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1,688 results on '"Singh, Neeraj"'

11. xLP: Explainable Link Prediction for Master Data Management

Author

Ganesan, Balaji, Pasha, Matheen Ahmed, Parkala, Srinivasa, Singh, Neeraj R, Mishra, Gayatri, Bhatia, Sumit, Patel, Hima, Naganna, Somashekar, and Mehta, Sameep

Subjects
Computer Science - Artificial Intelligence
Abstract

Explaining neural model predictions to users requires creativity. Especially in enterprise applications, where there are costs associated with users' time, and their trust in the model predictions is critical for adoption. For link prediction in master data management, we have built a number of explainability solutions drawing from research in interpretability, fact verification, path ranking, neuro-symbolic reasoning and self-explaining AI. In this demo, we present explanations for link prediction in a creative way, to allow users to choose explanations they are more comfortable with., Comment: 8 pages, 4 figures, NeurIPS 2020 Competition and Demonstration Track. arXiv admin note: text overlap with arXiv:2012.05516

Published
2024

12. A Complete Fragment of LTL(EB)

Author

Ferrarotti, Flavio, Rivière, Peter, Schewe, Klaus-Dieter, Singh, Neeraj Kumar, and Ameur, Yamine Aït

Subjects
Computer Science - Logic in Computer Science, 68Q60, 68N30
Abstract

The verification of liveness conditions is an important aspect of state-based rigorous methods. This article investigates this problem in a fragment $\square$LTL of the logic LTL(EB), the integration of the UNTIL-fragment of Pnueli's linear time temporal logic (LTL) and the logic of Event-B, in which the most commonly used liveness conditions can be expressed. For this fragment a sound set of derivation rules is developed, which is also complete under mild restrictions for Event-B machines., Comment: 22 pages

Published
2024

16. Medical and Surgical Management of the Failed Pancreas Transplant

Author

Casey, Michael J, Murakami, Naoka, Ong, Song, Adler, Joel T, Singh, Neeraj, Murad, Haris, Parajuli, Sandesh, Concepcion, Beatrice P, Lubetzky, Michelle, Pavlakis, Martha, Woodside, Kenneth J, Faravardeh, Arman, Basu, Arpita, Tantisattamo, Ekamol, Aala, Amtul, Gruessner, Angelika, Dadhania, Darshana, Lentine, Krista, Cooper, Matthew, Parsons, Ronald F, and Alhamad, Tarek

Subjects
Biomedical and Clinical Sciences, Clinical Sciences, Organ Transplantation, Clinical Research, Transplantation, Good Health and Well Being, Cardiovascular medicine and haematology, Clinical sciences, Immunology
Abstract

Despite the continued improvements in pancreas transplant outcomes in recent decades, a subset of recipients experience graft failure and can experience substantial morbidity and mortality. Here, we summarize what is known about the failed pancreas allograft and what factors are important for consideration of retransplantation. The current definition of pancreas allograft failure and its challenges for the transplant community are explored. The impacts of a failed pancreas allograft are presented, including patient survival and resultant morbidities. The signs, symptoms, and medical and surgical management of a failed pancreas allograft are described, whereas the options and consequences of immunosuppression withdrawal are reviewed. Medical and surgical factors necessary for successful retransplant candidacy are detailed with emphasis on how well-selected patients may achieve excellent retransplant outcomes. To achieve substantial medical mitigation and even pancreas retransplantation, patients with a failed pancreas allograft warrant special attention to their residual renal, cardiovascular, and pulmonary function. Future studies of the failed pancreas allograft will require improved reporting of graft failure from transplant centers and continued investigation from experienced centers.

Published
2024

17. HCDIR: End-to-end Hate Context Detection, and Intensity Reduction model for online comments

Author

Singh, Neeraj Kumar, Ghosh, Koyel, Mahapatra, Joy, Garain, Utpal, and Senapati, Apurbalal

Subjects
Computer Science - Computation and Language, Computer Science - Artificial Intelligence
Abstract

