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Philippe Thomas

Publications and source records attributed to Philippe Thomas.

18 recordsLinked to original sources

Ab-initio study of structural, vibrational and non-linear optical properties of (TiO2)-(Tl2O)-(TeO2) glasses

This paper reports on a systematic first-principles molecular dynamics investigation of binary (TlO$_{0.5}$)$_{y}$-(TeO$_2$)$_{1-y}$ and ternary $(TiO$_{2}$)$_{x}$-(TlO$_{0.5}$)$_{y}$-(TeO$_2$)_{1-x-y}$ tellurite glasses. The obtained structural models are validated against available measured X-ray pair distribution functions. In the binary system, increasing TlO$_{0.5}$ content induces network depolymerization through the reduction of Te coordination number, the substitution of Te-O-Te linkages with Te=O$^{-}$...Tl$^{+}$ units, and the proliferation of non-bridging oxygens. In addition, rings analysis demonstrates a loss of the network connectivity via the opening of small n-membered rings. In contrast, TiO$_2$ acts as a network former in ternary glasses, preserving Te coordination number, and promoting a high fraction of bridging oxygens. Ti atoms induces a network repolymerization that manifests through the formation of smaller Ti-containing n-membered rings thereby balancing the strong effect of Tl$_2$O modifier. Beside the structural analysis, we also computed Raman spectra and non-linear optical properties on the obtained large periodic models. Our results reproduce experimental trends in Raman band shifts with composition, while nonlinear optical calculations show that <$\chi^{(3)}$> remains stable with TlO$_{0.5}$ addition in binary glasses, consistent with experiment. In the case of ternary systems, we find that the inclusion of a small fraction of TiO$_2$ preserves the high optical nonlinearity of the TeO$_2$ network while maintaining the overall network connectivity. These results establish a predictive framework for tailoring the atomic structure and nonlinear optical response of tellurite glasses through the controlled interplay of modifiers nature and concentration.

cond-mat.mtrl-sci

Atomic scale structure and dynamical properties of (TeO$_2$)$_{1-x}$-(Na$_2$O)$_{x}$ glasses through first-principles modeling and XRD measurements

We resort to first-principles molecular dynamics, in synergy with experiments, to study structural evolution and Na$^+$ cation diffusion inside (TeO$_2$)$_{1-x}$-(Na$_2$O)$_{x}$ (x = 0.10-0.40) glasses. Experimental and modeling results show a fair quantitative agreement in terms of total X-ray structure factors and pair distribution functions, thereby setting the ground for a comprehensive analysis of the glassy matrix evolution. We find that the structure of (TeO$_2$)$_{1-x}$-(Na$_2$O)$_{x}$ glasses deviates drastically from that of pure TeO$_2$ glass. Specifically, increasing the Na$_2$O concentration leads to a reduction of the coordination number of Te atoms, reflecting the occurrence of a structural depolymerization upon introduction of the Na$_2$O modifier oxide. The depolymerization phenomenon is ascribed to the transformation of Te-O-Te bridges into terminal Te-O non bridging oxygen atoms (NBO). Consequently, the concentration of NBO increases in these systems as the concentration of the modifier increases, accompanied by a concomitant reduction in the coordination number of Na atoms. The structure factors results show a prominent peak at 1.4 A, that becomes more and more pronounced as the Na2O concentration increases. The occurrence of this first sharp diffraction peak is attributed to the growth of Na-rich channels inside the amorphous network, acting as preferential routes for alkali-ion conduction inside the relatively stable Te-O matrix. These channels enhance the ion mobility.

cond-mat.mtrl-sci

A Dataset for Pharmacovigilance in German, French, and Japanese: Annotating Adverse Drug Reactions across Languages

