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732 results on '"Agrawal, Neha"'

10. List of contributors

Author

Acharya, Amitabha, primary, Agrawal, Neha, additional, Ahlawat, Navin, additional, Bajpai, Sangeeta, additional, Banaspati, Atrayee, additional, Bathla, Manik, additional, Batish, Daizy Rani, additional, Bhavsar, Kalpesh V., additional, Chandimala, U.R., additional, Dhushane, S., additional, Gaur, Pooja, additional, Gautam, Ayushi, additional, Guleria, Praveen, additional, Gupta, Ipsa, additional, Hasan, Ziaul, additional, Husain, Arbab, additional, Jagtap, Uday P., additional, Jandrotia, Rupali, additional, Kalita, Debajit, additional, Kamboj, Monika, additional, Khanam, Afreen, additional, Khare, Puja, additional, Kumar, Vineet, additional, Lateef, Muneera, additional, Mahajan, Priyanka, additional, Mandal, Mamun, additional, Mishra, Disha, additional, Pal, Probir Kumar, additional, Pandey, Garima, additional, Pandey, Versha, additional, Poonia, Amrita, additional, Prasai, Jiwan Raj, additional, Rajkhowa, Sanchayita, additional, Rao, Vasundhara, additional, Saini, Trilok Chand, additional, Sarkar, Abhijit, additional, Sarma, Jyotirmoy, additional, Sarmah, Nirupamjit, additional, Shanker, Karuna, additional, Sharma, Mansi, additional, Sharma, Priya, additional, Singh, Harminder Pal, additional, Singh, Pardeep, additional, Srivastav, Ajeet K., additional, Srivastava, Madhumita, additional, Srivastava, Nupur, additional, Swapana, Ningombam, additional, Tiwari, Neerja, additional, and Yadav, Anisha, additional

Published
2024
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11. Training Large-Vocabulary Neural Language Models by Private Federated Learning for Resource-Constrained Devices

Author

Xu, Mingbin, Song, Congzheng, Tian, Ye, Agrawal, Neha, Granqvist, Filip, van Dalen, Rogier, Zhang, Xiao, Argueta, Arturo, Han, Shiyi, Deng, Yaqiao, Liu, Leo, Walia, Anmol, and Jin, Alex

Subjects
Computer Science - Machine Learning, Computer Science - Computation and Language, Computer Science - Cryptography and Security
Abstract

Federated Learning (FL) is a technique to train models using data distributed across devices. Differential Privacy (DP) provides a formal privacy guarantee for sensitive data. Our goal is to train a large neural network language model (NNLM) on compute-constrained devices while preserving privacy using FL and DP. However, the DP-noise introduced to the model increases as the model size grows, which often prevents convergence. We propose Partial Embedding Updates (PEU), a novel technique to decrease noise by decreasing payload size. Furthermore, we adopt Low Rank Adaptation (LoRA) and Noise Contrastive Estimation (NCE) to reduce the memory demands of large models on compute-constrained devices. This combination of techniques makes it possible to train large-vocabulary language models while preserving accuracy and privacy.

Published
2022

20. Rings

Author

Khattar, Dinesh, Agrawal, Neha, Khattar, Dinesh, and Agrawal, Neha

Published
2023
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31. Groups

Author

Khattar, Dinesh, Agrawal, Neha, Khattar, Dinesh, and Agrawal, Neha

Published
2023
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41. Functional interactions between posttranslationally modified amino acids of methyl-coenzyme M reductase in Methanosarcina acetivorans.

Author

Nayak, Dipti, Liu, Andi, Agrawal, Neha, Rodriguez-Carerro, Roy, Dong, Shi-Hui, Mitchell, Douglas, Nair, Satish, and Metcalf, William

Subjects
Amino Acids, Archaeal Proteins, Catalytic Domain, Methanosarcina, Methylation, Methyltransferases, Models, Molecular, Mutation, Operon, Oxidoreductases, Phenotype, Protein Processing, Post-Translational, Protein Subunits, Temperature
Abstract

The enzyme methyl-coenzyme M reductase (MCR) plays an important role in mediating global levels of methane by catalyzing a reversible reaction that leads to the production or consumption of this potent greenhouse gas in methanogenic and methanotrophic archaea. In methanogenic archaea, the alpha subunit of MCR (McrA) typically contains four to six posttranslationally modified amino acids near the active site. Recent studies have identified enzymes performing two of these modifications (thioglycine and 5-[S]-methylarginine), yet little is known about the formation and function of the remaining posttranslationally modified residues. Here, we provide in vivo evidence that a dedicated S-adenosylmethionine-dependent methyltransferase encoded by a gene we designated methylcysteine modification (mcmA) is responsible for formation of S-methylcysteine in Methanosarcina acetivorans McrA. Phenotypic analysis of mutants incapable of cysteine methylation suggests that the S-methylcysteine residue might play a role in adaption to mesophilic conditions. To examine the interactions between the S-methylcysteine residue and the previously characterized thioglycine, 5-(S)-methylarginine modifications, we generated M. acetivorans mutants lacking the three known modification genes in all possible combinations. Phenotypic analyses revealed complex, physiologically relevant interactions between the modified residues, which alter the thermal stability of MCR in a combinatorial fashion that is not readily predictable from the phenotypes of single mutants. High-resolution crystal structures of inactive MCR lacking the modified amino acids were indistinguishable from the fully modified enzyme, suggesting that interactions between the posttranslationally modified residues do not exert a major influence on the static structure of the enzyme but rather serve to fine-tune the activity and efficiency of MCR.

Published
2020

43. Thermal Stability Analysis of Graded-Channel Dual-Material Double-Gate (GCDMDG) MOSFET for Analog Application

Author

Priya, Anjali, Gupta, Abhinav, Agrawal, Neha, Rai, Sanjeev, Mishra, Ram Awadh, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Hirche, Sandra, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Li, Yong, Series Editor, Liang, Qilian, Series Editor, Martín, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Möller, Sebastian, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zhang, Junjie James, Series Editor, Dhawan, Amit, editor, Tripathi, Vijay Shanker, editor, Arya, Karm Veer, editor, and Naik, Kshirasagar, editor

Published
2022
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44. Vitiligo Prediction Using Autoencoder

Author

Agrawal, Neha, Aurelia, Sagaya, Kacprzyk, Janusz, Series Editor, Pal, Nikhil R., Advisory Editor, Bello Perez, Rafael, Advisory Editor, Corchado, Emilio S., Advisory Editor, Hagras, Hani, Advisory Editor, Kóczy, László T., Advisory Editor, Kreinovich, Vladik, Advisory Editor, Lin, Chin-Teng, Advisory Editor, Lu, Jie, Advisory Editor, Melin, Patricia, Advisory Editor, Nedjah, Nadia, Advisory Editor, Nguyen, Ngoc Thanh, Advisory Editor, Wang, Jun, Advisory Editor, Das, Asit Kumar, editor, Nayak, Janmenjoy, editor, Naik, Bighnaraj, editor, Dutta, Soumi, editor, and Pelusi, Danilo, editor

Published
2022
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