Fabrication and Characterization of Pzt Thin-Film Vibrators for Micromotors

For the first time we have characterized a micromotor driven by a piezoelectric PZT (PbZrxTi1-xO3) thin film. Sputter and sol-gel techniques have been applied for the deposition of the PZT films onto a silicon stator membrane, which is 20-30 mu m thick and has a diameter of 4 mm. The amplitudes of the membrane deflections are measured by means of laser interferometry. They are as large as 800 nm V-1 at the first resonance (26 kHz) and 60 nm V-1 at 1 kHz. This is one order of magnitude larger than previously reported for a ZnO-activated device of similar geometry. The motor operates at 1-3 V-r.m.s., with speeds of up to 200 rpm at 1.1 V-r.m.s. and torques of 35 nN m at 2.5 V-r.m.s. and 1 mN force between rotor and stator. Compared with the conceptually identical ZnO version published by Racine et al., this is an improvement by a factor of three in speed per volt. Taking into account the linear increase of the torque with the stator vibration frequency, the torque per volt is a factor of two higher. A long-term test of 100 h showed no degradation of the motor performance

Chat or learn ::a data-driven robust question-answering system

We present a voice-based conversational agent which combines the robustness of chatbots and the utility of question answering (QA) systems. Indeed, while data-driven chatbots are typically user-friendly but not goal-oriented, QA systems tend to perform poorly at chitchat. The proposed chatbot relies on a controller which performs dialogue act classification and feeds user input either to a sequence-to-sequence chatbot or to a QA system. The resulting chatbot is a spoken QA application for the Google Home smart speaker. The system is endowed with general-domain knowledge from Wikipedia articles and uses coreference resolution to detect relatedness between questions. We present our choices of data sets for training and testing the components, and present the experimental results that helped us optimize the parameters of the chatbot. In particular, we discuss the appropriateness of using the SQuAD dataset for evaluating end-to-end QA, in the light of our system’s behavio

THROMBOPHLEBITE CEREBRALE APRES INJECTION INTRA-THECALE DE CORTICOIDES (A PROPOS DE DEUX CAS)

TOURS-BU Médecine (372612103) / SudocPARIS-BIUM (751062103) / SudocSudocFranceF

Fasciite nécrosante : étude d'une série de 13 cas au CHRU de ReimsTitre

REIMS-BU Santé (514542104) / SudocPARIS-BIUM (751062103) / SudocSudocFranceF

A consolidated dataset for knowledge-based question generation using predicate mapping of linked data

In this paper, we present the ForwardQuestions data set, made of human-generated questions related to knowledge triples. This data setresults from the conversion and merger of the existing SimpleDBPediaQA and SimpleQuestionsWikidata data sets, including the mapping of predicates from DBPedia to Wikidata, and the selection of ‘forward’ questions as opposed to ‘backward’ ones. The new data set can be used to generate novel questions given an unseen Wikidata triple, by replacing the subjects of existing questions with the new one and then selecting the best candidate questions using semantic and syntactic criteria. Evaluation results indicate that the question generation method using ForwardQuestions improves the quality of questions by about 20% with respect to a baseline not using ranking criteria