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155 results on '"Shukla, Shashwat"'

3. Lunar Volatiles and Solar System Science

Author

Prem, Parvathy, Kereszturi, Ákos, Deutsch, Ariel N., Hibbitts, Charles A., Schmidt, Carl A., Grava, Cesare, Honniball, Casey I., Hardgrove, Craig J., Pieters, Carlé M., Goldstein, David B., Barker, Donald C., Needham, Debra H., Hurley, Dana M., Mazarico, Erwan, Dominguez, Gerardo, Patterson, G. Wesley, Kramer, Georgiana Y., Brisset, Julie, Gillis-Davis, Jeffrey J., Mitchell, Julie L., Szalay, Jamey R., Halekas, Jasper S., Keane, James T., Head, James W., Mandt, Kathleen E., Robinson, Katharine L., Luchsinger, Kristen M., Magaña, Lizeth O., Siegler, Matthew A., Landis, Margaret E., Poston, Michael J., Petro, Noah E., Lucey, Paul G., Killen, Rosemary M., Li, Shuai, Narendranath, Shyama, Shukla, Shashwat, Barrett, Thomas J., Stubbs, Timothy J., Orlando, Thomas M., and Farrell, William M.

Subjects
Astrophysics - Instrumentation and Methods for Astrophysics, Astrophysics - Earth and Planetary Astrophysics
Abstract

Understanding the origin and evolution of the lunar volatile system is not only compelling lunar science, but also fundamental Solar System science. This white paper (submitted to the US National Academies' Decadal Survey in Planetary Science and Astrobiology 2023-2032) summarizes recent advances in our understanding of lunar volatiles, identifies outstanding questions for the next decade, and discusses key steps required to address these questions.

Published
2020

4. Adaptive Chemotaxis for improved Contour Tracking using Spiking Neural Networks

Author

Shukla, Shashwat, Pathak, Rohan, Saraswat, Vivek, and Ganguly, Udayan

Subjects
Quantitative Biology - Neurons and Cognition, Computer Science - Neural and Evolutionary Computing
Abstract

In this paper we present a Spiking Neural Network (SNN) for autonomous navigation, inspired by the chemotaxis network of the worm Caenorhabditis elegans. In particular, we focus on the problem of contour tracking, wherein the bot must reach and subsequently follow a desired concentration setpoint. Past schemes that used only klinokinesis can follow the contour efficiently but take excessive time to reach the setpoint. We address this shortcoming by proposing a novel adaptive klinotaxis mechanism that builds upon a previously proposed gradient climbing circuit. We demonstrate how our klinotaxis circuit can autonomously be configured to perform gradient ascent, gradient descent and subsequently be disabled to seamlessly integrate with the aforementioned klinokinesis circuit. We also incorporate speed regulation (orthokinesis) to further improve contour tracking performance. Thus for the first time, we present a model that successfully integrates klinokinesis, klinotaxis and orthokinesis. We demonstrate via contour tracking simulations that our proposed scheme achieves an 2.4x reduction in the time to reach the setpoint, along with a simultaneous 8.7x reduction in average deviation from the setpoint.

