A quasi-random sampling approach to image retrieval

In this paper, we present a novel approach to contentsbased image retrieval. The method hinges in the use of quasi-random sampling to retrieve those images in a database which are related to a query image provided by the user. Departing from random sampling theory, we make use of the EM algorithm so as to organize the images in the database into compact clusters that can then be used for stratified random sampling. For the purposes of retrieval, we use the similarity between the query and the clustered images to govern the sampling process within clusters. In this way, the sampling can be viewed as a stratified sampling one which is random at the cluster level and takes into account the intra-cluster structure of the dataset. This approach leads to a measure of statistical confidence that relates to the theoretical hard-limit of the retrieval performance. We show results on the Oxford Flowers dataset. 1

Reduced basis methods and high performance computing. Applications to non-linear multi-physics problems

International audienceWe present an open-source framework for the reduced basis methods implemented in the library Feel++ [3,4] and we consider in particular multi-physics, possibly non-linear, applications [1,2] which require high performance computing. We present how the mathematical methodology and technology scale with respect to complexity and the gain obtained in industrial context [1]. We present also briefly our first developments on low-rank methods within our framework with our colleagues from ECN. One of the main application presented is developed with the Laboratoire National des Champs Magnétiques Intenses (LNCMI), a large french equipment, allowing researchers to do experiments with magnetic fields up to 35T provided by water cooled resistive electro-magnet. Existing technologies (material properties,...) are pushed to the limits and users require now specific magnetic field profiles or homogeneous fields. These constraints and the international race for higher magnetic fields demand conception tools which are reliable and robust. The reduced basis methodology is now part of this tool chain. Another domain of application we will consider in the talk is fluid flows, both Stokes and Navier-Stokes.[1] Cécile Daversin, Stéphane Veys, Christophe Trophime, Christophe Prud'Homme. A Reduced Basis Framework: Application to large scale non-linear multi-physics problems http://hal.archives-ouvertes.fr/hal-00786557 [2] Elisa Schenone, Stéphane Veys, Christophe Prud'Homme. High Performance Computing for the Reduced Basis Method. Application to Natural Convection http://hal.archives-ouvertes.fr/hal-00786560[3] http://www.feelpp.org [4] C. Prudhomme, V. Chabannes, V. Doyeux, M. Ismail, A. Samake, G. Pena. Feel++ :A Computational Framework for Galerkin Methods and Advanced NumericalMethods, ESAIM Proc., Multiscale Coupling of Complex Models in Scientific Computing, 38 (2012), 429–455

Individual variability in value-based decision making: behavior, cognition, and functional brain topography

Decisions often require weighing the costs and benefits of available prospects. Value-based decision making depends on the coordination of multiple cognitive faculties, making it potentially susceptible to at least two forms of variability. First, there is heterogeneity in brain organization across individuals in areas of association cortex that exhibit decision-related activity. Second, a person’s preferences can fluctuate even for repetitive decision scenarios. Using functional magnetic resonance imaging (fMRI) and behavioral experiments in humans, this project explored how these distinct sources of variability impact choice evaluation, localization of valuation in the brain, and the links between valuation and other cognitive phenomena. Group-level findings suggest that valuation processes share a neural representation with the “default network” (DN) in medial prefrontal cortex (mPFC) and posterior cingulate cortex (PCC). Study 1 examined brain network variability in an open dataset of resting-state fMRI (n=100) by quantitatively testing the hypothesis that the spatial layout of the DN is unique to each person. Functional network topography was well-aligned across individuals in PCC, but highly idiosyncratic in mPFC. These results highlighted that the apparent overlap of cognitive functions in these areas should be evaluated within individuals. Study 2 examined variability in the integration of rewards with subjective costs of time and effort. Two computerized behavioral experiments (total n=132) tested how accept-or-reject foraging decisions were influenced by demands for physical effort, cognitive effort, and unfilled delay. The results showed that people’s willingness to incur the three types of costs differed when they experienced a single type of demand, but gradually converged when all three were interleaved. The results could be accounted for by a computational model in which contextual factors altered the perceived cost of temporal delay. Finally, Study 3 asked whether the apparent cortical overlap between valuation effects and the DN persisted after accounting for individual variability in brain topography and behavior. Using fMRI scans designed to evoke valuation and DN-like effects (n=18), we reproduced the idiosyncratic network topography from Study 1, and observed valuation-related effects in individually identified DN regions. Collectively, these findings advance our taxonomic understanding of higher-order cognitive processes, suggesting that seemingly dissimilar valuation and DN-related functions engage overlapping cortical mechanisms

