741 research outputs found
An Introduction to Declarative Programming in CLIPS and PROLOG
We provide a brief introduction to CLIPS—a declarative/logic programming language for implementing expert systems—and PROLOG—a declarative/logic programming language based on first-order, predicate calculus. Unlike imperative languages in which the programmer specifies how to compute a solution to a problem, in a declarative language, the programmer specifies what they what to find, and the system uses a search strategy built into the language. We also briefly discuss applications of CLIPS and PROLOG
The Little-Hopfield model on a Random Graph
We study the Hopfield model on a random graph in scaling regimes where the
average number of connections per neuron is a finite number and where the spin
dynamics is governed by a synchronous execution of the microscopic update rule
(Little-Hopfield model).We solve this model within replica symmetry and by
using bifurcation analysis we prove that the spin-glass/paramagnetic and the
retrieval/paramagnetictransition lines of our phase diagram are identical to
those of sequential dynamics.The first-order retrieval/spin-glass transition
line follows by direct evaluation of our observables using population dynamics.
Within the accuracy of numerical precision and for sufficiently small values of
the connectivity parameter we find that this line coincides with the
corresponding sequential one. Comparison with simulation experiments shows
excellent agreement.Comment: 14 pages, 4 figure
Effects of Water Stress on Seed Production in Ruzi Grass \u3ci\u3e(Brachiaria ruziziensis Germain and Everard)\u3c/i\u3e
Water stress at different stages of reproductive development influenced seed yield in Ruzi grass differently. Under mild water stress, the earlier in the reproductive developmental stage the stress was applied (before ear emergence) the faster the plants recovered and the less the ultimate damage to inflorescence structure and seed set compared with the situation where water stress occurred during the later stages after inflorescences had emerged. Conversely, severe water stress before ear emergence had a severe effect in damaging both inflorescence numbers and seed quality. Permanent damage to the reproductive structures resulted in deformed inflorescences. Moreover, basal vegetative tillers were stunted and were capable of only limited regrowth after re-watering
Dynamics of on-line Hebbian learning with structurally unrealizable restricted training sets
We present an exact solution for the dynamics of on-line Hebbian learning in
neural networks, with restricted and unrealizable training sets. In contrast to
other studies on learning with restricted training sets, unrealizability is
here caused by structural mismatch, rather than data noise: the teacher machine
is a perceptron with a reversed wedge-type transfer function, while the student
machine is a perceptron with a sigmoidal transfer function. We calculate the
glassy dynamics of the macroscopic performance measures, training error and
generalization error, and the (non-Gaussian) student field distribution. Our
results, which find excellent confirmation in numerical simulations, provide a
new benchmark test for general formalisms with which to study unrealizable
learning processes with restricted training sets.Comment: 7 pages including 3 figures, using IOP latex2e preprint class fil
Slowly evolving geometry in recurrent neural networks I: extreme dilution regime
We study extremely diluted spin models of neural networks in which the
connectivity evolves in time, although adiabatically slowly compared to the
neurons, according to stochastic equations which on average aim to reduce
frustration. The (fast) neurons and (slow) connectivity variables equilibrate
separately, but at different temperatures. Our model is exactly solvable in
equilibrium. We obtain phase diagrams upon making the condensed ansatz (i.e.
recall of one pattern). These show that, as the connectivity temperature is
lowered, the volume of the retrieval phase diverges and the fraction of
mis-aligned spins is reduced. Still one always retains a region in the
retrieval phase where recall states other than the one corresponding to the
`condensed' pattern are locally stable, so the associative memory character of
our model is preserved.Comment: 18 pages, 6 figure
Statistical Mechanics of Support Vector Networks
Using methods of Statistical Physics, we investigate the generalization
performance of support vector machines (SVMs), which have been recently
introduced as a general alternative to neural networks. For nonlinear
classification rules, the generalization error saturates on a plateau, when the
number of examples is too small to properly estimate the coefficients of the
nonlinear part. When trained on simple rules, we find that SVMs overfit only
weakly. The performance of SVMs is strongly enhanced, when the distribution of
the inputs has a gap in feature space.Comment: REVTeX, 4 pages, 2 figures, accepted by Phys. Rev. Lett (typos
corrected
Phase transitions in optimal unsupervised learning
We determine the optimal performance of learning the orientation of the
symmetry axis of a set of P = alpha N points that are uniformly distributed in
all the directions but one on the N-dimensional sphere. The components along
the symmetry breaking direction, of unitary vector B, are sampled from a
mixture of two gaussians of variable separation and width. The typical optimal
performance is measured through the overlap Ropt=B.J* where J* is the optimal
guess of the symmetry breaking direction. Within this general scenario, the
learning curves Ropt(alpha) may present first order transitions if the clusters
are narrow enough. Close to these transitions, high performance states can be
obtained through the minimization of the corresponding optimal potential,
although these solutions are metastable, and therefore not learnable, within
the usual bayesian scenario.Comment: 9 pages, 8 figures, submitted to PRE, This new version of the paper
contains one new section, Bayesian versus optimal solutions, where we explain
in detail the results supporting our claim that bayesian learning may not be
optimal. Figures 4 of the first submission was difficult to understand. We
replaced it by two new figures (Figs. 4 and 5 in this new version) containing
more detail
On the center of mass of Ising vectors
We show that the center of mass of Ising vectors that obey some simple
constraints, is again an Ising vector.Comment: 8 pages, 3 figures, LaTeX; Claims in connection with disordered
systems have been withdrawn; More detailed description of the simulations;
Inset added to figure
Generalizing with perceptrons in case of structured phase- and pattern-spaces
We investigate the influence of different kinds of structure on the learning
behaviour of a perceptron performing a classification task defined by a teacher
rule. The underlying pattern distribution is permitted to have spatial
correlations. The prior distribution for the teacher coupling vectors itself is
assumed to be nonuniform. Thus classification tasks of quite different
difficulty are included. As learning algorithms we discuss Hebbian learning,
Gibbs learning, and Bayesian learning with different priors, using methods from
statistics and the replica formalism. We find that the Hebb rule is quite
sensitive to the structure of the actual learning problem, failing
asymptotically in most cases. Contrarily, the behaviour of the more
sophisticated methods of Gibbs and Bayes learning is influenced by the spatial
correlations only in an intermediate regime of , where
specifies the size of the training set. Concerning the Bayesian case we show,
how enhanced prior knowledge improves the performance.Comment: LaTeX, 32 pages with eps-figs, accepted by J Phys
Stemofoline ethyl acetate solvate
Crystals of the title compound, C22H29NO5·C4H8O2, {[systematic name: (2R,3R,5R,5aS,6R,8aR,9S)-(5Z)-5-[3-butylÂtetraÂhydro-6-methyl-2,5-methano-4,3,8a-[1]propanÂyl[3]ylÂideneÂfuro[3,2-f][1,4]oxazepin-7(5H)-ylÂidene]-4-methÂoxy-3-methylÂfuran-2(5H)-one ethyl acetate solvate} were isolated from the root extracts of Stemona aphylla (Stemonaceae). The structure closely resembles those of stemofoline derivatives which have previously been reported. InterÂmolecular contacts are observed between some C-bonded H atoms and nearby O atoms, perhaps indicating weak interÂactions which could influence the packing of species within the unit cell
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