213,827 research outputs found
Message passing optimization of Harmonic Influence Centrality
This paper proposes a new measure of node centrality in social networks, the
Harmonic Influence Centrality, which emerges naturally in the study of social
influence over networks. Using an intuitive analogy between social and
electrical networks, we introduce a distributed message passing algorithm to
compute the Harmonic Influence Centrality of each node. Although its design is
based on theoretical results which assume the network to have no cycle, the
algorithm can also be successfully applied on general graphs.Comment: 11 pages; 10 figures; to appear as a journal publicatio
Achieving mitigation and adaptation to climate change through sustainable agroforestry practices in Africa
Peer reviewedPublisher PD
Representations of stream processors using nested fixed points
We define representations of continuous functions on infinite streams of discrete values, both in the case of discrete-valued functions, and in the case of stream-valued functions. We define also an operation on the representations of two continuous functions between streams that yields a representation of their composite. In the case of discrete-valued functions, the representatives are well-founded (finite-path) trees of a certain kind. The underlying idea can be traced back to Brouwer's justification of bar-induction, or to Kreisel and Troelstra's elimination of choice-sequences. In the case of stream-valued functions, the representatives are non-wellfounded trees pieced together in a coinductive fashion from well-founded trees. The definition requires an alternating fixpoint construction of some ubiquity
Isoelastic Agents and Wealth Updates in Machine Learning Markets
Recently, prediction markets have shown considerable promise for developing
flexible mechanisms for machine learning. In this paper, agents with isoelastic
utilities are considered. It is shown that the costs associated with
homogeneous markets of agents with isoelastic utilities produce equilibrium
prices corresponding to alpha-mixtures, with a particular form of mixing
component relating to each agent's wealth. We also demonstrate that wealth
accumulation for logarithmic and other isoelastic agents (through payoffs on
prediction of training targets) can implement both Bayesian model updates and
mixture weight updates by imposing different market payoff structures. An
iterative algorithm is given for market equilibrium computation. We demonstrate
that inhomogeneous markets of agents with isoelastic utilities outperform state
of the art aggregate classifiers such as random forests, as well as single
classifiers (neural networks, decision trees) on a number of machine learning
benchmarks, and show that isoelastic combination methods are generally better
than their logarithmic counterparts.Comment: Appears in Proceedings of the 29th International Conference on
Machine Learning (ICML 2012
The pre-Lie structure of the time-ordered exponential
The usual time-ordering operation and the corresponding time-ordered
exponential play a fundamental role in physics and applied mathematics. In this
work we study a new approach to the understanding of time-ordering relying on
recent progress made in the context of enveloping algebras of pre-Lie algebras.
Various general formulas for pre-Lie and Rota-Baxter algebras are obtained in
the process. Among others, we recover the noncommutative analog of the
classical Bohnenblust-Spitzer formula, and get explicit formulae for operator
products of time-ordered exponentials
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