Magnetization Process of One-Dimensional Quantum Antiferromagnet: The Product Wavefunction Renormalization Group Approach

The product-wavefunction renormalization group method, which is a novel numerical renormalization group scheme proposed recently,is applied to one-dimensional quantum spin chains in a magnetic field. We draw the zero-temperature magnetization curve of the spin chains, which excellently agrees with the exact solution in the whole range of the field.Comment: 14 pages, LaTeX, 5 non-embedded figures, to be published in Physics Letters

Combining Textual and Visual Information for Image Retrieval in the Medical Domain

In this article we have assembled the experience obtained from our participation in the imageCLEF evaluation task over the past two years. Exploitation on the use of linear combinations for image retrieval has been attempted by combining visual and textual sources of images. From our experiments we conclude that a mixed retrieval technique that applies both textual and visual retrieval in an interchangeably repeated manner improves the performance while overcoming the scalability limitations of visual retrieval. In particular, the mean average precision (MAP) has increased from 0.01 to 0.15 and 0.087 for 2009 and 2010 data, respectively, when content-based image retrieval (CBIR) is performed on the top 1000 results from textual retrieval based on natural language processing (NLP)

Matrix spans in max-plus algebra and a graph-theoretic approach to switching max-plus linear systems

The relation between graph theory and max-plus algebra has been well studied since the inception of max-plus algebra. It has been shown that any square matrix over the maxplus semiring can be represented as a weighted directed graph. Furthermore, properties of these matrices, such as irreducibility and its (unique) eigenvalue, can be determined by its graph-theoretical interpretation. However, this graph-theoretical interpretation has not yet been extended to SMPL systems. Switching max-plus-linear (SMPL) systems are an extension of max-plus-linear systems (MPL) for modelling discrete-event systems. While for MPL systems the system is described by one max-plus-linear state equation and one max-plus-linear output equation, for SMPL systems the system is described by more than one mode of operation, each consisting of its own unique max-plus-linear state equation and max-plus-linear output equation. The different modes allow for more efficient modelling of changes to the structure of the system. The switching between the different modes of operation can be deterministic, stochastic or a combination of the two. Due to the fact that max-plus algebra is an idempotent algebra and there is no opposite operation to max-plus addition, vectors spaces in max-plus algebra cannot be defined in the same way as for conventional algebra. As a result, determining the span of matrices has to be performed in a different way than for matrices in conventional algebra as matrix ranks are also defined in a different way. Determining the span of matrices in both max-plus algebra and conventional algebra is important as it allows for the calculation of the set of states that can be accessed (reached) by MPL systems and LTI systems respectively. The purpose of this thesis is three-fold: firstly, a method is developed for accurately determining the span of max-plus matrices, secondly, this method is applied to MPL systems with the purpose of determining the set of accessible states for autonomous and non-autonomous MPL systems and establishing the necessary conditions for structural controllability by making use of its graphical representation and thirdly, to model SMPL systems and also establish properties such as structural controllability by means of a graph-theoretic framework.Mechanical Engineering | Systems and Contro

Using clustering to enhance text classification

This paper addresses the problem of learning to classify texts by exploiting information derived from clustering both train-ing and testing sets. The incorporation of knowledge result-ing from clustering into the feature space representation of the texts is expected to boost the performance of a classi-fier. Experiments conducted on several widely used datasets demonstrate the effectiveness of the proposed algorithm es-pecially for small training sets

Text Classification Using Clustering

This paper addresses the problem of learning to classify texts by exploiting information derived from both training and testing sets. To accomplish this, clustering is used as a complementary step to text classification, and is applied not only to the training set but also to the testing set. This approach allows us to estimate the location of the testing examples and the structure of the whole dataset, which is not possible for an inductive learner. The incorporation of the knowledge resulting from clustering to the simple BOW representation of the texts is expected to boost the performance of a classifier. Experiments conducted on tasks and datasets provided in the framework of the ECDL/PKDD 2006 Challenge Discovery on personalized spam filtering, demonstrate the e#ectiveness of the proposed approach. The experiments show substantial improvements on classification performance especially for small training sets

Using Hierarchical Clustering to Enhance Classification Accuracy

Abstract. A new approach to classification is presented based on COBWEB, an unsupervised conceptual clustering algorithm. The modifications proposed improved the classification accuracy by 2.32 % and up to 7.25 % in the Period Disambiguation system that was built in order to test the efficiency of the approach. The system can be trained across different domains and languages. It has been tested on the Brown Corpus and on a collection of articles from Greek financial newspapers achieving accuracy 99.18 % and 99.35 % respectively.