Adaptive text mining: Inferring structure from sequences

Text mining is about inferring structure from sequences representing natural language text, and may be defined as the process of analyzing text to extract information that is useful for particular purposes. Although hand-crafted heuristics are a common practical approach for extracting information from text, a general, and generalizable, approach requires adaptive techniques. This paper studies the way in which the adaptive techniques used in text compression can be applied to text mining. It develops several examples: extraction of hierarchical phrase structures from text, identification of keyphrases in documents, locating proper names and quantities of interest in a piece of text, text categorization, word segmentation, acronym extraction, and structure recognition. We conclude that compression forms a sound unifying principle that allows many text mining problems to be tacked adaptively

Data compression for estimation of the physical parameters of stable and unstable linear systems

A two-stage method for the identification of physical system parameters from experimental data is presented. The first stage compresses the data as an empirical model which encapsulates the data content at frequencies of interest. The second stage then uses data extracted from the empirical model of the first stage within a nonlinear estimation scheme to estimate the unknown physical parameters. Furthermore, the paper proposes use of exponential data weighting in the identification of partially unknown, unstable systems so that they can be treated in the same framework as stable systems. Experimental data are used to demonstrate the efficacy of the proposed approach

Tools for multiaxial validation of behavior laws chosen for modeling hyper-elasticity of rubber-like materials

We present an experimental approach to discriminate hyper-elastic models describing the mechanical behavior of rubber-like materials. An evaluation of the displacement field obtained by digital image correlation allows us to evaluate the heterogeneous strain field observed during these tests. We focus on the particular case of hyper-elastic models to simulate the behavior of some rubber-like materials. Assuming incompressibility of the material, the hyper-elastic potential is determined from tension and compression tests. A biaxial loading condition is obtained in a multiaxial testing machine and model predictions are compared with experimental results

Modelling, identification and application of phenomenological constitutive laws over a large strain rate and temperature range

A review of the different phenomenological thermo-viscoplastic constitutive models often applied to forging and machining processes is presented. Several of the most common models have been identified using a large experimental database (Hor et al., 2013). The latter consists of the tests were done in compression on cylindrical shaped specimens and in shear using hat-shaped specimens. The comparison between these different models is shown that the group of decoupled empirical constitutive models (e.g. the Johnson and Cook (1983) model), despite their simple identification procedures, are relatively limited, especially over a large range of strain rates and temperatures. Recent studies have led to the proposal of coupled empirical models. Three models in this class have also been studied. The Lurdos (2008) model shows the best accuracy but requires a large experimental database to identify its high number of parameters. After this comparison, a constitutive equation is proposed by modifying the TANH model (Calamaz et al., 2010). Coupling between the effects of strain rate and temperature is introduced. This model is easier to identify and does not require knowledge of the saturation stress. Compared to other models, it better reproduces the experimental results especially in the semi-hot and hot domains. In order to study real machining conditions, an orthogonal cutting tests is considered. The comparison between experimental test results and numerical simulations conducted using the previously identified constitutive models shows that the decoupled empirical models are not capable of reproducing the experimental observations. However, the coupled constitutive models, that take into account softening, improve the accuracy of these simulations

Drawing Elena Ferrante's Profile. Workshop Proceedings, Padova, 7 September 2017

Elena Ferrante is an internationally acclaimed Italian novelist whose real identity has been kept secret by E/O publishing house for more than 25 years. Owing to her popularity, major Italian and foreign newspapers have long tried to discover her real identity. However, only a few attempts have been made to foster a scientific debate on her work. In 2016, Arjuna Tuzzi and Michele Cortelazzo led an Italian research team that conducted a preliminary study and collected a well-founded, large corpus of Italian novels comprising 150 works published in the last 30 years by 40 different authors. Moreover, they shared their data with a select group of international experts on authorship attribution, profiling, and analysis of textual data: Maciej Eder and Jan Rybicki (Poland), Patrick Juola (United States), Vittorio Loreto and his research team, Margherita Lalli and Francesca Tria (Italy), George Mikros (Greece), Pierre Ratinaud (France), and Jacques Savoy (Switzerland). The chapters of this volume report the results of this endeavour that were first presented during the international workshop Drawing Elena Ferrante's Profile in Padua on 7 September 2017 as part of the 3rd IQLA-GIAT Summer School in Quantitative Analysis of Textual Data. The fascinating research findings suggest that Elena Ferrante\u2019s work definitely deserves \u201cmany hands\u201d as well as an extensive effort to understand her distinct writing style and the reasons for her worldwide success

Audiovisual preservation strategies, data models and value-chains

This is a report on preservation strategies, models and value-chains for digital file-based audiovisual content. The report includes: (a)current and emerging value-chains and business-models for audiovisual preservation;(b) a comparison of preservation strategies for audiovisual content including their strengths and weaknesses, and(c) a review of current preservation metadata models, and requirements for extension to support audiovisual files