Computation-Performance Optimization of Convolutional Neural Networks with Redundant Kernel Removal

Deep Convolutional Neural Networks (CNNs) are widely employed in modern computer vision algorithms, where the input image is convolved iteratively by many kernels to extract the knowledge behind it. However, with the depth of convolutional layers getting deeper and deeper in recent years, the enormous computational complexity makes it difficult to be deployed on embedded systems with limited hardware resources. In this paper, we propose two computation-performance optimization methods to reduce the redundant convolution kernels of a CNN with performance and architecture constraints, and apply it to a network for super resolution (SR). Using PSNR drop compared to the original network as the performance criterion, our method can get the optimal PSNR under a certain computation budget constraint. On the other hand, our method is also capable of minimizing the computation required under a given PSNR drop.Comment: This paper was accepted by 2018 The International Symposium on Circuits and Systems (ISCAS

Alternative Ingredient Recommendation: A Co-occurrence and Ingredient Category Importance Based Approach

As many people will refer to a recipe when cooking, there are several recipe-sharing websites that include lots of recipes and make recipes easier to access than before. However, there is often the case that we could not get all the ingredients listed on the recipe. Prior research on alternative ingredient substitution has built a recommendation system considering the suitability of a recommended ingredient with the remained ingredients. In this paper, in addition to suitability, we also take the diversity of the ingredient categories and the novelty of new combination of ingredients into account. Besides, we combine suitability with novelty as an index, to see whether our method could help find out a new combination of ingredients that is possibly to be a new dish. Our evaluation results show that our proposed method attains a comparable or even better performance on each perspective

A General Procedure for the Regioselective Synthesis of Aryl Thioethers and Aryl Selenides Through C–H Activation of Arenes

A general procedure for the synthesis of aryl thioethers and aryl selenides in one-pot through sequential iridium-catalyzed C–H borylation and copper-promoted C–S and C–Se bond formation is described. Functional groups including chloro, nitro, fluoro, trifluoromethyl, and nitrogen-containing heterocycles were all tolerated under the reaction conditions. Importantly, not only aryl thiols and selenides but also their alkyl analogs were suitable coupling partners, and the products were obtained in good yields with high meta regioselectivity

Manganese-Catalyzed Cross-Coupling of Thiols with Aryl Iodides

Here we report the manganesecatalyzedcoupling reaction of thiols witharyl iodides, giving the aryl thioethers ingood to excellent yields; the system showsgood functional group tolerance and enablesthe sterically demanding aryl iodides tocouple with thiols