The IDSA and the homogeneous sphere: Issues and possible improvements

In this paper, we are concerned with the study of the Isotropic Diffusion Source Approximation (IDSA) (Baxter et al., Phys. Rev. E 73, 046118, 2006) of radiative transfer. After having recalled well-known limits of the radiative transfer equation, we present the IDSA and adapt it to the case of the homogeneous sphere. We then show that for this example the IDSA suffers from severe numerical difficulties. We argue that these difficulties originate in the min-max switch coupling mechanism used in the IDSA. To overcome this problem we reformulate the IDSA to avoid the problematic coupling. This allows us to access the modeling error of the IDSA for the homogeneous sphere test case. The IDSA is shown to overestimate the streaming component, hence we propose a new version of the IDSA which is numerically shown to be more accurate than the old one. Analytical results and numerical tests are provided to support the accuracy of the new proposed approximation.Comment: 25 pages, 8 figures, accepted for publication in DCDS-

Re-conceptualiser notre expérience de l’environnement audio-visuel qui nous entoure : l’individuation, entre attention et mémoire

Notre mémoire prend en charge de re-conceptualiser notre nouvel environnement audio-visuel et l’expérience que nous en faisons. À l’ère du numérique et de la dissémination généralisée des images animées, nous circonscrivons une catégorie d’images que nous concevons comme la plus à même d’avoir un impact sur le développement humain. Nous les appelons des images-sons synchrono-photo-temporalisées. Plus spécifiquement, nous cherchons à mettre en lumière leur puissance d’affection et de contrôle en démontrant qu’elles ont une influence certaine sur le processus d’individuation, influence qui est grandement facilitée par l’isotopie structurelle qui existe entre le flux de conscience et leur flux d’écoulement. Par le biais des recherches de Bernard Stiegler, nous remarquons également l’important rôle que jouent l’attention et la mémoire dans le processus d’individuation. L’ensemble de notre réflexion nous fait réaliser à quel point le système d’éducation actuel québécois manque à sa tâche de formation citoyenne en ne dispensant pas un enseignement adéquat des images animées.This thesis re-conceptualizes our new audio-visual environment and analyses the experience we make of it. In the digital age marked by the dissemination of moving images, we circumscribe a category of images which we see as the most likely to have an impact on human development. We call it synchrono-photo-temporalized images-sounds. Specifically, we seek to highlight their power of affection and control by showing that they have some influence on the process of individuation, an influence which is greatly facilitated by the structural isotopy between the stream of consciousness and the flow of motion images. By examining the research of Bernard Stiegler, we also note the important roles attention and memory play in the process of individuation. This thinking makes us realize how the current education system in Quebec fails in its mission to give a good civic education by not providing an adequate teaching of moving images

Unsupervised classification to improve the quality of a bird song recording dataset

Open audio databases such as Xeno-Canto are widely used to build datasets to explore bird song repertoire or to train models for automatic bird sound classification by deep learning algorithms. However, such databases suffer from the fact that bird sounds are weakly labelled: a species name is attributed to each audio recording without timestamps that provide the temporal localization of the bird song of interest. Manual annotations can solve this issue, but they are time consuming, expert-dependent, and cannot run on large datasets. Another solution consists in using a labelling function that automatically segments audio recordings before assigning a label to each segmented audio sample. Although labelling functions were introduced to expedite strong label assignment, their classification performance remains mostly unknown. To address this issue and reduce label noise (wrong label assignment) in large bird song datasets, we introduce a data-centric novel labelling function composed of three successive steps: 1) time-frequency sound unit segmentation, 2) feature computation for each sound unit, and 3) classification of each sound unit as bird song or noise with either an unsupervised DBSCAN algorithm or the supervised BirdNET neural network. The labelling function was optimized, validated, and tested on the songs of 44 West-Palearctic common bird species. We first showed that the segmentation of bird songs alone aggregated from 10% to 83% of label noise depending on the species. We also demonstrated that our labelling function was able to significantly reduce the initial label noise present in the dataset by up to a factor of three. Finally, we discuss different opportunities to design suitable labelling functions to build high-quality animal vocalizations with minimum expert annotation effort

The Kolmogorov-Smirnov test for the CMB

We investigate the statistics of the cosmic microwave background using the Kolmogorov-Smirnov test. We show that, when we correctly de-correlate the data, the partition function of the Kolmogorov stochasticity parameter is compatible with the Kolmogorov distribution and, contrary to previous claims, the CMB data are compatible with Gaussian fluctuations with the correlation function given by standard Lambda-CDM. We then use the Kolmogorov-Smirnov test to derive upper bounds on residual point source power in the CMB, and indicate the promise of this statistics for further datasets, especially Planck, to search for deviations from Gaussianity and for detecting point sources and Galactic foregrounds.Comment: Improved significance of the results (which remain unchanged) by using patches instead of ring segments in the analysis. Added sky maps of the Kolmogorov-parameter for original and de-correlated CMB ma

Dual-comb spectroscopy with a phase-modulated probe comb for sub-MHz spectral sampling

We present a straightforward and efficient method to reduce the mode spacing of a frequency comb based on binary pseudo-random phase modulation of its pulse train. As a proof of concept, we use such a densified comb to perform dual-comb spectroscopy of a long-delay Mach–Zehnder interferometer and a high-quality-factor microresonator with sub-MHz spectral sampling. Since this approach is based on binary phase modulation, it combines all the advantages of other densification techniques: simplicity, single-step implementation, and conservation of the initial comb’s power