Warning: This paper contains examples of the language that some people may find offensive. Detecting and reducing hateful, abusive, offensive comments is a critical and challenging task on social media. Moreover, few studies aim to mitigate the intensity of hate speech. While studies have shown that context-level semantics are crucial for detecting hateful comments, most of this research focuses on English due to the ample datasets available. In contrast, low-resource languages, like Indian languages, remain under-researched because of limited datasets. Contrary to hate speech detection, hate intensity reduction remains unexplored in high-resource and low-resource languages. In this paper, we propose a novel end-to-end model, HCDIR, for Hate Context Detection, and Hate Intensity Reduction in social media posts. First, we fine-tuned several pre-trained language models to detect hateful comments to ascertain the best-performing hateful comments detection model. Then, we identified the contextual hateful words. Identification of such hateful words is justified through the state-of-the-art explainable learning model, i.e., Integrated Gradient (IG). Lastly, the Masked Language Modeling (MLM) model has been employed to capture domain-specific nuances to reduce hate intensity. We masked the 50\% hateful words of the comments identified as hateful and predicted the alternative words for these masked terms to generate convincing sentences. An optimal replacement for the original hate comments from the feasible sentences is preferred. Extensive experiments have been conducted on several recent datasets using automatic metric-based evaluation (BERTScore) and thorough human evaluation. To enhance the faithfulness in human evaluation, we arranged a group of three human annotators with varied expertise.

Published
2023

18. Convolutional Neural Networks Exploiting Attributes of Biological Neurons

Author

Singh, Neeraj Kumar and Pal, Nikhil R.

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

In this era of artificial intelligence, deep neural networks like Convolutional Neural Networks (CNNs) have emerged as front-runners, often surpassing human capabilities. These deep networks are often perceived as the panacea for all challenges. Unfortunately, a common downside of these networks is their ''black-box'' character, which does not necessarily mirror the operation of biological neural systems. Some even have millions/billions of learnable (tunable) parameters, and their training demands extensive data and time. Here, we integrate the principles of biological neurons in certain layer(s) of CNNs. Specifically, we explore the use of neuro-science-inspired computational models of the Lateral Geniculate Nucleus (LGN) and simple cells of the primary visual cortex. By leveraging such models, we aim to extract image features to use as input to CNNs, hoping to enhance training efficiency and achieve better accuracy. We aspire to enable shallow networks with a Push-Pull Combination of Receptive Fields (PP-CORF) model of simple cells as the foundation layer of CNNs to enhance their learning process and performance. To achieve this, we propose a two-tower CNN, one shallow tower and the other as ResNet 18. Rather than extracting the features blindly, it seeks to mimic how the brain perceives and extracts features. The proposed system exhibits a noticeable improvement in the performance (on an average of $5\%-10\%$) on CIFAR-10, CIFAR-100, and ImageNet-100 datasets compared to ResNet-18. We also check the efficiency of only the Push-Pull tower of the network., Comment: 20 pages, 6 figures

Published
2023

29. Trust Value Method for False Data Injection Detection in Smart Grid

Author

More, Shreyash, Singh, Neeraj, Yadav, Atul Kumar, Mahajan, Vasundhara, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, 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, 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, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Oneto, Luca, Series Editor, Panigrahi, Bijaya Ketan, 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, Zamboni, Walter, Series Editor, Zhang, Junjie James, Series Editor, Tan, Kay Chen, Series Editor, Mahajan, Vasundhara, editor, Chowdhury, Anandita, editor, Singh, Sri Niwas, editor, and Shahidehpour, Mohammad, editor

Published
2024
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31. A Complete Fragment of LTL(EB)

Author

Ferrarotti, Flavio, primary, Rivière, Peter, additional, Schewe, Klaus-Dieter, additional, Singh, Neeraj Kumar, additional, and Ameur, Yamine Aït, additional

Published
2024
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33. The Perspectives of General Nephrologists Toward Transitions of Care and Management of Failing Kidney Transplants

Author

Alhamad, Tarek, Murad, Haris, Dadhania, Darshana M, Pavlakis, Martha, Parajuli, Sandesh, Concepcion, Beatrice P, Singh, Neeraj, Murakami, Naoka, Casey, Michael J, Ji, Mengmeng, Lubetzky, Michelle, Tantisattamo, Ekamol, Alomar, Omar, Faravardeh, Arman, Blosser, Christopher D, Basu, Arpita, Gupta, Gaurav, Adler, Joel T, Adey, Deborah, Woodside, Kenneth J, Ong, Song C, Parsons, Ronald F, and Lentine, Krista L

Subjects
Biomedical and Clinical Sciences, Clinical Sciences, Clinical Research, Kidney Disease, Transplantation, Organ Transplantation, Renal and urogenital, Adult, Humans, Nephrologists, Kidney Transplantation, Nephrology, Immunosuppression Therapy, Surveys and Questionnaires, re-transplantation, failing kidney allograft, transition of care, immunosuppression management, multidisciplinary team, Surgery, Clinical sciences
Abstract

