The Diagonalized Newton Algorithm for Nonnegative Matrix Factorization

Non-negative matrix factorization (NMF) has become a popular machine learning approach to many problems in text mining, speech and image processing, bio-informatics and seismic data analysis to name a few. In NMF, a matrix of non-negative data is approximated by the low-rank product of two matrices with non-negative entries. In this paper, the approximation quality is measured by the Kullback-Leibler divergence between the data and its low-rank reconstruction. The existence of the simple multiplicative update (MU) algorithm for computing the matrix factors has contributed to the success of NMF. Despite the availability of algorithms showing faster convergence, MU remains popular due to its simplicity. In this paper, a diagonalized Newton algorithm (DNA) is proposed showing faster convergence while the implementation remains simple and suitable for high-rank problems. The DNA algorithm is applied to various publicly available data sets, showing a substantial speed-up on modern hardware.Comment: 8 pages + references; International Conference on Learning Representations, 201

Improving Source Separation via Multi-Speaker Representations

Lately there have been novel developments in deep learning towards solving the cocktail party problem. Initial results are very promising and allow for more research in the domain. One technique that has not yet been explored in the neural network approach to this task is speaker adaptation. Intuitively, information on the speakers that we are trying to separate seems fundamentally important for the speaker separation task. However, retrieving this speaker information is challenging since the speaker identities are not known a priori and multiple speakers are simultaneously active. There is thus some sort of chicken and egg problem. To tackle this, source signals and i-vectors are estimated alternately. We show that blind multi-speaker adaptation improves the results of the network and that (in our case) the network is not capable of adequately retrieving this useful speaker information itself

La pluie et le topoclimat

Lorsqu'on étudie les potentialités agroéconomiques d'une région à relief accidenté, une question importante qui se pose est de savoir s'il y a des orientations et des inclinaisons privilégiées au point de vue pluviosité. De plus, il est aussi important de comparer l'action érosive de l'eau, d'un versant à un autre, en fonction de l'inclinaison et de l'orientation. Dans le cadre de cet article, on propose un modèle original permettant de répondre à ces questions. Ce modèle est particulièrement bien adapté aux problèmes agronomiques. En effet, la modélisation proposée apporte de multiples informations permettant d'optimaliser l'utilisation des espaces ruraux en régions de collines et donc de microclimats liés à la topographie. De plus, la méthodologie présentée est très simple et peut ainsi être mise en oeuvre sans disposer d'appareils sophistiqués, ceux-ci pouvant être considérés comme complément utile mais non indispensable. Le modèle présenté est donc parfaitement adapté à tout pays en voie de développement à agriculture sur relief accidenté et donc à microclimat lié à la topographie. (Résumé d'auteur

Interviewing strategies to understand people’s perceptions of wildlife

Considering the highly anthropogenic nature of conservation problems today, it appears vital to integrate social sciences in the biology-dominated landscape of wildlife conservation. Using social sciences methods to understand people’s perceptions can provide us a unique chance to shed light on the complexities of the human-wildlife interface. When doing so, we have the choice between adopting (1) a deductive approach or (2) an inductive approach. Questionnaires, and the robust numerical data they deliver offer the opportunity to test hypotheses through statistical analyse. However, it is sometimes seen a reductionist because it restricts the data collection to factors supposedly important. On the other hand, the data collected in qualitative research and the theories it generates can be perceived as anecdotal but revealed to be an effective conservation tool for the comprehensive overview it gives. Now, which method should be used in conservation to understand people’s perceptions? This surely depends on the context and the questions we wish to address. I will focus here on the “semi-structured interview” technique in view of the balance it represents between the efficiency of questionnaires and the receptivity of unstructured interviews. This allows directing the conversation towards areas of interest without restricting the interviewee to these points, therefore establishing a climate prone to discussion. Conclusively, this technique has the potential to show how social features of our society shape our relationship with the surrounding environment. Furthermore, it may bring up valuable information on sensitive aspects of the issue, hence informing conservation practitioners

Radar and video as the perfect match : a cooperative method for sensor fusion

Accurate detection and tracking of road users is essential for driverless cars and many other smart mobility applications. As no single sensor can provide the required accuracy and robustness, the output from several sensors needs to be combined. Especially radar and video are a good match, because their weaknesses and strengths complement each other. Researchers from IPI – an imec research group at Ghent University – developed a new technique to optimize radar-video fusion by exchanging information at an earlier stage

Cycling around bends: the effect of cycling speed on steering and gaze behavior

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