Pascal Cuxac

Characterization of the moduli of elasticity of an anisotropic rock using dynamic and static methods

Abstract The tests reported in this paper were intended to determine the mechanical characteristics of a slate. This rock has a very definite discontinuous planar anisotropy superimposed on mineral lineation. As a first step, ultrasonic measurements were carried out to determine the main structural axes. The dynamic moduli of elasticity can be calculated if the velocities of the compressional and shear waves are known. This in turn allows a complete stiffness matrix to be calculated. As a second step, taking account of the ultrasonic measurements, the moduli of elasticity were determined from uniaxial and triaxial loading tests. The confining pressure is shown to have a definite influence on Youngs modulus, measured at right-angles to the schistosity. Calculating the shear modulus is a fairly complex operation; clinotropic tests with loading-unloading cycles are necessary for an understanding of the large difference in observed values between dynamic and static shear moduli. The laws of evolution of the moduli as a function of confinement levels are validated by compressibility tests.

Variations to incremental growing neural gas algorithm based on label maximization

Neural clustering algorithms show high performance in the general context of the analysis of homogeneous textual dataset. This is especially true for the recent adaptive versions of these algorithms, like the incremental growing neural gas algorithm (IGNG) and the labeling maximization based incremental growing neural gas algorithm (IGNG-F). In this paper we highlight that there is a drastic decrease of performance of these algorithms, as well as the one of more classical algorithms, when a heterogeneous textual dataset is considered as an input. Specific quality measures and cluster labeling techniques that are independent of the clustering method are used for the precise performance evaluation. We provide new variations to incremental growing neural gas algorithm exploiting in an incremental way knowledge from clusters about their current labeling along with cluster distance measure data. This solution leads to significant gain in performance for all types of datasets, especially for the clustering of complex heterogeneous textual data.

Optimizing text classification through efficient feature selection based on quality metric

Feature maximization is a cluster quality metric which favors clusters with maximum feature representation as regard to their associated data. In this paper we show that a simple adaptation of such metric can provide a highly efficient feature selection and feature contrasting model in the context of supervised classification. The method is experienced on different types of textual datasets. The paper illustrates that the proposed method provides a very significant performance increase, as compared to state of the art methods, in all the studied cases even when a single bag of words model is exploited for data description. Interestingly, the most significant performance gain is obtained in the case of the classification of highly unbalanced, highly multidimensional and noisy data, with a high degree of similarity between the classes.

Efficient supervised and semi-supervised approaches for affiliations disambiguation

The disambiguation of named entities is a challenge in many fields such as scientometrics, social networks, record linkage, citation analysis, semantic web…etc. The names ambiguities can arise from misspelling, typographical or OCR mistakes, abbreviations, omissions… Therefore, the search of names of persons or of organizations is difficult as soon as a single name might appear in many different forms. This paper proposes two approaches to disambiguate on the affiliations of authors of scientific papers in bibliographic databases: the first way considers that a training dataset is available, and uses a Naive Bayes model. The second way assumes that there is no learning resource, and uses a semi-supervised approach, mixing soft-clustering and Bayesian learning. The results are encouraging and the approach is already partially applied in a scientific survey department. However, our experiments also highlight that our approach has some limitations: it cannot process efficiently highly unbalanced data. Alternatives solutions are possible for future developments, particularly with the use of a recent clustering algorithm relying on feature maximization.

Enhancing Patent Expertise through Automatic Matching with Scientific Papers

This paper focuses on a subtask of the QUAERO research program, a major innovating research project related to the automatic processing of multimedia and multilingual content. The objective discussed in this article is to propose a new method for the classification of scientific papers, developed in the context of an international patents classification plan related to the same field. The practical purpose of this work is to provide an assistance tool to experts in their task of evaluation of the originality and novelty of a patent, by offering to the latter the most relevant scientific citations. This issue raises new challenges in categorization research as the patent classification plan is not directly adapted to the structure of scientific documents, classes have high citation or cited topic and that there is not always a balanced distribution of the available examples within the different learning classes. We propose, as a solution to this problem, to apply an improved K-nearest-neighbors (KNN) algorithm based on the exploitation of association rules occurring between the index terms of the documents and the ones of the patent classes. By using a reference dataset of patents belonging to the field of pharmacology, on the one hand, and a bibliographic dataset of the same field issued from the Medline collection, on the other hand, we show that this new approach, which combines the advantages of numerical and symbolical approaches, improves considerably categorization performance, as compared to the usual categorization methods.

