Language acquisition and machine learning

In this paper, we review recent progress in the field of machine learning and examine its implications for computational models of language acquisition. As a framework for understanding this research, we propose four component tasks involved in learning from experience - aggregation, clustering, characterization, and storage. We then consider four common problems studied by machine learning researchers - learning from examples, heuristics learning, conceptual clustering, and learning macro-operators - describing each in terms of our framework. After this, we turn to the problem of grammar acquisition, relating this problem to other learning tasks and reviewing four AI systems that have addressed the problem. Finally, we note some limitations of the earlier work and propose an alternative approach to modeling the mechanisms underlying language acquisition

Machine learning : techniques and foundations

The field of machine learning studies computational methods for acquiring new knowledge, new skills, and new ways to organize existing knowledge. In this paper we present some of the basic techniques and principles that underlie AI research on learning, including methods for learning from examples, learning in problem solving, learning by analogy, grammar acquisition, and machine discovery. In each case, we illustrate the techniques with paradigmatic examples

Smoothing Proximal Gradient Method for General Structured Sparse Learning

We study the problem of learning high dimensional regression models regularized by a structured-sparsity-inducing penalty that encodes prior structural information on either input or output sides. We consider two widely adopted types of such penalties as our motivating examples: 1) overlapping group lasso penalty, based on the l1/l2 mixed-norm penalty, and 2) graph-guided fusion penalty. For both types of penalties, due to their non-separability, developing an efficient optimization method has remained a challenging problem. In this paper, we propose a general optimization approach, called smoothing proximal gradient method, which can solve the structured sparse regression problems with a smooth convex loss and a wide spectrum of structured-sparsity-inducing penalties. Our approach is based on a general smoothing technique of Nesterov. It achieves a convergence rate faster than the standard first-order method, subgradient method, and is much more scalable than the most widely used interior-point method. Numerical results are reported to demonstrate the efficiency and scalability of the proposed method.Comment: arXiv admin note: substantial text overlap with arXiv:1005.471

Antiproton-Hydrogen annihilation at sub-kelvin temperatures

The main properties of the interaction of ultra low-energy antiprotons ($E\le10^{-6}$ a.u.) with atomic hydrogen are established. They include the elastic and inelastic cross sections and Protonium (Pn) formation spectrum. The inverse Auger process ($Pn+e \to H+\bar{p}$) is taken into account in the framework of an unitary coupled-channels model. The annihilation cross-section is found to be several times smaller than the predictions made by the black sphere absorption models. A family of $\bar{p}H$ nearthreshold metastable states is predicited. The dependence of Protonium formation probability on the position of such nearthreshold S-matrix singularities is analysed. An estimation for the $H\bar{H}$ annihilation cross section is obtained.Comment: latex.tar.gz file, 22 pages, 9 figure

Spatial and temporal variability of CO2 emisions in soils under conventional tillage and no-till farming

Agricultural soils can act as a carbon sink depending on the soil management practices employed. As a result of this functional duality, soil management systems are present in international documents relating to climate change mitigation. Agricultural practices are responsible for 14% of total greenhouse gas emissions (GHG’s) (MMA, 2009)(1). Conservation agriculture (CA) is one of the most effective agricultural systems for reducing CO2 emissions, as it increases the sequestration of atmospheric carbon in the soil. In order to assess the performance of CA in terms of CO2 emissions, a field trial was conducted comparing soil derived CO2 fluxes under No-till (NT) farming and under conventional tillage. Three pilot farms were selected in the cereal-growing area of southern Spain, located in Las Cabezas de San Juan (Seville), Carmona (Seville) and Cordoba. Each pilot farm comprises six experimental plots with an approximate area of five hectares; three of the six plots implement CA practices, while the other three use conventional tillage techniques. The subdivision of each tillage system into 3 plots allowed the simultaneous cropping of the three crops of the wheat-sunflower-legume rotation each year. Results showed that carbon dioxide emissions were 31 to 91% higher in tilled soils than in untilled soils, and that there was a great seasonal variability of CO2 emissions, as weather conditions also differed considerably for the different sampling periods. In all cases, the CO2 fluxes emitted into the atmosphere were always higher when soil was subject to conventional tillage