Matching hierarchical structures for shape recognition

In this thesis we aim to develop a framework for clustering trees and rep- resenting and learning a generative model of graph structures from a set of training samples. The approach is applied to the problem of the recognition and classification of shape abstracted in terms of its morphological skeleton. We make five contributions. The first is an algorithm to approximate tree edit-distance using relaxation labeling. The second is the introduction of the tree union, a representation capable of representing the modes of structural variation present in a set of trees. The third is an information theoretic approach to learning a generative model of tree structures from a training set. While the skeletal abstraction of shape was chosen mainly as a exper- imental vehicle, we, nonetheless, make some contributions to the fields of skeleton extraction and its graph representation. In particular, our fourth contribution is the development of a skeletonization method that corrects curvature effects in the Hamilton-Jacobi framework, improving its localiza- tion and noise sensitivity. Finally, we propose a shape-measure capable of characterizing shapes abstracted in terms of their skeleton. This measure has a number of interesting properties. In particular, it varies smoothly as the shape is deformed and can be easily computed using the presented skeleton extraction algorithm. Each chapter presents an experimental analysis of the proposed approaches applied to shape recognition problems

Geotechnical Engineering for the Preservation of Monuments and Historic Sites III

The conservation of monuments and historic sites is one of the most challenging problems facing modern civilization. It involves, in inextricable patterns, factors belonging to different fields (cultural, humanistic, social, technical, economical, administrative) and the requirements of safety and use appear to be (or often are) in conflict with the respect of the integrity of the monuments. The complexity of the topic is such that a shared framework of reference is still lacking among art historians, architects, structural and geotechnical engineers. The complexity of the subject is such that a shared frame of reference is still lacking among art historians, architects, architectural and geotechnical engineers. And while there are exemplary cases of an integral approach to each building element with its static and architectural function, as a material witness to the culture and construction techniques of the original historical period, there are still examples of uncritical reliance on modern technology leading to the substitution from earlier structures to new ones, preserving only the iconic look of the original monument. Geotechnical Engineering for the Preservation of Monuments and Historic Sites III collects the contributions to the eponymous 3rd International ISSMGE TC301 Symposium (Naples, Italy, 22-24 June 2022). The papers cover a wide range of topics, which include: 　 - Principles of conservation, maintenance strategies, case histories - The knowledge: investigations and monitoring - Seismic risk, site effects, soil structure interaction - Effects of urban development and tunnelling on built heritage - Preservation of diffuse heritage: soil instability, subsidence, environmental damages The present volume aims at geotechnical engineers and academics involved in the preservation of monuments and historic sites worldwide

Transmissibility-based monitoring and combination of damage feature decisions within a holistic structural health monitoring framework