User-generated data sources have gained significance in uncovering Adverse Drug Reactions (ADRs), with an increasing number of discussions occurring in the digital world. However, the existing clinical corpora predominantly revolve around scientific articles in English. This work presents a multilingual corpus of texts concerning ADRs gathered from diverse sources, including patient fora, social media, and clinical reports in German, French, and Japanese. Our corpus contains annotations covering 12 entity types, four attribute types, and 13 relation types. It contributes to the development of real-world multilingual language models for healthcare. We provide statistics to highlight certain challenges associated with the corpus and conduct preliminary experiments resulting in strong baselines for extracting entities and relations between these entities, both within and across languages.

cs.CL

MultiTACRED: A Multilingual Version of the TAC Relation Extraction Dataset

Relation extraction (RE) is a fundamental task in information extraction, whose extension to multilingual settings has been hindered by the lack of supervised resources comparable in size to large English datasets such as TACRED (Zhang et al., 2017). To address this gap, we introduce the MultiTACRED dataset, covering 12 typologically diverse languages from 9 language families, which is created by machine-translating TACRED instances and automatically projecting their entity annotations. We analyze translation and annotation projection quality, identify error categories, and experimentally evaluate fine-tuned pretrained mono- and multilingual language models in common transfer learning scenarios. Our analyses show that machine translation is a viable strategy to transfer RE instances, with native speakers judging more than 83% of the translated instances to be linguistically and semantically acceptable. We find monolingual RE model performance to be comparable to the English original for many of the target languages, and that multilingual models trained on a combination of English and target language data can outperform their monolingual counterparts. However, we also observe a variety of translation and annotation projection errors, both due to the MT systems and linguistic features of the target languages, such as pronoun-dropping, compounding and inflection, that degrade dataset quality and RE model performance.

cs.CL

Fabrication and characterization of iodine photonic microcell for sub-Doppler spectroscopy and laser stabilization

We report on the development of all-fiber stand-alone Iodine-filled Photonic Microcells demonstrating record absorption contrast at room temperature. The microcell s fiber is made of inhibited coupling guiding hollow-core photonic crystal fibers. The fiber-core loading with Iodine was undertaken at 10-1 - 10-2mbar vapor pressure using a novel gas-manifold based on metallic vacuum parts with ceramic coated inner surfaces for corrosion resistance. The fiber is then sealed on the tips and mounted on FC/APC connectors for better integration with standard fiber components. The stand-alone microcells display Doppler lines with contrasts up to 73% in the 633nm wavelength range, and an insertion loss between 3 to 4dB. Sub-Doppler spectroscopy based on saturable absorption has been carried out to resolve the hyperfine structure of the P(33)6-3 lines at room temperature with a full-width at half maximum of 24MHz on the b4 component with the help of lock-in amplification. Also, we demonstrate distinguishable hyperfine components on the R(39)6-3 line at room temperature without any recourse to signal-to-noise ratio amplification techniques.

physics.atom-ph

Cross-lingual Approaches for the Detection of Adverse Drug Reactions in German from a Patient's Perspective

In this work, we present the first corpus for German Adverse Drug Reaction (ADR) detection in patient-generated content. The data consists of 4,169 binary annotated documents from a German patient forum, where users talk about health issues and get advice from medical doctors. As is common in social media data in this domain, the class labels of the corpus are very imbalanced. This and a high topic imbalance make it a very challenging dataset, since often, the same symptom can have several causes and is not always related to a medication intake. We aim to encourage further multi-lingual efforts in the domain of ADR detection and provide preliminary experiments for binary classification using different methods of zero- and few-shot learning based on a multi-lingual model. When fine-tuning XLM-RoBERTa first on English patient forum data and then on the new German data, we achieve an F1-score of 37.52 for the positive class. We make the dataset and models publicly available for the community.

cs.CL

SIA: A Scalable Interoperable Annotation Server for Biomedical Named Entities

Recent years showed a strong increase in biomedical sciences and an inherent increase in publication volume. Extraction of specific information from these sources requires highly sophisticated text mining and information extraction tools. However, the integration of freely available tools into customized workflows is often cumbersome and difficult. We describe SIA (Scalable Interoperable Annotation Server), our contribution to the BeCalm-Technical interoperability and performance of annotation servers (BeCalm-TIPS) task, a scalable, extensible, and robust annotation service. The system currently covers six named entity types (i.e., Chemicals, Diseases, Genes, miRNA, Mutations, and Organisms) and is freely available under Apache 2.0 license at https://github.com/Erechtheus/sia.

