bluuebunny/crossref_metadata_2025 huggingface dataset:
The Hepatitis B Virus Polymerase Mutation rtV173L Is Selected during Lamivudine Therapy and Enhances Viral Replication In Vitro
Authors: Delaney William E. , Yang Huiling , Westland Christopher E. , Das Kalyan , Arnold Eddy , Gibbs Craig S. , Miller Michael D. , Xiong Shelly
Published: October 2003

Therapy of chronic hepatitis B virus (HBV) infection with the polymerase inhibitor lamivudine frequently is associated with the emergence of viral resistance. Genotypic changes in the YMDD motif (reverse transcriptase [rt] mutations rtM204V/I) conferred resistance to lamivudine as well as reducing the in vitro replication efficiency of HBV. A second mutation, rtL180M, was previously reported to partially restore replication fitness as well as to augment drug resistance in vitro. Here we report the functional characterization of a third polymerase mutation (rtV173L) associated with resistance to lamivudine and famciclovir. rtV173L was observed at baseline in 9 to 22% of patients who entered clinical trials of adefovir dipivoxil for the treatment of lamivudine-resistant HBV. In these patients, rtV173L was invariably found as a third mutation in conjunction with rtL180M and rtM204V. In vitro analyses indicated that rtV173L did not alter the sensitivity of wild-type or lamivudine-resistant HBV to lamivudine, penciclovir, or adefovir but instead enhanced viral replication efficiency. A molecular model of HBV polymerase indicated that residue rtV173 is located beneath the template strand of HBV nucleic acid near the active site of the reverse transcriptase. Substitution of leucine for valine at this residue may enhance polymerization either by repositioning the template strand of nucleic acid or by affecting other residues involved in the polymerization reaction. Together, these results suggest that rtV173L is a compensatory mutation that is selected in lamivudine-resistant patients due to an enhanced replication phenotype.
Hendra Virus V Protein Inhibits Interferon Signaling by Preventing STAT1 and STAT2 Nuclear Accumulation
Authors: Rodriguez Jason J. , Wang Lin-Fa , Horvath Curt M.
Published: October 2003

The V protein of the recently emerged paramyxovirus, Nipah virus, has been shown to inhibit interferon (IFN) signal transduction through cytoplasmic sequestration of cellular STAT1 and STAT2 in high-molecular-weight complexes. Here we demonstrate that the closely related Hendra virus V protein also inhibits cellular responses to IFN through binding and cytoplasmic sequestration of both STAT1 and STAT2, but not STAT3. These findings demonstrate a V protein-mediated IFN signal evasion mechanism that is a general property of the known
Henipavirus
species.
Binding of Transmissible Gastroenteritis Coronavirus to Brush Border Membrane Sialoglycoproteins
Authors: Schwegmann-Wessels Christel , Zimmer Gert , Schröder Bernd , Breves Gerhard , Herrler Georg
Published: October 2003

Transmissible gastroenteritis coronavirus (TGEV) is a porcine pathogen causing enteric infections that are lethal for suckling piglets. The enterotropism of TGEV is connected with the sialic acid binding activity of the viral surface protein S. Here we show that, among porcine intestinal brush border membrane proteins, TGEV recognizes a mucin-type glycoprotein designated MGP in a sialic acid-dependent fashion. Virus binding assays with cryosections of the small intestine from a suckling piglet revealed the binding of TGEV to mucin-producing goblet cells. A nonenteropathogenic mutant virus that lacked a sialic acid binding activity was unable to bind to MGP and to attach to goblet cells. Our results suggest a role of MGP in the enteropathogenicity of TGEV.
Multiple Viral Determinants Mediate Myopathogenicity in Coxsackievirus B1-Induced Chronic Inflammatory Myopathy
Authors: Tam Patricia E. , Weber-Sanders Melissa L. , Messner Ronald P.
Published: October 2003

Mice infected with myopathic coxsackievirus B1 Tucson (CVB1
T
) develop chronic inflammatory myopathy (CIM) consisting of hind limb weakness and inflammation. Amyopathic virus variants are infectious but attenuated for CIM. In this report, viral clones, chimeras, and sequencing were used to identify viral determinants of CIM. Chimeras identified several regions involved in CIM and localized a weakness determinant to nucleotides 2493 to 3200 of VP1. Sequencing of multiple clones and viruses identified five candidate determinants that were strictly conserved in myopathic viruses with one located in the 5′ untranslated region (UTR), three in the VP1 capsid, and one in the 3C protease. Taken together, these studies implicate Tyr-87 and/or Val-136 as candidate determinants of weakness. They also indicate that there are at least two determinants of inflammation and one additional determinant of weakness encoded by myopathic CVB1
T
.
Retrotransposition and Cell-to-Cell Transfer of Foamy Viruses
Authors: Heinkelein Martin , Rammling Matthias , Juretzek Thomas , Lindemann Dirk , Rethwilm Axel
Published: October 2003

