Constructivism (international relations)

In international relations (IR), constructivism is a social theory that asserts that significant aspects of international relations are shaped by ideational factors. The most important ideational factors are those that are collectively held; these collectively held beliefs construct the interests and identities of actors. In contrast to other prominent IR approaches and theories (such as realism and rational choice), constructivists see identities and interests of actors as socially constructed and changeable; identities are not static and cannot be exogenously assumed. Similar to rational choice, constructivism does not make broad and specific predictions about international relations; it is an approach to studying international politics, not a substantive theory of international politics. Constructivist analyses can only provide substantive explanations or predictions once the relevant actors and their interests have been identified, as well as the content of social structures. The main theories competing with constructivism are variants of realism, liberalism, and rational choice that emphasize materialism (the notion that the physical world determines political behavior on its own), and individualism (the notion that individual units can be studied apart from the broader systems that they are embedded in). Whereas other prominent approaches conceptualize power in material terms (e.g. military and economic capabilities), constructivist analyses also see power as the ability to structure and constitute the nature of social relations among actors. Nicholas Onuf has been credited with coining the term constructivism to describe theories that stress the socially constructed character of international relations. Since the late 1980s to early 1990s, constructivism has become one of the major schools of thought within international relations. The earliest constructivist works focused on establishing that norms mattered in international politics. Peter J.

Wasserstein Adversarial Regularization for learning with label noise

Devis Tuia, Sylvain Lobry, Nicolas Courty

Noisy labels often occur in vision datasets, especially when they are obtained from crowdsourcing or Web scraping. We propose a new regularization method, which enables learning robust classifiers in presence of noisy data. To achieve this goal, we propose ...

2021

Chemiscope: interactive structure-property explorer for materials and molecules

Michele Ceriotti, Guillaume André Jean Fraux

The number of materials or molecules that can be created by combining different chemical elements in various proportions and spatial arrangements is enormous. Computational chemistry can be used to generate databases containing billions of potential struct ...

2020

Mapping and classifying molecules from a high-throughput structural database

Michele Ceriotti, Félix Benedito Clément Musil, Sandip De, Teresa Ingram, Carsten Baldauf

High-throughput computational materials design promises to greatly accelerate the process of discovering new materials and compounds, and of optimizing their properties. The large databases of structures and properties that result from computational search ...

Biomed Central Ltd2017