Published
2020

5. Structured Mean-field Variational Inference and Learning in Winner-take-all Spiking Neural Networks

Author

Shukla, Shashwat, Shimazaki, Hideaki, and Ganguly, Udayan

Subjects
Quantitative Biology - Neurons and Cognition
Abstract

The Bayesian view of the brain hypothesizes that the brain constructs a generative model of the world, and uses it to make inferences via Bayes' rule. Although many types of approximate inference schemes have been proposed for hierarchical Bayesian models of the brain, the questions of how these distinct inference procedures can be realized by hierarchical networks of spiking neurons remains largely unresolved. Based on a previously proposed multi-compartment neuron model in which dendrites perform logarithmic compression, and stochastic spiking winner-take-all (WTA) circuits in which firing probability of each neuron is normalized by activities of other neurons, here we construct Spiking Neural Networks that perform \emph{structured} mean-field variational inference and learning, on hierarchical directed probabilistic graphical models with discrete random variables. In these models, we do away with symmetric synaptic weights previously assumed for \emph{unstructured} mean-field variational inference by learning both the feedback and feedforward weights separately. The resulting online learning rules take the form of an error-modulated local Spike-Timing-Dependent Plasticity rule. Importantly, we consider two types of WTA circuits in which only one neuron is allowed to fire at a time (hard WTA) or neurons can fire independently (soft WTA), which makes neurons in these circuits operate in regimes of temporal and rate coding respectively. We show how the hard WTA circuits can be used to perform Gibbs sampling whereas the soft WTA circuits can be used to implement a message passing algorithm that computes the marginals approximately. Notably, a simple change in the amount of lateral inhibition realizes switching between the hard and soft WTA spiking regimes. Hence the proposed network provides a unified view of the two previously disparate modes of inference and coding by spiking neurons.

Published
2019

16. A Computational Quantum Chemical Approach for Surface Assimilation Effects of Well‐Known Voltage Sensitive Potassium Channel Blockers (4‐Aminopyridine and 3,4‐Diaminopyridine) over Carbon Nanotube.

Author

Pandey, Anoop Kumar, Shukla, Shashwat, Yadav, O. P., Singh, Vijay, and Dwivedi, Apoorva

Subjects
*POTASSIUM channels, *CARBON nanotubes, *POTASSIUM antagonists, *FRONTIER orbitals, *MOLECULAR orbitals, *CHEMICAL properties, *DRUG delivery systems
Abstract

The compound 3,4‐diaminopyridine (3,4‐AP), a derivative of 4‐aminopyridine (4‐AP), is used in the symptomatic treatment of fatigue in multiple sclerosis as well as Lambert–Eaton myasthenia syndrome whereas the parent compound 4‐AP is used as an acetylcholine‐releasing drug, in serum, saliva, and urine. 4‐AP is an experimental drug that may reduce symptoms in some people with multiple sclerosis, particularly those who are more sensitive to heat. The carbon nanotubes (CNTs) have inside cavity tethering on the surface upon functionalization and adsorption on the walls of CNTs, which provides extra ability to carry pharmaceutical agents through multiple ways. Due to these benefits, this communication will discuss the comparative changes in geometry, electronic properties, and shielding parameters of 4‐AP and 3,4‐AP absorbed C56H16. The DFT/B3LYP/6–31G (d, p) method is used for the first time to report electronic structure and interaction parameters on the CNT surface. The same level theory is used to discuss the thermodynamic stability of CNT‐4‐AP/3,4‐AP. The calculated UV spectra of CNT are compared with UV spectra of CNT‐4‐AP and 3,4‐AP by using the same level theory in a water solvent, which provides a better comprehension of CNT as a drug delivery system after absorption of the 4‐AP and 3,4‐AP in the human body. The nature and strength of interactions have been discussed with the help of AIM analysis, and the frontier orbital highest occupied molecular orbital–lowest unoccupied molecular orbital (HOMO–LUMO) gap, chemical softness, and chemical hardness have been calculated to understand its complete chemical properties. The characters of the frontier molecular orbitals are discussed and analyzed by comparing the DOS spectra of CNT with CNT‐4‐AP/3,4‐AP. [ABSTRACT FROM AUTHOR]

Published
2024
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21. Design of Spiking Rate Coded Logic Gates for C. elegans Inspired Contour Tracking

