Text Summarization by Sentence Extraction and Syntactic Pruning

Nous présentons une méthode hybride pour le résumé de texte, en combinant l'extraction de phrases et l'élagage syntaxique des phrases extraites. L'élagage syntaxique est effectué sur la base d’une analyse complète des phrases selon un parseur de dépendances, analyse réalisée par la grammaire développée au sein d'un logiciel commercial de correction grammaticale, le Correcteur 101. Des sous-arbres de l'analyse syntaxique sont supprimés quand ils sont identifiés par les relations ciblées. L'analyse est réalisée sur un corpus de divers textes. Le taux de réduction des phrases extraites est d’en moyenne environ 74%, tout en conservant la grammaticalité ou la lisibilité dans une proportion de plus de 64%. Étant donné ces premiers résultats sur un ensemble limité de relations syntaxiques, cela laisse entrevoir des possibilités pour une application de résumé automatique de texte.

INVESTIGATIONS ON THE SOLVENT EXTRACTION AND LUMINESCENCE OF LANTHANOIDS WITH MIXTURES OF HETEROCYCLIC β- DIKETONE S AND VARIOUS NEUTRAL OXO-DONORS

The thesis entitled “ Investigations on the solvent extraction and luminescence of lanthanoids with mixtures of heterocyclic β-diketone S and various neutral oxo-donors” embodies the results of investigations carried out on the solvent extraction of trivalent lanthanoids with various heterocyclic β-diketones in the presence and absence of neutral oxo-donors and also on the luminescent studies of Eu3+-heterocyclic β-diketonate complexes with Lewis bases. The primary objective of the present work is to generate the knowledge base, especially to understand the interactions of lanthanoid-heterocyclic β-diketonates with various macrocyclic ligands such as crown ethers and neutral organophosphorus extractants , with a view to achieve better selectivity. The secondary objective of this thesis is to develop novel lanthanoid luminescent materials based on 3-phenyl-4-aroyl-5-isoxazolones and organophosphorus ligands, for use in electroluminescent devices. In the beginning it describes the need for the development of new mixed-ligand systems for the separation of lanthanoids and the development and importance of novel luminescent lanthanoid- β-diketonate complexes for display devices. The syntheses of various para substituted derivatives of 4-aroyl-5-isoxazolones and their characterization by various spectroscopic techniques are described. It also investigate the solvent extraction behaviour of trivalent lanthanoids with 4-aroyl-5-isoxazolones in the presence and absence of various crown ethers such as 18C6, DC18C6, DB18C6 and B18C6. Elemental analysis, IR and H NMR spectral studies are used to understand the interactions of crown ethers with 4-aroyl-5-isoxazolonate complexes of lanthanoids. The synergistic extraction of trivalent lanthanoids with sterically hindered 1-phenyl-3-methyl-4-pivaloyl-5-pyrazolone in the presence of various structurally related crown ethers are studied. The syntheses, characterization and photyphysical properties of Eu3+-4-aroyl-5-isoxazolonate complexes in the presence of Lewis bases like trictylphosphine oxide or triphenylphosphine oxide were studied.

“Improved Feature Extraction and Classification Techniques for Multispectral Brain Magnetic Resonance Images”

Magnetic Resonance Imaging (MRI) is a multi sequence medical imaging technique in which stacks of images are acquired with different tissue contrasts. Simultaneous observation and quantitative analysis of normal brain tissues and small abnormalities from these large numbers of different sequences is a great challenge in clinical applications. Multispectral MRI analysis can simplify the job considerably by combining unlimited number of available co-registered sequences in a single suite. However, poor performance of the multispectral system with conventional image classification and segmentation methods makes it inappropriate for clinical analysis. Recent works in multispectral brain MRI analysis attempted to resolve this issue by improved feature extraction approaches, such as transform based methods, fuzzy approaches, algebraic techniques and so forth. Transform based feature extraction methods like Independent Component Analysis (ICA) and its extensions have been effectively used in recent studies to improve the performance of multispectral brain MRI analysis. However, these global transforms were found to be inefficient and inconsistent in identifying less frequently occurred features like small lesions, from large amount of MR data. The present thesis focuses on the improvement in ICA based feature extraction techniques to enhance the performance of multispectral brain MRI analysis. Methods using spectral clustering and wavelet transforms are proposed to resolve the inefficiency of ICA in identifying small abnormalities, and problems due to ICA over-completeness. Effectiveness of the new methods in brain tissue classification and segmentation is confirmed by a detailed quantitative and qualitative analysis with synthetic and clinical, normal and abnormal, data. In comparison to conventional classification techniques, proposed algorithms provide better performance in classification of normal brain tissues and significant small abnormalities.

