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Data Science for Nano Image Analysis, 2021
In situ TEM and other dynamic imaging instruments alter the landscape of materials science and engineering. The unique and unprecedented ability to observe the transformation of nanoscale objects as it occurs is unparalleled in comparison to other material characterization methods, and is of tremendous value to material scientists and engineers who ...
Chiwoo Park, Yu Ding
semanticscholar +2 more sources
In situ TEM and other dynamic imaging instruments alter the landscape of materials science and engineering. The unique and unprecedented ability to observe the transformation of nanoscale objects as it occurs is unparalleled in comparison to other material characterization methods, and is of tremendous value to material scientists and engineers who ...
Chiwoo Park, Yu Ding
semanticscholar +2 more sources
A dual-LSTM framework combining change point detection and remaining useful life prediction
Reliability Engineering & System Safety, 2021Remaining Useful Life (RUL) prediction is a key task of Condition-based Maintenance (CBM). The massive data collected from multiple sensors enables monitoring the complex systems in near real-time.
Zunya Shi, Abdallah A. Chehade
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Sequential change‐point detection: Computation versus statistical performance
WIREs Computational Statistics, 2022Change‐point detection studies the problem of detecting the changes in the underlying distribution of the data stream as soon as possible after the change happens. Modern large‐scale, high‐dimensional, and complex streaming data call for computationally (
Haoyun Wang, Yao Xie
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Testing stationarity and change point detection in reinforcement learning
Annals of Statistics, 2022We consider offline reinforcement learning (RL) methods in possibly nonstationary environments. Many existing RL algorithms in the literature rely on the stationarity assumption that requires the system transition and the reward function to be constant ...
Mengbing Li +3 more
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LIFEWATCH: Lifelong Wasserstein Change Point Detection
IEEE International Joint Conference on Neural Network, 2022Change point detection methods offer a crucial ca-pability in modern data analysis tasks characterized by evolving time series data in the form of data streams.
Kamil Faber +4 more
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Parallelization of Change Point Detection
The Journal of Physical Chemistry A, 2017The change point detection method ( Watkins , L. P. ; Yang , H. J. Phys. Chem. B 2005 , 109 , 617 ) allows the objective identification and isolation of abrupt changes along a data series. Because this method is grounded in statistical tests, it is particularly powerful for probing complex and noisy signals without artificially imposing a kinetics ...
Nancy Song, Haw Yang
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Multiscale Change Point Detection
Theory of Probability & Its Applications, 2017zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Suvorikova, A., Spokoiny, V.
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On Covert Communication Against Sequential Change-Point Detection
IEEE Transactions on Information Theory, 2021We investigate covert communication under a sequential change-point detection (SCPD) framework, where a transmitter, Alice, attempts to communicate reliably with a receiver, Bob, over an additive white Gaussian noise channel, while simultaneously ...
Ke-Wen Huang, Huiming Wang, H. Poor
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Sequential Change-Point Detection for Mutually Exciting Point Processes
Technometrics, 2021We present a new CUSUM procedure for sequential change-point detection in self- and mutually-exciting point processes (specifically, Hawkes networks) using discrete events data.
Haoyun Wang +4 more
semanticscholar +1 more source