The B -> pi K Puzzle and New Physics

The present B -> pi K data is studied in the context of the standard model (SM) and with new physics (NP). We confirm that the SM has difficulties explaining the B -> pi K measurements. By adopting an effective-lagrangian parametrization of NP effects, we are able to rule out several classes of NP. Our model-independent analysis shows that the B -> pi K data can be accommodated by NP in the electroweak penguin sector.Comment: 4 pages (revtex

Polarization States in B -> rho K* and New Physics

The standard-model explanations of the anomalously-large transverse polarization fraction fT in B -> phi K* can be tested by measuring the polarizations of the two decays B+ -> rho+ K*0 and B+ -> rho0 K*+. For the scenario in which the transverse polarizations of both B -> rho K* decays are predicted to be large, we derive a simple relation between the fT's of these decays. If this relation is not confirmed experimentally, this would yield an unambiguous signal for new physics. The new-physics operators which can account for the discrepancy in B -> pi K decays will also contribute to the polarization states of B -> rho K*. We compute these contributions and show that there are only two operators which can simultaneously account for the present B -> pi K and B -> rho K* data. If the new physics obeys an approximate U-spin symmetry, the B -> phi K* measurements can also be explained.Comment: 20 pages, latex, no figures. Minor changes to references and Table 1. Minor modification of terms; more complete description of triple-product asymmetry. Analysis and conclusions unchange

Apprentissage de représentations musicales à l'aide d'architectures profondes et multiéchelles

L'apprentissage machine (AM) est un outil important dans le domaine de la recherche d'information musicale (Music Information Retrieval ou MIR). De nombreuses tâches de MIR peuvent être résolues en entraînant un classifieur sur un ensemble de caractéristiques. Pour les tâches de MIR se basant sur l'audio musical, il est possible d'extraire de l'audio les caractéristiques pertinentes à l'aide de méthodes traitement de signal. Toutefois, certains aspects musicaux sont difficiles à extraire à l'aide de simples heuristiques. Afin d'obtenir des caractéristiques plus riches, il est possible d'utiliser l'AM pour apprendre une représentation musicale à partir de l'audio. Ces caractéristiques apprises permettent souvent d'améliorer la performance sur une tâche de MIR donnée. Afin d'apprendre des représentations musicales intéressantes, il est important de considérer les aspects particuliers à l'audio musical dans la conception des modèles d'apprentissage. Vu la structure temporelle et spectrale de l'audio musical, les représentations profondes et multiéchelles sont particulièrement bien conçues pour représenter la musique. Cette thèse porte sur l'apprentissage de représentations de l'audio musical. Des modèles profonds et multiéchelles améliorant l'état de l'art pour des tâches telles que la reconnaissance d'instrument, la reconnaissance de genre et l'étiquetage automatique y sont présentés.Machine learning (ML) is an important tool in the field of music information retrieval (MIR). Many MIR tasks can be solved by training a classifier over a set of features. For MIR tasks based on music audio, it is possible to extract features from the audio with signal processing techniques. However, some musical aspects are hard to extract with simple heuristics. To obtain richer features, we can use ML to learn a representation from the audio. These learned features can often improve performance for a given MIR task. In order to learn interesting musical representations, it is important to consider the particular aspects of music audio when building learning models. Given the temporal and spectral structure of music audio, deep and multi-scale representations are particularly well suited to represent music. This thesis focuses on learning representations from music audio. Deep and multi-scale models that improve the state-of-the-art for tasks such as instrument recognition, genre recognition and automatic annotation are presented

Velocity-aided Attitude Estimation for Accelerated Rigid Bodies

Two nonlinear observers for velocity-aided attitude estimation, relying on gyrometers, accelerometers, magnetometers, and velocity measured in the body-fixed frame, are proposed. As opposed to state-of-the-art body-fixed velocity-aided attitude observers endowed with local properties, both observers are (almost) globally asymptotically stable, with very simple and flexible tuning. Moreover, the roll and pitch estimates are globally decoupled from magnetometer measurements

La nouvelle physique dans le système des mésons B

Mémoire numérisé par la Direction des bibliothèques de l'Université de Montréal