The flat limit of three dimensional asymptotically anti-de Sitter spacetimes

In order to get a better understanding of holographic properties of gravitational theories with a vanishing cosmological constant, we analyze in detail the relation between asymptotically anti-de Sitter and asymptotically flat spacetimes in three dimensions. This relation is somewhat subtle because the limit of vanishing cosmological constant cannot be naively taken in standard Fefferman-Graham coordinates. After reformulating the standard anti-de Sitter results in Robinson-Trautman coordinates, a suitably modified Penrose limit is shown to connect both asymptotic regimes.Comment: 11 pages revtex fil

Isolated factorizations and their applications in simplicial affine semigroups

We introduce the concept of isolated factorizations of an element of a commutative monoid and study its properties. We give several bounds for the number of isolated factorizations of simplicial affine semigroups and numerical semigroups. We also generalize $\alpha$-rectangular numerical semigroups to the context of simplicial affine semigroups and study their isolated factorizations. As a consequence of our results, we characterize those complete intersection simplicial affine semigroups with only one Betti minimal element in several ways. Moreover, we define Betti sorted and Betti divisible simplicial affine semigroups and characterize them in terms of gluings and their minimal presentations. Finally, we determine all the Betti divisible numerical semigroups, which turn out to be those numerical semigroups that are free for any arrangement of their minimal generators

Quid pro Quo: National Institutions and Sudden Stops in International Capital Movements

The paper explores the incidence of sudden stops in capital flows on the incentives for building national institutions that secure property rights in a world where sovereign defaults are possible equilibrium outcomes. Also thepaper builds upon the benchmark model of sovereign default and direct creditor sanctions by Obstfeld and Rogoff (1996). In their model it is in the debtor country’s interest to “tie its hands” and secure the property rights of lenders as much as possible because this enhances the credibility of the country’s romise to repay and prevents default altogether. It incorporate two key features of today’s international financial markets that are absent from the benchmark model: the possibility that lenders can trigger sudden stops in capital movements, and debt contracts in which lenders transfer resources to the country at the start of the period, which have to be repaid later. The papershows that under these conditions the advice “build institutions to secure repayment at all costs” may be very bad advice indeed.

Sparse Linear Models applied to Power Quality Disturbance Classification

Power quality (PQ) analysis describes the non-pure electric signals that are usually present in electric power systems. The automatic recognition of PQ disturbances can be seen as a pattern recognition problem, in which different types of waveform distortion are differentiated based on their features. Similar to other quasi-stationary signals, PQ disturbances can be decomposed into time-frequency dependent components by using time-frequency or time-scale transforms, also known as dictionaries. These dictionaries are used in the feature extraction step in pattern recognition systems. Short-time Fourier, Wavelets and Stockwell transforms are some of the most common dictionaries used in the PQ community, aiming to achieve a better signal representation. To the best of our knowledge, previous works about PQ disturbance classification have been restricted to the use of one among several available dictionaries. Taking advantage of the theory behind sparse linear models (SLM), we introduce a sparse method for PQ representation, starting from overcomplete dictionaries. In particular, we apply Group Lasso. We employ different types of time-frequency (or time-scale) dictionaries to characterize the PQ disturbances, and evaluate their performance under different pattern recognition algorithms. We show that the SLM reduce the PQ classification complexity promoting sparse basis selection, and improving the classification accuracy