Norm diffusion within international organizations: a case study of the World Bank

Constructivists often argue that International Organizations (IOs) diffuse norms throughout the international system. This article asks the question: if IOs promote and diffuse specific norms within world politics, where do these norms come from? In particular, this analysis seeks to formulate how IOs' identities emerge in issue areas where rationalist theories give limited explanation, such as the environment. This article posits that IOs interact with and consume norms from non-state actors such as transnational advocacy networks, a process overlooked by the constructivist analysis of institutions. This is examined through a case study of the World Bank's environmental identity where transnational advocacy networks played an important role in the Bank's shift towards sustainable development, through processes characterized here as direct and indirect socialization. This article demonstrates that the Bank's shift was more than instrumental as a result of this interaction, and that constructivists therefore need to examine the role of IOs as norm consumers as well as norm diffusers.

Theorizing norm diffusion within international organizations

International Organizations (IOs) promote and diffuse norms within world politics. This prompts the question: where do these norms come from? This inquiry analyses how IOs have been perceived within the emerging norms literature where IOs are 'norm diffusers' within the international system, and finds that the way in which IOs themselves internalize norms has not been taken into account. This poses a potentially fruitful new avenue of inquiry into why and when IOs behave as norm diffusers. An interpretation of when and why IOs internalize norms is offered by positing that IO identities are not fixed and that they are 'norm consumers' socialized by state and non-state actors.

An unified framework based on p-norm for feature aggregation in content- based image retrieval

Feature aggregation is a critical technique in content- based image retrieval systems that employ multiple visual features to characterize image content. In this paper, the p-norm is introduced to feature aggregation that provides a framework to unify various previous feature aggregation schemes such as linear combination, Euclidean distance, Boolean logic and decision fusion schemes in which previous schemes are instances. Some insights of the mechanism of how various aggregation schemes work are discussed through the effects of model parameters in the unified framework. Experiments show that performances vary over feature aggregation schemes that necessitates an unified framework in order to optimize the retrieval performance according to individual queries and user query concept. Revealing experimental results conducted with IAPR TC-12 ImageCLEF2006 benchmark collection that contains over 20,000 photographic images are presented and discussed.

Design of a norm-bounded LQG controller for power distribution networks with distributed generation

This paper presents a novel excitation control design to improve the voltage profile of power distribution networks with distributed generation and induction motor loads. The system is linearised by perturbation technique. Controller is designed using the linear-quadratic-Gaussian (LQG) controller synthesis method. The LQG controller is addressed with norm-bounded uncertainty. The approach considered in this paper is to find the smallest upper bound on the H∞ norm of the uncertain system and to design an optimal controller based on this bound. The design method requires the solution of a linear matrix inequality. The performance of the controller is tested on a benchmark power distribution system. Simulation results show that the proposed controller provides impressive oscillation damping compared to the conventional excitation controller.

The role of masculine norm conformity in men's and women's relational satisfaction

 This thesis highlighted that men’s conformity to aspects of masculinity manifest in their use of communication and conflict resolution strategies, which in turn contribute to men and their female partners’ relational satisfaction. These findings improve knowledge regarding the role of socialised gender norms in relationships and emphasise the importance of adopting a gender-informed approach to treatment practices.

Mixed-norm sparse representation for multi view face recognition

Face recognition with multiple views is a challenging research problem. Most of the existing works have focused on extracting shared information among multiple views to improve recognition. However, when the pose variation is too large or missing, 'shared information' may not be properly extracted, leading to poor recognition results. In this paper, we propose a novel method for face recognition with multiple view images to overcome the large pose variation and missing pose issue. By introducing a novel mixed norm, the proposed method automatically selects candidates from the gallery to best represent a group of highly correlated face images in a query set to improve classification accuracy. This mixed norm combines the advantages of both sparse representation based classification (SRC) and joint sparse representation based classification (JSRC). A trade off between the ℓ1-norm from SRC and ℓ2,1-norm from JSRC is introduced to achieve this goal. Due to this property, the proposed method decreases the influence when a face image is unseen and has large pose variation in the recognition process. And when some face images with a certain degree of unseen pose variation appear, this mixed norm will find an optimal representation for these query images based on the shared information induced from multiple views. Moreover, we also address an open problem in robust sparse representation and classification which is using ℓ1-norm on the loss function to achieve a robust solution. To solve this formulation, we derive a simple, yet provably convergent algorithm based on the powerful alternative directions method of multipliers (ADMM) framework. We provide extensive comparisons which demonstrate that our method outperforms other state-of-the-arts algorithms on CMU-PIE, Yale B and Multi-PIE databases for multi-view face recognition.

Visual object clustering via mixed-norm regularization

Many vision problems deal with high-dimensional data, such as motion segmentation and face clustering. However, these high-dimensional data usually lie in a low-dimensional structure. Sparse representation is a powerful principle for solving a number of clustering problems with high-dimensional data. This principle is motivated from an ideal modeling of data points according to linear algebra theory. However, real data in computer vision are unlikely to follow the ideal model perfectly. In this paper, we exploit the mixed norm regularization for sparse subspace clustering. This regularization term is a convex combination of the l1norm, which promotes sparsity at the individual level and the block norm l2/1 which promotes group sparsity. Combining these powerful regularization terms will provide a more accurate modeling, subsequently leading to a better solution for the affinity matrix used in sparse subspace clustering. This could help us achieve better performance on motion segmentation and face clustering problems. This formulation also caters for different types of data corruptions. We derive a provably convergent algorithm based on the alternating direction method of multipliers (ADMM) framework, which is computationally efficient, to solve the formulation. We demonstrate that this formulation outperforms other state-of-arts on both motion segmentation and face clustering.