Given the benefits of its low storage requirements and high retrieval efficiency, hashing has recently received increasing attention. In particular, cross-modal hashing has been widely and successfully used in multimedia similarity search applications. However, almost all existing methods employing cross-modal hashing cannot obtain powerful hash codes due to their ignoring the relative similarity between heterogeneous data that contains richer semantic information, leading to unsatisfactory retrieval performance. In this paper, we propose a tripletbased deep hashing (TDH) network for cross-modal retrieval. First, we utilize the triplet labels, which describes the relative relationships among three instances as supervision in order to capture more general semantic correlations between cross-modal instances. We then establish a loss function from the inter-modal view and the intra-modal view to boost the discriminative abilities of the hash codes. Finally, graph regularization is introduced into our proposed TDH method to preserve the original semantic similarity between hash codes in Hamming space. Experimental results show that our proposed method outperforms several state-of-the-art approaches on two popular cross-modal datasets.

PANI nanotubes with diameters of 100–150 nm with conductivities of single nanotubes as high as 30.6 S · cm−1 were directly oxidized with APS in the absence of hard templates and acidic dopants. The high conductivity was shown to result from protons produced during the polymerization. The pH decreases with increasing polymerization time, and the morphology changes from hollow spheres to short and long tubes. It is proposed that the micelles formed by the aniline monomer in aqueous solution serve as a soft template for forming hollow spheres at the initial stage. As the polymerization proceeds, these hollow spheres are linearly aggregated and elongated to form short and long tubes.magnified image

A multiwalled carbon nanotube∕polyaniline composite with cablelike morphology has been synthesized by an in situ chemical oxidative polymerization directed with cationic surfactant cetyltrimethylammonium bromide. It is interestingly found that with increasing carbon nanotube loading from 0 to 24.8wt%, the conductivity increases by two orders of magnitude and the Mott’s characteristic temperature T0 which depends on the hopping barrier decreases by three orders of magnitude. Furthermore, the low-temperature magnetoresistance has also changed the sign from positive to negative. The results reveal a strong coupling between the carbon nanotube and the tightly coated polymer chains, which enhances the average localization length and the electronic properties of the composites.

We report the electrical properties of a single conducting polyaniline nanotube measured by a standard four-terminal technique. Camphor sulfonic acid doped polyaniline nanotubes were self-assembled by a template-free method. The directly measured conductivity of the single polyaniline nanotube is very high (∼31.4 S/cm), and its temperature dependence follows the three-dimensional variable range hopping model. However, the bulk conductivity of the polyaniline nanotube pellets is much smaller than the nanotube itself (only 3.5×10−2 S/cm) and ln ρ(T) is linear in T−1/2, which is due to the large intertubular contact resistance. These results will help us to understand the conduction mechanism in conducting polymers.