A remarkable feature of the prototype foamy virus (PFV) replication pathway has been reported to consist of the ability to retrotranspose intracellularly with high efficiency (M. Heinkelein, T. Pietschmann, G. Jármy, M. Dressler, H. Imrich, J. Thurow, D. Lindemann, M. Bock, A. Moebes, J. Roy, O. Herchenröder, and A. Rethwilm, EMBO J. 19:3436-3345, 2000). PFV intracellular retrotransposition (IRT) was reported to be enhanced by coexpression of fusion-defective envelope protein. To investigate the possibility of cell-to-cell transfer of PFV genomes, which could mimic IRT, we performed cocultivation experiments with cells transfected with an IRT-competent and marker gene-expressing PFV vector together with cells expressing a different marker and measured cells positive for both markers. The findings corroborated the initial report on IRT of Env-deficient PFV. Furthermore, they indicated that viral cores that have incorporated fusion-deficient Env can be transferred from cell to cell in a cell type-specific manor. One possible explanation consists of a minor alternative cleavage site in Env that can be used to expose the fusion peptide of the Env transmembrane protein, which appears to be required for virus uptake.
Induction of Protective Immunity against Malaria by Priming-Boosting Immunization with Recombinant Cold-Adapted Influenza and Modified Vaccinia Ankara Viruses Expressing a CD8+-T-Cell Epitope Derived from the Circumsporozoite Protein ofPlasmodium yoelii
Authors: González-Aseguinolaza Gloria , Nakaya Yurie , Molano Alberto , Dy Edward , Esteban Mariano , Rodríguez Dolores , Rodríguez Juan Ramón , Palese Peter , García-Sastre Adolfo , Nussenzweig Ruth S.
Published: October 2003

We immunized mice with an attenuated (cold-adapted) influenza virus followed by an attenuated vaccinia virus (modified vaccinia virus Ankara), both expressing a CD8+-T-cell epitope derived from malaria sporozoites. This vaccination regimen elicited high levels of protection against malaria. This is the first time that the vaccine efficacy of a recombinant cold-adapted influenza virus vector expressing a foreign antigen has been evaluated.
A Note on the Dimensionality of Modified Knödel Graphs
Authors: Bermond Jean-Claude , Harutyunyan Hovhannes A. , Liestman Arthur L. , Perennes Stephane
Published: October 2003

We show that the edges of the modified Knödel graph can be grouped into dimensions which are similar to the dimensions of hypercubes. In particular, routing, broadcasting and gossiping, can be done easily in modified Knödel graphs using these dimensions.
Communication Issues in Parallel Systems with Optical Interconnections
Authors: Berthomé Pascal , Ferreira Afonso
Published: October 2003

In classical massively parallel computers, the complexity of the interconnection networks is much higher than the complexity of the processing elements themselves. However, emerging optical technologies may provide a way to reconsider very large parallel architectures where processors would communicate by optical means. In this paper, we compare some optically interconnected parallel multicomputer models with regard to their communication capabilities. We first establish a distinction of such systems, based on the independence of the communication elements embedded in the processors (transmitters and receivers). Then, motivated by the fact that in multicomputers some communication operations have to be very efficiently performed, we study communication problems, namely, broadcast and multi-broadcast, under the hypothesis of bounded fanout. Our results take also into account a bounded number of available wavelengths.
Efficient Communication in Folded Petersen Networks
Authors: Das Sajal K. , Hohndel Dirk H. , Ibel Maximilian , Öhring Sabine R.
Published: October 2003

Fast and efficient communication is one of the most important requirements in today's multicomputers. When reaching a larger scale of processors, the probability of faults in the network increases, hence communication must be robust and fault tolerant. The recently introduced family of folded Petersen networks, constructed by iteratively applying the cartesian product operation on the well-known Petersen graph, provides a regular, node– and edge-symmetric architecture with optimal connectivity (hence maximal fault-tolerance), and logarithmic diameter. Compared to the closest sized hypercube, the folded petersen network has a smaller diameter, lower node degree and higher packing density.

In this paper, we study fundamental communication primitives like single routing, permutation routing, one-to-all broadcasting, multinode-broadcasting (gossiping), personalized communications like scattering, and total exchange on the folded Petersen networks, considering two communication models, namely single link availability (SLA) and multiple link availability (MLA). We derive lower bounds for these problems and design optimal algorithms in terms of both time and the number of message transmissions. The results are based on the construction of minimal height spanning trees in the fault-free folded Petersen network. We further analyze these communication primitives in faulty networks, where processing nodes and transmission links cease working. This analysis is based on multiple arc-disjoint spanning trees, a construct also useful for analyzing other families of multicomputer networks.
Multitriangle: A Constant Node Degree Interconnection Network
Authors: Wu Jie , Qian Haifeng
Published: October 2003