cs.CL

A German Corpus for Fine-Grained Named Entity Recognition and Relation Extraction of Traffic and Industry Events

Monitoring mobility- and industry-relevant events is important in areas such as personal travel planning and supply chain management, but extracting events pertaining to specific companies, transit routes and locations from heterogeneous, high-volume text streams remains a significant challenge. This work describes a corpus of German-language documents which has been annotated with fine-grained geo-entities, such as streets, stops and routes, as well as standard named entity types. It has also been annotated with a set of 15 traffic- and industry-related n-ary relations and events, such as accidents, traffic jams, acquisitions, and strikes. The corpus consists of newswire texts, Twitter messages, and traffic reports from radio stations, police and railway companies. It allows for training and evaluating both named entity recognition algorithms that aim for fine-grained typing of geo-entities, as well as n-ary relation extraction systems.

cs.CL

Football and Beer - a Social Media Analysis on Twitter in Context of the FIFA Football World Cup 2018

In many societies alcohol is a legal and common recreational substance and socially accepted. Alcohol consumption often comes along with social events as it helps people to increase their sociability and to overcome their inhibitions. On the other hand we know that increased alcohol consumption can lead to serious health issues, such as cancer, cardiovascular diseases and diseases of the digestive system, to mention a few. This work examines alcohol consumption during the FIFA Football World Cup 2018, particularly the usage of alcohol related information on Twitter. For this we analyse the tweeting behaviour and show that the tournament strongly increases the interest in beer. Furthermore we show that countries who had to leave the tournament at early stage might have done something good to their fans as the interest in beer decreased again.

cs.SI

Using a Classifier Ensemble for Proactive Quality Monitoring and Control: the impact of the choice of classifiers types, selection criterion, and fusion process

In recent times, the manufacturing processes are faced with many external or internal (the increase of customized product rescheduling , process reliability,..) changes. Therefore, monitoring and quality management activities for these manufacturing processes are difficult. Thus, the managers need more proactive approaches to deal with this variability. In this study, a proactive quality monitoring and control approach based on classifiers to predict defect occurrences and provide optimal values for factors critical to the quality processes is proposed. In a previous work (Noyel et al. 2013), the classification approach had been used in order to improve the quality of a lacquering process at a company plant; the results obtained are promising, but the accuracy of the classification model used needs to be improved. One way to achieve this is to construct a committee of classifiers (referred to as an ensemble) to obtain a better predictive model than its constituent models. However, the selection of the best classification methods and the construction of the final ensemble still poses a challenging issue. In this study, we focus and analyze the impact of the choice of classifier types on the accuracy of the classifier ensemble; in addition, we explore the effects of the selection criterion and fusion process on the ensemble accuracy as well. Several fusion scenarios were tested and compared based on a real-world case. Our results show that using an ensemble classification leads to an increase in the accuracy of the classifier models. Consequently, the monitoring and control of the considered real-world case can be improved.

cs.LG

Clickbait Identification using Neural Networks

This paper presents the results of our participation in the Clickbait Detection Challenge 2017. The system relies on a fusion of neural networks, incorporating different types of available informations. It does not require any linguistic preprocessing, and hence generalizes more easily to new domains and languages. The final combined model achieves a mean squared error of 0.0428, an accuracy of 0.826, and a F1 score of 0.564. According to the official evaluation metric the system ranked 6th of the 13 participating teams.

cs.CL

An iterative closest point method for measuring the level of similarity of 3d log scans in wood industry

