61,994 research outputs found
Solvation forces in Ising films with long-range boundary fields: density-matrix renormalization-group study
Using the quasi-exact density-matrix renormalization-group method we
calculate the solvation forces in two-dimensional Ising films of thickness L
subject to identical algebraically decaying boundary fields with various decay
exponents p. At the bulk critical point the solvation force acquires a
universal contribution which is long-ranged in L due to the critical
fluctuations, a phenomenon known as the critical Casimir effect. For p = 2, 3
and 50, we study the scaling behaviour of the solvation force along the
pseudo-phase coexistence and along the critical and sub-critical isotherms.Comment: 9 pages, 6 figures, accepted to Molecular Physic
Considerations about multistep community detection
The problem and implications of community detection in networks have raised a
huge attention, for its important applications in both natural and social
sciences. A number of algorithms has been developed to solve this problem,
addressing either speed optimization or the quality of the partitions
calculated. In this paper we propose a multi-step procedure bridging the
fastest, but less accurate algorithms (coarse clustering), with the slowest,
most effective ones (refinement). By adopting heuristic ranking of the nodes,
and classifying a fraction of them as `critical', a refinement step can be
restricted to this subset of the network, thus saving computational time.
Preliminary numerical results are discussed, showing improvement of the final
partition.Comment: 12 page
Regression with Linear Factored Functions
Many applications that use empirically estimated functions face a curse of
dimensionality, because the integrals over most function classes must be
approximated by sampling. This paper introduces a novel regression-algorithm
that learns linear factored functions (LFF). This class of functions has
structural properties that allow to analytically solve certain integrals and to
calculate point-wise products. Applications like belief propagation and
reinforcement learning can exploit these properties to break the curse and
speed up computation. We derive a regularized greedy optimization scheme, that
learns factored basis functions during training. The novel regression algorithm
performs competitively to Gaussian processes on benchmark tasks, and the
learned LFF functions are with 4-9 factored basis functions on average very
compact.Comment: Under review as conference paper at ECML/PKDD 201
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