Sense abstractness, semantic activation, and word sense disambiguation

Concrete concepts are often easier to understand than abstract concepts. The notion of abstractness is thus closely tied to the organisation of our semantic memory, and more specifically our internal lexicon, which underlies our word sense disambiguation (WSD) mechanisms. State-of-the-art automatic WSD systems often draw on a variety of contextual cues and assign word senses by an optimal combination of statistical classifiers. The validity of various lexico-semantic resources as models of our internal lexicon and the cognitive aspects pertinent to the lexical sensitivity of WSD are seldom questioned. We attempt to address these issues by examining psychological evidence of the internal lexicon and its compatibility with the information available from computational lexicons. In particular, we compare the responses from a word association task against existing lexical resources, WordNet and SUMO, to explore the relation between sense abstractness and semantic activation, and thus the implications on semantic network models and the lexical sensitivity of WSD. Our results suggest that concrete senses are more readily activated than abstract senses, and broad associations are more easily triggered than narrow paradigmatic associations. The results are expected to inform the construction of lexico-semantic resources and WSD strategies.     

Genetic Parallel Programming: design and implementation

This paper presents a novel Genetic Parallel Programming (GPP) paradigm for evolving parallel programs running on a Multi-Arithmetic-Logic-Unit (Multi-ALU) Processor (MAP). The MAP is a Multiple Instruction-streams, Multiple Data-streams (MIMD), general-purpose register machine that can be implemented on modern Very Large-Scale Integrated Circuits (VLSIs) in order to evaluate genetic programs at high speed. For human programmers, writing parallel programs is more difficult than writing sequential programs. However, experimental results show that GPP evolves parallel programs with less computational effort than that of their sequential counterparts. It creates a new approach to evolving a feasible problem solution in parallel program form and then serializes it into a sequential program if required. The effectiveness and efficiency of GPP are investigated using a suite of 14 well-studied benchmark problems. Experimental results show that GPP speeds up evolution substantially.     

Diagnosis and treatment of aortic valve stenosis

The present review has attempted to summarize the classic symptoms and signs of aortic valve stenosis, especially in an adult. It is emphasized that all the classic signs rarely are present and their absence may mislead an unwary clinician. The diagnostic help provided by noninvasive tests, including echocardiography and phonocardiography, has been emphasized. A need for cardiac catheterization and angiography in most patients prior to corrective surgery is stressed. The natural history of the disease without operative intervention is dim and a significant risk of sudden death exists. The current surgical approach with immediate and long-term results is summarized. Finally, attention has been drawn to the special clinical circumstances when the aortic valve stenosis provides a strinkingly different clinical picture. We cannot find a better way to end this review than by quoting a warning note given by Thomas Lewis in 1920: "It is the faint cry of an anguished and fast failing muscle, which, when it comes, all should strain to hear, for it is not long repeated. A few months, a few years at most, and the end comes."     

A multi-objective genetic algorithm approach to rule mining for affective product design

A novel multi-objective genetic algorithm (GA)-based rule-mining method for affective product design is proposed to discover a set of rules relating design attributes with customer evaluation based on survey data. The proposed method can generate approximate rules to consider the ambiguity of customer assessments. The generated rules can be used to determine the lower and upper limits of the affective effect of design patterns. For a rule-mining problem, the proposed multi-objective GA approach could simultaneously consider the accuracy, comprehensibility, and definability of approximate rules. In addition, the proposed approach can deal with categorical attributes and quantitative attributes, and determine the interval of quantitative attributes. Categorical and quantitative attributes in affective product design should be considered because they are commonly used to define the design profile of a product. In this paper, a two-stage rule-mining approach is proposed to generate rules with a simple chromosome design in the first stage of rule mining. In the second stage of rule mining, entire rule sets are refined to determine solutions considering rule interaction. A case study on mobile phones is used to demonstrate and validate the performance of the proposed rule-mining method. The method can discover rule sets with good support and coverage rates from the survey data.     

