Nicolas Tremblay

Graph Wavelets for Multiscale Community Mining

We develop a signal processing approach to the multiscale detection of communities in networks, that is of groups of nodes well connected together. The method relies on carefully engineered wavelets on graphs to introduce the notion of scale and to obtain a local view of the graph from each node. Computing the correlations between wavelets centered at different nodes, one has access to a notion of similarity between nodes, thereby enabling a clustering procedure that groups nodes according to their community at the scale of analysis. By using a collection of random vectors to estimate the correlation between the nodes, we show that the method is suitable for the analysis of large graphs. Furthermore, we introduce a notion of partition stability and a statistical test allowing us to assess which scales of analysis of the network are relevant. The effectiveness of the method is discussed first on multiscale graph benchmarks, then on real data of social networks and on models for signal processing on graphs.

Strip, bind, and search: a method for identifying abnormal energy consumption in buildings

A typical large building contains thousands of sensors, monitoring the HVAC system, lighting, and other operational sub-systems. With the increased push for operational efficiency, operators are relying more on historical data processing to uncover opportunities for energy-savings. However, they are overwhelmed with the deluge of data and seek more efficient ways to identify potential problems. In this paper, we present a new approach called the Strip, Bind and Search (SBS); a method for uncovering abnormal equipment behavior and in-concert usage patterns. SBS uncovers relationships between devices and constructs a model for their usage pattern relative to other devices. It then flags deviations from the model. We run SBS on a set of building sensor traces; each containing hundred sensors reporting data flows over 18 weeks from two separate buildings with fundamentally different infrastructures. We demonstrate that, in many cases, SBS uncovers misbehavior corresponding to inefficient device usage that leads to energy waste. The average waste uncovered is as high as 2500 kWh per device.

Multiscale Anisotropic Texture Analysis and Classification of Photographic Prints: Art scholarship meets image processing algorithms

Texture characterization of photographic prints can provide scholars with valuable information regarding photographers? aesthetic intentions and working practices. Currently, texture assessment is strictly based on the visual acuity of a range of scholars associated with collecting institutions, such as museum curators and conservators. Natural interindividual discrepancies, intraindividual variability, and the large size of collections present a pressing need for computerized and automated solutions for the texture characterization and classification of photographic prints. In the this article, this challenging image processing task is addressed using an anisotropic multiscale representation of texture, the hyperbolic wavelet transform (HWT), from which robust multiscale features are constructed. Cepstral distances aimed at ensuring balanced multiscale contributions are computed between pairs of images. The resulting large-size affinity matrix is then clustered using spectral clustering, followed by a Ward linkage procedure. For proof of concept, these procedures are first applied to a reference data set of historic photographic papers that combine several levels of similarity and second to a large data set of culturally valuable photographic prints held by the Museum of Modern Art in New York. The characterization and clustering results are interpreted in collaboration with art scholars with an aim toward developing new modes of art historical research and humanities-based collaboration.

A Dynamical Network View of Lyon’s Vélo’v Shared Bicycle System

Community shared bicycle systems are an instance of public transportation systems that provide digital footprints of all the movements made using this system. The completeness of such dataset allows for their study using a complex system point of view. This chapter discusses how Lyon’s shared bicycle system, called Velo’v, can be seen as a dynamical complex network, and how using community detection methods gives interesting results thanks to the aggregation in space and/or time that communities propose.

Structure, thermodynamics and dynamics of the isotropic phase of spherical non-ionic surfactant micelles

We investigate a non-ionic surfactant (C(12)E(8))/water binary mixture, over a wide range of concentrations and temperatures (i.e. 1-35 wt.% of C(12)E(8) and 10-60 °C in temperature) by means of different experimental techniques: Small-Angle Neutron Scattering (SANS), Quasi Elastic Light Scattering (QELS) and High Frequency Rheology. The aims of this work are to provide information on structure, thermodynamics and dynamics of the isotropic phase of such a micellar system and, by combining these different types of information, to obtain a comprehensive image of the behaviour of this phase. Our results demonstrate that structural, thermodynamic and dynamic properties of these solutions are fully monitored by the temperature-induced changes in the ethylene-glycol chain hydration. They confirm that C(12)E(8) micelles are spherical and do not grow in the investigated range of concentrations and temperatures. They demonstrate that the interaction potential between C(12)E(8) micelles is more complicated than what was previously described, with an additional repulsive interaction. They allow us to put forward explanations for the Isotropic-Ordered phase transition as well as for the temperature behaviour of the viscosity of C(12)E(8) micellar solutions. Our investigation provides new and valuable information on the dynamics of these mixtures that reflect the complexity of the interaction potential between the C(12)E(8) micelles. It shows that concentrated solutions exhibit a viscoelastic behaviour that can be described by a simple Maxwell model.

Multiscale Anisotropic Texture Unsupervised Clustering for Photographic Paper

Texture characterization of photographic papers is likely to provide scholars with valuable information regarding artistic practices. Currently, texture assessment remains mostly based on visual and manual inspections, implying long repetitive tasks prone to inter- and even intra-observer variability. Automated texture characterization and classification procedures are thus important tasks in historical studies of large databases of photographic papers, likely to provide quantitative and reproducible assessments of texture matches. Such procedures may, for instance, produce vital information on photographic prints of uncertain origins. The hyperbolic wavelet transform, because it relies on the use of different dilation factor along the horizontal and vertical axes, permits to construct robust and meaningful multiscale and anisotropic representation of textures. In the present contribution, we explore how unsupervised clustering strategies can be complemented both to assess the significance of extracted clusters and the strength of the contribution of each texture to its associated cluster. Graph based filterbank strategies are notably investigated with the aim to produce small size significant clusters. These tools are illustrated at work on a large database of about 2500 exposed and non exposed photographic papers carefully assembled and documented by the MoMA and P. Messiers foundation. Results are commented and interpreted.

Bootstrapping under constraint for the assessment of group behavior in human contact networks

The increasing availability of time- and space-resolved data describing human activities and interactions gives insights into both static and dynamic properties of human behavior. In practice, nevertheless, real-world data sets can often be considered as only one realization of a particular event. This highlights a key issue in social network analysis: the statistical significance of estimated properties. In this context, we focus here on the assessment of quantitative features of specific subset of nodes in empirical networks. We present a method of statistical resampling based on bootstrapping groups of nodes under constraints within the empirical network. The method enables us to define acceptance intervals for various null hypotheses concerning relevant properties of the subset of nodes under consideration in order to characterize by a statistical test its behavior as normal or not. We apply this method to a high-resolution data set describing the face-to-face proximity of individuals during two colocated scientific conferences. As a case study, we show how to probe whether colocating the two conferences succeeded in bringing together the two corresponding groups of scientists.