The Fast and the Flexible: training neural networks to learn to follow instructions from small data

Learning to follow human instructions is a long-pursued goal in artificial intelligence. The task becomes particularly challenging if no prior knowledge of the employed language is assumed while relying only on a handful of examples to learn from. Work in the past has relied on hand-coded components or manually engineered features to provide strong inductive biases that make learning in such situations possible. In contrast, here we seek to establish whether this knowledge can be acquired automatically by a neural network system through a two phase training procedure: A (slow) offline learning stage where the network learns about the general structure of the task and a (fast) online adaptation phase where the network learns the language of a new given speaker. Controlled experiments show that when the network is exposed to familiar instructions but containing novel words, the model adapts very efficiently to the new vocabulary. Moreover, even for human speakers whose language usage can depart significantly from our artificial training language, our network can still make use of its automatically acquired inductive bias to learn to follow instructions more effectively

Experimental analysis of the rising damp by the comparison between different geometrical configurations: mono and multi-blocks of carparo and pietra leccese

The rising damp is the principal cause of the deterioration of the masonry in the existing constructions. Since carparo and pietra leccese are the most used materials in southern Italy, this study aims to underline the trend of the rising damp for the two building materials considering mono and multi- block configurations. All analyzes were developed with and without the influence of the Domodry® system

Orbital Optimization in the Density Matrix Renormalization Group, with applications to polyenes and \beta-carotene

In previous work we have shown that the Density Matrix Renormalization Group (DMRG) enables near-exact calculations in active spaces much larger than are possible with traditional Complete Active Space algorithms. Here, we implement orbital optimisation with the Density Matrix Renormalization Group to further allow the self-consistent improvement of the active orbitals, as is done in the Complete Active Space Self-Consistent Field (CASSCF) method. We use our resulting DMRGCASSCF method to study the low-lying excited states of the all-trans polyenes up to C24H26 as well as \beta-carotene, correlating with near-exact accuracy the optimised complete \pi-valence space with up to 24 active electrons and orbitals, and analyse our results in the light of the recent discovery from Resonance Raman experiments of new optically dark states in the spectrum.Comment: 16 pages, 8 figure

Renormalization-group at criticality and complete analyticity of constrained models: a numerical study

We study the majority rule transformation applied to the Gibbs measure for the 2--D Ising model at the critical point. The aim is to show that the renormalized hamiltonian is well defined in the sense that the renormalized measure is Gibbsian. We analyze the validity of Dobrushin-Shlosman Uniqueness (DSU) finite-size condition for the "constrained models" corresponding to different configurations of the "image" system. It is known that DSU implies, in our 2--D case, complete analyticity from which, as it has been recently shown by Haller and Kennedy, Gibbsianness follows. We introduce a Monte Carlo algorithm to compute an upper bound to Vasserstein distance (appearing in DSU) between finite volume Gibbs measures with different boundary conditions. We get strong numerical evidence that indeed DSU condition is verified for a large enough volume $V$ for all constrained models.Comment: 39 pages, teX file, 4 Postscript figures, 1 TeX figur

Kob-Andersen model: a non-standard mechanism for the glassy transition

We present new results reflecting the analogies between the Kob-Andersen model and other glassy systems. Studying the stability of the blocked configurations above and below the transition we also give arguments that supports their relevance for the glassy behaviour of the model. However we find, surprisingly, that the organization of the phase space of the system is different from the well known organization of other mean-field spin glasses and structural glasses.Comment: New reference added and one update

On the equivalence between stochastic baker's maps and two-dimensional spin systems

We show that there is a class of stochastic baker's transformations that is equivalent to the class of equilibrium solutions of two-dimensional spin systems with finite interaction. The construction is such that the equilibrium distribution of the spin lattice is identical to the invariant measure in the corresponding baker's transformation. We also find that the entropy of the spin system is up to a constant equal to the rate of entropy production in the corresponding stochastic baker's transformation. We illustrate the equivalence by deriving two stochastic baker's maps representing the Ising model at a temperature above and below the critical temperature, respectively. We calculate the invariant measure of the stochastic baker's transformation numerically. The equivalence is demonstrated by finding that the free energy in the baker system is in agreement with analytic results of the two-dimensional Ising model.Comment: 4 pages, 4 figure