950 research outputs found
A Continuous Opinion Dynamics Model Based on the Principle of Meta-Contrast
We propose a new continuous opinion dynamics model inspired by social psychology. It is based on a central assumption of self-categorization theory called principle of meta-contrast. We study the behaviour of the model for several network interactions and show that, in particular, consensus, polarization or extremism are possible outcomes, even without explicit introduction of extremist agents. The model is compared to other existing opinion dynamics models.Opinion Dynamics, Self-Categorization Theory, Consensus, Polarization, Extremism
The evolution of auditory contrast
This paper reconciles the standpoint that language users do not aim at improving their sound systems with the observation that languages seem to improve their sound systems. Computer simulations of inventories of sibilants show that Optimality-Theoretic learners who optimize their perception grammars automatically introduce a so-called prototype effect, i.e. the phenomenon that the learner’s preferred auditory realization of a certain phonological category is more peripheral than the average auditory realization of this category in her language environment. In production, however, this prototype effect is counteracted by an articulatory effect that limits the auditory form to something that is not too difficult to pronounce. If the prototype effect and the articulatory effect are of a different size, the learner must end up with an auditorily different sound system from that of her language environment. The computer simulations show that, independently of the initial auditory sound system, a stable equilibrium is reached within a small number of generations. In this stable state, the dispersion of the sibilants of the language strikes an optimal balance between articulatory ease and auditory contrast. The important point is that this is derived within a model without any goal-oriented elements such as dispersion constraints
Towards Efficient and Effective Deep Clustering with Dynamic Grouping and Prototype Aggregation
Previous contrastive deep clustering methods mostly focus on instance-level
information while overlooking the member relationship within groups/clusters,
which may significantly undermine their representation learning and clustering
capability. Recently, some group-contrastive methods have been developed,
which, however, typically rely on the samples of the entire dataset to obtain
pseudo labels and lack the ability to efficiently update the group assignments
in a batch-wise manner. To tackle these critical issues, we present a novel
end-to-end deep clustering framework with dynamic grouping and prototype
aggregation, termed as DigPro. Specifically, the proposed dynamic grouping
extends contrastive learning from instance-level to group-level, which is
effective and efficient for timely updating groups. Meanwhile, we perform
contrastive learning on prototypes in a spherical feature space, termed as
prototype aggregation, which aims to maximize the inter-cluster distance.
Notably, with an expectation-maximization framework, DigPro simultaneously
takes advantage of compact intra-cluster connections, well-separated clusters,
and efficient group updating during the self-supervised training. Extensive
experiments on six image benchmarks demonstrate the superior performance of our
approach over the state-of-the-art. Code is available at
https://github.com/Regan-Zhang/DigPro
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