Development of Techniques for the Automatic Extraction and Grade Detection of Glioma Tumors from Conventional Brain Magnetic Resonant Images

Cerebral glioma is the most prevalent primary brain tumor, which are classified broadly into low and high grades according to the degree of malignancy. High grade gliomas are highly malignant which possess a poor prognosis, and the patients survive less than eighteen months after diagnosis. Low grade gliomas are slow growing, least malignant and has better response to therapy. To date, histological grading is used as the standard technique for diagnosis, treatment planning and survival prediction. The main objective of this thesis is to propose novel methods for automatic extraction of low and high grade glioma and other brain tissues, grade detection techniques for glioma using conventional magnetic resonance imaging (MRI) modalities and 3D modelling of glioma from segmented tumor slices in order to assess the growth rate of tumors. Two new methods are developed for extracting tumor regions, of which the second method, named as Adaptive Gray level Algebraic set Segmentation Algorithm (AGASA) can also extract white matter and grey matter from T1 FLAIR an T2 weighted images. The methods were validated with manual Ground truth images, which showed promising results. The developed methods were compared with widely used Fuzzy c-means clustering technique and the robustness of the algorithm with respect to noise is also checked for different noise levels. Image texture can provide significant information on the (ab)normality of tissue, and this thesis expands this idea to tumour texture grading and detection. Based on the thresholds of discriminant first order and gray level cooccurrence matrix based second order statistical features three feature sets were formulated and a decision system was developed for grade detection of glioma from conventional T2 weighted MRI modality.The quantitative performance analysis using ROC curve showed 99.03% accuracy for distinguishing between advanced (aggressive) and early stage (non-aggressive) malignant glioma. The developed brain texture analysis techniques can improve the physician’s ability to detect and analyse pathologies leading to a more reliable diagnosis and treatment of disease. The segmented tumors were also used for volumetric modelling of tumors which can provide an idea of the growth rate of tumor; this can be used for assessing response to therapy and patient prognosis.

Sequential extraction and single-step cold-acid extraction: A feasibility study for use with freshwater-canal sediments

This investigation examines metal release from freshwater sediment using sequential extraction and single-step cold-acid leaching. The concentrations of Cd, Cr, Cu, Fe, Ni, Pb and Zn released using a standard 3-step sequential extraction (Rauret et al., 1999) are compared to those released using a 0.5 M HCl; leach. The results show that the three sediments behave in very different ways when subject to the same leaching experiments: the cold-acid extraction appears to remove higher relative concentrations of metals from the iron-rich sediment than from the other two sediments. Cold-acid extraction appears to be more effective at removing metals from sediments with crystalline iron oxides than the "reducible" step of the sequential extraction. The results show that a single-step acid leach can be just as effective as sequential extractions at removing metals from sediment and are a great deal less time-consuming.

Mining, grievance and conflict in rural Tanzania

This paper provides an extended analysis of the tensions that have surfaced between large-scale mine operators and artisanal miners in gold-rich areas of rural Tanzania. The literature on grievance is used to contextualise, these disputes, the underlying cause of which is artisanal miners' mounting frustration over not being able to secure viable concessions to work. Newly implemented legislation has, for the most part, empowered foreign large-scale mine operators, while simultaneously disempowering indigenous small-scale miners. In many cases, the former have addressed mounting security and community problems on their own. Until the country's major mine operators extend assistance to marginalised small-scale mining groups, the likelihood of violent conflict unfolding between these parties will increase.

Small-scale mining, poverty and economic development in sub-Saharan Africa: An overview

Artisanal and small-scale mining (ASM)-low tech, labour intensive mineral processing and excavation activity-is an economic mainstay in rural sub-Saharan Africa, providing direct employment to over two million people. This paper introduces a special issue on 'Small-scale mining, poverty and development in sub-Saharan Africa'. It focuses on the core conceptual issues covered in the literature, and the policy implications of the findings reported in the papers in this special issue. (C) 2009 Elsevier Ltd. All rights reserved.

