Stream dissolved oxygen (DO) dynamics are an outcome of metabolic activity and subsequently regulate ecosystem functions such as in‐stream solute and sediment reactions. The synchronization of DO signals in and across stream networks is both a cause and effect of the mode and timing of these functions, but there is limited empirical evidence for network patterns of DO synchrony. We used high frequency DO measurements at 42 sites spanning five catchments and stream orders to evaluate DO signal synchrony in response to variation in light (a driver of photosynthesis) and discharge (a control on DO signal spatial extent). We hypothesized that stream network DO synchrony arises when regional controls dominate: when light inputs are synchronous and when longitudinal hydrologic connectivity is high. By complement, we predicted that DO signal synchrony decreases as light becomes more asynchronous and stream flows decline or become discontinuous. Our results supported this hypothesis: greater DO signal synchrony arose with increasing light synchrony and flow connectivity. A model including these two controls explained 70% of variation in DO synchrony. We conclude that DO synchrony patterns within‐ and across‐networks support the current paradigm of discharge and light control on stream metabolic activity. Finally, we propose that DO synchrony patterns are likely a useful prerequisite for scaling subdaily metabolism estimates to network and regional scales.
- Award ID(s):
- NSF-PAR ID:
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- Proceedings of the 57th Design Automation Conference (DAC 2020), San Francisco, CA
- Medium: X
- Sponsoring Org:
- National Science Foundation
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The main difference between an online versus offline system is that an online system should always be causal and has minimum latency which is often defined by domain experts. The offline system, shown in Figure 2, uses two phases of deep learning models with postprocessing . The channel-based long short term memory (LSTM) model (Phase 1 or P1) processes linear frequency cepstral coefficients (LFCC)  features from each EEG channel separately. We use the hypotheses generated by the P1 model and create additional features that carry information about the detected events and their confidence. The P2 model uses these additional features and the LFCC features to learn the temporal and spatial aspects of the EEG signals using a hybrid convolutional neural network (CNN) and LSTM model. Finally, Phase 3 aggregates the results from both P1 and P2 before applying a final postprocessing step. 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Using the offline decoder and postprocessor, the model performed at 36.23% sensitivity with 9.52 FAs per 24 hours. The trained model was then evaluated with the online modules. The current performance of the overall online system is 45.80% sensitivity with 28.14 FAs per 24 hours. Table 2 summarizes the performances of these systems. The performance of the online system deviates from the offline P1 model because the online postprocessor fails to combine the events as the seizure probability fluctuates during an event. The modules in the online system add a total of 11.1 seconds of delay for processing each second of the data, as shown in Figure 3. In practice, we also count the time for loading the model and starting the visualizer block. When we consider these facts, the system consumes 15 seconds to display the first hypothesis. The system detects seizure onsets with an average latency of 15 seconds. Implementing an automatic seizure detection model in real time is not trivial. We used a variety of techniques such as the file locking mechanism, multithreading, circular buffers, real-time event decoding, and signal-decision plotting to realize the system. A video demonstrating the system is available at: https://www.isip.piconepress.com/projects/nsf_pfi_tt/resources/videos/realtime_eeg_analysis/v2.5.1/video_2.5.1.mp4. The final conference submission will include a more detailed analysis of the online performance of each module. ACKNOWLEDGMENTS Research reported in this publication was most recently supported by the National Science Foundation Partnership for Innovation award number IIP-1827565 and the Pennsylvania Commonwealth Universal Research Enhancement Program (PA CURE). Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the official views of any of these organizations. REFERENCES  A. Craik, Y. He, and J. L. 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