Enantioselective catalytic formation of polyfunctional molecules, such as non‐natural amino acids, is important for the efficient production of many chiral compounds. To this end, we present here the synthesis and evaluation of the catalytic activity of bifunctional peptide–thiourea organocatalysts. These hybrid organocatalysts consist of Pro‐Pro dipeptide and thiourea moiety connected via a 1,2‐diaminocyclohexane unit. These catalysts promoted challenging Mannich reaction between α‐imino esters and pyruvates, providing orthogonally protected oxo‐glutamate derivatives. The N‐tosyl‐protected imino ester, as the most active imine, was required to compensate for the poor reactivity of pyruvates. DFT calculations, NMR, and CD spectroscopy help elucidate the mode of action of these catalysts. Configuration of the dipeptide Pro‐Pro moiety is responsible for the sense of the stereoinduction of the Mannich reaction.
The article deals with genetic algorithms and their application in face identification. The purpose of the research is to develop a free and open-source facial composite system using evolutionary algorithms, primarily processes of selection and breeding. The initial testing proved higher quality of the final composites and massive reduction in the composites processing time. System requirements were specified and future research orientation was proposed in order to improve the results.
This paper aims at deeper exploration of the new field named auto-machine learning, as it shows promising results in specific machine learning tasks e.g. image classification. The following article is about to summarize the most successful approaches now available in the A.I. community. The automated machine learning method is very briefly described here, but the concept of automated task solving seems to be very promising, since it can significantly reduce expertise level of a person developing the machine learning model. We used Auto-Keras to find the best architecture on several datasets, and demonstrated several automated machine learning features, as well as discussed the issue deeper.
Abstract-Besides new technology, a huge volume of data in various form has been available for people. Image data represents a keystone of many research areas including medicine, forensic criminology, robotics and industrial automation, meteorology and geography as well as education. Therefore, obtaining specific information from image databases has become of great importance. Images as a special category of data differ from text data as in terms of their nature so in terms of storing and retrieving. Image Mining as a research field is an interdisciplinary area combining methodologies and knowledge of many branches including data mining, computer vision, image processing, image retrieval, statistics, recognition, machine learning, artificial intelligence etc. This review focuses researching the current image mining approaches and techniques aiming at widening the possibilities of facial image analysis. This paper aims at reviewing the current state of the IM as well as at describing challenges and identifying directions of the future research in the field.
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