Multimode VCSEL model for wide frequency-range RIN simulation

In this paper, we present an equivalent circuit model for oxide-confined AlGaAs/GaAs VCSEL with the noise contribution adapted to optomicrowave links applications. This model is derived from the multimode rate equations. In order to understand the modal competition process, we restrain our description to a two-modes rate equations system affected by the spectral hole-burning. The relative intensity noise (RIN) measurements which were achieved on a prober in Faraday cage confirm the low frequency enhancement described by the model. We validate our model for a wide frequency-range [1 MHz–10 GHz] and high bias level up to six times the threshold current

Minimizing makespan in flowshop with time lags

We consider the problem of minimizing the makespan in a flowshop involving maximal and minimal time lags. Time lag constraints generalize the classical precedence constraints between operations. We assume that such constraints are only defined between operations of the same job. We propose a solution method and present several extensions.Comment: 2 pages. Also available at http://hal.inria.fr/inria-0000014

Adaptive low-rank approximation and denoised Monte-Carlo approach for high-dimensional Lindblad equations

We present a twofold contribution to the numerical simulation of Lindblad equations. First, an adaptive numerical approach to approximate Lindblad equations using low-rank dynamics is described: a deterministic low-rank approximation of the density operator is computed, and its rank is adjusted dynamically, using an on-the-fly estimator of the error committed when reducing the dimension. On the other hand, when the intrinsic dimension of the Lindblad equation is too high to allow for such a deterministic approximation, we combine classical ensemble averages of quantum Monte Carlo trajectories and a denoising technique. Specifically, a variance reduction method based upon the consideration of a low-rank dynamics as a control variate is developed. Numerical tests for quantum collapse and revivals show the efficiency of each approach, along with the complementarity of the two approaches.Comment: 5 pages, 3 figures, Submitte

Noise and signal modeling of various VCSEL structures

Current evolution in Datacoms and Gigabit Ethernet have made 850nm Vertical Cavity Surface Emitting Lasers(VCSEL) the most important and promising emitter. Numerous different structures have been growth, to obtain bestcurrent confinement and then to control the emitted light modal behavior. We have developed a small signal equivalent electrical model of VCSEL including Bragg reflectors, active area, chip connection and noise behavior. Easy tointegrate with classical software for circuit studies, this model which is widely adaptable for different structures takesinto account the complete electrical environment of the chip. An experimental validation for RF modulation up to 10GHz has been realized on oxide confined VCSEL, demonstrating that the model could be used to get realistic valuesfor the VCSEL intrinsic parameters.Including Langevin noise sources into the rate equations and using the same electrical analogy, noise current andvoltage sources can be added to the model. It allows good prediction for the RIN function shape up to 10GHz formonomodal emitter

Regulation of tomato fruit ripening

Fruit ripening is a sophisticatedly orchestrated developmental process, unique to plants, that results in major physiological and metabolic changes, ultimately leading to fruit decay and seed dispersal. Because of their strong impact on fruit nutritional and sensory qualities, the ripeningassociated changes have been a matter of sustained investigation aiming at unravelling the molecular and genetic basis of fruit ripening. Tomato rapidly emerged as the model of choice for fleshy fruit research and a wealth of genetic resources and genomics tools have been developed, providing new entries into the regulatory mechanisms involved in the triggering and coordination of the ripening process. Some of the key components participating in the control of tomato fruit ripening have been uncovered, but our knowledge of the network of signalling pathways engaged in this complex developmental process remains fragmentary. This review highlights the main advances and emphasizes issues still to be addressed using the rapidly developing ‘omics’ approaches

Nuclear Magnetic Resonance in High Magnetic Field: Application to Condensed Matter Physics

In this review, we describe the potentialities offered by the nuclear magnetic resonance (NMR) technique to explore at a microscopic level new quantum states of condensed matter induced by high magnetic fields. We focus on experiments realised in resistive (up to 34~T) or hybrid (up to 45~T) magnets, which open a large access to these quantum phase transitions. After an introduction on NMR observable, we consider several topics: quantum spin systems (spin-Peierls transition, spin ladders, spin nematic phases, magnetisation plateaus and Bose-Einstein condensation of triplet excitations), the field-induced charge density wave (CDW) in high $T_c$~superconductors, and exotic superconductivity including the Fulde-Ferrel-Larkin-Ovchinnikov superconducting state and the field-induced superconductivity due to the Jaccarino-Peter mechanism.Comment: 19 pages, 6 figure

Reversible Destruction of Dynamical Localization

Dynamical localization is a localization phenomenon taking place, for example, in the quantum periodically-driven kicked rotor. It is due to subtle quantum destructive interferences and is thus of intrinsic quantum origin. It has been shown that deviation from strict periodicity in the driving rapidly destroys dynamical localization. We report experimental results showing that this destruction is partially reversible when the deterministic perturbation that destroyed it is slowly reversed. We also provide an explanation for the partial character of the reversibility.Comment: 4 pages, 2 eps figures (color

Prediction of miRNA-disease associations with a vector space model

MicroRNAs play critical roles in many physiological processes. Their dysregulations are also closely related to the development and progression of various human diseases, including cancer. Therefore, identifying new microRNAs that are associated with diseases contributes to a better understanding of pathogenicity mechanisms. MicroRNAs also represent a tremendous opportunity in biotechnology for early diagnosis. To date, several in silico methods have been developed to address the issue of microRNA-disease association prediction. However, these methods have various limitations. In this study, we investigate the hypothesis that information attached to miRNAs and diseases can be revealed by distributional semantics. Our basic approach is to represent distributional information on miRNAs and diseases in a high-dimensional vector space and to define associations between miRNAs and diseases in terms of their vector similarity. Cross validations performed on a dataset of known miRNA-disease associations demonstrate the excellent performance of our method. Moreover, the case study focused on breast cancer confirms the ability of our method to discover new disease-miRNA associations and to identify putative false associations reported in databases