We propose a constant node degree network topology, multitriangle, which is hierarchical, recursive, and expansive. First we introduce a corner cutting approach that generates a set of new network topologies (including multitriangles), followed by a formal definition of the multitriangle network and discussion of its properties. The salient features of this network are that it is a constant node degree network and it can be viewed as a hierarchical ring, a popular topology which has been adopted in several commercial systems. Algorithms for node-to-node routing, hierarchical ring routing, optimal ring routing, and broadcasting are presented. The multitriangle network is analyzed in terms of diameter, degree, average distance, and message density, and results are compared with other relevant networks.
A Scalable Diffusion Algorithm for Dynamic Mapping and Load Balancing on Networks of Arbitrary Topology
Authors: Heirich A.
Published: October 2003

The problems of mapping and load balancing applications on arbitrary networks are considered. A novel diffusion algorithm is presented to solve the mapping problem. It complements the well known diffusion algorithms for load balancing which have enjoyed success on massively parallel computers (MPPs). Mapping is more difficult on interconnection networks than on MPPs because of the variations which occur in network topology. Popular mapping algorithms for MPPs which depend on recursive topologies are not applicable to irregular networks. The most celebrated of these MPP algorithms use information from the Laplacian matrix of a graph of communicating processes. The diffusion algorithm presented in this paper is also derived from this Laplacian matrix. The diffusion algorithm works on arbitrary network topologies and is dramatically faster than the celebrated MPP algorithms. It is delay and fault tolerant. Time to convergence depends on initial conditions and is insensitive to problem scale. This excellent scalability, among other features, makes the diffusion algorithm a viable candidate for dynamically mapping and load balancing not only existing MPP systems but also large distributed systems like the Internet, small cluster computers, and networks of workstations.
Parsing Partially Ordered Multisets
Authors: Basten Twan
Published: October 2003

A partially ordered multiset or pomset is a generalization of a string in which the total order has been relaxed to a partial order. Strings are often used as a model for sequential computation; pomsets are a natural model for parallel and distributed computation.

By viewing pomsets as a generalization of strings, the question is raised whether concepts from language theory can be generalized to pomsets. An important area in the theory of languages is parsing theory. This paper develops the fundamentals of a parsing theory for pomsets, called PLR parsing. It is based on the LR-parsing technique, which is the most powerful deterministic parsing technique in language theory. The basic algorithm in the class of PLR parsing algorithms, the PLR(0) algorithm is explained in detail.
Learning with Natural Imprecision
Authors: Changizi Mark A.
Published: October 2003

The theorems of inductive inference in computational learning theory may be interpreted as the ultimate theoretical constraints on the abilities of finite machines to fabricate hypotheses and make predictions from information or data. However, all of the models of learning in this inductive inference literature require the hypothesis to be exactly correct infinitely often, have no way of measuring how far off a prediction of a hypothesis is, and require the hypothesis to make predictions that are directly measurable. Furthermore, most of the models of learning do not allow the learning of uncomputable functions, and of those that are capable of learning uncomputable functions, the literature has not explicitly noticed this property. New notions of learning that rectify these deficiencies are introduced and examined. The first criterion considers a hypothesis successful if each prediction is within δ(x) of the function to be learned on x, f(x). The second criterion considers a hypothesis successful if each prediction on x is equal to f on some domain element within ∊(x) of x, so long as the nearby value of the function f is within v(x) of f(x). These new learning criteria respectively model (a) learning in science with imprecise hypotheses, and (b) learning in science with hypotheses that ignore noisy and bad data, smoothing in image recognition with radius of smoothing ∊ and threshold v, and learning languages (i.e., two-valued functions) with imprecise hypotheses.
An Automated Procedure for Cluster Analysis of Multivariate Satellite Data
Authors: Waldemark Joakim
Published: October 2003

This paper studies the applicability of available cluster analysis methods on multivariate satellite data. Four different methods are studied, these are the Principal Component Analysis (PCA), K-means, Self Organizing Maps (SOM) and the Adaptive Resonance Theory (ART). Special focus is placed on the usefulness of these cluster analysis methods, that is, if the results generated by these methods are relevant and are of help to the physical analysis of the data. A combined SOM adaptive dynamic K-means procedure suitable for automated cluster analysis is presented. This new method is capable of achieving useful partition of multivariate satellite data which has not previously been studied.
Classification of Handwritten Digits using a RAM Neural Net Architecture
Authors: Jørgensen Thomas Martini
Published: October 2003

Results are reported on the task of recognizing handwritten digits without any advanced pre-processing. The result are obtained using a RAM-based neural network, making use of small receptive fields. Furthermore, a technique that introduces negative weights into the RAM net is reported. The results obtained on the task of recognizing handwritten digits is comparable with the best performances reported in the literature.
Dynamic Cell Structures for the Evaluation of Keypoints in Facial Images
Authors: Herpers R. , Witta L. , Bruske J. , Sommer G.
Published: October 2003

