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Tumblr, as a leading content provider and social media, attracts 371 million monthly visits, 280 million blogs and 53.3 million daily posts. The popularity of Tumblr provides great opportunities for advertisers to promote their products through sponsored posts. However, it is a challenging task to target specific demographic groups for ads, since Tumblr does not require user information like gender and ages during their registration. Hence, to promote ad targeting, it is essential to predict user's demography using rich content such as posts, images and social connections. In this paper, we propose graph based and deep learning models for age and gender predictions, which take into account user activities and content features. For graph based models, we come up with two approaches, network embedding and label propagation, to generate connection features as well as directly infer user's demography. For deep learning models, we leverage convolutional neural network (CNN) and multilayer perceptron (MLP) to prediction users' age and gender. Experimental results on real Tumblr daily dataset, with hundreds of millions of active users and billions of following relations, demonstrate that our approaches significantly outperform the baseline model, by improving the accuracy relatively by 81% for age, and the AUC and accuracy by 5\% for gender.

A strained dibenzoazacyclooctyne (DIBAC) derivative was introduced for the preparation of a rotaxane by strain-promoted azide–alkyne cycloaddition (SPAAC), also referred to as a copper-free click reaction. The DIBAC can efficiently act as a bulky reactive chain stopper to transform a pseudorotaxane architecture consisting of a diazo-functionalized dialkoxynaphthalene guest and a tetracationic cyclobis(paraquat-p-phenylene) (CBPQT4+) host into the corresponding [2]rotaxane. Furthermore, the use of the DIBAC is demonstrated to be limited to short rigid macrocycles, as it is unable to act as stopper for a rotaxane featuring a larger crown ether macrocyclic host.