Author

Shukla, Shashwat, Dutta, Sangya, Ganguly, Udayan, Hutchison, David, Series Editor, Kanade, Takeo, Series Editor, Kittler, Josef, Series Editor, Kleinberg, Jon M., Series Editor, Mattern, Friedemann, Series Editor, Mitchell, John C., Series Editor, Naor, Moni, Series Editor, Pandu Rangan, C., Series Editor, Steffen, Bernhard, Series Editor, Terzopoulos, Demetri, Series Editor, Tygar, Doug, Series Editor, Weikum, Gerhard, Series Editor, Kůrková, Věra, editor, Manolopoulos, Yannis, editor, Hammer, Barbara, editor, Iliadis, Lazaros, editor, and Maglogiannis, Ilias, editor

Published
2018
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26. Variant of Differential Evolution Algorithm

Author

Shukla, Richa, Hazela, Bramah, Shukla, Shashwat, Prakash, Ravi, Mishra, Krishn K., 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, Bhatia, Sanjiv K., editor, Mishra, Krishn K., editor, Tiwari, Shailesh, editor, and Singh, Vivek Kumar, editor

Published
2017
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36. The Plethora of Science Afforded by a Lunar Swirl : A White Paper to the Planetary Science Decadal Survey Committee

Author

Kramer, Georgiana, Deca, Jan, Shukla, Shashwat, Kohout, Tomas, Wang, Xu, Watkins, Ryan, Department of Geosciences and Geography, Geology and Geophysics, Department of Physics, and Planetary-system research

Subjects
115 Astronomy, Space science
Abstract

Whitepaper #166 submitted to the Planetary Science and Astrobiology Decadal Survey 2023-2032. Topics: surface/geological evolution; Mercury and/or the Moon; other science themes: Physics, Heliophysics, Magnetohydrodynamics, Volcanology, ISRU, Photometry Lunar swirls should be the top priority target of the next lunar mission. The swirls are a fascinating lunar feature, as well as a laboratory to study the solar wind, space weathering, plasma weathering, and plasma kinetics. In this white paper we present examples of broad scientific interest in lunar swirls as well as some example mission types.

Published
2021

39. Mission to Characterize Volatiles in Old, Cold, Permanently Shadowed Regions on the Moon

Author

Hurley, Dana, primary, Blewett, David T., additional, Cahill, Joshua, additional, Chabot, Nancy, additional, Greenhagen, Benjamin, additional, Hibbitts, Charles, additional, Klima, Rachel, additional, Lawrence, David, additional, Mandt, Kathleen, additional, Nunez, Jorge I., additional, Patterson, Wes, additional, Prem, Parvathy, additional, Stickle, Angela, additional, Deutsch, Ariel, additional, Delitsky, Mona, additional, Flahaut, Jessica, additional, Sowers, George, additional, Sefton-Nash, Eliot, additional, Brisset, Julie, additional, Zacny, Kris, additional, Shukla, Shashwat, additional, Grandidier, Jonathan, additional, Hosseini, Sona, additional, Liu, Yang, additional, Bhiravarasu, Sriram, additional, Stopar, Julie, additional, Barker, Donald, additional, Gertsch, Leslie, additional, Jha, Devanshu, additional, Colaprete, Anthony, additional, Elphic, Richard, additional, Banks, Maria, additional, Benna, Mehdi, additional, Farrell, William, additional, Saxena, Prabal, additional, Tucker, O.J., additional, Mitchell, Julie, additional, Needham, Debra, additional, Atkinson, Jared, additional, Barber, Simeon, additional, Hendrix, Amanda, additional, Kramer, Georgiana, additional, Siegler, Matthew, additional, Grava, Cesare, additional, Poston, Michael, additional, Retherford, Kurt, additional, Kumari, Nandita, additional, Barnes, Jessica, additional, Hayne, Paul, additional, Li, Shuai, additional, Lucey, Paul, additional, and Livengood, Timothy, additional

Published
2021
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40. Space Weathering Across the Solar System: Lessons from the Moon and Outstanding Questions