In this contribution Dynamic Cell Structures (DCS network) are applied to classify local image structures at particular facial landmarks. The facial landmarks such as the corners of the eyes or intersections of the iris with the eyelid are computed in advance by a combined model and data driven sequential search strategy. To reduce the detection error after the processing of the sequential search strategy, the computed image positions are verified applying a DCS network. The DCS network is trained by supervised learning with feature vectors which encode spatially arranged edge and structural information at the keypoint position considered. The model driven localization as well as the data driven verification are based on steerable filters, which build a representation comparable with one provided by a receptive field in the human visual system. We apply a DCS based classifier because of its ability to grasp the topological structure of complex input spaces and because it has proved successful in a number of other classification tasks. In our experiments the average error resulting from false positive classifications is less than 1%.
Gaussian Neural Networks for Glass Bottles Inspection: A Learning Procedure
Authors: Firmin C. , Hamad D. , Postaire J.-G. , Zhang R. D.
Published: October 2003

In glass bottle inspection, the defects detection is of first importance. For online system detection, high speed and robust detection of faults are highly required. Neural networks have recently, and successfully, been applied to fault detection in many manufacturing processes. In this study, a Gaussian neural network, an extension of the RBF network, trained through a competitive algorithm, has been chosen for fault detection. Four parameters extracted from images of the bottle are used as inputs of the network. The number of Gaussian units is adjusted by an informational criterion. Experimental results show that the performance of this network are better than classical parametric and non parametric classifiers.
Radar Image Segmentation Using Self-Adapting Recurrent Networks
Authors: Ziemke Tom
Published: October 2003

This paper presents a novel approach to the segmentation and integration of (radar) images using a second-order recurrent artificial neural network architecture consisting of two sub-networks: a function network that classifies radar measurements into four different categories of objects in sea environments (water, oil spills, land and boats), and a context network that dynamically computes the function network's input weights. It is shown that in experiments (using simulated radar images) this mechanism outperforms conventional artificial neural networks since it allows the network to learn to solve the task through a dynamic adaptation of its classification function based on its internal state closely reflecting the current context.
Application of a Multi-Structure Neural Network (MSNN) to Sorting Pistachio Nuts
Authors: Ghazanfari Ahmad , Kusalik Anthony , Irudayaraj Joseph
Published: October 2003

A multi-structure neural network (MSNN) classifier consisting of four discriminators followed by a maximum selector was designed and applied to classification of four grades of pistachio nuts. Each discriminator was a multi-layer feed-forward neural network with two hidden layers and a single-neuron output layer. Fourier descriptor of the nuts' boundaries and their area were used as the recognition features. The individual discriminators were trained using a biased technique and a back-propagation algorithm. The MSNN classifier gave an average classification performance of 95.0%. This was an increase of 14.8% over the performance of a multi-layer neural network (MLNN) with similar complexity for classifying the same set of patterns.
A Hybrid Classifier for Remote Sensing Applications
Authors: Ruppert Georg S. , Schardt Mathias , Balzuweit Gerd , Hussain Mushtaq
Published: October 2003

This paper presents a hybrid — unsupervised and supervised — classifier for land use classification of remote sensing images. The entire satellite image is quantized by an unsupervised Neural Gas process and the resulting codebook is labeled by a supervised majority voting process using the ground truth. The performance of the classifier is similar to that of Maximum Likelihood and is only a little worse than Multilayer Perceptrons while training and classifying requires no expert knowledge after collecting the ground truth. The hybrid classifier is much better suited to classifications with complex non-normally distributed classes than Maximum Likelihood. The main advantage of the Neural Gas classifier, however, is that it requires much less user interaction than other classifier, especially Maximum Likelihood.
Neural Network Visual Recognition for Automation of the Microelectromechanical Systems Assembly
Authors: Radjenović–Mrčarica J. , Mrčarica Ž. , Detter H. , Litovski V.
Published: October 2003

A neural network visual recognition system is developed. The system is intended for automation of microsystem assembly process. The recognition of microparts is insensitive on their position.This feature is enabled by calculation of the moment properties of the image during preprocessing. The system takes grey-level images and produces recognition code as the output. A feed-forward neural network is used for recognition. Learning of the neural network is performed by combination of standard backpropagation and resilient propagation rule. The system performance is satisfactory with respect to recognition accuracy and recognition time.
Growing 3D-SOMs with 2D-Input Layer as a Classification Tool in a Motion Detection System
Authors: Seiffert Udo , Michaelis Bernd
Published: October 2003

This paper describes the employment of an 'Adaptive Growing Three-Dimensional Self-Organizing Map' for the classification of images. First a short description of growing SOMs is given and the fundamental advantages are mentioned. Then an extension of the original SOM from two to three dimensions with growing feature is presented. By means of some selected examples the general behavior of the algorithm is illustrated.
Using Neural Networks to Automatically Detect Brain Tumours in MR Images
Authors: Dickson S. , Thomas B. T. , Goddard P.
Published: October 2003

Computer vision has been applied to many medical imaging problems with the aim of providing clinical tools to aid medical professionals. We present work being carried out to develop one such system to automatically detect a specific type of brain tumour from head MR images. The tumour under consideration is an acoustic neuroma, which is a benign tumour occurring in the acoustic canals.