Oscillation and convergence behaviours exhibited in an ‘unstable’ second‐order digital filter with saturation‐type non‐linearity

This letter explains the oscillatory behaviours exhibited in a second‐order digital filter with saturation‐type non‐linearity via the Hopf bifurcation theorem. It is shown that depending on the bifurcation parameter, the state variables may converge to zero even when the eigenvalues of the system matrix are outside the unit circle. Copyright © 2004 John Wiley & Sons, Ltd. abstract     

Immunology and adjuvant chemoimmunotherapy of breast cancer

Antibody against a breast carcinoma antigen was present in patients with breast carcinoma and other cancer more often (P less than .05) than in normal women. The incidence of antibody in women with breast carcinoma correlated with the presence or absence of gross tumor, and the titer of antibody paralleled the clinical course. These results suggest importance of a host-immune response to breast carcinoma. Fifty-seven patients with stage II carcinoma of the breast were entered into a prospective randomized adjuvant chemoimmunotherapy program of cyclophosphamide, methotrexate, and fluorouracil, and BCG vaccine +/- an irradiated allogeneic tumor cell vaccine. After 24 months of study, metastases occurred in two patients (3.5%) and a new primary carcinoma developed in the contralateral breast in two others, for an overall treatment failure rate of 7%. Adjuvant chemoimmunotherapy can delay early recurrence. Long-term follow-up is needed to assess the significance of these results.     

A simple characterization of optimal ARMA predictors

The coefficients of the optimal steady state k-step ahead predictor for an ARMA process in general depend on k. It is shown that a simple formula completely characterizes all these coefficients.     

A Novel Cartesian Plot Analysis for Fixed Monolayers That Relates Cell Phenotype to Transfer of Contents between Fibroblasts and Cancer Cells by Cell-Projection Pumping

We recently described cell-projection pumping as a mechanism transferring cytoplasm between cells. The uptake of fibroblast cytoplasm by co-cultured SAOS-2 osteosarcoma cells changes SAOS-2 morphology and increases cell migration and proliferation, as seen by single-cell tracking and in FACS separated SAOS-2 from co-cultures. Morphological changes in SAOS-2 seen by single cell tracking are consistent with previous observations in fixed monolayers of SAOS-2 co-cultures. Notably, earlier studies with fixed co-cultures were limited by the absence of a quantitative method for identifying sub-populations of co-cultured cells, or for quantitating transfer relative to control populations of SAOS-2 or fibroblasts cultured alone. We now overcome that limitation by a novel Cartesian plot analysis that identifies individual co-cultured cells as belonging to one of five distinct cell populations, and also gives numerical measure of similarity to control cell populations. We verified the utility of the method by first confirming the previously established relationship between SAOS-2 morphology and uptake of fibroblast contents, and also demonstrated similar effects in other cancer cell lines including from melanomas, and cancers of the ovary and colon. The method was extended to examine global DNA methylation, and while there was no clear effect on SAOS-2 DNA methylation, co-cultured fibroblasts had greatly reduced DNA methylation, similar to cancer associated fibroblasts.     

An efficient data mining method for learning Bayesian networks using an evolutionary algorithm-based hybrid approach

Given the explosive growth of data collected from current business environment, data mining can potentially discover new knowledge to improve managerial decision making. This paper proposes a novel data mining approach that employs an evolutionary algorithm to discover knowledge represented in Bayesian networks. The approach is applied successfully to handle the business problem of finding response models from direct marketing data. Learning Bayesian networks from data is a difficult problem. There are two different approaches to the network learning problem. The first one uses dependency analysis, while the second one searches good network structures according to a metric. Unfortunately, both approaches have their own drawbacks. Thus, we propose a novel hybrid algorithm of the two approaches, which consists of two phases, namely, the conditional independence (CI) test and the search phases. In the CI test phase, dependency analysis is conducted to reduce the size of the search space. In the search phase, good Bayesian network models are generated by using an evolutionary algorithm. A new operator is introduced to further enhance the search effectiveness and efficiency. In a number of experiments and comparisons, the hybrid algorithm outperforms MDLEP, our previous algorithm which uses evolutionary programming (EP) for network learning, and other network learning algorithms. We then apply the approach to two data sets of direct marketing and compare the performance of the evolved Bayesian networks obtained by the new algorithm with those by MDLEP, the logistic regression models, the na/spl inodot//spl uml/ve Bayesian classifiers, and the tree-augmented na/spl inodot//spl uml/ve Bayesian network classifiers (TAN). In the comparison, the new algorithm outperforms the others.