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

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

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

Combining two open source tools for neural computation (BioPatRec and Netlab) improves movement classification for prosthetic control

Background Controlling a myoelectric prosthesis for upper limbs is increasingly challenging for the user as more electrodes and joints become available. Motion classification based on pattern recognition with a multi-electrode array allows multiple joints to be controlled simultaneously. Previous pattern recognition studies are difficult to compare, because individual research groups use their own data sets. To resolve this shortcoming and to facilitate comparisons, open access data sets were analysed using components of BioPatRec and Netlab pattern recognition models. Methods Performances of the artificial neural networks, linear models, and training program components were compared. Evaluation took place within the BioPatRec environment, a Matlab-based open source platform that provides feature extraction, processing and motion classification algorithms for prosthetic control. The algorithms were applied to myoelectric signals for individual and simultaneous classification of movements, with the aim of finding the best performing algorithm and network model. Evaluation criteria included classification accuracy and training time. Results Results in both the linear and the artificial neural network models demonstrated that Netlab’s implementation using scaled conjugate training algorithm reached significantly higher accuracies than BioPatRec. Conclusions It is concluded that the best movement classification performance would be achieved through integrating Netlab training algorithms in the BioPatRec environment so that future prosthesis training can be shortened and control made more reliable. Netlab was therefore included into the newest release of BioPatRec (v4.0)

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

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

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

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