connections between vigilance and disruptive daytime behavior in the context of sleep deprivation and, second, to explore how naturalistic observations and pattern recognition can play a role in furthering our understanding of these connections. . .
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