Solvent extraction and lanthanide complexation studies with new terdentate ligands containing two 1,3,5-triazine moieties

The extracting agent 2,6-bis(4,6-di-pivaloylamino-1,3,5-triazin-2-yl)-pyridine (L-5) in n-octanol was found, in synergy with 2-bromodecanoic acid, to give D-Am/D-Eu separation factors (SFs) between 2.4 and 3.7 when used to extract the metal ions from 0.02-0.12 M HNO3. Slightly higher SFs (4-6) were obtained in the absence of the synergist when the ligand was used to extract Am(III) and Eu(III) from 0.98 M HNO3. In order to investigate the possible nature of the extracted species crystal structures of L-5 and the complex formed between Yb(III) with 2,6-bis(4,6-di-amino-1,3,5-triazin-2-yl)-pyridine (L-4) were also determined. The structure of L-5 shows 3 methanol solvent molecules all of which form 2 or 3 hydrogen bonds with triazine nitrogen atoms, amide nitrogen or oxygen atoms, or pyridine nitrogen atoms. However, L-5 is relatively unstable in metal complexation reactions and loses amide groups to form the parent tetramine L-4. The crystal structure of Yb(L-4)(NO3)(3) shows ytterbium in a 9-coordinate environment being bonded to three donor atoms of the ligand and three bidentate nitrate ions. The solvent extraction properties of L-4 and L-5 are far inferior to those found for the 2,6-bis-(1,2,4-triazin-3-yl)-pyridines (L-1) which have SF values of ca. 140 and theoretical calculations have been made to compare the electronic properties of the ligands. The electronic charge distribution in L-4 and L-5 is similar to that found in other terdentate ligands such as terpyridine which have equally poor extraction properties and suggests that the unique properties of L-1 evolve from the presence of two adjacent nitrogen atoms in the triazine rings.

Extraction and coordination studies of the unexplored bifunctional ligand carbamoyl methyl sulfoxide (CMSO) with uranium(VI) and cerium(III) nitrates. Synthesis and structures of [UO2(NO3)(2)((PhSOCH2CONBu2)-Bu-i)] and [Ce(NO3)(3)(PhSOCH2CONBu2)(2)]

The bifunctional carbamoyl methyl sulfoxide ligands, PhCH2SOCH2CONHPh (L-1), PhCH2SOCH2CONHCH2Ph (L-2), (PhSOCH2CONPr2)-Pr-i (L-3), PhSOCH2CONBu2 (L-4), (PhSOCH2CONBu2)-Bu-i (L-5) and PhSOCH2CON(C8H17)(2) (L-6) have been synthesized and characterized by spectroscopic methods. The selected coordination chemistry of L-1, L-3, L-4 and L-5 with [UO2(NO3)(2)] and [Ce(NO3)(3)] has been evaluated. The structures of the compounds [UO2(NO3)(2)((PhSOCH2CONBu2)-Bu-i)] (10) and [Ce(NO3)(3)(PhSOCH2CONBu2)(2)] (12) have been determined by single crystal X-ray diffraction methods. Preliminary extraction studies of ligand L-6 with U(VI), Pu(IV) and Am(III) in tracer level showed an appreciable extraction for U(VI) and Pu(IV) in up to 10 M HNO3 but not for Am(III). Thermal studies on compounds 8 and 10 in air revealed that the ligands can be destroyed completely on incineration. The electron spray mass spectra of compounds 8 and 10 in acetone show that extensive ligand distribution reactions occur in solution to give a mixture of products with ligand to metal ratios of 1 : 1 and 2 : 1. However, 10 retains its solid state structure in CH2Cl2.

