Deep Binary Reconstruction for Cross-modal Hashing

With the increasing demand of massive multimodal data storage and organization, cross-modal retrieval based on hashing technique has drawn much attention nowadays. It takes the binary codes of one modality as the query to retrieve the relevant hashing codes of another modality. However, the existing binary constraint makes it difficult to find the optimal cross-modal hashing function. Most approaches choose to relax the constraint and perform thresholding strategy on the real-value representation instead of directly solving the original objective. In this paper, we first provide a concrete analysis about the effectiveness of multimodal networks in preserving the inter- and intra-modal consistency. Based on the analysis, we provide a so-called Deep Binary Reconstruction (DBRC) network that can directly learn the binary hashing codes in an unsupervised fashion. The superiority comes from a proposed simple but efficient activation function, named as Adaptive Tanh (ATanh). The ATanh function can adaptively learn the binary codes and be trained via back-propagation. Extensive experiments on three benchmark datasets demonstrate that DBRC outperforms several state-of-the-art methods in both image2text and text2image retrieval task.Comment: 8 pages, 5 figures, accepted by ACM Multimedia 201

Multiple field-of-view MCAO for a Large Solar Telescope: LOST simulations

In the framework of a 4m class Solar Telescope we studied the performance of the MCAO using the LOST simulation package. In particular, in this work we focus on two different methods to reduce the time delay error which is particularly critical in solar adaptive optics: a) the optimization of the wavefront reconstruction by reordering the modal base on the basis of the Mutual Information and b) the possibility of forecasting the wavefront correction through different approaches. We evaluate these techniques underlining pros and cons of their usage in different control conditions by analyzing the results of the simulations and make some preliminary tests on real data.Comment: 10 pages, 5 figures to be published in Adaptive Optics Systems II (Proceedings Volume) Proceedings of SPI

Neurons and symbols: a manifesto

We discuss the purpose of neural-symbolic integration including its principles, mechanisms and applications. We outline a cognitive computational model for neural-symbolic integration, position the model in the broader context of multi-agent systems, machine learning and automated reasoning, and list some of the challenges for the area of neural-symbolic computation to achieve the promise of effective integration of robust learning and expressive reasoning under uncertainty