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Sketch-based cross-domain visual data retrieval is the process of searching for images or 3D models using sketches as input. Achieving feature alignment is a significantly challenging task due to the high heterogeneity of cross-domain data. However, the alignment process faces significant challenges, such as domain gap, semantic gap, and knowledge gap. The existing methods adopt different ideas for sketch-based image and 3D shape retrieval tasks, one is domain alignment, and the other is semantic alignment. Technically, both tasks verify the accuracy of extracted features. Hence, we propose a method based on the global feature correlation and the feature similarity for multiple sketch-based cross-domain retrieval tasks. Specifically, the data from various modalities are fed into separate feature extractors to generate original features. Then, these features are projected to the shared subspace. Finally, domain consistency learning, semantic consistency learning, feature correlation learning and feature similarity learning are performed jointly to make the projected features modality-invariance. We evaluate our method on multiple benchmark datasets. Where the MAP in Sketchy, TU-Berlin, SHREC 2013 and SHREC 2014 are 0.466, 0.473, 0.860 and 0.816. The extensive experimental results demonstrate the superiority and generalization of the proposed method, compared to the state-of-the-art approaches. The in-depth analyses of various design choices are also provided to gain insight into the effectiveness of the proposed method. The outcomes of this research contribute to advancing the field of sketch-based cross-domain visual data retrieval and are expected to be applied to a variety of applications that require efficient retrieval of cross-domain domain data.

An abundant supply of fresh water is one of the leading challenges of the 21st century (UNESCO. The United Nations World Water Development Report 2018: Nature-Based Solutions for Water;UNESCO: Paris, France, 2018; p 154). Here we describe a new approach to scavenge atmospheric water that employs a hierarchically ordered porous material with embedded particles (Lash, M. H.; Jordan, J. C.; Blevins, L. C.; Fedorchak, M. V.; Little, S. R.; McCarthy, J. J.Non-Brownian Particle-Based Materials with Microscale and Nanoscale Hierarchy. Ang. Chem. Int. Ed.201554, 5854−5858). This composite uses structure to amplify native material performance to realize synergy between the capture and storage and to ultimately qualitatively change the adsorption behavior of the hydrogel (from unfavorable to favorable). In this way we can capture moisture at significantly lower relative humidities than would otherwise be feasible with the native materials. Not only does this approach pose the potential for a cheap and low-energy source of clean water but it could also be modified for application across a variety of condensable vapor reclamations.