In this task, you are given a part of an article. Your task is to generate headline (title) for this text. Preferred headlines are under fifteen words.

Example Input: In many applications spanning from sensor to social networks, transportation systems, gene regulatory networks or big data, the signals of interest are defined over the vertices of a graph. The aim of this paper is to propose a least mean square (LMS) strategy for adaptive estimation of signals defined over graphs. Assuming the graph signal to be band-limited, over a known bandwidth, the method enables reconstruction, with guaranteed performance in terms of mean-square error, and tracking from a limited number of observations over a subset of vertices. A detailed mean square analysis provides the performance of the proposed method, and leads to several insights for designing useful sampling strategies for graph signals. Numerical results validate our theoretical findings, and illustrate the performance of the proposed method. Furthermore, to cope with the case where the bandwidth is not known beforehand, we propose a method that performs a sparse online estimation of the signal support in the (graph) frequency domain, which enables online adaptation of the graph sampling strategy. Finally, we apply the proposed method to build the power spatial density cartography of a given operational region in a cognitive network environment.
Example Output: Least Mean Squares Estimation of Graph Signals

Example Input: Résumé. Le crowdsourcing, un enjeu économique majeur, est le fait d’externaliser une tâche interne d’une entreprise vers le grand-public, la foule. C’est ainsi une forme de sous-traitance digitale destinée à toute personne susceptible de pouvoir réaliser la tâche demandée généralement rapide et non automatisable. L’évaluation de la qualité du travail des participants est cependant un problème majeur en crowdsourcing. En effet, les contributions doivent être contrôlées pour assurer l’efficacité et la pertinence d’une campagne. Plusieurs méthodes ont été proposées pour évaluer le niveau d’expertise des participants. Ce travail a la particularité de proposer une méthode de calcul de degrés d’expertise en présence de données dont l’ordre de classement est connu. Les degrés d’expertise sont ensuite considérés sur des données sans ordre pré-établi. Cette méthode fondée sur la théorie des fonctions de croyance tient compte des incertitudes des réponses et est évaluée sur des données réelles d’une campagne réalisée en 2016.
Example Output: Une mesure d’expertise pour le crowdsourcing

Example Input: This work studies the representational mapping across multimodal data such that given a piece of the raw data in one modality the corresponding semantic description in terms of the raw data in another modality is immediately obtained. Such a representational mapping can be found in a wide spectrum of real-world applications including image/video retrieval, object recognition, action/behavior recognition, and event understanding and prediction. To that end, we introduce a simplified training objective for learning multimodal embeddings using the skip-gram architecture by introducing convolutional “pseudowords:” embeddings composed of the additive combination of distributed word representations and image features from convolutional neural networks projected into the multimodal space. We present extensive results of the representational properties of these embeddings on various word similarity benchmarks to show the promise of this approach.
Example Output:
MULTIMODAL SKIP-GRAM USING CONVOLUTIONAL PSEUDOWORDS