Terahertz imaging with sub-wavelength resolution by femtosecond laser filament in air

Terahertz (THz) imaging provides cutting edge technique in biology, medical sciences and non-destructive evaluation. However, due to the long wavelength of the THz wave, the obtained resolution of THz imaging is normally a few hundred microns and is much lower than that of the traditional optical imaging. We introduce a sub-wavelength resolution THz imaging technique which uses the THz radiation generated by a femtosecond laser filament in air as the probe. This method is based on the fact that the femtosecond laser filament forms a waveguide for the THz wave in air. The diameter of the THz beam, which propagates inside the filament, varies from 20 {\mu}m to 50 {\mu}m, which is significantly smaller than the wavelength of the THz wave. Using this highly spatially confined THz beam as the probe, THz imaging with resolution as high as 20 {\mu}m (~{\lambda}/38) can be realized.Comment: 10 pages, 7 figure

MCDAN: a Multi-scale Context-enhanced Dynamic Attention Network for Diffusion Prediction

Information diffusion prediction aims at predicting the target users in the information diffusion path on social networks. Prior works mainly focus on the observed structure or sequence of cascades, trying to predict to whom this cascade will be infected passively. In this study, we argue that user intent understanding is also a key part of information diffusion prediction. We thereby propose a novel Multi-scale Context-enhanced Dynamic Attention Network (MCDAN) to predict which user will most likely join the observed current cascades. Specifically, to consider the global interactive relationship among users, we take full advantage of user friendships and global cascading relationships, which are extracted from the social network and historical cascades, respectively. To refine the model's ability to understand the user's preference for the current cascade, we propose a multi-scale sequential hypergraph attention module to capture the dynamic preference of users at different time scales. Moreover, we design a contextual attention enhancement module to strengthen the interaction of user representations within the current cascade. Finally, to engage the user's own susceptibility, we construct a susceptibility label for each user based on user susceptibility analysis and use the rank of this label for auxiliary prediction. We conduct experiments over four widely used datasets and show that MCDAN significantly overperforms the state-of-the-art models. The average improvements are up to 10.61% in terms of Hits@100 and 9.71% in terms of MAP@100, respectively

Managing polyglot systems metadata with hypergraphs

A single type of data store can hardly fulfill every end-user requirements in the NoSQL world. Therefore, polyglot systems use different types of NoSQL datastores in combination. However, the heterogeneity of the data storage models makes managing the metadata a complex task in such systems, with only a handful of research carried out to address this. In this paper, we propose a hypergraph-based approach for representing the catalog of metadata in a polyglot system. Taking an existing common programming interface to NoSQL systems, we extend and formalize it as hypergraphs for managing metadata. Then, we define design constraints and query transformation rules for three representative data store types. Furthermore, we propose a simple query rewriting algorithm using the catalog itself for these data store types and provide a prototype implementation. Finally, we show the feasibility of our approach on a use case of an existing polyglot system.Peer ReviewedPostprint (author's final draft

Exact and Consistent Interpretation for Piecewise Linear Neural Networks: A Closed Form Solution

Strong intelligent machines powered by deep neural networks are increasingly deployed as black boxes to make decisions in risk-sensitive domains, such as finance and medical. To reduce potential risk and build trust with users, it is critical to interpret how such machines make their decisions. Existing works interpret a pre-trained neural network by analyzing hidden neurons, mimicking pre-trained models or approximating local predictions. However, these methods do not provide a guarantee on the exactness and consistency of their interpretation. In this paper, we propose an elegant closed form solution named $OpenBox$ to compute exact and consistent interpretations for the family of Piecewise Linear Neural Networks (PLNN). The major idea is to first transform a PLNN into a mathematically equivalent set of linear classifiers, then interpret each linear classifier by the features that dominate its prediction. We further apply $OpenBox$ to demonstrate the effectiveness of non-negative and sparse constraints on improving the interpretability of PLNNs. The extensive experiments on both synthetic and real world data sets clearly demonstrate the exactness and consistency of our interpretation.Comment: KDD 201