Two-dimensional Lorentz-violating Chern-Simons-like action

We demonstrate generation of the two-dimensional Chern-Simons-like Lorentz-breaking action via an appropriate Lorentz-breaking coupling of scalar and spinor fields at zero as well as at the finite temperature and via the noncommutative fields method and study the dispersion relations corresponding to this action.Comment: 10 pages, references added, version accepted to Physics Letters

Grandparents and women's participation in the labor market

The conciliation of work and family life is a challenge to most women. In some countries, although not in southern Europe, women make significant use of part-time schedules as a way of balancing work and family life. Informal care, typically care by grandparents, is an alternative. It is cheap, trustworthy, and possibly compatible with non-standard labor schedules. In this paper we investigate how childcare by grandparents affects the probability of working of mothers in southern European countries. We empirically evaluate the verification and the significance of such an effect, accounting for a potentially endogenous grandparent-caring status.labor market, women, childcare, grandparents, ageing.

Comment on "Energies of the ground state and first excited 0+0^{+} in an exactly solvable pairing model"

We comment on a recent application of the RPA method and its extensions to the case of the two-level pairing model by N. Dinh Dang [1].Comment: 5 pages, 1 figure, submitted to EPJ

Lexicon Infused Phrase Embeddings for Named Entity Resolution

Most state-of-the-art approaches for named-entity recognition (NER) use semi supervised information in the form of word clusters and lexicons. Recently neural network-based language models have been explored, as they as a byproduct generate highly informative vector representations for words, known as word embeddings. In this paper we present two contributions: a new form of learning word embeddings that can leverage information from relevant lexicons to improve the representations, and the first system to use neural word embeddings to achieve state-of-the-art results on named-entity recognition in both CoNLL and Ontonotes NER. Our system achieves an F1 score of 90.90 on the test set for CoNLL 2003---significantly better than any previous system trained on public data, and matching a system employing massive private industrial query-log data.Comment: Accepted in CoNLL 201