Akanksha puri biography sample

  • Experience: Ad_astra official · Education: Smt.Indira Gandhi College of.
  • Experience: Cognizant · Education: Jaypee Institute Of Information Technology · Location: Pune · 500+ connections on LinkedIn.
  • Two cases demonstrate that full reliance on biological stains may lead to either wrongful convictions or no prosecution at all.

    • •

    Abstract

    Chronic kidney disease pathogenesis involves both tubular and vascular injuries. Despite abundant investigations to identify the risk factors, the involvement of chronic endothelial dysfunction in developing nephropathies is insufficiently explored. Previously, soluble thrombomodulin (sTM), a cofactor in the activation of protein C, has been shown to protect endothelial function in models of acute kidney injury. In this study, the role for sTM in treating chronic kidney disease was explored by employing a mouse model of chronic vascular activation using endothelial-specific TNF-α-expressing (tie2-TNF) mice. Analysis of kidneys from these mice after 3 mo showed no apparent phenotype, whereas 6-mo-old mice demonstrated infiltration of CD45-positive leukocytes accompanied by upregulated gene expression of inflammatory chemokines, markers of kidney injury, and albuminuria. Intervention with murine sTM with biweekly subcutaneous injections during this window of disease developm

    •

    Synthetic Multimodal Question Generation

    Ian Wu∗♢  Sravan Jayanthi∗♢  Vijay Viswanathan♣  Simon Rosenberg♢\AND Sina Pakazad♢  Tongshuang Wu♣  Graham Neubig♣\AND

    ♢C3 AI   ♣Carnegie Mellon University
    {ian.wu, sravan.jayanthi, simon.rosenberg, sina.pakazad}@c3.ai
    {sherryw, gneubig}@cs.cmu.edu, vijayv@andrew.cmu.edu

    Abstract

    Multimodal Retrieval Augmented Generation (MMRAG) is a powerful approach to question-answering over multimodal documents. A key challenge with evaluating MMRAG is the paucity of high-quality datasets matching the question styles and modalities of interest. In light of this, we föreslå SMMQG, a synthetic data generation framework. SMMQG leverages interplay between a retriever, large language model (LLM) and large multimodal model (LMM) to generate question and answer pairs directly from multimodal documents, with the questions conforming to specified styles and modalities. We use SMMQG to generate an MMRAG dataset of 1024 questions over Wi

  • akanksha puri biography sample

    • •

    Akanksha Mehndiratta

    BIOGRAPHY

    I completed my B. Tech and M. Tech in the field of Computer Science and Engineering from Jaypee Institute of Information and Technology. My current research interests include Machine Learning and Artificial Intelligence .I participated in many International and National conference in India I have been actively involved in organizing various workshops at institute and department level.

    WORK EXPERIENCE (10+ Years in Academics)

    EDUCATION

    • Ph.D. (Computer Science & Engineering) Pursuing
    • M.Tech. (Computer Science and Engineering)
    • B.Tech.(Computer Science and Engineering)

    INTEREST AREA(S)

    • Artificial Intelligence
    • Natural Language Processing
    • Computational Linguistics

    PARTICIPATION/ORGANIZING MEMBER IN SEMINARS/ WORKSHOPS/COURSES

    Attended:

    • Two week AICTE Training And Learning (ATAL) Academy Blended/Hybrid FDP on "Applied Data Science with Machine Learning" in 2023.
    • Online one week Faculty Development Programme on “Full Stack Engine