INESC-ID

About: INESC-ID is a nonprofit organization based out in Lisbon, Portugal. It is known for research contribution in the topics: Computer science & Context (language use). The organization has 932 authors who have published 2618 publications receiving 37658 citations.

Matrix Converter-Based Active Distribution Transformer

Abstract: This paper proposes a matrix converter-based active distribution transformer (MC-ADT) with enhanced control functionalities to be used in smart grids (SG). The proposed MC-ADT uses a matrix converter (MC) connected to a transformer inserted in series with the grid, and allows: 1) real-time voltage regulation of the low-voltage side of the MC-ADT, based on an adjustable reference value defined by the SG requirements and bounded by the standard values; 2) capability to regulate the LV grid voltage even in case of sags, voltage rises, and overvoltages in the transformer medium-voltage side; and 3) contribution to power factor correction in the MV side. The MC-ADT grid voltage regulators are synthesized, establishing the MC reference currents. To guarantee the tracking of the MC input and output reference currents, the space vector representation, together with sliding-mode direct control techniques, are used. The obtained experimental and simulation results show that the proposed system is able to regulate the LV grid voltages even for sags and overvoltages up to 20% in the MV side, and contributes to power factor correction in MV, while presenting fast dynamic response, without overshoot and almost zero steady-state error.

Learning tractable word alignment models with complex constraints

Abstract: Word-level alignment of bilingual text is a critical resource for a growing variety of tasks. Probabilistic models for word alignment present a fundamental trade-off between richness of captured constraints and correlations versus efficiency and tractability of inference. In this article, we use the Posterior Regularization framework (Graca, Ganchev, and Taskar 2007) to incorporate complex constraints into probabilistic models during learning without changing the efficiency of the underlying model. We focus on the simple and tractable hidden Markov model, and present an efficient learning algorithm for incorporating approximate bijectivity and symmetry constraints. Models estimated with these constraints produce a significant boost in performance as measured by both precision and recall of manually annotated alignments for six language pairs. We also report experiments on two different tasks where word alignments are required: phrase-based machine translation and syntax transfer, and show promising improvements over standard methods.

Geometric Detection Algorithms for Cavities on Protein Surfaces in Molecular Graphics: A Survey

Abstract: Detecting and analyzing protein cavities provides significant information about active sites for biological processes (eg, protein-protein or protein-ligand binding) in molecular graphics and modeling Using the three-dimensional structure of a given protein (ie, atom types and their locations in 3D) as retrieved from a PDB (Protein Data Bank) file, it is now computationally viable to determine a description of these cavities Such cavities correspond to pockets, clefts, invaginations, voids, tunnels, channels, and grooves on the surface of a given protein In this work, we survey the literature on protein cavity computation and classify algorithmic approaches into three categories: evolution-based, energy-based, and geometry-based Our survey focuses on geometric algorithms, whose taxonomy is extended to include not only sphere-, grid-, and tessellation-based methods, but also surface-based, hybrid geometric, consensus, and time-varying methods Finally, we detail those techniques that have been customized for GPU (Graphics Processing Unit) computing

On Solving Boolean Multilevel Optimization Problems

Abstract: Many combinatorial optimization problems entail a number of hierarchically dependent optimization problems. An often used solution is to associate a suitably large cost with each individual optimization problem, such that the solution of the resulting aggregated optimization problem solves the original set of hierarchically dependent optimization problems. This paper starts by studying the package upgradeability problem in software distributions. Straightforward solutions based on Maximum Satisfiability (MaxSAT) and pseudo-Boolean (PB) optimization are shown to be ineffective, and unlikely to scale for large problem instances. Afterwards, the package upgradeability problem is related to multilevel optimization. The paper then develops new algorithms for Boolean Multilevel Optimization (BMO) and highlights a large number of potential applications. The experimental results indicate that the proposed algorithms for BMO allow solving optimization problems that existing MaxSAT and PB solvers would otherwise be unable to solve.