7,569 research outputs found
Market and State: The Perspective of Constitutional Political Economy
The paper approaches the "market versus state" issue from the perspective of constitutional political economy, a research program that has been advanced as a principal alternative to traditional welfare economics and its perspective on the relation between market and state. Constitutional political economy looks at market and state as different kinds of social arenas in which people may realize mutual gains from voluntary exchange and cooperation. The working properties of these arenas depend on their respective constitutions, i.e. the rules of the game that define the constraints under which individuals are allowed, in either arena, to pursue their interests. It is argued that "improving" markets means to adopt and to maintain an economic constitution that enhances consumer sovereignty, and that "improvement" in the political arena means to adopt and to maintain constitutional rules that enhance citizen sovereignty. --Economics of rules,welfare economics,constitution of markets,constitution of politics
Consensus clustering in complex networks
The community structure of complex networks reveals both their organization
and hidden relationships among their constituents. Most community detection
methods currently available are not deterministic, and their results typically
depend on the specific random seeds, initial conditions and tie-break rules
adopted for their execution. Consensus clustering is used in data analysis to
generate stable results out of a set of partitions delivered by stochastic
methods. Here we show that consensus clustering can be combined with any
existing method in a self-consistent way, enhancing considerably both the
stability and the accuracy of the resulting partitions. This framework is also
particularly suitable to monitor the evolution of community structure in
temporal networks. An application of consensus clustering to a large citation
network of physics papers demonstrates its capability to keep track of the
birth, death and diversification of topics.Comment: 11 pages, 12 figures. Published in Scientific Report
Self-Supervised Learning with an Information Maximization Criterion
Self-supervised learning allows AI systems to learn effective representations
from large amounts of data using tasks that do not require costly labeling.
Mode collapse, i.e., the model producing identical representations for all
inputs, is a central problem to many self-supervised learning approaches,
making self-supervised tasks, such as matching distorted variants of the
inputs, ineffective. In this article, we argue that a straightforward
application of information maximization among alternative latent
representations of the same input naturally solves the collapse problem and
achieves competitive empirical results. We propose a self-supervised learning
method, CorInfoMax, that uses a second-order statistics-based mutual
information measure that reflects the level of correlation among its arguments.
Maximizing this correlative information measure between alternative
representations of the same input serves two purposes: (1) it avoids the
collapse problem by generating feature vectors with non-degenerate covariances;
(2) it establishes relevance among alternative representations by increasing
the linear dependence among them. An approximation of the proposed information
maximization objective simplifies to a Euclidean distance-based objective
function regularized by the log-determinant of the feature covariance matrix.
The regularization term acts as a natural barrier against feature space
degeneracy. Consequently, beyond avoiding complete output collapse to a single
point, the proposed approach also prevents dimensional collapse by encouraging
the spread of information across the whole feature space. Numerical experiments
demonstrate that CorInfoMax achieves better or competitive performance results
relative to the state-of-the-art SSL approaches
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