output
20042015
most citedStellar matter with a strong magnetic field within density-dependent relativistic models

61 citations

Showing 2015Show all

6 papers · 1 filter

quant-ph2015

Reply to "Comment on 'Bose-Einstein condensation with a finite number of particles in a power-law trap'"

A Jaouadi, M Telmini, Eric Charron

In this reply we show that the criticisms raised by J. Noronha are based on a misapplication of the model we have proposed in [A. Jaouadi, M. Telmini, E. Charron, Phys. Rev. A 83,…

math.PR20152 cited

Diffusion hitting times and the Bell-shape

Wissem Jedidi, Thomas Simon

Consider a generalized diffusion on R with speed measure m, in the natural scale. It is known that the conditional hitting times have a unimodal density function. We show that thes…

cs.AI20152 cited

Uncertainty in Ontology Matching: A Decision Rule-Based Approach

Amira Essaid, Arnaud Martin, Grégory Smits +1

Considering the high heterogeneity of the ontologies pub-lished on the web, ontology matching is a crucial issue whose aim is to establish links between an entity of a source ontol…

cs.AI20151 cited

Second-Order Belief Hidden Markov Models

Jungyeul Park, Mouna Chebbah, Siwar Jendoubi +1

Hidden Markov Models (HMMs) are learning methods for pattern recognition. The probabilistic HMMs have been one of the most used techniques based on the Bayesian model. First-order…

cs.AI20152 cited

Belief Hidden Markov Model for speech recognition

Siwar Jendoubi, Boutheina Ben Yaghlane, Arnaud Martin

Speech Recognition searches to predict the spoken words automatically. These systems are known to be very expensive because of using several pre-recorded hours of speech. Hence, bu…

cond-mat.mes-hall201519 cited

Electric field-induced valley degeneracy lifting in uniaxial strained graphene: evidence from magnetophonon resonance

Mohamed Assili, Sonia Haddad, Woun Kang

A double peak structure in the magneto-phonon resonance (MPR) spectrum of uniaxial strained graphene, under crossed electric and magnetic fields, is predicted. We focus on the