Author

Thompson, Michelle, primary, Barnes, Jessica, additional, Blewett, David, additional, Cahill, Joshua, additional, Denevi, Brett, additional, Hanna, Kerri Donaldson, additional, Gillis-Davis, Jeff, additional, Glotch, Tim, additional, Jha, Devanshu, additional, Kramer, Georgiana, additional, Kumari, Nandita, additional, Legett, Carey, additional, Moriarty, Daniel, additional, Retherford, Kurt, additional, Shukla, Shashwat, additional, Shusterman, Morgan, additional, and Varatherajan, Indhu, additional

Published
2021
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41. Exploring end-member volcanism on the Moon at the Aristarchus Plateau

Author

Jawin, Erica, primary, Glotch, Timothy, additional, Watkins, Ryan, additional, Jozwiak, Lauren, additional, Valencia, Sarah, additional, Meyer, Heather, additional, Yingst, R. Aileen, additional, Denevi, Brett, additional, Moriarty, Daniel, additional, Needham, Debra, additional, Besse, Sebastien, additional, Shukla, Shashwat, additional, Jolliff, Brad, additional, Gaddis, Lisa, additional, Bennett, Kristen, additional, and Clark, Jaclyn, additional

Published
2021
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42. Artificial Intelligence for the Advancement of Lunar and Planetary Science and Exploration

Author

Varatharajan, Indhu, primary, Angerhausen, Daniel, additional, Antoniadou, Eleni, additional, Bickel, Valentin, additional, D'Amore, Mario, additional, Faragalli, Michele, additional, López-Francos, Ignacio, additional, Maiti, Abhisek, additional, Potter, Ross, additional, Shneider, Carl, additional, Shukla, Shashwat, additional, Soboczenski, Frank, additional, Srishankar, Nishan, additional, and Watkins, Ryan, additional

Published
2021
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43. Lunar polar volatile resources: Obtaining their origin prior to extraction

Author

Farrell, William, primary, Beltran, Esther, additional, Prem, Parvathy, additional, Poston, Michael, additional, Deutsch, Ariel, additional, Hurley, Dana, additional, Retherford, Kurt, additional, Hayne, Paul, additional, Horanyi, Mihaly, additional, Orlando, Thomas, additional, Gillis-Davis, Jeffrey, additional, Dominguez, Gerardo, additional, Shukla, Shashwat, additional, Szalay, Jamey, additional, Needham, Debra, additional, Landis, Margaret, additional, Mandt, Kathleen, additional, Bennett, Christopher, additional, and Grava, Cesare, additional

Published
2021
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44. Science Opportunities offered by Mercury’s Ice-Bearing Polar Deposits

Author

Deutsch, Ariel, primary, Chabot, Nancy, additional, Maiti, Abhisek, additional, Luspay-Kuti, Adrienn, additional, Kereszturi, Akos, additional, Lucchetti, Alice, additional, Virkki, Anne, additional, Colaprete, Anthony, additional, Vorburger, Audrey, additional, Byron, Ben, additional, Jones, Brant, additional, Anzures, Brendan, additional, Butler, Bryan, additional, Schmidt, Carl, additional, Ernst, Carolyn, additional, Grava, Cesare, additional, Klimczak, Christian, additional, Dong, Chuanfei, additional, Hamill, Colin, additional, Hardgrove, Craig, additional, Blewett, David, additional, Lawrence, David, additional, Hickson, Dylan, additional, Rivera-Valentin, Edgard, additional, Costello, Emily, additional, Mazarico, Erwan, additional, Filacchione, Gianrico, additional, Bacon, Giovanni, additional, Neumann, Gregory, additional, Susorney, Hannah, additional, Brown, Holly, additional, Varatharajan, Indhu, additional, Wilson, Jack, additional, Kloos, Jacob, additional, Head, James, additional, Szalay, Jamey, additional, Steckloff, Jordan, additional, Miller, Kelly, additional, Cannon, Kevin, additional, Luchsinger, Kristen, additional, Rubanenko, Lior, additional, Magana, Lizeth, additional, Landis, Margaret, additional, Gritsevich, Maria, additional, Schneegurt, Mark, additional, Slade, Martin, additional, Siegler, Matthew, additional, Pajola, Maurizio, additional, Sarantos, Menelaos, additional, Poston, Michael J., additional, Sori, Mike, additional, Delitsky, Mona, additional, Pinilla-Alonso, Noemi, additional, Schorghofer, Norbert, additional, Prem, Parvathy, additional, Byrne, Paul, additional, Hayne, Paul, additional, Lucey, Paul, additional, James, Peter, additional, Pokorny, Petr, additional, Jr., Ronald J. Vervack,, additional, Killen, Rosemary, additional, Potter, Ross, additional, Solomon, Sean, additional, Shukla, Shashwat, additional, Bhiravarasu, Sriram, additional, Hauck, Steven, additional, Orlando, Thomas, additional, Stubbs, Timothy, additional, Bickel, Valentin, additional, Eke, Vincent, additional, and Farrell, William, additional

