Optical resolution of racemic mixtures of arginine and alanine was performed by chiral selective nanofiltration membrane. The chiral selective layer of the membrane was prepared by interfacial polymerization of metaphenylenediamine, trimesoyl chloride, and S (-)-2-acetoxypropionyl chloride (S-2-actoxpcl) in situ on the top of polysulfone ultrafiltration membrane. S-2-actoxpcl consists of a chiral carbon atom that has an induced chiral environment in the membrane. The membranes were characterized by FTIR, scanning electron microscopy, and atomic force microscopy to establish a structure-performance relationship. The optical resolution was performed on the membrane testing module and the effect of process parameters was determined. The results indicated that the incorporation of S-2-actoxpcl made membrane chiral selective hence membranes performed optical resolution. The resolution capacity increased by increasing S-2-actoxpcl in polymerizing solution up to 0.03% but any increase beyond 0.03% reduces the resolution capacity. More than 92% enantiomeric excess of D enantiomer was observed in the permeate of the membrane which was prepared from 0.07% trimesoyl chloride and 0.03% S-2-actoxpcl. The membrane prepared without the chain terminator exhibited less volumetric flux but more solute rejection compared to those prepared with the chain terminator. The flux of the membrane increases as the amount of the chain terminator in the reaction increases.

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Social media conversations unfold based on complex interactions between users, topics and time. While recent models have been proposed to capture network strengths between users, users' topical preferences and temporal patterns between posting and response times, interaction patterns between topics has not been studied. We argue that social media conversations naturally involve interacting rather than independent topics. Modeling such topical interaction patterns can additionally help in inference of latent variables in the data such as diffusion parents and topics of events. We propose the Hidden Markov Hawkes Process (HMHP) that incorporates topical Markov Chains within Hawkes processes to jointly model topical interactions along with useruser and user-topic patterns. We propose a Gibbs sampling algorithm for HMHP that jointly infers the network strengths, diffusion paths, the topics of the posts as well as the topictopic interactions. We show using experiments on real and semisynthetic data that HMHP is able to generalize better and recover the network strengths, topics and diffusion paths more accurately that state-of-the-art baselines. More interestingly, HMHP finds insightful interactions between topics in real tweets which no existing model is able to do. This can potentially lead to actionable insights enabling, e.g., user targeting for influence maximization.

India is an agro-based economy and proper information about agricultural practices is the key to optimal agricultural growth and output. In order to answer the queries of the farmer, we have build an agricultural chatbot based on the dataset from Kisan Call Center. This system is robust enough to answer queries related to weather, market rates, plant protection and government schemes. This system is available 24*7, can be accessed through any electronic device and the information is delivered with the ease of understanding. The system is based on a sentence embedding model which gives an accuracy of 56%. After eliminating synonyms and incorporating entity extraction, the accuracy jumps to 86%. With such a system, farmers can progress towards easier information about farming related practices and hence a better agricultural output. The job of the Call Center workforce would be made easier and the hard work of various such workers can be redirected to a better goal.

A biography of a person is the detailed description of several life events including his education, work, relationships and death. Wikipedia, the free web-based encyclopedia, consists of millions of manually curated biographies of eminent politicians, lm and sports personalities, etc. However, manual curation e orts, even though e cient, su ers from signi cant delays. In this work, we propose an automatic biography generation framework BioGen. BioGen generates a short collection of biographical sentences clustered into multiple events of life. Evaluation results show that biographies generated by BioGen are signi cantly closer to manually wri en biographies in Wikipedia. A working model of this framework is available at nlpbiogen.herokuapp.com/ home/ CCS CONCEPTS•Arti cial intelligence →Natural language processing;