A new incremental growing neural gas algorithm based on clusters labeling maximization: application to clustering of heterogeneous textual data

Neural clustering algorithms show high performance in the usual context of the analysis of homogeneous textual dataset. This is especially true for the recent adaptive versions of these algorithms, like the incremental neural gas algorithm (IGNG). Nevertheless, this paper highlights clearly the drastic decrease of performance of these algorithms, as well as the one of more classical algorithms, when a heterogeneous textual dataset is considered as an input. A new incremental growing neural gas algorithm exploiting knowledge issued from clusters current labeling in an incremental way is proposed as an alternative to the original distance based algorithm. This solution leads to obtain very significant increase of performance for the clustering of heterogeneous textual data. Moreover, it provides a real incremental character to the proposed algorithm.

A New Feature Selection and Feature Contrasting Approach Based on Quality Metric: Application to Efficient Classification of Complex Textual Data

Feature maximization is a cluster quality metric which favors clusters with maximum feature representation as regard to their associated data. In this paper we go one step further showing that a straightforward adaptation of such metric can provide a highly efficient feature selection and feature contrasting model in the context of supervised classification. We more especially show that this technique can enhance the performance of classification methods whilst very significantly outperforming (+80%) the state-of-the art feature selection techniques in the case of the classification of unbalanced, highly multidimensional and noisy textual data, with a high degree of similarity between the classes.

A concept for inferring `frontier research' in grant proposals

This paper discusses a concept for inferring attributes of ‘frontier research’ in peer-reviewed research proposals under the popular scheme of the European Research Council (ERC). The concept serves two purposes: firstly to conceptualize, define and operationalize in scientometric terms attributes of frontier research; and secondly to build and compare outcomes of a statistical model with the review decision in order to obtain further insight and reflect upon the influence of frontier research in the peer-review process. To this end, indicators across scientific disciplines and in accord with the strategic definition of frontier research by the ERC are elaborated, exploiting textual proposal information and other scientometric data of grant applicants. Subsequently, a suitable model is formulated to measure ex-post the influence of attributes of frontier research on the decision probability of a proposal to be accepted. We present first empirical data as proof of concept for inferring frontier research in grant proposals. Ultimately the concept is aiming at advancing the methodology to deliver signals for monitoring the effectiveness of peer-review processes.

A new efficient and unbiased approach for clustering quality evaluation

Traditional quality indexes (Inertia, DB, …) are known to be method-dependent indexes that do not allow to properly estimate the quality of the clustering in several cases, as in that one of complex data, like textual data. We thus propose an alternative approach for clustering quality evaluation based on unsupervised measures of Recall, Precision and F-measure exploiting the descriptors of the data associated with the obtained clusters. Two categories of index are proposed, that are Macro and Micro indexes. This paper also focuses on the construction of a new cumulative Micro precision index that makes it possible to evaluate the overall quality of a clustering result while clearly distinguishing between homogeneous and heterogeneous, or degenerated results. The experimental comparison of the behavior of the classical indexes with our new approach is performed on a polythematic dataset of bibliographical references issued from the PASCAL database.

Mining Research Topics Evolving Over Time Using a Diachronic Multi-source Approach

The acquisition of new scientific knowledge and the evolution of the needs of the society regularly call into question the orientations of research. Means to recall and visualize these evolutions are thus necessary. The existing tools for research survey give only one fixed vision of the research activity, which does not allow performing tasks of dynamic topic mining. The objective of this paper is thus to propose a new incremental approach in order to follow the evolution of research themes and research groups for a scientific discipline given in terms of emergence or decline. These behaviors are detectable by various methods of filtering. However, our choice is made on the exploitation of neural clustering methods in a multi-view context. This new approach makes it possible to take into account the incremental and chronological aspect of information by opening the way to the detection of convergences and divergences of research themes and groups.