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Computer-assisted automated synthesis. III. Synthesis of substituted N-(carboxyalkyl) amino-acid tert-butyl ester derivatives

A versatile automated synthesis apparatus, equipped with a chemical artificial intelligence, was developed to prepare and isolate a wide variety of compounds. The apparatus was to the synthesis of substituted N-(carboxyalkyl)amino-acids. The apparatus [1,2] is composed of units for performing various tasks,for example reagent supply, reaction, purification and separation, each linked to a control system. All synthetic processes, including washing and drying of the apparatus after each synthetic run, were automatically performed from the mixing of the reactants to the isolation of the products as powders or crystals. The reaction of an amino-acid tertbutyl ester acetic acid salt with a 2-keto acid sodium salt produces an unstable intermediate, Schiff base, which is reduced with sodum cyanoborohydride to give a substituted N-(carboxyalkyl)aminoacid tert-butyl ester sodium salt. The equilibrium and the consecutive reactions were controlled by adding sodium cyanoborohydride using the artificial intelligence software, which contained novel kinetic equations [3] and substituent effects [4]

ESPnet-ONNX: Bridging a Gap Between Research and Production

In the field of deep learning, researchers often focus on inventing novel neural network models and improving benchmarks. In contrast, application developers are interested in making models suitable for actual products, which involves optimizing a model for faster inference and adapting a model to various platforms (e.g., C++ and Python). In this work, to fill the gap between the two, we establish an effective procedure for optimizing a PyTorch-based research-oriented model for deployment, taking ESPnet, a widely used toolkit for speech processing, as an instance. We introduce different techniques to ESPnet, including converting a model into an ONNX format, fusing nodes in a graph, and quantizing parameters, which lead to approximately 1.3-2$\times$ speedup in various tasks (i.e., ASR, TTS, speech translation, and spoken language understanding) while keeping its performance without any additional training. Our ESPnet-ONNX will be publicly available at https://github.com/espnet/espnet_onnxComment: Accepted to APSIPA ASC 202