Published
2021
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45. Lunar Volatiles and Solar System Science

Author

Prem, Parvathy, primary, Kereszturi, Ákos, additional, Deutsch, Ariel N., additional, Hibbitts, Charles A., additional, Schmidt, Carl A., additional, Grava, Cesare, additional, Honniball, Casey I., additional, Hardgrove, Craig J., additional, Pieters, Carlé M., additional, Goldstein, David B., additional, Barker, Donald C., additional, Needham, Debra H., additional, Hurley, Dana M., additional, Mazarico, Erwan, additional, Dominguez, Gerardo, additional, Patterson, G. Wesley, additional, Kramer, Georgiana Y., additional, Brisset, Julie, additional, Gillis-Davis, Jeffrey J., additional, Mitchell, Julie L., additional, Szalay, Jamey R., additional, Halekas, Jasper S., additional, Keane, James T., additional, Head, James W., additional, Mandt, Kathleen E., additional, Robinson, Katharine L., additional, Luchsinger, Kristen M., additional, Magaña, Lizeth O., additional, Siegler, Matthew A., additional, Landis, Margaret E., additional, Poston, Michael J., additional, Petro, Noah E., additional, Lucey, Paul G., additional, Killen, Rosemary M., additional, Li, Shuai, additional, Narendranath, Shyama, additional, Shukla, Shashwat, additional, Barrett, Thomas J., additional, Stubbs, Timothy J., additional, Orlando, Thomas M., additional, and Farrell, William M., additional

Published
2021
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49. Artificial Intelligence for the Advancement of Lunar and Planetary Science and Exploration

Author

Varatharajan, Indhu, Bickel, Valentin, Angerhausen, Daniel, Antoniadou, Eleni, Shukla, Shashwat, Maiti, Abhisek, Potter, Ross, Srishankar, Nishan, Soboczenski, Frank, Shneider, Carl, Faragalli, Michelle, and Amore, Mario

Abstract

Over the past decades of NASA’s inner solar system exploration, data obtained from the Moon alone accounts for ~76%. Most of the lunar orbital spacecraft of the past and present carried imaging cameras and spectrometers (including multispectral and hyperspectral payloads), as well as a large variety of other passive and active instruments. For example, NASA’s Lunar Reconnaissance Orbiter (LRO) has been operating for more than 10 years, providing us with ~1206 TB of lunar data which amounts to ~99.5% of the total data contributed by NASA built instruments. Given recent advances in instrument and communication capabilities, the amount of data returned from spacecraft is expected to keep rising quickly. The white paper focus on potential components of AI and ML that could help to accelerate the future exploration of the Moon and other planetary bodies. The white paper highlights on selected AI/ML-based approaches for lunar and planetary surface science and exploration, the need for open-source availability of training, validation, and testing datasets for AI-ML based approaches, and need for opportunities to further bridge the gap between industry and academia for advancing AI-ML based research in lunar and planetary science and exploration.

Published
2020

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