The management of failing kidney allograft and transition of care to general nephrologists (GN) remain a complex process. The Kidney Pancreas Community of Practice (KPCOP) Failing Allograft Workgroup designed and distributed a survey to GN between May and September 2021. Participants were invited via mail and email invitations. There were 103 respondents with primarily adult nephrology practices, of whom 41% had an academic affiliation. More than 60% reported listing for a second kidney as the most important concern in caring for patients with a failing allograft, followed by immunosuppression management (46%) and risk of mortality (38%), while resistant anemia was considered less of a concern. For the initial approach to immunosuppression reduction, 60% stop antimetabolites first, and 26% defer to the transplant nephrologist. Communicating with transplant centers about immunosuppression cessation was reported to occur always by 60%, and sometimes by 29%, while 12% reported making the decision independently. Nephrologists with academic appointments communicate with transplant providers more than private nephrologists (74% vs. 49%, p = 0.015). There are heterogeneous approaches to the care of patients with a failing allograft. Efforts to strengthen transitions of care and to develop practical practice guidelines are needed to improve the outcomes of this vulnerable population.

Published
2023

42. Generating strategies for a national comeback in pancreas transplantation: A Delphi survey and US conference report

Author

Abrams, Peter, Alachkar, Nada, Alhamad, Tarek, Ali, Nicole, Axelrod, David, Basu, Arpita, Bloom, Roy, Burke, George, Concepcion, Beatrice, Cooper, Matthew, Craig-Shapiro, Rebecca, Dadhania, Darshana, Doshi, Mona, Drachenberg, Cinthia, Dunn, Ty, Forbes, Rachel, Fridell, Jonathan, Friedewald, John, Gaber, Osama, Gruessner, Rainer, Gruessner, Angelika, Josephson, Michelle, Kandaswamy, Raja, Kaufman, Dixon, Kudva, Yogish, Kukla, Aleksandra, Kumar, Vineeta, Lentine, Krista L., Lu, Yee, McElroy, Lisa, Neidlinge, Nikole, Niederhaus, Silke, Nishio-Lucar, Angie, Odorico, Jon, Parajuli, Sandesh, Parsons, Ronald, Pastan, Stephen, Pavlakis, Martha, Rao, Swati, Ratner, Lloyd, Romeo, Giulio, Sawinski, Deirdre, Scalea, Joseph, Sellers, Marty, Singh, Neeraj, Slaker, Dirk, Stock, Peter, Stratta, Robert, Turgeon, Nicole, Witkowski, Piotr, Woodside, Kenneth, Yadav, Anju, Parsons, Ronald F., Dunn, Ty B., Fridell, Jonathan A., Jesse, Michelle T., and Axelrod, David A.

Published
2024
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45. Should Transplant Nephrology pursue recognition from the Accreditation Council for Graduate Medical Education (ACGME)?

Author

Singh, Neeraj, Anand, Prince M, Gupta, Gaurav, Sawinski, Deirdre, Fix, Oren, Adey, Deborah, Akalin, Enver, Zayas, Carlos, Dadhania, Darshana, Doshi, Mona, Cibrik, Diane, Gupta, Mallika, Parsons, Ronald, Leca, Nicolae, Santos, Rowena Delos, Concepcion, Beatrice P, Nishio Lucar, Angie G, Ong, Song, Sridhar, Vikas Srinivasan, Parajuli, Sandesh, Zachariah, Mareena, Mehta, Shikha, Soliman, Karim, Shawar, Saed, Husain, Syed Ali, Preczewski, Luke, Friedewald, John, Mohan, Sumit, Wiseman, Alexander, Samaniego, Millie, Kumar, Vineeta, Tanriover, Bekir, and Bloom, Roy

Published
2024
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50. Review on the Applications of Multichannel Analysis of Surface Wave (MASW) in Indian Subcontinent

Author

Singh, Neeraj, Imam, Ashhad, Bera, Sourav, Sharma, Keshav K., Kumar, Virendra, Srivastava, A. K. L., Correia, José A. F. O., Series Editor, De Jesus, Abílio M. P., Series Editor, Ayatollahi, Majid Reza, Advisory Editor, Berto, Filippo, Advisory Editor, Fernández-Canteli, Alfonso, Advisory Editor, Hebdon, Matthew, Advisory Editor, Kotousov, Andrei, Advisory Editor, Lesiuk, Grzegorz, Advisory Editor, Murakami, Yukitaka, Advisory Editor, Carvalho, Hermes, Advisory Editor, Zhu, Shun-Peng, Advisory Editor, Bordas, Stéphane, Advisory Editor, Fantuzzi, Nicholas, Advisory Editor, Susmel, Luca, Advisory Editor, Dutta, Subhrajit, Advisory Editor, Maruschak, Pavlo, Advisory Editor, Fedorova, Elena, Advisory Editor, Fonseca de Oliveira Correia, José António, editor, and Choudhury, Satyabrata, editor

Published
2023
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