Pulse Profile Variability of PSR J1022+1001 in NANOGrav Data

Autor: Fiore, William, McLaughlin, Maura A., Agazie, Gabriella, Anumarlapudi, Akash, Archibald, Anne M., Arzoumanian, Zaven, Baker, Paul T., Brook, Paul R., Cromartie, H. Thankful, Crowter, Kathryn, DeCesar, Megan E., Demorest, Paul B., Dey, Lankeswar, Dolch, Timothy, Ferrara, Elizabeth C., Fonseca, Emmanuel, Freedman, Gabriel E., Garver-Daniels, Nate, Gentile, Peter A., Glaser, Joseph, Good, Deborah C., Hazboun, Jeffrey S., Jennings, Ross J., Jones, Megan L., Kaplan, David L., Kerr, Matthew, Lam, Michael T., Lorimer, Duncan R., Luo, Jing, Lynch, Ryan S., McEwen, Alexander, McMann, Natasha, Meyers, Bradley W., Ng, Cherry, Nice, David J., Pennucci, Timothy T., Perera, Benetge B. P., Pol, Nihan S., Radovan, Henri A., Ransom, Scott M., Ray, Paul S., Schmiedekamp, Ann, Schmiedekamp, Carl, Shapiro-Albert, Brent J., Stairs, Ingrid H., Stovall, Kevin, Susobhanan, Abhimanyu, Swiggum, Joseph K., Wahl, Haley M.

Pulse profile stability is a central assumption of standard pulsar timing methods. Thus, it is important for pulsar timing array experiments such as the North American Nanohertz Observatory for Gravitational Waves (NANOGrav) to account for any pulse

Externí odkaz: http://arxiv.org/abs/2412.05452

On-Chain Credit Risk Score (OCCR Score) in DeFi

Autor: Ghosh, Rik, Datta, Arka, Aggarwal, Vidhi, Sinha, Sudipan, Nag, Abhimanyu

Decentralized Finance (DeFi) and smart contracts are the next generation and fast-growing market for quick and safe interaction between lenders and borrowers. However for maintaining a streamline ecosystem it is necessary to understand the risk assoc

Externí odkaz: http://arxiv.org/abs/2412.00710

The NANOGrav 15 year Data Set: Removing pulsars one by one from the pulsar timing array

Autor: Agazie, Gabriella, Anumarlapudi, Akash, Archibald, Anne M., Arzoumanian, Zaven, Baier, Jeremy G., Baker, Paul T., Becsy, Bence, Blecha, Laura, Brazier, Adam, Brook, Paul R., Burke-Spolaor, Sarah, Casey-Clyde, J. Andrew, Charisi, Maria, Chatterjee, Shami, Cohen, Tyler, Cordes, James M., Cornish, Neil J., Crawford, Fronefield, Cromartie, H. Thankful, Crowter, Kathryn, DeCesar, Megan E., Demorest, Paul B., Deng, Heling, Dey, Lankeswar, Dolch, Timothy, Ferrara, Elizabeth C., Fiore, William, Fonseca, Emmanuel, Freedman, Gabriel E., Gardiner, Emiko C., Garver-Daniels, Nate, Gentile, Peter A., Gersbach, Kyle A., Glaser, Joseph, Good, Deborah C., Guertin, Lydia, Gultekin, Kayhan, Hazboun, Jeffrey S., Jennings, Ross J., Johnson, Aaron D., Jones, Megan L., Kaiser, Andrew R., Kaplan, David L., Kelley, Luke Zoltan, Kerr, Matthew, Key, Joey S., Laal, Nima, Lam, Michael T., Lamb, William G., Larsen, Bjorn, Lazio, T. Joseph W., Lewandowska, Natalia, Liu, Tingting, Lorimer, Duncan R., Luo, Jing, Lynch, Ryan S., Ma, Chung-Pei, Madison, Dustin R., McEwen, Alexander, McKee, James W., McLaughlin, Maura A., McMann, Natasha, Meyers, Bradley W., Meyers, Patrick M., Middleton, Hannah, Mingarelli, Chiara M. F., Mitridate, Andrea, Moore, Christopher J., Ng, Cherry, Nice, David J., Ocker, Stella Koch, Olum, Ken D., Pennucci, Timothy T., Perera, Benetge B. P., Pol, Nihan S., Radovan, Henri A., Ransom, Scott M., Ray, Paul S., Romano, Joseph D., Runnoe, Jessie C., Saffer, Alexander, Sardesai, Shashwat C., Schmiedekamp, Ann, Schmiedekamp, Carl, Schmitz, Kai, Shapiro-Albert, Brent J., Siemens, Xavier, Simon, Joseph, Siwek, Magdalena S., Fiscella, Sophia V. Sosa, Stairs, Ingrid H., Stinebring, Daniel R., Stovall, Kevin, Susobhanan, Abhimanyu, Swiggum, Joseph K., Taylor, Stephen R., Turner, Jacob E., Unal, Caner, Vallisneri, Michele, Vecchio, Alberto, Vigeland, Sarah J., Wahl, Haley M., Witt, Caitlin A., Wright, David, Young, Olivia

Evidence has emerged for a stochastic signal correlated among 67 pulsars within the 15-year pulsar-timing data set compiled by the NANOGrav collaboration. Similar signals have been found in data from the European, Indian, Parkes, and Chinese PTAs. Th

Externí odkaz: http://arxiv.org/abs/2411.14846

Multi-Modal Forecaster: Jointly Predicting Time Series and Textual Data

Autor: Kim, Kai, Tsai, Howard, Sen, Rajat, Das, Abhimanyu, Zhou, Zihao, Tanpure, Abhishek, Luo, Mathew, Yu, Rose

Current forecasting approaches are largely unimodal and ignore the rich textual data that often accompany the time series due to lack of well-curated multimodal benchmark dataset. In this work, we develop TimeText Corpus (TTC), a carefully curated, t

Externí odkaz: http://arxiv.org/abs/2411.06735

In-Context Fine-Tuning for Time-Series Foundation Models

Autor: Das, Abhimanyu, Faw, Matthew, Sen, Rajat, Zhou, Yichen

Motivated by the recent success of time-series foundation models for zero-shot forecasting, we present a methodology for $\textit{in-context fine-tuning}$ of a time-series foundation model. In particular, we design a pretrained foundation model that

Externí odkaz: http://arxiv.org/abs/2410.24087