Should We Learn Probabilistic Models for Model Checking? A New Approach and An Empirical Study

Many automated system analysis techniques (e.g., model checking, model-based testing) rely on first obtaining a model of the system under analysis. System modeling is often done manually, which is often considered as a hindrance to adopt model-based system analysis and development techniques. To overcome this problem, researchers have proposed to automatically "learn" models based on sample system executions and shown that the learned models can be useful sometimes. There are however many questions to be answered. For instance, how much shall we generalize from the observed samples and how fast would learning converge? Or, would the analysis result based on the learned model be more accurate than the estimation we could have obtained by sampling many system executions within the same amount of time? In this work, we investigate existing algorithms for learning probabilistic models for model checking, propose an evolution-based approach for better controlling the degree of generalization and conduct an empirical study in order to answer the questions. One of our findings is that the effectiveness of learning may sometimes be limited.Comment: 15 pages, plus 2 reference pages, accepted by FASE 2017 in ETAP

Position Models and Language Modeling

International audienceIn statistical language modelling the classic model used is $n$-gram. This model is not able however to capture long term dependencies, \emph{i.e.} dependencies larger than $n$. An alternative to this model is the probabilistic automaton. Unfortunately, it appears that preliminary experiments on the use of this model in language modelling is not yet competitive, partly because it tries to model too long term dependencies. We propose here to improve the use of this model by restricting the dependency to a more reasonable value. Experiments shows an improvement of 45\% reduction in the perplexity obtained on the Wall Street Journal language modeling task

Joule heating and high frequency nonlinear effects in the surface impedance of high Tc superconductors

Using the dielectric resonator method, we have investigated nonlinearities in the surface impedance Zs = Rs + jXs of YBa2Cu3O7 thin films at 10 GHz as function of the incident microwave power level and temperature. The use of a rutile dielectric resonator allows us to measure the precise temperature of the films. We conclusively show that the usually observed increase of the surface resistance of YBa2Cu3O7 thin film as function of microwave power is due to local heating

A Comparison of Noise Reduction Techniques for Robust Speech Recognition

. This report presents the integration of several noise reduction methods into the frontend for speech recognition developed at IDIAP. The chosen methods are : Spectral Subtraction, Cepstral Mean Subtraction and Blind Equalization. These dierent methods are studied from a theoretical point of view. Their implementation is described and they are tested on the Numbers95 speech database. A good noise robustness is obtained by combining two of these methods, like Spectral Subtraction with Cepstral Mean Subtraction or Spectral Subtraction with Blind Equalization. The later combination is found to be more appropriate for real recognition systems since it is frame synchronous. A comparison with Jah-RASTA-PLP is also given. Acknowledgements: The support of the OFES under the grant for the \Speech, Hearing and Recognition" (SPHEAR) project # OFES 970299 is gratefully acknowledged. The work described in this report beneted from fruitful discussions with Chac Mokbel. IDIAP{RR 99-10 1 Content..

Using spatially balanced sampling designs to optimise cost-efficiency of long term monitoring programs: application to Manila clam in Arcachon Bay. (3rd best oral communication award)

AFFInternational audienc

Optimizing cost-efficiency of long term monitoring programs by using spatially balanced sampling designs: The case of manila clams in Arcachon bay

ACLInternational audienc

De nouveaux packages pour sélectionner des points d'échantillonnage spatialement équilibrés sous R.

AFFInternational audienc

Listériose et grossesse. Protocole de prise en charge au sein de l’hôpital Necker-Enfants–Malades

International audienceListeriosis is a rare and severe food-borne infection. The clinical and biologica presentation is not specific. Complications such as fetal loss, prematurity < 32 WG (weeks of gestation) and neonatal infection are reported in 80 % of cases. Diagnosis is made by the isolation of Listeria monocytogenes in any sample of maternal, fetal or neonatal origin. Treatment relies on a combination of amoxicillin and gentamicin.La listériose est une infection rare et grave d’origine alimentaire. Sa présentation et biologique est non spécifique et l’infection se complique de perte fœtale, de grande prématurité ou d’infection néonatale dans 80 % des cas. Le diagnostic est porté sur l’identification de Listeria monocytogenes de tout prélèvement d’origine maternelle fœtale ou néonatale. Le traitement repose sur une combinaison d’amoxicilline et de gentamicine