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312 results on '"Kumar, Shashi"'

28. Ulcerative colitis is characterized by a plasmablast-skewed humoral response associated with disease activity

Author

Uzzan, Mathieu, Martin, Jerome C., Mesin, Luka, Livanos, Alexandra E., Castro-Dopico, Tomas, Huang, Ruiqi, Petralia, Francesca, Magri, Giuliana, Kumar, Shashi, Zhao, Qing, Rosenstein, Adam K., Tokuyama, Minami, Sharma, Keshav, Ungaro, Ryan, Kosoy, Roman, Jha, Divya, Fischer, Jeremy, Singh, Harpriya, Keir, Mary E., Ramamoorthi, Nandhini, Gorman, William E. O’, Cohen, Benjamin L., Rahman, Adeeb, Cossarini, Francesca, Seki, Akihiro, Leyre, Louise, Vaquero, Sonia Tejedor, Gurunathan, Sakteesh, Grasset, Emilie K., Losic, Bojan, Dubinsky, Marla, Greenstein, Alexander J., Gottlieb, Zoe, Legnani, Peter, George, James, Irizar, Haritz, Stojmirovic, Aleksandar, Brodmerkel, Carrie, Kasarkis, Andrew, Sands, Bruce E., Furtado, Glaucia, Lira, Sergio A., Tuong, Zewen K., Ko, Huaibin M., Cerutti, Andrea, Elson, Charles O., Clatworthy, Menna R., Merad, Miriam, Suárez-Fariñas, Mayte, Argmann, Carmen, Hackney, Jason A., Victora, Gabriel D., Randolph, Gwendalyn J., Kenigsberg, Ephraim, Colombel, Jean Frederic, and Mehandru, Saurabh

Published
2022
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33. Rule based complex event processing for IoT applications: Review, classification and challenges.

Author

Kumar, Shashi Shekhar and Agarwal, Sonali

Subjects
*LITERATURE reviews, *STREAMING video & television, *INTERNET of things, *ALGORITHMS, *CLASSIFICATION
Abstract

In recent decades, data aggregation and correlation have emerged as a significant and challenging area of research for finding useful relationships between different features of streaming data. Similarly, complex event processing (CEP) has emerged as a vital tool for aggregating and finding patterns from streaming data such as video streaming, real‐time stock trades, sensor data and more. In this context, rule‐based classifier algorithms are widely employed to discover valuable patterns from streaming data and cause‐and‐effect relationships between events. However, streaming data is dynamic and complex in nature, and it is not feasible to update the rules manually by domain experts continuously. On the contrary, dynamic data sometimes fails to adopt rules, which leads to sub optimal CEP implementation. Rule‐based CEP systems come up with their own set of challenges, which primarily includes rules adaptability for streaming IoT data. In dynamic environment, shifting patterns in data streams can impact the performance of the CEP system, as well as the correlation between events, and finding useful patterns is very much challenging. In this article, our objective is to provide a comprehensive survey for CEP using rule‐based algorithms applied across various domain and applications. We present a broad literature review using a sequential expansion of methods used for CEP, which primarily include event producers, preprocessing of events, robust rule implementation, and decision support. Additionally, we present a detailed classification of approaches used for different applications. Finally, after applying the rigorous review, it was feasible to present the state‐of‐the‐art research, which focuses on designing robust rules, application specific insights, scalable event‐driven architectures and finding the domain's trends with significant challenges and future scope. [ABSTRACT FROM AUTHOR]

Published
2024
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34. Inferring the Variability of Dielectric Constant on the Moon from Mini-RF S-Band Observations.

Author

Shukla, Shashwat, Patterson, Gerald Wesley, Maiti, Abhisek, Kumar, Shashi, and Dutton, Nicholas

Subjects
LUNAR soil, PERMITTIVITY, LUNAR exploration, DEEP learning, RADIO frequency, LUNAR craters
Abstract

The physical properties of lunar regolith are crucial for exploration planning, hazard assessment, and characterizing scientific targets at global and polar scales. The dielectric constant, a key property, offers insights into lunar material distribution within the regolith and serves as a proxy for identifying volatile-rich regoliths. Miniature radio frequency (Mini-RF) on the Lunar Reconnaissance Orbiter (LRO) provides a potential tool for mapping the lunar regolith's physical nature and assessing the lunar volatile repository. This study presents global and polar S-band Mini-RF dielectric signatures of the Moon, obtained through a novel deep learning inversion model applied to Mini-RF mosaics. We achieved good agreement between training and testing of the model, yielding a coefficient of determination (R2 value) of 0.97 and a mean squared error of 0.27 for the dielectric constant. Significant variability in the dielectric constant is observed globally, with high-Ti mare basalts exhibiting lower values than low-Ti highland materials. However, discernibility between the South Pole–Aitken (SPA) basin and highlands is not evident. Despite similar dielectric constants on average, notable spatial variations exist within the south and north polar regions, influenced by crater ejecta, permanently shadowed regions, and crater floors. These dielectric differences are attributed to extensive mantling of lunar materials, impact cratering processes, and ilmenite content. Using the east- and west-looking polar mosaics, we estimated an uncertainty (standard deviation) of 1.01 in the real part and 0.03 in the imaginary part of the dielectric constant due to look direction. Additionally, modeling highlights radar backscatter sensitivity to incidence angle and dielectric constant at the Mini-RF wavelength. The dielectric constant maps provide a new and unique perspective of lunar terrains that could play an important role in characterizing lunar resources in future targeted human and robotic exploration of the Moon. [ABSTRACT FROM AUTHOR]

Published
2024
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