Show, Attend and Read: A Simple and Strong Baseline for Irregular Text Recognition

Recognizing irregular text in natural scene images is challenging due to the large variance in text appearance, such as curvature, orientation and distortion. Most existing approaches rely heavily on sophisticated model designs and/or extra fine-grained annotations, which, to some extent, increase the difficulty in algorithm implementation and data collection. In this work, we propose an easy-to-implement strong baseline for irregular scene text recognition, using off-the-shelf neural network components and only word-level annotations. It is composed of a $31$-layer ResNet, an LSTM-based encoder-decoder framework and a 2-dimensional attention module. Despite its simplicity, the proposed method is robust and achieves state-of-the-art performance on both regular and irregular scene text recognition benchmarks. Code is available at: https://tinyurl.com/ShowAttendReadComment: Accepted to Proc. AAAI Conference on Artificial Intelligence 201

New Negentropy Optimization Schemes for Blind Signal Extraction of Complex Valued Sources

Blind signal extraction, a hot issue in the field of communication signal processing, aims to retrieve the sources through the optimization of contrast functions. Many contrasts based on higher-order statistics such as kurtosis, usually behave sensitive to outliers. Thus, to achieve robust results, nonlinear functions are utilized as contrasts to approximate the negentropy criterion, which is also a classical metric for non-Gaussianity. However, existing methods generally have a high computational cost, hence leading us to address the problem of efficient optimization of contrast function. More precisely, we design a novel “reference-based” contrast function based on negentropy approximations, and then propose a new family of algorithms (Alg.1 and Alg.2) to maximize it. Simulations confirm the convergence of our method to a separating solution, which is also analyzed in theory. We also validate the theoretic complexity analysis that Alg.2 has a much lower computational cost than Alg.1 and existing optimization methods based on negentropy criterion. Finally, experiments for the separation of single sideband signals illustrate that our method has good prospects in real-world applications

3,3′-(p-Phenyl­enedimethyl­ene)di­imidazol-1-ium bis­(3-carb­oxy-4-hydroxy­benzene­sulfonate) dihydrate

In the title compound, C14H16N4 2+·2C7H5O6S−·2H2O, the 3,3′-(p-phenyl­enedimethyl­ene)diimidazol-1-ium dication lies on a crystallographic inversion center. In the crystal structure, dications, anions and solvent water mol­ecules are linked via O—H⋯O, N—H⋯O and C—H⋯O hydrogen bonds, and C—H⋯π inter­actions, forming a three-dimensional network containing R 2 2(4), R 2 4(12), R 4 4(22), R 8 10(32) and R 12 14(66) ring motifs