13C n.m.r. investigation on the nitrogen methylation of some azabenzenes

The 1H and 13C n.m.r. spectra of N-methylated pyridine, pyridazine, pyrimidine and pyrazine and N,N-dimethylated pyrimidine and pyrazine have been recorded and analysed. The change in the 13C chemical shifts under the influence of N-methylation (Δδ) in the diazabenzenes could be predicted by the Δδ values of pyridine. A comparison of the Δδ values of N-methylation with those of N-protonation showed that both reactions have a similar effect

Theoretical study on the protonation of AZA-aromatics

The protonation of azanaphthalenes and azabenzenes has been studied theoretically using CNDO/2 wavefunctions and perturbation theory in order to examine the correlation between pKa values and quantum-mechanical quantities

Weakly Supervised Domain-Specific Color Naming Based on Attention

The majority of existing color naming methods focuses on the eleven basic color terms of the English language. However, in many applications, different sets of color names are used for the accurate description of objects. Labeling data to learn these domain-specific color names is an expensive and laborious task. Therefore, in this article we aim to learn color names from weakly labeled data. For this purpose, we add an attention branch to the color naming network. The attention branch is used to modulate the pixel-wise color naming predictions of the network. In experiments, we illustrate that the attention branch correctly identifies the relevant regions. Furthermore, we show that our method obtains state-of-the-art results for pixel-wise and image-wise classification on the EBAY dataset and is able to learn color names for various domains.Comment: Accepted at ICPR201

Exploiting Unlabeled Data in CNNs by Self-supervised Learning to Rank

For many applications the collection of labeled data is expensive laborious. Exploitation of unlabeled data during training is thus a long pursued objective of machine learning. Self-supervised learning addresses this by positing an auxiliary task (different, but related to the supervised task) for which data is abundantly available. In this paper, we show how ranking can be used as a proxy task for some regression problems. As another contribution, we propose an efficient backpropagation technique for Siamese networks which prevents the redundant computation introduced by the multi-branch network architecture. We apply our framework to two regression problems: Image Quality Assessment (IQA) and Crowd Counting. For both we show how to automatically generate ranked image sets from unlabeled data. Our results show that networks trained to regress to the ground truth targets for labeled data and to simultaneously learn to rank unlabeled data obtain significantly better, state-of-the-art results for both IQA and crowd counting. In addition, we show that measuring network uncertainty on the self-supervised proxy task is a good measure of informativeness of unlabeled data. This can be used to drive an algorithm for active learning and we show that this reduces labeling effort by up to 50%.Comment: Accepted at TPAMI. (Keywords: Learning from rankings, image quality assessment, crowd counting, active learning). arXiv admin note: text overlap with arXiv:1803.0309