Task-Driven Common Representation Learning via Bridge Neural Network

This paper introduces a novel deep learning based method, named bridge neural network (BNN) to dig the potential relationship between two given data sources task by task. The proposed approach employs two convolutional neural networks that project the two data sources into a feature space to learn the desired common representation required by the specific task. The training objective with artificial negative samples is introduced with the ability of mini-batch training and it's asymptotically equivalent to maximizing the total correlation of the two data sources, which is verified by the theoretical analysis. The experiments on the tasks, including pair matching, canonical correlation analysis, transfer learning, and reconstruction demonstrate the state-of-the-art performance of BNN, which may provide new insights into the aspect of common representation learning.Comment: To appear in AAAI-19 proceeding

Volume confinement induced microstructural transitions and property enhancements of supramolecular soft materials

The rheological properties of supramolecular soft functional materials are determined by the networks within the materials. This research reveals for the first time that the volume confinement during the formation of supramolecular soft functional materials will exert a significant impact on the rheological properties of the materials. A class of small molecular organogels formed by the gelation of N-lauroyl-L-glutamic acid din-butylamide (GP-1) in ethylene glycol (EG) and propylene glycol (PG) solutions were adopted as model systems for this study. It follows that within a confined space, the elasticity of the gel can be enhanced more than 15 times compared with those under un-restricted conditions. According to our optical microscopy observations and rheological measurements, this drastic enhancement is caused by the structural transition from a multi-domain network system to a single network system once the average size of the fiber network of a given material reaches the lowest dimension of the system. The understanding acquired from this work will provide a novel strategy to manipulate the network structure of soft materials, and exert a direct impact on the micro-engineering of such supramolecular materials in micro and nano scales