The hybrid system developed integrates neural networks with more conventional techniques used for computer vision tasks.

A database of MR images from 50 patients has been assembled and the acoustic neuromas present in the images have been labelled by hand. Using this data, neural networks (MLPs) have been developed to classify the images at the pixel level to achieve a targeted segmentation. The data used to train and test the MLPs developed, consists of the grey levels of a square of pixels, the pixel to be classified being the centre pixel, together with its global position in the image.

The initial pixel level segmentation is refined by a series of conventional techniques. It is combined with an edge-region based segmentation and a morphological operation is applied to the result. This processing produces clusters of adjacent regions, which are considered to be candidate tumour regions. For each possible combination of these regions, features are measured and presented to neural networks which have been trained to identify structures corresponding to acoustic neuromas. Using this approach, all the acoustic neuromas are identified together with three false positive errors.
Self-Supervised Learning in Cooperative Stereo Vision Correspondence
Authors: Decoux Benoît
Published: October 2003

This paper presents a neural network model of stereoscopic vision, in which a process of fusion seeks the correspondence between points of stereo inputs. Stereo fusion is obtained after a self-supervised learning phase, so called because the learning rule is a supervised-learning rule in which the supervisory information is autonomously extracted from the visual inputs by the model. This supervisory information arises from a global property of the potential matches between the points. The proposed neural network, which is of the cooperative type, and the learning procedure, are tested with random-dot stereograms (RDS) and feature points extracted from real-world images. Those feature points are extracted by a technique based on the use of sigma-pi units. The matching performance and the generalization ability of the model are quantified. The relationship between what have been learned by the network and the constraints used in previous cooperative models of stereo vision, is discussed.
A Neural Network Based Artificial Vision System for Licence Plate Recognition
Authors: Draghici Sorin
Published: October 2003

This paper presents a neural network based artificial vision system able to analyze the image of a car given by a camera, locate the registration plate and recognize the registration number of the car. The paper describes in detail various practical problems encountered in implementing this particular application and the solution used to solve them.

The main features of the system presented are: controlled stability-plasticity behavior, controlled reliability threshold, both off-line and on-line learning, self assessment of the output reliability and high reliability based on high level multiple feedback.

The system has been designed using a modular approach. Sub-modules can be upgraded and/or substituted independently, thus making the system potentially suitable in a large variety of vision applications.

The OCR engine was designed as an interchangeable plug-in module. This allows the user to choose an OCR engine which is suited to the particular application and to upgrade it easily in the future. At present, there are several versions of this OCR engine. One of them is based on a fully connected feedforward artificial neural network with sigmoidal activation functions. This network can be trained with various training algorithms such as error backpropagation. An alternative OCR engine is based on the constraint based decomposition (CBD) training architecture.

The system has showed the following performances (on average) on real-world data: successful plate location and segmentation about 99%, successful character recognition about 98% and successful recognition of complete registration plates about 80%
Video Sequence Compression via Supervised Training on Cellular Neural Networks
Authors: Rodríguez Luis , Zufiria Pedro J. , Berzal J. Andrés
Published: October 2003

In this paper, a novel approach for video sequence compression using Cellular Neural Networks (CNN's) is presented. CNN's are nets characterized by local interconnections between neurons (usually called cells), and can be modeled as dynamical systems. From among many different types, a CNN model operating in discrete-time (DT-CNN) has been chosen, its parameters being defined so that they are shared among all the cells in the network.

The compression process proposed in this work is based on the possibility of replicating a given video sequence as a trajectory generated by the DT-CNN. In order for the CNN to follow a prescribed trajectory, a supervised training algorithm is implemented. Compression is achieved due to the fact that all the information contained in the sequence can be stored into a small number of parameters and initial conditions once training is stopped.

Different improvements upon the basic formulation are analyzed and issues such as feasibility and complexity of the compression problem are also addressed.

