539 research outputs found
On-Line AdaTron Learning of Unlearnable Rules
We study the on-line AdaTron learning of linearly non-separable rules by a
simple perceptron. Training examples are provided by a perceptron with a
non-monotonic transfer function which reduces to the usual monotonic relation
in a certain limit. We find that, although the on-line AdaTron learning is a
powerful algorithm for the learnable rule, it does not give the best possible
generalization error for unlearnable problems. Optimization of the learning
rate is shown to greatly improve the performance of the AdaTron algorithm,
leading to the best possible generalization error for a wide range of the
parameter which controls the shape of the transfer function.)Comment: RevTeX 17 pages, 8 figures, to appear in Phys.Rev.
The application of inelastic neutron scattering to investigate the interaction of methyl propanoate with silica
A modern industrial route for the manufacture of methyl methacrylate involves the reaction of methyl propanoate and formaldehyde over a silica-supported Cs catalyst. Although the process has been successfully commercialised, little is known about the surface interactions responsible for the forward chemistry. This work concentrates upon the interaction of methyl propanoate over a representative silica. A combination of infrared spectroscopy, inelastic neutron scattering, DFT calculations, X-ray diffraction and temperature-programmed desorption is used to deduce how the ester interacts with the silica surface
Statistical Mechanics of Learning in the Presence of Outliers
Using methods of statistical mechanics, we analyse the effect of outliers on
the supervised learning of a classification problem. The learning strategy aims
at selecting informative examples and discarding outliers. We compare two
algorithms which perform the selection either in a soft or a hard way. When the
fraction of outliers grows large, the estimation errors undergo a first order
phase transition.Comment: 24 pages, 7 figures (minor extensions added
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
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
Lactation failure in Src knockout mice is due to impaired secretory activation
<p>Abstract</p> <p>Background</p> <p>Mammary gland development culminates in lactation and is orchestrated by numerous stimuli and signaling pathways. The Src family of nonreceptor tyrosine kinases plays a pivotal role in cell signaling. In order to determine if Src plays a role in mammary gland development we have examined mammary gland development and function during pregnancy and lactation in mice in which expression of Src has been eliminated.</p> <p>Results</p> <p>We have characterized a lactation defect in the Src-/- mice which results in the death of over 80% of the litters nursed by Src-/- dams. Mammary gland development during pregnancy appears normal in these mice; however secretory activation does not seem to occur. Serum prolactin levels are normal in Src-/- mice compared to wildtype controls. Expression of the prolactin receptor at both the RNA and protein level was decreased in Src-/- mice following the transition from pregnancy to lactation, as was phosphorylation of STAT5 and expression of milk protein genes. These results suggest that secretory activation, which occurs following parturition, does not occur completely in Src-/- mice. Failed secretory activation results in precocious involution in the mammary glands of Src-/- even when pups were suckling. Involution was accelerated following pup withdrawal perhaps as a result of incomplete secretory activation. In vitro differentiation of mammary epithelial cells from Src-/- mice resulted in diminished production of milk proteins compared to the amount of milk proteins produced by Src+/+ cells, indicating a direct role for Src in regulating the transcription/translation of milk protein genes in mammary epithelial cells.</p> <p>Conclusion</p> <p>Src is an essential signaling modulator in mammary gland development as Src-/- mice exhibit a block in secretory activation that results in lactation failure and precocious involution. Src appears to be required for increased expression of the prolactin receptor and successful downstream signaling, and alveolar cell organization.</p
Fixed Points of Hopfield Type Neural Networks
The set of the fixed points of the Hopfield type network is under
investigation. The connection matrix of the network is constructed according to
the Hebb rule from the set of memorized patterns which are treated as distorted
copies of the standard-vector. It is found that the dependence of the set of
the fixed points on the value of the distortion parameter can be described
analytically. The obtained results are interpreted in the terms of neural
networks and the Ising model.Comment: RevTEX, 19 pages, 2 Postscript figures, the full version of the
earler brief report (cond-mat/9901251
Retarded Learning: Rigorous Results from Statistical Mechanics
We study learning of probability distributions characterized by an unknown
symmetry direction. Based on an entropic performance measure and the
variational method of statistical mechanics we develop exact upper and lower
bounds on the scaled critical number of examples below which learning of the
direction is impossible. The asymptotic tightness of the bounds suggests an
asymptotically optimal method for learning nonsmooth distributions.Comment: 8 pages, 1 figur
Double-Blind Controlled Assessment of the Effect of Intra-Articular Hydrocortisone and Urokinase in Rheumatoid Arthritis
The merits of intra-articular urokinase in the treatment of rheumatoid arthritis are discussed. The results of a double-blind controlled study of its use in the knee joints following intra-articular hydrocortisone are presented
Statistical Mechanics of Soft Margin Classifiers
We study the typical learning properties of the recently introduced Soft
Margin Classifiers (SMCs), learning realizable and unrealizable tasks, with the
tools of Statistical Mechanics. We derive analytically the behaviour of the
learning curves in the regime of very large training sets. We obtain
exponential and power laws for the decay of the generalization error towards
the asymptotic value, depending on the task and on general characteristics of
the distribution of stabilities of the patterns to be learned. The optimal
learning curves of the SMCs, which give the minimal generalization error, are
obtained by tuning the coefficient controlling the trade-off between the error
and the regularization terms in the cost function. If the task is realizable by
the SMC, the optimal performance is better than that of a hard margin Support
Vector Machine and is very close to that of a Bayesian classifier.Comment: 26 pages, 12 figures, submitted to Physical Review
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