Extraction and determination of ellagic acid content in chestnut bark and fruit

Chestnuts are an important economic resource in the chestnut growing regions, not only for the fruit, but also for the wood. The content of ellagic acid (EA), a naturally occurring inhibitor of carcinogenesis, was determined in chestnut fruits and bark. EA was extracted with methanol and free ellagic acid was determined by HPLC with UV detection, both in the crude extract and after hydrolysis. The concentration of EA was generally increased after hydrolysis due to the presence of ellagitannins in the crude extract. The concentration varied between 0.71 and 21.6 ing g(-1) (d.w.) in un-hydrolyzed samples, and between 2.83 and 18.4 mg g(-1) (d.w.) ill hydrolyzed samples. In chestnut fruits, traces of EA were present in the seed, with higher concentrations in the pellicle and pericarp. However, all fruit tissues had lower concentrations of EA than had the bark. The concentration of EA in the hydrolyzed samples showed a non-linear correlation with the concentration in the unhydrolyzed extracts. (C) 2008 Elsevier Ltd. All rights reserved.

Facial feature extraction and selection by Gabor wavelets and boosting

In this paper, we present a feature selection approach based on Gabor wavelet feature and boosting for face verification. By convolution with a group of Gabor wavelets, the original images are transformed into vectors of Gabor wavelet features. Then for individual person, a small set of significant features are selected by the boosting algorithm from a large set of Gabor wavelet features. The experiment results have shown that the approach successfully selects meaningful and explainable features for face verification. The experiments also suggest that for the common characteristics such as eyes, noses, mouths may not be as important as some unique characteristic when training set is small. When training set is large, the unique characteristics and the common characteristics are both important.

A neurofuzzy network knowledge extraction and extended gram-Schmidt algorithm for model subspace decomposition

This paper introduces a new neurofuzzy model construction and parameter estimation algorithm from observed finite data sets, based on a Takagi and Sugeno (T-S) inference mechanism and a new extended Gram-Schmidt orthogonal decomposition algorithm, for the modeling of a priori unknown dynamical systems in the form of a set of fuzzy rules. The first contribution of the paper is the introduction of a one to one mapping between a fuzzy rule-base and a model matrix feature subspace using the T-S inference mechanism. This link enables the numerical properties associated with a rule-based matrix subspace, the relationships amongst these matrix subspaces, and the correlation between the output vector and a rule-base matrix subspace, to be investigated and extracted as rule-based knowledge to enhance model transparency. The matrix subspace spanned by a fuzzy rule is initially derived as the input regression matrix multiplied by a weighting matrix that consists of the corresponding fuzzy membership functions over the training data set. Model transparency is explored by the derivation of an equivalence between an A-optimality experimental design criterion of the weighting matrix and the average model output sensitivity to the fuzzy rule, so that rule-bases can be effectively measured by their identifiability via the A-optimality experimental design criterion. The A-optimality experimental design criterion of the weighting matrices of fuzzy rules is used to construct an initial model rule-base. An extended Gram-Schmidt algorithm is then developed to estimate the parameter vector for each rule. This new algorithm decomposes the model rule-bases via an orthogonal subspace decomposition approach, so as to enhance model transparency with the capability of interpreting the derived rule-base energy level. This new approach is computationally simpler than the conventional Gram-Schmidt algorithm for resolving high dimensional regression problems, whereby it is computationally desirable to decompose complex models into a few submodels rather than a single model with large number of input variables and the associated curse of dimensionality problem. Numerical examples are included to demonstrate the effectiveness of the proposed new algorithm.

Robust neurofuzzy rule base knowledge extraction and estimation using subspace decomposition combined with regularization and D-optimality

A new robust neurofuzzy model construction algorithm has been introduced for the modeling of a priori unknown dynamical systems from observed finite data sets in the form of a set of fuzzy rules. Based on a Takagi-Sugeno (T-S) inference mechanism a one to one mapping between a fuzzy rule base and a model matrix feature subspace is established. This link enables rule based knowledge to be extracted from matrix subspace to enhance model transparency. In order to achieve maximized model robustness and sparsity, a new robust extended Gram-Schmidt (G-S) method has been introduced via two effective and complementary approaches of regularization and D-optimality experimental design. Model rule bases are decomposed into orthogonal subspaces, so as to enhance model transparency with the capability of interpreting the derived rule base energy level. A locally regularized orthogonal least squares algorithm, combined with a D-optimality used for subspace based rule selection, has been extended for fuzzy rule regularization and subspace based information extraction. By using a weighting for the D-optimality cost function, the entire model construction procedure becomes automatic. Numerical examples are included to demonstrate the effectiveness of the proposed new algorithm.