Finally, some examples with real video sequences illustrate the applicability of the method.
Automatic Segmentation and Classification of Outdoor Images Using Neural Networks
Authors: Campbell N. W. , Thomas B. T. , Troscianko T.
Published: October 2003

The paper describes how neural networks may be used to segment and label objects in images. A self-organising feature map is used for the segmentation phase, and we quantify the quality of the segmentations produced as well as the contribution made by colour and texture features. A multi-layer perceptron is trained to label the regions produced by the segmentation process. It is shown that 91.1% of the image area is correctly classified into one of eleven categories which include cars, houses, fences, roads, vegetation and sky.
An Adaptive Training Method for Optimal Interpolative Neural Nets
Authors: Liu Tsong-Zen , Yen Chen-Wen V.
Published: October 2003

In contrast to conventional multilayered feedforward networks which are typically trained by iterative gradient search methods, an optimal interpolative (OI) net can be trained by a noniterative least squares algorithm called RLS-OI. The basic idea of RLS-OI is to use a subset of the training set, whose inputs are called subprototypes, to constrain the OI net solution. A subset of these subprototypes, called prototypes, is then chosen as the parameter vectors of the activation functions of the OI net to satisfy the subprototype constraints in the least squares (LS) sense. By dynamically increasing the numbers of subprototypes and prototypes, RLS-OI evolves the OI net from scratch to the extent sufficient to solve a given classification problem. To improve the performance of RLS-OI, this paper addresses two important problems in OI net training: the selection of the subprototypes and the selection of the prototypes. By choosing subprototypes from poorly classified regions, this paper proposes a new subprototype selection method which is adaptive to the changing classification performance of the growing OI net. This paper also proposes a new prototype selection criterion to reduce the complexity of the OI net. For the same training accuracy, simulation results demonstrate that the proposed approach produces smaller OI net than the RLS-OI algorithm. Experimental results also show that the proposed approach is less sensitive to the variation of the training set than RLS-OI.
Classification of User Expertise Level by Neural Networks
Authors: Leung Siu Cheong , Fulcher John
Published: October 2003

A neural network approach to low-level user modeling is described, in the context of text editing tasks using the [Formula: see text] editor. Knowledge of a user's expertise is extracted automatically, based on their interaction with [Formula: see text] over a two week period. A MLP classifier which uses [Formula: see text] learning and incorporates output data fuzzification is developed to classify users into one of five expertise levels. Classification into the correct level is achieved in around 80% of the cases, with misclassification being restricted to adjacent classes. The neurofuzzy system is seen to outperform not only the binary classifier of Beale [1989], but also production rule and inductive expert systems developed especially for comparison purposes in this study.
Translation-, Rotation-, Scale-, and Distortion-Invariant Object Recognition Through Self-Organization
Authors: Shams Soheil
Published: October 2003

The task of visual object recognition is often complicated by the fact that a single 3-D object can undergo a number of transformations which can substantially alter its projection onto a 2-D surface, such as the retina. Such transformations include translation of the object in the visual field, changes in the size of the object, its orientation in the 2-D plane and the viewing perspective. For a general pattern recognition system to detect and recognize an object after such transformations, it must be able to associate widely differing patterns with the same object label. In this paper, a novel self-organizing model, called the Multiple Elastic Modules (MEM), is presented which attempts to solve this problem by searching a multi-dimensional space, where each axis is defined by one of the transformations (e.g scale, translation, rotation, etc.). A particular object of a specific size, orientation and spatial location is mapped onto a single point in this space. Of course, distortions and minor variations in an object's image will expand this point to a small localized area in this multi-dimensional space. Such a powerful representation scheme comes at a cost of high computational demand due to the combinatorially large search space. The MEM approach to solving this problem efficiently partitions the solution space to search the most promising areas for the correct match. Simulation results are presented on detecting a stick-figure object under translation, distortion, scale, and rotation transformations in a cluttered background.
Supervised Adaptive Hamming Net for Classification of Multiple-Valued Patterns
Authors: Hung Cheng-An , Lin Sheng-Fuu
Published: October 2003

A Supervised Adaptive Hamming Net (SAHN) is introduced for incremental learning of recognition categories in response to arbitrary sequence of multiple-valued or binary-valued input patterns. The binary-valued SAHN derived from the Adaptive Hamming Net (AHN) is functionally equivalent to a simplified ARTMAP, which is specifically designed to establish many-to-one mappings. The generalization to learning multiple-valued input patterns is achieved by incorporating multiple-valued logic into the AHN. In this paper, we examine some useful properties of learning in a P-valued SAHN. In particular, an upper bound is derived on the number of epochs required by the P-valued SAHN to learn a list of input-output pairs that is repeatedly presented to the architecture. Furthermore, we connect the P-valued SAHN with the binary-valued SAHN via the thermometer code.
Recognition of Rotating Images Using an Automatic Feature Extraction Technique and Neural Networks
Authors: Verma Brijesh
Published: October 2003

This paper presents a new automatic feature extraction technique and a neural network based classification method for recognition of rotating images. The image processing technique extracts global features of an image and converts a large size image into a one-dimensional small vector. A special advantage of the proposed technique is that the extracted features are the same even if the original image is rotated with rotation angles from 5 to 355 or rotated and a little bit distorted. The proposed approach technique is based on simple co-ordinate geometry, fuzzy sets and neural networks. The proposed approach is very easy in implementation and its has been developed in C++ on a Sun workstation. The experimental results have demonstrated that the proposed approach performs successfully on a variety of small as well as large scale rotated and distorted images.
Data Mining of Inputs: Analysing Magnitude and Functional Measures
Authors: Gedeon Tamás D.
Published: October 2003

