1 code implementation • 27 Feb 2024 • Hongyu Shen, Yici Yan, Zhizhen Zhao
In DeepDRK, a generative model grounded in a transformer architecture is introduced to better achieve the "swap property".
no code implementations • 28 Jul 2022 • Hongyu Shen, Jinoh Oh, Shuai Zhao, Guoyin Wang, Tara Taghavi, Sungjin Lee
Then we propose a graph convolutional network(GCN) based model, namely Personalized Dynamic Routing Feature Encoder(PDRFE), that generates personalized customer representations learned from the built graph.
no code implementations • 1 Jan 2021 • Hongyu Shen, Layne C. Price, Mohammad Taha Bahadori, Franziska Seeger
While protein sequence data is an emerging application domain for machine learning methods, small modifications to protein sequences can result in difficult-to-predict changes to the protein's function.
no code implementations • 24 Nov 2020 • Qing Gao, Mingtao Pei, Hongyu Shen
In contrast to current lifelogging/egocentric datasets, our dataset is suitable for lifestyle analysis as images are taken with short intervals to capture activities of short duration; moreover, images are taken continuously from morning to evening to record all the activities performed by a user.
no code implementations • 26 Nov 2019 • E. A. Huerta, Gabrielle Allen, Igor Andreoni, Javier M. Antelis, Etienne Bachelet, Bruce Berriman, Federica Bianco, Rahul Biswas, Matias Carrasco, Kyle Chard, Minsik Cho, Philip S. Cowperthwaite, Zachariah B. Etienne, Maya Fishbach, Francisco Förster, Daniel George, Tom Gibbs, Matthew Graham, William Gropp, Robert Gruendl, Anushri Gupta, Roland Haas, Sarah Habib, Elise Jennings, Margaret W. G. Johnson, Erik Katsavounidis, Daniel S. Katz, Asad Khan, Volodymyr Kindratenko, William T. C. Kramer, Xin Liu, Ashish Mahabal, Zsuzsa Marka, Kenton McHenry, Jonah Miller, Claudia Moreno, Mark Neubauer, Steve Oberlin, Alexander R. Olivas, Donald Petravick, Adam Rebei, Shawn Rosofsky, Milton Ruiz, Aaron Saxton, Bernard F. Schutz, Alex Schwing, Ed Seidel, Stuart L. Shapiro, Hongyu Shen, Yue Shen, Leo Singer, Brigitta M. Sipőcz, Lunan Sun, John Towns, Antonios Tsokaros, Wei Wei, Jack Wells, Timothy J. Williams, JinJun Xiong, Zhizhen Zhao
Multi-messenger astrophysics is a fast-growing, interdisciplinary field that combines data, which vary in volume and speed of data processing, from many different instruments that probe the Universe using different cosmic messengers: electromagnetic waves, cosmic rays, gravitational waves and neutrinos.
no code implementations • 6 Mar 2019 • Hongyu Shen, Daniel George, E. A. Huerta, Zhizhen Zhao
Denoising of time domain data is a crucial task for many applications such as communication, translation, virtual assistants etc.
no code implementations • 5 Mar 2019 • Hongyu Shen, E. A. Huerta, Eamonn O'Shea, Prayush Kumar, Zhizhen Zhao
Upon confirming that our models produce statistically consistent results, we used them to estimate the astrophysical parameters $(m_1, m_2, a_f, \omega_R, \omega_I)$ of five binary black holes: $\texttt{GW150914}, \texttt{GW170104}, \texttt{GW170814}, \texttt{GW190521}$ and $\texttt{GW190630}$.
no code implementations • 1 Feb 2019 • Gabrielle Allen, Igor Andreoni, Etienne Bachelet, G. Bruce Berriman, Federica B. Bianco, Rahul Biswas, Matias Carrasco Kind, Kyle Chard, Minsik Cho, Philip S. Cowperthwaite, Zachariah B. Etienne, Daniel George, Tom Gibbs, Matthew Graham, William Gropp, Anushri Gupta, Roland Haas, E. A. Huerta, Elise Jennings, Daniel S. Katz, Asad Khan, Volodymyr Kindratenko, William T. C. Kramer, Xin Liu, Ashish Mahabal, Kenton McHenry, J. M. Miller, M. S. Neubauer, Steve Oberlin, Alexander R. Olivas Jr, Shawn Rosofsky, Milton Ruiz, Aaron Saxton, Bernard Schutz, Alex Schwing, Ed Seidel, Stuart L. Shapiro, Hongyu Shen, Yue Shen, Brigitta M. Sipőcz, Lunan Sun, John Towns, Antonios Tsokaros, Wei Wei, Jack Wells, Timothy J. Williams, JinJun Xiong, Zhizhen Zhao
We discuss key aspects to realize this endeavor, namely (i) the design and exploitation of scalable and computationally efficient AI algorithms for Multi-Messenger Astrophysics; (ii) cyberinfrastructure requirements to numerically simulate astrophysical sources, and to process and interpret Multi-Messenger Astrophysics data; (iii) management of gravitational wave detections and triggers to enable electromagnetic and astro-particle follow-ups; (iv) a vision to harness future developments of machine and deep learning and cyberinfrastructure resources to cope with the scale of discovery in the Big Data Era; (v) and the need to build a community that brings domain experts together with data scientists on equal footing to maximize and accelerate discovery in the nascent field of Multi-Messenger Astrophysics.
no code implementations • 27 Nov 2017 • Hongyu Shen, Daniel George, E. A. Huerta, Zhizhen Zhao
Gravitational wave signals are often extremely weak and the data from the detectors, such as LIGO, is contaminated with non-Gaussian and non-stationary noise, often containing transient disturbances which can obscure real signals.
no code implementations • 20 Nov 2017 • Daniel George, Hongyu Shen, E. A. Huerta
The detection of gravitational waves with LIGO and Virgo requires a detailed understanding of the response of these instruments in the presence of environmental and instrumental noise.
no code implementations • 22 Jun 2017 • Daniel George, Hongyu Shen, E. A. Huerta
The exquisite sensitivity of the advanced LIGO detectors has enabled the detection of multiple gravitational wave signals.