Nearest neighbor search is a fundamental problem in various research fields like machine learning, data mining and pattern recognition. Recently, hashing-based approaches, for example, locality sensitive hashing (LSH), are proved to be effective for scalable high dimensional nearest neighbor search. Many hashing algorithms found their theoretic root in random projection. Since these algorithms generate the hash tables (projections) randomly, a large number of hash tables (i.e., long codewords) are required in order to achieve both high precision and recall. To address this limitation, we propose a novel hashing algorithm called density sensitive hashing (DSH) in this paper. DSH can be regarded as an extension of LSH. By exploring the geometric structure of the data, DSH avoids the purely random projections selection and uses those projective functions which best agree with the distribution of the data. Extensive experimental results on real-world data sets have shown that the proposed method achieves better performance compared to the state-of-the-art hashing approaches.

Aircraft icing refers to ice formation and accumulation on the windward surface of aircrafts. It is mainly caused by the striking of unstable supercooled water droplets suspended in clouds onto a solid surface. Aircraft icing poses an increasing threat to the safety of flight due to the damage of aerodynamic shape. This review article provides a comprehensive understanding of the preparation and anti-icing applications of the superhydrophobic coatings applied on the surface of aircrafts. The first section introduces the hazards of aircraft icing and the underlying formation mechanisms of ice on the surface of aircrafts. Although some current anti-icing and de-icing strategies have been confirmed to be effective, they consume higher energy and lead to some fatigue damages to the substrate materials. Considering the icing process, the functional coatings similar to lotus leaf with extreme water repellency and unusual self-cleaning properties have been proposed and are expected to reduce the relied degree on traditional de-icing approaches and even to replace them in near future. The following sections mainly discuss the current research progress on the wetting theories of superhydrophobicity and main methods to prepare superhydrophobic coatings. Furthermore, based on the bouncing capacity of impact droplets, the dynamic water repellency of superhydrophobic coatings is discussed as the third evaluated parameter. It is crucial to anti-icing applications because it describes the ability of droplets to rapidly bounce off before freezing. Subsequently, current studies on the application of anti-icing superhydrophobic coatings including the anti-icing mechanisms and application status are introduced in detail. Finally, some limitations and issues related to the anti-icing applications are proposed to provide a future outlook on investigations of the superhydrophobic anti-icing coatings.

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Recently, hashing techniques have been widely applied to solve the approximate nearest neighbors search problem in many vision applications. Generally, these hashing approaches generate 2 c buckets, where c is the length of the hash code. A good hashing method should satisfy the following two requirements: 1) mapping the nearby data points into the same bucket or nearby (measured by the Hamming distance) buckets. 2) all the data points are evenly distributed among all the buckets. In this paper, we propose a novel algorithm named Complementary Projection Hashing (CPH) to find the optimal hashing functions which explicitly considers the above two requirements. Specifically, CPH aims at sequentially finding a series of hyperplanes (hashing functions) which cross the sparse region of the data. At the same time, the data points are evenly distributed in the hypercubes generated by these hyperplanes. The experiments comparing with the state-of-the-art hashing methods demonstrate the effectiveness of the proposed method.1 The corresponding binary hash bit can be simply computed as: y k (x) = (1 + h k (x))/2.