Deep Multi-view Learning to Rank

We study the problem of learning to rank from multiple information sources. Though multi-view learning and learning to rank have been studied extensively leading to a wide range of applications, multi-view learning to rank as a synergy of both topics has received little attention. The aim of the paper is to propose a composite ranking method while keeping a close correlation with the individual rankings simultaneously. We present a generic framework for multi-view subspace learning to rank (MvSL2R), and two novel solutions are introduced under the framework. The first solution captures information of feature mappings from within each view as well as across views using autoencoder-like networks. Novel feature embedding methods are formulated in the optimization of multi-view unsupervised and discriminant autoencoders. Moreover, we introduce an end-to-end solution to learning towards both the joint ranking objective and the individual rankings. The proposed solution enhances the joint ranking with minimum view-specific ranking loss, so that it can achieve the maximum global view agreements in a single optimization process. The proposed method is evaluated on three different ranking problems, i.e. university ranking, multi-view lingual text ranking and image data ranking, providing superior results compared to related methods.Comment: Published at IEEE TKD

Improving Semantic Embedding Consistency by Metric Learning for Zero-Shot Classification

This paper addresses the task of zero-shot image classification. The key contribution of the proposed approach is to control the semantic embedding of images -- one of the main ingredients of zero-shot learning -- by formulating it as a metric learning problem. The optimized empirical criterion associates two types of sub-task constraints: metric discriminating capacity and accurate attribute prediction. This results in a novel expression of zero-shot learning not requiring the notion of class in the training phase: only pairs of image/attributes, augmented with a consistency indicator, are given as ground truth. At test time, the learned model can predict the consistency of a test image with a given set of attributes , allowing flexible ways to produce recognition inferences. Despite its simplicity, the proposed approach gives state-of-the-art results on four challenging datasets used for zero-shot recognition evaluation.Comment: in ECCV 2016, Oct 2016, amsterdam, Netherlands. 201

Historical forest biomass dynamics modelled with Landsat spectral trajectories

Acknowledgements National Forest Inventory data are available online, provided by Ministerio de Agricultura, Alimentación y Medio Ambiente (España). Landsat images are available online, provided by the USGS.Peer reviewedPostprin

Modeling the Structure and Complexity of Engineering Routine Design Problems

This paper proposes a model to structure routine design problems as well as a model of its design complexity. The idea is that having a proper model of the structure of such problems enables understanding its complexity, and likewise, a proper understanding of its complexity enables the development of systematic approaches to solve them. The end goal is to develop computer systems capable of taking over routine design tasks based on generic and systematic solving approaches. It is proposed to structure routine design in three main states: problem class, problem instance, and problem solution. Design complexity is related to the degree of uncertainty in knowing how to move a design problem from one state to another. Axiomatic Design Theory is used as reference for understanding complexity in routine design

Temperature structure of the intergalactic medium within seven nearby and bright clusters of galaxies observed with XMM-Newton

Aims. We map the temperature structure of the intra-cluster medium (ICM) within a nearly complete X-ray flux limited sample of galaxy clusters in the redshift range z=[0.045,0.096]. Our sample contains seven bright clusters of galaxies observed with XMM-Newton: Abell 399, Abell 401, Abell 478, Abell 1795, Abell 2029, Abell 2065, Abell 2256. Methods. We use a multi-scale spectral mapping algorithm especially designed to map spectroscopic observables from X-ray extended emission of the ICM. Derived from a former algorithm using Haar wavelets, our algorithm is now implemented with B-spline wavelets in order to perform a more regular analysis of the signal. Results. For the four clusters in our sample that are major mergers, we find a complex thermal structure with strong thermal variations consistent with their dynamics. For two of them, A2065 and A2256, we perform a 3-d analysis of cold front features evidenced from the gas temperature and brightness maps. Furthermore, we detect a significant non-radial thermal structure outside the cool core region of the other 3 more "regular" clusters, with relative amplitudes of about about 10%. We investigate possible implications of this structure on the mass estimates of the "regular" clusters A1795 and A2029, by extracting surface brightness and temperature profiles from sectors correspondings to the hottest and coldest regions in the maps. While compensating with surface brightness for A2029, leading to consistent mass profiles, the temperature structure leads to significant mass discrepancies in the innermost region of A1795.Comment: published in A&