FINITE SIZE SCALING FOR FIRST ORDER TRANSITIONS: POTTS MODEL

The finite-size scaling algorithm based on bulk and surface renormalization of de Oliveira (1992) is tested on q-state Potts models in dimensions D = 2 and 3. Our Monte Carlo data clearly distinguish between first- and second-order phase transitions. Continuous-q analytic calculations performed for small lattices show a clear tendency of the magnetic exponent Y = D - beta/nu to reach a plateau for increasing values of q, which is consistent with the first-order transition value Y = D. Monte Carlo data confirm this trend.Comment: 5 pages, plain tex, 5 EPS figures, in file POTTS.UU (uufiles

Hamiltonian symplectic embedding of the massive noncommutative U(1) Theory

We show that the massive noncommutative U(1) theory is embedded in a gauge theory using an alternative systematic way, which is based on the symplectic framework. The embedded Hamiltonian density is obtained after a finite number of steps in the iterative symplectic process, oppositely to the result proposed using the BFFT formalism. This alternative formalism of embedding shows how to get a set of dynamically equivalent embedded Hamiltonian densities.Comment: 16 pages, no figures, revtex4, corrected version, references additione

Transformer-based language models for semantic search and mobile applications retrieval

Search engines are being extensively used by Mobile App Stores, where millions of users world-wide use them every day. However, some stores still resort to simple lexical-based search engines, despite the recent advances in Machine Learning, Information Retrieval, and Natural Language Processing, which allow for richer semantic strategies. This work proposes an approach for semantic search of mobile applications that relies on transformer-based language models, fine-tuned with the existing textual information about known mobile applications. Our approach relies solely on the application name and on the unstructured textual information contained in its description. A dataset of about 500 thousand mobile apps was extended in the scope of this work with a test set, and all the available textual data was used to fine-tune our neural language models. We have evaluated our models using a public dataset that includes information about 43 thousand applications, and 56 manually annotated non- exact queries. The results show that our model surpasses the performance of all the other retrieval strategies reported in the literature. Tests with users have confirmed the performance of our semantic search approach, when compared with an existing deployed solution.info:eu-repo/semantics/acceptedVersio

Operatorial quantization of Born-Infeld Skyrmion model and hidden symmetries

The SU(2) collective coordinates expansion of the Born-Infeld\break Skyrmion Lagrangian is performed. The classical Hamiltonian is computed from this special Lagrangian in approximative way: it is derived from the expansion of this non-polynomial Lagrangian up to second-order variable in the collective coordinates. This second-class constrained model is quantized by Dirac Hamiltonian method and symplectic formalism. Although it is not expected to find symmetries on second-class systems, a hidden symmetry is disclosed by formulating the Born-Infeld Skyrmion %model as a gauge theory. To this end we developed a new constraint conversion technique based on the symplectic formalism. Finally, a discussion on the role played by the hidden symmetry on the computation of the energy spectrum is presented.Comment: A new version of hep-th/9901133. To appear in JP