The problem of data encoding and feature selection for training back-propagation neural networks is well known. The basic principles are to avoid encrypting the underlying structure of the data, and to avoid using irrelevant inputs. This is not easy in the real world, where we often receive data which has been processed by at least one previous user. The data may contain too many instances of some class, and too few instances of other classes. Real data sets often include many irrelevant or redundant input fields. This paper examines the use of weight matrix analysis techniques and functional measures using two real (and hence noisy) data sets.

The first part of this paper examines the use of the weight matrix of the trained neural network itself to determine which inputs are significant. A new technique is introduced and compared with two other techniques from the literature. We present our experience and results on some satellite data augmented by a terrain model. The task was to predict the forest supra-type based on the available information. A brute force technique eliminating randomly selected inputs was used to validate our approach.

The second part of this paper examines the use of measures to determine the functional contribution of inputs to outputs. Inputs which include minor but unique information to the network are more significant than inputs with higher magnitude contribution but providing redundant information, which is also provided by another input. A comparison is made to sensitivity analysis, where the sensitivity of outputs to input perturbation is used as a measure of the significance of inputs.

This paper presents a novel functional analysis of the weight matrix based on a technique developed for determining the behavioral significance of hidden neurons. This is compared with the application of the same technique to the training and test data. Finally, a novel aggregation technique is introduced.
On Neural Blind Separation with Noise Suppression and Redundancy Reduction
Authors: Karhunen J. , Cichocki A. , Kasprzak W. , Pajunen P.
Published: October 2003

Noise is an unavoidable factor in real sensor signals. We study how additive and convolutive noise can be reduced or even eliminated in the blind source separation (BSS) problem. Particular attention is paid to cases in which the number of sensors is larger than the number of sources. We propose various methods and associated adaptive learning algorithms for such an extended BSS problem. Performance and validity of the proposed approaches are demonstrated by extensive computer simulations.
A Supervised Learning Network Based on Adaptive Resonance Theory
Authors: Zhou J. , Bennett S.
Published: October 2003

A neural network architecture, fuzzy ART with logistic discrimination (ART-LD), is introduced as a method of realising the pattern recognition task in a supervised learning manner. The system is formed by the hierarchical organisation of two network modules: a fuzzy ART and a logistic discrimination. The learning consists of two separate stages. Firstly, the fuzzy ART and a logistic discrimination. The learning consists of two separate stages. Firstly, the fuzzy ART module self-organises the input patterns into category clusters, whose operations are governed by fuzzy set theory and "competitive learning" dynamics that ensure fast and stable learning. Then the outputs, which can be interpreted as fuzzy memberships of an input pattern to the encoded categories, provide the spatial distance informtion that is generalised by the subsequent logistic discrimination to give the final prediction. Examples are presented, and the generalisation capabilities of ART-LD are demonstrated through two simulated and one real classification problem.
Invariant Object Recognition Using Higher-Order Neural Networks, Line-Segment Spectra and Multi-Resolution Training
Authors: Augusteijn Marijke F. , Winterbottom Michael C.
Published: October 2003

A second-order neural network architecture is introduced that achieves invariant recognition with respect to an object's position and orientation in an image. This network does not show the combinatorial growth in network size as image size is increased, which is commonly observed in higher-order architectures. A new concept called an object's line-segment spectrum is introduced. It is argued that the weights of the second-order architecture are determined by these line-segments. Training time then becomes a function of object size rather than image size. The network is tested on the 26 capital letters of the alphabet. It is shown that in this application a multi-resolution training approach leads to reduced training time and improved performance.
The Convergence of Backpropagation Trained Neural Networks for Various Weight Update Frequencies
Authors: Torresen Jim
Published: October 2003

One of the problems concerning the backpropagation training of feed-forward neural networks is the effect of the weight update frequency. This aspect influences the efficiency of parallel implementations of the training algorithm where the training vectors are distributed among processors. In this paper the convergence of two applications for various weight update intervals is reported. Further, several models are proposed for describing convergence and learning rate aspects in the context of a set of weight update intervals. The results show that the convergence by updating the weights after each training vector leads to about 10 times less number of training iterations compared to updating the weights only ones for the whole training set.
Real-Time Neural Network Based Camera Localization and its Extension to Mobile Robot Control
Authors: Choi Doo-Hyun , Oh Se-Young
Published: October 2003

