Monoclinic and Correlated Metal Phase in VO_2 as Evidence of the Mott Transition: Coherent Phonon Analysis

In femtosecond pump-probe measurements, the appearance of coherent phonon oscillations at 4.5 THz and 6.0 THz indicating the rutile metal phase of VO_2 does not occur simultaneously with the first-order metal-insulator transition (MIT) near 68^oC. The monoclinic and correlated metal(MCM) phase between the MIT and the structural phase transition (SPT) is generated by a photo-assisted hole excitation which is evidence of the Mott transition. The SPT between the MCM phase and the rutile metal phase occurs due to subsequent Joule heating. The MCM phase can be regarded as an intermediate non-equilibrium state.Comment: 4 pages, 2 figure

Genomic diversity among Basmati rice (Oryza sativa L) mutants obtained through 60Co gamma radiations using AFLP markers

Mutation breeding can be considered successful in obtaining new cultivars and broadening the genetic base of rice crop. In order to obtain new varieties of rice with improved agronomic and grain characteristics, gamma radiation (60Co) has been used to generate novel mutants of the Basmati rice. In this study rice cultivars; Basmati-370 and Basmati-Pak, were exposed to different doses of gamma radiations and stable mutants along with parents were studied for genomic diversity on the basis of molecular marker (AFLP). Morphological data showed that mutants of Basmati-370 performed well for yield and yield components and grain physical parameters whereas, the mutant EL-30-2-1 has extra long rain trait as compared to the parent (Basmati-Pak). The genetic variations determined through AFLP revealed a total of 282 scorable bands, out of which 108 (37.81%) were polymorphic. The number of fragments produced by various primers combinations ranged from 11 - 26 with an average of 17.63fragments per primer combination. Maximum 26 bands were amplified with P-AAG/M-CAG primer combination and minimum one band was amplified with P-ATG/M-CTA primer combination. Two groups of genotypes were detected; group-A had DM-1-30-3-99, DM-1-30-34-99 and EF-1-20-52-04 mutants along with parent Basmati-370, whereas the group-B contained EL-30-2-1 and parent Basmati-Pak. The results of AFLP analysis indicated that the rate of polymorphism was 4.43% (DM-1-30-3-99), 4.25% (DM-1-30-34-99) and 6.38% (EF-1-20-52-04) among the genomes of mutants and parent Basmati-370, respectively, whereas polymorphism rate was 5.32% between genome of EL-30-2-1 and Basmati-Pak. The study further confirmed that the use of gamma radiations is an effective approach for creating new rice germplasm

Pipelines for Procedural Information Extraction from Scientific Literature: Towards Recipes using Machine Learning and Data Science

This paper describes a machine learning and data science pipeline for structured information extraction from documents, implemented as a suite of open-source tools and extensions to existing tools. It centers around a methodology for extracting procedural information in the form of recipes, stepwise procedures for creating an artifact (in this case synthesizing a nanomaterial), from published scientific literature. From our overall goal of producing recipes from free text, we derive the technical objectives of a system consisting of pipeline stages: document acquisition and filtering, payload extraction, recipe step extraction as a relationship extraction task, recipe assembly, and presentation through an information retrieval interface with question answering (QA) functionality. This system meets computational information and knowledge management (CIKM) requirements of metadata-driven payload extraction, named entity extraction, and relationship extraction from text. Functional contributions described in this paper include semi-supervised machine learning methods for PDF filtering and payload extraction tasks, followed by structured extraction and data transformation tasks beginning with section extraction, recipe steps as information tuples, and finally assembled recipes. Measurable objective criteria for extraction quality include precision and recall of recipe steps, ordering constraints, and QA accuracy, precision, and recall. Results, key novel contributions, and significant open problems derived from this work center around the attribution of these holistic quality measures to specific machine learning and inference stages of the pipeline, each with their performance measures. The desired recipes contain identified preconditions, material inputs, and operations, and constitute the overall output generated by our computational information and knowledge management (CIKM) system.Comment: 15th International Conference on Document Analysis and Recognition Workshops (ICDARW 2019