92,956 research outputs found
Additive versus multiplicative clause weighting for SAT
This paper examines the relative performance of additive and multiplicative clause weighting schemes for propositional satisfiability testing. Starting with one of the most recently developed multiplicative algorithms (SAPS), an experimental study was constructed to isolate the effects of multiplicative in comparison to additive weighting, while controlling other key features of the two approaches, namely the use of random versus flat moves, deterministic versus probabilistic weight smoothing and multiple versus single inclusion of literals in the local search neighborhood. As a result of this investigation we developed a pure additive weighting scheme (PAWS) which can outperform multiplicative weighting on a range of difficult problems, while requiring considerably less effort in terms of parameter tuning. W
The Asymptotic Performance of Linear Echo State Neural Networks
In this article, a study of the mean-square error (MSE) performance of linear
echo-state neural networks is performed, both for training and testing tasks.
Considering the realistic setting of noise present at the network nodes, we
derive deterministic equivalents for the aforementioned MSE in the limit where
the number of input data and network size both grow large. Specializing
then the network connectivity matrix to specific random settings, we further
obtain simple formulas that provide new insights on the performance of such
networks
Dismantling the Mantel tests
The simple and partial Mantel tests are routinely used in many areas of
evolutionary biology to assess the significance of the association between two
or more matrices of distances relative to the same pairs of individuals or
demes. Partial Mantel tests rather than simple Mantel tests are widely used to
assess the relationship between two variables displaying some form of
structure.
We show that contrarily to a widely shared belief, partial Mantel tests are
not valid in this case, and their bias remains close to that of the simple
Mantel test.
We confirm that strong biases are expected under a sampling design and
spatial correlation parameter drawn from an actual study.
The Mantel tests should not be used in case auto-correlation is suspected in
both variables compared under the null hypothesis. We outline alternative
strategies. The R code used for our computer simulations is distributed as
supporting material
No lack of relative power of the Dickey-Fuller tests for unit roots
This paper shows numerically that the lack ofpower and size distortions of the Dickey-Fuller type test for unit roots (very well documented in the unit root literature) are similar to and in many situations even smaller than the lack of power and size distortions of the standard Student-t tests for stationary roots of an AR model
Testing I(1) against I(d) alternatives with Wald Tests in the presence of deterministic components
This paper analyses how to test I(1) against I(d), d<1, in the presence of deterministic components in the DGP, by extending a Wald-type test, i.e., the (Efficient) Fractional Dickey-Fuller (EFDF) test, to this case. Tests of these hypotheses are important in many economic applications where it is crucial to distinguish between permanent and transitory shocks because I(d) processes with d<1 are mean-reverting. On top of it,
the inclusion of deterministic components becomes a necessary addition in order to analyze most macroeconomic variables. We show how simple is the implementation of the EFDF in these situations and argue that, in general, has better properties than LM tests. Finally, an empirical application is
provided where the EFDF approach allowing for deterministic components is used to test for long-memory in the GDP p.c. of several OECD countries, an issue that has important consequences to discriminate between growth
theories, and on which there has been some controversy
Phylogenetic information complexity: Is testing a tree easier than finding it?
Phylogenetic trees describe the evolutionary history of a group of
present-day species from a common ancestor. These trees are typically
reconstructed from aligned DNA sequence data. In this paper we analytically
address the following question: is the amount of sequence data required to
accurately reconstruct a tree significantly more than the amount required to
test whether or not a candidate tree was the `true' tree? By `significantly',
we mean that the two quantities behave the same way as a function of the number
of species being considered. We prove that, for a certain type of model, the
amount of information required is not significantly different; while for
another type of model, the information required to test a tree is independent
of the number of leaves, while that required to reconstruct it grows with this
number. Our results combine probabilistic and combinatorial arguments.Comment: 15 pages, 3 figure
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