The feasibility of using neural networks for camera localization and mobile robot control is investigated here. This approach has the advantages of eliminating the laborious and error-prone process of imaging system modeling and calibration procedures. Basically, two different approaches of using neural networks are introduced of which one is a hybrid approach combining neural networks and the pinhole-based analytic solution while the other is purely neural network based. These techniques have been tested and compared through both simulation and real-time experiments and are shown to yield more precise localization than analytic approaches. Furthermore, this neural localization method is also shown to be directly applicable to the navigation control of an experimental mobile robot along the hallway purely guided by a dark wall strip. It also facilitates multi-sensor fusion through the use of multiple sensors of different types for control due to the network's capability of learning without models.
Stability Analysis of a Class of Noise Perturbed Neural Networks
Authors: Meyer-Bäse Anke
Published: October 2003

We establish robustness stability results for a specific type of artificial neural networks for associative memories under parameter perturbations and determine conditions that ensure the existence of asymptotically stable equilibria of the perturbed neural system that are near the asymptotically stable equilibria of the original unperturbed neural network. The proposed stability analysis tool is the sliding mode control and it facilitates the analysis by considering only a reduced-order system instead of the original one and time-dependent external stimuli.
Symmetry Breaking and Training from Incomplete Data with Radial Basis Boltzmann Machines
Authors: Nijman Marcel J. , Kappen Hilbert J.
Published: October 2003

A Radial Basis Boltzmann Machine (RBBM) is a specialized Boltzmann Machine architecture that combines feed-forward mapping with probability estimation in the input space, and for which very efficient learning rules exist. The hidden representation of the network displays symmetry breaking as a function of the noise in the dynamics. Thus, generalization can be studied as a function of the noise in the neuron dynamics instead of as a function of the number of hidden units. We show that the RBBM can be seen as an elegant alternative of k-nearest neighbor, leading to comparable performance without the need to store all data. We show that the RBBM has good classification performance compared to the MLP. The main advantage of the RBBM is that simultaneously with the input-output mapping, a model of the input space is obtained which can be used for learning with missing values. We derive learning rules for the case of incomplete data, and show that they perform better on incomplete data than the traditional learning rules on a 'repaired' data set.
High Resolution Measurement of Translucent Plastic Wall Thicknesses by Computerized Tomography and Neural Networks
Authors: Hemminger Thomas L. , Farrell Robert E.
Published: October 2003

Today there is a great deal of interest in the field of plastics design. Several methods can be employed to create plastic products such as injection molding, compression and transfer molding, and blow molding. This paper is concerned with blow molding which is a procedure employed to create hollow plastic containers such as those used to contain liquids and solids in the wholesale and retail markets. An important aspect of blow molding is the measurement of the wall thickness of semi-liquid plastic before the molding procedure has been initiated. Minimization of waste is rapidly becoming a critical consideration within the plastics community due to the cost of raw polymers. Unfortunately, it is also an extremely difficult task to measure the thickness considering the high temperatures and elasticity of the polymers in question. This paper presents initial research on a non-invasive approach for wall thickness measurements of semi-liquid plastics through the utilization of computerized tomography and neural networks. The work described here is based on simulations and on modeling data obtained through experimental means. This technique can be extended to other fields of research as well, such as those related to the development of glass and ceramic products.
SISO Nonlinear System Identification Using a Fuzzy-Neural Hybrid System
Authors: Lin Cheng-Jian
Published: October 2003

This paper describes a fuzzy-neural hybrid system for the identification of nonlinear dynamic systems with unknown parameters. The proposed model takes the form of a context-sensitive module in which a fuzzy system is used as a function module and a multilayer neural network is used as a context module. Fuzzy-neural hybrid systems with a decomposed structures reduce complexity and thus accelerate the learning process. Also, the parameters of a fuzzy system have clear physical meanings, which makes it possible to incorporate a priori knowledge into the selection of initial parameter values and constraints among parameter values. Since hybrid systems correspond to networks, it is feasible to construct fast, parallel devices to implement these models for practical applications. The gradient descent method for the adjustment of parameters in hybrid systems is discussed. Simulations demonstrate that the hybrid identification models suggested here for SISO dynamic systems are quite effective.
Static and Dynamic State Feedback Control Model of Basal Ganglia-Thalamocortical Loops
Authors: Lörincz A.
Published: October 2003

It is argued that a novel control architecture, the Static and Dynamic State (SDS) feedback scheme, which utilizes speed-field tracking, exhibits global stability, and allows on-line tuning by any adaptation mechanism without canceling stability if certain structural conditions are met, can be viewed as a model of basal ganglia-thalamocortical loops since (1) the SDS scheme predicts the neuronal groups that fit neuronal classification in the supplementary motor area, the motor cortex and the putamen, (2) the structural stability conditions require parallel channels, a feature that these loops provide, and (3) the SDS scheme predicts two major disorders that can be identified as Parkinson's and Huntington's diseases. Simulations suggests that the basal ganglia work outside the realm of the stability condition allowed by the robustness of the scheme and required for increased computation speeds.