L’étude des relations internationales: Objet, méthode, perspectives

Este artículo ha sido traducido con el permiso de la editorial Revue française de science politique, Presses de Sciences Po (http://www.cairn. info/revue-francaisede- science-politique. htm). Traducido por Sebastián Crescentin

Proximity, maps and conflict: New measures, New maps and New findings

This article introduces two new datasets. The first is a new interstate distance dataset. It is recognized that different theories regarding distance and conflict will call for different understandings of “distance” and accordingly, ten different types of distance measurement are presented. Moreover, it is argued that in order for a distance dataset to contain accurate distances, it is necessary for it to be based on maps reflecting state border changes over time. As such, a new map dataset is presented, including annualized maps for all states, stored in KML format. It will be shown that the frequent border changes experienced by states can have large impacts on distance calculations. The significance of the relationship between distance and conflict will be tested for the ten different types of distance measurement, not with the aim of finding a “best measure” but in order to demonstrate that distance remains an important variable and that each different form of distance measure can be significant

Adaptation de domaine non supervisée pour la reconnaissance de la langue par régularisation d'un réseau de neurones

National audienceLes systèmes automatiques d’identification de la langue subissent une dégradation importante de leurs performances quand les caractéristiques acoustiques des signaux de test diffèrent fortement des caractéristiques des données d’entraînement. Dans cet article, nous étudions l’adaptation de domaine non supervisée d’un système entraîné sur des conversations téléphoniques à des transmissions radio. Nous présentons une méthode de régularisation d’un réseau de neurones consistant à ajouter à la fonction de coût un terme mesurant la divergence entre les deux domaines. Des expériences sur le corpus OpenSAD15 nous permettent de sélectionner la Maximum Mean Discrepancy pour réaliser cette mesure. Cette approche est ensuite appliquée à un système moderne d’identification de la langue reposant sur des x-vectors. Sur le corpus RATS, pour sept des huit canaux radio étudiés, l’approche permet, sans utiliser de données annotées du domaine cible, de surpasser la performance d’un système entraîné de façon supervisée avec des données annotées de ce domaine

Unsupervised regularization of the embedding extractor for robust language identification

International audienceState-of-the-art spoken language identification systems are constituted of three modules: a frame-level feature extractor, a segment-level embedding extractor and a final classifier. The performance of these systems degrades when facing mismatch between training and testing data. Most domain adaptation methods focus on adaptation of the final classifier. In this article , we propose a model-based unsupervised domain adaptation of the segment-level embedding extractor. The approach consists in a modification of the loss function used for training the embedding extractor. We introduce a regularization term based on the maximum mean discrepancy loss. Experiments were performed on the RATS corpus with transmission channel mismatch between telephone and radio channels. We obtained the same language identification performance as supervised training on the target domains but without using labeled data from these domains

Metric learning loss functions to reduce domain mismatch in the x-vector space for language recognition

International audienceState-of-the-art language recognition systems are based on dis-criminative embeddings called x-vectors. Channel and gender distortions produce mismatch in such x-vector space where em-beddings corresponding to the same language are not grouped in an unique cluster. To control this mismatch, we propose to train the x-vector DNN with metric learning objective functions. Combining a classification loss with the metric learning n-pair loss allows to improve the language recognition performance. Such a system achieves a robustness comparable to a system trained with a domain adaptation loss function but without using the domain information. We also analyze the mismatch due to channel and gender, in comparison to language proximity, in the x-vector space. This is achieved using the Maximum Mean Discrepancy divergence measure between groups of x-vectors. Our analysis shows that using the metric learning loss function reduces gender and channel mismatch in the x-vector space, even for languages only observed on one channel in the train set

The Romantic Socialist Origins of Humanitariamism

“Humanitarian” (humanitaire) came into use in French contemporaneously with the emergence of romantic socialism, and in the context of the rebuilding of post-revolutionary French society and its overseas empire beginning in the 1830s. This article excavates this early idea of humanitarianism, documenting an alternative genealogy for the term and its significance that has been overlooked by scholars of both socialism and humanitarianism. This humanitarianism identified a collective humanity as the source of its own salvation, rather than an external, well-meaning benefactor. Unlike liberal models of advocacy, which invoked individualized actors and recipients of their care, socialists privileged solidarity within their community and rejected the foundational logic of liberal individualism. In tracing this history, this article considers its importance for contemporary debates about humanitarianism’s imperial power dynamics