Over the past forty years, intensive research has been carried out in the field of structural health monitoring (SHM), since the identification of damage at an early stage contributes to avoiding structural failure and reducing maintenance costs. In particular, the monitoring of wind turbines has gained special interest, since there is an increasing number of installed wind turbines worldwide and a large number of wind turbines which have reached or will soon reach their design lifespan. This thesis focuses on vibration-based SHM methods, which observe features describing the dynamics of a structure. Moreover, this work is based on the conception that the consideration of SHM should not only involve the observation of damage-sensitive features, but should also address further aspects, such as the effect of environmental and operational conditions (EOCs) and the statistical pattern recognition approaches used for decision making. Wind turbines are complex structures which operate in a challenging environment. Most of the vibration-based approaches rely on assumptions which are violated, for example, during the operation of a wind turbine, raising doubts concerning their accuracy. Furthermore, there is a plethora of damage-sensitive features, alternatively called condition parameters (CPs), which can be used to assess the state of a structure. However, up to the present moment, little research has been conducted on the combination of damage feature selected and on the exploitation of decision making processes for improving the detection rates of damage when it exists. This work introduces a new vibration-based CP, which does not rely on any significant assumptions. The new CP is based on an output-only version of an autoregressive model with exogenous input (ARX), which is essentially a transmissibility function (TF) model. The poles of the model are therefore called TF poles. The proposed CP is based on the observation of TF pole migration due to structural changes. Several experimental datasets are used to explore the sensitivity of TF poles to damage, while the concept of implementing TF poles as a CP in unsupervised mode is described. The new CP is integrated into a three-tier SHM framework which performs data normalizaton (tier 1), extracts the CP for analysis (tier 2) and subsequently makes use of hypothesis testing (tier 3). This framework using TF poles is validated on the fatigue test data of a full-scale rotor blade. This work also proposes the implementation of adaptive boosting (AdaBoost) for the combination of decisions obtained from several damage features in order to attain a new and more accurate decision rule. The proposed concept is integrated into the aforementioned three-tier SHM framework and is used for combining the decisions of vibration-based damage features. However, the proposed concept can be implemented after any SHM process, even if other SHM approaches are employed. The concept of implementing Adaboost within the three-tier SHM framework is outlined and validated on the data of an operating 3~kW wind turbine. Finally, different damage features, including the proposed CP, are compared with respect to their sensivity to damage and sensitivity to EOC variability based on rotor blade fatigue tests

Statistical methods for sparse functional object data: elastic curves, shapes and densities

Many applications naturally yield data that can be viewed as elements in non-linear spaces. Consequently, there is a need for non-standard statistical methods capable of handling such data. The work presented here deals with the analysis of data in complex spaces derived from functional L2-spaces as quotient spaces (or subsets of such spaces). These data types include elastic curves represented as d-dimensional functions modulo re-parametrization, planar shapes represented as 2-dimensional functions modulo rotation, scaling and translation, and elastic planar shapes combining all of these invariances. Moreover, also probability densities can be thought of as non-negative functions modulo scaling. Since these functional object data spaces lack a natural Hilbert space structure, this work proposes specialized methods that integrate techniques from functional data analysis with those for metric and manifold data. In particular, but not exclusively, novel regression methods for specific metric quotient spaces are discussed. Special attention is given to handling discrete observations, since in practice curves and shapes are typically observed only as a discrete (often sparse or irregular) set of points. Similarly, density functions are usually not directly observed, but a (small) sample from the corresponding probability distribution is available. Overall, this work comprises six contributions that propose new methods for sparse functional object data and apply them to relevant real-world datasets, predominantly in a biomedical context

Intelligent on-demand radio resource provisioning for green ultra-small cell networks

This thesis studies intelligent on-demand radio resource provisioning involving sleep mode operation in ultra Small Cell Networks (SCNs). Sleep modes are low power states of base stations. The purpose of the research is to investigate how appropriate traffic information can be adopted in sleep mode operation schemes for SCNs with different architectures. A novel protocol-friendly sleep mode operation algorithm based on Adaptive Traffic Perception is proposed for distributed SCN architectures. It is proved robust to different SCN layouts with the reduction in the average power consumption of base stations being more than 35% while maintaining the Quality of Service. The Traffic-aware Cell Management scheme adopting Direction of Arrival information is particularly designed to eliminate the necessity of computation for sleeping base stations. This scheme is shown to significantly reduce the side effects associated with the sleep mode operation, including system overheads and the increasing user transmission power. For SCNs using centralised architectures, such as Cloud Radio Access Networks, Hotspot-oriented Green Frameworks are proposed for different information availabilities, which achieve almost 80% reduction in power consumption of Remote Radio Heads at low traffic levels. A clustering technique is utilised for the optimisation of the placement of active Remote Radio Heads, lowering the average user transmission power. The amount of reduction depends on the completeness of the information and can exceed 70% compared with the state-of-the-art. A type II Matern Hard-core Point Process is used for modelling SCNs. The derivation and approximation of its distance distributions are also proposed. The distance distributions are used for the probabilistic theoretical analysis of some metrics of the sleep mode operation