Search Results for author: T. Chen

Found 3 papers, 2 papers with code

Periodic activity from a fast radio burst source

1 code implementation • 28 Jan 2020 • The CHIME/FRB Collaboration, M. Amiri, B. C. Andersen, K. M. Bandura, M. Bhardwaj, P. J. Boyle, C. Brar, P. Chawla, T. Chen, J. F. Cliche, D. Cubranic, M. Deng, N. T. Denman, M. Dobbs, F. Q. Dong, M. Fandino, E. Fonseca, B. M. Gaensler, U. Giri, D. C. Good, M. Halpern, J. W. T. Hessels, A. S. Hill, C. Höfer, A. Josephy, J. W. Kania, R. Karuppusamy, V. M. Kaspi, A. Keimpema, F. Kirsten, T. L. Landecker, D. A. Lang, C. Leung, D. Z. Li, H. -H. Lin, B. Marcote, K. W. Masui, R. Mckinven, J. Mena-Parra, M. Merryfield, D. Michilli, N. Milutinovic, A. Mirhosseini, A. Naidu, L. B. Newburgh, C. Ng, K. Nimmo, Z. Paragi, C. Patel, U. -L. Pen, T. Pinsonneault-Marotte, Z. Pleunis, M. Rafiei-Ravandi, M. Rahman, S. M. Ransom, A. Renard, P. Sanghavi, P. Scholz, J. R. Shaw, K. Shin, S. R. Siegel, S. Singh, R. J. Smegal, K. M. Smith, I. H. Stairs, S. P. Tendulkar, I. Tretyakov, K. Vanderlinde, H. Wang, X. Wang, D. Wulf, P. Yadav, A. V. Zwaniga

Fast radio bursts (FRBs) are bright, millisecond-duration radio transients originating from extragalactic distances.

High Energy Astrophysical Phenomena

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Hybrid Machine Learning Models of Classifying Residential Requests for Smart Dispatching

1 code implementation • 22 Dec 2019 • T. Chen, J. Sun, H. Lin, Y. Liu

This paper presents a hybrid machine learning method of classifying residential requests in natural language to responsible departments that provide timely responses back to residents under the vision of digital government services in smart cities.

Benchmarking BIG-bench Machine Learning +3

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Sequential geophysical and flow inversion to characterize fracture networks in subsurface systems

no code implementations • 14 Jun 2016 • M. K. Mudunuru, S. Karra, N. Makedonska, T. Chen

In this approach, we first estimate bounds on the statistics for the DFN fracture orientations using microseismic data.

Clustering

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