In the Canadian's lumber industry, simulators are used to predict the lumbers resulting from the sawing of a log at a given sawmill. Giving a log or several logs' 3D scans as input, simulators perform a real-time job to predict the lumbers. These simulators, however, tend to be slow at processing large volume of wood. We thus explore an alternative approximation techniques based on the Iterative Closest Point (ICP) algorithm to identify the already processed log to which an unseen log resembles the most. The main benefit of the ICP approach is that it can easily handle 3D scans with a variable number of points. We compare this ICP-based nearest neighbor predictor, to predictors built using machine learning algorithms such as the K-nearest-neighbor (kNN) and Random Forest (RF). The implemented ICP-based predictor enabled us to identify key points in using the 3D scans directly for distance calculation. The long-term goal of this ongoing research is to integrated ICP distance calculations and machine learning.

cs.CV

A batching and scheduling optimisation for a cutting work-center: Acta-Mobilier case study

The purpose of this study is to investigate an approach to group lots in batches and to schedule these batches on Acta-Mobilier cutting work-center while taking into account numerous constraints and objectives. The specific batching method was proposed to handle the Acta-Mobilier problem and a mathematical formalisation and genetic algorithm were proposed to deal with the scheduling problem. The proposed algorithm has been embedded in software to optimise production costs and emphasis the visual management on the production line. The application is currently being used in Acta-Mobilier plant and shows significant results

cs.NE

Improving production process performance thanks to neuronal analysis

Product quality level is become a key factor for companies' competitiveness. A lot of time and money are required to ensure and guaranty it. Besides, motivated by the need of traceability, collecting production data is now commonplace in most companies. Our paper aims to show that we can ensure the required quality thanks to an "on-line quality approch" and proposes a neural network based process to determine the optimal setting for production machines. We will illustrate this with the Acta-Mobilier case, which is a high quality lacquerer company.

cs.SE

Nonlinear optical properties of TeO$_2$ crystalline phases from first principles

We have computed second and third nonlinear optical susceptibilities of two crystalline bulk tellurium oxide polymorphs: $α$-TeO$_{2}$ (the most stable crystalline bulk phase) and $γ$-TeO$_{2}$ (the crystalline phase that ressembles the more to the glass phase. Third order nonlinear susceptibilities of the crystalline phases are two orders of magnitude larger than $α$-SiO$_{2}$ cristoballite, thus extending the experimental observations on glasses to the case of crystalline compounds. While the electronic lone pairs of Te contribute to those large values, a full explanation of the anisotropy of the third order susceptibility tensor requires a detailed analysis of the structure, in particular the presence of helical chains, that seems to be linked to cooperative non-local polarizabilty effects. Our results demonstrate that first-principles simulations are a powerful predictive tool to estimate nonlinear optical susceptibilitites of materials.

cond-mat.mtrl-sci

How deals with discrete data for the reduction of simulation models using neural network

Simulation is useful for the evaluation of a Master Production/distribution Schedule (MPS). Also, the goal of this paper is the study of the design of a simulation model by reducing its complexity. According to theory of constraints, we want to build reduced models composed exclusively by bottlenecks and a neural network. Particularly a multilayer perceptron, is used. The structure of the network is determined by using a pruning procedure. This work focuses on the impact of discrete data on the results and compares different approaches to deal with these data. This approach is applied to sawmill internal supply chain

cs.NE

Sélection de la structure d'un perceptron multicouches pour la réduction dun modèle de simulation d'une scierie

Simulation is often used to evaluate the relevance of a Directing Program of Production (PDP) or to evaluate its impact on detailed scénarii of scheduling. Within this framework, we propose to reduce the complexity of a model of simulation by exploiting a multilayer perceptron. A main phase of the modeling of one system using a multilayer perceptron remains the determination of the structure of the network. We propose to compare and use various pruning algorithms in order to determine the optimal structure of the network used to reduce the complexity of the model of simulation of our case of application: a sawmill.

cs.NE

Elagage d'un perceptron multicouches : utilisation de l'analyse de la variance de la sensibilité des paramètres

The stucture determination of a neural network for the modelisation of a system remain the core of the problem. Within this framework, we propose a pruning algorithm of the network based on the use of the analysis of the sensitivity of the variance of all the parameters of the network. This algorithm will be tested on two examples of simulation and its performances will be compared with three other algorithms of pruning of the literature

cs.NE